HESA for Planners

1. HESA for Planners

2. Objectives • Identify best practice around quality assurance and use of data • Improve our understanding of check documentation and how it can be utilised • Introduce the downloadable files and how they can be used • Outline the future information landscape • Better understand the IRIS outputs • Learn from each other

4. “You never finish HESA, you abandon it” Utilisation of time – ‘opportunity cost’ Efficient and cost-effective procedures Best practice Collaborative approach to data Resource Systems that work for the organisation

5. How to be good… • Data ownership: • Systems (storage issues) • People • Translation: • From HEIs internal data language to an external data language • The extent to which these match • The variety of external languages that an HEI has to work with • Documentation • How, who, when • Education • Value of data and transparency

6. Evidence (or anecdote) from the KIS • New requirement – high profile • Data spread across institutions • No documentation • Little/no control • No standardisation/comparability • Variable quality • Variable approaches to storage • …being assembled and managed in spreadsheets

8. Spreadsheets • Often created by people who don’t understand principles of sound data management • Conflate data and algorithms • Almost impossible to QA • Spread and mutate like a virus Search “Ray Panko spreadsheets”

9. The institutional perspective

10. HESA for planners Wednesday, 15 May 2013 Alison Hartrey Head of Planning SOAS, University of London

11. HESPA – what we do • Higher Education Strategic Planners Association • provide a forum & network to discuss shared areas of interest • provide a focus for planning input to statutory bodies etc. • promote awareness and understanding of planning issues • contribute to the professional development of planners • link with planning & associated communities

12. HESPA - governance • Executive drawn from across the sector • includes representation from England, Scotland & Wales • post-1992 and pre-1992 institutions • various mission groups and independent HEIs • working towards a full transition under the PHES umbrella • flat rate membership fee per institution (2013/14) • NATIONAL-PLANNERS-GROUP@jiscmail.ac.uk

13. HESA – planning role at SOAS • Direct responsibility for: • Student Record • Aggregate Offshore Record • Institutional Profile Record • Key Information Set • Oversight of other HESA returns and ability to download check documentation and data supply • Produce NSS data for Ipsos MORI and supply DLHE data to the University of London Careers Service

14. HIN target list and UCAS files • Used when we are adapting our “as delivered” HESA extracts to ensure the correct data is being extracted • UCAS files (*J and level 3 qualifications expansion file) used as a double-check against the qualifications records held in the student records system

15. Check documentation • Used when answering Minerva queries • Used for basic data checks (cost centres, major source of tuition fees) • Used across years for credibility checks • Used for FOI responses

16. Data supply • Used in a variety of institutional reporting to ensure consistency of student population across years – in particular • annual review & financial statements • OFFA/widening participation reporting • equality & diversity reporting • annual programme review updates • FOI requests • …. but we could use it for a lot more

17. IRIS • Another check on cost centres and student load (HESES recreation) • Used for a wide variety of HEFCE checks and HEFCE funding streams - RDP, SNC, TRAC(T), NSP • Tendency to concentrate on just the reports needed at the time due to concentration on the HESA Student Record and the time lines

18. HESA performance indicators in HE in the UK • Underlying data from the HESA performance indicators is written back into the core student record system: • LPN, state school, previous HE, entry qualifications, • SEC, region of domicile, young/mature, mode, level • Used by Planning and the Widening Participation Team for onward analysis and reporting • Allows us to more easily compare whole population and special interest groups as well as assess the impact of initiatives across a time line

19. Embedding HESA into institutional thinking – a rough guideHESA for Planners, May 2013 John Busby, Deputy Director of Corporate Planning

20. The contention • Institutions that embed HESA and HESA requirements in their thinking are more likely to: • submit higher quality data; • extract more value from HESA data to inform their development (benchmarking, business intelligence, …). • be better place to respond where policy impact/funding is derived from HESA data (HEFCE SNC, NSP …) • Thinking = people / resources, skills, policy, standards, processes (and attitude?) • HESA = in the broadest sense (not just Student, and a provider of information as well as a collector)

21. Signs of embedding • Senior Management awareness and interest (depth of involvement in institutional sign off, reporting) • Close working between compilers of HESA Student, HESA Staff and HESA Finance-HEBCIS, other exercises (e.g. KIS, TRAC(T), OFFA, REF), Finance, Planning, HEFCE liaison • Widespread use of HEIDI, purchases of bespoke data by institution or amongst groups of institutions to develop BI • HESA collections seen as a requirement but also an opportunity (institutional reflection, comparative data) • Understanding permeates academic departments / schools / faculties

22. A self-assessment • Last report to most senior governance body on trends from HESA data? Extent of involvement in sign-off? (Senior Management awareness) • Last time HESA Student and Staff compiler met, or met with the TRAC(T) compiler, or REF team? (Close working) • Last time HESA data from HEIDI was warehoused, or a bespoke data set specified and purchased? (Use of data) • Last time collection of a HESA field prompted institutional reflection? (HESA as opportunity) • Last time a member of academic staff was helped to understand HESA data? (Widespread knowledge)

