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Hiring Data Engineers

Thu, 16 Apr 2026 14:53:32 GMT

Hiring Data Engineers in 2026: What Good Looks Like Now

Hiring a Data Engineer sounds straightforward until you start the process.

On paper, the brief looks simple. You need someone who can build pipelines, improve data quality, work in the cloud and help the business make better use of its data. In practice, though, a lot of hiring processes stall because the role has been scoped too broadly, the interview process tests the wrong things, or different stakeholders want completely different outcomes from the same hire.

That is one of the biggest reasons companies struggle to hire strong Data Engineers.

It is not always a talent shortage problem. Often, it is a definition problem.

The first issue: “Data Engineer” means different things to different businesses

A Data Engineer in one company might be focused on moving legacy reporting into a modern cloud platform. In another, they are building production-grade pipelines that feed machine learning models. Somewhere else, they are effectively acting as an Analytics Engineer, cleaning up transformation layers and improving trust in reporting.

All three businesses might advertise exactly the same title.

That is where hiring gets difficult.

Before going to market, it helps to answer one question clearly:

What do we actually need this person to do in the first 12 months?

If the answer is vague, the shortlist usually is too.

What strong Data Engineering briefs look like in 2026

The best briefs are outcome-led, not tool-led.

A lot of job descriptions still read like a shopping list:

SQL
Python
Snowflake
dbt
Airflow
AWS, Azure or GCP
stakeholder skills
CI/CD
streaming
governance experience

Individually, none of that is unreasonable. The problem is that it often describes three different people, not one.

A stronger brief usually starts with the business need.

For example:

Are you modernising a legacy data estate?
Are you trying to improve reliability and observability?
Do you need cleaner data products for analytics teams?
Are you building infrastructure to support AI or machine learning use cases?
Do you need someone hands-on, or someone who can also influence architecture and roadmap?

When that is clear, the skill set becomes easier to define and the hiring process becomes faster.

The types of Data Engineer hires companies most commonly confuse

One of the biggest causes of wasted time in data hiring is combining very different profiles into one role.

1. Platform-focused Data Engineers

These are the people who help design and build the foundations. They are often strongest around infrastructure, orchestration, performance, scalability and cloud-native ways of working.

2. Product-aligned Data Engineers

These hires sit closer to stakeholders and delivery teams. They are often better suited to environments where data is being treated as a product and where business value needs to be delivered quickly.

3. Analytics Engineering-style profiles

Some businesses say they need a Data Engineer when, in reality, they need someone who can improve modelling, transformation and reporting trust. In those cases, the role may sit much closer to analytics engineering than platform engineering.

4. ML or AI-supporting Data Engineers

As more businesses invest in AI, there is increasing demand for engineers who can support data readiness, model pipelines, feature availability and production workflows. That does not make every Data Engineer an AI Engineer, but it does change what “good” can look like in some teams.

If these distinctions are not made early, businesses often attract the wrong market and lose time mid-process.

What good Data Engineers are being assessed on now

The strongest candidates are rarely just “good with tools”.

Technical depth still matters, of course. SQL remains fundamental. Python is still highly valuable in many environments. Cloud experience is often essential. But the better hiring processes go beyond checking whether someone has worked with a specific stack.

In 2026, the better questions are often:

Can they explain the trade-offs behind the choices they made?
Have they improved reliability, scalability or cost-efficiency?
Can they work across engineering and business conversations?
Do they understand how data is actually consumed downstream?
Have they worked in a messy environment, not just a polished one?

That last point matters more than many hiring teams realise.

There is a big difference between someone who has inherited a mature platform and someone who has helped shape one. Both can be good hires, but they are not the same hire.

What companies still get wrong in interviews

It is easy to create an interview process that filters for people who interview well but does not properly test whether they can succeed in your environment.

Some of the most common issues we see are:

interview stages that are too long
different stakeholders assessing different versions of the role
technical tests that are disconnected from the day job
no clear distinction between must-haves and nice-to-haves
waiting too long to align salary expectations with the market

Strong candidates, particularly in data engineering, do not usually stay available for long. If the process is unclear or slow, they will often disengage before the final stage.

Permanent or contract?

This depends on the problem you are trying to solve.

If you need someone to build long-term capability, improve standards and become part of the fabric of the team, a permanent hire is often the better option.

If you need specialist support for a migration, platform implementation or short-term delivery push, a contractor can be a very effective solution.

The mistake is using one by default without being honest about the real requirement.

Sometimes businesses say they want a permanent hire because that feels like the safer choice. But if the need is immediate and highly specialised, contract can be the better answer. Equally, some businesses lean on contractors for too long when what they really need is a long-term capability builder.

Salary is still important, but clarity is often the bigger issue

Yes, salary matters. Strong Data Engineers know where demand sits, and the better ones are usually not applying blindly to everything on the market.

But budget is not always the only blocker.

In many cases, businesses lose good candidates because:

the role is too broad
the value proposition is weak
the brief changes mid-process
the team cannot explain the impact of the hire
the interview process creates doubt

Good candidates are not just assessing the pay. They are assessing whether the opportunity makes sense.

So, what does good look like now?

Good looks like clarity.

Good looks like knowing whether you need a platform builder, a product-minded engineer or someone closer to analytics engineering.

Good looks like a process that tests real capability, not just keyword familiarity.

Good looks like a brief that matches the actual business need rather than trying to cover every possible requirement in one hire.

And good looks like moving with intent when you find the right person.

Because in Data Engineering, the market is competitive, but it is often poor scoping, not just scarcity, that causes the biggest hiring delays.

Hiring Data Engineers in 2026?

If you are planning to hire and want a clearer view of what the market looks like, what good should look like for your team, or how to shape the brief before going live, KDR Talent Solutions can help.

We work with businesses across data and AI hiring, helping teams define roles properly and connect with the right talent in a competitive market.

Need help hiring a Data Engineer? Get in touch with the KDR team.

Data and AI Job Market Review

Thu, 02 Apr 2026 13:03:23 GMT

There is a lot being said about data and AI hiring at the moment

...but from our perspective as a recruitment business working across the UK, EU and USA, the reality is way more nuanced than the rhetoric around whether “the market is flying” or “the market has stalled” (we’ve heard both recently during conversations with our community)

What we’d say, candidly, is this that the market is active, but it is selective and that has consequences both on the hiring side as well as for those looking to move on in their career.

In the UK, the ONS view in January and February 2026 does not suggest any real rebound yet. In the January release, vacancies edged up to 734,000 for October to December 2025, which was only a modest increase on the previous rolling quarter. Then in the February release, vacancies moved to 726,000 for November 2025 to January 2026, slightly higher than the comparable earlier period, but lower than the 734,000 reported a month before. So, the direction of travel is not one of meaningful recovery, if anything, the ONS is telling us the market remains broadly flat, with small month-to-month movements rather than any genuine surge in hiring appetite.

That being said, while the ONS is telling us the UK vacancies market remains broadly flat, more nuanced data suggests the data and AI market is fragmenting rather than simply slowing, with Data Engineering, Governance and AI-related hiring proving more resilient than the wider job market. For example, in the UK, Data Engineer demand looks firmer than the broader vacancies backdrop: IT Jobs Watch shows 1,068 permanent UK vacancies in the six months to March 2026, up from 749 in the equivalent period a year earlier. Data Governance also looks relatively healthy at 1,165 vacancies, up from 797 a year earlier. That suggests demand has improved, but not to the point where employers can be loose on process or vague on what good looks like.

On the AI side, the trend is stronger again. Indeed’s data shows AI-related hiring continuing to grow even while wider hiring remains subdued. Its UK report said 5.6% of UK job postings mentioned AI as of the end of October 2025, the highest share on record at that point, while its US labour market update said the Indeed AI Tracker reached 4.2% of US postings in December 2025 and that nearly 45% of data and analytics postings now contain AI-related terms. So, AI is clearly outperforming the wider market in terms of hiring intensity and skill demand.

On a broader job market perspective, in the US, the picture is slightly better on activity. CompTIA reported that active employer postings for tech occupations rose to 505,045 in February 2026, including more than 230,000 new postings in the month, after a January increase from December. In other words, hiring intent improved as Q1 progressed, but it improved from a cautious base rather than from a position of strength. Building on that, data and ai roles were starting from a higher base and are outperforming the overall job market.

That backdrop explains what many of us in the market have felt on the ground, clients are hiring, but with far less room for error.

If we look at ITJobs Watch for example and take Data Management as a job title, it shows that UK vacancies citing Data Management dropped to 931 from 1,728 year on year, and median pay fell from £75,000 to £60,000. That is a meaningful reset. It doesn’t mean Data Management is unimportant. In fact we have seen quite the opposite within clients. But what I think is happening here is that employers are being more specific, instead of hiring generic “data management” capability, they are niching into sharper disciplines such as governance, quality, architecture, master data and platform-led engineering.

That is probably the biggest market pivot going into Q2:the era of broad, catch-all data hiring is fading. The era of targeted, outcome-led hiring is here.

On the AI side, the signal is stronger. LinkedIn’s latest labour market report also showed roughly 1.3 million AI jobs created globally between 2023 and 2025, including 177,000 AI Engineer roles, while forward-deployed engineer demand grew 42x and AI engineer demand 13x over that period.

That global picture is interesting because it tells us where budgets are going. The money is still flowing into AI, but increasingly into applied AI, AI infrastructure, and roles that connect engineering to commercial delivery. Reuters has reported major ongoing AI infrastructure investment from firms such as Oracle, Google, Nvidia and Databricks, even as some companies also trim headcount elsewhere or use AI to run leaner teams. So globally, the picture seems to be emerging that more AI equates to different jobs, different skill mixes, and more pressure on productivity.

That brings us to what we think happens in Q2 2026. We think we are heading into a quarter where the strongest employers keep hiring in a focused way, while others remain cautious and wait for more economic clarity. In the UK especially, the wider labour market is still fragile enough that I would expect measured hiring rather than aggressive expansion. In the US, I would expect continued demand for platform, engineering and AI-enabled roles, but with scrutiny on headcount and ROI staying high.

This creates an interesting hiring environment where it could be easy to confuse a fuller market with an easier market. What I would say to hiring managers is yes, candidate availability has improved in some areas. But the best Data Engineers, Governance specialists, MDM professionals and AI talent are still not hanging around for long, especially when the brief is clear and the business case is compelling. The companies winning right now are the ones doing three things well: defining the problem properly, running a tight process, and being realistic on value rather than trying to buy premium capability at discounted rates.

For candidates, we would say, this is not a bad market, but it is a less forgiving one. Generic profiles are struggling more. Clear specialism wins. In Data Engineering, that means demonstrating platform depth, production delivery and business impact, not just tool lists. In Data Management, it means being able to show where you sit across governance, quality, MDM, metadata, privacy or architecture, and what outcomes you have driven. In AI, it increasingly means being able to connect model capability to operational reality. Employers are far less interested in theory than they were 12 months ago.

Our overall read for Q2 is that the market has not switched off but it has evolved with Data Engineering staying relatively strong, Data Governance and MDM remaining important, particularly where regulation, trust and AI readiness intersect and AI continues to attract investment, but the winners will be people and businesses who can turn that investment into something practical. In a more mature market, clarity beats hype every single time.

UK Data and AI Job Market Trends

Thu, 01 Jan 2026 10:30:08 GMT

What’s coming for the UK Data and AI Job Market? Trends, Predictions & What to Watch

As we move into another year, the UK tech landscape is undergoing a fascinating transformation. On one hand, there are clear signs of economic strain: hiring across many industries is slowing, businesses are exercising caution and junior-level opportunities are thinning out. But on the other, there’s a growing hunger for strategic tech investment, particularly in the areas of data engineering, AI and cloud infrastructure.

While the general job market may feel cool to the touch, the specialised corners of the technology profession are not. Roles that sit at the intersection of cloud, data and AI are not only surviving the economic slowdown, they’re thriving in it. Let’s explore what’s really going on.

Job vacancies reportedly declined steadily through the second half of last year, and unemployment crept up to its highest level in four years, sitting at around 5.1%. Business confidence took a hit, and many companies scaled back large recruitment drives, particularly for entry-level positions or generalist roles.

Strategic Investment in Cloud, AI and Real-Time Data

The resilience of tech hiring in the face of economic restraint can be explained by one simple truth: when times are tough, businesses double down on what makes them more efficient. And right now, nothing delivers efficiency like automation, intelligent data use and scalable cloud infrastructure. AI is no longer a ‘future concept’ whilst it’s not fully embedded yet, businesses spent a lot of last year laying the proper foundations for success in the following year. Our annual State of Data report backs this up. It seems 2024 was adopt ai at any cost, 2025 made people stop and realise what is required in the background to ensure the limitations of the tech are not exposed and this in turn made people realise they need the right talent to run effectively. Governance and quality roles long side AI engineers were needed to set the business up for success.

Similarly, the cloud has shifted from being a “nice-to-have” to a critical business enabler. Technologies like Google Cloud Platform (GCP) became essential to support scalable data pipelines, real-time analytics and machine learning operations. As companies migrate more workloads to the cloud, they’re seeking professionals who can bridge the gap between infrastructure and innovation. That’s where data engineers, MLOps engineers and cloud architects come in and why their roles are becoming central to hiring strategies now.

Data Engineering

Data engineering continues to be one of the most in-demand specialisms across the UK. In many ways, it’s the unsung hero of modern AI adoption. Businesses now realise that machine learning models are only as good as the data pipelines behind them, clean, reliable, real-time data has become a competitive necessity.

As time goes on, we’ll see the role of the data engineer evolve beyond traditional ETL work. Engineers are increasingly expected to build and manage real-time streaming pipelines, work closely with AI teams and contribute to the architecture of modern, cloud-native data platforms. GCP skills in particular are seeing a rise in demand, as companies lean into scalable, secure and cost-effective data infrastructure.

And it’s not just about technical skills. There’s growing emphasis on data governance, lineage, and quality, areas that are critical to building trustworthy AI systems. That means the most valuable data engineers today are the ones who can wear multiple hats: part builder, part strategist, part custodian.

AI Roles: From Research to Real-World Impact

As previously discussed, it’s no longer just about hiring data scientists to build proof-of-concept models. Employers now want AI engineers who can deploy solutions in production, integrate them into business processes and demonstrate clear ROI.

As such, and as LinkedIn Emerging Roles report indicates, new hybrid roles are emerging, think AI Product Engineers, Machine Learning Infrastructure Specialists, or even AI Translators, professionals who bridge the gap between research and execution. These individuals are helping to move AI out of the lab and into the heart of real businesses.

While some entry-level positions are being absorbed by automation, the tools themselves have created new opportunities. Companies are increasingly seeking talent who can fine-tune models, engineer effective prompts and integrate LLMs into operational workflows. These are not experimental projects, they’re shaping customer service, marketing, operations and even product development in real time.

GCP: A Quiet Powerhouse Behind Data & AI

While AWS and Azure have long dominated the UK cloud scene, Google Cloud Platform is steadily gaining ground, particularly among companies prioritising AI and data-heavy applications. GCP’s deep integration with machine learning tools and its flexible pricing model make it a popular choice for organisations building modern AI platforms.

In general we’re seeing rising demand for GCP specialists, especially those with experience in BigQuery, Dataflow, Vertex AI and real-time analytics. Employers are increasingly looking for engineers who can navigate the complexities of hybrid cloud architecture, cost-optimise their pipelines, and support both analytics and ML workflows on the same platform.

Importantly, companies are no longer satisfied with general cloud knowledge. They want platform specialists who can deliver measurable value. This trend is shifting the cloud hiring landscape from “can you work in the cloud?” to “can you build something that, not only, performs, but scales and saves us money?”

A Shift Toward Specialisation and Value Creation

One of the most significant trends shaping the job market is a shift away from broad, high-volume hiring and toward focused, high-impact roles. Employers are thinking more strategically about where they invest, and the result is a sharper focus on people who can deliver real outcomes.

Entry-level and generalist roles are under slightly more pressure, automation is replacing many of the repetitive or routine tasks previously handled by junior team members. At the same time, skilled professionals who understand how to partner with automation and enhance it are becoming more valuable than ever.

This means candidates need more than just technical credentials. Employers are looking for soft skills such as communication skills, business acumen and an ability to deliver results in a real-world setting. Certifications still matter, especially in cloud and AI domains, but hands-on experience and demonstrable impact will always win out for the best clients.

Looking Ahead: What This Means for Hiring & Career Planning

For hiring managers, this year will be about smart hiring, not just filling seats, but bringing in talent that aligns with transformation goals. That might mean investing in fewer hires but offering more competitive packages to secure the right people. It also means looking beyond titles and focusing on skills, adaptability and learning potential.

For candidates, it’s an opportunity to get ahead by specialising. Whether it’s mastering GCP, diving deeper into data engineering frameworks, or learning how to integrate AI responsibly into live systems, those who continue to evolve their skillsets will remain in demand.

In short, the tech market isn’t shrinking, it’s maturing. Roles in data, AI and cloud are no longer experimental sidelines, they are core business functions. And in a climate where every hire matters, specialists who can unlock real business value will continue to have an edge.

If you’re a business looking to scale your data and AI teams, or a candidate navigating your next move in a changing market, KDR Talent Solutions is here to help. Our team of specialist recruiters is deeply embedded in these ecosystems, and we’d love to share more insights tailored to your goals.

How data leadership is changing

Tue, 02 Dec 2025 12:00:21 GMT

Poor management is now the number one reason data professionals consider leaving their roles

The State of Data report shows that poor management is now the number one reason data professionals consider leaving their roles, above salary, workload and flexibility. To address issues around leadership skills here are some predictions that may help the best leaders take competitive advantage.

Prediction 1: “Translator” roles will become more formal

The most effective data leaders today are those who bridge the gap between technical and business teams and advocate for data at board level. On that note whilst they are there, the best ones also protect teams from unrealistic expectations.

Next year, there could be more formalised translator roles like Data Product Managers, Analytics Leads, Head of Data Value, etc.

These roles will be ideal for senior analysts and engineers who are strong communicators, people who enjoy stakeholder engagement as much as technical problem-solving or individuals ready to influence strategy.

Prediction 2: Leadership capability will be a hiring criterion, not an afterthought

Because leadership quality is such a strong driver of retention and performance, we expect 2026 hiring to place more emphasis on coaching and mentoring capability. Experience of building psychologically safe environments will help with this as will evidence of enabling, rather than micromanaging, technical teams.

Takeaway for data professionals:
Start thinking about which path excites you most whether that’s deep expertise, people leadership, or translation, and make conscious moves in that direction now.

Download the full report here.

Skill predictions for the data industry

Tue, 02 Dec 2025 11:13:34 GMT

The data from the State of Data report shows that focus may shift next year

With 74% of organisations already reporting that they’ve adopted AI in some form, and 67% saying their data usage has improved year-on-year, the foundations are clearly being laid. The focus for 2026 won’t be adoption, it will be accountability, scalability and ownership . We looked at the themes within the report to predict where we think the industry is heading next year.

Prediction 1: Model ownership becomes a board level question

The report shows AI product ownership, MLOps and model governance becoming part of everyday language in data teams.

Next year, we expect this to contribute to more AI Product Owner and Data Product Owner roles as well as clearer lines of accountability between engineering, data science and the business. For data professionals, that will potentially mean more time documenting assumptions and limitations and explaining models to non-technical stakeholders.

Skills wise this means that more than ever those soft skills will come into play. If you feel like you are very technically capable but maybe lack confidence around soft skills like communication or influencing etc, 2026 might finally be the year you decide to invest in yourself and really double down on these crucial business skills.

Prediction 2: Data quality finally moves from “problem” to “platform requirement”

Poor data quality is still the number one blocker to AI progress so next year sorting out the data quality and governance issue will become a strategic advantage.

By next year, data professionals will start to think more about creating stronger data contracts between source systems and downstream consumers. Data quality checks will need to be embedded into pipelines, not added as an afterthought and if they are not already, data governance teams will work more closely with engineers, not just reporting into compliance.

If you work in engineering, analytics or governance, the ability to design and enforce “quality by default” processes will become a key differentiator.

Prediction 3: AI skills expectations will broaden, not just deepen

Lack of in-house AI skills remains one of the top three blockers today. Next year, the skill ask is likely to shift from “Can you build a model?” to “Can you deploy and maintain a model?” “Can you co-design an AI solution with product, risk and operations?” or “Can you translate capabilities and constraints to stakeholders?”

The most valuable data professionals will blend technical literacy , operational thinking and communication skills and that’s where we expect career progression and salary growth to concentrate.

Download the full report here

What data talent wants

Mon, 24 Nov 2025 10:00:26 GMT

Flexibility is Non-Negotiable - what data professionals really want from hybrid working in 2025

In the early days of remote work, flexibility was a perk. In 2025, it’s a dealbreaker.

This year’s State of Data 2025 report makes that crystal clear:

63% of UK data professionals say they would consider leaving their job if hybrid flexibility were reduced.

That’s not a soft preference. That’s a hard line in the sand and a clear signal to employers.

What the Data Says

While most professionals still work between 1–3 days per week in the office, expectations have changed. The hybrid model isn’t just about location anymore, it’s about purpose.

Are team days being used effectively?

Are office requirements tied to collaboration, client-facing needs or development goals, or just legacy thinking?

Is flexibility offered equally, or are policies manager-dependent?

The report reveals that role-based, purpose-driven presence policies are increasingly preferred and organisations embracing these are seeing stronger engagement and retention.

Why flexibility matters in the data industry

Data professionals, especially engineers, analysts and AI specialists, often need:

Long stretches of deep focus

Access to distributed teams and systems

Asynchronous collaboration

Autonomy to solve complex problems

Rigid, in-office mandates interrupt these workflows. Worse, they risk sending a message that trust is conditional and output is measured by visibility, not impact.

The organisations thriving in 2025 are rethinking hybrid presence as part of intentional workplace design, and they’re doing things like:

Scheduling collaborative office days around projects, not policies

Designing workflows that don’t rely on real-time approvals

Offering clarity on “why” in-office time matters for specific roles

And that makes a difference.

The risk of inflexibility

Flexibility isn’t a perk for this talent pool, it’s part of what makes high-performing data teams effective and the data shows companies that ignore this shift risk:

Losing top performers

Slowing delivery

Struggling to hire, especially in candidate-short specialties

Meanwhile, those who embrace role-appropriate hybrid working are better positioned to attract and retain the most in-demand talent.

Download the full State of Data 2025 report to see how top organisations are evolving their working models to reflect what data teams actually need.

Next in the blog series: Why poor leadership, not salary, is driving data professionals to leave.

AI Implementation blockers - The State of Data Series

Mon, 17 Nov 2025 10:00:13 GMT

What’s Really Blocking (AI) Progress in 2025?

In an industry that moves fast and talks faster, it’s easy to assume that the biggest obstacles to data and AI progress are abstract: fear of job loss, confusion about AI’s purpose, or a lack of executive buy-in.

But this year’s State of Data 2025 report paints a more grounded picture, one that’s both more practical and more solvable.

The top 3 blockers in 2025 are operational, not existential

When asked what’s really holding them back, UK data professionals didn’t cite hype or hesitation. They pointed to tangible operational challenges:

Poor data quality (28%)
Lack of in-house AI skills (21%)
Managing stakeholder expectations (16%)

These are not philosophical barriers. They are process, skill and communication gaps that many high-performing organisations are already tackling head-on.

Why This Matters

The shift from cultural to operational blockers signals an industry that’s maturing. Previous blockers are being replaced by implementation challenges that require structure, not speculation.

This means that organisations aren’t asking “Should we do AI?” anymore.

They’re asking:

“Do we have the right foundations?”
“Are we ready to scale what we’ve already started?”
“Who owns the outcomes?”

And that’s good news. Because unlike abstract fears, operational blockers are fixable, if you have the right talent, tools, and leadership.

Solving the Right Problems

Let’s break these blockers down:

Poor Data Quality
You can’t do AI, or even BI, on bad data. The most successful organisations are moving data quality upstream, embedding governance into ingestion pipelines and engineering standards.

Lack of In-House AI Skills
While external tools and vendors can help, real value comes from internal capabilities. Upskilling, cross-functional collaboration, and product ownership of AI use cases are key levers.

Stakeholder Expectation Management
As AI becomes more embedded, misunderstandings around capability vs. hype are creating friction. The best teams are communicating clearly, documenting limitations and aligning expectations from the start.

The Takeaway for Leaders

If your transformation efforts are stuck, it’s probably not because your team doesn’t care, or your tools aren’t good enough. It’s because your operations aren’t aligned with the realities of scaling data and AI.

Start by auditing:

Where quality control lives (and where it doesn’t)
Where AI skills sit (and where they’re needed)
Where expectations are misaligned (and how they’re managed)

Then, invest in change you can control and measure.

Download the full report for insights on how top-performing teams are solving these blockers in real-world environments.

Next in the blog series: What hybrid flexibility really means for data professionals in 2025 .

Data Usage Trends - The State of Data Series

Mon, 10 Nov 2025 10:30:01 GMT

Data usage is improving, but it’s not because of more tools

For years, improving data usage has often been seen as a tooling problem: better platforms, smarter dashboards, more automation. But the 2025 edition of The State of Data suggests something different and far more foundational, is happening.

A Quiet Revolution in Data Usage

This year, 67% of UK respondents said their organisation’s data usage has improved year-on-year, a promising statistic in a landscape still shaped by complexity, disruption and economic caution.

So what’s driving this shift?

Not new platforms.

Not bigger teams.

Not AI (yet).

Instead, 42% of respondents cited “clear definitions of what data is needed and why” as the strongest reason for the improvement, more than any other factor listed.

That’s significant. And it reveals something important: the most impactful changes are coming from the alignment between people and purpose, not software and spend.

Clarity over complexity

When teams understand what data they need, how it will be used, and what outcomes it’s tied to, they:

Ask better questions.
Build more reliable pipelines.
Reduce rework and duplication.
Improve decision-making with less noise.

In other words, they stop treating data as an abstract resource and start treating it as a strategic asset.

This shift isn’t about tooling, it’s about thinking.

The Business-Data Bridge

The 2025 report highlights a growing awareness that business users play a critical role in data quality and usage , not just the technical teams.

Many organisations have begun prioritising:

Clear communication of data requirements between functions.
Upstream quality control , not just post-hoc data cleansing.
Business involvement in governance practices , including ownership of specific datasets.

It’s this cultural and structural evolution, not flashy new platforms, that’s helping organisations unlock more value from their data.

Takeaway for Data Leaders

If you’re still looking to drive better usage outcomes by buying new tools, the data suggests a pause for reflection.

Start instead with:

✅ Cross-functional workshops to clarify data needs.
✅ Governance structures that encourage collaboration.
✅ Education and enablement for business users.
✅ Metrics that reflect business impact, not just data availability.

These are not one-time initiatives. They’re the foundations of sustainable, scalable data maturity.

Download the full report for more insights into how top-performing organisations are bridging the data-business divide and what it means for your team.

Next up in the blog series: What’s Really Blocking Progress in 2025?

The State of Data

Fri, 07 Nov 2025 10:00:17 GMT

The State of Data report provides a vital pulse check on the UK’s data industry

Drawing on the perspectives of UK data professionals across different sectors, it reveals a landscape that is maturing, but still uneven in its progress.

Signs of progress: Adoption, usage and mindset

At headline level, the results are promising:

74% of organisations now report adopting AI , up from 66% in 2024.
67% say their organisation's data usage has improved year-on-year.
New strategies like product ownership of AI use cases , MLOps , and data contracts are beginning to appear in everyday operations, signalling real shifts in capability.

But these advances aren’t universal, and they’re not necessarily coming from new tools or hires. The report shows that progress is being driven by foundational work: clarity in data definitions, stronger alignment between teams, and a mindset shift from firefighting to strategic enablement.

Challenges are evolving, not disappearing

While fear and uncertainty were once dominant blockers, 2025’s respondents cite more tangible (but equally tough) challenges:

Data quality (28%) remains the top barrier to AI implementation.
A lack of in-house AI skills (21%) continues to slow momentum.
Stakeholder expectation management (16%) is still one of the top three blockers, showing that misalignment and hype are still costing progress.

These are real, solvable problems, but they require deeper operational change, not just executive ambition.

What else is shifting in 2025?

Hybrid working remains a priority:
63% of professionals say they would consider leaving a role if hybrid flexibility was reduced, proving that location policies still play a key role in talent retention.

Governance is being redefined :
Far from a box-ticking compliance function, governance is increasingly viewed as the enabler of scale. Terms like data contracts , model risk governance and AI product ownership now form part of the modern data vocabulary, a shift from last year’s more tactical focus.

Leadership is under scrutiny :
Poor management, not salary or workload, is the number one reason data professionals are leaving their roles. This trend spans all levels and specialisms, highlighting the need for leadership that understands the evolving needs of technical talent.

What’s Next?

The data sector is not slowing down, but it is becoming more discerning.

Adoption is no longer the primary goal. Teams are focused on scalability, accountability, and value creation with a sharper eye on how to make AI and data work in the real world.

In this blog series, we’ll explore the report’s key themes across usage, AI maturity, engineering enablement, governance, and the talent landscape.

Navigate to the full State of Data 2025 Report to dive into the full dataset, commentary and actionable insights.

For previous year's State of Data Reports, click here.

Data Governance Isn’t Optional And Financial Services Know It

Mon, 01 Sep 2025 13:59:08 GMT

If you work in financial services, you’ll know that “data governance” has become the backbone of compliance

If you work in financial services, you’ll know that “data governance” has become the backbone of compliance, customer trust and competitive advantage. Yet across the UK, banks and insurers are finding that while the need for governance expertise has grown, the supply of qualified talent simply isn’t keeping pace.

So why exactly is demand for data governance professionals rising so sharply and what can organisations do to secure the skills they need?

Why Data Governance Matters More Than Ever

Financial services firms are under pressure from every angle. Regulators like the FCA expect robust data governance frameworks, while GDPR and other global standards have raised the bar on compliance. For firms that get it wrong, the consequences include eye-watering fines and reputational damage that can last for years.

But governance isn’t only about risk management. Companies with strong governance benefit from cleaner data, faster reporting and better decision-making. It also enables innovation, whether that’s in customer personalisation, product development, or AI adoption. Put simply: if the data isn’t well governed, the strategy will fail.

The Hiring Bottleneck
The challenge is that demand has outstripped supply. Right now, UK firms are competing not just with each other, but also with technology companies, consultancies and even start-ups, all chasing the same pool of experienced governance professionals.

This has led to inflated salaries in London and other financial hubs, as well as longer time-to-hire for senior roles. We’re also seeing a trend towards hybrid skill sets, candidates who can combine regulatory knowledge with technical expertise, which narrows the pool even further.

The Skills in Highest Demand

From our work in data recruitment, three skill areas stand out as most sought after:

Data lineage and metadata management, ensuring firms know exactly where their data comes from and how it is used.

Regulatory knowledge, from GDPR to Basel III, organisations want candidates who understand the rules inside out.

Cross-functional communication, governance professionals must be able to bridge the gap between IT, compliance, risk and senior leadership.

These aren’t just technical skills; they’re strategic capabilities that directly impact a firm’s ability to operate effectively.

What Can Firms Do?

So how can UK financial services firms respond to this talent shortage? Investing in long-term pipelines provides a real boost. That means graduate programmes, upskilling initiatives and apprenticeships that build capability from within.

Second, employers must think carefully about their employer brand. Data professionals want to work somewhere that values governance as a strategic function, not a back-office task. Messaging matters and so does culture.

Finally, partnering with a specialist recruitment agency can make all the difference. Generalist recruiters may not understand the nuances of these roles, but a data-focused agency can help map the market, identify the right candidates and reduce your time-to-hire.

The demand for data governance talent in UK financial services isn’t going away. If anything, it will intensify as regulation tightens and competition for data-driven advantage grows. Firms that act now by investing in talent pipelines, strengthening their employer brand, and working with the right partners will be best placed to secure the skills they need.

At KDR Talent Solutions, we specialise in data recruitment for financial services. If you’re struggling to attract or retain governance talent, get in touch…we’ll help you build a team that lasts.

FAQs

Q1. Why is data governance so important in financial services?
Data governance ensures financial institutions meet regulatory requirements, reduce risk, and maintain customer trust. Without strong governance, firms face compliance fines and struggle to make accurate business decisions.

Q2. The most in-demand skills for data governance roles include:

Data lineage and metadata management to ensure transparency and traceability across the data lifecycle.
Regulatory and compliance expertise – particularly around GDPR, Basel III, CCPA, and emerging AI/data regulations.
Data quality and stewardship – the ability to define, monitor, and enforce data standards.
Communication and stakeholder management – bridging the gap between IT, compliance, and business leadership.
Tool proficiency – experience with platforms like Collibra, Informatica, or Alation is increasingly desirable.

As data governance matures into a strategic function, employers are also looking for professionals who can align governance with business outcomes and support broader data initiatives like analytics, AI, and digital transformation.

Q3. Why is it hard to recruit data governance professionals in the UK?
There’s a shortage of experienced candidates, especially in London. Competition from tech companies and consultancies, combined with rising salaries and demand for hybrid skill sets, can make hiring challenging.

Q4. How can financial services firms attract more data governance talent?
Firms could invest in graduate pipelines, strengthen their employer brand (and more specifically their data team brand) and work with specialist recruitment agencies who understand the data talent market.

Governance-Driven Adoption of Synthetic Data in Financial Services

Mon, 01 Sep 2025 12:59:51 GMT

It’s been a big year for synthetic data.

With the FCA’s Synthetic Data Expert Group (SDEG) publishing its final report, it’s clear we’re moving beyond experimentation and towards adoption particularly within regulated environments like financial services. But with progress comes pressure. How do we ensure synthetic data is not only powerful but safe, transparent and fair?

For those of my network working in data governance, quality, ethics and risk, this report offers both cautionary insights and practical direction.

What’s the Point of Synthetic Data?

At its core, synthetic data aims to mimic real datasets, preserving statistical patterns while stripping out personal or sensitive information. In financial services, this unlocks a few big opportunities:

Enabling secure model development without exposing real customer data
Testing systems under various stress scenarios
Supporting innovation where data is limited or legally constrained
Training AI models without breaching data protection laws

Sounds promising. But as the SDEG rightly points out: synthetic is not risk-free.

Synthetic data might be a privacy enhancer but it still carries risk. Poor generation techniques, unclear documentation, or hidden biases can all undermine trust and compliance. The FCA makes one thing clear: confidence in synthetic data isn't a technical problem, it's a governance challenge.

“Confidence is built through transparency, consistency and shared understanding.” FCA Synthetic Data Expert Group

Foundations for Responsible Use

The SDEG outlines three key governance foundations:

Frameworks, Controls & Processes. Think stage gates, escalation pathways and oversight mechanisms.

2. Roles & Responsibilities: Who owns what from data generation to model deployment.

3. Documentation & Monitoring: A clear, auditable trail of decisions, trade-offs, and updates throughout the lifecycle.

These aren’t ‘nice-to-haves’, they’re prerequisites. Without them, synthetic data projects risk becoming compliance liabilities rather than innovation enablers. Drawing on AI ethics and model risk frameworks, the report offers a nine-point blueprint for good governance:

Accountability with clear ownership, even with third-party vendors
Safety. Systems should be robust and reliable
Transparency with clear visibility into data generation and use
Explainability, humans must understand how outputs are produced
Privacy & Security: Risks of re-identification must be minimised
Fairness, mitigate bias and prevent discrimination
Agency, human oversight remains critical
Suitability, align tech use with ethical and practical context
Continuous Monitoring, it’s a lifecycle, not a one-time review

For governance and data leaders, these principles offer a structure for embedding synthetic data safely into your ecosystem, not treating it as an isolated experiment.

Practical Recommendations from the FCA

The report is refreshingly actionable it's not just theory. Here are a few standout recommendations:

Conduct value vs. risk assessments before kicking off a project

Run privacy impact assessments (DPIAs) early and often

Use robust validation such as “Train Synthetic, Test Real” (TSTR)

Document trade-offs between privacy, fidelity, and utility

Maintain auditability across every decision and output

Engage cross-functional stakeholders — governance, legal, data science, compliance

This isn’t just about ticking boxes. It’s about ensuring synthetic data is not only usable, but trusted.

What Does This Mean for Data Leaders?

Whether you’re a Chief Data Officer, Head of Governance, or Data Risk Manager, the message is clear:

Synthetic data has moved into the mainstream. It will become increasingly embedded into how firms design models, test systems and share data. But governance must evolve with it.

Some questions to help keep you on track...

Do our governance frameworks account for synthetic data risks?

Are we involving the right people (examples might be legal, compliance, ethics team) at the right time?

Can we explain our synthetic data decisions to a regulator?

If not, it may be time to act.

Synthetic data offers incredible promise, but trust can’t be faked. That’s why strong governance isn’t optional. It’s the foundation for responsible innovation, better models, and ultimately, safer outcomes for customers and businesses alike.

This FCA report is a valuable blueprint for what “good” looks like.

If you’re building or scaling your data governance team to tackle this challenge, I’d love to hear from you.

Let’s connect.

�� mark.townsend@kdrtalentsolutions.com

The Best Hiring Strategies

Mon, 01 Sep 2025 08:21:48 GMT

Taken from our annual survey of community members and the State of Data and State of Technology Reports, the following trends will help you to formulate your hiring strategy.

The current market dictates that candidates have more choice around which roles they want When there is more choice, the perception from the hiring side can be that there is a candidate-short market. However, this is not necessarily the case.

Our experience is that there is an abundance of people with the right skill sets for client roles. The issue is instead around engagement. Because people are being contacted, sometimes multiple times a day, about new roles, they are not engaging at the levels they once did.

What can hiring clients do to mitigate this? Now, more than ever it is important to get your employee proposition right. Candidates are looking for remote and hybrid work environments primarily. Benchmark your salaries to ensure they are competitive – speak to your recruitment partner who will be able to help with this. Benefits packages have gone through some interesting iterations over the last couple of years as businesses seek to be the most attractive, however from our research and speaking to hundreds of candidates over the last year, it’s always the basics that matter most: the right salary, the right working environment and flex, bonus levels, pension scheme contributions. Get these nailed first and then focus on providing strong reasons to choose your business over another: Consider team dynamics, management styles, purpose of the business and role, career progression, wellbeing programmes, but perhaps most importantly focus heavily on the hiring and onboarding process. We would always help a client evaluate their processes because we have seen first-hand how the good, the bad and the ugly affect success rates. Provide a good experience during the hiring stage with frequent open communication between all the stakeholders and cement this in with a robust onboarding experience with clear signposting, training if needed, support, communication and check-ins and you are more likely to attract and retain the people you work so hard to hire.

What else should you be focusing on?

Hire for Skills and Experience not Academic achievements.

The trend for skills-based hiring is thankfully increasing with Forbes reporting a 63% increase in companies hiring in this way compared to previous years. This strategy gets a thumbs up from us as it not only addresses the needs of the team but help create a more diverse team. Not everyone wants to, or can go on to, higher education, and real-world learning is just as valid as theory-based learning in a classroom. Experienced candidates without university degrees are finally getting the opportunities they deserve.

Keep Hiring Processes Short and Timely

When we polled our community of data and tech professionals most of them preferred a 2-stage process. Where this is not possible, making sure communication lines are open is important to ensure the candidate remains engaged in the process. Timely feedback and communication between interview stages helps. Data from a 2016 career builder study showed that 83% of respondents said that their experience would be greatly improved if the hiring process and length was clearly communicated from the beginning whilst 52% said they were greatly frustrated by the lack of communication during the process.

Salary Benchmarking

Across the board salaries have increased. Where there used to be London weighting, candidates can work from any location with the occasional visit to the office, which has opened up data and tech hubs around the country. Most noticeably growth areas such as Glasgow, Manchester and Leeds are helping to close the salary gap across the North/South divide. We have seen in the past huge hikes across some functions with companies wanting to retain existing staff and attract new ones. Where companies were unable to compete on salary, the best were able to compete on culture and L&D opportunities. Conducting a thorough salary benchmarking exercise will help you understand which camp you are in and whether you need to look at benefits, culture or progression opportunities as alternative attraction points.

Workforce Planning

Companies who follow workforce planning principals are more likely to be able to anticipate and plan for change, making them more agile than any competitors that don’t. Knowing what talent you require before the point of need and ensuring the plans meet business objectives makes complete sense, however most companies only address recruitment at the point of need. Tackling data and analytics recruitment in this transactional way costs a business, time, money and competitive market positioning not to mention delivery schedules. For success, workforce planning should be on your agenda.

Hiring GCP Data Engineers in 2025

Wed, 06 Aug 2025 08:07:51 GMT

Google Cloud Platform (GCP) may be third in market share but it has grown to become a significant player alongside AWS and Azure.

It offers an enterprise-level, scalable and robust platform for organisations dealing with big data, machine learning, or real-time analytics. GCP is typically chosen by users for its open pricing, which enables businesses to gain better visibility and predictability of cloud spend.

When you consider the tech, GCP is greatly respected as a data services leader, especially well-liked among businesses interested in hybrid or multi-cloud strategies due to Anthos and good open-source integrations. While AWS leads the pack at about 29% market share and Azure is at around 22%, GCP is the growth leader in the machine learning and AI area, and companies interested in next-generation data platforms. As of Q1 2025, GCP has about 12% of the global cloud market share, up from about 11% in 2024. Forecasts suggest the growth to about 15% by 2027. The expansion is mainly a result of its AI and machine learning offerings and budget-friendly infrastructure like TPUs. However, the talent pool available is smaller compared to AWS and Azure, so senior GCP Data Engineers are scarce and highly sought after.

