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#IWMW17 #A7
The Sixty Minute (Data Dashboard)
Makeover – in 1 hour 30 minutes!
Marieke Guy, QAA
Jon Rathmill, University of Kent
Tuesday 11th July 2017
16:00 – 17:30

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#IWMW17 #A7
Introductions

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• Data analyst at Quality Assurance Agency for higher
education (QAA)
• QAA mission is to safeguard standards and improved
the quality of UK higher education, wherever it is
delivered in the world
• Does this by:
 delivering elements of revised operation model for quality assessment
 managing assessment process for TEF
 regulating Access to HE qualification
 maintaining UK Quality Code
 advising on degree awarding powers
 carrying out review of Alternative Providers
 Strategic international work (TNE)
Marieke Guy - QAA

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Jon Rathmill - University of Kent
• Planning Analyst in the Planning and Business
Information Office (PBIO)
• Previously worked in FE college MI department
• PBIO is responsible for the provision of management
information, both external and internal, on the
complete range of student academic activity.
• Migrating to use of Qlikview to display data and
statistics.
 Allows users to filter and shape reports to meet their needs
 Quicker to disseminate data
 Visualisations improve understanding
 Standardised design across dashboards to help users
 Allows greater data control – only certain users see certain things

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Overview of the Workshop
Time Session
Introduction session
16:00 – 16:30 Data in Higher Education (MG & JR)
Practical session: Sixty second dashboards
16:30 – 16:45 User stories (All)
16:45 – 17:00 Data sources (All)
17:00 – 17:20 Designing a dashboard (All)
Show and tell and feedback
17:20 – 17:30 Delegates present their dashboard (All)

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Data in Higher
Education

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Data Landscape

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• DLHE – Destination of Leavers in HE
• NSS – National Student survey
• LEO – Longitudinal Education Outcomes
• POLAR – Participation of Local Areas
• PRES – Postgraduate Research Experience Survey
• KIS – Key Information Sets
• HE-BCI – HE Business Community Interaction
• JACs – Joint Academic Coding System
• HECos – HE Classification of Subjects
• CAH – Common Aggregation Hierarchy
• HESA – Higher Education Statistics Agency
• TEF – Teaching Excellence Framework
• REF – Research Excellence Framework
Acronyms…

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“The HE sector has always been a data-rich
sector, and universities generate and use
enormous volumes of data each day.
However, the sector has not yet capitalised
on the enormous opportunities presented by
the data revolution, and is lagging behind
other sectors in this area.”
From Bricks to Clicks report, Higher education Commission

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• Data everywhere in the HE sector:
 Collection by HESA and through surveys
 Use in TEF, REF, league tables
• Data collection/use is (to some extent) in hand
(HEDIIP/data Futures) but data analysis isn’t
• Data ownership and management is slowly evolving
• Data seeping in to all aspects of HE provision and
decision making: programme design, retention, WP,
learning analytics
• Many staff concerned about their data capabilities
• Agencies need to work together (Bell review)
Data in HE

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“A business intelligence dashboard (BI
dashboard) is a software interface that
provides preconfigured or customer defined
metrics, statistics, insights and visualisation
into current data.
It allows users to view instant results into
the live performance state of business or
data analytics.”
Technopedia

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Where the Work is - IPPR
http://wheretheworkis.org/ - Institute for Public Policy Research

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Some Unis doing really well…
Liverpool HopeDe Montfort
Gloucestershire

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Business Intelligence
Analytics Lab Project

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• HESA data:
 Summary data from the HESA Student, Staff and Finance submissions
 HE Business and Community Interaction (HE-BCI), Estates Management
and Destinations collections
 Performance Indicators, Student Staff Ratios, Aggregate Offshore Record
(AOR)
• Heidi - web-based management information
service developed for accessing, extracting and
manipulating data
• Heidi plus – New BI service, more granularity,
more visualisation opportunities
, Heidi and

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• National analytics experimentation project led by
Jisc and HESA
• Aim to refresh Heidi Plus content with insights from
a wide range of alternative data sources
• Teams comprise staff from multiple institutions
• Teams themed – agile approach
• Working towards ‘proof of concept’ dashboards
• These dashboards will be further developed by
HESA
• 130 people, 70 institutions, on 6th round
BI Analytics Labs

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Labs Overview

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Team member Institution
Tom Wale University of Oxford (Product owner )
Jon Rathmill University of Kent
Carolyn Deeming Plymouth University
Marieke Guy QAA
Elena Hristozova University of Nottingham
Myles Danson Jisc (Scrum Master)
Kris Popat Cetis (Data Wrangler)
Neil Richards HESA/Jisc (Data viz)
Team Tom

