hesa for planners. objectives identify best practice around quality assurance and use of data...
TRANSCRIPT
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HESA for Planners
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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
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“You never finish HESA, you abandon it”
Utilisation of time – ‘opportunity cost’
Best practiceCollaborative approach to data
Efficient and cost-effective procedures
Systems that work for the organisationResource
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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
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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
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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”
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The institutional perspective
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A Planning Perspective on HESA ReturnsFidelma Hannah, Director of Planning
Loughborough University
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Overview
Responsibility for completing HESA returns lies with relevant sections of the University but Planning has the role of:
co-ordinating the returns ensuring appropriate governance, data assurance and consistency
between all HESA returns disseminating HESA data across the University
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Co-ordination
The Planning Office produces a schedule of Statutory Returns listing all HESA, HEFCE and other Funding Agency returns, identifying: Submission dates Ways in which data is used Process for completion Independent checking and sign-off process
The Planning & Finance Offices are accountable to the Vice-Chancellor and Audit Committee for the verification and accuracy of the data returns.
The Planning Office liaises with all relevant sections of the University to ensure that returns are completed, checked and signed off in accordance with the schedule.
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Co-ordination, contd.
Planning is: Involved most directly with preparation and checking of HESA student
return
BUT Has a significant and increasing input into the processes used for other
HESA returns
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Responsibility for Completion of HESA Returns
Student Return – Student Office, Academic Registry Staff Return – Human Resources Finance Return – Finance Office HEBCI – Enterprise Office/Planning Office Destination of Leavers - Careers Estates Management Statistics – Facilities Management Institutional Return – Planning Office
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Other Student - Related Returns
HESES TRAC OFFA Teaching Agency Skills Funding Agency Education Funding Agency REF
All of these returns incorporate HESA data
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Governance and the role of Audit Committee
The University’s Audit Committee must provide assurance about the management and quality assurance of data provided to HEFCE, the Higher Education Statistics Agency (HESA) and other public bodies.
This is a requirement of the HEFCE Financial Memorandum and Accountability and Audit Code of Practice introduced on 1 August 2008.
Audit Committee reviews the schedule of statutory returns annually and also receives regular reports from internal and HEFCE auditors on the various returns.
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Data Assurance – in year
Planning: Liaises closely with Student Office during preparation of HESES return as
this helps to ensure data quality in year
Co-ordinates monthly Data Management Group meetings Membership : IT Services, Planning, Student Office, Research Student Office,
Careers and Admissions
Reviews the funding and monitoring data produced by HEFCE after HESA return has been submitted
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Data Assurance during HESA preparation
Planning: Maintains regular contact with Student Office during preparation of HESA
return Uses the HEFCE recreation files extensively to check data quality before
HESA student return is finally committed (This includes detailed examination of individualised student files)
Undertakes a comprehensive review of check documentation at commit stage with cross-checking by Finance Office
Retains comprehensive records and an audit trail of the checking processes
Joins the briefing meeting with VC before sign-off
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Consistency across HESA Returns
Vital to ensure that data is consistent across HESA Staff, Student and Finance returns because data will be combined
Important to align JACS, Cost Centres and UOAs Implications for subject mapping must be considered Implications for funding must be considered ,e.g. JACS codes and cost
centres both used to determine additional funding for very high cost subjects
Added complexity of Key Information Sets
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Disseminating Bench-marking Data and Comparisons
Use of HEIDI to generate bench-marking data at subject level including: Student: Staff Ratios NSS Employability Degree Classifications International/UK/EU students Completion rates
Production of institutional profile data such as: Student profile Income & Expenditure profile Cost Centre profile
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Final Comments
Understanding HESA data is becoming even more critical in current HE environment
Ensuring the accuracy of HESA data is important for future funding streams (SNC monitoring, additional funding for high-cost subjects, WP indicators, etc.)
Effort invested will make future income streams more reliable, avoiding claw-back in later years.
BUT Complexity and cross-checking is increasing demands on Universities.