With the above in mind and in order to appeal to this smaller talent pool, businesses must know the abilities of these candidates. From our data, we can give the following analysis:

The labour market is competitive so extended recruitment processes or excessive job requirements will cause you to lose good applicants. Our advice would be to consider your requirements before posting job ads and eliminate old requirements. Old job postings get caught in the net when hiring managers feel pressure, and although the situation is completely understandable it also delays and jams the hiring process with the wrong candidates.

Regarding recruitment procedures, most respondents to our annual state of data survey preferred a two-stage process comprising an interview and a technical test (if appropriate). Ideally, the best would be three stages that comprise a screening interview, a technical test (if required), and then in-depth interview with team members so that candidates and potential employers can get to know each other.

While certain applicants will prioritise job security or pay, most GCP engineers are driven by learning and impact. Emphasising opportunities to contribute to cutting-edge projects, rather than humdrum routine tasks, will get you applicants who bring innovation and inquisitiveness to your business.

An experienced recruiter within the GCP space can assist you in reaching out to passive talent, provide salary range guidance, and expedite the hiring process.

Final Thought: GCP Data Engineers are not merely a new hire; they are key to unleashing your data's full potential. As GCP's market share is expected to continue growing and demand for AI continues to rise, recruiting top talent now can provide your company with a competitive edge for years to come.

FAQ 1: Why is demand for GCP skill rising in the UK?

The UK economy is adopting Google Cloud more and more, particularly in industries like fintech, healthtech, and media. Companies want to use GCP's strengths in data analytics, AI, and multi-cloud, but they lack experienced GCP engineers. This shortage is driving up demand and compensation.

FAQ 2: What are the most in-demand GCP job roles in the UK right now?

The top GCP roles UK companies are hiring for in 2025 include:

- GCP Data Engineer (BigQuery, Dataflow, Pub/Sub)

- GCP Cloud Architect

- Google Cloud DevOps Engineer

- Machine Learning Engineer with GCP

- GCP Platform Engineer (with Kubernetes, Terraform)

FAQ 3: What are UK employers searching for in terms of GCP skills in 2025?

The most in-demand Google Cloud skills in the UK are:

- BigQuery, Dataflow, and Pub/Sub expertise

- Data pipeline skills in Python, Java, or Scala

- Terraform-based Infrastructure as Code (IaC)

- CI/CD tooling and cloud-native architecture

- Vertex AI, TensorFlow, and other GCP machine learning integration experience

FAQ 4: What are UK businesses doing to recruit and hire the top GCP engineers?

UK employers can do the following to hire the top GCP professionals:

- Highlight AI, big data, or multi-cloud innovation initiatives

- Offer flexible work schedules (hybrid or remote)

- Speed up the interview and offer process

- Get affiliated with a specialized cloud recruitment agency

- Hire the services of GCP engineers hiring specialists in the UK

FAQ 5: What is the career prospect for GCP professionals in the UK?

With Google Cloud's market share expected to grow from 12% in 2025 to around 15% by 2027, GCP engineers and architects are positioned for strong career opportunities, competitive salaries, and heightened demand across various industries. There is no better time to upskill or transition into a GCP-specified role.

Check out our latest jobs here

Proud Sponsors of DAMA UK

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 21 Jul 2025 09:00:25 GMT

KDR Talent Solutions is a proud sponsor of DAMA UK

KDR Talent Solutions is teaming up with DAMA UK to bring data professionals fresh insights, events and perks.

The alliance is designed to blend KDR’s data market experience, and our established network with DAMA’s renowned thought leadership, delivering timely research, practical guidance and exclusive opportunities directly to DAMA members.

We have planned a series of value add content comprising webinars, market reports and advice sessions and we are looking forward to offering DAMA Members:

Free UK salary benchmarking for core data roles
Discounted recruitment fees when you hire through us
One-on-one mentorship sessions with our own Mark Townsend

Community is at the heart of what we believe is important. Relationship building, trust and knowledge sharing is key and this sponsorship of DAMA UK is the perfect way to provide added value to the data community.

About KDR Talent Solutions
Since 2003, we’ve been matching top-tier data talent with the organisations that need them. Honest, community-minded, and always plugged into the latest trends, KDR drives the data industry forward through tailored recruitment and market research.

What employers are looking for when hiring Artificial Intelligence professionals.

Mon, 09 Jun 2025 09:47:12 GMT

As we move into summer 2025, the AI job market is very buoyant.

From computer vision to large language models (LLMs), the opportunities are still growing.

At KDR Talent Solutions, we speak to candidates every day who are excited about AI but unsure of how to make their next move. Whether you’re just getting started or looking to pivot from another tech discipline, this guide will help you take practical steps toward your next AI opportunity.

1. Understand the landscape.

AI roles come in many shapes and sizes, machine learning engineers, computer vision specialists, data scientists, MLOps professionals, and LLM researchers, just to name a few.

Top tip: Start by mapping out the kinds of problems you want to solve. Are you more interested in building models, deploying them, or working with the data that feeds them? Your passion and direction will help narrow your search and in turn, make your applications sharper.

2. Show value.

Hiring managers in AI want to see your work. Whether it’s a GitHub repo, a Kaggle competition entry, or a blog breaking down how you fine-tuned a model, evidence of hands-on experience compliments a tailored CV. Employers want to see the value you will bring to their business.

Bonus: Share your work online and tag relevant communities. Visibility can lead to opportunity.

3. Get Clear on the tech stack

In-demand tools and frameworks vary depending on the role. But here’s a snapshot of what we’re seeing right now:
Machine Learning & LLMs: Python, PyTorch, Hugging Face, LangChain, Vector DBs (like Pinecone or Weaviate)
Computer Vision: OpenCV, TensorFlow, YOLO and increasingly, diffusion models
MLOps/Deployment: MLflow, Docker, Kubernetes, Metaflow, SageMaker (AWS)
Data Engineering Support for AI: GCP, BigQuery, Airflow, dbt

If you’re missing some of these, don’t panic, employers value learning agility. Start skilling up and be ready to talk about what you’re currently learning and how that can be practically applied to solve business problems.

4. Tailor your applications for the role

AI job descriptions can often be buzzword-heavy. Drill into the job description and cut through the noise by showing how your skills directly relate to the company’s use case. If the company hasn’t published their use case, speak to the recruiter who is working the role as they will often have that insight and will be able to help you provide the best examples that align your skills to the issues the company is wanting to solve via the hire.

Tip from our recruiters: If a job posting mentions computer vision and LLMs, focus your CV and cover letter on the one you’re strongest in. Employers would rather see depth than a scattergun of acronyms. Tie in the teck stack with the business value you provided in your current or previous role.

5. Partner with the right recruiter

Working with a recruiter who specialises in AI makes a massive difference. At KDR, our team understands the nuances of the market, from early-stage startups building LLM infrastructure to established businesses embedding AI into core systems.

We’re not just matching keywords, we’re listening to what you want and making sure the companies we put you forward to are serious about AI, not just riding the hype.

Ready to make your move in AI? Let’s chat. Drop us a message or explore our live roles here .

Hiring an AI Team

Tue, 03 Jun 2025 14:18:33 GMT

For CTOs, building an AI team isn’t as simple as hiring a few machine learning engineers.

The real challenge lies in aligning hiring decisions to the stage of AI maturity your business is in, from early experimentation through to productisation and scale.

Depending on where you sit within the AI model maturity cycle, will determine how to approach building the team. We have broken down the typical scenarios our clients come to us with below, to help you identify how peers typically approach recruitment for each stage.

STAGE 1 – EXPLORATORY

Objective : Identify where AI adds business value.

At this stage, you’re typically asking questions like:

Can AI solve a real user or business problem? Is the data available and clean enough? What are the success metrics?

Hiring focus:

At this point you’d benefit from being lean, strategic and experimental. You need people who can test hypotheses fast. The kinds of profiles that are good for this stage are usually:

AI R&D profiles (often ex-academia or deep learning experts).

Why?

1. They’re trained to ask new questions, test hypotheses and explore uncharted territory. They are great at solving novel, undefined problems where there’s no playbook yet so they are ideal for greenfield AI use cases where innovation is the goal.

2. They have a strong grasp of machine learning theory, especially in areas like:

Neural networks

Generative models (GANs, VAEs)

Reinforcement learning

Transformers / LLMs

3. They can assess whether a model architecture is fundamentally appropriate and not just whether it works theoretically.

4. They are skilled in quick prototyping using tools like PyTorch and TensorFlow and comfortable building proofs-of-concept to validate ideas quickly. They are also used to iterative experimentation, which is critical in the early stages of AI discovery.

5. They are able to write clearly, summarise experiments and communicate findings to technical peers or senior stakeholders.

6. They typically stay up to date with the latest research from NeurIPS, ICML, and arXiv and are great at identifying new trends and adapting them to business contexts (e.g. fine-tuning a new LLM architecture).

Where They May Need Support

They may lack experience deploying models at scale or integrating them into real-world systems.

Business Alignment. They might need guidance to focus on commercial outcomes rather than academic elegance.

Strategic data scientists are also a good hire at this stage.

Why?
At this early stage, the question isn’t "Can we build it?" … it’s "Should we build it?"

Strategic data scientists excel at:

Understanding business problems and translating them into data questions
Assessing AI feasibility given the available data and resources
Prioritising use cases based on ROI potential, complexity and strategic fit

They connect technical possibilities with commercial priorities which is something a purely technical ML engineer may not be equipped to do. Essentially they are product thinkers with technical fluency. They’ll save you time by identifying whether the model should be built in the first place. Also, this profile tends to have enough business context to align with stakeholders and enough technical depth to assess data quality, completeness and structure. They can communicate effectively with product managers and business leads (to understand needs) as well as data engineers or researchers (to shape solutions). They’re often the glue that connects domain expertise with AI capability. They work iteratively and handle ambiguity well.

Stage 1 is often fuzzy. You don’t have a clear scope, complete data, or guaranteed value. Strategic data scientists thrive in that space because they're comfortable working with incomplete information and they pivot quickly when new insights emerge.

What you don’t need yet:

A full-stack ML engineer focused on pipelines

A team of data engineers to productionise anything

A model that's 99% accurate because 70% might be enough to prove value

Key CTO decision: Do you build a proof of concept in-house or partner with a vendor/research lab?

STAGE 2: PROTOTYPE TO MINIMUM VIABLE PRODUCT

Objective: Prove that AI can work in your domain, technically and commercially.

This is where many AI projects stall. The prototype works in isolation, but integrating it with your systems, infrastructure and users is a new challenge.

Hiring focus: Start bridging research and production. At this stage you’ll likely need:

MLOps/Platform engineers to take models out of notebooks

Backend developers to build data services or APIs

Data engineers to build the pipeline from source to inference

At this point: Avoid hiring a “team of scientists” without engineering support. They’ll build models no one can use.

STAGE 3: SCALE PHASE FROM VIABLE PRODUCT TO REAL PRODUCT

Objective: Make AI part of your infrastructure and roadmap.

At this point your hiring focus should be on specialists who can embed AI into your production stack and roadmap because now, the goal is no longer to prove that AI can work, it's to ensure that it works consistently, reliably and safely at scale, embedded into your existing infrastructure and product delivery pipeline.

MLOps + DevOps integration

Cloud-native data engineering (GCP, AWS, Azure)

AI-savvy product managers

Responsible AI expertise (compliance, bias, transparency)

CTO insight: This is where you build a cross-functional AI capability, not just a team. Because at Stage 3, the goal isn’t just to have a dedicated "AI team" off in a corner building models. The goal is to embed AI into how your entire organisation delivers value across products, platforms and teams.

If you stop at hiring a smart AI team, you’ll hit scaling problems. However, if you invest in the systems, processes and people to support AI across the business you’re building a capability that’s scalable, resilient and strategically valuable.

In practice, this means:

AI/ML engineers work closely with product, DevOps and customer teams

There’s shared infrastructure for data, model deployment and monitoring

Teams are trained to understand how to interact with AI systems (not just the AI experts)

AI isn’t a "lab project", it’s part of how products are built and improved

STAGE 4: EMBEDDED AI WITH ORGANISATION-WIDE ENABLEMENT

Objective: AI becomes a core part of how your organisation delivers value.

AI is part of multiple products and you’re scaling across teams and markets. Data governance and cross-functional alignment are critical.

Hiring focus:

Leadership: Head of AI / AI Product Director

Federated data science teams aligned to business units

AI Ops / model governance leads

Enablement roles: data product managers, internal platforms

CTO Role: Start thinking about AI capability architecture not just headcount.

Bonus: Aligning Hiring to AI Investment ROI

Every AI hire should map to a clear product or business metric.

CTOs who succeed with AI aren’t the ones with the biggest teams they’re the ones with:

Alignment between AI and product strategy

Cross-functional collaboration from day one

A roadmap for scaling AI capability, not just model accuracy

Need help hiring at the right AI maturity stage?

At KDR Talent Solutions, we understand the difference between hiring for PoC vs platform. Let’s build a team that moves with your needs.

Moving Beyond AI Bias: How the Right Talent Solves the Hard Problems

Wed, 30 Apr 2025 10:57:59 GMT

Bias in AI isn’t a new problem.

If you’re working in machine learning, computer vision or data science, you’ve likely already encountered the challenges around data imbalance, opaque models or underperforming systems across different demographic groups.

What matters now is what we do about it and more importantly, who we hire to fix it going forward. Most AI bias issues don’t stem from bad intentions, they come from gaps in team capability or oversight. So, what is the solution? Building multidisciplinary teams that are proactive, not reactive. That means hiring not just engineers and data scientists, but ethics-trained ML practitioners who can apply fairness metrics and bias mitigation techniques during development, not after the fact. On top of this, data curation experts who understand representation, annotation quality and provenance and then product and policy professionals who can balance innovation with governance and regulatory frameworks.

This isn’t about hiring generalists who “care about bias.” It’s about embedding specialist roles at every stage of the model lifecycle.

Standards are catching up, so should your talent strategy. With the launch of ISO/IEC 42001:2023, AI governance has a new global benchmark and this standard helps organisations build structured AI management systems with clear responsibilities, continuous monitoring, and ethical oversight.

But as we all know, a framework is only as good as the people implementing it. In my opinion, hiring individuals who understand (or can upskill into) ISO-aligned practices will be critical. Whether that’s a responsible AI lead who maps ethical risks, or an MLOps engineer integrating fairness evaluations into pipelines, the ability to align people with processes is where real progress will be made.

The most impressive AI teams I speak to right now aren’t waiting for regulation to force their hand. They’re running a variety of checks:

Running bias audits as a core KPI, not as an afterthought.
Pairing research scientists with ethicists in model validation.
Incentivising teams to publish fairness benchmarks alongside performance metrics.
Prioritising candidates with experience in causal inference, adversarial testing, or explainable AI, not just Kaggle scores.

And I believe, this mindset shift is changing the game from compliance to competitive advantage.

Where we come in

At KDR, we’re not just helping organisations find AI talent, we’re helping them find the right talent to build trustworthy, future-proof systems. I work closely with candidates who are actively shaping the ethical AI space, and who want to work for companies taking these issues seriously.

If you’re looking to scale a team that’s technically sharp and ethically aware, or if you’re struggling to find that niche mix of applied ML and governance expertise, we can help.

Because fixing AI bias isn’t just a technical problem. It’s a talent problem. And solving it starts with who you hire.

Data Governance in 2025: The hidden hiring challenge for financial services

Wed, 30 Apr 2025 10:42:13 GMT

Data governance has always been a critical foundation for financial services firms

In 2025, however, the landscape has shifted dramatically.

The rise of AI is fundamentally reshaping how data is created, processed and used across financial institutions (although admittedly maybe less emphasis as yet on the how data is created). With more decisions becoming data-enabled and automated, the need for robust, transparent governance frameworks has never been greater. Meanwhile, new regulations like the EU’s Artificial Intelligence Act and a focus on ISO 42001 are introducing stricter requirements for explainability, data traceability and accountability in AI systems, raising compliance demands across the sector. Alongside these shifts, public scrutiny of how organisations manage personal data has intensified. Clients, investors and regulators alike now expect higher standards of data privacy, security and ethical usage.

It is no longer enough to have a framework ‘on paper’; financial institutions now need dynamic, embedded data governance strategies that support both innovation and compliance.

Yet, as the need for stronger governance grows, an important challenge is emerging: finding the right talent to lead and deliver it.

So in this industry, why is Data Governance under a new spotlight?

Financial services organisations today are navigating a complex environment. There’s AI and machine learning adoption, requiring robust data lineage and model explainability. On top of this there are tighter regulations on data movement and cloud storage and increasing board-level scrutiny, as governance failures are seen as direct business risks. Client expectations around transparency and data ethics are rising. In short data governance has evolved. It is no longer simply about compliance and having standards in place; it is about enabling safe, sustainable data usage.

To answer this, the hiring challenge is becoming increasingly clear. Blended skillsets are rare. Companies need professionals who can define, build and embed a governance framework and the knowledge of the technical side of modern data stacks. More than ever, communication skills are critical because data governance leaders must bridge and engage multiple business areas like legal, risk, business and technology teams and this can be a rare combination when so many isolated data governance programmes have happened in the past. For those that are open to transferable skills from other industries, competition is fierce. Financial services firms are battling tech companies, fintechs and consultancies for the same pool of data-savvy talent. However this route at least widens access to available talent, rather than relying on the network of candidates with a data background within financial services.

We have noticed that contractor demand is rising. When permanent hiring projects are causing delays, organisations are turning to interim leaders and specialist consultants to fill urgent gaps.

How can financial services organisations stay ahead?

1. Hire for potential, not perfection. The "unicorn" candidate may not exist. Focus on adaptable, high-potential hires who have the required soft skills and can grow into complex governance roles.

2. Invest in cross-training. Future governance leaders may already be within your other teams.

3. Offer visible, influential roles. Data governance must be positioned as a strategic careerpath, not just regulatory box-ticking or stand-alone programme of work

4. Streamline your hiring processes. In a competitive market, speed and decisiveness are critical to securing top talent.

At least for now, data governance is a strategic enabler for financial services organisations. Those who act now to secure and grow the right talent will be best placed to lead in an increasingly complex, regulated and data-driven future.

At KDR Talent Solutions, we specialise in helping financial institutions find the next generation of data governance and leadership talent. If you would like to discuss your hiring strategy, we would love to hear from you.

Get in touch with the KDR team today.

Data Engineering vs Data Science – Which Should Your Business Invest In?

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Thu, 03 Apr 2025 10:22:09 GMT

When it comes to building a high-performing data team, how do you decide where to invest first?

As a hiring manager, you already know the basic differences between Data Engineering and Data Science.But when it comes to building a high-performing data team, how do you decide where to invest first? The reality is, without the right foundation, even the best data scientists will struggle to deliver value. Here, we explore when to prioritise Data Engineering vs. Data Science, how they impact business outcomes and what strategic hiring decisions will set you up for success.

The Strategic Role of Data Engineering

When Data Engineering comes first

Many businesses jump straight to hiring data scientists, expecting immediate insights. However, without structured, clean and accessible data, their work is slowed down by endless data wrangling. Key reasons to prioritise Data Engineering:

· When your data is disorganised, inconsistent or difficult to access, making analytics unreliable.

· When you don't have well-structured data pipelines, causing bottlenecks in data flow.

· When data silos exist, preventing cross-functional teams from accessing critical insights.

· When data scientists spend more time cleaning and preprocessing data than analysing it.

· When your business needs real-time analytics and scalable infrastructure to support AI and machine learning.

What Happens Without Data Engineers?

Imagine hiring a top-tier data scientist, only to have them spend 80% of their time fixing broken data pipelines instead of building models. This is a common issue when businesses lack a solid data engineering foundation.

The Role of Data Science in Business Growth

Once data pipelines are well-structured, data scientists unlock the true value of your data by:

· Building AI & machine learning models to optimise decision-making.

· Providing predictive analytics that drive revenue and efficiency.

· Uncovering hidden trends that give a competitive advantage.

· Enhancing automation in business processes through intelligent data-driven strategies.

When to Invest in Data Science

If your data is already structured and accessible, hiring data scientists can take your business intelligence to the next level. Consider prioritising Data Science if:

· You want to move from reactive to predictive decision-making.

· Your competitors are leveraging AI-driven insights and you need to keep up.

· You need to extract advanced insights from large datasets to inform business strategy.

Hiring strategy – decision time, do you need one, the other or both?

Scenario 1: Your business lacks a scalable data pipeline

Priority: Hire data engineers first.

Why? Without a structured foundation, data scientists will spend most of their time fixing data rather than analysing it.

Scenario 2: Your data infrastructure is strong, but insights are lacking

Priority: Invest in Data Science.

Why? If data is accessible and well-structured, data scientists can extract value quickly through advanced analytics and AI.

Scenario 3: You need real-time AI-driven decision making

Priority: Hire both Data Engineers and Data Scientists.

Why? A combined team ensures optimised data pipelines and cutting-edge AI models to maximise business value.

FAQs

1. Can data scientists handle data engineering tasks?

While some data scientists have engineering skills, forcing them to manage pipelines reduces efficiency. Hiring dedicated data engineers ensures smoother workflows and faster insights.

2. Should I hire a generalist who can do both?

A “full-stack” data expert can be valuable in early-stage startups, but for long-term scalability, separate roles provide greater specialisation and efficiency.

3. How do I build a balanced data team?

Start with Data Engineers to ensure a strong data infrastructure. Once the foundation is solid, bring in Data Scientists to extract insights and build AI-driven solutions.

Conclusion: Making the Right Investment

Rather than choosing one over the other, businesses should see Data Engineering and Data Science as complementary investments. Building a strong data infrastructure first ensures that Data Scientists can drive maximum value.

Next Steps

Need help hiring top Data Engineers or Data Scientists? Our team at KDR Talent Solutions specialises in finding the best AI and data professionals for your business. Contact us today!

The AI Skills Gap - Why Companies Struggle (and what to do about it)

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Thu, 03 Apr 2025 10:12:33 GMT

Demand for AI talent is booming, but filling roles can still feel challenging for some.

Despite a wave of AI bootcamps, MSc programmes and online certs, the real-world AI skills gap risks blocking innovation and slowing team scaling.

So, what’s actually going wrong? And what do high-performing hiring managers do differently to fix it?

Diagnosing the AI Skills Gap

The talent pool isn’t empty, but the skill sets often don’t align with what’s needed in production environments. Many job specs still optimise for “AI research” rather than applied, product-focused AI. The result can then be a mismatch to required outcomes, not because candidates lack skill, but because their experience often lies in different contexts, like academic research or prototype environments, rather than fully integrated production systems.

Actionable fix:

Stop hunting for unicorns and start mapping your needs to adjacent, trainable profiles. What do we mean by this? For example, let’s say you are trying to hire MLOps Engineers, the people who can productionise machine learning models, automate pipelines, manage versioning, monitoring, and deployment. But that specific skill set is still relatively niche and evolving.

The idea here is instead of waiting to find the perfect MLOps engineer, identify strong backend engineers with solid Python experience (the language of most ML frameworks), good knowledge of CI/CD pipelines, testing, containerisation, etc as well as being comfortable working with APIs, microservices, and deployment environments.

These engineers already have the foundational skills required to transition into MLOps quickly. You're just layering on ML-specific tools and workflows.

Backend engineers are already used to building reliable, scalable services, managing code versioning, testing and deployment, collaborating with DevOps teams and writing clean, maintainable Python code.

To make the transition over to MLOps, they would need to learn tools like MLflow, Airflow, Kubeflow, or Metaflow, model versioning and experimentation tracking, ML pipeline orchestration, monitoring performance and drift in production and serving models as APIs or microservices. Because they already know Python and software engineering principles, they can ramp up fast. Especially when working alongside data scientists who can handle the modelling, while they focus on deployment, automation and reliability.

Instead of endlessly searching for “experienced MLOps engineers”, which are scarce, you can re-skill strong backend engineers already in your team or hire backend engineers from outside with an interest in ML and invest in targeted upskilling. It’s a faster, more sustainable way to build MLOps capability internally.

How to spot the right type of candidate for this pivot when hiring:

They have a background in Spark/Kafka, cloud infra, batch and real-time data workflows

They have experience working with Data Science teams but never formally in ML

They are familiar with data lineage, observability and operational handoffs

They mention streaming data, event-driven architecture, or data product ownership

Why This Works:

Model performance depends just as much on the data pipeline as the algorithm. Yet many teams separate these too rigidly. Upskilling data engineers into ML pipeline engineers creates a bridge, in essence you have someone who then understands the nuances of data at scale, can speak both "infra" and "model" and builds production-ready, retrainable ML workflows. Essentially, they become the backbone of scalable, reliable and resilient AI systems.

Another approach could be to identify role aligned professionals with deep domain knowledge in business relevant areas like finance, healthcare, or biotech and upskilling them in applied machine learning, so they can help design, interpret and even build AI models that deliver real, business-aligned impact.

If you are considering this to help bolster your pipeline, some examples of job titles that may be suitable to pivot might be:

A credit risk analyst in finance

A clinical data manager in healthcare

A supply chain planner in pharma

An actuary, lab scientist, R&D lead, or commercial ops analyst

Because they already understand the context and challenges inside their industry, they know what success looks like for a model in practice. They work with data every day, just not always as an engineer. The goal isn’t to turn them into full-stack machine learning engineers, but to equip them with enough applied ML knowledge to become high-impact collaborators, bridging the gap between technical teams and real-world business needs.

Why It Works:

From speaking to clients, sometimes the issues they encounter stem from the lack of communication or understanding from the deep technical knowledge of the AI engineer on the one hand, and the business functions needs on the other. Using domain specialists as bridges provides the domain insight needed to ensure AI solutions align with practical, high-impact business goals. AI often fails not because of bad models, but because the models weren’t designed with real-world complexity or regulatory nuance in mind. A finance analyst or medical expert brings that critical context. Most of these experts already work in data-rich environments. They know the quirks, gaps, and sensitivities. That’s half the battle in building reliable ML systems. On top of this, applied ML is now more accessible with tools like:

AutoML platforms (DataRobot, H2O, Vertex AI)

Low-code notebooks (Jupyter, Colab)

ML libraries (Scikit-learn, XGBoost)

Whilst external ML hires often need time to get up to speed on industry context, regulatory nuance and stakeholder priorities. Domain experts already speak that language fluently. With structured support and collaboration, bridging the technical gap becomes far more achievable, and the results, more immediately relevant.

Giving high-performing subject-matter experts a path into applied ML also keeps them engaged, invested and future-proofed. Many are already self-learning tools like Python or AutoML platforms, structured training and guided mentorship can unlock real value, fast.

Realistic Examples in Practice:

In healthcare: A clinical ops manager learns Python and model evaluation basics.

She now collaborates with the data science team to shape models for patient trial matching, offering context that improves both performance and clinical trust.

In banking: A credit analyst upskills in AutoML and interpretable model techniques.

He works alongside ML engineers to improve default prediction models, bringing in real-world logic, policy nuance, and exception handling that wouldn’t be obvious from the data alone.

The Bottom Line:

Domain experts + ML training = applied AI that delivers.

They won’t replace your ML engineers.

But they’ll make your models more relevant, faster to deploy, and more likely to succeed in the real world.

Another challenge compounding the skills gap is that AI education doesn’t touch messy data or scaling as effectively as real world scenarios.

Academia teaches model building on clean datasets. But in the real world? Data is incomplete, inconsistent, or scattered across silos and AI needs to be both accurate and deployable.

To help ensure you are getting the right candidates through we would suggest prioritising candidates who can talk through:

Real-world trade-offs (e.g. latency vs. accuracy)

Model lifecycle challenges (drift, retraining)

Data validation and governance practices

Cross-functional AI integration (Ops, Product, Data)

Also, partner with universities, yes, but focus on building an internal bridge: mentor junior hires with domain knowledge but no applied AI background. It’s the fastest way to future-proof your pipeline.

The third issue is the best candidates might never hit the open market

Elite AI engineers often get pre-emptively hired by hyperscalers or VC-backed startups. By the time your job ad’s live, they’re already gone.

In these instances, it may be tricky for you to compete on key motivators such as:

Salary, AI infrastructure and research opportunities

Instead tap into other known candidate motivators such as:

High-impact, high-ownership work

Greenfield projects and architectural freedom

Remote-first culture with a good work-life balance

Clear pathways to influence and progression

Actionable fix:

If you can’t outbid the likes of Google, out-“meaning” them. Lead with your mission, your roadmap and your real impact.

Your job descriptions might be working against you

Some AI job specs are outdated, overly broad, or ask for “10 years of experience” in technologies that are 4 years old. It’s an easy mistake to make as many time poor hiring managers or TA’s re-use legacy job descriptions and just tweak them. The problem with this however is that it prevents fresh thinking about what is really required for the role. To make job descriptions work for you it’s worth overhauling any that are older than 12 months to ensure that only the relevant requirements are added.

Actionable fix:

Strip out credentials and focus on capabilities

Separate “must-have” from “nice-to-have”, ruthlessly

Align job titles to market norms: use role titles that reflect what the person will actually be doing and that candidates recognise and search for.

Test the JD with someone external (your best candidates won’t read past paragraph two if it’s fluff)

Examples of common misaligned job titles:

Posting a role as “AI Engineer”

…but it’s actually about building data pipelines and infrastructure.

A better title would be: ML Ops Engineer or Data Engineer (ML focus)

Advertising a “Machine Learning Engineer”

…but the role is mostly about research or exploratory modelling.

A better title would be: Applied Research Scientist or Data Scientist (ML focus)

Listing a “Data Scientist”

…but the job is heavily focused on deploying models to production and managing model lifecycle.

A better title would be: ML Engineer or MLOps Engineer

Why It Matters:
It attracts the right talent because the best candidates self-identify by title. If the label doesn’t match the role, they won’t apply. It also improves clarity for internal stakeholders: It’s easier to scope, budget and align expectations when the title reflects actual responsibilities. As an added bonus it saves time in the hiring process because you’ll spend less time filtering through irrelevant applications from people misled by the title.

How to close the AI skills gap (for real)

1. Build internal conversion pipelines

Your next AI engineer might already be on your team. Create structured pathways for:

Backend to MLOps

Analyst to ML Modelling

Platform to AI Infrastructure

Data engineer to Model integration

Use learning budgets to fund certifications, but bolster them with on-project exposure and mentorship.

2. Target non-obvious hiring channels

Instead of posting on generic job boards:

Mine OSS contributors to relevant GitHub repos

Look at AI hackathons or Kaggle leaderboard talent

Engage in niche communities (HuggingFace, LangChain Discords)

Partner with companies sunsetting AI projects, great talent can often get let go when strategy shifts.

3. Rethink your hiring process

Speed wins. Top AI candidates expect offers in 1–2 weeks. But speed alone isn’t enough.

Make your process a selling tool:

Give a sneak peek at real problems they'll solve

Let them meet future collaborators (not just HR and a whiteboard)

Share your AI roadmap and how this role drives it

4. And don’t forget: career trajectory options , tooling autonomy and the chance to architect real-world AI systems often provide a draw just as strongly as clarity over compensation,

FAQs

Q: Why is hiring AI talent still so hard?

Because you're often hiring into an ecosystem (infra, data quality, orchestration), not just a role. Solving the surrounding tech debt makes hires more successful.

Q: Can non-AI engineers really transition successfully?

Yes, if your environment allows for structured learning, real-world practice and support. Focus on trainability, not perfection.

Q: How do smaller companies win against FAANG offers?

They don’t compete, they differentiate. Sell your mission, your flexibility and your culture of ownership.

Q: How do we future proof our hiring strategy?

Stop building around static roles, instead start building learning organisations.

What a “learning organisation” looks like:

Instead of just filling skill gaps, learning organisations invest in capability building. That means:

Hiring for adaptability and learning velocity, not just current expertise

Upskilling internal talent from adjacent areas

Creating space and structure for continuous learning (e.g. dedicated time for experimenting with new tools, internal tech talks, certifications)

Encouraging cross-functional collaboration, so AI doesn’t live in a silo

Empowering employees to evolve with the tech, not fall behind it

What this means for hiring strategy:

Hire for core competencies, not just tools

Build job specs that focus on problem-solving and learning ability

Look for people excited by change, not just stability

Create career paths that allow for growth into emerging roles

Partner with recruitment teams who understand how talent evolves

The AI skills gap won’t close overnight, but companies that act now will win later. The future belongs to those who are able to build adaptable, multi-skilled teams, create space for learning, not just hiring, focus on delivery, not just job titles

Need to build your AI team?

KDR Talent Solutions works with leading organisations to find and develop high-impact AI talent, from MLOps experts to applied NLP engineers. Let’s talk.

Message us or visit www.kdrtalentsolutions.com

5 strategies to attract & retain talent

Mon, 17 Mar 2025 18:24:20 GMT

5 ways to attract talent in the current climate

The tech job market has changed and is continuing to evolve. The landscape of this sector has been impacted in several ways attracting and hiring technology talent is, without a doubt a challenge. It’s important to understand as a hiring company the candidates and what they are looking for.

Today, IT candidates look beyond big names when they search for a job. They seek out a company’s culture, the flexibility within the working structure, diversity, company benefits, career growth and not to mention transparency across the whole business itself.

It’s important to note that building a reputation where employees really appreciate your approach and want to work for you takes time and consistency. Here at KDR we are here to help you with those steps to attract talent in the current climate.

Here are 5 ways to attract and retain talent:

Transparency

Let’s be honest here: with so much demand for top talent in the technology field it is important for you as a company to come across clearly and coherently. Candidates will look through your social presence/website, job ads and develop a perception of the business and what it stands for.

If you are truly wanting to hire the best talent, you need to be prepared to offer several benefits that are competitive and transparent. But it doesn’t stop there, what does career progression look like? Is your structure transparent whereby they can visualise working their way through the company. Does your company culture match internally what it portrays externally?

You need to be clear about your onboarding process, and the career development opportunities as well as any additional learning opportunities. In the current climate candidates are having to continuously upskill, they know if they stop learning, they will be left behind in the market. Thinking about your employee’s development is a huge attraction and very important currently.

An appealing career path needs to be available. Candidates want to know they can progress and it’s just as important to retain them as an employee by offering to help them achieve their goals.

Having regular catchups to provide insights into an employee’s performance is important for their development. This will help identify their aspirations and individual goals. They will also provide an opportunity to discuss any room for improvements and to provide guidance where needed so that there are no surprises when it comes to the appraisal.

Employers need to be aware that everyone’s set of skills and needs are different, so are their career paths. This isn’t us saying have 100+ different paths on offer as that’s not sustainable. However, providing each employee with their own individual plan, containing their learning objectives, mentoring and progression planning will help to attract and retain professionals.

Company Culture

Company culture is an eco-system that needs to be nurtured in order to thrive. A successful culture brings people together and allows everyone to work as a team. Having a healthy culture allows your team to thrive and should align to your company values.

To attract the best talent, it’s critical for companies to build a reputation whereby candidates are able to instantly recognise your culture and what you stand for and ultimately aspire to want to work for you!

Having the right culture will create an environment where employees don’t want to move on. A positive culture impacts business growth, longevity, and the teams’ results. Showcasing a commitment to a healthy company culture will attract the best talent.

Here are just a few things to how you can showcase your company culture.

Jobs Ads

Write job ads that reflect what your business is about and what it can offer. This is really important when it comes to attracting the best talent. What is a typical day like? Are you a formal business? Casual? Try to capture this in your job ad and showcase how your business appreciates its employees. Highlight your benefits and be clear with what you expect from a potential hire and what they can expect from the company

Language

Company culture needs to be positively displayed in all marketing communications internally and externally. Candidates and clients appreciate transparency in how you address and communicate with potential employees. This is showcase via job ads, company website, interviews, and the onboarding process as well as evidenced behaviours

Values

Highlight your company values. What stands out about your current team that highlights what your values and culture represent? Most, if not all, companies have a values page and it’s important for the organisation to use these values to drive performance

Benefits

The candidate led market requires close scrutiny of your benefits package to see if it is competitive. To attract and retain top talent, a company needs to be forward-thinking and have benefits that really resonate with professionals. A benefits package shows a real loyalty and commitment to each professional working for you, not to mention it is the first thing a professional looks at when considering to a take job with you. Providing a good benefit package will make employees feel motivated and rewarded for all their work and efforts. A focus on employee wellbeing and how working at an organisation will enhance their life is the space where benefits packages are moving into.

Diversity

With so much demand for top talent and for a business to remain competitive, it is crucial to obtain the best people. A diverse and inclusive workforce is necessary to drive innovation, creativity, and strategy. To reap the benefits of a diverse workforce and attract the best talent companies must ensure that their reach for candidates is as wide as possible. Internal processes need to ensure they don’t contain inherent biases around gender, ethnicity, sexual orientation, disability, or socioeconomic backgrounds.

In order to succeed KDR has a few tips:

Consistency is key

In order to succeed consistency is key. You need to make sure you follow a process of collecting data, analysing it and addressing issues that arise. Consistent internal and external communication around the subject is key.

Training

Having regular discussions and training sessions with a focus on diversity is essential for companies to help build an inclusive culture environment. One of the key blockers in changing the workforce is the lack of understanding and inherent unconscious biases. The recruitment process is a great place to open up positions to a wider talent pool as long as all aspects of the process are reviewed for inclusion along the way.

Language

When you want to appeal to a more diverse pool of candidates, language matters. From job descriptions to blogs you need to be mindful of the language used to not off put potential candidates

Diverse interview panels

Having a diverse hiring panel is a step towards increasing diversity within your business. It provides a fairer opportunity for every candidate going through the interview process.

Increasing diversity is essential for every organisation to attract the best talent. It is a commitment but essential, it will be a challenge but take one step at a time and don’t forget to measure your progress. For more on Diversity and how to attract & retain the best talent head over to our whitepaper here

Autonomy, Mastery & Purpose

It’s not all about an environment where if you do well, you’ll get a reward. Not everyone will connect with this approach and employees can get lost in the drive to find a way to be praised or promoted. Research shows that combining autonomy, mastery and purpose into a person’s role will do a lot more for motivation once the basic needs for a certain salary level are taken care of.

Autonomy

Proactively looking into autonomy motivators is a great way to help employees direct their working structure. By rethinking your ‘set rules’ (for example office hours, flexibility, KPI’s, dress code) you can increase staff autonomy, build trust, and improve your reputation to attract future talent not to mention retain employees as well.

To support autonomy in the workplace you need to provide your employees with control over their workload and how they want to complete it. Empowerment is everything and allowing autonomy will empower employees to deliver more.

Mastery

Are you providing your employees with an opportunity to be motivated by success through improving their skills via continual learning and/or practice? To attract the best talent, you need to have an array of learning objectives to progress and grow them in area/s they are keen on exploring.

Purpose

It's human instinct to have a purpose, we all need to wake up in the morning and know we are contributing to something bigger than ourselves. Successful companies in this area ensure their employees understand how their role fits into the bigger picture. Motivation is proven to increase when this is very clearly understood by a team.

Summary

The key to attracting the best talent and increasing performance in this current climate is employee engagement. The best talent is looking beyond the old order of employment. Successful talent attractive companies are focusing on what matters: They are engaging candidates early with clear company values, vision and mission. They showcase well company culture, with current employees acting as advocates. They are opening up their roles to as wide an audience as possible and ensuring their culture is inclusive and they invest in individual career paths, motivators and ongoing development of skills.

For more help in attracting the best talent contact us today! To find out more you can see more advice on hiring in this current environment on Slideshare here.

How to align your EVP to your talent strategy

Mon, 17 Mar 2025 13:37:36 GMT

How to create a modern EVP and align it to your Recruitment Process: A five point approach.

A strong Employer Value Proposition is a valuable addition to any business. Research from Gartner shows that 65% of candidates they surveyed had pulled out of a recruitment process because of an unattractive EVP. If this is a more widespread sentiment, outside of the respondent sample Gartner ran the study for, then organisations really can’t afford to let this slip.

From a recruitment business perspective, it is obvious to us when a client has a great EVP versus one that doesn’t, and we have helped businesses refine these to help make them an employer of choice. Once you have one, how can you use it to help with the hiring process? Here we look at what makes a great EVP and how to align it to the recruitment process to help increase hires and then retention of great talent to your business.

What makes a great EVP?

Since the pandemic it has become apparent that people’s needs are changing. Businesses need to shift towards a more people centric approach to the EVP with a stronger focus on the experience and feelings of the people that make up the business rather than the experience of work itself. How does this translate into tangible outcomes? Autonomy and mastery have long been identified as drivers of motivation; Dan Pink has a great YouTube video on this from 2016 based on an MIT study. A modern EVP will take these elements and embed them into the employee experience. It will focus on why we give a particular benefit rather than focusing on what we give.

If we look at what forms the basis of a modern employee experience (which shifts focus from employee to human being), we can break it down into 5 different elements.

Connectivity: To colleagues, to community, to selves and family
Meaning: A sense of purpose to an employee’s work experience (purpose)
Well-being: A deeper drive to help employees feel cared for, moving beyond provision to action
Growth: Personal and professional growth (mastery)
Autonomy: A greater degree of flexibility around work patterns

Questions to ask to help form a modernised EVP.

To help colleagues feel more connected you could look at your policies and culture around inclusivity.

Do you survey your team to understand where there are gaps in feelings of belonging?
Do you have inclusion goals as part of manager KPI’s?
Are there any personal initiative opportunities for employees to own and execute?
Are managers aware of the importance of supporting their employees, families and community and are they trained on this area?
Are there opportunities for employees to anonymously provide feedback on their experiences within the business so there is a feeling of safety around this subject?
What family friendly benefits do you offer?
Are managers trained in empathy?
Do managers have access to guides to help them build conversations based on trust around emotional needs?

