The Local Government Reorganisation (LGR) is the most significant shake-up of local administration in the UK for over 50 years. Central government has argued simpler local government structures will create efficiencies, with the savings put back into front-line services - while increasing public understanding of who is responsible for delivering those services will improve accountability. Redrawing the boundaries of local government on this scale is a considerable undertaking, but since it’s taking place at the height of the Data Age – and the dawn of the AI Age – multiple opportunities will emerge. Specifically, the transition allows organisations to look at overlapping processes, systems and data and ask not only “which of these is best?” but also “what should the local government of the future look like?”.

Central government has already expressed its views on key focus areas. The Prime Minister has championed the use of AI for creating public sector efficiency savings, whilst the LGR White Paper emphasises that devolved powers must be underpinned by strong data and analytical capabilities. But whilst these are laudable aims, the “Blueprint for a Modern Digital Government” warns that public sector infrastructure is fragmented and often locked in legacy systems. From our experience, public sector organisations have also historically lacked the funds to truly invest in broad digital skills – so even when digital technologies are rolled out across organisations, they are rarely used effectively.

Fragmented legacy systems and poor digital skills are weak foundations for cementing the much-heralded benefits of AI. In fact, they lead to inconsistent and incorrect outputs, stalled implementations and a lack of adoption amongst the very staff that the digital solutions are supposed to help. Instead, the LGR should be embraced as a once-in-a-generation opportunity to build robust digital foundations through modern Data Governance and accessible, trusted data sources.

The value of data governance in local councils

It’s no-longer unusual to hear that “data is an asset”. The Blueprint for a Modern Digital Government states that data should be treated as a public asset, shared (responsibly) between different public bodies. But just as with physical assets, not all data has the same worth. Local governments hold a wealth of detailed information on their residents, for example, but if that data is poorly maintained and can’t be used, it has little value.

Data Governance is not just “something for IT” but an issue the whole organisation needs to be involved in solving. Data Owners must be drawn from business unit leads and process owners – key players who will ensure the business has reliable, high-quality data it can trust. In-turn, they must be supported by central coordination to maintain consistent data standards across different areas of the organisation.

The Local Government Association has highlighted that siloed working is one of the most deeply entrenched structural barriers local government faces when attempting digital transformation. Consequently, without well-managed change and new governance frameworks, this age-old problem will only be compounded when local government organisations are combined. Indeed, even where existing data has been well maintained, the task of merging the data held by two areas of local government is not simple. For instance, meta data must be updated, duplicated data removed, data owners and stewards re-appointed and more.

As with any other asset, data must be maintained to approved standards – with clearly defined and assigned responsibilities. Having an agreed, well-known and well-followed data governance framework is key to implementing future automation and AI solutions.

Data integration: building a single source of truth

Companies and councils alike often operate on a set of fragmented data, sourced from a combination of legacy systems. Subsequently, this is taped together with ad-hoc integrations that have lasted well past their intended lifespan. Quality is therefore thus reduced as data cycles back and forth between operational systems, and with information spread across teams and platforms, local bodies are prevented from having a unified view over their data.

The solution is to sever the ties between systems and replace them with a centralised repository of data, connected to each system. Put simply, a data warehouse. In a digital-first government, this is no longer a luxury, but a necessity. When implemented well and with the correct governance in place, creating a single source of truth provides a trustworthy foundation on which to adapt policy to the needs of local communities.

For newly merged councils, consolidating the disparate legacy systems and data from existing bodies is crucial. But while it would be expensive and time-intensive to replace systems or develop integrations between them, a data warehouse allows seamless integration of the data sets the systems produce and consume. Differences in data types and formats can be reconciled to agreed standards through the integration process. Plus, data that was previously underutilised can then be used to create a fuller picture of the community with access democratised to SMEs across the council.

Crucially, a warehouse also reduces the effort of locating data, particularly when staff are getting to grips with data and systems from a merged authority. This is just as true for AI as it is for human operators, with AI agents thriving off well labelled, organised and maintained data – rather than struggling with poorly-labelled data that is difficult to locate.

Laying the digital foundations for AI

The risks from failing to integrate and standardise data foundations are significant. Failing to use data for AI or automation activities will leave councils inefficient and languishing behind central government expectations. But using incorrect data to train a predictive model could lead to biased outcomes and poor allocation of funds. Meanwhile, holding data with unfit security measures could result in personally identifiable information being surfaced when queried by an AI agent. Additionally, poorly tagged and mismatched metadata could lead to the failure of automated tasks, increasing the need for human input.

At RSM, we have a full spectrum of experience in digital transformation to support you in building out a future-fit foundation that will allow you to use data and AI to create better outcomes for your internal and external customers. If you’d like to discuss how to simplify your data estate, ensure high quality data and create trusted data sources for use with modern digital solutions, please speak to Data, Analytics and Insights Partner Sarah Belsham.

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