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Learn how the Office for Students leveraged Databricks to accelerate insights across millions of student records and enable AI-ready decision making.
8 hours → minutes | Processing time for a 300-million-record data job after moving to Databricks
1/2 day | To complete a student segmentation analysis that previously took two analysts two weeks
The Office for Students regulates more than 400 higher education providers across England and manages data spanning millions of student records over decades. As the scale and complexity of analysis grew, legacy systems could no longer keep pace. By moving to Databricks, the organisation transformed how its teams access, analyse and act on data, dramatically accelerating insight generation while creating a more flexible foundation for AI-driven decision support.
The Office for Students is focused on ensuring a high quality of higher education for all students across England through data-informed regulation that supports the quality, fairness and accountability of the higher education system. The team examines student and provider data, including student outcomes, provider reporting, enrollment patterns, student continuation data and indicators that may signal risks to education quality or student experience across higher education providers.
However, the limitations of a legacy analytics platform had become impossible to work around. Their data team managed data on every student who had touched higher education in England, up to 3 million records per year, drawn from the JISC, Department for Education, Universities and Colleges Admissions Service (UCAS), the Student Loans Company and other sources spanning 15 to 20 years. The system had originally been designed for analysis of quantitative data, but the demands on the organisation had evolved far beyond what the legacy platform could support efficiently.
One of the clearest examples was a data wrangling process used to create the infrastructure for monitoring student outcomes. The workflow processed approximately 300 million records and took 8 hours to complete on the legacy environment. Beyond performance limitations, incorporating unstructured and qualitative data required manual workarounds that slowed analysis and limited the organisation’s ability to work with emerging data sources.