As the UK local government and civic funding space has evolved, grant programmes run by individual boroughs have scaled significantly in complexity. Millions of pounds in public capital are deployed or committed annually across fragmented frameworks. Charities report on their activities, council officers log evaluation metrics in a local spreadsheet, and then sometimes report back to the Mayor’s office to evaluate the programme.

I wasn’t looking at council grant programmes to answer a question as large as “how should grants be designed or evaluated?”. I’m interested in whether these systems can become learning systems — if councils already monitor delivery for the committee, can that data be made usable enough to see patterns across the city and over time?

The programmes themselves have a wide range of approaches:

Funding streams

• Transparent, open-data-driven (e.g. London Councils Pan-London Programme)
• Closed, officer-managed allocations
• Co-designed and participatory budgeting processes (e.g. Brent Voluntary Sector Innovation Fund)

Evaluation models

• Traditional prospective grant monitoring
• Narrative-heavy scrutiny reviews (e.g. Hackney VCS Grants)
• Hyper-local discretionary ward budgets (e.g. Islington Local Initiatives Fund)

This diversity of thought on how to best run, monitor, and evaluate such programmes has led to innovative community-focused outcomes, but it has also deeply fragmented outcome capital data and completely obscured cross-borough transparency.

I think there is insight in connecting these dots. Right now 360Giving captures the award side, but evaluations are fragmented, narrative and siloed by borough. This means you can ask ‘what got funded?’ much more easily than ‘what happened?’. Councils are doing the hard work of monitoring, but the data could be structured to answer bigger questions about decisions across time:

• When the same organisation holds grants in multiple boroughs, do they look healthy in one place and stuck in another?
• Are some themes systematically riskier across London: cost-of-living, mental health, immigration advice, or is that just one borough’s portfolio?
• When an officer leaves, can anyone see the last eight quarters without digging through old packs?

To try and surface that, I built in three phases:

Layer 1: The London Borough Coverage Index. A public index that attempts to map London’s “data capacity gap”. It scores boroughs on data accessibility — awards vs evaluations published, reporting cadence, and join feasibility. (Not all boroughs are scanned currently.)

Layer 2: Pan-London. A single active multi-year funding round — London Councils Grants. Data extracted from quarterly packs into an explorer with insights.

Layer 3: Borough Deep-dive. A single active multi-year funding round — Tower Hamlets’ Mayor’s Community Grants Programme. Manually extracted from quarterly packs into an explorer: 111 projects, ward and theme filters.

The aim of the current version is firstly to show the utility of the data by manually extracting unstructured evaluation notes. The larger aim is to make a case for councils to publish the raw data, so that extracting insights currently buried in committee PDFs can be automated, and interoperability between siloed borough grants tooling can increase. Ultimately, the goal is to make grant administration more efficient for council officers, elected members, and under-resourced grassroots grantees — as well as to make coordination and institutional memory sharing across London’s 32 boroughs easier.