2026 DATA TRENDS FOR THE UK PUBLIC SECTOR

Across local government, central government, and the NHS, organisations are under pressure to deliver more with less. Budget constraints are forcing tough choices, but they are also driving a shift from short-term fixes toward more strategic planning where a credible multi-year case for modernisation can be made.

In local and regional government, we are seeing renewed momentum in data and AI as a lever for improved service delivery, particularly as authorities prepare for reorganisation and seek to reduce technical debt that prevents meaningful change. There is also growing focus on adjacent markets such as housing associations, where legacy constraints and resource shortages make “lightweight” modernisation approaches attractive.

For many years, cross-government data sharing was more aspiration than reality. In 2026, that gap is closing. Legislative and strategic direction has made it clear that public servants must be able to access data across services securely and effectively, because the outcomes government is accountable for cannot be delivered within organisational boundaries.

Trust has become a defining theme of public sector transformation. As organisations increase data sharing and adopt AI, citizens are more aware of and more concerned about how their data is used. Trust is shaped by transparency, accountability, fairness, and whether organisations can demonstrate robust safeguards.

At the same time, governance challenges inside organisations remain significant. Across the public sector, governance maturity varies widely: some teams have clear stewardship roles, metadata practices and access models, while others still rely on informal approaches that do not scale.

Public sector organisations are under rising fiscal pressure and scrutiny. Technology spend is high, and the operational burden of legacy infrastructure continues to drain capacity and budgets that could otherwise fund service improvements.

Modernisation is now less about innovation theatre and more about resilience and sustainability. Organisations need platforms that reduce duplication, improve cost visibility, and enable them to scale without expensive re-engineering.

AI has become a dominant theme in public sector conversations. However, many decision-makers struggle to articulate specific AI goals. Often what they are really asking for is better data: more accessible, more reliable, and better governed because without that, AI will either fail or introduce unacceptable risk.

AI systems amplify whatever they are fed. If the underlying data is incomplete, biased, inconsistent or fragmented, AI outputs will be too.

Even when organisations have the right technology plans, capability often limits progress. Digital, data and analytical skills shortages remain persistent across the public sector. Teams are stretched, recruitment is difficult, and turnover in specialist roles reduces continuity.

This creates a transformation gap: organisations invest in tools but cannot fully adopt them. It also shapes platform decisions. Increasingly, public bodies value solutions that are quicker to implement, require fewer specialist engineers, and support familiar skills such as SQL and Python.

The ability to access and integrate high-quality data is now foundational to the public sector’s operational effectiveness. Yet the State of Digital Government Review highlights striking gaps: only 27% of organisations believe they have a meaningful operational view of their services, and 70% say their data landscape is fragmented and not interoperable.

This fragmentation has direct consequences. It slows decision making, undermines performance reporting, increases risk and prevents real-time insight. For AI initiatives, poor interoperability leads to inconsistent model inputs and unreliable outputs.

Technical debt remains one of the most persistent barriers to progress in the public sector. Budget pressures often push organisations toward short-term savings rather than long-term investment, and spending cycles can deprioritise service maintenance and improvement, creating a compounding legacy burden.

Legacy systems limit flexibility, slow delivery, and increase operational risk. They also consume time: analysts and engineers spend energy maintaining pipelines, reconciling data discrepancies, and managing fragile dependencies rather than delivering higher-value insight.

As AI and automated decision-making become more common, citizens increasingly expect transparency, fairness and accountability from public services. Ethics is no longer an abstract principle; it is being operationalised through standards, documentation expectations and governance requirements.

In practical terms, this means organisations must understand their data pipelines, control access to sensitive data, document how automated decisions are made, and provide mechanisms for challenge and review.