Transforming data from exhaust to infrastructure

In a recent piece for UKAuthority Chief Digital Officer for London, Theo Blackwell MBE, argued that the UK is starting to treat public data as the core infrastructure for service delivery, albeit progress remains uneven and fragile. Too many flagship programmes still treat data as “exhaust” from policy or technology initiatives, rather than a first‑order design concern with its own roadmap, standards and ownership.

Public data will only become the “core infrastructure” for better public services if government treats it like a designed system, not a happy by‑product of programmes. Datnexa’s view is that the next wave of transformation, especially in adult social care and local government, depends on making shared data infrastructure a deliberate, funded and governed asset in its own right.

Without putting data at the centre of everything the result will be familiar on the frontline: fragmented records, repeated assessments and staff who work heroically around the system to get residents what they need. Local leaders know they cannot deliver integrated care, prevention or AI‑enabled services on top of brittle spreadsheets and siloed line‑of‑business systems. The question is no longer whether public data is infrastructure it is whether we are prepared to fund, govern and operate it as such over the long term.

What “public data as infrastructure” really means

For Datnexa, treating data as infrastructure has three concrete implications for public bodies.

• A clear, shared data architecture across placeLocal authorities, NHS partners and care providers need a reference architecture for how data flows, not just a list of systems. That means agreeing core canonical data sets (people, places, providers, care episodes), common identifiers, and patterns for secure sharing via APIs rather than one‑off integrations.

• Service‑led design of data, not just techData models should be shaped by real user journeys: a carer trying to arrange respite, a housing officer spotting early signs of crisis, a social worker managing complex safeguarding. Starting from these journeys forces clarity on what “good data” looks like: timely, linkable, explainable and trusted by practitioners.

• Long‑term operational stewardshipInfrastructure implies operations: monitoring quality, managing schema changes, onboarding new partners, and ensuring that AI or analytics products don’t quietly degrade data over time. This is where many programmes falter; funding covers build, not run. Councils and ICSs need named data product owners, measurable SLAs, and governance that reports to the same boards that oversee finance and risk.

  
  

The adult social care test

Adult social care is where rhetoric about public data as infrastructure collides with reality. Demand is rising, workforces are under pressure, and councils are trying to deliver strengths‑based, preventative services with systems that were never designed for multi‑agency, AI‑enabled working.

Yet the sector also shows what is possible when data is treated as a shared asset. Integrated Care Systems are beginning to use shared data platforms to coordinate services and understand population need, while some local authorities are building digital and data “foundations” that span housing, social care, and public health. These efforts remain the exception, not the rule, and they often depend on a handful of champions rather than a sustainable model of cross‑sector investment. For Datnexa, adult social care is the proving ground: if we can make data work there, with its complexity, sensitivity and mixed economy of providers, we can make it work anywhere.

AI will amplify whatever data foundation we give it

The latest UKAuthority research highlights widespread concern among digital leaders about the quality, fragmentation and governance of public sector data as they move towards AI. Leaders are clear that AI is now politically central, tied to fiscal pressures and expectations of a more responsive state, but also that current data infrastructure is not consistently ready.

AI will not rescue poor data; it will amplify its weaknesses. Deploying powerful models on top of inconsistent codes, missing context and opaque lineage risks unfair decisions, regulatory breaches and a collapse in public trust. Conversely, where organisations have invested in strong master data management, APIs and robust governance, AI can genuinely augment staff, target interventions earlier and personalise support. The strategic choice is stark: invest in data as infrastructure now, or accept that many AI pilots will stall at the point where they should scale.

A practical agenda for local leaders

Datnexa’s work with local government and care organisations suggests five practical moves for leaders who want to turn the “public data as infrastructure” ambition into operational reality.

• Make data a board‑level risk and assetTreat key data sets and data platforms as you would estates or finance, with explicit risk ownership and strategic investment plans. Align this with AI governance so data quality, accountability and ethics are considered together, not in separate conversations.

• Start with a place‑based data blueprintBring together local government, ICSs, voluntary sector and suppliers to agree a single, pragmatic blueprint for how data will be shared across your place. Focus first on a small number of high‑value journeys, for example, hospital discharge, falls prevention or transitions from children’s to adults’ services, and design the flows and standards from there.

• Invest in APIs and interoperability, not one‑off integrationsUKAuthority’s work on APIs shows the momentum behind using APIs as a core public service asset, rather than project‑specific plumbing. Local digital teams should prioritise reusable API patterns and shared components that can be applied across programmes, easing future AI integration and supplier changes.

• Treat data products as living servicesCurated data sets, dashboards and AI models should have owners, roadmaps and feedback loops in the same way as frontline digital services. That includes capacity for continuous improvement and the ability to respond when policy, coding standards or partner landscapes change.

• Build capability and culture alongside technologyNone of this is purely technical. Councils and partners need multidisciplinary teams who understand commissioning, care practice, information governance, and modern data engineering. Training, communities of practice and clear operating models for data‑sharing agreements are as important as any platform choice.

Datnexa believes that if public bodies treat data as designed, shared infrastructure, with architecture, governance and operations to match, they can unlock safer, fairer and more sustainable services in adult social care and beyond. If you were to pick one service journey in your area to act as the catalyst for this kind of shared data infrastructure, which would you choose first?