Meaning:

Do you have a defined purpose that employees buy into?
Do you have a cross functional team that meet to discuss societal issues and deeper meaningful work-related issues to gain diverse perspectives?
Is there a transparent policy on communicating wider societal issues so that employees can feel connected to wider issues beyond the business framework?
Have you created a defined set of values that employees buy into?

Wellbeing:

Do you gather employee input for benefit design?
Do you have any well-being champions within the business? Are they equipped with time and tools to help them in this role?
Do you regularly communicate the employee assistance programme (do you have one?)
Is talk around mental wellbeing normalised around the business?
Do you have any trained mental health first aiders?
Are managers trained on creating personal connections with team members?
Is wellbeing an ongoing conversation within the business?

Growth (personal and professional):

Do you have an established mentoring programme in place and training for the mentors?
Are your managers equipped to have conversations with team members around personal and professional growth goals?
Are employees empowered to design their own personal development with support from the business?
Is there an environment where employees can communicate what they would like to do at any point in their career? (degree, diploma, courses)
Do you use social media or internal comms to encourage employees to share their personal growth with colleagues or the wider community?

Autonomy:

Do you allow deep flexibility over where, when, how much and with whom they work?
For guidance around what to provide flexibility on, look at: work-life balance, location, innovation, risk taking and workspace.
Do you allow teams to co create their own flexible working patterns?
Have you identified areas of each role that do allow for flexibility (keeping in mind productivity of the business as a whole)
If you are opening up flexibility, have you provided managers with the tools to have the conversations with team members to be accountable for their productivity?
Do managers have an environment where they can share and learn from peers, and have you identified high performing managers and teams where flexibility is high as well as productivity for benchmarking purposes?

If you have decided to overhaul your EVP and have looked at incorporating a more person centric policy, perhaps using the questions above to help form that policy, you can then translate the answers into benefits and make cultural references to the way the business is run and the impact on the employee within your document. These coupled with the progression, rewards, remuneration, values and purpose will form the total package of the employee experience whilst working within the business and this is what will help retain team members and attract new ones.

Once you have the EVP you should use it within the hiring process. Candidates will be researching your business so the starting point is in how you communicate your EVP. Does your website reflect the components within the EVP? Have you created career pages on the site? Do you demonstrate tangibly the employee (human) experience of working at your business over your social media channels. In essence if someone researched your business as a potential candidate, would they see what you want them to see? Is the application process easy or if using an agency do you trust their attraction processes, have you spoken about translating the EVP into the recruitment phase? A good Search agency will help you align your EVP to the full hiring process.

Secondly you need to make sure your job adverts are optimised for the EVP. Ensure you have checked that the language used is inclusive and gender neutral you need to include the core elements of the EVP within it.

Interview Stage:

For those candidates that are not successful, the messaging around this is a good opportunity to back up the EVP. Many businesses forget that candidates who are unsuccessful now, may become employees of the future. If they have a bad experience at this stage you may be missing out on top talent in the future. Ensure each applicant receives comms around the fact that their application will not be taken any further.

For those that you want to interview, this is a great opportunity to showcase your EVP at this early stage. Hopefully inclusivity is prominent so make sure you ask about any reasonable adjustments that the candidate may need to attend the interview. This can be communicated in alignment with your EVP with curated wording to make the candidate feel safe revealing these adjustments (often hidden disabilities stay hidden because of previous bad experiences).

Once you have them at interview stage, it is the first opportunity for the candidate to come into contact (after the information gathering stage) with the people that represent your company culture. The welcome they receive, the interview structure, feedback and experience of the environment should accurately reflect the experience you have showcased so far in the materials they have seen about the value you can offer them.

Onboarding:

Once through the door, the onboarding is the next important phase in the continuation of your EVP. The welcome they received during the recruitment and interview stage should seamlessly fit into the early days of joining your business. If this is disjointed it can lead to feelings that they were reeled in with recruitment tools rather than authentic representation of your culture and values. Align the devised EVP to the onboarding process and ensure team and manager buy in.

Retention:

A candidate experience only turns into an employee experience once that hire feels integrated and feels a sense of belonging to the team and business. The faster this happens the better for the long term prospects of the hire and the employee. This is where walking the walk shines, the continued authentic representation of the EVP shows the team that they continue to be aligned to the very things that attracted them in the first place.

If you are looking for help with aligning your EVP to your hiring process, speak to one of our Search team specialists.

Balancing innovation and stability, career choices in data & Ai

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Wed, 26 Feb 2025 11:49:10 GMT

Data and AI professionals can find themselves at a crossroads

Data and AI professionals can find themselves at a crossroads: should they chase the cutting edge of innovation or opt for the stability of established technologies? We would argue that striking the right balance between these two forces is key to long-term career success and satisfaction.

The Innovation Argument

Data and AI are at the heart of some of the most ground-breaking advancements today. From generative AI models to real-time data processing, companies are racing to leverage the latest technologies. Professionals who immerse themselves in innovation can:

Stay ahead of the curve, by learning new AI techniques and programming languages as this makes candidates more competitive.
If you get the possibility to put yourself forward to work on transformative projects, cutting-edge AI applications in whatever field you operate in can offer exciting challenges and further your knowledge.
Command higher salaries with skills sets and expertise in the latest AI tools, because they often translate into lucrative job opportunities.

However, the constant demand to upskill and adapt to emerging trends can be exhausting. The rapid evolution of AI technologies also means that skills may become obsolete faster than in other fields.

The Case for Stability

While innovation fuels progress, stability provides a strong foundation. Many companies rely on robust, well-established data infrastructures that require ongoing maintenance, optimisation, and governance. Professionals focused on stability can:

Ensure business continuity. Data governance, compliance and security are critical for long-term operational success. AI for example relies on strong data governance and security.
Regardless of the technology that is currently coming thick and fast, mastering fundamental concepts in data engineering, data quality and database management still provides long-term career value.
Potential burnout avoidance. A structured work environment with well-defined processes can (but not always!) be less stressful than constantly adapting to new technologies.

While stability provides predictability, it may also limit exposure to the latest innovations, potentially slowing career growth in highly dynamic industries.

Finding the Right Balance

So how can professionals navigate this balance? The key is in knowing yourself, here are some strategies to help you get there:

Identify Your Career Goals. Do you thrive on solving new challenges, or do you prefer building strong, lasting systems? Understanding your motivations will guide your career decisions.
Blend Innovation with practicality because many roles, such as data engineering and AI product development, require both cutting-edge skills and foundational expertise.
Choose the right industry for you. Some sectors, like fintech and autonomous systems, demand rapid innovation, while others, such as insurance or healthcare, emphasise stability and compliance.
Continuous learning with a purpose will get you where you want to be. Rather than chasing every trend, focus on technologies that align with long-term industry needs.
Network and seek mentorship because engaging with professionals in your field can provide insights into how others successfully balance innovation and stability.

A successful career in Data and AI doesn’t necessarily require choosing between innovation and stability, it’s about finding the right mix. By strategically positioning themselves, professionals can enjoy the excitement of innovation while ensuring long-term career security. Whether you're drawn to building AI-driven solutions or ensuring data reliability, balancing these two forces is the key to sustainable career growth in the evolving world of Data and AI.

What are the costs of a bad hire and how to get it right

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Wed, 26 Feb 2025 10:20:54 GMT

A bad hire can cost far more than just a few months' salary ...

How to Avoid Common Recruitment Pitfalls

Hiring the right talent in AI and Data is a critical challenge for businesses looking to drive innovation and stay ahead of the competition. However, a bad hire can cost far more than just a few months' salary because it can lead to missed project deadlines, technical debt, team disruptions or a weakened market position. In this article, we’ll explore the risks of mis-hiring in AI and data roles and provide actionable strategies to get recruitment right the first time.

The True Cost of a Bad Hire

1. Financial Impact

Hiring and onboarding a new employee is a costly process. Research suggests that a bad hire can cost a company up to three times the employee's annual salary in the UK, according to the Recruitment & Employment Confederation (REC) . These figures underscore the financial burden of replacing an ineffective hire, particularly in high-paying AI and data roles.

2. Productivity Loss

A mis-hire in AI or data engineering can lead to inefficiencies, missed deadlines or poor-quality work that requires rework from other team members. A project that should have accelerated business insights or improved AI capabilities may instead slow down or fail entirely.

3. Impact on Team Morale

A poor hire can disrupt team dynamics, lower morale or increase turnover among top-performing employees. AI and data teams thrive on collaboration, and when a team member lacks the necessary skills or maybe even work ethic, others are left to pick up the slack, leading to frustration and burnout.

4. Reputational Damage

In high-stakes fields like AI and data, mistakes can have far-reaching consequences. A mis-hire could lead to poor model performance, data security risks, or compliance failures which in turn can damage client trust and the company’s reputation.

Common Pitfalls in AI & Data Recruitment and How to Avoid Them

1. Prioritising Technical Skills Over Business Impact

It’s tempting to hire candidates with the most impressive AI models or technical capabilities, but success in AI and data roles also requires business alignment . Candidates should not only have technical expertise but also understand how their work impacts the company's strategic goals.

Solution: Incorporate real-world problem-solving assessments into the interview process to evaluate how candidates approach business challenges and not just coding exercises.

2. Relying Solely on Keywords in CV’s

AI and data professionals often list numerous tools and frameworks on their resumes, but that doesn’t always equate to hands-on expertise.

Solution: If you are not using a specialist recruiter to screen candidates, conduct in-depth technical interviews with practical coding tasks and system design discussions to assess a candidate’s real-world proficiency.

3. Overlooking Soft Skills

As you know, data scientists, engineers or solutions architects don’t work in isolation, they collaborate with product teams, executives and other stakeholders.

Solution: Evaluate communication, influencing, adaptability skills and teamwork through behavioural based interview questions (Try using the STAR technique if you are not currently).

4. Ignoring Cultural Fit

AI and data teams function best when aligned with a company’s values and long-term vision. A technically skilled but culturally misaligned employee can lead to disengagement and turnover.

Solution: Involve multiple stakeholders in the hiring process to ensure alignment with company culture and work environment. Ensure the hiring panel understand what they are assessing for and have consistency in their approach.

5. Rushing the Hiring Process

The demand for AI and data talent is high, but hiring under pressure can lead to costly mistakes.

Solution: Partner with specialised AI and data recruitment agencies that understand the market, reducing time-to-hire whilst maintaining quality.

Getting Recruitment Right the First Time

1. Define Clear Role Requirements

Ensure job descriptions accurately reflect both the technical expertise and business impact expected from the role.

2. Use a Structured Hiring Process

Implement a multi-stage interview process, including technical assessments, business problem-solving discussions and cultural fit evaluations. Ensure this process is used for each candidate to establish consistency and fairness.

3. Offer Competitive Packages & Growth Opportunities

Top AI and data professionals have many opportunities so ensure your company stands out by offering career growth , a strong company culture and attractive compensation packages .

A bad hire in AI or data isn’t just an inconvenience it’s a costly mistake that can impact finances, productivity, team morale and company reputation. By refining your hiring process, prioritising both technical and business acumen and working with specialised recruiters, you can ensure that your AI and data teams drive innovation rather than disruption.

Need help finding the right AI & data talent? KDR Talent Solutions specialises in connecting businesses with the best professionals in AI, data engineering, and solutions engineering. Get in touch today to build your high-performing team!

Exclusive Insights - What AI Leaders are saying

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 24 Feb 2025 14:57:14 GMT

As AI adoption accelerates across industries

As AI adoption accelerates across industries, AI leaders face both exciting opportunities and significant challenges. To stay ahead, organisations must understand the key concerns and priorities of AI decision-makers. This report compiles exclusive insights from AI leaders on the most pressing topics, including innovation, talent, ethical AI, and future trends.

Key Insights from AI Leaders

Bias in AI models remain a top concern among AI professionals.

Companies face increasing pressure to implement transparent and explainable AI solutions.

Regulatory changes in AI governance are on the horizon, making compliance a growing focus.

Takeaway: Organisations must proactively integrate fairness, accountability and transparency in their AI frameworks to build trust.

Many AI projects fail to progress beyond proof-of-concept due to data silos and infrastructure limitations.

AI leaders emphasise the need for better collaboration between data teams and business stakeholders.

Scalable AI solutions require stronger governance, model monitoring and cloud investments.

Takeaway: To achieve long-term success, AI initiatives must align with business goals and be supported by robust infrastructure.

AI leaders continue to highlight the skills shortage as a major hurdle.

Specialised areas such as MLOps, NLP, computer vision, and AI ethics are in high demand.

Companies must compete not just on salary but on career development and learning opportunities.

Takeaway: Investing in upskilling and reskilling initiatives is crucial to bridging the AI talent gap.

AI teams thrive in environments that encourage cross-functional collaboration and experimentation.

Remote and hybrid work models are reshaping team dynamics and talent acquisition strategies.

Retaining top AI talent requires a focus on work-life balance and meaningful projects.

Takeaway: Fostering an inclusive, innovative workplace culture is essential for attracting and retaining AI professionals.

AI leaders predict increased adoption of generative AI, autonomous AI agents, and AI-driven automation.

The integration of AI with IoT, robotics, and edge computing is expected to transform multiple industries.

AI’s role in decision intelligence and predictive analytics will become more prominent.

Takeaway: Staying ahead of AI advancements requires continuous investment in emerging technologies and strategic foresight.

Organisations that prioritise responsible AI development, invest in workforce skills, and cultivate strong workplace cultures will be best positioned for future growth. By understanding the key insights shared by AI leaders, businesses can make informed decisions to shape the future of AI within their operations.

AI & Data Science Salaries in the UK...Are You Paying Competitively?

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 24 Feb 2025 14:37:17 GMT

With AI and data science driving business transformation

With AI and data science driving business transformation, competition for top talent remains fierce. Companies that fail to offer competitive salaries risk losing skilled professionals to competitors willing to pay more. In this report, we analyse the latest AI and data science salary trends in the UK with regional variations. We also provide strategies to ensure you remain competitive in attracting and retaining top talent.

Here are the key takeaways based on conversations with employers and candidates in this field.

1. Rising salaries due to high demand

AI and machine learning engineers continue to command high salaries as organisations prioritise AI-driven innovation. On top of this, the numbers of qualified and experienced professionals are still not quite there compared to the range of opportunities available, so companies are competing for the best.

LinkedIn reports a 37% increase over the last year in professionals with the job title Artificial Intelligence Engineer or Generative AI Engineer. After London the next largest cohort of talent sits in the Manchester area.

Salaries for AI specialists have increased by 15-20% over the past two years.

How to stay competitive:
Regularly benchmark salaries to stay competitive and offer performance-based incentives or share options.

2. Regional salary variations

London provides the highest salaries, with AI engineers earning an average of £99,000 - £160,000 annually.

Midlands also provide competitive salaries, typically ranging between £105,000 - £115,000.

North of England & Scotland still command high salaries ranging from £92,000 – £98,000.

To remain competitive and to gain access to the main cohorts of talent, consider remote or flexible working models.

3. The Importance of Non-Monetary Benefits

Salary is important, but career growth, flexibility and learning opportunities also influence job decisions. Another key area is to ensure you have a robust data strategy and governance framework, as the solidity of these often dictate the success of the AI initiative. Candidates know this, often moving on due to the lack of one or both in their existing employer.

Companies with robust learning & development programmes retain employees longer.

Remain competitive by showcasing data and AI initiatives, talking a strong game around the frameworks and environment someone would be working within and offering strong upskilling initiatives, mentorship programmes (if appropriate for role and possibility) and flexible work arrangements.

4. Competitive Compensation Strategies for AI Talent

Equity & profit-sharing is increasingly attractive.

Performance-based incentives help retain top talent.

Comprehensive benefits are expected. Health, wellness and work-life balance incentives matter more than ever.

Think beyond base salaries and ensure total compensation aligns with UK industry benchmarks and employee expectations.

Exclusive Insights - what AI Leaders are saying

1. Skills in High Demand

According to LinkedIn, the fastest growing skills lie within multivariate statistics and AI leaders are emphasising the growing need for specialists in LLM, Gen AI, Support Vector Machine and AI ethics.

Demand for professionals with MLOps and data engineering expertise is increasing.

2. Salary expectations vs reality

AI Leaders feel many AI professionals expect salaries above market rates, leading to mismatches when hiring.

Salary transparency and clear career progression paths are key to managing expectations.

Be upfront about salaries but align salary offers with market realities while offering clear growth opportunities.

3. The Role of Workplace Culture

AI leaders stress the importance of collaborative and research-driven cultures.

Bear in mind that flexibility in work arrangements can be a major deciding factor for job seekers.

A strong workplace culture can be a key differentiator in talent retention.

By combining competitive pay with career growth opportunities and work flexibility, organisations can attract and retain top AI and data science professionals in 2025 and beyond.

AI in 2025 - The challenges leaders need to prepare for

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 24 Feb 2025 12:06:33 GMT

The coming years will bring technological advancements

The coming years will bring technological advancements, regulatory changes and according to government data, talent shortages that will challenge organisations aiming to scale AI effectively. In this article, we explore the key challenges Data and AI leaders are facing in 2025 and the main strategies they are using to overcome them.

Key Challenges in AI for 2025

1. Scaling AI across the business

Many organisations struggle to move AI projects from proof-of-concept to full-scale implementation.

Integration with existing systems, data silos and lack of alignment with business objectives remain major hurdles.

Solution: Leaders are developing their AI strategy and the best ones are aligned with clear business outcomes, investing in scalable infrastructure and encouraging cross-functional collaboration.

2. Ethical AI and Bias Mitigation

AI models can inadvertently introduce biases, leading to unfair outcomes and reputational risks.

Increased regulatory scrutiny requires organisations to adopt transparent and explainable AI models.

Solution: Ethical AI frameworks are being explored, those successfully forging forward are conducting regular bias audits and ensuring diverse datasets for training models.

3. Talent Shortages and Skill Gaps

The demand for AI talent far exceeds supply, making it difficult to attract and retain skilled professionals.

Upskilling existing employees and fostering a culture of continuous learning are essential.

Solution: Invest in employee training, establish partnerships with universities and niche recruitment experts and offer competitive compensation packages.

4. AI Governance and Compliance

With evolving regulations and industry-specific compliance requirements, organisations must stay ahead of legal changes.

Lack of standardised AI governance frameworks can create compliance risks.

Solution: Implement strong AI governance policies, establish dedicated compliance teams and stay informed on global AI regulations.

5. Managing AI Model Performance and Reliability

AI models degrade over time due to data drift, requiring ongoing monitoring and retraining.

Ensuring the reliability of AI-driven decisions is critical, particularly in high-stakes industries like healthcare and finance.

Solution: Deploy AI monitoring tools, automate model retraining processes, and establish clear performance benchmarks.

As AI adoption accelerates, organisations must proactively address these challenges to maximise AI’s value. By implementing robust strategies for scaling, ethics, talent management, governance and performance monitoring, AI leaders can drive sustainable innovation and stay ahead in the competitive landscape of 2025.

AI Talent - How to Attract & Retain the Best

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 24 Feb 2025 11:30:04 GMT

The demand for AI and data science talent has surged in recent years

The demand for AI and data science talent has surged in recent years, with companies across industries racing to build AI-driven solutions. This boom has also created competition, where attracting and retaining the best professionals has become increasingly challenging. In this report, we explore the key factors influencing AI talent retention and provide actionable strategies for AI leaders.

Firstly let look at why AI Professionals leave a role

Lack of career growth. AI professionals seek continuous learning and career progression. Without clear pathways for development and with the current market providing such choice for roles, many look around for better offerings.

Burnout & workload. High expectations, project complexity or scope creep and unrealistic deadlines can lead to burnout, causing valuable team members to leave.

Better compensation offers. Competitive salaries, equity options and benefits lure top talent away. Benchmarking your offering mitigates this.

Lack of purpose. AI professionals often seek meaningful work that aligns with their values. Increasingly this is falling into the ethical AI development and impactful projects camp.

Poor leadership & culture. A misalignment between leadership expectations and team dynamics can often result in disengagement.

Taking all that into account, here's how to go about attracting the best AI or data science candidates:

1. Offer competitive compensation & benefits

Benchmark salaries regularly to ensure alignment with industry trends.

Provide performance-based incentives, stock options if possible and learning budgets.

2. Create a culture of innovation & learning

Encourage knowledge sharing, mentorship programmes and AI research initiatives.

Support team members in attending conferences and obtaining certifications.

3. Create clear career growth paths

Implement structured career progression frameworks.

Offer leadership training and cross-functional collaboration opportunities.

4. Prioritise work-fife balance & well-being

Introduce flexible work arrangements, such as remote/hybrid options if they are currently not available.

Promote mental health programmes and manageable workloads.

5. Align AI work with meaningful impact

Ensure projects have ethical AI considerations and align with societal benefits.

Allow team members to contribute to open-source AI initiatives and research.

Conclusion
Winning the best AI talent requires more than just competitive salaries. You need to create an environment that allows for learning, well-being, and purpose-driven work. By implementing these strategies, you'll be in a healthier position to attract and retain top AI professionals.

What $500 billion investment in AI means for start ups and scale ups

Wed, 29 Jan 2025 09:57:40 GMT

AI has transformed the technological landscape....

AI has transformed the technological landscape, presenting both opportunities and challenges for startups and scale-ups in the United States. As solutions architects, understanding the evolving policy environment is crucial for effectively integrating AI into business models.

The current U.S. AI policy landscape

In January 2025, President Donald Trump announced the "Stargate" private sector deal, a collaboration with tech giants like OpenAI, Oracle, and SoftBank, aiming to invest up to $500 billion in AI infrastructure over the next four years. This project underscores the administration's commitment to bolstering AI capabilities domestically.

Concurrently, an executive order was signed to promote AI development free from ideological bias, emphasizing human flourishing, economic competitiveness, and national security. This order mandates a review of existing policies to eliminate barriers to AI innovation.

Implications for Solutions Architects

For solutions architects in startups and scale-ups, these developments offer several considerations:

The Stargate project signifies substantial investments in AI infrastructure, potentially providing access to advanced resources and partnerships beneficial for emerging companies.

The executive order's focus on removing barriers to AI innovation may lead to a more favorable regulatory climate, encouraging experimentation and rapid deployment of AI solutions.

With increased government support, there is an opportunity to leverage national initiatives to enhance product offerings, ensuring alignment with the latest technological advancements.

Strategic Recommendations

To navigate this evolving landscape:

Stay informed and regularly monitor policy changes and government initiatives to understand their impact on AI development and deployment.

See if there is scope to leverage government programs. Explore opportunities within federal AI projects that may offer support or collaboration prospects for startups.

Despite the push for rapid innovation, maintain a focus on ethical AI practices to ensure long-term sustainability and public trust.

By aligning with national priorities and leveraging available resources, solutions architects can effectively integrate AI into their organizations, driving innovation and growth in the competitive U.S. market.

Drowning in CV’s in 2025: Advice for Hiring Managers

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Tue, 28 Jan 2025 11:11:17 GMT

Everyone is talking about how technology is being used within the recruitment process

Everyone is talking about how technology is being used within the recruitment process, especially how AI is influencing the space along with how technology is increasing the rate of applications. Hiring Managers often bear the brunt of this, especially if a central recruitment function is not understanding the nuances of the roles in your teams. So, what do you do if you haven’t got access to new technology which can help cut the workload involved in reviewing CV’s or can’t influence what is being used?

Even without control over your company’s technology stack, there are several impactful steps you can take to attract and retain top talent in today’s competitive job market. These strategies focus on fostering human connections, leveraging existing resources creatively, and aligning hiring processes with current workforce trends.

1. Champion Skills-Based Hiring

You don’t need fancy software to assess candidates based on their skills rather than traditional qualifications. By creating detailed role profiles that highlight the specific competencies required, you can focus interviews and assessments on real-world skills. Use practical exercises, scenario-based questions, or sample projects to gauge candidates’ ability to perform the role effectively.

To do this correctly you need to this: Partner with current team members to create a list of key skills for each role. Use simple tools such as collaborative platforms to keep track of your evaluations. Then make sure everyone who is hiring has access to this list, including the central talent acquisition team.

2. Create Opportunities for Upskilling and Growth

While you may not control your company’s formal learning and development programmes, you can build an internal culture of upskilling if one doesn’t exist. Employees are more likely to stay engaged when they feel their career is progressing. Offer mentorship opportunities, job-shadowing programmes, or even informal learning groups.

Practical Tip : Suggest cross-functional projects or rotation programmes to expose team members to new challenges. Highlight career progression opportunities during interviews to appeal to growth-oriented candidates.

3. Personalise Candidate Engagement

AI tools can automate communication, but nothing replaces the human touch. Hiring managers can still make a strong impact by building genuine relationships with candidates. Make a habit of reaching out personally, offering clarity on the process, and providing meaningful feedback. These comms can be templated to save having to write out the same “process” information but with room to personalise where time and context permits.

Practical Tip: Create a “personal touch” checklist for every candidate. Simple gestures like personalised follow-ups or introducing candidates to team members during the hiring process can make a lasting impression.

4. Leverage Existing Data to Plan Ahead

Even without advanced analytics platforms, you can still work proactively. Use past hiring data, team feedback and industry research to anticipate future talent needs. Collaborate with your HR team to identify potential gaps before they arise. Sometimes there is a disconnect between what a hiring manager knows from 1:1’s with their team and their progression plans, and what “the business” knows in terms of future needs. If this information is known and shared in real time, it saves time interviewing people when they are not needed.

Practical Tip : Yes, it’s on top of your day job however, setting aside time each quarter to review hiring needs, match and tweak to fit your real time teams needs with your HR or Talent Acquisition team means you are saving time in the long run with irrelevant CV reviews (C# when to need C++ anyone?) and interviews for roles that are not needed. Track turnover rates and emerging team needs to stay ahead of the curve.

5. Promote Your Team’s Unique Culture

Employer branding doesn’t have to be a massive company-wide initiative. Most people forget to focus on their team’s culture, which is important to get across to new employees. As a hiring manager, you can highlight your team’s strengths and culture during recruitment. Use testimonials, social media, or even your own LinkedIn profile to showcase why candidates should join your team.

Practical Tip: Encourage your team to share success stories or moments of achievement that reflect your group’s values. Share these in job descriptions or during interviews to help candidates visualise themselves in the role.

6. Prioritise Flexibility and Work-Life Balance

While your company’s policies on hybrid work or flexible hours may already be set, you can emphasise the importance of work-life balance within your own team. Candidates are drawn to managers who value their well-being.

Practical Tip: Highlight specific examples of how your team supports flexibility during interviews. If possible, offer tailored solutions like staggered start times or the availability of remote workdays.

7. Drive diversity at the top level

While broader DEI strategies may be outside your control, you can still encourage an inclusive environment within your team. Focus on removing bias from your own hiring practices by using structured interviews and diverse interview panels. Make a conscious effort to ensure your job descriptions are inclusive and appeal to a broad audience.

Practical Tip: Use free resources, like gender-neutral job description tools (Kat Matfield is a free online tool or just plug it into ChatGPT), and ensure where possible that a diverse mix of employees is involved in your hiring decisions.

Hiring managers play a vital role in building a strong talent pipeline, even without access to cutting-edge tools or technologies. By focusing on people-first strategies, leveraging the resources at your disposal, and prioritising long-term relationships, you can position your team as a destination for top-tier talent.

Ultimately, success lies in your ability to balance the human aspect of hiring with strategic thinking—proving that even without the latest tech, you can still build a pipeline that delivers value now and in the future.

What the AI Opportunities Action Plan Means for Data and Tech Professionals

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Tue, 28 Jan 2025 10:55:02 GMT

The UK government has released its ambitious AI Opportunities Action Plan

The UK government has released its ambitious AI Opportunities Action Plan, which outlines a comprehensive strategy to position Britain as a global leader in artificial intelligence. With a clear focus on leveraging AI for economic growth, public service transformation and national security, this plan lays the groundwork for an exciting new era in the data and technology sectors. For businesses seeking to innovate and candidates looking to advance their careers, this roadmap presents transformative opportunities.

As it’s rather a long document, we have read the plan and summarised it below. If they follow through on the plans then it should provide a fantastic foundation for clients and job seekers alike who are working in this space.

Key Highlights of the AI Opportunities Action Plan

Step 1 is to lay the foundations for success

The government plans to build a secure and sustainable AI infrastructure. Key to this is expanding the AI Research Resource (AIRR) by 2030. This resource is a national hub for AI-focused computational power, data access and collaboration. For example the AIRR will offer large-scale computing capabilities required to train advanced AI models, which typically demand immense processing power.

Segmented into sector specific clusters of national priority (e.g. healthcare or climate) the resource aims to integrate public and private data assets, enabling researchers and organisations to utilise rich datasets for AI innovation. It’s designed to help scale AI solutions from research to real-world application by creating partnerships between academia, startups as well as enterprises. One of the areas of concern is sustainability so it also aims to help ensure AI development is aligned with long-term ecological goals in mind.

In essence, the AIRR is the backbone of the UK’s strategy to lead in global AI innovation, providing the resources required for breakthroughs in areas like large language models, robotics and AI for science.

Adding to this foundation will be the launch of a National Data Library (NDL) to responsibly unlock public and private data assets. For businesses, access to curated datasets will enable more accurate model training, predictive analytics and innovation in AI-driven solutions.

Opportunities

Companies contributing to the Library could benefit from public recognition and partnerships and data becoming central to AI development. Candidates skilled in data governance, engineering and AI-driven analysis should find increased opportunities to work with these assets.

As part of the plans, the report points to the significant skills gaps and how the UK is falling behind other countries so the recommendation is to train tens of thousands of AI professionals and establish scholarships to attract top talent. There is also an opportunity here to access apprenticeship funding for existing members of staff to upskill themselves in AI related topics.

Part of laying the foundations relies on robust regulation and earned trust. The UK’s regulatory framework aims to be flexible and proportionate, ensuring that it supports innovation while mitigating risks. Regulators in key sectors (for example: healthcare, finance or transport) are encouraged to provide clear, tailored rules for AI applications in their industries. This allows for tailored approaches with perhaps more credible outcomes. Through institutions like the AI Safety Institute, they want to tread a fine line between taking a pro-innovation approach but also providing a robust framework of regulation within which to operate. Although UK-based, the AI Safety Institute is designed to work with like-minded countries and organisations to shape global norms and standards for AI governance. By maintaining partnerships with key allies, the institute ensures that the UK's approach to AI safety aligns with and contributes to international frameworks.

The Next Step would be to transform lives through AI adoption

Via a “scan, pilot and scale” approach they want businesses of all sizes to be able to take advantage of the opportunities. This should be an exciting period for business and employees alike as they get involved in many more mini pilot projects rather than getting bogged down with unwieldy large-scale projects without proof of concept.

The report also cites UK based AI Growth Zones which are areas identified that could help with the smooth acceleration of AI innovation, for example simplifying planning permission for data centres (access to land, energy and resources)

What does this mean for data and tech businesses?

It should provide a springboard for innovation. The government’s investment in AI infrastructure, such as the AIRR and AI Growth Zones, will create an environment ripe for innovation. Clients in the data and technology sectors can expect access to cutting-edge tools, robust computational resources, and enhanced collaboration opportunities through public-private partnerships.

The opportunity lies in access to the National Data Library. Businesses can leverage the National Data Library to access high-value datasets, accelerating AI model development and deployment.

Companies should explore government-backed initiatives, such as innovation sandboxes, to test and scale new technologies while reducing regulatory risks.

Pro-innovation regulation will help. Balanced regulations and assurance frameworks provide clarity and confidence for businesses to integrate AI into their operations.

Clients should engage with sector-specific AI regulatory guidance and invest in upskilling teams to meet emerging compliance standards.

With initiatives to train tens of thousands of AI professionals and attract global talent, businesses will benefit from an increasingly skilled workforce.

Clients can tap into a diverse pool of AI talent equipped to develop innovative solutions.

Collaborate with universities and AI training programmes to secure emerging talent and offer compelling career development opportunities.

An Expanding Job Market

The plan’s focus on AI adoption and innovation will create demand for specialists in areas such as data engineering, machine learning, computer vision, and AI governance.

AI Growth Zones and national AI initiatives will generate job openings in both emerging startups and established organisations.

Candidates should upskill in high-demand areas, such as natural language processing, cloud-based data engineering, and AI ethics.

So what are the pathways to progression?

Apprenticeships, AI-focused higher education programmes, and lifelong learning opportunities will make it easier for professionals to enter or advance in the AI field. Conversion courses and industry-relevant certifications will open doors for individuals from non-AI backgrounds to transition into AI roles. If you are interested in following this path, take advantage of government-funded programmes to gain relevant qualifications and position yourself as a valuable asset in the AI-driven job market. You can find out where the apprenticeships are run here: https://findapprenticeshiptraining.apprenticeships.education.gov.uk/courses/561 (apprenticeship course and funding are also available for existing and senior members of the team).

Looking Ahead

The AI Opportunities Action Plan presents a bold vision for integrating artificial intelligence into every aspect of the UK’s economy and society. For clients in the data and tech sectors, this is a call to embrace innovation and position themselves as leaders in their fields. For candidates, it’s an invitation to develop new skills and seize emerging opportunities in a job market where for the first time in a very long time, we just don’t know how this technology will evolve.

Now is the time to act. Whether you’re looking to hire top talent, adopt transformative technologies, or make your next career move, the opportunities in AI are boundless.

What is increasing the demand for Data Scientists?

Sun, 19 Jan 2025 09:21:48 GMT

In this article, we’ll take a look at some of the latest trends in data science that are shaping the future of this field.

With advancements in machine learning, artificial intelligence, and cloud computing, data science has become an increasingly important tool for businesses and organisations of all sizes. The ability to collect, analyse, and interpret data has never been more vital, and as a result, data science has become one of the most in-demand skills on the job market.

One of the biggest trends in data science today is the increased focus on Explainable AI (XAI). XAI refers to the ability to understand, interpret, and explain the decision-making processes of AI systems, and it is becoming increasingly important as AI is used in areas such as healthcare, finance, and the criminal justice system where transparency and accountability are needed. This trend is expected to grow even more in the coming years, as organisations and governments around the world look for ways to ensure that AI systems are trustworthy and that they are making decisions in a responsible and ethical manner.

Automated machine learning (AutoML)

This is another trend that is rapidly gaining popularity in the data science community. AutoML is a technology that automates many of the tasks involved in building and training machine learning models, making it easier for data scientists to develop models and get results faster. This technology is ideal for organisations that want to scale their machine learning initiatives and for those that don’t have the technical expertise in-house. AutoML also has the potential to democratise machine learning by making it more accessible to businesses of all sizes and by helping to close the skills gap in the data science community (Chat GPT is a case in point).

The Rise of Edge Computing

is a growing trend in data science that is focused on processing data at the edge of a network, rather than in a central location such as a data centre. Edge computing plays an important role as the volume of data generated by Internet of Things (IoT) devices and other connected devices continues to grow. By processing data at the edge, organisations can reduce the amount of data that needs to be transmitted over the network, reducing latency and improving the responsiveness of their systems. This trend is expected to continue to grow in the coming years, as more and more devices become connected and the need for real-time processing and analysis of data increases.

The Emergence of Augmented Analytics is another trend that is gaining pace in the data science community. Augmented analytics is a technology that uses machine learning algorithms to automate and optimise many of the tasks involved in data analysis, such as data preparation, feature engineering, and model selection. This technology has the potential to greatly improve the speed and efficiency of data analysis, and it is expected to become more widely adopted as organisations look for ways to get more value from their data.

Deepfake videos are a growing concern in the field of data science, as well as in society more broadly. Deepfake technology uses advanced machine learning algorithms to manipulate and generate synthetic media, such as videos or images, that appear to be authentic but are actually fake. This technology can be used to create videos of people doing or saying things that they never actually did or said, and it has the potential to cause significant harm, particularly in the realm of politics and disinformation. Detecting deepfakes can be difficult, as the technology is getting increasingly sophisticated and is able to produce very convincing synthetic media. This makes it important for data scientists to develop new techniques for detecting deepfakes, such as by analysing the subtle differences between real and fake videos or by using meta-data to determine the origin of a video, they can work to develop open-source tools and technologies that make it easier to detect deepfakes and to prevent their spread, or they can engage in public education and awareness campaigns to help people understand the dangers of deepfakes and how to protect themselves.

According to our own KDR data and job search websites, the number of job postings for data scientists in the UK has been steadily increasing, and the demand for the role is expected to continue to grow in the future. The average salary for a data scientist in the UK is over £75,000 per year, which is competitive compared to other tech roles in the country.

With all these advancements in technology, data scientists are in high demand in the UK and this trend is expected to continue as organisations continue to collect and analyse more data and seek individuals with the right combination of technical skills, creativity, and business know how to help them turn their data into actionable insights that drive growth and competitiveness.

How to Stay Ahead in Your Data Career: Trends and Technologies for 2025

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 06 Jan 2025 09:18:24 GMT

Chances are if you are a data professional you might be thinking about what 2025 will bring

Chances are if you are a data professional you might be thinking about what 2025 will bring in terms of career, technological advancements and therefore what to focus on in terms of learning and development. Staying ahead requires not just keeping up with trends but actively adapting and evolving your skill set. We wanted to outline some of the key trends and technologies that we are being asked about by clients and pass this information over to you to help you decide where to put your focus in 2025. So here goes, here are the key trends and technologies that we think will define data careers in 2025, and we've thrown in some bonus actionable steps to future-proof your career to boot.

1. AI and Machine Learning Integration

It probably goes without saying but AI and machine learning (ML) will continue to dominate the data landscape, driving innovation across all industries. To prepare the ground in 2025, we think your focus should be on:

Data Readiness: Our Annual State of Data Report has shown that data management, in particular data governance has been a major focus for the last 3 years for businesses. Getting this aspect right is fundamental in order to create a solid foundation for generative AI as it typically needs accurate, governed data to be effective. According to some industry reports, companies are planning on bringing in on average around 15 new gen AI tools to the business this year, therefore in 2025 ensure that you are focusing on getting data "AI ready" before introducing new gen AI tools into the mix - this might mean accelerating the adoption of tools that can tag, catalogue and clean data for AI use.

AutoML : Automated machine learning platforms are enabling non-technical users to build predictive models, democratising AI and slowly shifting the role of data professionals to overseeing, optimising and innovating.

What you can do to pave the way for AutoML tools:

Again, shift your focus towards data preparation and curation. As mentioned above, clean, well-structured and annotated data is essential for AutoML tools to perform effectively.

Develop pipelines and workflows ensuring to automate repetitive data tasks to ensure consistency and reliability.

Gain expertise in tool integration. Look on platforms like Pluralsight for courses to learn how to integrate AutoML platforms with existing analytics and business intelligence tools.

Advocate for team collaboration and promote knowledge sharing across teams to ensure that business users understand the capabilities and limitations of AutoML tools.

It's important to stay involved in quality control during the integration phase. Review and validate models produced by AutoML systems to ensure they align with business goals and ethical standards.

2. Real-Time Data Processing

Businesses are prioritising systems that deliver actionable insights instantly and real-time data processing has become more accessible to a wider pool of companies due to cost efficiencies brought about by cloud computing.

How to stay ahead:

Learn about real-time analytics tools such as Apache Kafka, Flink and Spark Streaming.

Understand the principles of event-driven architectures and data pipelines.

Explore the integration of edge computing technologies for low-latency data processing.

3. Synthetic Data Generation

As organisations grapple with data privacy concerns and the need for large, diverse datasets for training purposes, synthetic data generation is emerging as a game-changer. By creating artificial data that mimics real-world scenarios, businesses can accelerate AI model training and ensure compliance with privacy regulations.

How to stay ahead:

Learn the basics of synthetic data generation tools (examples might be Gretel.ai, Synthesis AI and Mostly AI).

Understand how to balance realism (making synthetic data look and behave like real-world data) with privacy (ensuring the synthetic data doesn't reveal sensitive information) when generating synthetic datasets.

Explore use cases in industries like healthcare, finance or retail to see how synthetic data can solve domain-specific challenges.

Stay informed about ethical guidelines and legal considerations around synthetic data usage.

4. Data Governance and Ethics

As data privacy regulations become stricter worldwide, businesses must ensure compliance while maintaining data usage. Transparency, ethical considerations, and robust governance will help achieve compliance.

How to stay ahead:

Stay informed about global data protection laws like GDPR, CCPA and emerging frameworks.

If appropriate, acquire certifications in data governance (e.g., CDMP or CIPP).

5. Cloud-Native Data Solutions

Cloud computing continues to revolutionise how organisations manage and analyse data. Hybrid and multi-cloud strategies are becoming the norm, offering scalability and flexibility.

How to stay ahead:

Develop expertise in platforms like AWS, Azure, and Google Cloud.

Learn Kubernetes and Docker for managing containerised applications.

Understand cloud-native data warehousing tools like Snowflake and BigQuery.

6. The Rise of DataOps

DataOps, the agile methodology for data management has gained traction. It focuses on improving communication, integration and automation to streamline the data lifecycle.

How to stay ahead:

Master tools like dbt, Apache Airflow, and Git for data version control.

Learn CI/CD principles for data pipelines.

Emphasise collaboration and documentation to enhance team productivity.

7. Upskilling in Data Visualisation and Storytelling

Year on year one area that differentiates those that are successful on their data strategy execution from those that are not, is the ability to tell compelling stories with the data analysed. Professionals are expected to communicate insights effectively to diverse audiences, some technical, most not. Tools and techniques for visualisation and storytelling will remain in high demand.