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HESA – Living and Learning
Becs Lambert
Senior Assistant Registrar Strategic Planning and AnalyticsUniversity of Warwick
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Outline
1. The Warwick context
2. Warwick’s HESA process
3. Sign-off, Verification and Quality Assurance
4. Issues
5. Positives
6. Challenges moving forward
7. Using HESA data – the HEIDI API
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My context…
Maternity leave cover
Planning: responsibility for Enrolment, HESES, HESA, KIS, student numbers…
…October crunch point for key Planning activities
Student reporting and HESA experience = HEIDI (basically nil)
= …baptism of fire
+
+
+
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Warwick context…
Student number related returns (HESA, HESES, KIS)
Located in the Deputy Registrar’s Office, but close liaison with Academic Registry re: data input, quality, implications.
One key member of staff (data input, liaison, query resolution, data quality management, Minerva, etc. etc.)
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The HESA process- Warwick SITS update schedule (positives and negatives)
- Prep and housekeeping from April – address learning points from previous year,
implement procedures for HESA changes, check ‘usual suspects’
- Strong use of validation kit to identify issues
- Use of internal access databases to cross-check HESA return data and ensure
comprehensive data checks
- Aim for early as possible submit/commit schedule to front load schema and
business validation issues
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Sign-off, verification and data quality
• Validation kit is a good early prep tool (though limitations)• Check docs and Minerva are key tools (post markers for further
internal analysis)• Two year historical comparison of return data – explain or check • Student level data checks (targeted)• Scrutiny of check docs by Assistant Registrar (close to student data,
highlight potential issues, discrepancies)
Verification and Data Quality
• Senior level oversight and final sense check of numbers• VC involvement• Understanding of downstream implications of the return
Sign-off
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Issues
Workload in ‘peak season’
Reconciliation reports (HESA/HESES)
Ownership
Mis-match of needs (HESA rules v internal processes)
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StrengthsStrong HESA and institutional expertise (also a
negative??)
Collegial spirit
Established and clear process for generation, checking, verification and submission of HESA return
Strong data quality focus throughout the year given BI focus of office
Minerva
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Challenges moving forward
Look to be less reactive to HESA data quality issues
More structure understanding of HESA implications and responsibilities across data owning departments
Increased use of FAMD docs
Re-vamp of process documentation (Business Continuity)
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Before…
• Flexible report writing with drag and drop interface for usability but can be slow to build large reports
• Direct output to Excel or XML file
• Limited to 125 columns for extracts (eg. Finance Table 5b has 490 columns of data times 3 years = 1470 columns = 12 separate extract files)
• People like cross tabular reports, but data warehouses need flat data files so the extracts need to be transformed prior to loading
• Our data transformation was based on a VBA script in Excel, but needed to be customised for each extract (different numbers and column groupings)
• Depending on the extract size many files may need to be processed and concatenated together
Using HESA data – the HEIDI API
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• Turning this:
into this:
is relatively slow and painful!
HEIDI > Data Warehouse
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• API permits rapid extraction of large volumes of data in warehouse-friendly format
• Based on standard web services technology
• Difficult to use and requires specialist technical skills but very powerful and fast
• Generate a custom url to produce a response (eg.
https://heidi.hesa.ac.uk/api/1.0/datareport?rowtype=3297&year=61422&domain=3311&valuetype=4008&field=61432
produces a report of UCAS Accepted Applicants for 2011/12 by Institution and Gender)
• Extracts produced as single files containing data and metadata (field descriptions)
• Simple direct loading into warehouse
• XML shredded (transformed) into data tables using the native query language capabilities
HEIDI API > Data warehouse
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• Lessons Learnt:
• API is not a “magic bullet” but is a useful additional tool
• Harvesting HESA data for BI analysis now down from days to hours, but specialist skills and knowledge still required
• Current API needs simplifying and extending to allow multi-year and multi-value extracts
• Next steps for Warwick:
• Use of the API still requires a number of steps – plan is to more tightly integrate the extract and loading of data using SQL Server Integration Services
• Provision of standardised self-service reporting capability for power users to extract and analyse HESA data contained in the warehouse
HEIDI API > Data warehouse
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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
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Using check documentation
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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
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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
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Task
1. In your groups, or individually, complete check documentation tasks 1-4
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How can check doc be used?
