Public sector · Local government
70% of UK public sector organisations say their data landscape is not co-ordinated or interoperable. Inside a council, that fragmentation is now the single biggest barrier to Local Government Reorganisation, Section 114 recovery and every service delivery target sitting on the executive team's desk.
Those headline numbers from the 2026 UK public sector data trends report describe most councils we work with. They explain why LGR programmes stall on the data-joining question, why cyber-resilience audits keep finding surprises, and why boards approve digital investment that never quite lands as visible service improvements.
Key takeaways
70%
Say data is fragmented, not interoperable
58%
Cite skills shortages as top barrier
8+
Councils under Section 114 since 2018
£45bn
Annual modernisation savings unrealised
LGR is not just a governance restructure. It is the largest data-joining exercise UK local government has faced since the 1974 reforms. Councils being folded into new unitaries bring twenty or thirty years of siloed systems: revenues and benefits on one, adult social care on another, housing on a third, licensing and planning on a fourth. Each has its own IDs, its own hierarchies, its own definition of "resident".
The new unitary cannot deliver its statutory services on day one without a data layer that resolves the same resident across those systems. Snowflake's secure data sharing model lets teams work across legacy datasets without physically moving them until the joining logic is proven. That reduces both cost and information governance risk during the transition.
At least eight English councils have issued Section 114 notices since 2018. The financial pattern is often the same: transformation programmes that promised savings then delivered smaller savings than modelled, on longer timescales. Cost predictability is now a first-order requirement, not a nice-to-have.
Consumption-based platforms let councils see, month by month, exactly what a workload is costing. That transparency matters more than the architecture diagram when Section 151 officers are asking hard questions.
Councils sit at the crossroads of public services. Safeguarding needs data from social care, schools, health and police. Housing allocations sit next to homelessness prevention, adult social care and mental health. Post-Grenfell building safety needs joined data between council estates, housing associations and Fire and Rescue.
Every serious case review published in the past five years has identified the same root cause: the relevant data existed, but it did not reach the person who needed it in time. Governed data sharing removes that friction. It also produces the audit trail that follows every serious incident.
Most councils already run Microsoft. Power BI is embedded, licences are already paid, Fabric is the path of least resistance for procurement. That does not automatically make it the right architecture for every workload.
| Council workload | Microsoft Fabric | Snowflake |
|---|---|---|
| Predictable BI on Power BI | Strong fit | Works, but overkill |
| LGR resident-matching across legacy systems | Limited | Strong fit |
| Adult social care caseload forecasting | Constrained by capacity units | Elastic compute, strong fit |
| Consumption cost transparency | Capacity-based, can creep | Consumption-based, transparent |
| Small in-house team ownership | Fine if Microsoft-first | Fine either way |
Fabric suits BI-first councils with predictable workload profiles. Snowflake fits better where cost transparency, workload elasticity and cross-organisation data sharing are load-bearing. Most councils will end up with both, joined through governed pipelines, once the workload analysis is done.
For a council under Section 114 pressure, a platform that gives you predictable monthly costs and a clear audit story matters more than the architecture diagram.
Council data touches vulnerable residents. Homeless application forms, safeguarding records, benefit claims, adult care assessments. When councils are hacked (Hackney, Redcar, Copeland), the consequence is not just a service outage. It is public trust erosion that lasts years.
Only 26% of the public believe government uses AI responsibly. That gap shows up the moment a resident asks how their data is being used to make a decision about them.
The most valuable council AI use cases are predictive: which tenancies are at risk of homelessness, which properties need retrofit priority, which adult care packages need review before they escalate to hospital admission. None of those models work on data that is not connected first.
58% of public sector organisations cite skills shortages as their top barrier. Councils have small data teams that turn over. Platforms that reduce operational overhead and rely on familiar SQL skills are not a nice-to-have. They are a constraint on what your team can actually deliver.
Start with the LGR data-joining problem, not the reporting layer. Resident matching across legacy systems is the workload that pays back fastest and reduces reorganisation risk.
Frame everything in resident outcomes. Fewer handoffs on adult social care, faster homeless application decisions, cleaner housing allocations. Anything that reads as an ongoing service contract risks being deferred.
Treat cyber and compliance as non-negotiable table stakes. Every hacked council has been a lesson learned in public. Your platform choice has to survive an audit before it earns a business case.
Build it as a knowledge transfer engagement. Your team owns it long after the partner leaves. Choose a platform your team can actually run.
Prove it before you commit
A 30-day Snowflake Proof of Value on your priority workload (LGR resident-matching, adult social care caseload, council tax collection or repairs). Two days of Catalyst BI consultancy. Real cost numbers. No multi-year commitment.
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For the full picture across the UK public sector, including all eight trends and the supporting research:
2026 UK Public Sector Data Trends →