How to stay ahead:

Hone your skills in tools like Tableau, Power BI and Looker.

Learn the principles of design and storytelling to craft compelling narratives.

Practice translating complex data into actionable insights for decision-makers - Learn to speak "CEO", focus on the business outcome not on the technical details when gaining stakeholder buy in.

9. The Growing Role of Soft Skills

While technical expertise is essential, soft skills like communication, influencing, leadership and adaptability are increasingly critical for career growth.

How to stay ahead:

If possible, invest in leadership and communication training.

Cultivate a growth mindset to navigate changes and challenges effectively.

Build a professional network to stay informed and share knowledge.

Conclusion

The data industry in 2025 will continue to be defined by advancements, legislation constraints and evolving business needs. By staying informed, continuously learning and strategically positioning yourself, you can remain competitive and thrive in this dynamic field. Embrace the trends, master the tools and remember the ability to adapt is the most valuable skill of all.

How to attract & identify top talent in 2025's competitive market

Jo.Dionysiou@kdrtalentsolutions.com (Jo Dionysiou) — Mon, 06 Jan 2025 09:18:13 GMT

The KDR Way to attract top talent in 2025's competitive market

As we enter 2025, the demand for specialised data and tech talent is on the increase. The market started opening up during Q4 and Q1 has started positively with more companies opening up roles or building out teams. Companies that have held back on recruitment due to over hiring in previous periods or hiring freezes are now opening up roles and the competition for talent is high. We wanted to help companies to adopt targeted strategies to attract and retain these skilled professionals. We have taken our knowledge of the industry and what professionals are looking for, based on thousands of conversations and our annual survey of the community we serve and filtered the answers to serve you the best.

1. Showcase tech stacks and innovation

An area often forgotten by hiring companies lies in the showcasing of tech stacks and the innovations that come out of collaborations with the data team and the rest of the business. Professionals in tech-centric roles are drawn to companies that lead in innovation. We would suggest highlighting your use of cutting-edge technologies, such as Google Cloud Platform (GCP) tools, advanced AI frameworks or big data analytics. If you have use cases, emphasise projects that showcase practical applications of machine learning models or analytics in real-world scenarios, which appeal strongly to candidates in these fields.

2. Promote your learning and development culture

We can’t emphasise enough how much top data and tech talent seek environments that prioritise professional growth and learning. We would suggest if you do offer well-structured programmes for continuous education, then really put this front and centre of your hiring campaigns – include certifications that can help a professional grow in their area of speciality (like GCP certification for data engineers for example), and if you run regular workshops on the latest technologies in AI, machine learning, and data analytics mention this. Emphasising a commitment to staying at the forefront of technology can be a significant draw and help to lift you into the employer of choice level.

3. Showcase your attitude towards ethical AI or sustainable data

Talking about innovation and adoption of life-changing technologies, as AI and machine learning become more integrated into business processes, demonstrating ethical AI practices is something that is being discussed more by candidates coming through our client processes. Hopefully you have a plan or process in place for this, and if you do then make sure you promote your company’s commitment to ethical data usage, transparency in AI algorithms and efforts to mitigate biases. This attracts professionals who are not only skilled but also value responsible innovation.

5. Offer flexibility around working arrangements

2024 seemed to be all about trying to encourage people back into fully on-site roles, however offering remote work options, flexible hours and results-oriented work environments over fully on-site environments make your company attractive. Not only that, it also opens up the talent market to people who are not in your local area, to people who have the talent and ability but also have to pick the kids up from school and to a more diverse group of people. As an aside, consider providing competitive benefits tailored to data and tech professionals, such as subscriptions to premium development platforms, paid time for personal projects, or sabbaticals to pursue tech innovations as this speaks to their motivations around why they choose employers.

6. Engage with relevant communities

A great way to build access to people before they are looking is by building relationships within specific data and tech communities. For companies looking to hire, ideas around participation might be to sponsor hackathons, meetups, and conferences that cater to the community you wish to engage. This not only enhances your visibility as an employer of choice but also allows you to engage with potential candidates informally and understand their career aspirations and motivations.

7. Share stories and case studies of successful projects

It can be very powerful to share inspiring stories of success, whether that be of an individual or a team project that made a difference to the business. Often people are looking for ways to visualise what it would be like to work somewhere, bringing these projects, successes or team members to life for them can make the difference between a candidate wanting to be put forward or accepting a role or not. Choose ones that involve innovation. Showcase how these projects have driven success for your clients or made a significant impact in the business. This demonstrates the team’s capability and commitment to delivering high-value, cutting-edge work.

8. If you are using recruitment agencies, make sure they are selling your EVP (i.e all of the above)

A good agency will fill roles for you, but a great agency will act as your brand ambassador, fill roles, grow future pipelines, create word of mouth opportunities and build stronger loyalties to your opportunities by fully understanding your EVP and storytelling. Niche agencies are usually better at this as they are able to not only do the above but also fully understand the technical aspects of the role.

2024 in Review: Trends in the Data, Analytics, and Technology Industries

Jo.Dionysiou@kdrtalentsolutions.com (Jo Dionysiou) — Tue, 03 Dec 2024 15:10:39 GMT

The data, analytics and technology sectors continued to evolve throughout 2024

The data, analytics and technology sectors continued to evolve throughout 2024, driven by advances in AI, regulatory shifts and of course the long and rumbling issue of companies needing to find skilled talent. The economic and political backdrop to all this meant that for some industry sectors it was BAU but for others they found they were unable to execute on their planned growth. Here’s a snapshot of the year’s major trends and what they meant for professionals in these industries:

Key Highlights from 2024

Fiverr’s report on AI usage and their supporting video “nobody cares” pulled the focus onto how people are actually using AI, driving home the message that it’s not the usage of the tech that is an issue, it’s on the skill of the individual and whether they are able to get the best out of the tech. Talent shortage within the AI sector was high in the UK during 2024 as businesses adopted aspects of it more prolifically than they had during 2023. COP29 also highlighted a significant gap within the sustainability sector for digital skills necessary to harness AI effectively. Coupled with the fact that AI training was relatively low compared to this shortage of skills so this is potentially an area to look at in 2025 if you want to remain relevant.

Data streaming took off in 2024, with platforms like Kafka and Confluent enabling businesses to make swift, informed decisions based on real-time data. AI needs access to reliable and constant data in order to be effective and up to date so with an increase in AI usage there comes a need for businesses to get the foundations for a steady stream of data correct.

Salary Trends: Up, down and everything in-between!

Not surprisingly the sectors that did well during 2024 were those that utilised AI – gen AI, large language models and computer vision professionals experienced salary increases of 10-20% in high demand markets and competition for this talent was fierce. Digital transformation projects, like the data streaming ones mentioned earlier, also provided a good basis for salary upticks for Cloud Infrastructure professionals involved in the sector. Data roles within the insurance and financial sectors did well, with some roles fairing better than others, Analysts for example saw between a 5%-6% increase whereas ML and AI specialists salaries experienced on average a 10% growth.

In demand skills

Predictably, skills in building, fine tuning and deploying LLMs were really hot. Familiarity with frameworks like TensorFlow, NumPy, PyTorch, SciKit Learn and Hugging Face helped professionals gain new employment.

Data Engineers with niche component skills such as Snowflake, AWS, Azure and Google Cloud Platform were in high demand as companies expanded cloud adoption, the expansion of big data initiatives and the necessity for robust data infrastructure prompted organisations to invest in these skilled data engineers. Gen AI skills across areas like content creation, customer service and predictive analytics meant those that took the time to learn how to implement the technology into their roles were more in demand than those that did not.

Another discipline where professionals were in high demand was data privacy and AI governance for a variety of reasons around AI implementation, regulatory changes.

In terms of programming languages Python remained dominant but Go and Rust gained traction for their performance and efficiency in large-scale applications.

Challenges in 2024

Hiring freezes and layoffs driven by macro-economic uncertainties led to hiring slowdowns in some sectors. The hybrid work trend persisted with fully remote roles becoming scarcer and companies started to increase on-site days citing collaboration and culture as the main driver. Forward thinking businesses found ways to continue the culture with a blended workforce in place and continued to benefit from a wider talent pool outside of site locality.

Advice for 2025 regardless of sector

If you are looking to grow professionally in 2025 these are tried and tested methods to help you achieve your goals:

1. Continuous Skill Development

Regardless of sector, we would advise looking at obtaining certifications in high-demand areas that compliment your role. An obvious one would be anything AI related or cloud computing. Stay updated with emerging technologies and methodologies through online courses, workshops, and industry seminars.

2. Expand Your Professional Network

The best way to engage with peers is to attend conferences, webinars and networking events to connect with peers and industry leaders. Consider becoming a member of relevant organisations to access resources and opportunities for collaboration.

3. Enhance Your Personal Brand

The power of sharing insights and projects on platforms like LinkedIn should not be underestimated. Not only does it indicate to new employers the level of thought leadership or expertise, it also can lead to new avenues like side projects, speaker opportunities, and new idea generation. Regardless of whether you post or not, create a portfolio highlighting your work, achievements and skills because this makes everything easier for you if you ever need to present such things to potential employers.

4. Seek Career Advancement Opportunities

Seek out industry salary benchmarks (or contact us for ours!) to help advocate effectively for raises or promotions.

Sometimes being open to lateral moves or new positions that offer growth and learning opportunities that aren’t a direct linear move can be a great way to advance your career in the long run.

5. Consider taking on projects or initiatives outside of your usual role remit that showcase your leadership capabilities and potential for advancement. This of course depends on your company, however most companies will be running initiatives alongside BAU activities, whether that’s people or business improvement related.

When we look at the progressing initiatives within this sector in 2025 there is plenty to look forward to. Open finance will continue to develop, data driven companies will grow in numbers and capability, government policies are in place to support the growth of this sector and roles will continue to be added to the growing portfolio of skills sets, data ethicists and quantum computing included. For career advice, role discussion, help with your CV or hiring please do get in touch and have a great festive period, ready for whatever 2025 brings!!

Using Candidate Motivators in your Data Hiring Strategies

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Fri, 29 Nov 2024 09:57:17 GMT

Data from the State of Data 2024

Hiring Strategies for the Motivators of Men and Women in the Data Industry: Insights from the State of Data 2024

Recruiting the best people for the team can sometimes feel like trying to find a needle in a haystack. The problem that some companies create without realising is they exclude candidates from the process by not tapping into the known motivators for staying in, moving into or leaving a role. Motivators are the core drivers that encourage professionals to join and remain within an organisation. The report highlights career progression, meaningful work, flexibility, and leadership vision as primary motivators. These factors not only help to attract talent but also ensure long-term engagement and retention.

The State of Data 2024 report also offers valuable insights into the motivators for both men and women working in data roles, which are crucial for organisations seeking to attract and retain top talent. By tapping into these motivators, companies will naturally showcase and highlight projects and benefits that resonate well with both men and women, rather than just appealing to one or the other. As we delve into the findings from the report, we see that while there are many similarities in the motivators for men and women in the data field, subtle differences exist that can inform hiring strategies. By aligning recruitment practices with these motivators, businesses will create a more inclusive hiring process.

What’s The Common Ground? Career Progression and Interesting Projects

Across both men and women, career progression and interesting projects were consistently identified as the top motivators for staying in a role. The ability to influence business strategy was also highly valued, regardless of gender. This suggests that, for both men and women, providing opportunities for growth and the chance to engage in meaningful, challenging work are paramount to job satisfaction.

Organisations can leverage this information by creating clear career development paths and ensuring that their teams understand the impact each project has on the business. Ensuring that employees understand how their contributions align with the company’s broader business strategy will not only help retain talent but also improve overall performance.

Key Motivators for Women in Data

For women in the data industry, hybrid-working and flexibility were among the top motivators. As mentioned before, the ability to influence business strategy ranked higher that the ability to work on interesting projects. Career progression was ranked 5 ^th .

Women in the data industry are increasingly prioritising workplaces that offer a sense of purpose, where they can make an impact and influence strategy.

Key Motivators for Men in Data

Men, on the other hand, placed slightly more emphasis on financial rewards, with salary ranking higher among their motivators compared to women. Career progression was the top answer and the ability to influence business strategy was voted 3 ^rd . Interestingly, men in data roles were more likely to place emphasis on a sense of belonging than women, so culture is important to them. The second most important factor to the male respondents was the ability to work on interesting projects.

Tailoring Hiring Strategies to Motivators

Flexibility and work-life balance: Organisations looking optimise their chances of attracting top female talent into the hiring process, should consider emphasising flexibility in their work models, particularly the option for hybrid or remote work. With work-life balance being a significant motivator for women, providing flexible working hours or remote working options can make a substantial difference in attracting women to the data field.

Career Progression: Both men and women prioritise career progression, but the path to advancement should be clearly defined and accessible to all employees. Organisations should invest in training programs, mentorship opportunities, and leadership development initiatives. A transparent career progression framework helps employees of all genders feel valued and ensures they see a future within the company.

Inclusive Culture : An inclusive workplace culture is especially important now, perhaps with the change in working arrangements over the last 4 years or so. Companies should ensure that remote or hybrid employees have access to the same opportunities as those that are fully office based. This includes providing leadership opportunities, addressing unconscious bias, and encouraging a collaborative environment.

Compensation: While both genders value career growth, men are more likely to consider financial rewards as a significant motivator. Offering competitive salaries and performance-based incentives is essential for attracting and retaining male talent. However, compensation should be structured in a way that is equitable across genders, ensuring fairness and transparency in pay.

Challenging and High-Impact Work: Most were motivated by the opportunity to take on high-impact projects and significant responsibilities. Organisations should seek to align their recruitment efforts by providing candidates with a clear understanding of the type of work they will be doing, emphasising the strategic value of the roles and the potential for personal and professional growth.

Conclusion: One Size Doesn’t Fit All

The State of Data 2024 reveals that while there are many overlapping motivators for both men and women in the data industry, subtle differences exist. By understanding these differences and aligning hiring strategies accordingly, organisations can attract and retain top talent more effectively.

For companies looking to build a diverse, high-performing data team, it’s crucial to recognise the importance of both career progression and flexibility, while also considering how financial rewards and challenging work can appeal to different segments of the workforce. Fostering a culture of inclusivity and offering opportunities for personal growth will ensure that organisations remain competitive.

By tailoring hiring strategies to these motivators, businesses can create a more engaged and loyal workforce, driving both individual success and organisational growth in the data industry.

Steps to Anchor Recruitment in Motivators

1. Define Your Employee Value Proposition (EVP)

Craft a strong EVP that emphasises the motivators your organisation offers. Highlight elements such as:

Opportunities for growth and development (training, leadership programmes).

Flexible working arrangements (remote or hybrid models).

Diversity and inclusion initiatives that foster belonging.

Tip: Showcase your team’s contribution to business outcomes and emphasise unique project opportunities to stand out in the market.

2. Leverage Data Insights

Use analytics to gain a deep understanding of workforce trends and motivations. For example:

Analyse survey data to uncover why current employees joined and chose to stay.

Benchmark motivators against industry standards to refine your offerings.

3. Diversify Hiring Channels

To access a diverse talent pool, expand your outreach beyond traditional channels. Strategies include:

Engaging with professional networks that support underrepresented groups.

Promoting inclusivity in job adverts to attract a broader range of candidates.

Example: Successful organisations prioritise motivators such as hybrid working options for women and culture-focused benefits for underrepresented groups to foster inclusivity.

4. Adopt Skills-Based Hiring

Move away from traditional role-based hiring and focus on skills-based recruitment. This approach opens opportunities to a wider pool of candidates and aligns with motivators like career progression and engaging projects, by valuing adaptability and potential over former experience.

5. Enhance the Candidate Experience

The recruitment process reflects your organisation’s culture. Ensure that:

Communication is clear and timely at every stage.

Feedback is provided, with transparency throughout the hiring journey.

Interview processes are streamlined to avoid frustrating candidates.

NOTE: Many candidates cite poor communication and prolonged processes as major deterrents in recruitment.

6. Invest in Leadership Development

Leaders play a critical role in delivering on motivators. Equip your leadership team with:

Training to create inclusive and supportive team environments.

Tools to align individual goals with overarching business strategies.

Measuring Success in Diverse Hiring

To evaluate the impact of motivator-based recruitment:

Monitor metrics such as retention rates, employee satisfaction, and diversity indices.

Conduct regular reviews to align hiring strategies with evolving workforce trends.

To find out more, download the State of Data Report

How Data Usage has Changed since 2015

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Mon, 18 Nov 2024 08:34:21 GMT

It’s interesting to join the dots backwards....

It’s interesting to join the dots backwards when you collect data over a long period of time. We wanted to compare how data usage and challenges had shifted over the last decade. So, we dug out previous surveys and taking the main trackable data points this is what we discovered.

Since 2015 respondent businesses have improved their data usage significantly. The data has moved from 71% rating their organisations' data usage as average or poor to 36% in the latest report in 2024. In earlier surveys there was a general sentiment of disillusionment in effectively using data, business buy in was harder to secure and senior stakeholders didn’t always understand the full value of what data could bring.

Fast forward to 2024, as mentioned, the data usage figure significantly improved with 36% of respondents rating their data usage as average or poor. This suggests a shift towards better integration of data into business strategies. The shifts in the wider market contributing to this improvement have seen the data community moving towards education and storytelling, better governance and of course technology.

We wanted to compare how the barriers to successful data usage have shifted or stayed the same over the years.

When questioned in 2015, respondents believed that the major barriers included data quality, ineffective governance, and a lack of skills within their organisations. In 2024, while barriers such as data governance and skill shortages remain critical issues, there is now a growing focus on AI and data strategy as emerging or growing challenges.

Turning to leadership skills, had the skill requirements of data leaders changed over the last 10 years?

Back in 2015, skills such as listening, questioning, and influencing were highlighted as essential for data leaders. In 2024 there was a rise in respondents focusing on the importance of long-term vision in leadership. We speculate that the landscape of change that data functions are operating within, whether that’s economic, political, technology based or legislative means that the community feel the importance of long-term vision is more valid now than in previous years.

The data quality piece was interesting and shows a significant shift in approaches.

In early surveys organisations were primarily focused on reactive measures to improve data quality; for example, data cleansing was the main way respondents felt that data quality could be improved. These days the approach has shifted towards proactive strategies that emphasise clear definitions of data needs and engaging users in understanding data quality. In other words, education programmes and prevention has overtaken mop up activities.

How is data being used?

In 2015, organisations primarily used data to understand profitability and sales costs. In 2024, there has been a shift towards using data to understand customer behaviour and identify growth opportunities, reflecting a more customer-centric approach.

What the data shows is that from 2015 to 2024 there has been a notable improvement in data usage and the strategic importance of data within organisations. Whilst challenges persist, particularly regarding quality, skills and governance, there is a clear trend towards leveraging advanced technologies like AI and adopting a more holistic view of data's role in business growth.

To find out more - download the full State of Data here

How AI Startups and Solutions Engineers Can Drive Sustainability Amid COP29 and Political Shifts

Wed, 13 Nov 2024 10:49:48 GMT

As COP29 highlights the urgency of climate action

As COP29 highlights the urgency of climate action, sustainability is an increasingly significant priority in the US tech sector. Startups and solution engineering teams in AI and machine learning (ML) are at the forefront of this transformation, with innovative solutions that can contribute directly to the COP29 agenda. However, potential political changes, including the return of a Trump administration, could shape the regulatory environment and influence the adoption of sustainable practices. Here’s how startups and AI solutions engineering can align with COP29 goals, navigate potential political shifts, and leverage emerging skills to drive impactful environmental solutions.

COP29 and Its Potential Impacts on AI and ML Startups

COP29’s agenda includes stringent targets to reduce emissions and improve resource efficiency, aiming to push businesses to adopt climate-conscious practices. For startups in AI and ML, this presents both an opportunity and a responsibility. Solutions engineering teams developing AI-driven products have the potential to create powerful tools for industries needing to meet COP29’s sustainability targets. From energy-efficient data processing to predictive analytics for resource conservation, AI startups can innovate in ways that contribute to global climate goals.

Many AI and ML startups are already embracing sustainable practices, from optimising energy use in data centers to creating products that predict and manage environmental impacts. For example, companies can develop AI models that track and forecast carbon emissions, enabling industries to adjust their operations for greater sustainability. Additionally, startups are well-positioned to offer AI-powered solutions for monitoring supply chain emissions, resource consumption, and other environmental metrics critical to COP29’s goals.

How a Trump Administration Could Impact COP29 Compliance

A Trump administration may impact the COP29 agenda in the US, particularly through changes in regulatory support. Historically, the Trump administration deprioritised climate regulations, focusing instead on deregulation to encourage corporate growth. If a similar approach is taken, some companies might experience less regulatory pressure to adopt COP29’s sustainability guidelines. However, as the demand for sustainable business practices continues to grow globally, AI and ML startups are likely to continue developing solutions that align with these values, driven by market demand rather than regulatory compliance alone.

Regardless of policy changes, consumer expectations and investor pressures for sustainability remain strong. Many AI startups have recognised that sustainable practices and products are beneficial for both their market positioning and long-term viability. Even without stringent regulations, solution engineering teams are likely to continue innovating AI tools that drive sustainable transformation, aligning with COP29 goals by choice rather than by mandate.

Skill Sets Shaping Sustainable Innovation in AI and ML

The demand for skills in sustainable technology and AI ethics is growing as COP29 approaches. Solution engineering teams now require not only technical expertise in AI and ML but also knowledge of sustainable design principles, energy-efficient data processing, and compliance with environmental standards. Startups focusing on sustainability are likely to prioritise hiring talent with a background in these areas, as well as in data governance and analytics skills that support environmentally responsible data usage.

In addition to core technical skills, AI and ML startups are looking for professionals who can understand and integrate sustainability into product design. This includes knowledge of carbon footprint analysis, environmental impact forecasting, and the ability to implement energy-saving optimisations in AI infrastructure. As COP29 pushes companies to adopt climate-aligned practices, data scientists, engineers, and solutions architects with skills in sustainable AI will be highly sought after.

How AI and ML Products Benefit Businesses and Support the COP29 Agenda

AI and ML products offer numerous benefits to businesses looking to enhance their sustainability practices, directly supporting COP29’s agenda. For example, predictive analytics models can help companies anticipate and mitigate environmental risks by analysing trends in resource consumption, emissions, and waste generation. This enables businesses to make proactive adjustments that reduce their ecological footprint and lower costs.

AI-driven automation is another area where ML startups can make a significant impact. Automating resource-intensive processes, such as energy usage tracking, inventory management, and waste reduction, enables companies to operate more sustainably and comply with evolving environmental standards. Solutions engineering teams can also develop tools that support carbon offsetting initiatives, helping companies meet net-zero goals through data-driven insights and optimisation strategies.

In summary, COP29 presents both challenges and opportunities for AI and ML startups in the US. Whether or not a Trump administration aligns with COP29 goals, the demand for sustainable solutions from consumers, investors, and global markets is likely to propel innovation. Startups and solution engineering teams can drive sustainable practices forward by integrating environmentally conscious AI products, investing in the right skills, and recognising the long-term benefits of sustainability for their businesses and the planet.

Driving sustainability - key insights from the State of Data and Cop 29

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Wed, 13 Nov 2024 10:26:27 GMT

As COP29 runs this week, sustainability is transitioning from a corporate responsibility initiative to a regulatory mandate

As COP29 runs this week, sustainability is transitioning from a corporate responsibility initiative to a regulatory mandate, particularly in the UK. KDR’s latest "State of Data" report reveals how the rise in sustainability regulations is reshaping data strategy and tech infrastructure across industries. With data and tech professionals facing the challenge of integrating sustainability into core operations, the report’s insights provide guidance on navigating compliance and leveraging data for sustainable growth.

The Role of Data Engineering and Google Cloud Platform (GCP)

Under the new UK Sustainability Disclosure Requirements (SDR), data engineering will be crucial for handling the large volumes of data needed for climate-related reporting. Google Cloud Platform (GCP) allows organisations to consolidate data, reducing the reliance on energy-intensive, on-premises data centres while using scalable tools aligned with SDR’s data demands. Companies adopting GCP for data engineering can streamline their data infrastructure and reduce carbon emissions, helping them meet SDR’s rigorous reporting requirements.

Artificial Intelligence (AI), particularly large language models (LLMs) embedded in SaaS applications, plays a key role in environmental impact predictions and sustainability modelling. KDR's research within the State of Data indicates that companies adopting AI not only boost efficiency but enhance their ability to comply with regulations such as the Task Force on Climate-related Financial Disclosures (TCFD). By using LLMs, companies can detect patterns, assess risks, and simulate sustainable strategies, supporting SDR compliance and setting a global example in sustainable innovation. As always the challenge found by many is attracting and retaining the right skill sets needed for the job as well as budgetary constraints.

Data Management and Digital Analytics: Meeting Complex Compliance Standards

The complexity of UK regulations—especially SDR and TCFD—places high demands on data management. For UK companies, data management systems must be robust, transparent, and capable of delivering accurate sustainability data for auditing purposes. KDR’s State of Data report underscores the importance of data governance to ensure information is both reliable and readily available. Analytics allow digital and data teams to monitor sustainability metrics in real-time, helping businesses achieve compliance with minimal delays and maximise carbon reporting accuracy.

The Future of Data and Sustainability in the UK

COP29’s emphasis on digital sustainability aligns with the report’s focus on future-proofing data strategies. By 2025, KDR anticipates a stronger emphasis on AI, data literacy, and data quality as businesses adapt to sustainability standards that continue to evolve. This shift points to a workforce where data professionals skilled in sustainable practices, AI ethics, and environmental analytics will be in high demand. As companies prepare for future regulatory developments, investment in data talent and technology will be essential to stay competitive.

To download the full State of Data report click here

5 Take Aways from the Latest State of Data Report

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Tue, 05 Nov 2024 11:28:23 GMT

Based on the insights from the report, here are five key pieces of advice for data or tech professionals:

1. Align your data strategies with business objectives! One of the main reasons for unsuccessful data initiatives is the misalignment between data and business strategies. Ensure that your data strategy directly supports and integrates with your overall business goals to drive meaningful impact and business buy in.

2. Prioritise data governance and quality because data governance remains a critical skill gap, and poor data quality continues to be a recurring challenge. Invest in improving data governance frameworks and emphasise communication across departments to ensure that data inputs are clean, controlled, and understood by all stakeholders.

3. Embrace AI with realistic expectations. While AI adoption is growing, the report highlights challenges such as talent shortages and managing business expectations. Approach AI implementation incrementally, focusing on tasks like automation and business intelligence while managing expectations around its transformative potential.

4. Invest in skills development: The ongoing skills gap, particularly in AI, data governance, and business intelligence, is a significant challenge. Prioritise continuous learning and development for yourself and your team to stay ahead of emerging technologies and industry demands.

5. Develop Stronger Stakeholder Management: As AI and data play an increasing role in strategic decision-making, managing expectations and ensuring clear communication between the tech function and business units is critical. Develop strong stakeholder management and communication skills to navigate the complexities of AI and data adoption effectively.

Read the full report here.

Data and Tech candidates, this is what employers are looking for!

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Wed, 25 Sep 2024 07:56:51 GMT

A view from the data recruitment experts..

As we step into the final months of 2024, the job market in data and technology is continuing to improve for both employers and candidates. However it is still very competitive so we wanted to bring some advice to those seeking jobs currently to help you create the strongest foundation you can in order to secure your next role.

Current Job Market Trends

The demand for professionals in data and tech remains high, with many companies looking to grow out their data and tech functions either to accommodate emerging technologies and the need for digital transformation or to provide a redress to the last few years of leaner hiring strategies. These emerging technologies are also driving the creation of new roles, particularly in AI and cybersecurity. Meanwhile, the "Big Stay"—where fewer professionals are actively job hunting—means competition for available positions is fierce, making it crucial for job seekers to stand out where possible.

What are employers looking for?
Employers are looking for a variety of attributes in a candidate and to a large extent it will depend on the culture and role. As a general rule however, candidates who can demonstrate how they have benefited an employer are always the ones that stand out. Outcomes focused examples provided within the CV and interview bring to life how a candidate will instantly bring value to their business. Many people make the mistake of listing out projects and tech stacks but we would advise to make sure you include the value your contribution brought to the business. Particular skills always go down well regardless of required technology: problem solving, adaptability, communication and influencing skills are ones that spring to mind.

Focus on your CV initially

Your CV should clearly highlight relevant technical skills and successful project outcomes. Use metrics and specific examples to demonstrate your impact on previous roles. Additionally, include any continuous learning efforts, such as certifications or courses, especially in high-demand areas like AI or machine learning.

Then prepare well for the interview

Prepare to discuss specific technologies you’ve worked with, but also be ready to address how you can contribute to the company’s culture and long-term goals. Demonstrating knowledge of the company’s tech stack and current projects can set you apart but be prepared to discuss how you handle project challenges and adapt to new technologies, reflecting your flexibility and problem-solving skills. Talk about how the outcomes you contributed to or created contributed to the overall business strategy and research the company well so that you can create comparisons with the potential employers’ needs. This is where working with a great recruitment consultant will help because they will be able to brief you on client expectations, team culture, business strategy, the background to a role opening, the overall direction of the data or technology function and the purpose for the team and specific role. Ensure you have questions prepared for the hiring managers during the interview as it shows interest.

Final words of advice

Staying informed about the latest technologies and market trends, while continuously upgrading your skills, are also solid strategies for navigating the tech job market in Autumn 2024. Approach your job search with a clear strategy for your CV (objective review of how you are presenting information and update if required) and interviews, and you'll increase your chances of landing your desired role in this competitive field. Work with a reputable recruitment partner, not only is it an easier route into clients, the good ones provide invaluable advice, prep and guidance along the way.

A Comprehensive Guide to Implementing Data Governance for AI

Tue, 20 Aug 2024 10:38:04 GMT

Data governance and AI are deeply intertwined.

As artificial intelligence (AI) becomes increasingly integral to business operations, a few of you have been talking to me about the importance of robust data governance. As you know, AI platforms rely on vast amounts of data to function effectively, and without proper governance, organisations risk compromising data quality, security, and ethical standards. This guide will help you navigate the complexities of data governance for AI, offering practical steps for implementation and aligning with UK regulatory frameworks.

Data governance and AI are deeply intertwined. AI systems depend on large datasets to learn and make decisions. The quality, integrity, and ethical use of this data directly impact the outcomes of AI models. Therefore, implementing strong data governance is crucial for ensuring that AI systems are reliable, fair, and compliant with legal and ethical standards.

There are 2 key aspects where data governance and AI intersect: data quality and compliance. It’s a given that high-quality data is essential for training accurate and reliable AI models but regulatory requirements, such as the UK’s GDPR, demand strict controls over how data is collected, processed, and used, especially in AI systems. Data governance frameworks must therefore incorporate ethical considerations, such as:

transparency (stakeholders should be informed about the nature of the data, the purpose of its use, and the logic behind AI decision-making processes)

fairness (think about regularly auditing datasets for representativeness and bias, as well as implementing fairness metrics in the AI model evaluation process)

accountability (this includes assigning responsibility for data governance, monitoring AI system performance, and ensuring there are mechanisms in place to address and rectify any issues that arise, such as erroneous or harmful AI decisions)

data minimisation (collecting only the data necessary for specific AI functions which in turn reduces the risk of misuse and enhances privacy protections).

To recap, you need to ensure, the data is fit for purpose, ethical standards are upheld and regulatory compliance is maintained as well as ensuring that data security is enforced. This step-by-step checklist should help get you on the right track.

Step 1 . The first thing to do is to assemble a cross-functional data governance team

Why? A successful data governance initiative requires expertise from various domains, including data science, IT, legal, compliance, and business operations.

How? Form a governance committee that includes representatives from each of these areas. This team will be responsible for developing policies, overseeing implementation, and ensuring ongoing compliance with data governance and AI ethics standards.

Step 2. Define Data Governance Objectives and AI-Specific Principles

Why? Clear objectives and guiding principles help make sure that data governance efforts are aligned with organisational goals and AI-specific needs.

How? Discuss and define objectives that focus on maintaining the 3 key pillars of data quality, protection of sensitive data, and ensuring ethical AI use.

Some suggested areas to consider include:

Fairness: AI models should be free from bias and should not discriminate against any group.

Transparency: AI decisions must be explainable and understandable to all stakeholders.

Accountability: There should be clear accountability for AI-driven outcomes.

Step 3. Conduct a Data Audit with an AI Focus

Why? Understanding your data landscape is crucial for identifying gaps that could affect AI model performance or ethical use.

How? Audit your data sources, focusing on the quality, accuracy, and representativeness of the data used in AI training. Identify any potential biases and assess the security of sensitive data.

Step 4. Develop Data Governance Policies Tailored for AI

Why? AI platforms have unique requirements, and governance policies must reflect these needs.

How? Draft policies that cover:

Data collection and usage ensuring data used in AI models is collected with proper consent and is relevant to the AI’s purpose.

Bias detection and mitigation implementing processes to regularly check AI models for bias and take corrective actions.

Model transparency establishing guidelines for documenting AI models and ensuring their decisions are explainable.

Data access controls, defining who can access data, especially sensitive information used in AI, and under what conditions.

Step 5. Implement AI-Specific Data Governance Tools and Processes

Why? Technology is essential for enforcing data governance policies effectively.

How? Invest in tools that support data cataloguing, data quality management, and bias detection in AI models, such as data catalogues, data profiling tools, and AI fairness toolkits.

Examples of these might be:

Data Catalogues: Tools like Collibra or Alation help organisations create and maintain an inventory of data assets, making it easier to track data lineage, manage metadata, and ensure consistent data usage across AI projects.

Data Profiling and Quality Management: Solutions like Talend or Informatica allow for the assessment and improvement of data quality. These tools can automatically detect anomalies, cleanse data, and enforce quality rules, ensuring that AI models are trained on accurate and reliable data.

Bias Detection and Fairness Toolkits: Tools such as IBM’s AI Fairness 360 or Google’s What-If Tool are specifically designed to identify and mitigate biases in AI models. These tools provide metrics and visualisations that help in assessing fairness, allowing organisations to take corrective actions when necessary. Then make sure you are doing regular data quality checks, bias audits, and AI model transparency assessments. Ensure these tools and processes are integrated into your AI development lifecycle.

Importantly, the following is then advised.

Step 6. Establish Training and Awareness Programs

Why? Employees need to understand the intersection of data governance and AI ethics.

How? Develop training programmes that cover data governance policies, AI ethics, and legal requirements (such as UK GDPR). Other ideas include running regular workshops, e-learning modules, and putting out internal communications to ensure that all employees are aware of their responsibilities.

Finally,

Step 7. Monitor, Review, and Update Your Data Governance Framework

Why? AI and data governance are dynamic fields, requiring continuous improvement.

How? Set up a system for ongoing monitoring of AI models to ensure compliance with data governance policies. Conduct regular reviews and update your framework as new risks, technologies, or regulations emerge. Engage with external auditors to validate your practices.

Optional extra

Step 8. Engage with External Stakeholders and Regulatory Bodies

Why? Transparency and accountability are essential for building trust with stakeholders.

How? Regularly communicate your data governance practices to external stakeholders, including customers, partners, and regulators. Stay aligned with UK regulatory guidance from bodies like the Information Commissioners Office (ICO) and Centre for Data Ethics and Innovation (CDEI). Also you might consider publishing transparency reports that outline your AI practices and data governance efforts.

Interview & CV advice - latest thinking

Tue, 20 Aug 2024 10:27:51 GMT

5 common mistakes it’s easy to make when applying and interviewing for roles.

We speak to clients a lot so in this competitive market we thought it would be useful to pass on some information about some of the common mistakes people make when applying for roles and going into interview.

Mike Thacker-Cooke – Head of Recruitment

1. I would say the top CV mistake people make is not tailoring their CV to the role. Sounds like a lot of fiddly work? Yes, it can be, especially when you are already in a full-time role. However, it represents the difference between the most successful candidates who stand out when put in front of a potential employer and those that get passed over. Keeping a CV the same for all applications can mean your relevance for the role gets missed.

If you need some assistance, speak to the recruiter who is working the role and they will always be willing to help before they put you forward. Any good recruiter will advise you, help you change the CV and complete a tailored write up highlighting all the relevant experience you have for the role and why the hiring manager should invite you to interview.

Top tip on how to go about this exercise: read the job description or advert and pick out the key skills and technologies mentioned. Also note the type of organisation it is and the work pattern they are asking for. Armed with this information look at your CV, does it reflect the advert well? If it doesn’t and you know you have the relevant experience, start to bring the information higher up your CV. Look at your opening statement, do you include those specific skills and experience in the opener? Have you highlighted key technologies in bold? Have you included the industry, if that is relevant? Do you mention that you currently work in a hybrid environment 3 days a week (for example), have you put that you thrive in a corporate/start-up/scale up environment? All these little tweaks make it easier for a hiring manager to spot when they are looking through many applications. If you are working with a recruiter taking this approach is also helpful, although as mentioned they will work with you to help you achieve the optimum CV and submission.

Ben Dugdale – Principal Consultant

2. Interviews can be a source for a few mistakes to creep in. The top mistake people make is not preparing enough or preparing in the right way.
How much is enough and what to focus on?
As a guideline, understand your CV really well. Sounds simple but so many people forget this in the interview. By that I mean, understand how you have contributed to a business. What was the business impact of a project you worked on, or an output you created. Know what skills you have and technologies you have worked with. Layer this with any other achievements you reached, so you are highlighting how you helped move the business forward or how you developed in the role. Hiring Managers are looking for clear reasons to add you to a team – what value will you bring? Employers effectively want 2 things: they want to like you and they want to know how you will improve their business. The problem some people run into is not linking their experience to an outcome, this is the easiest way to demonstrate to a potential employer the immediate value you will bring to the team. You may need to do some research around figures and how much you saved a business or brought into the business – if revenue related. If it’s not revenue related, how did you improve processes or solve an existing business challenge? List them and get your figures clear in your head. Secondly understand the business you are interviewing for. Research the company, the industry, news items, press releases, ask questions about their strategy, tell the interviewer what you like about the business and its approach to xyz, if you are not sure about something, ask. Also don’t forget to research the interviewer. Understanding a bit about who is interviewing you beforehand helps to build rapport.

Haroon Choudry – Strategy Consultant

3. Another mistake people make during the interview is not thinking of it as a two-way process. Most people spend the majority of their time at work, so it is important to know whether it is the place where you really want to spend all that time and energy. Questions like, what are the opportunities for development? what is the team structure? how mature is the data strategy? what does the tech stack look like? what is the culture? Help you assess the kind of environment you would be walking into. Based on what is important to you, think of some questions and make sure you are asking them as part of the conversation rather than tacking them onto the end. Sometimes the client will stipulate that questions can be asked at the end, but most won’t and will enjoy the 2-way flow of natural conversation.

Hally Barlow - Recruitment Consultant

4. Regardless of your current situation, never speak badly about your current employer. If you are looking to leave a toxic workplace, a good work around is to mention that you are looking for an environment that is more aligned with your values (and then go on to list them). This way you are not directly criticising your boss or team. When you do complain about an employer it can sow seeds of doubt in potential employers minds (how will you talk about them for example if they were to hire you?) Candidates can also sometimes forget to let their personalities shine through. Technical ability is great (and called for) but don’t forget you will be working side by side other people, and they want to know they can connect with you and be able to easily progress projects. Personality is important and an over reliance on technical ability can sometimes be a mistake.

Advice from KDR

5. Make sure you are voicing any reasonable adjustments needed up front. All clients completely understand and it is absolutely ok to ask if needed. Without prior knowledge of hidden conditions (for example), clients can jump to conclusions if the interview puts you at a disadvantage and you don’t perform as well as you would have liked. With reasonable adjustments in place, there is no room for assumptions and jumping to the wrong conclusions as to why someone performs or behaves in a certain way.

Data Job Market Analysis Q3

Tue, 23 Jul 2024 09:00:00 GMT

In Q2 when I wrote my review, the last thing I thought I would be writing about this quarter was an election!

Nevertheless, the recent elections have brought interesting changes to the data and technology industry. As the owner of a data and technology recruitment company, it helps me and the team provide a better service when we understand how these political shifts will affect our industry, job market dynamics, and the overall business environment we are all working within. Firstly, as a backdrop to the current job market, I can see that the overall percentage drop from Q1 to Q2 was 3.2%, however these figures are still tracking 25% higher than the number of recorded job vacancies 10 years ago (ONS data). As mentioned in my previous roundups, industries like insurance, financial services and technology are enjoying periods of relative growth, and this is reflected in the uptick we are seeing in client requests from these specific sectors.

We saw in Labour’s manifesto that they announced a series of economic policies aimed at boosting innovation and digital transformation, but what does this mean? The R&D funding introduced back in 2000 by Labour will remain and funding for innovation and research remains stable. Other policies include tax incentives for tech startups, and increased grants for developing cutting-edge technologies such as artificial intelligence (AI), blockchain, and quantum computing. They also announced they would remove planning barriers to data centres and ensure their industrial policies encourage the development of Artificial Intelligence. The new Government have a difficult road ahead of them, with the debt to GDP ratio hovering around 100% since 2020 but they do seem to be taking a supportive stance and it is expected to foster growth and innovation in the data and technology sector, attracting investments and encouraging new ventures.

It will be interesting to see who or rather which companies are included on the industrial strategy board. Let’s see if some major tech companies land soon as this will be a good indicator of how serious they are about supporting the tech and data industry of the UK. The UK is the start up leader of Europe and Labour seem keen to capitalise on this, however post Brexit we need to ensure the country remains attractive to start ups given the shrunken market size we can now offer them compared to Europe, USA or China.

