This session will examine the challenges faced within institutions as they seek to leverage existing institutional data repositories. Through the lens of real life case histories, we will examine some of the drivers behind the disparate practices that make data sharing so challenging within a university and point to successful strategies for tackling these challenges.

1. You can use it when your sister is
done with it!!!
Challenges in intra-institutional data sharing
José Sigouin – University of Toronto
Robert Dirstein – Dirstein Consulting Inc.
November 19, 2014

2. Show of Hands
 Work with a research information system
 Stand-alone RIS
 RIS interacts with other systems

3. University Org Chart
President
VP Research &
Innovation
Research
Services Offices
VP & Provost
Deans
Chairs/Directors
VP Human
Resources
CFO
VP University
Operations

4. Definitions – What’s a University
 Derived from the Latin universitas
magistrorum et scholarium, or “community
of teachers and scholars”. (Wikipedia)
 I have sometimes thought of the modern
university as a series of individual faculty
entrepreneurs held together by a common
grievance over parking (Clark Kerr – President of
the University of California)

5. What data is collected?
 Data that has to be collected to support
management of the institution
– Who works at the institution
– What are the research dollars, who has them,
what are the rules
• Data that is under institutional control
 Data that is not directly under institutional
control
– International dimension of research
– Publications, citations, collaborations

6. U of T’s RIS exists primarily to
manage research funds
 Most used measure: Funds Awarded (spending limits
allowed by sponsors over defined periods of time, aka
Award, aka Budget)
 Not the same as Research Revenue (purely FIS
territory)
 Not the same as Research Expenditures (purely FIS
territory)
All 3 are valid measures of research input

7. “Data is only as good as the process that collects it.”
Anonymous

8. RIS data exist primarily to manage
research funds
Missing:
 Donations in support of research – tracked in DIS
 Service contracts
 In-kind
Derivative uses:
 Internal allocations
 Success rates
 Participation rates
 Demographic and gender analyses

9. Internal Allocations Where RIS Data
Has to Be Right
1. Federal indirect costs distribution
2. Allocation of VPRI portfolio costs to divisions
 value of funding
 # applications
 # active funds

10. Success Rate
 Is competition deadline always entered?
 Is it accurate?
 Is it complete? What if an applicant bypassed the research
office and ends up being unsuccessful?
 Is it checked by anyone?
 Does it drive a process?

11. Data is only as good as the processes it drives.

12. Participation Rates
# funded fac members with grants in Dept A
# eligible faculty w/ main appointment in Dept A
Can lead to puzzling participation rates,
sometimes even greater than 100%

13. Better approach:
1. Identify eligible faculty members
with main appointments in Dept
A
2. Among that group, identify who
is funded (regardless of where
grants are administered)
# funded fac members w/ main appointment in Dept A
# eligible faculty w/ main appointment in Dept A

14. How many faculty members does
the University of Toronto have?
Facts & Figures 2013
What is the difference?
Why does it matter?

15. Gender Analysis

16. Demographic Analysis

17. Does Everything Add Up?
 Double-counting?
 Undercounting?
2012-13
Council Reported by Councils* CAUBO**
CIHR $816,385,752 $813,545,000
NSERC $798,228,209 $790,737,000
SSHRC $254,064,436 $249,431,000
Grand Total $1,868,678,398 $1,853,713,000
* Apri l to March; l imi ted to Col leges , including CEGEPs , and Univers i ties in Canada (U of T analys i s )
** Varying fi scal years ; excludes CEGEPs
Note, however, that SSHRC and CIHR reported-by-counci l numbers include NCEs but NSERC's don't.
 Considerable hidden noise in these
apparently matching data

18. Case Study - MRA
 Approval for submission of research
proposals to external funders
– All such (research) proposals must be approved by the Principal
Investigator and the appropriate officials in the Administering
Unit and/or the academic division housing the Administering
Unit, and then submitted to the appropriate unit of the office of
the Vice-President, Research and Innovation for institutional
review and approval before being sent to the sponsoring
organization. (Research Administration Policy)
 Automation of workflows to ensure adherence
to the Research Administration Policy

19. Approval Schematic Application
Chair/Primary
Appointment
Request to
Administer in
Different Unit?
Yes No
Admin
Chair
Hospital
?
Vice
Dean?
Hospital
?
Vice
Dean?
Dean? Dean?
Provost? Provost?
University
Approvers
University
Approvers
• Assistant may
prepare and
forward to PI
• PI submits
goes to
Chair/Director
of Unit of
Primary
Appointment
• Request to
Administer in a
different unit?
• No
• Yes
• Other
Approvals
required based
on escalation
rules

20. Data
 What’s needed?
– Personnel information, organizational
structure and relationships
 Who needs it?
– Research Services Office
 Who owns it?
– HR
• (sort of)

21. What we discovered
 Multiple official versions of the org
structure
– HRIS
– FIS
– ROSI
– SAKIA
 Why? – each addressed the business
needs of the owner

22. Which one?
 HRIS – provides the people, the type of
appointment and the relationships of
people and positons to units
 Unfortunately no system differentiation
between academic and non-academic
units
 Data quality issues

23. Why is data quality so important?

24. Resulting System Landscape
FIS
RIS
HRIS
EHS
MRA
MRAP
Plan 90
ROSI MRHP
UTORauth

26. Resulting System Landscape
FIS
RIS
HRIS
EHS
MRA
MRAP
Plan 90
ROSI MRHP
UTORauth

27. Ongoing challenges
 Need to understand consequences of
actions
 Institutional Structure, Decision/Budgetary
Authority & Culture favour Atomization

28. Solutions
 Look past the detritus of current practice
and examine objectives
 To what use will the data be put?
– Now
– In the future
 Leadership
 Communication