• Use the check documentation guide produced by HESA as a starting point
• Many of the items provide year on year comparisons:
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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
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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
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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’….
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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
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Item 2a - Qualifications awarded
What is the difference between 2 and 2a?Item 2 Item 2a
Shows the qualifications awarded to students in the format that will be published in the student volume
Displays the year on year differences using the qualifiers field of XQLEV501 (including the split out of PGCE and Post grad cert in Education.
Used to check that the qualification awarded are, in the main, those which they were aiming for
More consistent with the SFR variance figures
Item 2a - Qualifications obtained by students on HE course by level of qualification obtained and mode of study (2012/13 and 2011/12)
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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
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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
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Other reports
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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
Data submitted
Quality assurance
Queries raised
Queries resolved
Sign-off
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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:
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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
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Using downloadable files
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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
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Using downloadable files
• The files should be utilised to:- Carryout additional DQ checks- Benchmarking- Planning/forecasting- Improve efficiency (recreating data from scratch
unnecessary)
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League tables
• Student staff ratios by institution and cost centre • First degree (full-time for Guardian) qualifiers by institution and league table subject
group• Average total tariff scores on entry for first year, first degree students by institution
and league table subject group. • Data is restricted to tariffable qualifications on entry (QUALENT3 = P41, P42, P46,
P47, P50, P51, P53, P62, P63, P64, P65, P68, P80, P91) (Times applies ‘under 21’ restriction, Guardian applies ‘full-time’ restriction)
• Full-time, first degree, UK domiciled leavers by Institution, League table subject, Activity
• Full-time, first degree, UK domiciled leavers entering employment• Graduate employment/Non graduate employment/Unknown• Positive destinations /Negative destinations • Expenditure on academic departments (Guardian)• Expenditure on academic services (Guardian, Times, CUG)• Expenditure on staff and student facilities (Times, CUG)
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Who cares? Why bother?
• …because you can’t afford not to care• In what space does student recruitment take place?• Extended coverage…• Subject based…• Supply and demand are linked to measures of quality• Internationalisation of Higher Education
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But be aware of the tail wagging the dog…
• Collectively we can become obsessed about specific measures…
• …and dangerously on the wrong type of measures…• …and instead of good (or accurate) ranking being
born out of doing your day to day business well, it becomes fuelled by quick fixes
• Measurement culture tends to trade long-term value for short-term gains…
• …this holds true in ‘data world’
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But there are gains to be sought – both in terms of quality and benchmarking“Firstly, you need a team with the skills and motivation to succeed. Secondly, you need to understand what you want to achieve.Thirdly, you need to understand where you are now.Then, understand ‘aggregation of marginal gains’. Put simply….how small improvements in a number of different aspects of what we do can have a huge impact to the overall performance of the team.”
Dave Brailsford, Performance Director of British Cycling
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But there are gains to be sought – both in terms of quality and benchmarking“Firstly, you need a team with the skills and motivation to succeed. Secondly, you need to understand what you want to achieve.Thirdly, you need to understand where you are now.Then, understand ‘aggregation of marginal gains’. Put simply….how small improvements in a number of different aspects of what we do can have a huge impact to the overall performance of the team.”
Dave Brailsford, Performance Director of British Cycling
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Is there a correlation between this spread and league table positioning?