I am hoping with the government’s emphasis on digital transformation and their support for open banking and open finance technology within the financial industries that the demand for skilled professionals across data science, machine learning, data governance, cybersecurity, and software development continues to grow. With the rise of AI, increase in data (estimations show 90% of worlds data has been produced int the last 2 years), and the complexities of regulations on the digital sector we have seen in influx of data governance requirements. Companies are increasingly looking for candidates with expertise across AI and data governance to stay competitive. With this melting pot of regulation, supportive policies, increasing reliance on data I can see the job market returning to a more stable and competitive environment, which I am sure after the volatility of the last few years this will be welcomed by many, both job seekers and businesses alike.

As a quick overview I believe small companies stand to benefit significantly from the new economic policies, tax and funding opportunities. These companies will also need to navigate the new regulatory landscape, which may require additional resources to ensure compliance. SMEs are in a strong position to capitalise on the increased demand for digital transformation. With the government's support, these companies can invest in new technologies and expand their offerings. As above they will need to balance growth with one eye on compliance and Enterprise organisations will benefit from a hopefully more stable business environment, however with the increase in Cyber-attacks, ensuring compliance in this area will be a challenge.

An area close to my heart, is around early talent and apprenticeships. On this, the government has allocated more funds specifically for apprenticeships and training programs in the tech sector, which means more opportunities for both companies and individuals to receive financial support. Specifically, subsidy rates for training programs have been increased, reducing the financial burden on companies and making it more attractive for them to hire apprentices and invest in training. A route that is worth considering with the shortfall across skills gaps and diversity.

The recent elections have set the stage for significant growth and transformation in the data and technology industry. With supportive economic policies, increased demand for tech talent, and a focus on digital innovation, the industry has the basis of a really strong foundation for growth. However, businesses must navigate new regulatory requirements and adapt to changes in the labour market to fully capitalise on these opportunities.

Prompt Engineering for HR and Talent Acquisition

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Wed, 17 Jul 2024 10:51:29 GMT

As an HR or Talent Acquisition professional understanding and leveraging prompt engineering can significantly enhance your efficiency and effectiveness.

This playbook aims to guide you through the essential aspects of prompt engineering tailored specifically to address common hiring challenges.

We will focus on 6 core areas that we have identified as key moments within a typical hiring process and by the end you will have the knowledge and framework to write your own prompts saving you hours of work.

What is Prompt Engineering?

Prompt engineering involves designing and refining prompts to get the most accurate, relevant, and helpful responses from AI tools. In the context of HR, it can streamline processes, improve candidate engagement, and provide deeper insights into recruitment data. The key to mastering this tool is in how you interact with it, how specific you are and the level of security you are able to put in place around it.

Before you get started - just a quick note around security. In order to upload and protect company documents or candidate applicant records make sure you have an enterprise edition of any open-source AI models you may be accessing, as this provides a level of security you won’t be able to achieve with personal or free accounts. If you can’t get access to an enterprise edition it is still possible to upload documents, however you must be sure that the information is anonymised before you do as there is no guarantee that the information is ringfenced.

Start by planning WHAT you want to use it for.

Examples

1.Crafting job adverts

2.Screening candidates

3.Interview question creation

4.Analyse recruitment data

5.Enhance candidate experience

6.Improve diversity and inclusion

USE CASE 1: JOB ADVERT

The key to getting the best outcome for your prompt lies within the language you use and how specific you are. If we take the first example above, “crafting job descriptions”, most people make the mistake of taking the following approach to writing a prompt: “craft a job advert for a data engineer”.

Whilst the AI tool will likely produce a job advert that covers most of the attributes of a data engineer position, it is more likely to produce something that neither represents your brand, the specifics of the role within your business or be representative of the tone of your business.

Instead, follow these steps and you will produce a much more usable and specific result.

1.List the essential skills, experience, and qualifications the role and ideal person requires.

2.Highlight the unique selling points around what makes the role and your brand attractive.

3.Then structure your prompt correctly, e.g: Create a job advert for the position of [Job Title] at [Company Name]. The ideal candidate should have [number] years of experience in [industry/field], and be proficient in [specific skills]. Key responsibilities include [list key responsibilities]. Highlight the benefits of working at [Company Name], including [list benefits]. The tone should be [include desired tone: corporate, friendly, warm, professional etc]. Include a statement around diversity and inclusion, and include a statement around reasonable adjustment requirements at the end. Include instructions for application [email: name.surname@companyname.com with your CV or provide details of your online portal and your process].

USE CASE 2: SCREENING CANDIDATES

Using AI to help screen your candidates, especially when there are large volumes of applicants for any given role is a really good use case. You have to be very specific and meticulous in your prompting and processes so that you ensure you are not accidentally screening people out of the process.

First, identify the essential qualifications, skills and experiences required for the role. These are your "must-haves" that every candidate should meet. Distinguish between essential and desirable qualifications to help with candidate prioritisation down the line.

Assign points for each qualification and skill. Use a standardised scale (e.g., 1-10) to maintain consistency in evaluation. Weight the points according to the importance of the qualifications. For example, critical skills could have a higher point value.

Anonymise candidate profiles to prevent bias in the evaluation process. Use a unique candidate reference number for each profile to maintain confidentiality and help with organisation. (Note: If you have an enterprise edition of your AI platform you can run a prompt to remove contact details from a CV and replace with an assigned candidate reference number). The steps and prompt for this would be:

Step 1: Upload CVs, provide your ZIP file or upload individual CVs

The use this prompt:

Remove personal details from the uploaded records. Personal details to remove are:

Name
Address
Phone number
Email
Any other identifiable information

Candidate Reference Format:

Use the format Candidate001, Candidate002, etc.

Placement of Candidate Reference:

Place the candidate reference at the top of the first page of each CV.

Spreadsheet Logging:

Log the original name and the assigned candidate number in a spreadsheet named Candidate_Log.xlsx.
Ensure that each profile includes relevant information aligned with the identified qualifications and skills. This could look like: the entire CV minus the personal details and just the candidate reference number in their place.

Upload anonymised CV’s and upload the scoring system spreadsheet of skills and qualifications required. You can then move on to craft a detailed evaluation prompt:

Evaluate the following anonymised candidate profiles for the position of [Job Title] at [Company Name]. Use the scoring system based on the listed criteria on the uploaded spreadsheet. [or Insert Detailed Criteria List]. For each candidate, provide a score based on their qualifications and a brief summary explaining the reasoning behind the score. Please refer to the candidate by their unique reference number.

USE CASE 3: INTERVIEW QUESTIONS

Looking at the interview questions next, you need to develop relevant and insightful interview questions, that are consistent and universal for each candidate
.

The 3 steps you would take are:

1.Spend time looking at the core competencies important for the role. Focus on skills, experience and cultural value add (cultural value add as opposed to cultural fit is where you look at what your team needs to increase diversity with harmony v’s herd thinking where people are too similar and where the typical outcome results in stifled innovation or diversity of thinking).

2. Ensure questions are specific to the job requirements.

3. Use the following prompt template for generating interview questions: Generate a list of interview questions for the position of [Job Title] at [Company Name]. Questions should assess the candidate’s experience in [specific area], their skills in [specific skills], and what they will bring to the team. Use this culture profile [insert culture profile of existing team] to devise questions aimed at finding candidates that will compliment and add value and diversity, without conflict, rather than looking for exact culture fit.

USE CASE 4: RECRUITMENT DATA

Extracting actionable insights from recruitment data is another area that AI can help you to speed up the process. This section requires a little more work upfront than some of the other sections, but once this is done it is a matter of plugging the latest data into the prompt to gain the insights.

1.The first thing you need to do is to identify the key metrics you want to measure. Some examples of the metrics that matter to most are time-to-hire, source of hire, and candidate quality. Gather this information – potentially on a spreadsheet – and upload to the AI platform.

2.Structure your prompt using the following template: Analyse the recruitment data for [Company Name]. Using the uploaded spreadsheets as the data source, focus on the following metrics: [list metrics]. Provide insights and recommendations based on the data trends.
Maintaining communication and engagement with candidates throughout the recruitment process is important. When it comes to enhancing candidate experience AI can be a useful tool to craft personalised messages for use in automated follow up communications.

USE CASE 5: ENHANCING CANDIDATE EXPERIENCE

Most applicant tracking systems have built in automations so you might just need to prompt for the wording to add into these follow up emails. If you don’t have this ATS technology and are relying on spreadsheets to monitor candidates in process then the prompt could also provide status updates for you.

We have outlined two options below.

For those without access to an ATS which also provides an automated follow up email, the steps to follow and the prompt text might look like this:

1.Record candidate details and process status on an excel spreadsheet.

2.Use this prompt text: Using the uploaded spreadsheet for reference, draft a follow-up email for each candidate who applied for the position of [Job Title] at [Company Name].

The email should:

Thank the candidate for their application.
Provide an update on the status.
Outline the next steps based on the current status.

Specific instructions:

If the status is [insert status], the next steps are [insert next steps].
If the status is [insert status], the next steps are [insert next steps].
If the status is [insert status], the next steps are [insert next steps].

Ensure the following:

Maintaining the Log:
Add a column named "Previous Status" to the spreadsheet to log the status from the previous run.
Update this column with the current statuses before each new run.

Identifying and Noting Candidates:
Compare the "Current Status" with the "Previous Status" for each candidate.
Add a column named "Flag" to mark candidates whose status hasn’t changed.
If a candidate's current status matches the previous status, mark them in the "Flag" column.

If you have an ATS and just need the text to feed into your follow up email templates, use the following prompt:

Draft a follow-up email for each candidate who applied for the position of [Job Title] at [Company Name].

The email should:

Thank the candidate for their application.
Provide an update on the status.
Outline the next steps based on the current status.

Specific instructions: There are 3 status email updates I require. Use the instructions below and create 3 versions of the above email according to the status.

If the status is [insert status], the next steps are [insert next steps].
If the status is [insert status], the next steps are [insert next steps].
If the status is [insert status], the next steps are [insert next steps].

USE CASE 6: DIVERSITY & INCLUSION

Using AI to review your adverts is an effective and efficient way to help you understand what changes should be made to make the advert more inclusive and attract a more diverse pool of candidates.

Use the following prompt text to help you speed up this process:

Review the job description for [Job Title] at [Company Name] and suggest changes to ensure it promotes diversity and inclusion. Highlight any biased language and recommend more inclusive alternatives.

BEST PRACTICE
The suggested 6 use cases should stand you in good stead to create an efficient recruitment process that saves you time and effort once set up.

The following best practices can help you if you are looking to expand your prompt library and create your own or you want to tweak those provided to fit more with your requirements.

1.Be specific. Provide clear instructions to ensure you get what you need from the AI tool.

2. Test the prompt against a small batch of records. At this stage it is a good idea to use the same records within an existing manual/human process and check to see how similar or different the outcomes are. If you need to tweak see below.

2.Based on the responses you receive, make sure you iterate and refine your prompts. You will get a better result and the system learns to produce tighter results.

3.Avoid overly complex language or multiple requests in a single prompt.

4.Regularly test the effectiveness of your prompts and measure their impact on your recruitment processes.

Developing Data Leadership

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Tue, 11 Jun 2024 07:45:22 GMT

We conducted research amongst our data leader network to find out the different ways they develop their team....

As a Data Leader, HR or Talent Team Member, developing and nurturing professionals to progress on to data leadership positions is important for retention and offering career growth for home grown talent. But given all the pressures on teams and individuals to deliver BAU activities not to mention special projects that crop up what are the best ways to improve career pathing for data professionals?

There are many different approaches that you can take that encompass mentoring, training, and providing clear pathways for career development. We conducted research amongst our data leader network to find out the different ways they develop their team and combined that with latest thinking on the subject to provide this guide.

We provide this advice a lot but it really does all start with defining roles and responsibilities . In order to get people up to the standards required to progress, they need to know what they need to achieve or demonstrate in order to get to where they want to go. An exercise in clearly defining the different data leadership roles within your organisation which outline the skills, experiences, and accomplishments required for each level up to that point is both a task in itself and the route to success.

Bonus: Create visual career maps that outline potential career trajectories for employees, including horizontal moves that can provide valuable cross-functional experience.

Develop robust training programmes that cover both technical skills (e.g., advanced analytics, machine learning) and soft skills (e.g., leadership, communication). Soft skills are often overlooked, however for professionals looking to progress towards data leadership roles they are crucial. In our annual State of Data report the skills that are voted as the most important for data leaders aren’t centred around technical competence but around listening and communicating as well as strategy. Indeed, those teams that had been able to execute on their data strategy were those that felt they had excellent communication across leadership, C-Suite and business units and this comes down to individuals at the top ensuring ideas, plans and policies are clearly communicated across the business. It could be that you develop customised leadership training programmes that take an individual on a journey, or that the programme is modular and non-specific to data leadership, yet still delivers on the skill sets required further downstream.

Bonus: Encourage and support employees in obtaining relevant certifications and attending courses, workshops, and conferences.

One idea that works well is to pair potential leaders with experienced data leaders who can provide guidance, feedback, and support. Coaching and mentoring as tools are successful due to their foundation in the social learning theory, where individuals learn through observing and modelling experienced leaders. These interactions boost the confidence of emerging leaders to tackle new challenges. Grounded in Vygotsky's Zone of Proximal Development , mentors and coaches provide essential guidance that extends the learner's capacity beyond what they could achieve alone. Continuous feedback and reflective practices within these relationships enable practical skill enhancement and self-awareness. Additionally, techniques like motivational interviewing help in uncovering intrinsic motivations, aligning personal goals with professional growth. Together, these psychological principles help to create a supportive and effective environment where robust leadership skills can be nurtured through practical experience with a guide.

One area that some of the Data Leaders we spoke to lean on was around providing real-world evidence through project leadership opportunities. This is where employees are given the opportunity to lead data projects or initiatives, allowing them to demonstrate their leadership capabilities and gain hands-on experience. This is a highly effective way to boost confidence, demonstrate skills as well as areas that need improvement for employees who are looking to progress.

A couple of data leaders we spoke to talked about the creation of innovation labs or sandbox environments where employees could experiment with new technologies and approaches without the fear of failure. This set up is perfect for creating an environment and culture of innovation. It encourages, collaboration, communication skills and leadership. If you are looking to set something like this up, one example would be where employees could access a monthly “lab” day where they were allowed to work on diverse projects or aspects of their ongoing projects with other team members to get a different perspective and potentially different outcomes. Developing internal networks and communities of practice where data professionals can share knowledge, best practices, and support each other’s growth is very beneficial to both the company and the individual.

The consensus around the mix required to develop data leaders of the future seems to be to provide continuous investment in people, clear pathways for growth and a supportive culture . This will hopefully ensure that the organisation has a pipeline of capable leaders ready to take the data strategy forward.

5 Key Benefits of Incorporating Interns into Your Business

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Tue, 28 May 2024 13:43:51 GMT

Interns can be a valuable asset to any organisation for a variety of reasons. Here are five compelling reasons to consider using interns:

1. Interns often bring new ideas and fresh perspectives to the workplace. Being typically younger and perhaps more in tune with current trends and technologies, they can offer innovative solutions and insights that may not have been considered by more experienced staff. One example of this was for a client who implemented an internship programme where one of the interns invented a product that helped them reach IPO status.

2. Internships allow companies to nurture and evaluate potential future employees in a real-world setting. This can be a strategic approach to recruiting, as it gives both the employer and the intern the opportunity to assess fit before making a commitment to permanent employment.

3. Interns can handle tasks that support the broader BAU goals of a team, freeing up experienced employees to focus on more complex projects. This can increase overall productivity and efficiency within the team.

4. Interns are often less expensive to hire than full-time employees. This cost-effectiveness makes them an attractive option for roles that require less experience or for special projects where additional help is needed temporarily.

5. Offering internships allows your organisation to contribute to the education and development of future professionals. It’s a way to give back to the community by helping students gain the skills and experience they need to succeed in their chosen careers.

Entering the workforce can be a tricky process for younger people or those who are newly trained. These benefits make internships a mutually beneficial arrangement, providing valuable work experience for the interns and tangible benefits for the organisation. If you are interested in exploring internships, KDR Talent Solutions has partnered with institutions offering the exact skillsets our clients typically look for. Contact us today for more details.

Are ED&I Initiatives Losing Ground?

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Fri, 03 May 2024 08:20:34 GMT

It turns out it’s all in the delivery.

A recent report by Oxford University delved into whether anecdotal comments around ED&I losing ground, support or adoption within British businesses was founded or not. And thankfully it seems that the idea that the country is split into two camps of for and against EDI is not true. 94% of the British public have heard of ED&I and the majority is in favour of it (62%). It turns out it’s all in the delivery, with the majority of British people understanding the need for these initiatives and programmes. This article looks at the very latest thinking on what individuals and businesses need to do to really make an impact in the area of ED&I.

The study uses the “British Seven” to distinguish between cohorts and their typical responses on topics and across all of the seven groups there is agreement that ED&I leads to fairer outcomes for groups. The two groups that had stronger beliefs around ED&I not leading to fairer outcomes were amongst the 2 cohorts of loyal nationals and backbone conservatives. However even within these two groups there was acknowledgement that it helps (42% and 46%).

One of the main issues lies in ED&I training. Whilst each cohort more or less (depending on the group) felt that ED&I training had made a difference to the people that it is meant to help, there was still a percentage that felt it had either made no difference or actually made it worse. The main concerns were around the delivery of the training, where some modules were seen as tick box exercises and not tailored to the organisations’ needs or workforce. These types of training felt like back covering exercises rather than genuine attempts to help the situation and there was a certain level of hostility towards this approach. Many organisations tie this kind of training (along with health and safety, data protection and time management for example) into bonus structures which can feel very performative and non-committal to the end user. Rather than trying to educate and move the dial, people tend to rush through the modules to tick off that it has been completed. The preference is for in person training as it feels a two way process.

The biggest concern across all cohorts is that ED&I can be used as a tool to chastise or shut people down. 7 in 10 Britons feel that people are made to feel stupid for not understanding the latest thinking around ED&I. So if ED&I initiatives are set up so that they are perceived as ways to expose people for not understanding what ED&I is all about then they risk alienating the people they are trying to bring along.

One of the takeaways from the report is that those responsible for ED&I need to keep their initiatives grounded in the reality of the workforce rather than taking it to abstract levels of discussion. On top of this, people engage better when shown lived experiences within their workforce rather than politicised contentious theories based outside of the workplace. The majority of people don’t want their workplace to be politicised. They just want to work in a fair working environment which is extended to all colleagues. Outside pressures make people feel uncomfortable around the subject.

Use of language is really important for ED&I to succeed in the workplace. The evidence suggests that terms such as woke, and culture wars are a complete turn off and not helpful if companies want workforce buy in. The most unhelpful term identified was “white privilege”, where apart from one of the British seven cohort (progressive activists) all felt it was more unhelpful than helpful as a term to explain the advantages that white people can sometimes have over other communities. Interestingly, across all cohorts the age group with the most even split on this term was between 17-24 year olds. Perhaps a level of acceptance of this term and the wrongs surrounding what it is trying to convey is growing finally. One other explanation is that most white Britons take this statement to mean they themselves have had a life without hardship, many of which have of course had a life full of hardship, and feel it dismisses their lived experience, whereas 17-24 year olds have perhaps still been shielded from many of the hardships of adulthood, mortgages, bills, family responsibilities and difficulty in finding a job or job losses for example.

Public perception of how they are perceived based on a series of statement shows they don’t feel that employers treat people equally. Generally the results showed that across all the different cohorts people generally feel that if you are unemployed, from a working class background, have a strong regional accent or are from an ethnic minority group then employers take you less seriously. Whereas if you have gone to private school, live in London, are rich or have a university degree then employers are more likely to take you seriously.

To sum up, in the UK the discussions around ED&I and the role organisations play in it are less polarised than in the USA. Having said that, some debates have been very acrimonious and there is some evidence that the increasingly political and divisive rhetoric around it is serving to mask a broader consensual attitude by the general population.

HR professionals and hiring managers within data and analytics who want to move the dial and make a real impact around ED&I in their teams or the wider business could look at tailoring their approaches. Here’s a strategic guide to reinvigorating your organisation’s EDI initiatives:

Contextualise EDI Activities

Tailor your EDI initiatives to fit the unique needs of your workplace. What works for a global consultancy may not suit a tech startup or an insurance business. Grounding EDI in the everyday roles and responsibilities of your team not only garners more support but also enhances job performance. How to do this? Easier said than done! However let’s think about it…

Start with…
Conducting a thorough assessment of the current workplace culture and diversity. This can include employee surveys, focus groups, and data analysis to understand the existing demographics and the perceptions of inclusion among staff. This helps identify specific areas that need attention and ensures that initiatives are relevant to the company's current state.

Based on the assessment, define clear and measurable goals that are relevant to the business's needs. These goals should address both broad and specific issues identified in the initial assessment. For instance, if a tech company lacks gender diversity in its engineering teams, a goal might be to increase the recruitment and retention of women in those roles.

Align ED&I goals with the overall business objectives. For example, if a company aims to enhance innovation, emphasise how diverse teams are shown to be more creative and produce a wider range of ideas. This alignment helps to get support from all levels of the organisation.

Different departments may face unique challenges and opportunities in terms of diversity and inclusion. Tailor initiatives to these needs. For example, a sales team might benefit from training on cultural competence to better connect with a diverse clientele, while the HR department might focus on bias-free hiring practices. Software engineers may need to understand the role of bias within their code and the eventual outcome.

Define how each role in the company can contribute to ED&I goals. This could mean training team leaders on inclusive leadership practices or integrating diversity and inclusion metrics into performance evaluations. By making ED&I part of the day-to-day responsibilities, employees are more likely to engage with and support these initiatives.

Offer training sessions that are tailored to the needs of the business and its employees. This can range from workshops on unconscious bias to education about different cultures and communities. The training should also equip employees with practical tools and strategies to create an inclusive environment and focus on real-world stories and experiences to humanise EDI.

Encourage an environment where questions are welcomed and honest mistakes in EDI discussions aren’t met with harsh criticism . This creates a safe space for learning and growth, reducing the anxiety around "saying the wrong thing" and promoting genuine understanding.

Recognise and respect the boundary between professional conduct and personal opinions, especially in today's digital age where lines can blur. Clear guidelines about workplace behaviour help maintain a respectful environment without encroaching on personal freedoms.

Emphasise how EDI initiatives remove barriers and create a level playing field , which leads to fairer outcomes and opportunities based on merit. This aligns with the public’s view that EDI should lead to more deserving outcomes.

Avoid exclusive language that creates division . Instead, use language that demonstrates how EDI benefits everyone in the organisation, not just specific groups. This inclusive framing helps minimise resistance and builds a more cohesive team ethos.

EDI should not be championed by only one group within your organisation. Broaden your range of messengers to include diverse voices across ideological spectrums to foster wider acceptance and deeper engagement across all levels of your team.

Be judicious in addressing social issues. Focus your organisation's engagement on matters that directly relate to your sector and your stakeholders' interests. This targeted approach ensures relevance and supports wider public endorsement.

As always, if you need help with your ED&I initiatives, get in touch!

Recruitment ROI - Are you getting the most out of your hiring strategies?

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Tue, 30 Apr 2024 07:35:38 GMT

Understanding the cornerstone of recruitment ROI – Quality of hire

We think it’s fair to say, it’s been a difficult time for businesses of late, balancing the need to hire with the need to keep costs in check so we wanted to take a look at helping you understand the Return on Investment (ROI) in recruitment because it’s not just about the cost—it's about the value each hire brings to your organisation over time.

As a starting point, we would argue, the cornerstone of recruitment ROI is the 'Quality of Hire' (QoH). How well someone provides positive outcomes for the business is not an easy measurement to capture, but it is insightful and informs how well the rest of the recruitment processes are working. It is also not a metric you can put in place immediately, so you need to wait a few months after a new hire before you can even start to measure if effectively.

Step one would be to set clear, measurable criteria for what constitutes a "quality hire"

Examples would be*:
Performance Metrics
Productivity Metrics: Amount of code written against requirements, Impact on sales, Number of meetings booked, Delivery of products, Deadlines met
Culture fit & Engagement Metrics

*pick the ones that are relevant to your business

Some businesses prefer to look at retention or turnover rates but these are perhaps a bit more risky to use as these figures can also be linked to wider company issues such as culture, ineffective management, or poor onboarding practices.

Then look at the ratings for each indicator chosen.

Performance Metrics: Conduct regular performance reviews and ratings by supervisors.

Productivity Metrics: Output, sales numbers, delivery of projects or other quantifiable work products.

Engagement and cultural fit metrics: Surveys or assessments measuring how well the employee integrates and aligns with company culture.

Retention Metrics (if used): The length of time employees stay with the company, indicating their satisfaction and fit.

Measure your baseline: what do the current performance metrics look like for your existing workforce? When you know this information you have a benchmark for any new hires you make.

Pick your indicators and then divide them by the number you choose, for example:
QoH = (New hire performance + new hire engagement + culture fit)/3
QoH = (80% + 85% + 90%)/3
QoH = 85%

Once you have the baseline, and some new hires data (please note this process may take up to 12 months depending on the frequency of hires you make), you can start to use the findings to inform and improve your hiring processes.

Use your analysis to identify strengths and weaknesses in your hiring process. If certain sources of hire consistently lead to high-quality employees, for example, you might focus more resources there. Conversely, if some hires are not meeting expectations, investigate why and adjust your processes accordingly.

Quality of hire is not a one-time measurement but a continuous process. Regularly review and adjust your criteria, KPIs, and analysis to reflect changes in your organization’s goals and the job market.

Establish a feedback loop between hiring managers, the HR team, and new hires. Use this feedback to refine your hiring criteria, the onboarding process, and other factors that influence quality of hire.

Understanding the Cost Dynamics of Recruitment

Cost per Hire

Every penny spent in the recruitment process counts. So, understanding your 'Cost per Hire' is a crucial metric. This includes all expenses — from advertising to recruiter fees and the administrative costs of onboarding. A comprehensive view of these costs helps in identifying areas where you can optimise and reduce expenses without compromising the quality of candidates.

You can measure Cost per Hire using this formula:

Cost per Hire (CPH) = Total External Costs + Total Internal Costs/Total Number of Hires

Where: Total External Costs include expenses such as advertising, job fairs, agency fees, and any other expenses paid to external vendors or third parties.

Total Internal Costs are costs related to the internal recruitment process, such as the salaries of HR staff and hiring managers, referral bonuses, and recruiting technology or tools. *At this point some businesses also include cost of the position not being filled, where the cost of projects not meeting deadlines, lost business or the cost of the extra man hours people put in to cover the unfilled roles are added in.

The Total Number of Hires refers to the total number of positions filled during the period you're measuring.

Speed and Efficiency Matter – Understanding time to hire and what to do about it.

Time to Fill

The 'Time to Fill' metric is a double-edged sword; while you don’t want to rush decisions and compromise on quality, efficiency is key. Unfilled extended vacancies can hamper productivity and morale. Streamlining your recruitment process to reduce time to fill, without sacrificing candidate vetting, can significantly improve your ROI by minimising productivity loss.

The formula to use for this metric is as follows:

Time to Fill: Measures the number of days from when a job requisition is opened until an offer is accepted by the candidate. It helps in understanding the speed of the recruiting process.

Time to Fill = Day Offer Accepted − Day Job Role Opened

Leverage the Right Sources – Understanding source of hire

Source of Hire

Not all recruitment channels are created equal. Identifying your most effective 'Source of Hire' allows you to allocate your budget more effectively, ensuring you’re investing in channels that attract high-calibre candidates. Whether it's job boards, social media, or employee referrals, focusing on high-yield sources can enhance both the efficiency and effectiveness of your recruitment efforts.

Understanding & Optimising the Candidate Journey

Applicant Dropout Rate and Offer Acceptance Rate

The experiences candidates have during the recruitment process can make or break their decision to join your organisation. A high 'Applicant Dropout Rate' may indicate issues with your recruitment process, while a low 'Offer Acceptance Rate' suggests your offers may not be competitive. Paying attention to these metrics and optimising the candidate journey can significantly improve your recruitment ROI.

For our clients, understanding and maximizing the ROI of recruitment is about making every hire count, optimising each step of the recruitment process, and leveraging technology to gain insights and efficiency. By focusing on the quality of hire, managing costs effectively, ensuring efficiency in the recruitment process, optimising sources of hire, and enhancing the candidate experience, organisations can significantly improve their recruitment ROI. As recruitment consultants, our role is to guide you through this complex landscape, providing the insights and tools you need to attract, hire, and retain the top talent that will drive your organisation forward.

4 Megatrends Affecting The Hiring Landscape

Fri, 26 Apr 2024 14:03:07 GMT

The hiring and workplace landscape is currently being shaped by four megatrends.

The hiring and workplace landscape is currently being shaped by four megatrends. Firstly however, as a backdrop to this discussion, HR and executives agree that people are both the key asset and one of the main risk factors to an organisation’s growth.

Our discussions with the data and technology community highlights shifts in work design, productivity and employee expectations and it’s an interesting area that organisations must navigate through to attract and retain talent effectively. Layered over these discussions are the economic factors that are a very real issue for some in the workforce. The cost of work “crisis” is at the forefront of people’s minds and it means that companies are having to look at reward packages that look to assist with these problems.

Taking these megatrends one by one, let’s understand how this is impacting the workplace.

Culture and AI

Unless you’ve been living under a rock, you’ll know that AI is changing the future of work. Organisations are focusing on enhancing productivity through AI, assessment, and work design. This involves solving the productivity equation by leveraging technology whilst ensuring that the work environment remains conducive to human needs and capabilities. What does this mean for people in the data and tech communities? Everything - from the policies that need to be drafted around use of AI to the adoption of new skills to implement processes that will enhance the ways people work. One of the areas we feel strongly about is providing clear guidance for employees around how to implement and leverage AI. We've seen more and more reports coming out around “AI gone bad” where the fundamentals were lacking and employees were left to implement and figure it out as they went along. The implementation of AI means creating a work environment founded on guidance then trust, fairness, and inclusion. Equitable pay, fairness around upskilling, role design and promotion need to form part of its approach.

With all these changes and external pressures, building a resilient company culture is more crucial than ever, with emphasis on teams that are aware of risks and work to maintain a company’s core health. This trend is about enhancing the organisation's ability to withstand various challenges, ensuring both business continuity and employee well-being.

Cost of Work

The 2nd mega trend sweeping the world of work is related to the so-called cost of work crisis. Workers across the globe have understood how changing the way they work is helping their household finances. In 2020 (if role appropriate), once many were forced to step away from the 5-day on site model it became very clear as to how they then benefited financially and/or emotionally from working from home. These costs include commuting expenses, childcare, wardrobe, and the less quantifiable impacts on time and energy. The realisation that the traditional office model may impose a significant burden on employees is driving some employers to rethink their approach to work arrangements and organisations are now starting to rethink their strategies to attract and retain talent by addressing these costs head-on. Some examples around this would be re-evaluating remote work policies, enhancing benefits packages, or adopting innovative solutions to reduce the total cost of work for employees. Rather than returning to previous working models, there's a challenge and opportunity for employers to reassess traditional expectations and focus on outcomes rather than where the work is done. A 2023 Gallup study found that “hybrid workers have higher engagement, better overall wellbeing and lower turnover risk than fully on-site workers who are remote-capable” On top of this “leaders and managers report that hybrid work has reduced burnout, improved retention and expanded talent pools in their organisation”. There are of course what could be considered negative effects, such as less collaboration or communication as well as the hidden issues around recognition and promotion, however the data would argue that these are areas for reworking rather than a reason to state the hybrid model is broken. The cost of work crisis is acting as a catalyst for both employees and employers to re-evaluate the structure of work. It's pushing towards more flexible arrangements that can better meet the needs of the modern workforce, with hybrid working models emerging as a key component of this evolution.

Trend 3 looks at the adoption of Four-Day workweeks

In 2022 a UK based experiment conducted amongst 61 companies showed that the concept works well for both the employer and the employee. Of the 61 companies 93% of them had continued the model into 2023 and reported, better employee wellbeing outcomes, higher productivity, better customer service and lower costs. It seems the key to success (like most things) lies in planning and communication. Looking further afield, analysis by MIT cites that although larger companies like Microsoft in Japan specifically and Lever in New Zealand took part in a similar trial and reported positive results, many larger organisations are slower to adopt the model due to rigid frameworks. Of course there are challenges, some industries need people to be available 24/7 (healthcare for example), so for now small and mid-sized companies are leading the way. If you are interested in looking at this model for your organisation this website has some fantastic resources:

The final trend focuses on skills-based hiring

A variety of factors have contributed to the shift towards skills-based hiring. A tight labour market, rise of AI tools within the workplace, greater commitment to EDI, and shift away from fully onsite working models means that finally skills v’s degrees and/or which University a person attended, is providing the best indicator of a successful hire. For decades the degree was a ticket in, but with a reported 13% of graduates workplace ready post degree, alternatives to the narrative are being embraced. It is a more equitable approach to diversifying the workforce and studies show that tenure is increased on average by 9% when this approach is taken. To successfully navigate this beneficial trend, here are a few tips.

Start by conducting a thorough job redesign – from job descriptions and job architectures where all previous assumptions about what the job profile requires is challenged and defined.

If it’s not in place already, implement some fundamentals around EDI programmes, C-suite buy in, education for the workforce, unconscious bias training conducted for hiring panels and managers, your EVP attended to etc.

Define a skills-based framework for promotions and career development.

These trends collectively signal a dynamic shift in the fundamental aspects of employment and organisational strategies. As work becomes more intertwined with advanced technology like AI, and as economic realities push for new work models such as remote or hybrid formats and four-day workweeks, companies are forced to rethink not just how work is done, but also how it's valued and rewarded. This evolution is leading to a redefinition of job roles, a greater emphasis on employee well-being, and the adoption of more inclusive hiring practices based on skills rather than educational background. Consequently, these changes are likely to increase job satisfaction and loyalty, creating a more engaged workforce. Ultimately, as organisations adapt to these emerging trends, they not only enhance their competitiveness but also contribute to a more equitable and responsive labour market.

5 Tips for skills based hiring

Jo.Dionysiou@kdrrecruitment.com (Jo Dionysiou) — Wed, 24 Apr 2024 08:23:05 GMT

One of the biggest trends in 2024 is a focus by employers on skills based hiring

One of the biggest trends in 2024 is a focus by employers on skills based hiring v’s more traditional recruitment which focuses instead on qualifications, academic institutions and former employer companies. Our advice to companies seeking to harness the full potential of their data-driven initiatives is to prioritise strategic skill alignment when hiring data technology and analytics professionals. This is a more focused approach to recruitment and ensures that new hires not only fill technical gaps but also align closely with the company's long-term strategic goals, thereby maximising the impact of their data-driven projects. It also really drives inclusive recruitment practices which is what you should all be striving to nail.

Understanding Strategic Skill Alignment

Strategic skill alignment involves matching candidate skills and potential with the company's current and future needs. This goes beyond assessing technical capabilities to include soft skills, adaptability, and alignment with the company's culture and strategic vision. By prioritising this alignment, companies can enhance team cohesion, drive innovation, and better adapt to evolving market dynamics.

We have seen from our annual State of Data Report how important softer skills are to the community. In fact the more senior the role, the more important they become for influencing, project management, buy in, communicating across departments stakeholders and suppliers.

Top 5 tips to successful implementation of skills based hiring processes:

1. Before starting the hiring process, identify the specific skills that are crucial for success in the role. Break these down into hard skills (e.g., programming languages, analytics tools) and soft skills (e.g., communication, influencing, teamwork). This inventory will then serve as a basis for evaluating all candidates consistently.

2. Write job descriptions that highlight the skills and competencies needed rather than focusing solely on degrees and certifications. This not only widens your applicant pool but also attracts candidates who are confident in their abilities to perform the job. A word of warning here though, make sure you are being very clear on skill expectation. An experiment reported by WEF showed that the clearer job adverts were around the expectations, the more women were likely to apply. (You will have heard about how women are very likely to self-select themselves out of the application process if they perceive they are not qualified v's men who tend to apply regardless). The way around this is to avoid woolly language instead providing prescriptive instructions like "we invite applications from people with xyz"

3. Tailor interview questions to directly assess the candidate’s proficiency in key areas. Use behavioural interview techniques to ask candidates to describe past experiences that demonstrate their skills and how they've applied them in real-life scenarios.

4. Commit to the continuous development of employees’ skills post-hire. Provide training and learning opportunities that allow employees to upgrade their skills as needed, which helps in retaining talent and keeping your team competitive.

5. There is some argument to utilise skills assessments but note that there is also evidence that providing skills tests upfront can thin out your pipeline as professionals are increasingly time poor. If you are considering it, this could include technical tests, problem-solving exercises, or project simulations relevant to the role. These assessments can help to objectively evaluate a candidate's ability to perform job-specific tasks.

Why use this technique?

Employees whose skills and career aspirations align with their roles are more likely to be engaged and productive because it promotes job satisfaction and employee retention by aligning roles with individual career aspirations and competencies.

Skills aligned professionals are more adept at identifying and pursuing innovative opportunities that align with business goals and because the skills are aligned with the company’s strategy, a company can adapt and grow more effectively - driving continuous improvement.

Skill alignment helps in forecasting future talent needs and preparing the workforce for upcoming challenges and technological advancements.

In 2024, as companies increasingly rely on data to make informed decisions and drive business outcomes, the importance of hiring data technology and analytics professionals who are strategically aligned with the company’s goals cannot be overstated. By emphasising strategic skill alignment in their hiring practices, companies can build more diverse, effective, innovative, and adaptable teams.

Data Job Market Analysis Q2 2024

Fri, 19 Apr 2024 09:29:05 GMT

From my perspective as a data and technology recruitment business owner, I am keen to provide insights into the market for my network.

Taking key learnings from last year, monitoring the market weekly and then reflecting on a quarterly basis helps us as a business to become more agile and I hope to pass on these insights to others.

Economy

As a backdrop, taking employment government data for Q1, we can see that the employment rate was at 75% for individuals aged 16 to 64 years, which was a slight decrease from the previous year. Unemployment was estimated at 3.9%, indicating a slight increase compared to the prior year (Office for National Statistics). Despite a challenging economic landscape, there's a silver lining as headline inflation is expected to near the 2% target, potentially improving living standards for low-income households in 2024. However, consumer prices are anticipated to be significantly higher than early 2021 levels, and corporate insolvencies are expected to grow, which could impact job security and creation (taken from a PwC report).

Growth

I feel quite optimistic about Q2 and the rest of 2024. I am encouraged to see many of our clients sharing growth plans, renewed interest in consultancy services and a wide range of job opportunities, spanning multiple industries. I can cautiously say, the job market has picked up. With that, however, follows other challenges, candidates are still experiencing the effects of 2023 – 400+ applications for one role is sadly becoming the norm, with competition still high and AI “assisting” job seekers who are applying for roles.

Comparing our proprietary data with wider market data we can see that so far in Q1 and going into Q2 and beyond, there is an uplift in employer hiring intent. For example, the Manpower Group Employment Outlook Survey highlighted a steady national employer hiring intent at +27%, led by sectors like Financial Services, Real Estate, and IT, which exactly reflects our own experience at KDR.

Markets

As mentioned, the finance and insurance market in particular has performed well in Q1 and the range of roles we have been working on shows how diverse the skillsets required are. Reflecting on conversations held across Q1, although there will be a continued need for cyber security, cloud, AI and machine learning skillsets, running alongside these more hyped skills is data governance. Data governance and wider data management skill sets are in high demand. According to data from our State of Data Annual Reports, data governance has been a priority over the last two years, before the influx of generative AI data (in particular), and it’s now in even more demand due to the amount of data flooding businesses and their systems. Often seen as the less attractive alternative for young talent coming into the sector, we have been working to elevate these functions amongst the student cohorts we worked with in Q1, particularly with Sheffield University (which has been cited as the global number 1 school for information management courses) and 2 academy trusts across the North West.

Trends

It has been interesting to see an increase in skills-based hiring amongst our client base. This of course helps with ED&I efforts and swerves the university and experience headlock we have perhaps seen a prevalence of in previous years.

Salaries

Looking at salaries, the prediction was for 5% growth this year. We are seeing a slight plateau of salary growth across our core sectors, however the heights reached over the last 2 years has far out-performed national average increases so this is not surprising. If you are unsure of where your roles currently stand against the market, I would advise conducting a salary benchmark exercise in Q2 to ensure that you remain competitive and that you are also not over-paying new roles. Incidentally, we have noticed a big increase in demand for contractors over permanent positions, this shift is part of a broader trend towards cost optimisation and reflects the industry's adaptation to economic pressures and the evolving work landscape.

Despite a challenging economic environment, we are finding that sectors showing robust growth offer promising opportunities for data and analytics professionals. Overall, I believe the Q2 outlook for data and analytics jobs in the UK is optimistic, with better outcomes for candidates on the market. As an aside note, I have noticed a growing trend over the course of 2023 and so far in ‘24 for AI assisted applications. I would recommend candidates keep their nerve and ensure every application is personalised and tailored to the role. Keep applying, despite seeing 100+ applicants, because with this rising trend for AI assisted applications, there is a real decline in the quality of applications coming through. You will shine through that 100 list of applicants if you stick to your skill sets and as previously mentioned tailor your application to the role and company. By that I mean, bring the skills mentioned in the job advert to the top of your CV and mention your experience and outcomes in these areas in the opening summary or cover letter.

