In Episode 26 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Aimee Smith, Chief Data Officer at the UK Government, where they discuss her move from 25 years in policing and law enforcement into government's top data role, and why she believes culture, risk appetite and misaligned investment incentives, not legislation or technology, are what's really blocking data sharing across Whitehall.

They also touch on the UK's evolving position on data and AI sovereignty, the real purpose behind the National Data Library, and how Aimee is building a framework to track and prove the value of government data.

They also discuss:

• Why Aimee left a 25-year career in policing to become the UK Government's Chief Data Officer.
• How she is building influence across government departments without having formal authority over them.
• Why she restructured the Chief Data Officer Council around a strategic data roadmap and sector-based leads.
• What makes data sharing across government harder than the equivalent challenge in the private sector.
• Why culture and risk appetite, not legislation or technology, are the biggest blockers to data sharing.
• How she plans to fix the incentive problem by changing how Treasury assesses technology and data investment bids.
• Why she wants government's most valuable data treated as critical national infrastructure.
• How linking investment to data assets rather than departments could unlock sharing.
• Why legacy technology is as much a data cataloguing problem as a modernisation one.
• What she believes better data sharing could unlock for citizens navigating public services.
• Why she thinks the data sovereignty debate currently overlooks the data itself.
• How she is working with cross-government commercial teams to embed sovereignty principles into procurement.
• What the National Data Library actually is, and the shifting priorities behind its original manifesto pledge.
• How the National Data Library differs from a centralised data lake.
• Why AI adoption depends on getting the underlying data foundations right first.
• How she is using AI as a way to keep data on the agenda inside government.
• Why she is cautious about how AI investment gets communicated to the public.
• What her three-part framework for tracking data value looks like (commercialisation, valuation, usage).
• How Treasury's new balance sheet requirement for data valuation came about.
• What she hopes to point to as evidence of progress in twelve months' time.

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