Challenge
Since 2006 Skills for Care has been collecting and analysing data on the thousands of adult social care workforce across England. Providing data to the Department of Health and Social Care helps better inform the decisions made by government when it comes to the care sector.
Many of their data analysis processes were manual, taking considerable time and not maximising data-driven insights. We worked with the team to transform their data processes to better provide intelligence quickly and accurately.
Our approach
We implemented the Amazon Web Services (AWS) data ecosystem. This helped us modernise Skills for Care’s data architecture and processing. The goal was to streamline processes, automate routine analysis and free up valuable time.
Working with the analytics team we helped put in place best practices in data engineering. We also worked with them to apply machine learning to improve their workforce estimates.
The next stage was to help Skills for Care identify what was needed to continue to develop this new data pipeline long after we left. They’re now fully prepared to continue to build their pipeline as new data becomes available.
The data engineering prototype developed in this project is the first step toward a new way of working. It will help us to report more frequently and accurately to support faster and better decision-making.
Will Fenton
Analytics Manager
Results we achieved together
Crucially the team can now import data from many data sources automatically. This has helped enrich their data, fill data gaps and review at a more granular level – improving data quality. Now that there’s no need to manually process data that team are saving time and effort. Analysis that used to take 2 days can be done in 15 minutes and the datasets available for review are also extended from one to 10 years. Data visualisations can now also better inform analysis, making it much easier to see trends over time.
For Skills for Care it’s now increasingly quick and easy to test new ideas. They are better prepared to identify and fix issues earlier when data changes outside of the organisation happen.
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