Why did we do this?

Local authorities sit in the middle of a web of information. Everything from social care for vulnerable children, waste collection, procurement, council tax collection to planning applications produces huge quantities of data. This data is sometimes garbled, hard to analyse or personal and sensitive. But it is potentially hugely beneficial in helping councils make services more targeted and effective, to allocate resources to where they will have the biggest impact, to save officer time in front and back office processes, and to provide insight into the causes of, and solutions to, costly social problems.

We wanted to help local authorities get more from the data they hold. Our research project articulated the ways in which data analytics can help local authorities to find savings and improve outcomes for people and communities.

What did we do?

We studied leading data innovations in local government. Our research included case studies, interviews, surveys, desk-based research and workshops. We produced practical insights and lessons addressing the common barriers to working with data and setting out a range of strategies for getting more value from local authority data.

What did we learn?

We distilled the research into a 10-step guide to data-led innovation in local government:

1. Start with a clear problem to be solved, for which data can offer impactful and actionable insight.
2. Gauge the level of support for data-led work in senior leadership and work to convince them of the importance of the project.
3. Start small, engage with end-users to find out where and how data could be used to make their day-to-day work easier. 4.
4. Be clear about ultimate objectives and how these will be measured.
5. Ensure there are realistic financial and staff resources allocated to the project.
6. Approach the work through a series of short, repeatable work cycles, which enable rapid development, testing and iteration.
7. Secure dedicated expertise for information governance and be specific about the purposes of sharing data. 8.
8. Test the product with end-users and take on board their feedback.
9. Be receptive to making decisions informed by data.
10. Evaluate the overall impact of the work against the original objectives.