UK local government devolution: unleashing the power of data

In local government devolution discussions it is only natural for councils to want greater powers and financial autonomy — goals which are undeniably crucial and pressing. To these long-standing asks I’d add another important enabler: unleashing the power of local data.

Right now we are experiencing a monumental shift due to digital transformation, where data plays a critical role in driving change, innovation and improving services across all sectors. To give more agency to local government, it’s essential to tap into the vast amount of data it possesses which it doesn’t (or can’t use) due to the combination of entrenched legacy technology, administrative and cultural barriers which have never been addressed by national policy.

Our legacy of data management in the UK is highly restrictive to economic growth and innovation. A generation of poorly planned outsourcing of our public technology systems means the old school technology industry ‘owns’ much of our data and restricts its use to solve policy issues… (Mike Bracken, “New data institutions are the key to London’s growth”, Evening Standard 28 July 2023")

So far data takes a backseat in devolution discussions. Past Devo- or City Deals have largely overlooked the importance of data sharing between councils, hindering the potential to meet need and deliver better services. Whitehall’s oblique commitment to local government data is also evident both in the National Data Strategy and current discussions about AI in public services — and although some efforts are being made with local authority care data and parking data, these projects seem oblique when set against Whitehall’s overall approach. By adopting different mindset to data collaboration in local public services, we can enable councils to innovate together and better meet the needs and aspirations of the people they serve.

Joining-up data for change

Local government data, when linked, holds huge potential for positive change. However, UK local government (and the NHS, Police, school academies and social housing) is notoriously federated and limited data sharing infrastructure and other barriers currently restrict the ability to work with data at scale. Access to a much larger pools of data and datasets — part of ‘Big Data,’ — allows for deeper analysis and understanding of complex issues spanning councils or regions and the common innovation.

In addition to rich datasets held by councils on demographics and place (often already combined through open data publishing), UK local government holds a wealth of transaction data on citizens, gathered through interactions like school enrolments/free school meals, social service records, waste collection, parking, transport and housing services. This data provides valuable insights into citizens’ needs, behaviours and preferences, allowing services to be tailored accordingly — but more often than not is locked behind commercial, technology or administrative firewalls. Not unreasonably, the personal nature of transaction data creates a higher data protection test for sharing .

Here are examples of the possibilities that can be seized by combining local authority datasets in three important areas of local government.

Care

Sharing local authority data with other councils facilitates the development of new services to support vulnerable individuals and improve preventive measures (and there are early signs of government’s ambitions in this with Care Data Matters). Examples include collaborative integrated care planning and predictive risk models to identify vulnerable individuals early. Data sharing also enables the improvements of telemedicine and telecare services, community support networks and the ability to trigger earlier social prescribing to prevent or delay the escalation of needs. Exchanging data on assistive technology allows for personalised solutions, enhancing independence and safety. Feedback mechanisms based on data improve adult social care services continuously, and multi-agency safeguarding arrangements protect vulnerable adults and ensure coordinated support from various service providers. These initiatives can promote a more proactive and comprehensive approach to supporting vulnerable people in the UK, especially when combined with the new NHS Patient Care Record and Secure Data Environments.

NetZero

I’ve written elsewhere how data is a critical part of improving delivery of NetZero initiatives (performance on which is rightly criticised by the National Audit Office over a series of reports). Examples could include coordinating cross-council renewable energy projects, parking (which the Department of Transport has been working on since 2019), EV charging infrastructure and cycling. Data on green building standards, carbon offsetting and green infrastructure initiatives can be shared to promote building retrofit. By combining data on vulnerable areas, extreme weather events, and social demographics councils can develop collaborative climate adaptation and resilience strategies to address common challenges Additionally, community engagement efforts and circular economy initiatives could also be improved.

Cost of Living

Collaborative data sharing among local authorities can offer crucial support to vulnerable individuals during the UK’s cost of living crisis. For instance, councils can identify those at risk of financial hardship by sharing income and service data, enabling targeted financial assistance. Data on housing availability informs the development of affordable housing initiatives for those struggling with housing costs. Energy consumption data helps identify vulnerable individuals facing high energy expenses, leading to energy efficiency programs and grants. Similarly, sharing data on food insecurity helps establish food support programs like food banks and community gardens. Collaborative efforts enable financial education, debt support services, transportation subsidies, telehealth services, and digital inclusion initiatives to reach those in need. Ultimately, early intervention strategies ensure timely support and well-being programs address stress and mental health challenges.