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Before you begin…
• Remember the different populations• Use derived fields (those beginning with an X!)• The INSTCAMP field can be used to better analyse
and understand your data
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Demonstration…
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Performance indicators
• The PI tables (available from the HESA website) give sector wide data on:
- Non-continuation rates- Widening participation of under-represented groups
and those in receipt of DSA- Research output- Employment of leavers• Included are benchmarks and definitions
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DDS
• http://www.hesa.ac.uk/content/view/2664
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Scenario planningThe impact of fee increases on applications
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Total applications 05/06 – 11/12
04/05 05/06 06/07 07/08 08/09 09/10 10/11 11/120
10,000
20,000
30,000
40,000
50,000
60,000
The Nottingham Trent UniversitySheffield Hallam University
heidi
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% change of total applications
05/06 06/07 07/08 08/09 09/10 10/11 11/12
-25.0
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
The Nottingham Trent University Sheffield Hallam University
heidi
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Total applications by regions
04/05 05/06 06/07 07/08 08/09 09/10 10/11 11/120
50,000
100,000
150,000
200,000
250,000
300,000
Total Yorkshire & the HumberTotal East Midlands
heidi
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NSS 05/6 results versus 06/7 applications heidi
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Student:Staff ratios 2005/06
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% change in applications by subject for sector
-12
-10
-8
-6
-4
-2
0
2
4
6
Total applications % change 2006/07
Nursing
Mass communications
Creative arts
Education
Law
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2006/07 Subject profile
Nottingham Trent University Sheffield Hallam University
Check documentation
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Performance indicators 2006/07
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The Nottingham Trent University Sheffield Hallam University0
10
20
30
40
50
60
70
80
90
2005/06 Building condition Total Non-residential - condition A & B
2005/06 Building condition Total Non-residential - condition A & B
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What we ‘know’…
- Some subject areas are more price elastic than others?
- Applicants take note of NSS?- Condition of the estate matters to applicants? - Some socio-economic groups are more affected by
fee increases than others• Each of these variables might have a value of x
number of applicants
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What we don’t know…
• …but could scenario plan for…- Future government policy on HE funding- Social/cultural/economic impact- The power of perception
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NSS 2011/12
Nottingham Trent Unviersity Sheffield Hallam University0
10
20
30
40
50
60
70
80
90
Overall satisfaction
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Student:Staff ratios
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2011/12 Subject profile
Nottingham Trent University Sheffield Hallam University
Check documentation
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Performance indicators 2011/12
• Was 2.3% difference, now 0.2%
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2005/06 Build
ing condition To
tal Non-Resi
dential
- Condition A &
B
2010/11 Build
ing condition To
tal Non-Resi
dential
- Condition A
0
10
20
30
40
50
60
70
80
90
100
The Nottingham Trent University Sheffield Hallam University
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heidi - % change of total applications
05/06 06/07 07/08 08/09 09/10 10/11 11/12
-25.0
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
The Nottingham Trent University Sheffield Hallam University
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heidi - % change of total applications
05/06 06/07 07/08 08/09 09/10 10/11 11/12 12/13
-25.0
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
The Nottingham Trent University Sheffield Hallam University
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Aggregation of marginal gains
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The power of perception
• …can be influenced by the power of datahttp://www.youtube.com/watch?v=ZWTJ_TPraLQ
• If you don’t like what they’re saying, change the conversation…
• …what data are you using on the website and is it the right data?
• …repositioning - find what you are good at and sell it (both internally and externally)….
• …but never neglect what you need to improve
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`
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Higher Education Information Database for Institutions
Estates Destinations
Finance HE-BCI
Student Staff
Applications Equality
National student survey
Derived statistics
heidi.hesa.ac.uk
Available to all HEIs
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Capabilities
Collate, cross-reference and interpret
information
View, create and export reports, charts
and custom tabulations
Generate aggregations, ratios and percentages
Benchmark the performance of your
institution against others
Use heidi data within your own business
intelligence software
Embed heidi reports and charts into your
own website(s)
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Notes and definitions provided
Adjust the year or the group from within the report
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Sharing
• New to heidi• Allows reports and
charts to be shared with others – Including non-heidi users
• Share by email using the ‘send to’ link
• Use the HTML code to embed reports or charts into websites
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Use groups and sub-groups to compliment
analysis of charts
Adjust the year or the groups from within the chart
Download to any version of PowerPoint
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Charts
New Radar Chart
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Application Programming Interface
• Does your HEI have it’s own data warehouse or BI system?
