50 minute session for Tableau users in Higher Ed in the UK outlining the collaborative work between Jisc and HESA and focusing on the opportunity to join an innovative national Agile Scrum analysis effort to produce Tableau dashboards

1. HESA and Jisc Business Intelligence Project
Myles Danson and Lee Baylis (Jisc) Beth Magovern (HESA)

2. HESA and Jisc
(with HESPA) BI
project overview

3. Jisc is the UK higher, further education
and skills sectors’ not-for-profit organisation
for digital services and solutions
Operate shared digital
infrastructure
and services
Provide trusted advice and
practical assistance for
universities, colleges and
learning providers
We…
Negotiate sector-wide deals
with IT vendors and
commercial publishers
3

4. Strategic priorities
4

5. Co-design partners and participation
142 ideas considered
24 defined and pitched
6 challenges prioritised
>100 senior stakeholders
prioritised ideas
(inc. 5 PVCs)
> 1000 colleagues consulted

6. Co-design challenges
Research at risk (R@R)
Prospect to alumnus (P2A) Learning analytics
Digital learning & capabilitiesImplementing FELTAG
Business intelligence
Hosting platform Hosting platform

7. 20/11/2015 7Pipeline

10. Heidi Plus
A new service offering;
Improved data content and functionality (new data
warehouse to optimise utility and processing speed)
Delivery of data sets throughTableau
New visualisations and dashboards
New training programme and support materials
Application programming Interface (API) retained for those
using own BI and analytics systems

11. More disaggregated (but still anonymised) student and staff
data
New approach developed to ensure Data Protection
compliance based on advice of top Data Protection barrister
Comprises:
framework of organisational and user agreements
new Data Protection training programme
three levels of access permission plus new Lead Contact
role
Access to more detailed and flexible data

12. Data sources
Data sources accessible
through Heidi Plus
Will be split into two
separate projects…

13. Silver data
Does not contain data relating to
individuals
Does not constitute ‘Personal
data’ as defined by the DPA
Use not restricted by this Data
Protection training

14. Gold data
Contains data relating to
individuals and therefore may
constitute ‘Personal data’ as
defined by the DPA
Must only be used in accordance
with the terms set out in the Heidi
Plus agreements and the DPA

15. Project infrastructure
Heidi Plus utilises a project
infrastructure to offer users the
required access to specific
functionality and data
Availability of these projects is
dependent on the user role type
assigned
Support
centre
HESA
dashboards
Shared
workbooks
Silver data
sources
Gold data
sources
Personal
workbooks

16. Access rights
There are 3 types of user roles available for Heidi Plus
and these levels of access determine the availability
of the projects, thus controlling what Heidi Plus
functionality and data sources are accessible
The ‘Roles explained’ guide is a useful training guide
Bronze Silver Gold

17. Beta 1 release - 9 organisations participated; ended on 4
September
Beta 2 release – underway now, 25 organisations
participating, ending 6 November.
Production release planned for:
Monday 30 November
Production release to include range of data sets with others
being added up to April 2016
Current Heidi decommissioned November 2016
Development schedule

18.  Data Protection webinar training being delivered to Lead Contacts
and optionally to Gold level users
 Lead Contact workshops planned for November and December in
London, Liverpool, Belfast and Edinburgh: hands-on training in using
the system
 See www.hesa.ac.uk/seminars-2015 for details of the workshops
 Training programme being planned for 2016
 Wide range of training materials also under construction
Training sessions and materials

19. Heidi Lab
Overview

20.  A new national analytics research and development project.
 Focuses on business questions that can’t be addressed through Heidi
Plus.
 3 cycles; Winter, spring, summer
 Technical; MS SQL Web & Business (elastic), DocumentDB (elastic),
Alteryx, Tableau server
Heidi Lab

21. As a: Outreach officer
When: Planning widening participation
recruitment
I want
to:
Better understand potential
student demographics
So I can: Achieve my targets in the most
efficient way
Contribute a user story
http://bit.ly/heidilab-user-
stories

22. What is agile?

23. Agile overview

24. Benefits of Agile
 Stakeholder engagement
 Transparency
 Early delivery
 Predictable costs and schedule
 Allows for change
 Focus on business value and on customers
 Improves quality

25. Heidi Lab Agile
approach

26. Heidi Lab Scrum in a slide

27. Analysis team effort
Activity Outputs Timing Method Duration Effort /
cycle
Identification of
challenge areas / team
planning Sprint
Planning
Challenge areas / data
wish list / development
plan
Week 1 F2F 1 day 1 day
Tableau training /
experts data session
Enhanced skills / data
sanity check, prep, load,
analysis
Week 1 F2F 2 days / 1 day 2 days
Remote team
development time
Weekly Scrum
Visualisations /
dashboards
Weeks 2, 3, 6, 7,
9, 10, 11
Remote 1 day (more
welcome)
7 days
Post-mortem /
Challenge area ID /
team planning
Sprint Retrospective /
Sprint planning
Revised working methods
/ data wish list /
development plan
Weeks 4, 8 F2F 1 day 2 days
Showcase event Priorities for service / new
challenge areas / data list
for next cycle
Week 12 F2F 1 day 1 day
Total effort 13 days