23. Potential obstacles • Pace of change and team workloads • Institutional boundaries between key functional areas (Registry, Planning, Finance, HR, QA function ...) • Disconnect between the business processes on which data quality depend and those that compile returns and interpret derived data • HESA compiler hidden in the ‘broom cupboard’? • Lack of senior management buy-in

24. What may help? • HESA integration or HESA co-ordination group (formal, informal, self-help) • Consider physical location of key systems teams and HESA compilers around Planning / Finance • Promote senior management awareness through: • Development of insightful business intelligence and tools from HESA data (internal reflection and comparator benchmarking) • Emphasise league table impact • Talk to your academic community about your data (social science researchers, ...)

25. Examples of using HESA data (Student)

26. Examples of using HESA data (Student)

27. Examples of using HESA data (benchmarking)

28. Some closing observations • Warehouse the HESA Student data supply tables from the data collection system (helps with interpretation of DDS, derived data exercises from HEFCE, FOIs) • Try the HEIDI API • Look forward to the new Cost Centre structure • Audits and auditors can be extremely useful • Ensure that there is joined up discussion in your institution of the expanded Institutional Profile return • Please support HESPA!

29. Any questions? John Busby, john.busby@york.ac.uk

30. Discussion • Discuss the following as a table: • How good are you at HESA? • Consider factors such as data ownership, documentation, staffing, knowledge, resilience, training, systems, data quality process – how extensive and sophisticated it is. How often you use HESA data and what for and how is the process and data managed/structured internally. What are the barriers and how do you overcome them? • Now consider and rate your own institution: 1st class 2:1 2:2 3rd Unclassified

31. Using check documentation

32. What is check documentation? An Excel workbook which displays the data in a series of tables Used by analysts at HESA for quality assurance Available after any successful test or full commit

33. Why should I use it? • Check documentation gives an overview of the submitted data which can help identify potential issues • Provides context to the queries raised by HESA • The institution will be able to spot anomalies that HESA would not • Comparison feature also useful for later commits/test commits to monitor changes Check doc is one of many reports and is best used in conjunction with other reports

34. Task • In your groups, or individually, complete check documentation tasks 1-4

35. How can check doc be used? • Use the check documentation guide produced by HESAas a starting point • Many of the items provide year on year comparisons:

36. Using check documentation Different populations and groupings are used for each item in the check documentation, including derived fields For 2012/13 the definitions sheet has moved to the coding manual

37. Who are those 5 students?? • To get the most out of check documentation and work out whether something is an error, you need to identify the records behind the table • To do this you can use Data Supply which contains much of the raw data submitted alongside the derived fields used by HESA • Pivot tables can be used to recreate items and identify particular cells

38. Identifying students: • The HESA for Planners manual contains instructions on recreating the populations and conditions used in check documentation • As an example we will recreate item 6a ‘Student cohort analysis’….

39. Check doc changes for 2012/13 • Revised tolerances • Items 1, 2 & 3 will now highlight year on year changes of +/- 10%/50 students • Item 11 will look at sector averages rather than just the previous year • Move to JACS3 and new cost centre coding frame • New Fees tab • More detailed breakdowns, summations and percentage changes added to enable checking

40. Item 2a - Qualifications awarded What is the difference between 2 and 2a?

41. Item 7 – Highest qualifications on entry • Now split into 7a & 7b ‘proportion of highest qualification on entry for first years’ • Subtotals also added to item 7a

42. Item 12 – average instance FTE • This item has been broken down further to provide a three way split of starters, leavers and ‘others’. • The different groups may have very different FTE values that impact the average

43. Other reports

45. Minerva …is the data query database operated by HESA • During data collection HESA (and HEFCE) raise queries through Minerva and institutions answer them • These responses are then reviewed and stored for future use by HESA and the institution

46. Using Minerva for quality assurance • Responses from previous years are retained in the Issue Report • Review targets set for the current year • Queries raised by HESA are prioritised:

47. Contextual Intelligence • At the request of the National Planners Group HESA have formulated a ‘public’ version of Minerva • Designed to give users of the data additional context • HESA has published a query to Minerva to which HEIs can add notes about their institution e.g. ‘we recently opened a new department’ • HESA will not interact with what is added • Will remain open throughout the year • HESA will extract and send the information to accompany data requests

48. Using downloadable files

49. Downloadable files • Data Supply (Core, subject, cost centre, module and qualifications on entry tables) • NSS inclusion (person and subject) and exclusion files • POPDLHE • TQI/UNISTATS • All available after every successful full and test commit

50. Using downloadable files • The files should be utilised to: • Carryout additional DQ checks • Benchmarking • Planning/forecasting • Improve efficiency (recreating data from scratch unnecessary)