Leading Data Teams In 2025

One of the common themes we’ve seen in recent years is that Data teams, in particular, have become overinflated and in some cases lacked efficiency. We're encouraging an informal discussion around the importance of talent density and how hiring problem solvers rather than specific technical skills is critical to success. The group will dive into how they identify these problem solvers and what attributes they look for within them, as well as having a wider conversation about how to get the most out of these teams to ensure a leaner yet more efficient approach to Data.

To tackle this, we hosted an in-person roundtable, where Data leaders from the likes of Raft.ai, Rightmove, Silver Lake, Beauty Pie, Auction Technology Group, Expana, Deliveroo and Planet, met up to discuss their challenges, and experiences surrounding talent density, leadership balance and hiring problem-solvers.

The target audience for this event was Data Leaders and specifically Data decision makers, from different sized organisations to encourage diverse career backgrounds and opinions.

Some of the more focused thoughts highlighted have been:

💡Talent Density: Strategies for building high-impact teams by ensuring quality and alignment across all team members.

💡Hiring for Problem-Solving: Shifting beyond technical skills and industry backgrounds to identify and develop problem-solvers within data teams.

💡Leadership Balance: Exploring how leaders can meet commercial demands while staying connected to their technical teams. 

What were the key take aways from the session:

Building High-Performing Data Teams
Talent density is everything.

Having a small number of exceptional people can drive more impact than a larger, less focused team. But how do you maintain high performance when teams are under pressure?

• Clear frameworks for measuring impact—not just individual skills, but alignment with business outcomes.
• Reducing team size can sometimes increase productivity by focusing efforts on key priorities.
• Recognising and rewarding foundational work, like strong data models and governance, which often go unnoticed but are critical to success.

Making data more than just a service function
Data teams can struggle to get a seat at the table—so how do you ensure your team is seen as strategic rather than just reactive?

• Aligning with commercial objectives—data should be solving business problems, not just delivering reports.
• External validation—getting buy-in from outside the team (e.g., town halls, exec sponsorship) helps shift perception.
• Hiring strategically balancing high-performers with fresh perspectives to drive innovation.

AI & business value
What’s the hype, what’s real, and how to get stakeholders on board.

• AI success depends on integration, not just innovation—how does it fit into existing workflows?
• Data quality is still king—AI is only as good as the data it’s trained on.
• Storytelling matters—using data-backed narratives to win over non-technical stakeholders.

If you'd like to hear more and are interested in attending one of our Data events, please reach out to our Head Of Data & Business Intelligence, nick@burnssheehan.co.uk