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’
Efficient and cost-effective procedures
Best practice
Collaborative approach to data
Resource
Systems that work for the organisation
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 Returns
Fidelma 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 Analytics
University 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
Verification
and Data
Quality
Sign-off
• 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)
• Senior level oversight and final sense check of numbers
• VC involvement
• Understanding of downstream implications of the return
Issues
Workload in ‘peak season’
Reconciliation reports (HESA/HESES)
Ownership
Mis-match of needs (HESA rules v internal
processes)
Strengths
Strong 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)
Using HESA data – the HEIDI API
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
HEIDI > Data Warehouse
• Turning this:
into this:
is relatively slow and painful!
HEIDI API > 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
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 planning
The impact of fee increases on applications
Total applications 05/06 – 11/12
heidi
60,000
50,000
40,000
The Nottingham Trent University
30,000
Sheffield Hallam University
20,000
10,000
0
04/05
05/06
06/07
07/08
08/09
09/10
10/11
11/12
% change of total applications
heidi
20.0
15.0
10.0
5.0
0.0
05/06
06/07
07/08
08/09
09/10
10/11
-5.0
-10.0
-15.0
-20.0
-25.0
The Nottingham Trent University
Sheffield Hallam University
11/12
Total applications by regions
heidi
300,000
250,000
200,000
Total Yorkshire & the Humber
150,000
Total East Midlands
100,000
50,000
0
04/05
05/06
06/07
07/08
08/09
09/10
10/11
11/12
NSS 05/6 results versus 06/7 applications heidi
Student:Staff ratios 2005/06
% change in applications by subject for sector
6
4
Nursing
2
Education
0
-2
-4
Creative arts
-6
-8
Law
-10
Mass communications
-12
Total applications % change 2006/07
2006/07 Subject profile
Nottingham Trent University
Check documentation
Sheffield Hallam University
Performance indicators 2006/07
2005/06 Building condition Total Non-residential - condition A & B
90
80
70
60
50
40
30
20
10
0
The Nottingham Trent University
Sheffield Hallam University
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
Overall satisfaction
90
80
70
60
50
40
30
20
10
0
Nottingham Trent Unviersity
Sheffield Hallam University
Student:Staff ratios
2011/12 Subject profile
Nottingham Trent University
Check documentation
Sheffield Hallam University
Performance indicators 2011/12
• Was 2.3% difference, now 0.2%
100
90
80
70
60
50
40
30
20
10
0
2005/06 Building condition Total Non-Residential - Condition A & B
The Nottingham Trent University
2010/11 Building condition Total Non-Residential - Condition A
Sheffield Hallam University
heidi - % change of total applications
20.0
15.0
10.0
5.0
0.0
05/06
06/07
07/08
08/09
09/10
10/11
-5.0
-10.0
-15.0
-20.0
-25.0
The Nottingham Trent University
Sheffield Hallam University
11/12
heidi - % change of total applications
20.0
15.0
10.0
5.0
0.0
05/06
06/07
07/08
08/09
09/10
10/11
-5.0
-10.0
-15.0
-20.0
-25.0
The Nottingham Trent University
Sheffield Hallam University
11/12
12/13
Aggregation of marginal gains
The power of perception
• …can be influenced by the power of data
http://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
Available to all HEIs
heidi.hesa.ac.uk
Capabilities
Collate, crossreference 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)
Adjust the year or the group from within the report
Notes and
definitions
provided
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
Download to any version of PowerPoint
Adjust the year or
the groups from
within the chart
Use groups and
sub-groups to
compliment
analysis of charts
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
Anthea Beresford – Data Assurance Consultant, HEFCE
May 2013
HEFCE’s Data Assurance Team
activity April 2013 to March 2014
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.
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.
Core roles of the Data Assurance
Team
3 distinct types of activity:
• data audit;
• data verification;
• data reconciliation.
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 (HEBCIS)
• Student Number Control (SNC)
• Equivalent and Lower Qualifications (ELQ)
Data audit – established work
• 2011-12 outturn review of FT UG HEFCE-funded student noncompletion 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 – developmental work
• Research HESA student data exploratory work; Higher
Education Business and Community Interaction Survey (HEBCIS); 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 verification
• 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/dataverifi
cation/
• 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 reconciliation
• 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 (cont.)
• 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/
Finally….
Any questions?
Thank you for listening
a.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
Sponsoring group
Chair of the Programme board
Programme board
Programme
Management Office
SRO/Programme Director
Project
A
Project
B
Project
C
RPG
Chair of the Programme board
Programme board
Programme
Management Office
SRO/Programme Director
Project
A
Project
B
Project
C
RPG
Chair of the Programme board
Programme board
Programme
Management Office
SRO/Programme Director
Project
A
Project
B
Project
C
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/training
e: training@hesa.ac.uk
t:
01242 211472
Follow us on Twitter: @HESATraining