28. Sector Adviser and data expert effort
Activity Outputs Timing Method Duration Effort /
cycle
Identification of
challenge areas / team
planning Sprint
Planning
Challenge areas / data
wish list / development
plan
Week 1 F2F 1 day 1 day
Remote team
development time
Weekly Scrum
Visualisations /
dashboards
Weeks 3, 5, 6, 7,
9, 10, 11
Remote 1 day (more
welcome)
2 days
Post-mortem /
Challenge area ID /
team planning
Sprint Retrospective /
Sprint planning
Revised working methods
/ data wish list /
development plan
Weeks 4, 8 F2F 1 day 2 days
All hands meeting and
general advice to Jisc /
HESA
Priorities for service / new
challenge areas / data list
for next cycle
Week 12 F2F 1 day 2 days
Total effort 7 days

29. 18 institutional Development team members (0.2 FTE HE BI / analyst /
data experts)
 Joining 4 regional teams
 4 senior BI / planning sector advisors (Product owners) @ 7 days /
team
 1 for each team (GaryTindell, Neil Barrett, Anita Jackson, Richard Elliot)
 4 Data support staff (Development team members) @ 7 days / team
 1 for each team
 4 Jisc facilitators (Scrum masters) @ 7 days / team
 1 for each team
Heidi Lab winter teams

30. Heidi Lab winter teams

31. Over the next month, we will facilitate colleagues in Strategic Planning
to be able to undertake competitor analysis in terms of subjects, HEI
location,Type of HEI (Russell Group, Post-92s, etc), tariff scores to
enable course/curriculum management planning to match national
and local demand. To complement this analysis, we will provide
evidence of local economic conditions with specific regard to labour
market composition (employment rates, SOC/SIC, Earnings &
Wages). If time permits, providing insights to Further education
providers and students regarding choice of study.
Team Gary (Tindell, UEL)

32. Labour market from ONS, KCS,SOC, SIC,
HESA DLI (gold)
league tables
programme titles via KIS
A level subjects and grades achieved
POLAR
Initial data sources

33. Create a dashboard to compare year-on-year the performance of my
institution against chosen institutions using the 3 main league tables
so that I can identify factors at the institutional and subject level that
hinder or support institutional goals over a 5-year period.
Team Anita (Jackson, Kent)

34. League tables
HESA institutional UKPRN list for 5 year period
5 years worth of league table data (main tables and subjects) from the
3 main table producers
HESA staff
HESA student
Initial data sources

35. Create a dashboard to compare year-on-year the performance of my
institution against chosen institutions at subject level
Team Neil (Barrett MMU)

36. League table measures, UKPRN table, Student counts statistical
releases, Staff count, HESA Pis, Finance, Estate quality and spend,
Size and shape by subject (institutional profile), Campus structure
(institutional profile), Mission group, region, urbanicity, medical school
Initial data sources

37. Over the next month, we will investigate a range of data sources to
support user epics 15 (planning officer evaluating student value added)
and 17 (planner developing strategic plan).
We'll also specifically work on IMD, Mosaic, Census and POLAR data
and prepare for postcode lookup from within our own institutions, as
well as look at which datasets are available for initial visualisation
work.
Team Richard (Elliot, Sunderland)

38.  HESA Staff & Student
 IMD / POLAR / Census/ Mosaic
 Athena SWAN
 Student data: Entry Profile / Profession/ Funding / NSS Outcomes /
DLHE Earnings
 Estates data: Quality / Function / Connectivity
Initial data sources

39. Heidi Lab and
tools

40. Heidi Lab secure environment
There are four main components for use by the teams:
 Data sources
 Alteryx (optional -- for transforming data)
 Tableau Desktop (for producing visualisations)
 download Microsoft Remote Desktop
 Tableau Server (for sharing visualisations with others)
 http://tableau-labs.data.alpha.jisc.ac.uk

41. The Heidi Lab environment
 Data is being made available under license => legal complications if
allowed out of environment
 Heidi lab runs in cycles, fixed number of licenses to be re-used
 => Secure environment required!

42. Candidates for the Heidi Lab environment
 Cloud first andTableau server needsWindows => MS Azure
 Initial requirements ideas – only wanted access to data via apps
=>trial Azure remote app
 Requirements becoming clearer (very agile), need an environment to
clean and transform data
 Remote app not very mature for complex desktop apps
=> move to remote desktop services (probably cheaper too)

43. Heidi lab overview

44. (Low shelf) data catalogue
Image: Anton Bielouso CC BY_SA 2.0Image: dankueck CC BY SA 2.0

45. Data catalogue
 Shortlisting and origins: http://is.gd.DataCatList
 Link to live Catalogue

46. Basecamp
 URL:http://bit.ly/heidi-lab-basecamp

47. Business
Intelligence
maturity

50. UK BI maturity dashboard
50 HEI responses (38%)
Why and how;
Recorded webinar
Make a return;
myles.danson@jisc.ac.uk

51. UK / US BI maturity dashboard
270 responses (27%)
Make a return;
myles.danson@jisc.ac.uk

52. Heidi Lab - Benefits
Numerous for Universities and team members
 Gain access to more varied dashboards by HE for HE
 Utilise a wider range of low shelf data sources
 Steps toward opening up access to high shelf data
 Work at national level
 Gain expertise in agile development

53.  http://www.business-intelligence.ac.uk
 ‘subscribe JISC-HESA-BUSINESS-INTEL’ to listserv@jiscmail.ac.uk
 Twitter @HESA @jisc #hesajiscbi
 Any team members here?
 Apply to join a spring or summer team?
 Offer user stories, data (or dashboards)?
http://bit.ly/heidilab-user-stories
 Poster
Get involved

54. Q & A