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• Employability (including TEF planner)
• Staff
• Market insights
• Library resources
• Finance
• FE – in particular manufacturing and how it links to
local FE colleges
• Research
Theme areas

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Research Student Experience
I want to: Understand the progress and
completion of my research students,
and the issues that they face
So that I can: Ensure that my institution
can improve its research student
experience and improve the proportion
graduating, and how this compares with
others
Brexit and International Exposure
I want to: Understand my exposure to the EU
by subject for research in terms of research
students, staff and research income
So that I can: Understand what gaps I might
have should European students, staff and
income dry up, and whether this is particularly
different from other institutions
Preparation for REF
I want to: Understand how my
institution’s activity since REF14
contributes to REF21
So that I can: Understand how my
institution’s performance compares
with others operating in the same
subject areas
User stories

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• Low shelf data
 Publicly available & openly licensed
 Vast, distributed, no common vocabulary, complex
 May be patchy
 Not designed to be combined with other data
 Examples include demographic, geo-spatial, international, census
• High shelf data
 Closed licensing - available by subscription or is locked to third
party organisations
 Examples include funding and regulatory, local councils, Government
bodies, fees and admissions, careers and trajectory, current study data,
staff, research, financial, estates or even institutions themselves
Low shelf vs high shelf data

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• HESA Student
• HESA Staff
• HESA Finance
• HEA – PRES data
• Eurostats
http://ec.europa.eu/eurostat/web/education-and-
training/data/database
• EU Community Research and Development
Information Service (CORDIS)
• RCUK – funding awarded – Gateway to Research
Data sources used

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BI Labs
Dashboards

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Kent Qlikview
Dashboards

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Practical session –
Designing dashboards

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Practical:
User stories (All)

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• Brainstorm possible themes
• Decide on a theme to be explored
• Craft up to three user stories for your dashboard
using the user stories template
In your groups

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User Stories
• As a: <user>
• When: <context>
• I want to: <feature>
• So I can: <benefit>

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Practical:
Data Sources (All)

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• Brainstorm HESA data fields that may be useful
(student, staff, finance, other)
• Brainstorm possible external data sources to use
• Have a look at: http://heidi-ckan.dev.jisc-betas.net/
• Think about connections between your data sets
(unique IDs)
• Think about the possible challenges your choices
may pose:
 High shelf vs low shelf data
 Data quality, availability, licence
 Time, cost, date
In your groups

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Practical: Designing
a Dashboard (All)

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• Pick an artist
• Agree on one user story to develop
• Create some sketches of what your dashboard
could look
 Think about potential users
 Consider usability, layout, colour
 Consider filters, searches, titles, legends, navigation
 Sense check that it tells an honest story!
In small groups

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“It’s not good enough for there to
be a looming fear in the sector; we
should have an open forum for
debate about the detail of data, and
the best ways to use it.”
Ant Bagshaw, Wonkhe

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• Make friends:
 Find out who deals with data in your organisation
 Find out what tools they use
 Build up links with them
• Think about data:
 Start thinking about data, visualising data and the complexities of data
 What data do you have? Google analytics, other?
 Are there opportunities for embedding it on your website?
• Watch out for the M5 (Jisc, HESA, QAA) data
conference (3rd November – London)
• Get your staff involved in the BI Analytics Labs
work (Myles.Danson@jisc.ac.uk)
Future activities

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Resources

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• BI Analytics labs website: https://business-intelligence.ac.uk/
• BI Analytics labs data catalogue: http://heidi-ckan.dev.jisc-betas.net/
• Tablea public: https://public.tableau.com/s/
• Tableau HE: https://www.tableau.com/solutions/topic/education-
higher-ed
• The Seven Hats of Visualisation Design: A 2017 Reboot –
Slideshare – by Andy Kirk:
https://www.slideshare.net/visualisingdata/the-seven-hats-of-
visualisation-design-a-2017-reboot
• Neil Richards Tableau Public:
https://public.tableau.com/profile/neil.richards
• Subscribe JISC-HESA-BUSINESS-INTEL’ to listserv@jiscmail.ac.uk
• Twitter @HESA @jisc #hesajiscbi
Useful resources

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• All images from:
 Courtesy of Jisc and HESA
 Pixabay – CC0 - pixabay.com/
 or author’s own
Credits

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qaa.ac.uk
enquiries@qaa.ac.uk
+44 (0) 1452 557000
© The Quality Assurance Agency for Higher Education 2016
Registered charity numbers: 1062746 and SC037786
#IWMW17 #A7
Thank you