A data professional’s guide to jobs in the insurance industry

Tue, 19 Mar 2024 09:11:54 GMT

The insurance industry has been undergoing significant transformations

The insurance industry has been undergoing significant transformations, driven largely by technological advancements and changing consumer behaviours. Two areas at the forefront of this evolution are telematics and Usage-Based Insurance (UBI).

Telematics and usage-based insurance (UBI) are at the forefront of revolutionising how drivers are assessed, priced and offered coverage. By leveraging cutting-edge technology, insurers can now provide more personalised insurance policies, shifting away from traditional models based on demographics and historical data. One example would be the use of this model for new drivers. Drive well and you are rewarded with lower fees the following year, drive badly and pay more or not at all!

The integration of these technologies into insurance products not only offers more personalised pricing models but also requires a unique set of skills for professionals within the sector. Understanding the job market within this context and the necessary competencies can help both newcomers and seasoned professionals navigate their careers in the insurance industry. Having worked with insurance companies for the best part of 20 years, we have witnessed the evolution in the skill sets required first hand.

For data and tech professionals exploring opportunities in the insurance sector, particularly within the realms of telematics and Usage-Based Insurance (UBI), the transition might seem straightforward at first glance. However, the application of data analysis, technical expertise, and innovation within insurance, especially in these niche areas, requires a nuanced understanding of the industry's unique challenges and opportunities.

Here's a list of what sets apart the skill set for data professionals in insurance compared to other industries:

1. Domain-Specific Data Interpretation

While strong data analytical skills are a fundamental requirement across industries, insurance demands a deep understanding of risk assessment, actuarial science, and behavioural analysis. Telematics and UBI, in particular, involve interpreting driving behaviours, environmental conditions, and other contextual factors that impact risk. This necessitates not just technical proficiency in data analysis but also an ability to understand and predict complex human behaviours within the framework of risk and insurance models.

2. Regulatory and Compliance Acumen

The insurance industry is heavily regulated, more so than many other sectors where data professionals might work. This introduces a layer of complexity in how data can be collected, used, and stored. Data professionals need to navigate these regulations skilfully, ensuring that telematics and UBI data practices comply with privacy laws, data protection regulations, and insurance-specific legal requirements. This regulatory acumen is crucial for designing products that are not only innovative but also legally sound.

3. Ethical Use of Data and Bias Mitigation

Data ethics takes on a heightened significance in insurance. The potential for bias in data collection, analysis, and application is a critical concern, especially when it comes to setting premiums or making claims decisions based on telematics and UBI data. Professionals in this space must be adept at identifying and mitigating biases in datasets and algorithms, ensuring that decisions are fair, ethical, and transparent. This includes a thorough understanding of how to responsibly use sensitive data and protect consumer privacy.

4. Innovative Product Development

Unlike other sectors where data might primarily support operations or decision-making, in insurance, especially with telematics and UBI, data is directly tied to product development. This requires data professionals to think like product managers, understanding market needs, consumer behaviours, and technology trends to innovate and create insurance products that meet evolving consumer expectations. This cross-disciplinary skill set bridges data science, technology, and business strategy.

5. Customer-Centric Analytics

The focus on customer-centric products in insurance, particularly with telematics and UBI, demands a different approach to data analysis. Professionals must leverage data not just for risk assessment but also to enhance customer engagement, personalise offerings, and improve the overall customer experience. This involves analysing data to derive insights into customer preferences, behaviours, and needs, and then translating those insights into actionable strategies.

Salaries for roles specialising in telematics and UBI tend to be competitive, reflecting the high demand and specialised skill set required. Career progression opportunities are plentiful, with paths leading to senior analytical roles, product management, and strategic leadership positions.

While the core data and technical skills remain similar, applying these in the insurance industry, and specifically within telematics and UBI, requires a tailored set of competencies. Depending on who you speak to, as a data professional you can enter the industry without prior experience, however on the whole, domain-specific knowledge, regulatory expertise, ethical considerations, a focus on innovative product development, and a customer-centric approach are what differentiate the skill set needed in insurance from other sectors. For data and tech professionals, understanding and cultivating these specialised skills can open up rewarding opportunities to drive innovation in insurance.

Building a collaborative data function

Mon, 04 Mar 2024 16:01:44 GMT

How to create collaborative data teams: Unlocking synergy

The ability to foster collaboration within data teams and across different business functions has emerged as a key differentiator for successful organisations. In fact, our State of Data survey revealed that the most successful teams are those that excel in collaboration both internally among themselves and externally with other business units. This finding is a clear call for teams seeking to optimise their data strategies and achieve better outcomes. If you are looking to extend collaboration across business functions, here's a quick guide to help unlock the full potential of your data strategies.

1.Foster a Culture of Open Communication

The bedrock of any collaborative effort is open and transparent communication. One area that sometimes falls short is in creating an environment where team members feel comfortable sharing ideas, challenges, and feedback. To help this grow successfully think about not only holding regular team meetings but also creating spaces for informal exchanges and encouraging the use of collaborative tools that facilitate seamless communication. Examples would be cross-functional team gatherings on-site or good old-fashioned team build / innovation days.

2. Break Down Silos

Data teams often work in isolation from the rest of the business, leading to silos that can hinder the effective execution of data strategies. To counteract this, it's beneficial to establish cross-functional ideation teams that include members from various departments such as IT, marketing, finance, and operations. This approach ensures a diversity of perspectives and expertise, and also helps to cement buy in to the data strategy. Having the wider business functions understand the aims and limitations of data strategies is really the cornerstone of a successful data strategy.

3. Invest in Collaboration Tools – no need to elaborate here!

Project management tools
Comms tools
Cloud based platforms for real-time data sharing.

4. Embrace Agile Methodologies

Adopting agile methodologies can greatly improve the collaboration within data teams and with other business units. Agile practices, such as sprint planning, daily stand-ups, and retrospectives, encourage continuous communication and adjustments based on feedback and changing requirements. This flexibility allows teams to be more responsive and aligned with business goals.

5. Prioritise Education and Cross-Training

A common understanding of the goals, tools, and methodologies used in data analysis and strategy implementation can enhance collaboration. Investing in education and cross-training sessions for team members, including those from non-technical backgrounds, ensures that everyone speaks the same language and appreciates the value and constraints of the initiatives that data can drive. In other words, set up an internal data literacy programme. If you do, our State of Data survey indicated that those that prioritised this spread of information around data, across the business, were more likely to succeed (execution of data strategy) than those that hadn’t done this.

6. Celebrate Successes and Learn from Failures

Finally, recognising and celebrating the achievements of collaborative efforts can reinforce the value of teamwork and encourage further cooperation. Along a similar theme, openly analysing and learning from failures without assigning blame can foster a culture of continuous improvement and resilience.

The insights from our State of Data survey and knowledge of building high performing data teams for clients, highlight a clear path forward for organisations aiming to leverage their data assets more effectively: prioritise collaboration. In doing so, they not only amplify their capabilities but also drive innovation and competitive advantage in today's dynamic market landscape.

Expert advice to help you get your next job in data science

Thu, 22 Feb 2024 15:53:16 GMT

The demand for data scientists and analysts continues into 2024

The demand for data scientists and analysts continues into 2024, but landing your dream role requires more than just technical skills. This guide, provides expert tips to navigate the competitive landscape to help secure your ideal data position.

1. Know Yourself: An area often overlooked, but a really important first step!

Knowing what you are good at and not so good at, and analysing your specific skill sets and passions helps you to identify your data strengths, interests, and desired industry/work environment.

Taking this a step further, by spending time upfront to define your dream job, looking at what tasks, challenges, and impact you want to make for example, gives you real clarity and this will guide your application strategy.

2. Sharpen Your Skills:

If you start researching the types of roles you are interested in, chances are there are technologies or methodologies that you haven’t worked in anger previously.

Python, SQL, statistics, and data visualisation are probably non-negotiables for the types of roles you are looking for. However, if you are rusty in some areas or need to understand how other technologies work, spend time on Code Project for example because that platform has some good free access to learning resources, or if you have PluralSight through work, use it to stay updated on industry trends and emerging technologies.

Another overlooked activity is to build a portfolio to showcase your best data projects, highlighting problem-solving abilities and communication skills.

And on that, subject remember that soft-skills like communication, teamwork, and critical thinking are crucial for success. One way to refine these is through networking and volunteering. Make sure to put them front and centre on your CV alongside your technical skills.

3. Craft a Compelling CV and Cover Letter:

Tailor your CV by aligning your skills and experience directly to each job description. A good tip is to focus on what you achieved and the impact it had on the business. To write a captivating cover letter you need to go beyond generic statements. Show genuine interest in the company and highlight why you're the perfect fit. A current growing trend is showing in the form of AI generated applications. If you are using AI to help you be more efficient in your application process, make sure you are checking the content. We have seen some really bad examples where the gen AI tables are still showing around the pasted text for example, that the statements are not tailored for the job and that the content produced does not sound human, which is rather missing the point of a tailored cover letter and CV!

4. Network and Build Relationships:

You can learn a lot from your peers so build time into your week to connect with data professionals. You can do this in a variety of ways like attending industry events, joining online communities, and leveraging LinkedIn.

Finally, reach out to us recruiters. Those recruiters that are specialised in data and technology can provide you with job opportunities and introductions to companies you might not have considered previously. Our job market knowledge and current trend insights can give you the edge in your job hunting strategies.

5. Ace the Interview:

A basic we are sure you’re aware of but worth mentioning just in case: Remember to research the company and role. Go even further if possible and try to demonstrate your understanding of their challenges and how you can contribute. This will be easier if you are working with a recruiter, as they will know this information.

Prepare for common data interview questions and practice explaining complex concepts clearly and concisely.

One activity that really makes a difference is to ask insightful questions. When you ask questions about the role, business or strategy you show your engagement and genuine interest in the company and the role.

Bonus Tip:

Stay up-to-date on data trends: Read industry publications, follow data leaders on social media, and participate in online courses to stay relevant and competitive.

By following these expert tips, you'll be well-equipped to navigate the data job market and land your dream role. Contact us if you feel you need any assistance, we are here to help! Best of luck in your data career journey!

Data Job Market Analysis Q1 2024

Tue, 20 Feb 2024 10:58:19 GMT

"Looking forward towards the next 12 months, I am optimistic about the opportunities that lie ahead for businesses and professionals in this space".

As the Managing Director of KDR Talent Solutions, my perspective on the data and analytics job market is informed by a deep understanding of the sector, external economic, political factors, and the dynamic nature of technology-driven industries. The last 12 months have been a period of significant fluctuation and adaptation, influenced by a variety of external factors. However, looking forward towards the next 12 months, I am optimistic about the opportunities that lie ahead for businesses and professionals in this space.

Overview of the Last 12 Months

The data and analytics job market has been significantly influenced by several key external factors. The biggest arguably has been the ongoing economic uncertainty around the globe. Global economic conditions, including inflation rates, interest rate hikes, and supply chain disruptions, have affected investment and hiring trends across industries. Despite these challenges, the demand for data and analytics talent is turning the corner, as companies who imposed headcount freezes to create lean and agile environments for survival now in Q1 return to the market with stronger than ever needs for talent. This partly is due to a realisation that leaner teams are struggling to deliver on customer demands.

Political factors such as regulatory changes concerning data privacy and security (e.g., GDPR in Europe, CCPA in California) and geopolitical tensions have led to a re-evaluation of data sovereignty and localisation strategies which in turn have increased the demand for professionals skilled in data governance and compliance.

Looking Forward

The ongoing effects of the COVID-19 pandemic have accelerated digital transformation initiatives, with businesses investing heavily in data and analytics to improve operational efficiency and customer experiences. We are finding that this has led to a surge in demand for data scientists, analysts, and engineers, but with perhaps the highest demand within data governance.

The rapid development of AI, machine learning, and cloud technologies that we saw during 2023 has seen businesses evolve some roles and we are more frequently seeing traditional roles expand to encompass specialised skills to leverage these technologies for competitive advantage. One growth area in particular can be found within the Microsoft Power Platform suite of technologies, with salaries and demand increasing above the curve of other disciplines.

Looking ahead, I am optimistic about the growth and opportunities in the data and analytics job market for several reasons. The drive for digital transformation is far from over. As businesses across all sectors continue to digitise their operations and explore new data-driven business models.

There is a growing recognition of the importance of building a data-driven culture within organisations. This cultural shift is creating more opportunities for data professionals to influence strategic decision-making and drive innovation.

The application of AI and machine learning is expanding beyond traditional sectors, creating new opportunities for data professionals . As businesses explore the potential of these technologies in areas such as healthcare, finance, and manufacturing, the demand for skilled professionals will rise.

Economic and political pressures are prompting businesses to focus on sustainable growth strategies . Data and analytics play a crucial role in identifying efficiencies, optimising operations, and reducing environmental impact, leading to increased opportunities in this space.

The normalisation of remote and hybrid work has opened up global talent pools , both for businesses seeking talent and for professionals seeking opportunities. This trend is expected to continue, despite many businesses calling for a return to the office, facilitating a more dynamic and diverse data and analytics job market.

Despite the challenges posed by external economic and political factors over the last 12 months, the data and analytics job market is poised for significant growth and opportunities in the coming year. At KDR Talent Solutions, we are excited to be at the forefront of this dynamic market, connecting talented professionals with innovative businesses that are shaping the future of industries . Our optimism is not just rooted in current trends but in the fundamental belief in the transformative power of data and analytics to drive progress and innovation and I for one am happy to play a small part in that space.

Unlocking the Benefits of Workforce Planning: A Guide for Talent Acquisition, HR, and Hiring Managers

Wed, 14 Feb 2024 14:41:07 GMT

The benefits of effective workforce planning are vast

Workforce planning, an essential component of human resources (HR) strategy, offers a structured approach to ensuring that an organisation has the right people, with the right skills, at the right time, and in the right places. For talent acquisition (TA) professionals, HR teams, and hiring managers, the benefits of effective workforce planning are vast, ranging from improved talent management and operational efficiency to enhanced strategic decision-making and competitive advantage.

Benefits Across the Board

Taking Talent Acquisition teams first, workforce planning enables TA teams to align recruitment strategies with business goals, ensuring that talent acquisition efforts are focused on areas of greatest strategic importance. By anticipating future talent needs, TA teams can engage in proactive sourcing, building talent pipelines and networks before critical needs arise, thus reducing time-to-hire for key positions.

For HR Departments , HR can use workforce planning to identify skills gaps, plan for succession, and develop targeted training and development programmes, ensuring the workforce is prepared to meet current and future challenges. Workforce planning also provides HR with data and insights to make informed decisions about talent management, compensation, benefits, and other HR policies, aligning them with business needs and employee expectations.

For Hiring Managers , with a clear understanding of current and future competency requirements, hiring managers can make more informed hiring decisions, improving the quality of hires and reducing turnover. Effective workforce planning ensures that teams have the necessary skills and capabilities to meet their objectives, enhancing overall performance and productivity.

Practical Tips for Starting Workforce Planning

If your organisation has not yet embarked on workforce planning, or if you are looking to refine your approach, consider the following practical tips:

Begin by conducting a thorough assessment of your current workforce, including skills inventories, performance data, and future potential. This assessment will serve as the foundation for all subsequent planning activities.
Work closely with business leaders to understand strategic goals and priorities. Workforce planning should be directly aligned with these business objectives to ensure that talent strategies support business needs.
Analyse the difference between current workforce capabilities and the skills and roles required to achieve future business objectives. This analysis will highlight areas where talent acquisition, development, or internal mobility may be needed.
Based on the identification of skills gaps and future needs, develop specific action plans to address these areas. Plans may include recruitment strategies, training programmes, succession planning, and workforce adjustments.

If you are looking to focus your efforts on the most effective areas instead of a step-by-step guide, then it might be worth considering the following in order of priority:

Strategic Alignment (High Benefit): Aligning workforce plans with business objectives ensures that all talent management efforts support overarching goals, providing significant strategic and competitive benefits.

Gap Analysis (Medium-High Benefit): Identifying current and future skills gaps enables targeted actions to close these gaps, improving workforce agility and preparedness.

Action Planning (Medium Benefit): Developing specific strategies for talent acquisition, development, and retention ensures that the organisation can meet its talent needs efficiently and effectively.

Monitoring (Medium-Low Benefit): Continuously monitoring progress allows for adjustments and improvements, ensuring ongoing alignment with business needs.

Quick Wins and Tips

Use HR data analytics to inform your workforce planning efforts. Data-driven insights can help identify trends, predict future needs, and measure the effectiveness of talent strategies.

Involve business leaders, managers, and employees in the planning process to gain buy-in and ensure that plans reflect the reality of business needs and workforce capabilities.

Prioritise planning efforts on roles that are critical to business success. Filling these roles with high-quality talent can have an outsized impact on organisational performance.

As you can appreciate, the business environment is constantly changing. Adopt flexible and adaptable workforce strategies that can be adjusted as business needs evolve.

By systematically implementing workforce planning, TA professionals, HR teams, and hiring managers can unlock significant benefits for their organisations, enhancing their ability to attract, develop, and retain the talent needed for sustained success.

Power Platform Jobs

Fri, 02 Feb 2024 11:30:47 GMT

The job market in 2024 for Power Platform professionals showcases a significant upward trajectory

The job market in 2024 for Power Platform professionals showcases a significant upward trajectory, reflecting the platform's growing impact on business processes and its substantial influence on the fields of data, analytics, and technology. Charlie Wilkinson-White , a specialist in data, analytics, and technology recruitment consultant advises that there is a growing demand for skilled professionals within the Power Platform domain, particularly in the UK. This demand is spurred by the platform's increasing adoption across businesses. To capitalise on this surge, professionals in the space would do well to get a deep understanding of the career landscape, including insights into potential salaries, job availability, and strategies for skill enhancement.

The successful implementation of Power Platform solutions requires a comprehensive approach that includes assessing business needs, strategic planning, focused execution, and continuous optimisation. Consequently, there are diverse opportunities for professionals interested in this technological specialty. The Power Platform job market attracts individuals from various backgrounds, such as software development, data science, business analysis, among others. Key skills that distinguish these professionals include expertise in data modelling, analytics, cloud services, and a nuanced understanding of business processes. Moreover, it is crucial for Power Platform professionals to continually update their skills to stay abreast of the platform's developments.

Salaries for Power Platform professionals in the UK exhibit a wide range based on factors like experience, role, and specific skill sets. While entry-level positions may offer salaries in the lower percentile (although still healthy for entry level roles!), experienced developers, analysts, and architects within the Power Platform domain can command higher wages, reflective of the high value placed on digital transformation capabilities by businesses. Additionally, competitive compensation packages provide further benefits, highlighting the strategic importance of these roles.

The job market is showing demand across the board, ranging from entry-level positions focusing on basic application development, data analysis, and workflow creation, to mid-level and senior-level roles that involve more sophisticated project delivery and leadership responsibilities. Specialised roles, such as Power Platform Solutions Architects or consultants adept at integrating Power Platform with other technologies, command complexity and strategic focus, thereby delivering high business value.

A quick and dirty search on google trends shows that Power BI Jobs is by far the highest trending search term over any of the other Platform components, even more so than Power Platform as an overall search term. This indicates a particularly high demand for skills in data analytics and business intelligence .

Looking ahead, the demand for Power Platform skills is poised to grow even further. Businesses increasingly depend on digital transformation strategies, driving the need for professionals who can leverage Power Platform to optimise operations, enhance data insights, and foster innovation. KDR Talent Solutions' data underscores this robust demand, pointing to a trend towards specialisation within technology roles. The future job market for Power Platform professionals is expected to be competitive, offering new career opportunities and playing a pivotal role in the strategic direction of UK businesses.

For professionals seeking to expand their skills in this area, leveraging networks on platforms such as LinkedIn is recommended for advice, collaboration on projects, and career advancement. Consider contacting experts like Charlie Wilkinson-White for insights into the Power Platform landscape and market dynamics can provide valuable guidance for navigating this evolving job market.

Building High Performance Data Teams - A no nonsense guide

Mon, 04 Dec 2023 15:29:20 GMT

A well-structured data team can be the difference between a good utilisation of insightful data and a company that is unable to access the benefits of the data produced

As our State of Data report indicated, those businesses that have invested in data, especially with the economic and political backdrop of the last 18 months, are the ones that have had reported successes in the areas of data usage, data strategy execution and growth. Building a high-performance data team is therefore crucial for the success of any organisation. A well-structured data team can be the difference between a good utilisation of insightful data and a company that is unable to access the benefits of the data produced. My recommendation would be to follow the flow below, focusing on specific areas within each check point because it allows you to stand above the whole hiring process and see what is going on in a more holistic way, which tends to lead to better results.

Foundation:

Roles and Responsibilities:

Crafting a high-performance data team begins with defining clear roles and responsibilities. One mistake I come across is when hiring managers keep running with legacy job roles and descriptions instead of considering what is needed going forward. It’s a mistake because the need for “speed to fill” overrides the better outcome for the business and team. Spend longer at this point to really define the where the skills gaps lie in the team, where the business objectives impact what your team can deliver, then look at each role and weed out any legacy requirements or expectations to ensure you are being as inclusive as possible and therefore reaching a wider talent pool. A note on team size. The size of your data team should be a reflection of the scale and complexity of your business operations. A smaller team may excel in agility, while a larger team can handle diverse data challenges. Striking the right balance ensures optimal resource utilisation.

Process:
Recruitment processes are often overlooked but really important aspects of building a high performing data team. I have written a whole article on recruitment processes here , but a boiled down version would be: start with strategic preparation, create inclusive job descriptions, make use of technology, unbias your selection and interview process, ensure you provide timely feedback, and then look at your onboarding, because all of these phases of the recruitment cycle affect who you attract and then keep engaged with your business v’s your competitors. Adherence to these basics significantly enhances the likelihood of recruiting the right talent and contributes to overall organisational success.

Identifying talent:
When seeking individuals for your data team, prioritise a blend of technical expertise and soft skills. Technical knowhow is the bedrock of a high-performance data team. Proficiency in programming languages (Python, R), database management (SQL, NoSQL), and knowledge of machine learning frameworks may be non-negotiable for example. Foster an environment that encourages continuous learning to keep pace with the rapidly evolving tech landscape and mitigate skills gap challenges.

Beyond technical acumen, soft skills are the glue that binds a data team. Our State of Data report found that effective communication, problem-solving, and a knack for translating complex insights into understandable narratives are vital. As effective communication is the linchpin of a successful data team, assess candidates not only on their technical acumen but also on their ability to convey complex insights in a clear and concise manner. Evaluate their experience in presenting findings to diverse audiences, gauging their capacity to bridge the gap between data and actionable business strategies.

Foster a collaborative culture where team members can articulate their findings to non-technical stakeholders. Ultimately, the most effective teams are those that are able to communicate the reason why data needs to be inputted in a certain way, so that those who are feeding the systems understand why they are doing what they are doing. High performing data teams collaborate and story tell their way into the hearts and minds of the business so keep this in mind when hiring, technical knowhow is great and completely necessary, but so is having team members who can then sell to the rest of the business.

A diverse team brings a spectrum of perspectives crucial for comprehensive problem-solving. Beyond gender and ethnicity, diversity should encompass varied professional backgrounds and experiences. This approach fosters creativity and resilience in tackling complex data challenges.

Structure: data governance models

A key consideration for the times we are experiencing currently is the agility of a team. For agility to thrive you need certain structures in place and here there are a few options to choose from which I will highlight below. Whichever structure you go for make sure you consider a few helpful characteristics such as agility, efficiency, influence, stakeholder clarity, stability, growth or flexibility.

Usually when companies are at the beginning of their data journey they adopt a fully centralised data governance model. It consolidates data management under a single, central authority. All data-related processes, storage, and decisions are controlled centrally, ensuring uniformity and compliance with organisational standards. This allows for strong control over data quality and governance, maintenance and updates are simplified and enforcement of security measures is easier. The problems that arise in contrast can be around lack of flexibility for specialised business units, potential for bottlenecks in data processing and it may not cater well to diverse business needs.

The federated model however, involves a decentralised approach where data is distributed across different autonomous units or departments. Each unit maintains control over its data but can share information with other units when necessary. This encourages autonomy among units and provides efficient data access for specific business needs, in turn reducing the burden on a central authority. However, with this fully federated approach, data governance challenges may arise. There can be potential for inconsistencies in data interpretation and it requires strong coordination among units.

This is why I believe most of the clients I speak to have adopted a spoke and wheel model:

In the spoke and wheel model, a central hub manages and disseminates information to satellite units (the "spokes"). The central hub serves as a focal point for data control, ensuring consistency and standardisation.

The choice between these models depends on the organisation's structure, business objectives, and the need for centralised control versus distributed autonomy. Some organisations may even adopt hybrid approaches, blending elements from multiple models to strike a balance between control and flexibility.

Business Buy-In:

To elevate your data team, securing support from the business is key. Transparent alignment of data initiatives with organisational goals ensures everyone in the business understands the function and impact of what the data team provides. Demonstrate the tangible impact of data-driven insights on revenue, customer satisfaction, or operational efficiency to gain continuous support.

Learning and Development

One of the key trends for HR professionals in 2024 will be reinvigorating L&D programmes. Stats indicate that more people are becoming more disengaged from their work. Studies have also shown that continued learning taps into the hippocampus and prefrontal cortex, promoting neuroplasticity and cognitive flexibility. At work, this serves as a motivator by triggering a sense of accomplishment, fostering professional growth, aiding in adaptation to change, promoting innovation, enhancing job satisfaction, providing a competitive edge, and increasing self-efficacy. This not only benefits individuals but also contributes to a more dynamic and successful workplace. Seek individuals who exhibit a proactive approach to staying updated on emerging technologies and industry trends. Enquire about their participation in training programs, certifications, or community engagement, demonstrating their commitment to professional growth and adaptability.

Building a high-performance data team requires a meticulous approach to talent acquisition, streamlined processes, and alignment with business objectives. Having worked with data leaders and teams over the last 10 years, I know that navigating these intricacies well takes time but ultimately ensures that the individuals you bring on board not only possess the requisite technical skills but also contribute to a collaborative and innovative team culture. By focusing on a holistic evaluation initially, you lay the foundation for a data team that not only meets but exceeds organisational expectations.

Demystifying the Data Recruitment Process

Mon, 13 Nov 2023 14:54:04 GMT

Defining the Data Recruitment Process

The process of recruiting the right talent for data-related positions can be quite challenging, my article aims to demystify the data recruitment process. I will explore the various aspects of this process, discuss common challenges and provide solutions and best practices.

Defining the Data Recruitment Process

During our free talent strategy assessment, we discovered that companies often undermine their own success due to flawed processes. As boring as it sounds, processes are the absolute basics and backbone to any good recruitment strategy. Get this right, you increase your success rates, get these wrong and you lose good people, not only new recruits but also existing staff (through frustration at either vacancies remaining unfilled, and projects being delayed – or through the added workload of the recruitment cycle that never ends).

Like most things, the first stage of any successful process lies in the preparation stage.

Preparing for Recruitment

Before diving into the data recruitment process, it is essential to have a clear strategy. Define the roles you need to fill, the skills required and the goals you want to achieve through data analysis. Running a mini workforce planning exercise is useful if you have the time and expertise. It helps you to define your starting point, eliminate any legacy expectations for roles and where the growth lies. If you don’t have the expertise/time we (or any decent recruitment partner) can help.

Identify key stakeholders.

Scenario 1
Talent acquisition team, hiring manager, candidates

Scenario 2
Recruitment partner, hiring manager, candidates
Scenario 3
Talent acquisition team, recruitment partner, hiring manager, candidates

Getting the chain of communication bang on becomes more important the more functions that are added to the process. Regardless of who is involved, we would suggest creating a process that flows as follows:

Stage 1
Thorough briefing of role requirements, current and future projects, current team set up and reporting lines, timelines, and expectation setting.

Identify hiring panel (and provide training on unconscious bias if required)

Stage 2

Run weekly progress reports on the pipeline, submitted candidates, profile accuracy, next steps.

Interwoven into stage 2 will be the interviews. Make sure to include timely feedback into the process.

Stage 3
Candidate engagement. Depending on the role, there can sometimes be quite a period between an offer being accepted and the start date. Keeping candidates engaged is important so set up some automated messages which drip feed company information to the candidates. If you have a recruitment partner, they will cover this off for you, regularly checking in with candidates to ensure they are fully engaged and excited about the new role and your business.

Deep dive into recruitment stages.

Identifying Key Data Requirements

Identifying the specific data requirements of your organisation is crucial. This includes understanding the type of data you need, the tools required for analysis and the skills and experience that candidates should possess.

Identifying technology

Technology throughout the recruitment cycle creates a much smoother process. There are many value add technologies you can use but ensuring that you have a mix of AI, automation and data analytics throughout the process will lead you to the best results.

Sourcing Data Talent

Once you've identified your data needs, it's time to source the right talent. Depending on where your team is at, there may be a flow of roles to fill – it could be you need to focus on the strategy first, in which case hiring a senior role first would be a good idea, then adding in analysts and other roles. Regardless of the order, the first point of call is to make sure the job description is inclusive (check for gender bias words), is relevant and showcases the businesses commitment to data, plus all the benefits (make sure to include career progression in this and check out our state of data report for the most sought after candidate requirements of 2023/4).

The next stage is to look at the best place to post your job: think about where the community hangs out and get promoting! As a side note here, work with your marketing/digital/HR team to ensure that your company EVP (employee value proposition) is well reflected across all your digital assets and any Glassdoor or Google reviews are responded to (especially if negative). If you want to make sure you are competitive, running salary benchmarking exercises and some quick competitor analysis will tell you where you are at. A good recruitment partner can be invaluable at this stage as they can save you time by conducting the afore mentioned analysis for you.

Interviewing and Selecting Candidates

The interview process should be rigorous, but not too onerous. You need a compromise between getting a good understanding of technical and soft skills as well as team fit, balanced against consideration for candidates’ time and motivation. If the stages/requirements are too onerous it turns candidates off, so whilst you could argue that the most dedicated and motivated will commit, there are a heck of a lot of very talented candidates who simply don’t have the time (work and family commitments) and if your competitors have a shorter interview stage process then you will lose out. We polled our candidates and found out that the most preferred interview process would be a two- stage process. Testing both technical skills and soft skills such as problem-solving and communication is a good idea but some companies potentially go too far and expect candidates to complete hours and hours of technical testing.

Select candidates who not only meet the job requirements but also align with the company culture. As a side note, company culture doesn’t necessarily mean everyone needs to be the same. Think about what your team is missing (as this brings diversity) and if necessary speak to your recruitment partner about using cultural assessment tools to help eliminate group thinking and create a diverse but harmonious team. Stick to your identified interview stages, going off-piste and adding in extra stages, will help you to lose good candidates. Communicate the process clearly to candidates from the off.

Feedback

After each interview stage, get feedback from and provide feedback to the candidate. This can be done via a recruitment partner, talent acquisition team or via the hiring manager, depending on the set up. Recruitment partners add value in many ways for example saving time, money and covering off candidate motivations and current commitments and expectations which saves time during the negotiation phase and mitigates dropouts. Timings for feedback is really important. One huge mistake companies make is not providing timely feedback, losing candidates interest in the process.

Timings in general

Aside from timely feedback, timing frameworks throughout the process are important to keep to. Good candidates often have 2 or 3 options in process, so you are literally in a race against your competitors. I am not saying to rush the process but definitely define and execute the process as communicated to help manage candidate expectation and engagement.

Onboarding and Training

After selecting candidates, it's essential to provide proper onboarding and training to ensure they can seamlessly integrate into your organisation and contribute effectively.

Final word

Recruitment processes can be as intricate as you make them. Getting the basics right makes a huge difference. As a bare minimum, use planning, definition and communication as your corner stones.

Navigating the Recruitment Tech Landscape: A Comprehensive Guide

Mon, 13 Nov 2023 13:27:57 GMT

There are 4 key areas to focus on when considering recruitment technology: Attraction, Selection, Engagement and Management.

Recruitment technology has come a long way and has revolutionised the way organisations identify, engage and hire talent, but in equal measures it can be a minefield. I wanted to pull together a roundup of the types of technology to consider when hiring, to help TA teams and hiring managers build the best environment for their hiring strategies to succeed.

There are 4 key areas to focus on when considering recruitment technology: Attraction, Selection, Engagement and Management. Each stage can be enhanced by technology.

Attraction:

When you are weighing up what technology to use at this stage, you need to consider what is important. I would say getting to market fast, accessing a diverse group of candidates and expressing your employee value proposition is key. And across all stages you need to consider candidate attraction. At this stage however, if you are including agencies into the supplier mix then you need to look at including submission portals. I would advise looking out for key features at this stage that cover off:

Diversity and Inclusion – Does the technology help you write gender neutral, compelling job adverts?
Advertising - Does it plug into programmatic advertising across a wide variety of inclusive channels. Programmatic advertising uses automated technology for media buying and can be a very cost-effective way to maximise your advertising spend.
Career site – A career site will uniquely showcase your brand and employee value proposition.
Talent Mobility - You may want to consider talent mobility as part of your attraction strategy. A good talent mobility platform will recommend internal vacancies to suitable candidates, create career paths and offer relevant skills training as well as opportunities for mentorship.
Referrals – Does the technology facilitate internal/external referrals?

Selection:
The selection stage of the recruitment process is critical for identifying the most suitable candidates for a job opening. Technology can greatly enhance the efficiency and effectiveness of this stage. There has been much made of the use of AI within the selections process. Although there are clearly many benefits (time/cost to fill, time saved for hiring teams) there are still concerns around system bias entered subconsciously during the coding stage. This is just something to consider and drill into with the supplier if you are wanting to go down the AI route.

Applicant Tracking Systems (ATS):

The most important technology to consider during selection stage is an applicant tracking system. ATS software helps manage and streamline the application and candidate screening process. It stores CV’s, tracks candidate progress and allows for easy collaboration among hiring team members. ATS software simplifies the management of a large volume of applicants, speeds up the initial screening, and ensures consistency in the evaluation process. For consistent and large volume hires I would argue that this is a non-negotiable piece of technology.

Video Interviewing Platforms:

Video interviewing platforms enable recorded or real-time video interviews. The beauty of using video is that all candidates can be screened with the same criteria and hiring managers get a flavour of the personality and body language of a candidate rather than using the CV and a phone call to complete the initial assessment. The available video platforms usually allow you to set pre-determined questions and the responses can be sent to the hiring panel for evaluation. The more consistent a process can be for all candidates, the more inclusive it becomes.

Pre-employment Assessment Tools:

This category of technologies assesses a candidate's skills, knowledge and/or personality traits. They may include coding tests, personality assessments and cognitive ability tests. Using these kinds of tools provide objective data for evaluating a candidate's suitability for the role.

A quick word of warning however, over-reliance on these tools can lead to skewed results as some tests make candidates nervous. One thing to consider is accessibility, some neuro-divergent candidates that are perfectly capable of thriving in a role can be put off these kinds of time pressured tests as they don’t perform well under pressure and lastly poorly designed assessments can introduce bias.

Culture assessment tools:

There are a whole host of tools to help you assess how a potential hire will impact team or company culture. I wouldn’t advise looking for tools to help identify “culture fit” as such, as this can create herd thinking which kills creativity and productivity. Rather, use these tools to help assess hires against company and team values and how potential hires can complement or impact these measures. A good platform will also help with existing staff retention and productivity as they seek to assess and motivate based on values and mission.

Engagement

Having a robust candidate engagement process ensures an Enhanced Talent Pool:

Engaging candidates in a positive and personalised manner builds a talent pool of interested and qualified individuals. This pool can be a valuable resource for future hiring needs, reducing the time and effort required to source new talent. Not only this but candidates feel valued and respected and so results in a positive candidate experience. One of the issues with a longer recruitment process can be dropouts, when you have a good engagement programme in place and the technology to support it, candidates feel part of the new business before they start, and it keeps them interested. A 3 month notice period as an example is a long time without any communication from the new employer so ensuring that you have a good engagement platform helps with buy in before a candidate starts.

Management

The management phase of the recruitment process involves onboarding new employees and ensuring a smooth transition into their roles. Technology plays a crucial role in simplifying administrative tasks, improving communication and facilitating the integration of new hires. Here are some examples of technology used during the management phase:

Onboarding Software:

Onboarding software streamlines the administrative tasks associated with bringing a new employee on board. All the forms can be stored electronically so document management and task checklists add to process flow. A good platform reduces paperwork, accelerates the onboarding process and ensures compliance with legal requirements.

Employee Self-Service Portals: These portals allow new hires to complete various administrative tasks, such as entering personal information and bank details. They allow employees to take ownership of their onboarding process and reduces HR's administrative burden.

Learning Management Systems (LMS):

Most companies will have mandatory training for all new starters. LMS platforms deliver training and development programs to new and existing employees. They offer value not only because they track progress and ensure compliance with mandatory training, but also help hiring managers suggest areas where their team can upskill, offering up courses to suit and keeping them motivated and engaged at the same time. With more people working remotely, these platforms accelerate training, provide a centralised platform for learning and ensures that employees acquire necessary and enhanced skills.

At this point you can consider HR Management Systems (HRMS) as part of the management process. HRMS platforms manage employee records, benefits and payroll. They can automate many HR processes, including time tracking and leave requests. Like most technologies these increase HR efficiency, reduces errors in payroll and benefits administration and provides a comprehensive view of employee data.

If you get your tech stack right (and remember what is right for one business is not necessarily right for another), you will see improved candidate experience, which has a positive knock-on effect towards employer brand, fill rates (time and cost) and the quality of your hires. You can expect a smoother onboarding process, continuous improvements as people feedback, referral opportunities, a positive impact on company culture and you can expect to make more data driven decisions around hiring and HR. The future of recruitment is likely to witness further technological innovations and predictive analytics, blockchain for verifying credentials or augmented reality interviews are just a few examples of what's on the horizon. Organisations need to stay agile, but at all costs human, and adapt to these changes to remain competitive in the talent market.

The Crucial Role of Data Quality and Governance: Insights from "The State of Data 2023/24" Survey

Mon, 13 Nov 2023 09:02:44 GMT

In the dynamic landscape of the UK's economic sector, the importance of data quality and governance cannot be overstated.

The findings from "The State of Data 2023/24" survey provide valuable insights into the challenges and opportunities businesses face in this realm.

Understanding the Landscape: A Data-Driven Approach

In the face of economic challenges, businesses prioritising data and technology have not only weathered the storm but capitalised on the insights and opportunities offered by these assets. The survey reveals a strategic advantage for companies emphasising data initiatives, showcasing their resilience and adaptability in navigating uncertain times.

The Data Section: A Closer Look at Data Quality and Governance

Continuous Improvement in Data Usage

Year on year, the survey monitors how businesses are utilising their available data. Since 2015, there has been a steady improvement in data usage, with a notable 5% increase since 2022. The survey indicates that businesses are recognising the value of data and actively working to enhance their data-related capabilities.

Key Areas for Improvement: Governance and Quality

Delving deeper, the survey highlights two crucial aspects that help improve data uptake: data governance and data quality. These emerged as the top two skills deemed to be lacking within data functions, with 15% and 14% of respondents identifying them respectively.

Solutions for Improving Data Quality

When asked about improving data quality, respondents pointed towards the need for clearly defining data usage across the business. Specifically, articulating what data is needed and why emerged as the most effective way to improve data quality, with 38% of respondents choosing this approach.

Communication is Key

The survey emphasises the importance of communication in the realm of data. Clear communication channels between data functions and business units, along with very clear data entry policies and governance, are highlighted as essential factors. It's evident that effective communication is a linchpin for successful data management.

Aligning Data Function with Business Strategy

Interestingly, the survey challenges the notion that alignment between data function and overall business strategy guarantees success. Despite 88% of respondents stating alignment, at least a third reported that data usage had remained static or poorly improved. This indicates that alignment alone is not a predictor of success; effective data governance and quality play pivotal roles.

The Path Forward: Balancing Innovation with Governance

As businesses navigate the complexities of the data landscape, striking a balance between innovation and governance becomes paramount. The survey suggests that businesses excelling in data utilisation, hiring execution, and business-data alignment also display enthusiasm for innovation, particularly in the realm of generative AI.

Conclusion: A Call for Strategic Data Management

In conclusion, the survey results underscore the critical importance of data quality and governance in the success of businesses operating in a data-centric environment. Striving for continuous improvement, clear communication, and a strategic balance between innovation and governance will position businesses for sustained success in an ever-evolving economic landscape.

Key Takeaways:

Continuous improvement in data usage signifies businesses recognising the value of data.

Data governance and quality are identified as top skills lacking within data functions.

Clearly defining data usage and improving communication are vital for enhancing data quality.

Alignment between data function and overall business strategy does not guarantee success; effective governance is essential.

State of Data: Unveiling the Power of Data: Trends and Insights for 2023

Fri, 03 Nov 2023 14:53:31 GMT

KDR’s 2023 State of Data survey revealed that 67% of respondents reported improved data usage

Since 2015, the path of data utilisation has shown consistent improvement and 2023 is no exception, with a 5% uptick in data usage compared to 2022.

KDR’s 2023 State of Data survey revealed that 67% of respondents reported improved data usage, up from 62% the previous year. This underscores the growing recognition of the significance of leveraging data for enhanced performance.

The Driving Factors Behind Improved Data Usage

Upon closer examination, two pivotal factors emerged as contributors to the better performance of businesses in data usage:

1. Skill Set Enhancement: Respondents whose businesses had invested in upskilling their teams in data-related disciplines yielded more positive results. By investing in skills, employees have the tools to handle data effectively, fostering a culture of data-driven decision-making.