• Use API to specify wanted heidi data and retrieve in a usable format
• API is aimed at users familiar with writing and understand programming code. HESA can provide support to colleagues involved with API at your institution
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The future
Re-designed DLHE data set
Federated user accounts
Benchmarking functionality
We are always keen to hear feedback, especially ideas and suggestions for
future release of heidi. Please send any
comments to [email protected]
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Sign up
heidi account• Contact your Local
Administrator for access to heidi – Expert account allows
access to all features– Standard account allows
access on a view mode
JISC • Mailing service which allows
heidi users to make new contacts, ask questions and share knowledge and best practice
www.jiscmail.ac.uk
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Welcome slide
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HESA for Planners (Student Record) Seminar
May 2013
Anthea Beresford – Data Assurance Consultant, HEFCE
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The aim of this session is to advise you of:• areas of HEFCE’s Data Assurance Team’s coverage from April 2013
to March 2014.
HEFCE’s Data Assurance Team activity April 2013 to March 2014
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Data assurance activity of the Data Assurance Team• team of 3, supplemented by a consultant;
• we are part of the overall data assurance framework;
• annual audit plan agreed by our Funding Round Process Board (internal Executive oversight) and our Audit Committee to which we report regularly and provide an annual report on audit outcomes;
• ever changing activity, for example, as funding rules change and new areas become important;
• interested in both funding and non-funding issues with data.
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3 distinct types of activity:
• data audit;
• data verification;
• data reconciliation.
Core roles of the Data Assurance Team
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Areas identified for attention from April 2013 to March 2014• Student funding data work:
• HESA student data verification work pre-sign-off;
• HESES verification work pre-sign-off;
• 2011-12 outturn review of FT UG HEFCE-funded student non-completion rates.
• Research Funding data work:
• Research income from Charities;
• Research income from Business;
• Research HESA student data exploratory work.
• Key Information Set (KIS) 2013/14
• Destination of Leavers from Higher Education (DLHE) 2011/12
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Areas identified for attention from April 2013 to March 2014 (cont.)• National Scholarship Programme (NSP)
• 2011-12 Funding and Monitoring Data (FAMD) (reconciliation) exercise
• BIS Service Level Agreement work:
• Access to Learning Fund (ALF).
• Higher Education Business and Community Interaction Survey (HE-BCIS)
• Student Number Control (SNC)
• Equivalent and Lower Qualifications (ELQ)
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• 2011-12 outturn review of FT UG HEFCE-funded student non-completion rates:• desk based review of a 5% random sample of FT UG HEFCE-funded students.
• National Scholarship Programme (NSP):• desk based request for explanations of differences between HESES11, HESA
2011-12 and HESES12 new entrant student numbers.
• Research income from Charities and Business; Key Information Set (KIS) 2013/14; Destination of Leavers from Higher Education (DLHE) 2011/12; Access to Learning Fund (ALF):• Pre-audit visit review of data; on-site visit; post visit follow-up; issue of audit
report with recommendations; completed action plan for approval; closure of audit; implementation of funding adjustments following the Appeals process, where relevant.
Data audit – established work
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• Current audit programmes can be found at:http://www.hefce.ac.uk/whatwedo/invest/institns/funddataaudit/dataaudit/
• Current audit reports can also be at that link. Note the new KIS 2012/13 report.
Data audit – established work
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• Research HESA student data exploratory work; Higher Education Business and Community Interaction Survey (HE-BCIS); Student Number Control (SNC) and Equivalent and Lower Qualifications (ELQ):• Pre-audit visit review of data; on-site visit; post visit follow-up; issue of audit
report with recommendations; completed action plan for approval; closure of audit; implementation of funding adjustments following the Appeals process, where relevant.
