HESA for Planners

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

“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

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

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

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”

The institutional perspective

A Planning Perspective on HESA ReturnsFidelma Hannah, Director of Planning

Loughborough University

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

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.

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

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

Other Student - Related Returns

HESES TRAC OFFA Teaching Agency Skills Funding Agency Education Funding Agency REF

All of these returns incorporate HESA data

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.

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

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

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

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

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.

HESA – Living and Learning

Becs Lambert

Senior Assistant Registrar Strategic Planning and AnalyticsUniversity of Warwick

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

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

+

+

+

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.)

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

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

Issues

Workload in ‘peak season’

Reconciliation reports (HESA/HESES)

Ownership

Mis-match of needs (HESA rules v internal processes)

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

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)

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

• Turning this:

into this:

is relatively slow and painful!

HEIDI > Data Warehouse

• 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

• 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

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

Using check documentation

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

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

Task

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

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:

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

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

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’….

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

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)

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

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

Other reports

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

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:

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

Using downloadable files

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

Using downloadable files

• The files should be utilised to:- Carryout additional DQ checks- Benchmarking- Planning/forecasting- Improve efficiency (recreating data from scratch

unnecessary)

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)

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

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’

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

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

Is there a correlation between this spread and league table positioning?

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

Demonstration…

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

DDS

• http://www.hesa.ac.uk/content/view/2664

Scenario planningThe impact of fee increases on applications

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

% 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

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

NSS 05/6 results versus 06/7 applications heidi

Student:Staff ratios 2005/06

% 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

2006/07 Subject profile

Nottingham Trent University Sheffield Hallam University

Check documentation

Performance indicators 2006/07

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

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

What we don’t know…

• …but could scenario plan for…- Future government policy on HE funding- Social/cultural/economic impact- The power of perception

NSS 2011/12

Nottingham Trent Unviersity Sheffield Hallam University0

10

20

30

40

50

60

70

80

90

Overall satisfaction

Student:Staff ratios

2011/12 Subject profile

Nottingham Trent University Sheffield Hallam University

Check documentation

Performance indicators 2011/12

• Was 2.3% difference, now 0.2%

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

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

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

Aggregation of marginal gains

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

• http://www.ucl.ac.uk/about-ucl

`

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

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)

Notes and definitions provided

Adjust the year or the group from within the report

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

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

Charts

New Radar Chart

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

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 heidi@hesa.ac.uk

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

heidi@hesa.ac.uk

JISC • Mailing service which allows

heidi users to make new contacts, ask questions and share knowledge and best practice

www.jiscmail.ac.uk

Welcome slide

HESA for Planners (Student Record) Seminar

May 2013

Anthea Beresford – Data Assurance Consultant, HEFCE

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

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.

3 distinct types of activity:

• data audit;

• data verification;

• data reconciliation.

Core roles of the Data Assurance Team

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

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)

• 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

• 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

• 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

• 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

• 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

• 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.)

Any questions?

Finally….

Thank you for listeninga.beresford@hefce.ac.uk

The HE information landscape

Update

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

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

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

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

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?

Governance?

• What will it do?

• What authority does it have to progress actions?

• How will the work be delivered and coordinated?

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

Programme board

SRO/Programme Director

Chair of the Programme board

Sponsoring group

Project A

Project B

Project C

Programme Management Office

Programme board

SRO/Programme Director

Chair of the Programme board

RPG

Project A

Project B

Project C

Programme Management Office

Programme board

SRO/Programme Director

Chair of the Programme board

RPG

Project A

Project B

Project C

Programme Management Office

www.hediip.ac.uk @hediip

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

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: training@hesa.ac.ukt: 01242 211472

Follow us on Twitter: @HESATraining