2. Improved Communication and Understanding: Organisations bridging the gap between the data function and other business units have witnessed remarkable improvements. Effective communication ensures that data-driven insights are translated into actionable strategies.

On the theme of communication, data story telling skills are a really important factor for successful outcomes. In episode 1 of the KDR podcast “data insights”, Scott Taylor, the data whisperer, goes into detail about how changing the way you talk about data with business functions, affects everything from buy-in and data quality to outcomes. Using business friendly language with a focus on the why not the how will dramatically improve the effectiveness of data strategies.

Job Function's Influence on Perceptions

An interesting observation from the survey is the influence of job roles on perceptions regarding factors driving data usage improvement. Chief Technology Officers (CTOs) were more likely to attribute improvement to C-Suite buy-in, while roles without direct insight into high-level decision-making tended to cite skill enhancements, data input improvements and better technology as primary reasons. Ultimately you need a mixture of all of the above, but communication and understanding is at the heart of every success story.

Key Areas for Data Optimisation

The survey highlights two vital areas for organisations looking to optimise their data usage:

1. Clear Communication Channels: We have covered this off above but it is safe to say establishing transparent and efficient communication channels between data functions and business units ensures each party understands data requirements and aligns with business objectives.

2. Data Entry Policies and Governance: Robust data governance and data quality are pivotal for reliable decision-making. Addressing data governance issues and implementing clear data entry policies enhance data quality.

Enhancing Data Quality

Looking at responses to how to improve data quality. Respondents identified the need to clearly define data usage across the organisation as the primary solution for improving data quality. This involves specifying required data and its importance which in turn enables data inputters to follow processes more effectively. Other strategies include tighter controls over data input and engaging with data inputters, other less popular responses favoured measures like appointing data stewards or using data dictionaries to improve data quality.

The Alignment Pitfall

You might assume that the respondents that stated their business strategy and data strategy were aligned would have seen higher levels of success. Interestingly, although 88% of respondents reported alignment, a significant proportion noted that data usage remained static or improved only slightly. This underscores that alignment must be complemented by factors such as skill development, clear communication and robust data governance.

Evolving Data Usage Trends

In recent years, businesses faced a challenging landscape shaped by factors like Brexit, the COVID-19 pandemic and geopolitical tensions. These external forces influenced business strategies and data usage patterns.

The most popular response for data usage priority was "understanding customer behaviour”, however notably, there was a significant shift toward using data to identify growth opportunities during 2023. In contrast to a notable decrease in businesses seeking cost-saving insights from data, which was the most popular response in 2022, this shift suggest that businesses were turning to data to illuminate avenues for growth in 2023 after a leaner 2022.

The journey of data usage improvement continues to mould the modern business landscape. Businesses that invest in skill development, foster clear communication, and prioritise data quality governance are poised for success. While alignment with overall business strategy is crucial, it must be complemented by proactive efforts to unlock the full potential of data. As we move forward, evolving trends in data usage highlight the dynamic nature of this field, emphasising the need for adaptation and innovation in our increasingly data-driven world.

Data Analysis Skills Shortage: Challenges and Solutions

Fri, 03 Nov 2023 12:31:46 GMT

Backdrop: The Growing Importance of Data Analysis

The demand for professionals skilled in data analysis is skyrocketing. The UK government report “Quantifying the UK Data Skills Gap”, found a shortfall of 178,000 to 234,000 individuals with data competency in the UK. It also found that nearly half (48 per cent) of businesses are recruiting for data roles, with 46 per cent struggling with recruitment. In October alone there were 4853 data analyst roles advertised across the UK and a quick and dirty search for skills gaps within data analytics in 2023 shows industry reports from marketing to HR claiming the biggest skills gaps in the respective functions is data analytics. Marketing week report, for example, that amongst its readership (presumably a mixture of agency and client-side) the biggest gap by far is data analysis. A good time to be a data analyst then!

The Skills Shortage Dilemma

This discrepancy between the supply and demand for data analysis skills has created a significant challenge for organisations. It is, as I crassly stated earlier, a good time to be a data analyst but that doesn’t help organisations to cover the gaps they are experiencing right now. They continue to struggle to find the right talent to harness the power of data.

Causes of Data Analysis Skills Shortages

What are the well documented factors contributing to such a gap? For starters data analytics provides such value to businesses that what once might have been a niche role within a few companies, now has spread across most functions within businesses, so the available resource is spread even thinner. Industry-wise it has been reported that 60% of those professionals in data analytics work within technology and IT, followed by industry at 17%, finance 15% and 7% in healthcare.

For some large enterprise level businesses each function would ideally have its own analyst – so HR would have a professional that understands the HR and people function and the data that helps a business grow, pivot, get the best out of the team. Whereas the finance team would have someone who works with the figures and reports to the commercial team/SLT to help steer business decisions.

According to a 2021 Ipsos Mori report the number of people leaving university as qualified data professionals entering into the workforce will not exceed 10k per year - a complete mismatch to the estimated 178,000 jobs requiring data skills.

So what can be done to address this. Against this backdrop we are advising clients to really drill down into their skills requirements and check that historical skills are still relevant for roles. One area that some businesses are missing a piece on is ensuring that what they offer is what the market is looking for. When we look at the current demands, many candidates are looking for career progression, they are looking to be upskilled, they are looking for clear pathways and want to know that within their role they are able to keep up which the rapidly changing technology and skillsets. With 80% of businesses reporting that they are unable to find the skill sets they need to execute on business goals and if you offer these kinds of benefits within your organisation, make sure you are publicising them well.

Our State of Data report indicated the areas where companies could focus their attention to attract top talent. We asked respondents to rank their primary reasons for departing a company. Leading the list was poor management, cited by 27% of respondents. These figures closely align with a recent CIPD report, in which 21% of the surveyed population (weighted to represent the UK population) identified poor management as a potential reason for leaving their jobs. It is crucial to conduct employee satisfaction surveys to detect issues early and mitigate attrition levels. The next significant factor driving employees to consider leaving their positions revolved around low salaries and unchallenging work. The same CIPD survey indicated that a higher proportion of the population is struggling to meet their financial commitments compared to 2022. To address the risk of employee turnover due to low pay or perceived low pay, it is advisable to ensure competitiveness through salary benchmarking and benefits assessments.

So if poor management is a driver for respondents leaving a job, what are the motivators to join a business? The data reveals that a significant motivator among individuals is their capacity to shape business strategy. In 2022, more women than men expressed motivation in this regard, and this trend continues into 2023, with 2% more women than men desiring the ability to influence business strategy. This motivational factor aligns with the concept of meaningful work and the well-established autonomy, mastery, and purpose theory. This theory suggests that once our basic needs are met, our motivation shifts away from monetary rewards and towards autonomy, continuous learning, and a sense of purpose in our work.

To ensure you remain competitive and to enhance the effectiveness of your recruitment outreach efforts, consider highlighting specific areas to attract a diverse pool of applicants. These could encompass:

• workplace policies (such as flexible hours, hybrid, remote, or office options, diversity, parental leave, menopause)

• corporate social responsibility initiatives related to charities or environmental targets

• opportunities for career advancement

• reporting structures

• organisational culture

• salary information. *It's worth noting that candidates are more inclined to apply for roles where salary details are provided, with statistics suggesting a significant impact on application rates.

Beyond the scope of the data survey, drawing from extensive candidate interviews within the data community, it's evident that data professionals are drawn to employers who take their data functions and strategies seriously. This sentiment is echoed in the "other" comments collected during the survey, with respondents expressing their desire to work for organisations where "data is taken seriously."

Beyond securing the existing talent and plugging any leaks that contribute to attrition levels, are there any other tactical hiring strategies to consider?

With the rise of low code no code technology, many more people are able to get involved with the data function. Albeit with less expertise but as an interim looking at introducing this technology and upskilling from within could be an option for companies struggling fill their roles.

Addressing the data analysis skills shortage requires a multi-pronged approach:

1. Promoting Education and Training

Encouraging the development of data analysis programs in educational institutions can help bridge the skills gap. Scholarships and incentives can attract more students to pursue data analysis as a career. Many companies get involved in young talent initiatives and sponsor events like hackathons to encourage and attract new talent.

2. Leveraging Data Analysis Tools

As mentioned earlier, the advancement of user-friendly data analysis tools allows individuals with limited technical expertise to perform data analysis. This widens the talent pool.

3. Encouraging Cross-Disciplinary Collaboration

Fostering collaboration between data analysts and professionals from other fields can help address the shortage by combining domain expertise with data analysis skills.

The shortage of data analysis skills is a significant challenge for businesses in the data-driven world. However, by promoting education and training, leveraging data analysis tools, encouraging cross-disciplinary collaboration, and implementing hiring and upskilling programs, businesses can address this issue and unlock the full potential of data to drive their objectives forward.

Beyond Numbers: The Human Stories Uncovered by Digital Analytics in Charitable Work

Tue, 31 Oct 2023 14:12:40 GMT

Charities are increasingly leveraging digital analytics

I have been working with charities for the last three years and have noticed how much my network has grown in this area. Charities are increasingly leveraging digital analytics as a powerful tool to enhance their operations, reach a wider audience and ultimately achieve their missions. I’ve seen the impact these professionals have had on the outcomes for the charities they work for and wanted to showcase how digital analytics can be a lifeline for charities, providing them with valuable insights and data-driven strategies to help them continue to make a meaningful impact in the world.

The foundational work that charities need to do to survive, is where many data teams focus their projects: during the funding stage where philanthropic organisations and grant-making institutions are more interested in funding projects that can demonstrate their impact through data. Data functions allow charities to track and measure the outcomes and impact of their programmes. By collecting and analysing data on the numbers of people they have helped and the subsequent success stories, charities can provide concrete evidence of their own effectiveness. This evidence is crucial for convincing grantors and donors that their contributions will make a tangible difference.

The most successful data teams then help charities to turn this evidence into compelling stories. The collated data enable charities to craft compelling narratives, using data to tell stories about the challenges they address and the lives they impact. These stories resonate with potential funders on a more emotional level, making them more likely to support the cause. Data can turn statistics into relatable, powerful human stories that inspire action.

Digital Analytics can identify the most effective channels for reaching their target audience with the stories created. Because digital analytics encompasses the collection, analysis and interpretation of data from various online sources such as website traffic, social media engagement, email marketing and more, Charities have a wide source of data to interpret.

Grantors and donors are often more inclined to support organisations that are embracing modern methods and technology to enhance their impact and utilising the data available to them in innovative ways shows that they are doing everything in their power to gain the attention of donors and therefore worthy of the grant. With data functions, charities can adapt their programmes to evolving needs. They can track trends, identify emerging issues and adjust their strategies accordingly. This agility is appealing to funders, as it ensures that their contributions remain relevant and impactful. Funders want to see that the charity is making a real difference. They assess past achievements and the potential impact of future projects and the data shows this.

From my experience in helping charities to set up data teams from scratch I know there are a few key things to bear in mind. If there is budget for a senior hire, the first hire for a data team in a charity should typically be a Chief Data Officer (CDO). Here's why, the CDO sets the strategic direction for the data team, which is critical from the outset. This ensures that data initiatives are aligned with the charity's mission and goals. Establishing data governance policies early on is essential to ensure data quality and compliance with privacy regulations and it’s better to have these in place from the beginning. Collaborating with various departments is essential for understanding their data needs (our latest State of Data Report indicates it is a key driver of successful data strategy execution), the CDO can facilitate this process from the start as it is usually an important skill they bring with them. The CDO can also assist in budgeting and allocating resources for the data team, ensuring that the team has what it needs to be successful.

The next most important hire would be a Data Strategist/Analyst because the Data Strategist or Analyst plays a pivotal role in translating the charity's mission and goals into actionable data strategies and projects. They collaborate with the CDO to define specific objectives, KPIs and data-related initiatives that align with the charity's mission. Their analytical skills are essential for project planning, ensuring that data-driven efforts are strategically focused.

I would then look at hiring a Data Engineer because they are responsible for building and maintaining the technical infrastructure required for data collection, storage and retrieval. They set up data pipelines, ETL (Extract, Transform, Load) processes and databases. With a well-structured data architecture in place, the charity can efficiently manage data, ensuring its quality and accessibility.

If there is budget for a full team, I would then look at hiring a Database Administrator to help manage the day-to-day operations of the data infrastructure. They ensure that data is secure, backed up and accessible when needed. Their role is crucial in maintaining data integrity and protecting sensitive donor and beneficiary information, which is a priority in the non-profit sector.

Data Scientists are skilled in advanced analytics and modelling. They can extract valuable insights from the charity's data, helping to make informed decisions, optimise programs and predict donor behaviour. Their analytical expertise is especially valuable for charities looking to enhance donor engagement and program impact measurement.

In an era of increasing data privacy regulations and ethical considerations, having a specialist who can manage data governance, compliance and ethical considerations is vital. This role focuses on ensuring data security, privacy and ethical data practices, reducing the risk of data-related issues.

Finally, Data Visualisation Specialists create meaningful and accessible data visualisations, making data insights understandable to non-technical stakeholders within the charity. This role is crucial for effective communication of data-driven findings and project outcomes.

The order of these hires can vary depending on the charity's specific needs and the size of the data team. However, generally, starting with a Data Strategist/Analyst and progressively adding roles that support data infrastructure, analysis, governance, and communication is a well-rounded approach to building a comprehensive data team. This order ensures that data initiatives are strategically planned, technically sound, and able to deliver actionable insights while maintaining data integrity and compliance. Regardless of hiring preferences, setting up a data team in a charity should start with a clear strategy and alignment with the charity's mission. The main priorities in for any team in 2023/4 revolve around ethical data use, donor engagement, impact measurement and data security, with an emphasis on adaptability and collaboration. As technology advances, charities that embrace analytics will thrive and achieve even greater impact in the ever-evolving digital landscape. Digital analytics is a lifeline for charities, equipping them with the tools and insights needed to operate efficiently, reach a broader audience, and make a more significant impact in their mission-driven work.

Unlocking Career Opportunities in the UK with Microsoft Power Platform

Mon, 16 Oct 2023 09:27:22 GMT

In recent years, the demand for professionals with expertise in Microsoft Power Platform has been on the rise.

In this article, I explore how mastering Microsoft Power Platform can open doors to exciting career prospects. I will look at its relevance, job prospects and how you can equip yourself with the necessary skills to stand out in this competitive field.

Microsoft Power Platform is at the forefront of data and automation transformation, offering tools that allow individuals to create apps, automate workflows and analyse data with ease. Microsoft Power Platform is a suite of tools that empowers individuals and organisations to create custom apps, automate workflows and analyse data. It comprises four major components: Power Apps, Power Automate, Power BI and Power Virtual Agents. These tools provide a seamless environment for developing solutions that can address a variety of business challenges. I am currently in the middle of gaining my certifications and I am blown away by the capabilities made possible by these components.

Relevance in the UK Job Market

The UK job market has seen a surge in demand for professionals who can effectively utilise Microsoft Power Platform. Statistics from major job boards show that professionals citing these skills on their profiles has increased by 60% in the last 12 months. The platform's versatility and adaptability make it an attractive choice for businesses across various sectors and professionals are investing in the skills.

There are a number of roles with these components at their core. As a Power Platform Developer, for example, you are responsible for designing, building and implementing Power Apps and Power Automate solutions. In the UK, the average salary for a Power Platform Developer is around £45,000 to £60,000 per year, with the median sitting at £55,000, depending on experience and location. Power Platform Consultants help organisations leverage the full potential of the platform. They guide businesses in designing effective solutions and integrating Power Platform tools. Salaries for Power Platform Consultants typically range from £50,000 to £70,000 annually. Power Platform Administrators ensure the smooth operation of the platform. They manage user access, security and system maintenance. The average salary for this role in the UK is approximately £40,000 to £55,000 per year.

Microsoft Power Platform has made significant inroads into various industries in the UK. In healthcare, for example, the applications are helping to streamline patient data management and appointment scheduling. In the financial sector the technology is used for automating processes such as loan approvals, fraud detection and customer data analysis plus they benefit from faster decision-making and enhanced security. Manufacturing companies utilise Power Platform for inventory management, production monitoring and quality control which results in reduced downtime and higher productivity.

Key Skills for Success

From learning the technology myself and from speaking to clients looking for talent, to excel in the field of Microsoft Power Platform you need a blend of technical skills, problem-solving skills plus so called softer skills like communication and influencing. The top skills I would recommend are proficiency in Microsoft Power Apps, Power Automate, Power BI, data analysis and the ability to design user-friendly solutions. Communication with business functions is so important before and after to ensure you are building and delivering what is needed.

To strengthen your credentials in Microsoft Power Platform, consider gaining relevant certifications. The following are pretty well respected and recognised: Microsoft Certified Power Platform Fundamentals which is the entry-level certification is ideal for those new to Power Platform. It covers the basics of Power Apps, Power Automate, and Power BI. Microsoft Certified Power Platform App Maker Associate focuses on the practical application of Power Platform tools, making you proficient in creating solutions for business needs. Whichever you choose, Microsoft Learn platform has numerous online courses and tutorials to pick from.

A career in Microsoft Power Platform offers several rewards and benefits. For one, there is high demand for skilled professionals and the salaries are competitive. Due to the different components and multitude of uses and applications there are many opportunities for growth and advancement plus you have the ability to make a significant impact within whole industries or businesses. Keeping up with the evolving technology and staying updated with the latest features and tools however can be demanding, but the greenfield aspect of designing, building and delivering solutions I believe far outweighs the fast-paced changes that arise.

Mastering Talent Mapping in HR

Wed, 11 Oct 2023 13:08:15 GMT

Understanding Talent Mapping

HR professionals are increasingly turning to talent mapping as a strategic tool to identify, nurture and retain top talent. In this comprehensive guide, we'll delve into the world of talent mapping in HR , exploring its benefits and how it can give your organisation a significant edge in talent acquisition.

Understanding Talent Mapping

What is Talent Mapping?

Talent mapping is a strategy that HR professionals use to help a business align their people needs to a business’s long-term goals. It involves identifying potential candidates, both internal and external, to meet the organisation's current and future talent needs. It's a proactive approach that fits within the workforce planning model and goes beyond traditional recruitment, focusing on building a talent pipeline for critical positions.

The Process of Talent Mapping

Much like the approach to workforce planning , the first step in talent mapping is to understand the organisation's strategic goals and identify the skills and competencies required to achieve them. You need to know whether these skills exist already, whether they can be internally acquired (through promotion, departmental job mobility, merging of roles or job shares for example). If you need to outsource talent then HR professionals can use various sources to identify potential candidates, including proprietary databases, social networks and industry research. Partnerships with knowledgeable recruitment agencies will quickly help you to identify potential candidates who possess the desired skills.

Candidates are then rigorously assessed through interviews, tests (if necessary to the role) and reference checks to ensure they are a good fit for the organisation. This is where your partnership with recruitment agencies starts paying dividends: their knowledge, network and skills will save you and your hiring managers time and money.

If you are looking internally (and any robust talent mapping process should include internal talent) any individuals identified as potential candidates should be provided with opportunities for skill development and career advancement within the organisation.

The Benefits of Talent Mapping

Talent mapping offers numerous advantages for organisations looking to enhance their HR strategies:

1. Proactive Talent Acquisition

By continuously identifying and nurturing potential candidates, organisations have an active pipeline of qualified candidates more readily available to critical roles. This in turn can reduce time to fill rates.

2. Reduced Recruitment Costs

Talent mapping helps teams to understand where the talent sits. Because you will be identifying internal candidates and building up pipelines you are able to reduce the associated costs of recruitment: time to hire, cost per hire, recruitment fees.

3. Improved Succession Planning

Succession planning becomes more effective because the talent mapping process identifies and develops potential leaders within the organisation. This ensures a smooth transition when key employees retire or leave their roles.

4. Enhanced Employee Engagement

Employees are more likely to stay engaged when they see opportunities for growth and advancement within the organisation. Talent mapping fosters a culture of development and progress, so make sure your talent mapping processes are well communicated.

5. Competitive Advantage

Many organisations don’t practice talent mapping, therefore those that do work with a robust talent mapping strategy gain a competitive edge by having a pipeline of skilled and capable individuals ready to take on challenges and drive innovation.

Challenges in Talent Mapping

While talent mapping offers significant benefits, it's not without its challenges, here are a few pitfalls to be aware of:

1. Data Accuracy

Databases are notorious for not being accurate (depending on data policies and how the data is inputted). It's essential to have robust policies in place so that everyone understands why it is so important to record the information accurately and consistently. This helps you to be super targeted when trying to fill a role with an existing candidate pool.

2. Talent Retention

Identifying top talent is one thing; retaining them is another challenge. Organisations must provide opportunities and incentives to keep these individuals engaged and committed.

3. Changing Business Landscape

The business environment is continually evolving and HR strategies must adapt. Staying up to date with industry trends and circling back to ongoing skill requirements (matched to business goals) is critical for effective talent mapping.

4. Privacy and Compliance

Talent mapping involves collecting and storing sensitive candidate data. HR departments must ensure they comply with data protection regulations to avoid legal issues.

5. Continuation
Ensuring the flow of information from leadership through to business function remains a constant. As business needs change, your talent map will also need to keep pace. Ensuring team skill sets, leavers information, new hires information and future business needs or projects are kept up to date is a huge task, but essential for this to work consistently.

Implementing Talent Mapping in Your Organisation

Top 4 tips to help you get started with talent mapping in HR:

1. Align with Business Goals

Understand your organisation's strategic objectives and tailor your talent mapping efforts to support these goals (skills needed, team gaps, growth areas, locations etc). Communicate regularly with teams and hiring managers to ensure their needs are being met/expectations managed. Sometimes plans are made at the beginning of the year but then reality if project delivery kicks in throughout the year and there needs to be some flex in the plans.

2. Invest in Technology

Leverage HR software and tools to streamline data collection and analysis, making your talent mapping efforts more efficient.

3. Build a Strong Team

Having skilled HR professionals who excel in talent mapping is crucial. Invest in training and development for your HR team.

4. Continuous Improvement

Regularly review and refine your talent mapping processes to stay agile and responsive to changing talent needs.

Talent mapping empowers organisations to stay ahead of the competition by identifying and nurturing the best talent. By implementing a strategic talent mapping approach, your organisation can achieve its goals more efficiently and effectively, securing a bright future. To help you get started KDR Talent Solutions developed a free talent strategy assessment with actionable outcomes. Contact haroon.choudry@kdrtalentsolutions.com to get going.

Maximising Impact as a Data Analyst in UK Housing

Wed, 11 Oct 2023 10:43:31 GMT

Data analysts play a pivotal role with the potential to reshape the UK housing sector.

The LGA noted in 2020 that the use of predictive analytics across housing and the public sector in general was still in its infancy. It has developed since then as more and more councils realise the benefits that can be enjoyed by adopting this approach. Traditional approaches to housing allocation and management have been giving way to innovative solutions, at the centre of which lies the expertise of data analysts. This article explores how data analysts are contributing to unlocking efficiencies in UK housing by harnessing the power of data.

Leveraging Data Analytics

Predictive maintenance is a data-driven approach that empowers data analysts to transform maintenance practices in the UK housing sector. Traditionally, maintenance has been a reactive process, where repairs are conducted only when equipment or systems break down. This approach can result in unexpected disruptions, increased repair costs and tenant inconvenience and distress. However, by harnessing historical data and analytics, data analysts are shifting the model to proactive maintenance.

Predictive Maintenance

Traditionally, assessing the condition of properties was a time-consuming and often subjective process. Data analysts are revolutionising this by developing automated property assessment models. These models use data on property age, maintenance history and local environmental factors to predict the condition of properties accurately. This not only streamlines maintenance but also aids in making informed decisions regarding property investment.

Looking at the specifics of predictive maintenance models. The first step is collecting and integrating data from various sources within the housing sector. This includes information about the properties including age, construction materials and maintenance history, as well as data on equipment and systems, like HVAC units or plumbing and electrical systems. Additionally, environmental factors such as weather conditions and location-specific data are considered.

By analysing historical maintenance records and equipment performance data, analysts detect subtle indicators that usually show up before equipment failures or deterioration. Armed with predictive models and real-time data, housing providers can then schedule preventive maintenance tasks at optimal times. This means that maintenance is performed when it's most cost-effective and before equipment reaches a critical failure point. As a result, breakdowns and costly emergencies are avoided. Predictive maintenance not only reduces the overall cost of maintenance but also looks after the rights of residents. They in turn experience fewer disruptions, enjoy more reliable services and have a more positive perception of the housing provider's efficiency and responsiveness.

Risk Analysis

Housing Associations are using data analytics to understand risk levels across the eco system. Areas such as property damage or non-compliance with regulations as well as tenant matching. By analysing tenant preferences, demographics, behaviour and rental history, analysts are helping housing providers match tenants with properties that best suit both their needs. This minimises tenant turnover, reduces vacant periods and ensures a more harmonious tenant-landlord relationship.

Much has been written about the benefits and pitfalls of AI in recent months but where this technology is being used within the sector, AI algorithms are making a real impact. Those that can forecast trends in housing needs accurately, for instance, can predict areas with growing demand for affordable housing, enabling associations to proactively allocate resources and plan developments accordingly.

There are a variety of other ways data teams within housing organisations are using AI. Some are harnessing its power to help their organisation hit sustainability targets. In this instance data analysts are using historical energy consumption data to identify patterns and trends in housing associations' properties. By leveraging AI algorithms, they can pinpoint energy inefficiencies, such as excessive heating or lighting usage. During the design stage data analysts are assisting in the development of environmentally friendly housing. By analysing data on sustainable building materials, energy-efficient technologies and construction practices, they are optimising building designs for minimal environmental impact. This includes selecting materials with lower carbon footprints and ensuring efficient insulation and ventilation systems are installed. Going back to what we were discussing earlier, data on tenant behaviour can then feed into educational pieces around sustainable practices like recycling or water conservation.

Predictive Rent Pricing

Setting rent prices was traditionally based on market trends and gut feeling. Data analysts however are changing this by developing predictive rent pricing models. These models consider factors such as location, property size, local demand and economic indicators to recommend optimal rent prices. This ensures that properties are competitively priced, maximising rental income for housing providers whilst keeping the balance fairer for tenants.

Often data departments are small within these types of organisations so the data team tends to have to jump on a variety of tasks, including ensuring the integrity of the data underpinning AI-driven decision-making. It can become quite a wide remit but by maintaining clean and consistent data, analysts contribute to more precise forecasts and recommendations. This helps housing associations make well-informed decisions regarding property management, investment strategies and tenant services ultimately enhancing the operational efficiency of housing associations.

The role of data analysts is undergoing a profound transformation. Beyond their traditional responsibilities, they are now grappling with vast and complex data volumes, real-time analysis demands and the integration of AI and machine learning for predictive insights. Alongside these technical skills they must navigate data privacy and ethical considerations while mastering the art of data visualisation for effective communication. Collaboration with different business functions and external agencies and a commitment to continuous learning are also essential. So, in this evolving landscape, data analysts are increasingly the linchpin, bringing together data, technology to uncover valuable insights and drive informed decisions.

Best Practices for Promoting Diversity and Inclusion in your Organisation

Fri, 06 Oct 2023 13:23:47 GMT

Understanding Diversity and Inclusion

Promoting diversity and inclusion has become a pivotal aspect of any organisation's talent or growth strategy. It's not just about doing what's ethically right; it also brings a whole host of benefits to businesses, including enhanced creativity, better decision-making and improved overall performance. In this article, we will delve deep into the best practices for promoting diversity and inclusion within your organisation, helping you create a workplace that not only attracts top talent but also fosters innovation and growth.

Understanding Diversity and Inclusion

Before we explore the best practices, let's establish a clear understanding of what diversity and inclusion entail. Diversity refers to the wide array of individual differences among people in an organisation, such as differences in race, gender, age, sexual orientation, disability, religion and more. Inclusion, on the other hand, involves valuing and respecting these differences and ensuring that all individuals feel a sense of belonging and are empowered to contribute their unique perspectives.

The Business Case for Diversity and Inclusion

Benefits

There are so many benefits to diversity, but here are just the very top liners. Diverse teams bring together individuals with distinct backgrounds and experiences, leading to a rich tapestry of ideas and perspectives. This diversity of thought fuels creativity and innovation and inclusive teams tend to make better decisions. When individuals from diverse backgrounds collaborate, they bring a variety of viewpoints, reducing the likelihood of groupthink, the death knell of innovation, agility and forward thinking. Diverse teams are also often better equipped to tackle complex problems. The difference in perspectives leads to more comprehensive analyses and creative solutions.

Challenges

The apparent mismatch between the desire for diversity and the outcome/reality of the make-up of a workforce is well documented, so what are some of the challenges businesses are facing? Two of the big areas sit in and around change and buy in. Implementing diversity and inclusion initiatives may encounter resistance from some employees who are uncomfortable with change, don’t think there is an issue in the first place, don’t understand or see their unconscious bias, don’t want to question their own belief system or don’t want to put the work in to create the framework and environment conducive to fostering diversity and inclusion. This can range from overhauling hiring processes to ensure they have been vetted for unconscious bias, management buy in and accountability, allyship awareness, use of language in the workplace like banter which manifest as microaggressions to setting up employee resource groups and creating consistent career progression frameworks and salary reviews/bandings. There can be costs associated with diversity and inclusion training and programs, however, these are often outweighed by the long-term benefits.

Best Practices for Promoting Diversity and Inclusion

1. Leadership Commitment

Genuine leadership commitment sets the tone for the entire organisation, encouraging employees to embrace these values more readily. If leadership commitment appears superficial, it can lead to scepticism and erode trust among employees.

2. Diverse Hiring Practices

Actively seeking diverse candidates during the hiring process ensures a broader talent pool and can lead to better-qualified employees. Some individuals may argue that prioritising diversity in hiring could lead to reverse discrimination, although this perspective is widely debated and mainly the result of a lack of education around the subject. People have got to be in it to win it as they say. If you don’t widen the net how can anyone from diverse groups ever be in with the chance of getting through to interview. Nobody is suggesting to hire based on a “quota", just that jobs need to be exposed to diverse groups in the first place to give them a chance of applying.

3. Inclusive Work Culture

Once hired, cultivating an inclusive work culture where all employees feel valued and heard fosters a sense of belonging and increases overall job satisfaction. This takes education and effort on the part of all employees. The challenge may come when establishing an inclusive culture may necessitate changes to existing policies and practices, which can face resistance. Communication and education plays a big part in overcoming resistance to change.

4. Training and Education

Providing diversity and inclusion training can heighten awareness and sensitivity among employees, resulting in a more inclusive workplace. Most people never intend on bringing their unconscious bias to the surface – training helps people become aware of what those biases may be.

5. Measurement and Accountability

Setting measurable goals and holding individuals and teams accountable for diversity and inclusion efforts ensures progress and continuous improvement. Be careful though, defining meaningful metrics can be challenging, and there's a risk of focusing solely on meeting quotas rather than fostering genuine inclusion.

Promoting diversity and inclusion in the workplace is not just a moral imperative but also a strategic business decision. Embracing diversity and fostering inclusion can lead to a more innovative, productive and successful organisation and while there may be challenges along the way, the long-term benefits far outweigh the drawbacks. Remember, diversity and inclusion are ongoing processes that require continuous commitment and effort.

The Importance of Effective Workforce Planning

Fri, 06 Oct 2023 13:15:03 GMT

Actionable insights into effective workforce planning

Workforce planning is a crucial factor in achieving business success. In today's competitive landscape, organisations are constantly striving to maintain a competitive edge and the quality of their workforce plays a pivotal role in this endeavour. This article explores the significance of effective workforce planning and offers insights into its successful implementation.

Understanding Workforce Planning

At its core, workforce planning is the strategic alignment of an organisation's workforce with its broader goals and objectives. It encompasses the evaluation of current staffing needs, identification of skill gaps and the formulation of strategies to ensure that the right people are in the right positions at the right time. While the concept may seem straightforward, it demands a deep understanding of the organisation's goals, industry trends and the dynamic job market.

The Benefits of Workforce Planning

Improved Efficiency: Workforce planning facilitates the optimisation of human resources within an organisation. By ensuring that employees possess the necessary skills and resources, businesses can enhance their operational efficiency, reduce costs and boost productivity.

Enhanced Agility: In today's rapidly changing business environment, adaptability is so important. Workforce planning empowers organisations to respond swiftly to evolving market conditions, making them more resilient in the face of uncertainty.

Talent Retention: Employees are more likely to remain with an organisation that values and nurtures their skills and pays attention to their career aspirations. Effective workforce planning can result in higher employee satisfaction and retention rates.

Implementing Effective Workforce Planning

Now that we recognise the importance of workforce planning, let's look at the steps required for its successful implementation.

Step 1: Define Your Business Goals

Start the workforce planning process by clearly defining your organisation's business goals. What are your short-term and long-term objectives? Establishing a clear vision serves as the foundation for your workforce planning efforts.

Step 2: Assess Current Workforce

Thoroughly assess your current workforce. Identify their strengths, weaknesses, skills and competencies. Are there disparities between the skills your organisation needs and those possessed by your employees?

Step 3: Identify Future Needs

Consider the future landscape of your industry. What trends and technologies are likely to impact your business? Predicting future workforce needs necessitates a profound understanding of market dynamics as well as business projections.

Step 4: Develop a Talent Acquisition Strategy

Once you've identified your future needs, you can get on with creating a talent acquisition strategy. Do you need to recruit new talent, upskill existing employees, transfer internally, or pursue a combination of all approaches? Think about recruitment channels, training programs and potential collaborations as well as internal job mobility.

Step 5: Monitor and Adjust

Workforce planning is an ongoing process. Continuously monitor your workforce, assess the effectiveness of your strategies and be prepared to make adjustments when necessary.

Effective workforce planning creates a solid foundation for an organisation to excel. Done well, it has the potential to elevate efficiency, foster agility and contribute to employee satisfaction. By aligning your workforce with your strategic goals and proactively adapting to change, you can position your organisation for enduring success.

How to craft a great CV: Expert tips from an experienced recruiter

Wed, 27 Sep 2023 13:36:40 GMT

A well-crafted CV helps you to stand out from the crowd

In the competitive landscape of job hunting , a well-crafted CV helps you to stand out from the crowd. At KDR we understand the significance of presenting yourself effectively to potential employers. Hally Barlow , speaks to data professionals all day long, looking at CV’s and submitting them to our clients’ roles. In this comprehensive guide, she shares expert insights and tips to help you create a CV that not only impresses but also outranks other candidates in the eyes of hiring managers.

Crafting a Winning CV

1. Start with a Strong Opening

Your CV's first impression matters. Many data job seekers make the mistake of being too technical immediately. Begin with a compelling summary that highlights your key strengths, experiences and career objectives. This snapshot should encapsulate your professional identity and grab the reader's attention. Remember it’s important to get across your personality as well as your technical expertise so a well written paragraph which conveys a little extra about what you are looking for (and why) and what you have to offer is a good hook.

2. Tailor Your CV for Each Job

This cannot be stressed enough. Often candidates make the mistake of creating one CV, forgetting that the main skill set required for the job might be the one that is buried deep within your experience one or two jobs back. Bring the key skills required for each role you are going for front and centre so that the hiring managers can see your relevancy straight away. One size does not fit all when it comes to CVs. This tailored approach demonstrates your genuine interest and suitability for the role.

3. Showcase Your Achievements

Rather than listing job responsibilities, emphasise your accomplishments. Use quantifiable data and metrics to illustrate your impact on previous employers. This paints a vivid picture of your contributions and distinguishes you from other applicants. So, for example if you created data sets for a project, also list the business outcomes to demonstrate the impact of those data sets. Saying that you increased customer engagement by 7% is more powerful than saying you increased customer engagement.

4. Highlight Relevant Skills

Identify key skills that are pertinent to the job you're applying for. Whether it's technical expertise, leadership qualities, or soft skills, ensure they feature prominently in your CV. This helps employers or recruiters quickly assess your suitability.

5. Education and Qualifications

Clearly outline your educational background, certifications and qualifications. Mention any relevant courses or training programs that enhance your candidacy. Remember, the relevance of your education may vary depending on the role. Once you have reached a certain level, you may want to minimise earlier qualifications (like GCSE’s or A’ levels for example) and just state the number achieved rather than listing each subject.

CV Essentials

6. Organise with Clarity

Present your CV in a clean and structured format. Use headings and bullet points to break down information into easily digestible sections. This improves readability and helps recruiters find the information they seek quickly.

7. Keep it Concise

While it's essential to provide comprehensive information, avoid unnecessary words. Let each word fight for their place on the page – if a sentence reads well without the word, take it out.

8. Proofread Thoroughly

So many errors go unchecked! Errors in spelling and grammar can undermine your professionalism. Proofread your CV meticulously to eliminate any mistakes. Consider seeking a second opinion from a trusted colleague or friend. As recruiters we will catch these mistakes but it helps if you really check beforehand.

10. Maintain an Online Presence

If you haven’t already, create an online professional profile, using platforms such as LinkedIn. An online presence reinforces your credibility and provides recruiters with additional information about you. LinkedIn provide an “open to work” feature if find yourself between contracts and this is a god way to get yourself noticed by recruiters or in house talent acquisition teams. When you post on these platforms, it is advisable to conduct yourself in a way that you would be happy for an employer to see. Although it is less formal than it was originally, it is still primarily a business networking platform.

In today's highly competitive job market, your CV is your ambassador. Craft it with care and precision to make a lasting impression on potential employers. By following these expert tips, you can create a CV that not only stands out but also ranks high in search results, increasing your chances of landing your dream job.

Key Takeaways:

Start your CV with a strong opening, tailoring it for each job application and quantify achievements rather than listing outcomes.

The structure of your CV should be clear, concise and error-free.

A well-crafted CV increases your chances of landing your desired job and impressing potential employers.

For an informal chat around what the data, analytics or technology job market looks like or how we can help you in your new job search. Contact us today .

Data Recruitment Strategies that Target Your Ideal Audience

Mon, 11 Sep 2023 13:29:55 GMT

When it comes to data recruitment, the landscape is more competitive than ever before.

In today's digital age, finding and attracting the right candidates with the necessary data skills can be a daunting task. This comprehensive guide will walk you through proven recruitment strategies to reach your ideal data audience. Whether you're a hiring manager or talent acquisition professional these insights will help you secure top talent for your organisation.

Introduction

Having the right people on your team can make all the difference. The success of your projects, the innovation in your products, and the growth of your business hinge on finding and recruiting individuals with the right data skills. To accomplish this, you need to tailor your recruitment efforts to reach the right audience effectively. Let's dive into strategies that will transform your data recruitment game.

Before Recruitment Begins: Understanding Your Ideal Data Audience

Before we delve into specific strategies, it's crucial to understand who your ideal data audience is. This involves defining the characteristics, skills, and experiences that your potential candidates should possess. To do this effectively, consider the following factors:

1. Data Roles and Specialisations

Identify the specific data roles and specialisations your organisation requires. Do you need data analysts, data scientists, data engineers, or machine learning experts? Understanding this will help you target the right professionals. Often, when building out a team, the roles you think you need first are not the ones that will help the team to grow. Asking your recruitment partner for advice on this can provide market wide insights, gained from many years in the industry and an understanding of what works and what doesn’t.

2. Skills and Qualifications

Determine the key skills and qualifications necessary for success in these roles. This might include proficiency in programming languages, data visualisation tools, statistical analysis, or machine learning frameworks. As a side note here, often lack of time means role descriptions carry forward legacy requirements from day dot. It really pays to rethink the job description at this point so that scope creep doesn’t set in further down the line, delaying the recruitment process even further.

3. Industry and Domain Knowledge

Consider the industry or domain expertise that's relevant to your organisation. Certain roles may require knowledge of finance, healthcare, e-commerce, or other specific sectors. However! Most roles really can support transferable “industry” skills as long as the job relevant skills are there, industries and legislations can be learned. This can help to open up your roles to wider audiences of relevant and innovative candidates.

4. Cultural Fit

Whilst team fit is important, consider what your team is lacking rather than what will fit in. A vibrant diverse team of unique individuals is healthy and innovative. Herd thinking can set in if people are hired because they are similar to everyone else.

5. Geographical Location

Depending on your organisation's location and remote work policies, you may need to target candidates within a specific geographical area or open your search to a global pool of talent. Your recruitment partner will have the insights needed to understand if there are pockets of talent around the countries you are interested in. Considering remote work is extremely beneficial as it completely opens up the talent pool, allowing you access to the best people rather than being constrained by a geographical area.

Now that you have a clear understanding of your ideal data recruitment audience, let's explore the strategies to attract them.

Crafting Compelling Job Descriptions

Highlight the Exciting Challenges

It's essential to make your job postings stand out from the rest. Rather than listing generic job responsibilities, focus on the exciting challenges and opportunities candidates will encounter. Use language that ignites curiosity and enthusiasm. Many data professionals are looking for companies where the data function has buy in and support from the rest of the business. If you know this to be the case, make sure to add this in to the job advert.

Showcase Your Company's Mission

Incorporate your organisation's mission and values into the job description. Candidates are more likely to be drawn to companies that align with their personal values and beliefs. If you have a commitment to diversity talk about how this is happening rather than adding in generic rhetoric around ED&I.

Offer Career Growth Opportunities

Top data professionals are often looking for opportunities to grow and develop their skills. Highlight your company's commitment to professional development and advancement.

Leverage LSI Keywords

Integrate Latent Semantic Indexing (LSI) keywords naturally into your job descriptions. These keywords are contextually related to your main keywords and can improve your search engine ranking.

