How data leadership is changing
Jo Dionysiou • 2 December 2025
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.
Using the results from the annual State of Data Report, this blog looks at some predictions for the data industry around focus and skills.
Data professionals have made it clear in the State of Data Report that they would leave if made to come into the office for more days than they currently do.
KDR Talent Solution's State of Data Report provides insights into the blockers to AI implementation, how these have evolved & what companies are doing about them.
