The Early Inclusion Collective will transform the support systems around children to be safety-focused and inclusive from the outset, built through evidence-based practices.
Providing better children‘s services data at lower cost
Since 2020 we have built and supported a community of children‘s services analysts to bring Python data analysis tools to more than 50 local authorities across the UK.
Why are we doing this?
Childrens’ services analysts have a wealth of data available to them to improve their services, but existing tools, such as Excel, that they have available to utilise this data are limited.
Whilst useful for some types of analysis – for example, the number of children using a service – more advanced functions, such as demand forecasting or data validation, can prove difficult.
What we are doing
Over the past two years, we have collaborated with Data to Insight, a community of local authority data professionals, to make Python-based analytical tools available to a wider audience.
To do so has meant applying the multidisciplinary skillsets in Social Finance’s Data + Digital Labs Team – including our technical expertise, community-building experience, and user research methodology – to overcome a number of challenges, as detailed below.
The challenge: How can we deploy a Python-based tool to all 152 local authorities?
These more advanced analytical tools need to be code-based (rather than built in Excel), which has limited the scalability of tools as they require local authorities to either:
- Share sensitive personal data, requiring lengthy information governance processes; or
- Install tools / software locally, involving lengthy IT approval processes
Our solution: Using a software package called Pyodide, we were able to load Python and run code in-browser, but working entirely locally, focusing on standard datasets. This means analysts can access the tool in seconds, like a website, but that no data leaves their machine.
The challenge: How can we ensure ongoing sustainability?
Tools need maintenance and updates (for example when data validation rules are updated, or when bugs are identified). We needed to identify a way to do this which was sustainable within existing local authority funding structures.
Our solution: We upskilled a community of children’s social care data analysts in Python, coached them to be able to code up rules, and built the infrastructure so they can edit and update in future.
The challenge: How do we ensure the tool is responsive to the needs of all 152 local authorities?
Each local authority will have their data stored in slightly different ways, and have different capabilities in accessing and engaging tools. Additionally, each will have different preferences around the analysis and functionality they would find most valuable.
Our solution: We established a replicable four-tier user research methodology to be used to develop and iterate upon new tools:
- At the centre was a core development team of analysts from Social Finance and Data to Insight, responsible for building the tool and setting priorities on a weekly basis. This stable, focused group always had feature design and implementation in their sights. They were also responsible for synthesising the other feedback heard, allowing for rapid turnaround in quick sprints.
- In the next layer were two local authorities who agreed to be regular testers and were reached out to multiple times during development. Their familiarity with the tool became an asset and allowed swift testing of new features.
- Outside this layer were a group of approximately 10 local authorities who each provided user experience feedback once during the process. Their ‘fresh eyes’ perspective provided the core team with regular insight into how the tool would land with its intended audience — Local Authorities who would be seeing the tool for the first time — upon release.
- In the final layer are the 151 local authorities connected to Data to Insight’s distribution network. By embedding development within this network, it should be possible to achieve a rapid take-up of the tool upon launch.
I’ve been using the new 903 data tool, and I just want to say that I think this will be a game changer in terms of data cleaning! Thanks so much to everyone involved in developing it.
Impact and insight
Through this process, we have thus far created two tools that are accessible to all 152 local authorities. We are currently developing more tools.
A free, web-based tool which allows childrens‘ services analysts to quickly see estimates of demand for residential, fostering, and supported accommodation placements up to three years out, and model changes they are considering – such as the creation of in-house provision, or a step-down service
The placement demand modelling tool:
- Automatically calculates the demand forecasts for sufficiency analysis.
- Supports commissioners to secure appropriate budgets for services.
- Helps commissioners to see and communicate the business case for a new or changed service.
A free, web-based tool which allows analysts to check their SSDA 903 data (the statutory return used to report on looked after children) throughout the year and fix errors as they arise.
The cleaning tool:
- Improves data quality.
- Distributes work across the year that is otherwise crammed into a three-month submission window, saving time.