Tony Blair Institute backs National Data Library

The UK has the potential to be a world leader in artificial-intelligence-driven innovation, research and data-enabled public services – but it is being handcuffed by its data infrastructure.

That’s according to the Tony Blair Institute (TBI), which has published a report supporting the UK Government’s creation of a National Data Library to unlock the value of public data and support AI development.

In the report, TBI describes the UK’s data infrastructure as “fragmented and unfit for purpose.”

It said that “public-sector data are locked in silos, access is slow and inconsistent, and there is no system to connect and use these data effectively, or any framework for deciding what additional data would be most valuable to collect given AI’s capabilities.”

As a result, research is stalled, AI adoption is held back, and the government struggles to plan services, target support and respond to emerging challenges.

However, the proposed National Data Library (NDL) – tabled as part of the Government’s Blueprint for Modern Digital Government – represents an opportunity for the UK to be the first worldwide to harness AI for public good, it said.

“It can help create the infrastructure needed to unlock the value of public-sector data alongside frameworks to identify and collect new types of data for breakthrough insights. Instead of a complicated web of systems and slow, one-off approvals, the NDL will establish a clear, secure way to access linked data sets, supporting AI innovation, better policymaking and research that drives economic growth. It will not centralise data but will put in place the legal, technical and governance structures to ensure that high-value data sets can be used efficiently while maintaining security and trust.”

However, TBI said this can only work if the NDL is “driven by vision, not just technology.”

It claimed too many past government data initiatives have focused on systems and processes rather than real-world outcomes.

“The NDL must not fall into the same trap. It must be built to remove systemic barriers to data use, support AI-driven discovery and help us understand what additional data would be most valuable to gather given AI’s analytical capabilities, delivering tangible economic and societal benefits. A narrow, technical approach will fail.”