Building an Appealing Career Page

Create Engaging Content

Your career page is a reflection of your organisation. Include compelling content, such as employee testimonials, videos, and success stories, to give potential candidates an inside look at your company culture.

Streamline Application Process

Make it easy for candidates to apply. Lengthy and complex application processes can deter qualified individuals. Ensure that the application process is user-friendly and efficient. Don’t make candidates jump through hoops, like sending in a CV and then filling out exactly the same information online. Ensure you have really robust communication touch points throughout the process. One of the biggest bug bears for candidates is when they don’t hear anything back after applying or going for interview.

Optimise for Mobile

In today's mobile-centric world, ensure that your career page is optimised for mobile devices. A responsive design enhances the user experience and attracts a broader audience.

Utilising Social Media and Professional Networks

Harness LinkedIn

LinkedIn is a goldmine for talent acquisition in the professional realm. Use your company's LinkedIn page to share job openings, engage with potential candidates, and build a strong network.

Leverage Industry Forums

Participate in industry-specific forums and communities. By contributing valuable insights and connecting with professionals, you can establish your organisation as a thought leader and attract talent.

Employee Advocacy

Encourage your employees to share job openings on their personal social media profiles. Employee referrals are a powerful tool for recruitment.

Partnering with Educational Institutions

Collaborate with Universities

Forge partnerships with universities and educational institutions. Establishing internship programs, offering guest lectures, and participating in career fairs can help you identify and nurture young talent. This is often an untapped area for organisations, however, although it requires some effort and planning, starting an early talent programme ensures you have a steady influx of new talent coming into the business.

Sponsor Data Competitions

On a similar note to the above point, sponsoring data competitions or hackathons can showcase your organisation's commitment to data excellence and attract highly motivated candidates who may be starting out on their career.

Networking Events and Conferences

Attend Industry Events

Participate in data-related conferences and events. These gatherings provide an excellent opportunity to connect with potential candidates and establish your organisation's presence.

Host Webinars and Workshops

Organise webinars and workshops on data-related topics. Sharing knowledge and expertise demonstrates your commitment to the field and can attract like-minded professionals. Basically the more outward facing content and activities that you can showcase, the better it is for candidates when researching your company. You will create a basis for becoming an employer of choice.

Employee Referral Programs

Incentivise Referrals

Implement an employee referral program with attractive incentives. This encourages your current employees to recommend qualified candidates from their professional networks.

Measuring and Iterating

Analyse Recruitment Metrics

Regularly track and analyse recruitment metrics to assess the effectiveness of your strategies. Metrics like time-to-fill, cost-per-hire, and candidate satisfaction can provide valuable insights.

Adapt and Improve

Based on your analysis, adapt your recruitment strategies. Experiment with different approaches and continually refine your methods to reach your ideal data recruitment audience more effectively.

Bridging the Generational Gap: A United Front for a Greener Future

Thu, 06 Jul 2023 15:59:05 GMT

In this blog, we delve into the generational divide in greentech adoption, the promising opportunities it offers for recruitment and jobs, and effective strategies to bridge the skills gap within the industry.

Greentech was developed to protect the environment, repair damage previously done and conserve what is left of Earth’s natural resources. While greentech is gaining broad acceptance, a clear generational divide exists in its adoption. The younger cohorts, Millennials and Gen Z, stand as the vanguard in the shift towards greentech, their choices fueled by deep-rooted concerns for environmental sustainability. As highlighted by a Deloitte survey, over 70% of these young respondents are willing to invest more in sustainable products.

On the other hand, Baby Boomers and Gen X show some hesitancy towards embracing greentech. This hesitation may stem from factors like resistance to change or a perceived lack of economic viability. However, growing awareness and understanding of environmental issues are promising. A study by the Pew Research Center found that 68% of Baby Boomers acknowledged the reality of human-induced climate change in 2020, up from 60% in 2018.

The economic barriers to greentech adoption are gradually being dismantled. The last decade saw the cost of solar panels drop by 89%, as reported by the International Renewable Energy Agency. This trend makes sustainable choices like solar power more financially accessible to all generations.

To bridge this generational divide, targeted efforts in education, outreach initiatives and the need for policy incentives, such as tax benefits for green investments are critical. These steps can encourage wider adoption of greentech across all ages, nurturing an intergenerational alliance for a greener future. As we work to narrow this divide, the reality of a sustainable future driven by greentech becomes increasingly within our grasp.

Unlocking New Opportunities in Recruitment and Jobs

As greentech continues to gain momentum and reshape industries, it also brings forth a wealth of new opportunities in the realm of recruitment and jobs. The transformation of industries towards sustainability or sustainable supply chains opens up diverse career paths for individuals passionate about making a positive impact on the environment. The renewable energy sector has witnessed remarkable growth, with the solar industry alone employing over 255,037 workers in the United States as of 2021 according to IREC’s National Solar Jobs Census. Similarly, the wind energy sector has seen substantial job creation, supporting over 120,000 jobs in 2021, as reported by the US Department of Energy. Moreover, greentech drives demand for professionals in fields such as energy-efficient building design, where the U.S. Bureau of Labor Statistics projects a faster-than-average growth rate of 8% from 2020 to 2030. By embracing greentech, individuals not only contribute to the transition towards a sustainable future but also tap into a world of exciting career prospects. Greentech is making its mark as an attractive and rewarding field for job seekers looking to contribute to a greener and more prosperous society.

Bridging the Skills Gap in Green Tech

Whilst the industry is in its relative infancy there is a real and perceived skills gap within GreenTech, so how can you combat this challenge? One area to look at is transferable skills from industries not related to GreenTech. For example, technical skill sets such as Java, Python or Ruby are easily transferable. Couple those with a passion or interest in the Green Sector and that is a powerful mix. When looking at existing teams, look to create diversity rather than hiring homogeneously. Hiring outside of Greentech but within the cohorts of interested generations (as mentioned earlier) means you can harness a core base set of skills layered with a passion to make a difference. People who make a career move are often motivated to stay to master the new required skills meaning retention rates improve.

Conclusion

As greentech continues to gain momentum and reshape industries, it simultaneously unlocks a plethora of opportunities in the realm of data recruitment and technology jobs. The transformation of industries towards sustainability creates a demand for skilled professionals dedicated to making a positive impact on the environment. From engineers specializing in renewable energy to architects designing energy-efficient buildings and from data analysts optimizing green technologies to farmers implementing sustainable agricultural practices, greentech paves the way for diverse and rewarding career paths. By embracing greentech, individuals not only contribute to the transition towards a sustainable future but also tap into a world of exciting prospects. With greentech as the driving force we can foster a workforce that is passionate about creating a greener and more prosperous society, ensuring a better future for both people and the planet.

Data Governance: The Vanguard of the Digital Age

Tue, 06 Jun 2023 10:42:39 GMT

In this blog, we delve into the world of data governance and its pivotal role in the digital age

In the world of business and technology, the language spoken with reverence is none other than that of data. This immaterial entity, underpins our global society, and functions as the lifeblood of modern organisations. Amidst the euphoria of digital transformation, however, lurks an often-ignored imperative — data governance. Its influence has been gradually growing, and its importance cannot be overstated in the face of our relentless march towards a data-driven future.

The Humble Beginnings

The seedling of data governance took root during the dawn of the information age when databases were first put into operation, helping businesses manage their operations. In these early stages, the need for data governance wasn't apparent. Data was often stored and processed on siloed systems, typically under the jurisdiction of individual business units or departments. Each unit had its own data handlers and managers, who were responsible for the quality and security of their data.

From Chaos to Order

This seemingly autonomous process, however, was a ticking time bomb. Duplication of effort, discrepancies in data interpretation and the lack of data standardisation led to inefficiencies, conflicts which led to disinformed decision-making. It was becoming increasingly clear that the way businesses dealt with data needed an overhaul. Enter: Data Governance

The Age of Data Governance

The sheer volume of data production today is astronomical. Consider this: The Big Data industry rocketed from $169 billion in 2018 to a staggering $274 billion in 2022, representing a 62% increase, according to our 2022 State of Data report. With such a vast sea of data at our disposal, it's no surprise that the necessity for data governance has surged dramatically.

It was evident that there was a shift in the skills gaps within data teams from the previous year from our State of Data report. A more evenly spread demand across Data Governance, Master Data Management, Software Engineering and Data Quality was noted, as opposed to the previous year where Data Governance dominated. This shift indicates the diversification of the data skills market in line with the rapid growth of the industry.

However, when we drilled into responses from the Chief Data Officers' (CDO) perspective, the main skills gap remains in the realm of Data Governance. This is hardly surprising, given the ceaseless flow of data through businesses. Another Gartner study included in our report revealed that a staggering 90% of respondents' data governance projects failed, indicating that even with data governance teams in place, the scale of data flowing through a business is causing widespread challenges.

The People Process in Data Governance

In our increasingly data-driven society, it's easy to fall into the trap of attributing all the power to the tools we use to manage and manipulate data. However, according to the 2022 State of Data report, a notable shift was observed, revealing a stronger focus on the people processes within data governance. This underscores the essential role of individuals and teams who handle the data and carry out the governance procedures, even in an era dominated by sophisticated software and algorithms.

Unpacking the Importance

There's a reason why people processes have taken centre stage in data governance. Despite technological advancements, data governance remains a fundamentally human endeavour. The choices made by data professionals - from data architects and stewards to Chief Data Officers - shape the direction and success of data governance initiatives. It's these individuals who set the standards, manage the data life cycle and instil a data-driven culture within an organisation.

With the explosion of data and the surge in the complexity of managing it, the need for skilled data professionals has never been more critical. As highlighted in our State of Data report, a primary challenge from a CDO's perspective remains the skills gap in data governance. Even with the best data governance tools at their disposal, without the right skills to implement and manage these tools, organisations are likely to struggle in their data governance endeavours.

Harking back to the Gartner report which showed an astounding 90% of data governance projects failed, it shows that even when data governance teams are armed with the right skills and are adept at their roles, it is still a challenge to lead successful data governance projects.

Nurturing a Data Governance Culture

The importance of the people processes extends beyond mere skills, it's also about nurturing a culture of data governance within the organisation. An effective data governance framework relies heavily on cooperation across different roles and levels of the organisation. It's the people who follow the procedures, adhere to the protocols to uphold the principles of data governance.

Promoting a culture of data governance means encouraging transparency, promoting data literacy and fostering a shared understanding of data's value and purpose. It's about aligning everyone - from executives to entry-level employees - towards a common goal: to leverage data responsibly and effectively.

The Future of Data Governance

Looking to the future, Data Governance was singled out as the main focus for respondents for the coming year, overshadowing Big Data/Data Lakes, which was the most popular response in 2021. This highlights the growing recognition of the importance of data governance in managing the ever-increasing complexity and volume of data.

As data continues to grow both in volume and complexity, jobs in data governance management will become more widespread and is poised to evolve. It will play an increasingly crucial role in emerging fields such as artificial intelligence and machine learning, where the quality and reliability of data are paramount.

Data Governance has journeyed from the side lines to centre stage. In this increasingly data centric world, its rise to prominence is a testament to the changing dynamics of data management and the increasing recognition of data as a crucial asset.

Building Support for a Diverse and Inclusive Workplace

Fri, 26 May 2023 07:34:02 GMT

Introduction:

In today's fast-paced and interconnected world, businesses understand the importance of fostering diverse and inclusive work environments. However, simply implementing ED&I initiatives for the sake of appearances is insufficient. To drive genuine progress and avoid superficial actions, it is vital to secure buy-in from senior leadership. In this blog, we will explore the significance of management buy-in for ED&I in the UK, providing supporting statistics to underscore its importance.

The Business Case for ED&I:

To convince senior leadership of the value of ED&I, it is crucial to present a strong business case. Highlighting the potential benefits for the organisation and its stakeholders can pave the way for management buy-in. These benefits include:

Increased Innovation: Research has shown that diverse teams foster creativity and innovation. By bringing together individuals with different backgrounds, experiences, and perspectives, organisations can tap into a wealth of ideas and problem-solving approaches. A study by McKinsey found that companies with diverse executive teams are 33% more likely to achieve above-average profitability.
Improved Talent Retention: Creating an inclusive environment where individuals feel valued and respected can contribute to better employee retention rates. When employees feel that their unique contributions are recognised and supported, they are more likely to remain with the organisation. A survey conducted by Deloitte revealed that 83% of millennials consider workplace inclusion a crucial factor when choosing an employer.
Enhanced Brand Reputation: Consumers are increasingly making purchasing decisions based on an organisation's commitment to diversity and inclusion. A study by Glassdoor found that 67% of job seekers consider a diverse workforce an important factor when evaluating job offers. By embracing ED&I, organisations can build a positive brand reputation, attracting both customers and top talent.

Engaging Leadership in the Process:

Securing management buy-in goes beyond presenting a compelling business case. It is equally important to actively involve leadership in ED&I initiatives and ensure their commitment to driving real change. Here are a few strategies to engage leadership:

Communicate the Benefits: Clearly articulate the advantages of ED&I to senior leaders, emphasising how a more diverse workforce aligns with their key performance objectives. Demonstrating how ED&I can help middle managers achieve their goals can be a powerful addition to the ethical argument.
Set Clear Goals and Metrics: Establish measurable objectives for ED&I initiatives and track progress over time. By providing leadership with tangible metrics, they can gauge the impact of their efforts and stay committed to driving change.
Provide Education and Training: Offer diversity and inclusion training programmes to senior leadership to deepen their understanding of the subject and equip them with the necessary tools to foster inclusive practices throughout the organisation.

Conclusion:

Securing management buy-in is crucial for the success of ED&I initiatives in the UK. By presenting a strong business case that highlights the potential benefits, such as increased innovation, improved talent retention, and enhanced brand reputation, organisations can gain the support of senior leadership. Engaging leadership in the process, involving them in initiatives, and ensuring their commitment to real change will further strengthen the organisation's ED&I efforts. By working together, businesses can create inclusive environments that drive success and foster a culture of equality and diversity.

Remember, diversity and inclusion are not only ethical imperatives but also strategic advantages that contribute to long-term organisational success.

Nurturing Mental Well-being: Overcoming Burnout in the Data, Analytics & Technology Sectors

dale.elder@kdrrecruitment.com (Dale Elder) — Mon, 15 May 2023 13:13:15 GMT

In this blog, we will explore effective strategies to combat burnout, nurture mental well-being and prevent anxiety for professionals in the data, analytics & technology sectors.

Introduction:

Professionals in the data, tech & analytics sectors face unique challenges that can significantly impact their mental well-being. Disturbingly, recent statistics indicate that a significant percentage of engineers in AI, Machine Learning & Data reported a decline in their mental health.

Understanding the Impact:

The fast-paced and high-pressure nature of these sectors can contribute to burnout. Long hours, tight deadlines, and the continuous pursuit of innovation can take a toll on professionals' mental and emotional health. Recognising this, it is vital to proactively address burnout and prioritise mental well-being either for yourself or your team.

Statistics Highlighting Burnout:

A survey conducted in 2022 revealed that 60% of professionals in the data, analytics & tech sectors experienced symptoms of burnout in the past year.

Additionally, 67% of respondents reported feeling overwhelmed by their workload, indicating the need for better work-life balance and stress management strategies. Surprisingly, only 30% of professionals felt adequately supported by their employers in managing their mental health. To help combat this, use some of the points below to inform your mental well-being strategy in the workplace.

Strategies for Mental Well-being:

Establish Work-Life Balance: Set clear boundaries between work and personal life. Dedicate time to recharge, engage in hobbies and spend quality time with loved ones.

Manage Workload: Prioritise tasks, delegate when possible, and communicate effectively to avoid excessive workloads. Recognise your limits and seek assistance or re-evaluate deadlines if necessary.

Foster Supportive Environments : Cultivate a supportive work culture that encourages open communication and provides resources for mental health support. Promote dialogue and destigmatise conversations about mental well-being.

Encourage Regular Breaks: Incorporate short breaks into your workday to recharge and refresh your mind. Stepping away from work, even for a few minutes, can boost productivity and prevent burnout.

Promote Physical Well-being : Engage in regular exercise, eat a balanced diet and prioritise sleep. Physical health plays a crucial role in maintaining mental well-being.

Develop Coping Strategies: Identify healthy coping mechanisms to manage stress, such as mindfulness exercises, deep breathing techniques, or engaging in activities that bring you joy and relaxation.

Seek Support: Don't hesitate to reach out for support when needed. Find out who your mental health first aiders are.Talk to trusted colleagues, friends, or professional counsellors who can provide guidance and help you navigate challenges.

Conclusion:

As we observe Mental Health Awareness Week, it is crucial to address the issue of burnout in the data, analytics & tech sectors. By implementing these strategies and prioritising mental well-being, individuals and organisations can create healthier work environments, improve job satisfaction and foster sustainable success. Let's support one another on the journey towards better mental health and prevent burnout in our workplaces.

How to Build a Career in Data Architecture

Tue, 11 Apr 2023 19:24:52 GMT

In this blog post, we will explore the steps you can take to build a successful career in data architecture.

Data architecture is a vital aspect of modern-day businesses. As companies collect and analyse more data than ever before, the role of data architects has become increasingly important. The systems that store, organise, and manage data must be designed, constructed, and maintained by them. Data architecture careers can be stimulating and fulfilling.

Get the necessary education and certifications

To become a data architect, you need a strong foundation in computer science, mathematics, and statistics. A lot of data architects are educated in computer science, information technology, or a closely related subject. Yet possessing a degree by itself might not be sufficient. You may also need to obtain certifications in data architecture, such as those offered by the Open Group or the Data Management Association (DAMA).

Gain hands-on experience

In addition to education and certifications, you need practical experience to become a successful data architect. You can gain this experience through internships, part-time jobs, or volunteering. Look for opportunities to work on projects that involve data architecture, such as database design or data modelling.

Develop your technical skills

To be a successful data architect, you need to be proficient in a range of technical skills. These may include data modelling, database design, data integration, and data governance. Database management solutions like Oracle, SQL Server, or MySQL should also be recognisable to you. Keep yourself up-to-date with the latest technologies and tools used in data architecture by attending conferences, workshops, and training programs.

Build your soft skills

Data architects also need strong interpersonal skills. They must be able to communicate clearly with all team members, including project managers, business analysts, and developers. They must also be able to explain technical concepts to non-technical stakeholders. Consider enrolling in courses in leadership, project management, and communication to develop your soft skills.

Network with other professionals

Connect with other businesspeople Every industry needs networking, and data architecture is no different. Attend conferences, join professional organisations, and participate in online forums to connect with other data architects. You can develop your professional reputation, learn about job chances, and remain current with industry trends by doing this.

Consider advanced education

If you want to advance your career in data architecture, you may need to pursue advanced education, such as a master's degree in data architecture or a related field. This can open up opportunities for higher-level positions and increased salary.

Building a career in data architecture requires a combination of education, practical experience, technical skills, soft skills, networking, and continuous learning. By following these steps, you can build a successful career in this exciting and in-demand field. At KDR Talent Solutions, we can help you find your dream job in data architecture or other related fields. Contact us today to learn more.

The Skills Needed to Become a Data Analyst

Tue, 28 Mar 2023 14:58:16 GMT

In this blog, we'll examine the fundamental skills required to succeed as a data analyst.

Data Analyst skills are expanding and hiring qualified data analysts is becoming key as more businesses try to capitalise on the worth of their data. But what skills are necessary to work in this role?

1. Analytical Thinking Skills

Being a data analyst means gathering raw data and turning it into insights. You must simplify complex information, spot trends, and draw meaningful conclusions. Think of yourself as a detective who finds patterns and stories in numbers.

2. Programming Basics: Python & R Skills

Data analysts often use Python or R to clean, process, and analyse data. Knowing scripting helps automate tasks and run smooth analyses. Even basic coding knowledge is valuable and it gets better with practice.

3. Effective Data Visualisation Skills

Once you find insights, you must present them clearly. Tools like Tableau, Power BI, or Excel charts are key. Great visuals help non-technical teams understand your findings. Keep visuals simple and focused on what matters.

4. Communicative Clarity Skills

Clear communication, both written and verbal, is essential. You’ll often explain technical insights to non-technical colleagues or leaders. Use simple language, relevant examples, and visual aids to make your point easily understandable.

5. Business Awareness Skills

The best analysts connect their work to company goals. Know the business strategy, top challenges, and success metrics. That way, your data walkthroughs aren’t just interesting, they’re impactful.

6. Sharp Attention to Detail

Data can be messy. You must check for errors, inconsistencies, and omissions. Spotting issues early avoids mistakes that could skew results. Being detail-oriented helps you deliver trustworthy outcomes.

7. Creative Problem-Solving Skills

Data analysis rarely goes exactly as planned. Whether it's cleaning issues or unexpected results, you’ll need to adapt. Think critically, try new approaches, and keep your end goal in sight.

As you can see data analyst skills are comprehensive and growing, if you need to speak to one of our consultants, you can contact us here :

The Top Data Analyst Tools to Use

Tue, 28 Mar 2023 14:58:13 GMT

In this blog post, we'll explore some of the top data analyst tools that are commonly used in the industry

The position of data analysts has expanded as firms rely more on data to make strategic decisions. These professionals are tasked with analysing and interpreting large volumes of data to identify trends and insights that can be used to guide business decisions. Data analysts use a range of tools and technology to do their jobs well.

Microsoft Excel

Data analysts have been using Microsoft Excel, a well-liked tool, for decades. It offers a variety of tools that make managing, organising, and analysing data simple. Excel is particularly useful for creating charts, graphs and tables that can be used to visualise data trends and patterns. Excel also allows data analysts to perform statistical analysis, regression analysis and other types of analysis on their data.

SQL

A popular computer language for maintaining and modifying data in relational databases is called Structured Query Language (SQL). Data analysts use SQL to extract data from databases, transform it and load it into other systems for analysis. SQL is particularly useful for performing complex queries and aggregations on large datasets.

Tableau

Tableau is a powerful data visualisation tool that is used by data analysts to create interactive dashboards and reports. It allows users to connect to multiple data sources and create customised visualisations that can be easily shared with others. Tableau is particularly useful for creating maps, charts and graphs that can be used to communicate complex data in a simple and intuitive way.

Python

Python is a popular programming language that is commonly used in data analysis. It offers a range of libraries and tools that make it easy to work with data, perform statistical analysis and create visualisations. Python is very helpful for doing advanced analytics tasks like machine learning.

R is yet another popular programming language for data analysis. It offers a range of packages and tools that make it easy to work with data, perform statistical analysis and create visualisations. R is particularly useful for performing complex statistical analysis and modelling.

Google Analytics

Data analysts utilise the potent tool Google Analytics to monitor user behaviour and website traffic. Important information like page visits, bounce rates, and conversion rates may all be monitored by users. Google Analytics also allows users to create custom reports and dashboards that can be used to analyse website performance and identify areas for improvement.

Apache Hadoop

Large datasets are stored and processed on a distributed basis using the open-source software architecture known as Apache Hadoop. It allows data analysts to work with data that is too large to be processed on a single machine. Large-scale complicated data analysis and machine learning tasks benefit greatly from Hadoop.

Conclusion

As companies become more data-driven, there is a rising need for qualified data analysts. To do their duties effectively, data analysts use a variety of tools and technologies. The tools we've highlighted in this post are some of the most commonly used in the industry. By mastering these tools, data analysts can improve their ability to analyse and interpret data and provide valuable insights to their organisation.

The Role of Business Intelligence in Digital Transformation

Sat, 25 Mar 2023 15:03:59 GMT

In this blog post, we will explore the importance of BI in data-driven decision-making and how it can benefit your organisation.

In today's hectic business world, data-driven decision-making is becoming more and more important. Business Intelligence (BI) plays a critical role in enabling organisations to collect, analyse, and interpret data, allowing them to make informed decisions that drive success.

What is Business Intelligence?

The act of gathering, analysing, and interpreting data to provide insights that can be applied to guide business choices is known as "business intelligence." It entails converting raw data into usable information that may be used to boost corporate performance through the use of technology, tools, and procedures. BI can encompass a wide range of activities, including data mining, data warehousing, dashboards, reporting, and analytics.

Why is BI important for Data-Driven Decision-Making?

BI is important for data-driven decision-making for several reasons. First, it helps organisations to identify trends and patterns in their data, which can provide insights into customer behaviour, market trends, and other key factors that impact business performance. Organisations might find growth prospects as well as potential dangers and difficulties by analysing this data.

Second, BI enables organisations to track key performance indicators (KPIs) and other metrics that are critical to measuring success. By monitoring these metrics, organisations can quickly identify areas where performance is lagging and take action to address these issues.

Finally, by offering insights into how various business components are working, BI may assist organisations in optimising their operations and processes. This can help companies find chances to increase productivity, cut costs, and increase revenues.

Benefits of BI for Data-Driven Decision-Making

There are many benefits to using BI for data-driven decision-making. Here are just a few:

Better insights: BI enables organisations to gain a deeper understanding of their data and the factors that impact their business performance. As a result, companies may be better able to make decisions based on data rather than gut feeling.

Improved efficiency: By using BI to identify areas where processes can be optimised, organisations can reduce costs and increase efficiency. This could enhance competitiveness and profitability. Increased agility: BI enables organisations to respond quickly to changes in the market, customer behaviour, and other factors that impact business performance. This can help companies keep one step ahead of the competition and take advantage of fresh opportunities.

Enhanced collaboration: BI tools enable teams to work together more effectively by providing a centralised platform for data analysis and reporting. By doing so, you can make sure that everyone is working toward the same goals, improve collaboration, and dismantle silos.

Conclusion

Business intelligence is essential to helping organisations gather, analyse, and interpret data so they can make successful decisions. By leveraging BI tools and techniques, organisations can gain deeper insights into their data, improve efficiency, increase agility, and enhance collaboration. If you are not already using BI as part of your data-driven decision-making process, now is the time to start. Contact KDR Talent Solutions recruitment to find out how we can help you recruit the right BI analysts to make the most of your data.

Creating equity with the EdTech industry

Fri, 24 Mar 2023 13:03:51 GMT

In a world where technological advancements within the Education industry are escalating change and transforming experiences and outcomes, there is a huge risk of creating inequitable learning platforms if investments of resources and awareness are not made soon.

The recent global pandemic pushed education systems into the digital age, with sudden and prolonged periods of remote learning. Consequences of an ill-equipped system are being felt as we speak: learner support needed (playing catch up) and disruption to onsite learning models being transferred “as is” to the online setting clearly not working. The education technology industry is now undergoing a digital revolution, with tools such as Artificial Intelligence, Machine Learning and automation redefining the norms in education and delivering a distinct set of skills for the young people of today. Not everyone has access to these experiences however and not every platform is adopting them. Schools often do not know the best option to choose. It’s important to close the equity gap in education technology as a whole to create a more positive effect not only for the students but the educational system holistically.

Education today

In order to analyse the social impact of EdTech, we must dive into the access and usage of such tech. Despite the increase in handheld devices, there are barriers that can prevent students from having access to high-quality tech at home or at school. Accessibility is so important as it can help reduce inequities between students and help those who are already marginalised within the school systems. Gaps that begin early in the education pipeline are likely to persist and widen into adulthood.

EdTech developers should take a more disciplined approach to equity. Being inclusive means making sure every individual can use the platform regardless of their specific circumstances. An example would be where an individual with hearing impairments is given the modification necessary to work within the EdTech platform.

The future of EdTech

There are approximately 1.5 million people with a learning disability in the UK and with the traditional school settings not likely to change any time soon, those students rely on innovations to support them with their diverse or specific learning needs. This is a real chance for the EdTech industry to add an extra layer of support and revolutionise ways of interacting and learning. Adjusting and collaborating with diverse groups will help the platforms evolve with everyone’s needs in mind. If diversity doesn’t exist within the community of developers, it is very likely that inclusive features will be missed (purely through the lack of diverse thinking on the team).

So just how diverse is the EdTech workforce currently?

Current estimations state that 40-50% of EdTech start-ups have at least one person of colour as the founder, however overwhelmingly the workforce is made up of white middle-class males. As founder of “Black and Brilliant”, Tony Effik stated in a Forbes interview “ In the race towards digital transformation and competitive advantage the real winners are those who solve human-computer interaction problems most efficiently to augment human intelligence. You can’t gain that advantage if you only build for a portion of humanity, and do that building with talent from just a portion of the population”. Taking this further, and to its natural conclusion, people with disabilities, autism, diverse gender identification and more should all be represented by the build team or consulted at the design stage.

EdTech is different to many other fast-growing tech fields in that gender representation is slightly better than the tech market as a whole (83% of tech execs are white males). Women are more prevalent in this field perhaps because there are more women across teaching as a whole and as we have seen there is a higher proportion of people of color within EdTech. This field is full of professionals who want to make the world a better place. EdTech tools can provide solutions for those working at home and help students/ professional’s regardless of their needs to engage and support without obstructing their learning.

Recommendations

EdTech companies have the potential to achieve a truly equitable and inclusive experience for all professionals, to achieve that the same principles that apply to inclusive hiring across all sectors and industries need to be applied. The talent exists, despite many claims that there is a lack of talent across underrepresented groups, however, the industry needs to evolve to ensure they are attracting and then retaining diverse groups within their business. Apart from adopting inclusive hiring practices, engaging with schools and other educational institutions to really assess the needs of the students themselves, ensuring that they engage with a truly diverse representation of the student body and educational trends, will go a long way to informing the design or modifications required. Inviting investment from more diverse groups will also help to ensure the needs of all, not just the majority, are being considered and then met. Considerations towards Data Learning cycles and their potential for bias are paramount.

Summary

Education is a collective endeavour and there needs to be the correct procedures and frameworks in place to create a more collaborative future as a UK Government report states “ Low confidence in technology slows adoption: Concerns around the effectiveness of data-driven technology, its impacts on educator and learner privacy, and the risk of bias are negatively affecting educator trust in data-driven technologies, acting as a barrier to adoption and successful integration into education”.

How to Build a Career in DevOps

Fri, 24 Mar 2023 13:03:48 GMT

In this blog, we'll guide you through the steps you need to take to build a career in DevOps, so you can start your journey with confidence!

DevOps has emerged as a crucial component in the software development industry, and it is no surprise that DevOps engineers are in demand in the job market. If you're interested in the area of software development, you will have come across the term 'DevOps'.. It's a way of working that brings together development and operations teams, making the software development process more efficient and streamlined.

If you're keen to pursue a career in DevOps, it's important to understand what skills and experiences are required to succeed in this exciting field.

Step 1 Learn the basics of DevOps.

The first step in building a career in DevOps is to learn the basics of DevOps. DevOps is a methodology that requires a deep understanding of software development, IT operations and automation tools. You should start by learning the fundamentals of software development, including programming languages, database management and web development. You should also familiarise yourself with IT operations, including system administration, networking and security. Once you have a good grasp of these fundamentals, you can start learning DevOps-specific tools and technologies like Docker, Jenkins and Kubernetes.

Step 2 Gain hands-on experience.

To further your career in DevOps, you must have hands-on experience with DevOps tools and technology.. You can gain this experience by taking up internships, participating in open source projects, or working on personal projects. Internships are a brilliant way to gain real experience working with DevOps technologies. Many companies offer internships specifically for DevOps engineers and these can be a great way to gain and learn from experienced professionals. Participating in open-source projects is another fantastic way to gain experience. Many DevOps tools and technologies are open source which will allow you to contribute to these projects to gain valuable experience and build your portfolio.

Step 3 Build your network.

Any career requires networking, and DevOps is no different. You should go to industry gatherings, participate in online forums, and network with other professionals.. Keeping you up to date on the newest trends and technology in order to create contacts with other industry experts.

Step 4 To stay on top of your game

It's crucial to keep up with the latest technologies and trends. This entails staying current by reading industry blogs, attending conferences, and engaging in forums. By doing this, you can stay informed about the latest developments and ensure that you're using the best tools and techniques to get the job done. Stay up to date and don't let fresh opportunities pass you by! By doing so, you'll be able to keep up with the latest developments in the field and stay ahead of the curve. With modern technologies emerging all the time, staying up to date is crucial if you want to remain competitive and successful in your DevOps career.

Step 5 Get certified

Certifications may be an excellent tool to authenticate your abilities and exhibit your expertise to prospective employers. There are several certifications available for DevOps professionals, including the AWS Certified DevOps Engineer, Google Cloud DevOps Engineer and Certified Kubernetes Administrator. Conclusion If you're thinking about a career in DevOps, you'll need to about software development, IT operations and automation tools.

You will benefit by learning the fundamentals of DevOps and getting some hands-on experience. It might take some hard work and dedication, but with the right attitude, you could pave the way to a successful career in DevOps and be a valuable asset to any organisation.

The Role of Data Engineering in Data Driven World

Fri, 24 Mar 2023 13:03:45 GMT

In this blog, we'll explore why data engineering is so crucial in a data-driven world and why businesses need to invest in data engineers.

In today's world, data is an incredibly valuable asset and businesses who make the most of it are more likely to succeed. This is where data engineering comes in - it's all about assembling and maintaining the infrastructure that helps companies collect, store, process and analyse vast amounts of data.

What is Data Engineering?

The act of planning, constructing and maintaining the infrastructure that supports companies to manage and process massive volumes of data is known as data engineering.

The Role of Data Engineering

Data is an incredibly valuable asset and businesses that can harness the power of data are more likely to succeed. Data engineering supporting businesses to control and process large quantities of data successfully. Here are some of KDR Talent Solutions key reasons why data engineering is essential in a data driven world.

Data Integration

Businesses gather data from various sources like social media, customer interactions and sales data. To make sense of this data, data engineers build the infrastructure that lets businesses to combine data from these sources into one, unified dataset. This helps businesses get a complete view of their operations and make informed decisions using real-time data.

Data Quality

Data quality is critical in businesses that depend heavily on data. Poor quality data can result in flawed analysis, incorrect predictions and ultimately, bad decision making. It's the job of data engineers to create data pipelines that ensure the data is clean, accurate and consistent. This way, the data can be trusted to make educated decisions that drive the success of the business.

Scalability

To keep up with the ever growing volumes of data, data engineers create data architectures that are capable of handling vast amounts of information. This means that businesses can keep collecting and analysing data without worrying about downtime or poor performance, even as they continue to grow.

Security

It's the job of data engineers to make sure that data is stored securely and that only authorised people can access it. They also make sure data is regularly backed up, just in case of a data breach or system failure, it can be quickly and easily recovered.

Data engineering is essential in enabling businesses to successfully manage and process large amounts of data. Businesses who invest in data engineering will be better positioned to gain insights from their data, make enlightened decisions to accomplish long term success.

At KDR Talent Solutions , we understand the value of data engineering and we work with our clients to help them build strong data engineering teams that can support their data driven initiatives.

The Role of Data Architecture in Data Science

Fri, 24 Mar 2023 13:03:41 GMT

In this blog, we will discuss the value of data architecture in data science and how it can help organisations improve valuable insights from their data.

Data architecture is a crucial aspect of the Data Science field that cannot be emphasised enough. It plays a vital role in ensuring the successful storage, management and application of data for various purposes. In other words, without a sound data architecture in place, Data Science efforts may not produce the desired results.

Here are of the key characteristics of the role of data architecture in data science:

Data modelling

In data science, data modelling involves creating and maintaining models that define the structure, relationships and rules governing how data is stored and accessed in a system. These models provide a blueprint for organising and managing data, which is essential for building accurate and reliable data driven models.

Data integration

This entails combining data from various sources to produce a unified picture of the data. This includes managing data quality, mapping data between different systems and ensuring that data is consistent and up to date.

Data storage

Data storage comprises the process of choosing and utilising suitable storage technologies and data structures for diverse forms of data, including structured, semi structured and unstructured data. This encompasses the selection of databases, data warehouses and data lakes that can efficiently handle massive amounts of data.

Data governance

In data science, data governance involves defining and enforcing policies for data security, privacy and compliance. This is required to ensure that data is used ethically and responsibly, and that it is protected from unauthorised access and abuse.

Data strategy

A data architecture plays a critical role in defining the overall data strategy for an organisation. This involves identifying business objectives, defining data requirements and designing the data infrastructure that supports these objectives. Data architects work closely with data scientists and other stakeholders to ensure that the data infrastructure is aligned with the needs of the business.

A good data architecture is vital in the realm of data science. It's similar to the foundation of a home without it, the entire building can collapse. A good data architecture ensures that data scientists can access, integrate and analyse data accurately and reliably, leading to the development of strong and scalable data driven models. If the data architecture is weak, it can cause significant problems for data scientists and their models. Therefore making it harder to work with data and compromising the accuracy of the results.

At KDR Talent Solutions, we specialise in providing recruitment solutions for data driven businesses and we understand the critical role that data architecture plays in the success of data science projects.

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Data Engineering: The Future

Fri, 24 Mar 2023 13:03:27 GMT

The future of the data, tech and analytics space is exciting however, there is a concern in both professionals keeping up to date with change and those that want to pursue a data engineering career that the changes are coming thick and fast.

By definition, the role of the data engineer is to get the right data sets lined up, ready and available to the people in the business that need to analyse it. Collaboration is key to this role.

The evolving role of the data engineer is being driven by an invasion of rapid technological advancements which is causing the industry to completely transform. The changes within data engineering have been impacted by many different tools such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT) and the cloud. On top of this, evolving business needs and higher expectations of the value that data can provide means that Data Engineers have moved from a support function to the owners of data flows.

Tech-related vacancies have had a surge in popularity in the last few years and currently make up around 13% of all UK vacancies in 2021, up from 12.3% in 2020. The resilience shown within the tech industry has increased massively during the pandemic, tech has literally saved the world of work during these testing times.

The role of a Data Engineer is central to any modern data-driven organisation. Technologies such as Big Query, Snowflake and any other Cloud warehousing platform for that matter helps the Data Engineer to accomplish complex tasks effectively. Therefore, freeing up their time to resource on other tasks. Technology has enabled them to focus on core data infrastructure, performance optimisation and flow.

Because the available technology is helping the role evolve, businesses are now looking for Data Engineers to be skilled in many different areas. Often Data Engineers are asked to demonstrate software engineering methodologies such as Agile as well as distributed systems, programming languages, SQL, ETL/ELT, Visualisation, analytics and data modelling. The rise in home working and increase in cyber-attacks over recent years has also led the data engineer to become knowledgeable in cyber security and data privacy as well as deploying sophisticated data masking solutions.

As the role evolves the demands increase and as a result, companies desire more complex data projects to squeeze the value from their data. It is no surprise or coincidence that Data Engineering jobs are enjoying a huge increase at the moment.

As the role of a Data Engineer develops into a more focused and specialised bracket of people that will require a new set of skills, technology is only getting more sophisticated. The industry has an opportunity to branch out and discover further ways to help better understand data.

How to be a good ally in the workplace

Fri, 24 Mar 2023 13:03:22 GMT

This blog covers some of the key areas to look at when considering how good an ally you are being in the workplace.

Ongoing learning is always needed to push the right attitudes and behaviours going forward and part of this is learning how to be a good ally in the workplace. We have undertaken company wide training on the subject and offer inclusive searches as part of our services packages but we are continually learning. We are lucky enough at KDR to have a diverse workforce of different social backgrounds, sexuality, religions, cultures and genders and we want it to be a place where everyone feels included and heard.

Points to consider:
Getting to know people helps with allyship – an obvious one but maybe one people can skim over, especially with hybrid working models becoming more widespread where the team isn’t always together on a daily basis. Relationship building, as we know helps collaboration and it also helps people advocate other people’s ideas. Part of building relationships relies on really being present for someone, so when you are having your next conversation with someone, make sure you aren’t also checking emails or answering other messages on teams. This helps to build rapport and signals to the other person you are really listening and present for them. Incidentally this is vital during meetings and video meetings also, facial expressions and body language are important, so smile and nod as well as asking for more information if you need it.

Listen to how people describe their own identity – there’s nothing worse than someone not pronouncing your name correctly, so take the time to listen to how people pronounce their name and use their pronouns (if these are used). When you mirror the language they use to describe themselves it shows them that you care.

Acknowledge cultural holidays and life milestones – if you have taken the time to get to know someone, also make sure you are acknowledging when things are happening in their life, whether religious festivals, birthdays, weddings, deaths, illness, promotions.

Help everyone to participate in team discussions – if someone is feeling “other” in anyway, they are less likely to join in. This includes people who may feel like an imposter in their role, so encourage participation from everyone.

If you notice that you’re in a room where everyone looks the same, maybe consider asking if certain colleagues should be invited into the meeting. This ensures their work and experience are also being tapped into.

If you do notice someone is not contributing or participating in events, discussions or even showing up on video calls (camera off) check in on them to make sure they are ok and if there is anything that you can do to help.

Acknowledging skills is so important – people who are underrepresented have reported having to prove their expertise repeatedly. One study showed two-thirds of women and people of colour reported this. This compares to 35% of white males. They also reported having their successes discounted and felt pressured to let white men take the lead. With these biases at play recognising someone’s expertise and ideas is all the more important. However, don’t discount the importance of recognising when their skills or expertise are being questioned. Give people a chance to shine by affirming their skills.

Provide regular feedback – women tend to receive less quality feedback that can help them make changes that will help them grow and become leaders. This can be down to managers worrying about how the feedback will be perceived. Also when women do get feedback it tends to be about how they communicate, for example “you are too aggressive” or “not assertive enough” rather than “if you develop this skill then it will help you with that outcome”.

There are many ways to show allyship in the workplace. It really takes a person to look outside of themselves and understand the potential struggles another person has had to overcome to get to where they are, or is continuing to experience. Be present, be aware and be kind, it makes such a difference to performance and team engagement, wellbeing and happiness.