The Evolving Role of the Data Team
The function of data teams has shifted from passive support roles to active strategic enablers, guiding decision-making, fostering innovation, and underpinning competitive advantage in an increasingly data-driven economy. The recent Data Impact Leadership Forum roundtable, hosted in Edinburgh by MBN Solutions and Brodies LLP, gathered 30 industry leaders to discuss how data teams are evolving and the skills they need to succeed.
Beyond STEM—Data Teams Need More Than Technical Skills
Traditionally, data teams have been built around technical expertise in STEM disciplines—statistics, programming, and database management. While these remain important, successful data teams today require a broader skill set.
As keynote speaker Elizabeth Hollinger highlighted, businesses increasingly value professionals with strong communication and storytelling abilities, often found in those with backgrounds in the arts and humanities. The ability to translate data into compelling narratives is crucial for making insights accessible and actionable. A striking example shared at the forum was a data visualisation expert with a background in textile design—an unconventional but highly effective approach to presenting complex data in ways that drive business decisions.
Collaboration at the Core—Why Data Can’t Work in Isolation
Gone are the days when data teams worked in silos, responding to ad hoc requests. Modern data teams thrive when embedded within cross-functional groups, working closely with product managers, engineers, and legal experts.
Mark Hunter, VP of Analytics & Product Operations at Wise, shared how their decentralised, mission-driven structure empowers data analysts to actively shape product decisions rather than simply support them. This approach is mirrored in many organisations where data teams act as partners rather than service providers.
However, the discussion also highlighted the challenge of cross-skilling. While upskilling team members across engineering, analytics, and governance is increasingly common, spreading employees too thin can dilute expertise. Finding the right balance, where specialisation is maintained without restricting broader collaboration, is key.
AI is Here—Now What?
AI and automation are reshaping industries, but rather than replacing jobs, they are transforming them. The forum discussion stressed that AI should be seen as an opportunity, shifting roles toward higher-value tasks rather than eliminating them.
For businesses, the challenge lies in upskilling employees to harness AI effectively. Training professionals with traditional SQL database experience for cloud-based roles, for example, allows organisations to retain institutional knowledge while integrating modern capabilities. Companies that invest in continuous learning will be best placed to adapt to this evolving landscape.
Governance is a Priority, Not a Footnote
As data teams become more embedded in business strategy, governance and compliance must be considered from the outset rather than as an afterthought.
Legal and compliance experts at the forum, including Martin Sloan and Steve Coates, emphasised the importance of involving legal teams early to ensure data initiatives align with privacy laws, intellectual property rights, and industry regulations. This proactive approach prevents costly missteps and ensures regulatory confidence.
No One-Size-Fits-All Approach to Building a Data Team
There is no universally ‘correct’ way to structure a data team. Whether centralised, decentralised, or operating under a hybrid model, success depends on strong governance and clear alignment with business objectives.
Leaders from FanDuel and Experian shared insights into how governance frameworks help prevent misalignment and inconsistencies in how core business metrics are interpreted. Without a clear framework, different teams risk defining key metrics, such as ‘active customers’, in ways that create operational confusion.
The Best Data Teams Put People Before Technology
As Simon Axon of Teradata noted, technology will always evolve, but the 'people model' must be strong enough to adapt alongside it. Leadership is critical in ensuring data teams remain agile, adaptable, and aligned with business priorities.
The evolution of data teams isn’t just about technology—it’s about people, collaboration, and strategic thinking. Organisations that embrace diverse skill sets, invest in upskilling, prioritise governance, and embed data within decision-making processes will be best positioned to lead the next wave of data-driven innovation.