Data audit – developmental work
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• HESA student data verification work pre-sign-off:• Desk based;
• Working in conjunction with HESA;
• Querying institutions on their data during the student data collection period to assist institutions in identifying potential data issues for correction before sign-off;
• We will publish guidance nearer the time at:
http://www.hefce.ac.uk/whatwedo/invest/institns/funddataaudit/dataverification/
• HESES verification work pre-sign-off:• Desk based;
• Working with institutions between initial submission and sign-off, obtaining explanations for data differences or changes to data.
Data verification
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• Reconciliation between HESES11 and HESES11 re-creation based on HESA 2011-12 data:• Desk based;
• Thresholds for selection;
• 2 stage process this year due to students with undetermined completion status (FUNDCOMP=3). The ‘Completion Status Survey’ is currently underway where we are asking institutions to update their completion status information for those who were returned as FUNDCOMP=3 in their 2011-12 return. The deadline for sign-off for this is 19 July 2013. We have therefore selected institutions who currently break the selection criteria thresholds. All institutions will be looked at again following submission of the Completion Status Survey;
• Gain explanations of differences;
• Action plan and amendment of data as necessary;
• Formal sign-off by institution;
• Implementation of any funding adjustment following an Appeals process.
Data reconciliation
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• The link to guidance on our website concerning this area of activity can be found at:http://www.hefce.ac.uk/whatwedo/reg/assurance/datareconciliations/
Data reconciliation (cont.)
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Any questions?
Finally….
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Thank you for [email protected]
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The HE information landscape
Update
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Recommendations to RPG
• Governance for data and information exchange across the sector
• Development of a common data language– Data model, lexicon, thesaurus
• Inventory of data collections• Specific data standards work
– JACS– Unique Learner Number
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2. Data model, lexicon and thesaurus
• Review of existing collections/definitions – the as is• Better understanding of differences/similarities
– In definitions– In terminology
• Coming from both angles:– What are collectors asking for? – What are institutions supplying?
• To inform future standardisation and data sharing discussion• Deliverables:
– Data model, lexicon and thesaurus– Maintenance plan
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3. Inventory of data collections
• HEBRG survey identified 550 lines of reporting• Very little detail (width)• Is it complete? (length)• We need a solid understanding of the current burden
– To help HEIs become more joined up in their reporting– To challenge data collectors to reduce duplication
• Deliverables:– Database of collections– Maintenance plan
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4a JACS development
• Problems:– JACS could have far broader use– Current structure has run out of space
• Analysis of requirements• Exploration of coding options• Deliverable:
– Road map for future development
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4b ULN implementation
• ULN widely accepted as a Good Thing– Reducing burden by replacing existing IDs– Adding value through better data linking/sharing
• What are the real barriers to adoption?• What would it take to resolve these issues?• Deliverable
– An assessment of where we currently are with ULN– Commitment to a roadmap?
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Governance?
• What will it do?
• What authority does it have to progress actions?
• How will the work be delivered and coordinated?
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Proposal
• A programme of work…• …made up of specific projects…• …overseen by a Programme Board…• …and reporting to a Sponsoring Group
• Utilising best practice from Managing Successful Programmes
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Programme board
SRO/Programme Director
Chair of the Programme board
Sponsoring group
Project A
Project B
Project C
Programme Management Office
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Programme board
SRO/Programme Director
Chair of the Programme board
RPG
Project A
Project B
Project C
Programme Management Office
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Programme board
SRO/Programme Director
Chair of the Programme board
RPG
Project A
Project B
Project C
Programme Management Office
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www.hediip.ac.uk @hediip
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HEDIIP
• Enhance the arrangements for the collection, sharing and dissemination of data and information
• Programme management office based at HESA• Publishing and maintaining the inventory of data
collections• Carry forward the established projects
– Common data language– Replacement for JACS– Implementation of the ULN
• Other strategic developments
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Keep in touch
If you require additional training help, including bespoke visits to your institution, get in touch with the training department…w: www.hesa.ac.uk/traininge: [email protected]: 01242 211472
Follow us on Twitter: @HESATraining