Many of these are already delivered by local councils and their partners — some are not — but not at scale, meaning we get less value from data than we should.

A case study of what could be is emerging with London’s work around homelessness data, currently held by the GLA, councils and voluntary service providers:

“Bringing data together to support London’s goal of making rough sleeping rare, brief and non-recurrent”

London’s work on homelessness data is an early illustration of this thinking. Rough sleeping data has traditionally been held across multiple different systems, which prevents users from understanding the full picture of the needs and journeys of the rough sleeping population. This makes it difficult to make strategic decisions about how best to support these vulnerable groups.

LOTI’s Strategic Insights Tool for Rough Sleeping (SITRS) is a new tool that has created the capability to bring data from across the rough sleeping ecosystem together into a single place to give decision-makers in GLA, London Councils, Local Authorities, and homelessness service providers, a clearer view of rough sleeping in their local area. The new tool merges and integrates multiple sources of data, by probabilistically-matching CHAIN records of London rough sleepers, In-Form instances from service providers that work with rough sleepers across London, and H-CLIC submissions from every London Local Authority. This means that for the first time, users of this tool are able to see the aggregated journeys of rough sleepers over time, as they show up through touch points in multiple systems, which include statutory homelessness applications, contacts with housing outreach officers, as they are seen bedding down; and interactions with service providers, who are commissioned to support them through various services.

Through the use of this tool, users are able to get actionable insights on how support can be improved, to make rough sleeping rare, brief and non-recurrent. By seeing the aggregated journeys of rough sleepers through various combined systems, users of this tool can better map the history and journeys of rough sleepers through their interaction with homelessness services. For example, by seeing the different inflows and outflows of rough sleeping by different boroughs, Local Authority or pan-London commissioners can make educated decisions about the effectiveness of different services and support, while forecasting and pre-empting rough sleeping trends over time. Local Authorities can also see the inflows of rough sleeping into their borough, by seeing previous statutory housing applications that rough sleepers may have made, across London Local Authorities.

The current system sets the foundation for potential sophisticated data science opportunities in the future. For example, predicting long-term outcomes that could help end users make better decisions on the types of interventions deployed for a given individual to elicit better long-term solutions within the available resources. Or forecasting the big picture trends in homelessness and rough sleeping to better understand upcoming demand which will enable forward thinking policy and funding decisions at a system level. Key to unlocking these opportunities is further data collaboration and integration; promoting the responsible sharing of data across London to build on the existing dataset. Accounting for all the touchpoints a rough sleeper might encounter throughout their journey, data from key services such as Health and Social Care, Prison and Probation Service, would enable users to have a detailed end-to-end view which will only further enhance effective decision and policy making.

This model and approach with data collaboration sitting at its core has the potential to transform services across London.

Safeguarding personal data

Clearly, robust safeguards are crucial when sharing personal data between local authorities to develop new citizen services which will require more coordinated resource. What I’m not arguing for here is a data ‘free for all’ each new collaborative service must be designed, defined and purpose-led. Design teams, like the the London Office of Technology and Innovation in London regional government, play an integral role and — as I’ve argued previously, Whitehall needs a different delivery approach too if it going to meet tough Net Zero targets.

Key measures include data minimisation (to collect only necessary data), obtaining explicit consent from individuals and employing anonymisation/pseudonymisation to safeguard identities. Data Protection Impact Assessments (DPIAs) help identify and manage risks, especially for sensitive information. Formal data sharing agreements clarify purposes, responsibilities, and scopes. Ensuring clear purpose limitation guarantees that shared data serves only agreed-upon functions, promoting trust and efficient, citizen-focused services. Alongside processes and culture, technology plays a role here too with data sharing agreement libraries (see the Information Sharing Gateway), data protection software (for example, the use of Dapian) and privacy enhancing technologies.

