The burden of data returns has been an on-going debate in higher education for many years. Many studies and initiatives on burden have been launched and although steps have been taken to manage and reduce burden, data burden remains a perpetual feature of debate about funding and regulation in UK higher education.
I have been involved many discussions about data burden over the past three decades and I have worked on both sides of HE data collection. In all this time I’ve had a strong sense that data burden is not well defined or consistently understood. We can only make genuine progress on reducing data burden if we can achieve a consensus on what burden is and what drives it.
In this project I set out a model of describing data burden and a number of conclusions that, I hope, will improve the debate about the drivers of data burden and how it can be managed. I seek feedback on the model and on the conclusions. What is right? What is wrong? What is missing?
Please email any comments or questions. I hope to refine and improve the model and publish the results of this work later in the year.
Andy Youell, May 2023
The model of data burden in funding and regulation
- Returns – setup and change
- Returns – data processes
- Returns – submission processes
- Reconciliation with other returns
- Funding and regulatory metrics