1.
Developing a research analytics service –
Discovery Phase
1 July 2019
Research Analytics Webinar
Rob Johnson (Research Consulting)

2.
Research Analytics Service
Research Analytics Service2
Running a discovery phase to scope a research analytics service
• Jisc’s members are increasingly focused on the use of data to inform their planning, though mindful
of GDPR and competition law
• A Jisc research analytics service would build on existing work undertaken in the area of learning
analytics, and would extend the scope of the existing Jisc Analytics Labs and Heidi Plus Community
Dashboards initiative to meet urgent policy drivers such as the Industrial Strategy, REF and KEF.
• A research analytics service offer from Jisc is not limited in scope to data just from Jisc services and
might also be based on data from HEI services, and/or third party (sector and commercial
organisations, etc) sources as well.

3.
3
ResearchAnalytics Sophistication Model
Maturity of Research Analytics Development
Researcher/Group/Organisational/SectorImpact
LimitedIntegrated
Immature Advanced
Aware
Experimentation
Organisational
transformation
Research group
Researcher
PGR student
Sector
transformation
Research
organisation
• Basic reports
• Log data
• Drill down reports
• Sample dashboards
• PGR dashboards
• Researcher
dashboards
• Business intelligence
reporting tools
• Cross-system data
integration
• Predictive models
• Measured by impact
and organisational
strategy
• Data sharing
capabilities
• Innovation
• Open data
• Sector-wide agility
Emerging

4.
Research Analytics Service
Research Analytics Service4
Discovery Phase
• Problem Definition
• Broad context
• Stakeholders
• 5 HEI case studies
• Problem dossier
• Refine solutions
• Wireframe prototypes
• Initial thoughts on business model, vision, competitive landscape, alignment with political and
international landscape, and technical approach
• Solution pitch to Investment Committee
• Active engagement with UKRI, Research England, and the Forum for Responsible Research Metrics
Double Diamond model (Copyright Design Council 2014)

5.
Assessing Research Analytics Capability – 5 case study HEIs
5
•Research analytics remains
immature in most HEIs.
•‘Research intelligence’ still
primarily qualitative in nature.
•Analytics expertise resides in
Planning & MI functions, and is
focussed on teaching & learning.
•Analytics maturity linked to size
and data availability, but also
internal leadership.
HighLowMedium
Immature Emerging Mature
Availabilityofdata
ResearchAnalytics Development
HEI A
HEI B
HEI C
HEI D
HEI E

6.
Application of research analytics
6
Thematic analysis of c.160 user stories derived from case studies, workshops and
interviews
Infrastructure and
technical capability
Resourcing and
skills
Negligible number
of user stories
relating to research
integrity

7.
Research analytics – Potential areas of focus
Insert footer7
Numberofuserstories

8.
Provenance of data needed to address the user stories
8
0
5
10
15
20
25
30
35
40
Strategy and
planning
Researcher
development and
careers
Financial
performance and
sustainability
Collaboration,
impact and
knowledge
exchange
Understanding and
evaluating
research
performance
Compliance with
legal, regulatory or
funder
requirements
Scholarly
communication
Research integrity
HEI data only
HEI & external data
External data only
Numberofuserstories

9.
NEXT STEPS
Insert footer9
•11 July - Interim findings presented to Forum for Responsible Research Metrics
•Mid-July – internal progress review with Jisc
•August – proceed to solution phase...?
•Autumn – solution pitch to Jisc investment committee?

10.
christopher.brown@jisc.ac.uk
jisc.ac.uk
rob.johnson@research-consulting.com
Rob Johnson
Director
Research Consulting)
Tel: 07795 117747
Twitter: @rschrobUK
Comments
and feedback?
www.research-consulting.com