Rethinking operating and delivery models

Predictive analytics enabled by shared data allows proactive measures to address challenges and enhance public safety, health, and well-being. This data can play a pivotal role in uncovering patterns and correlations, leading to innovative problem-solving approaches. For instance, the InnOvate programme across 5 south London boroughs (population 1.2 million) is using sensors and AI to proactively address issues like fly-tipping, noise pollution, floods through to safeguarding vulnerable older people. Work like this is important not just because authorities gain actionable insights they wouldn’t have previously, but that those insights have led to changes in the way they deliver services.

Linking large datasets from UK local authorities can also lead to the creation of new operating or business models for service delivery. This is preferable to the traditional way to create new ways of working, namely the shared service approach that often faces cumbersome coordination, governance and integration challenges. With data sharing innovation, local authorities gain flexibility and responsiveness: adapting services based on evolving needs and real-time data. External innovation opportunities can be boosted when researchers, start-ups and businesses can access data to propose novel solutions to existing challenges, resulting in more efficient approaches to service delivery.

NHS data opportunity

The NHS Patient Care Record presents a big opportunity. The record holds a wealth of medical data, enabling healthcare professionals to access crucial information about patients’ medical history, treatments and prescriptions — leading to improved healthcare delivery. Regionally, the creation of the London Data Service over the next 12 months will provide linked data for 10.7 million patients across London, which will be available for R&D and innovation purposes as well as the delivery of heath outcomes and productivity.

But when combined with social, housing, and environmental data from local authorities, we can start to understand the social and environmental context of patients potentially leading to more effective measures and improved health outcomes. With a broader view of peoples’ lives, healthcare professionals can focus on preventive healthcare measures and early identification of risk factors or health challenges can lead to more timely interventions and better management of chronic conditions.

What needs to be done

Securing resources to deliver data collaboration is essential to moving beyond ad-hoc approaches and projects towards a systematic and sustained approach to local government data. Striking a balance between decentralised-yet-coordinated is the biggest challenge and where Open Banking in the financial sector is interesting to explore as a model.

Open Banking, introduced in 2017 by the Competition and Markets Authority, requires the UK’s largest banks to allow customers to share their financial data with authorised providers. This initiative has encouraged competition, innovation, and transparency in retail banking, benefiting millions of users through financial management apps and payment tools.

While Open Banking standards are very much focused around consumers and their right to ‘own’ the data financial institutions hold on them (which might be an additional long term goal), it can offer valuable lessons for local government data exchange by introducing key principles and practices to improve data accessibility and innovation, such as:

• Standardised Data Sharing Framework: Create a standardised framework for data sharing among local government agencies, defining rules, protocols, and security measures for secure and efficient data sharing.
• Data Interoperability: Set up data interoperability standards to enable seamless data exchange between government departments and agencies, promoting data flow and accessibility.
• APIs for Data Access: Develop Application Programming Interfaces (APIs) for data access, allowing third-party applications and developers to access government data, encouraging innovation in services and solutions.
• Data Security and Privacy: Prioritise strong data security and privacy measures to ensure secure data handling and compliance with data protection regulations, maintaining public trust.

Recommendations for Policymakers to Improve Local Government Data Sharing in England

1. Revise the UK Government National Data Strategy: Update the national data strategy to cover all levels of government, including local authorities. Create a framework that encourages efficient data sharing and use among local authorities, fostering collaboration and innovation.
2. New legislative framework for data: Introduce laws to modernise data practices in local government and promote technological compatibility within the local government landscape.
3. Prioritise Data Infrastructure: Allocate funding and resources from central government to modernise data infrastructure, ensuring that local authorities have the necessary tools, training and skills to effectively utilise local data.
4. Explore Lessons from Open Banking: Investigate how insights from the UK’s Open Banking innovation can be applied to local government data exchange. Adapt principles of consent-based data sharing, standardized frameworks, APIs, data security, and interoperability to the context of local government.
5. Incentivise New Service Delivery Models: Encourage local authorities to explore and develop new operating or business models for service delivery, starting with areas such as care, NetZero initiatives, and addressing the cost of living.

By embracing these recommendations and recognising data as a fundamental element of devolution, local government can significantly improve the lives of citizens, deliver better services, and drive positive change at the community level.