Why now

The Student return is moving earlier and becoming more frequent.

The in-year programme changes when data has to be right. Here is what is planned and what the first Data Futures collection taught the sector.

Timeline

From one return a year to two.

  1. 2022/23

    First Data Futures collection

    A new data model and a new submission platform. Many providers submitted late.

  2. 2025/26

    Annual return, earlier sign-off

    The annual return continues, with the sign-off date moving earlier each year.

  3. 2027/28

    In-year collection trialled

    Providers begin submitting data during the academic year, not only after it.

  4. 2028/29

    Two returns a year

    The first mandatory year with an in-year return alongside the annual one.

Based on the in-year programme as currently published by Jisc (HESA). Dates may change; check the latest HESA guidance.

2022/23

What the independent review found.

In January 2025 the Office for Students published an independent review of the Data Futures programme, carried out by PwC. It describes a collection that stretched teams across the sector.

  • 42% of providers missed the extended deadline for their 2022/23 data.
  • One institution saw its error count rise from around 10,000 to 250,000 in a single year.
  • Errors were hard to trace. Teams could not always tell whether a problem came from their own data, the way it was transformed, their records system or the rules themselves.
  • Staff left, took sick leave or retired early.

Source: OfS, Independent review of the Data Futures programme, January 2025.

The root cause

Data is cleaned all year because it was never collected in HESA’s shape.

Student record systems are configured to run admissions, teaching, fees and awards. HESA’s model describes the same students in a different structure, with its own codes, its own relationships between records and its own rules.

The gap is closed by hand each year: extracts, spreadsheets, lookups and local knowledge held by a few people. When the return moves earlier and happens twice a year, that manual work has to happen more often, with less time in between.

For your team

Questions worth asking before the next collection.

How many weeks before the deadline do you first see a full list of errors?
Can you tell whether an error comes from the source data or from how it was transformed?
How much of the process depends on one or two people?
Could you produce the return twice a year with the team you have now?

See your own data in HESA’s shape

We can run a proof of value on an anonymised extract, so you can judge the results against a return you already know.

Talk to us or email askus@lyceumdata.com