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Enhancing Data Efficiency in Higher Education: The HEFCE Project Insights

The Data Efficiency Project, led by HEFCE and HESA, aimed to understand and reduce the data-related burdens faced by higher education institutions during the HESA Staff and Student returns. Funded by various educational bodies and executed by PricewaterhouseCoopers, the project identified operational barriers and proposed improvements. Key findings revealed the necessity of good governance, technology alignment, and staff training. The resulting Road Map serves as a guide for institutions to enhance data practices and alleviate burdens related to data collection.

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Enhancing Data Efficiency in Higher Education: The HEFCE Project Insights

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  1. The Data Efficiency Project Richard Puttock Head of Data and Management Information, HEFCE Andy Youell Director of Quality and Development, HESA SROC Bradford 2009-04-07

  2. The Data Efficiency Project • Funded by HEFCE, SFC, HEFCW, TDA and HESA • Undertaken by PriceWaterhouseCoopers • Report published on HEFCE web-site (Circular letter 28/2008)

  3. Objectives • To understand the data-related burdens and operational barriers that arise in institutions in delivering the HESA Staff and Student returns; • To identify and/or develop improvements to the existing process; • To produce a Road Map to implement the improvements identified; and • [To gather data on the nature of the additional burdens involved in data returns for Atypical staff]

  4. 80 70 60 50 40 Number of institutions 30 20 10 0 1 7 14 21 28 4 11 18 25 2 9 16 23 30 6 13 Aug Aug Aug Aug Aug Sep Sep Sep Sep Oct Oct Oct Oct Oct Nov Nov INSERT COMMIT CREDIBLE

  5. Key findings (1) • Good HEI engagement • Similar processes, different systems • Agreed good practice • Patchy existence of good practice

  6. Key findings (2) • Good practice is generally in HEIs hands • Numerous opportunities for better practice • Better practice is generally in sponsoring agencies' hands

  7. Sources of burden - student data (1) • Adapting to changes in the collection requirements • Correcting errors in the original data • Waiting for/correcting errors in the *J file • Meeting the return deadline within resource constraints

  8. Sources of burden - student data (2) • Entering the data into the system at the start of the process • Overcoming resistance to the process from colleagues • Aligning new IT systems with the return requirements in order to meet the deadline

  9. Sources of burden - student data (3) • Obtaining correct data from students during the process • Training new staff in time to meet the return deadline • Aligning HEI data structures with the HESA data structures

  10. Good practice (1) • Governance and culture • Senior ownership of data • Understanding data usage • People • Data team personnel • Technology • Technology assessment • Live quality assurance

  11. Good practice (2) • Process • Timely quality checking • Managing change • Supporting documentation • HESA protocol • Data • Single data view

  12. Next steps • Implementation group • Possible quick wins

  13. Implementation group • Chair – Nigel Thrift, VC Warwick • Members – • SROC • ARC • Others - AHUA, AUA planners forum, UPA, UCISA, JISC, HESA, funding bodies • Schedule

  14. Possible quick wins • Star J and UCAS liaison (A04 & A19) • Stability of student returns (A02) • Clarifying why “we” want data (A06) • Integration of HEFCE web-facility (A13)

  15. Finding the report • www.hefce.ac.uk • Publications • Research and Evaluation • 2008 • Or http://www.hefce.ac.uk/pubs/rdreports/2008/rd19_08/

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