The document discusses how data and collaboration can enhance decision making. It provides an overview of why using data and collaborating are beneficial, as well as some of the challenges. A case study is presented on how the Office of Institutional Research and Recreational Sports collaborated using data on facility usage, which found a relationship between usage and student retention. The collaboration strengthened both groups' understanding and ability to demonstrate the value of their work. The document encourages applying similar collaborations using data to other areas.

1. Using Data to Bring People
Together
Quality Fair 2010
Project and Change Management
Collaborators (PCMC) Session
Peter M. Radcliffe
Executive Director
Office of Planning and Analysis
University of Minnesota

2. Overview
• Why use data?
• Why collaborate?
• How can data enhance collaboration?
• What are the challenges?
• Case study: How has this worked in
practice?
• How could you apply this in your own
work?

3. How do we make good decisions?
Good decisions bring together…
• Evidence
– What does reality look like?
– How do the parts fit together?
• Context
– How will stakeholders respond?
– What resources are available?
– What are our goals?

4. How do we make bad decisions?
• Evidence is wrong
– Data is inaccurate
– Model (understanding of what factors lead
to what outcomes) is faulty
• Context is wrong
– Local culture is different or changing
– Resources are unavailable
– Contrary to goals of organization

5. How can data help us?
• More comprehensive than our personal
experience
• More recent than our personal
experience
• Clearer representation of underlying
trends
• Challenges us to examine our biases
and assumptions

6. Benefits of collaboration
• Sharing of knowledge and expertise
– Not all of the information you need for your
work can be found in your own area
– Your team may lack critical skills or
technologies
• Improved credibility
– Broader collaborations less likely to be
perceived as parochial and self-interested

7. Challenges to collaboration
• Problem definition
– What situation are we trying to change?
– Does evidence suggest the problem is real?
• Establishing agreement on outcomes
– How do we define success?
• Understanding stakeholders and interests
– What does each partner need?
– Are interests complimentary, opposed, or
orthogonal

8. Types and Uses of Data
• Descriptive data
– What is the state of the world in this area?
• Metrics
– What are our goals and standards of
progress?
• Analysis
– How do elements connect in this area?
– Can show causation or simply correlation

9. Data, truth, and stories
• Facts may speak for themselves, but they
donʼt say anything interesting on their own
• Data are summaries of a story – they can
help identify some of the critical points, but
they are not the story themselves
• The full “truth” requires putting
observations in context

10. Anecdotes and data
• “The plural of anecdote is data”
– Raymond Wolfinger 1
• Anecdotes, like data, are summaries of
experiences which highlight the elements of the
story that are most relevant for the discussion or
deemed most likely to persuade the audience
– A data set with a very small “n” (most anecdotes) has
very large error bounds on predictions
– A large data set is hard to understand without
statistics to summarize the information, sacrificing
some detail
– Combined, they make a fuller, more engaging
argument
1
Nelson W. Polsby. PS, Vol. 17, No. 4. (Autumn, 1984), pp. 778-781. Pg. 779: “Raymond Wolfinger's brilliant aphorism ʻthe plural of anecdote is dataʼ never
inspired a better or more skilled researcher. I e-mailed Wolfinger last year and got the following response from him: "I said 'The plural of anecdote is
data' some time in the 1969-70 academic year while teaching a graduate seminar at Stanford. The occasion was a student's dismissal of a simple
factual statement--by another student or me--as a mere anecdote. The quotation was my rejoinder.“

11. Trust, Bias, and Integrity
• “Figures won't lie, but liars will figure”
– Charles Grosvenor
• Dishonesty with numbers doesnʼt require
outright fabrication
• Selective use of data sources and definitions
can distort descriptions and analyses
• Best defense is transparency in data sources
and definitions, and use of institutional
standards for both whenever possible
• "Grosvenor, Charles H." The Oxford Dictionary of American Quotation. Hugh Rawson and Margaret Miner. Oxford
University Press 2008. Oxford Reference Online. Oxford University Press. University of Minnesota - Twin
Cities. 24 January 2010 <http://www.oxfordreference.com/views/ENTRY.html?subview=Main&entry=t251.e757>

12. Building the relationship
• Collaboration involves intruding on another
person or officeʼs business
• Need to understand their goals and needs,
as well as your own
• Must demonstrate you are committed to
their success, not just yours

13. Case Study: Recreational Sports and
Institutional Research
• Recreational sports had information on
facility usage collected from U-Card
system
• Had conducted surveys exploring how
students felt about the facilities and the
benefits students perceived from using
them
• Lacked ability to show impact on
educational outcomes without assistance
• Approached Institutional Research for
help

14. Project Background
•Social Interaction
•Tintoʼs Theory of Student Departure
•Braxton & Hirschy: Communal Potential

15. Project Background
CRF – High Communal Potential
Social Interaction
Social Integration
Persistence
Academic Success

16. Analysis and Preliminary Findings
•In 2004, Rec Sports
provided OIR with three
years of facility usage
data
•OIR connected that
data with retention
information, and found a
positive relationship
•Needed a multivariate
model to rule out
spurious relationship

17. Research Questions
Is CRF usage associated with better academic performance
and increased likelihood of being retained and graduating,
above and beyond other important predictors of academic
success?
First-term GPA
First-year retention
Graduating within 5 years

18. Influence of Visits to a CRF on
Probability of Returning for a Second
Year
# of First-term Visits to Campus Recreation Facilities

19. Significance of collaboration
• The story is that campus recreational
facilities usage is associated with higher
retention, even after controlling for many
other factors
• Benefits of partnership
– Recreational Sports strengthened their case
for investment in facilities
– Institutional Research gained more
understanding of student retention and
demonstrated the value of their modeling in
making decisions

20. Roles data played in collaboration
• Initial descriptive data and bivariate
analysis demonstrated showed promise of
effort and garnered support from
leadership
• Each office needed data and knowledge
only the other office could provide
• Analysis validated the understanding of
how facility usage connected to student
success
• Institutional metrics identified outcomes of
interest

21. Try this at home!
• What stories do you and your colleagues
tell and hear about why things happen in
your area?
• What do those stories tell you about the
important concepts to measure?
• How could you explore if those
experiences can be generalized?

22. Keep trying…
• Who has the information and skills you
need to access and assemble that
information?
– May be in local systems in your or other
offices, or in institutional records
– You may need to invent a way to collect it
• What does your group need to accomplish,
and how does that differ from your what
your collaborators need?
• How would you measure whether each of
you is succeeding?

23. Continuing the conversation
• Join the discussion on Moodle
– https://moodle.umn.edu/course/view.php?id=11
30
– Will post responses to all questions submitted
on the index cards
– Ask additional questions on Moodle or
contribute to the discussion
• Contact Peter: radcl002@umn.edu

24. Appendix

25. Metrics and Agreement on Goals
• At November 2009 Board of Regents
Meeting, the President, Provost, and
Executive Director of Planning and
Analysis presented a system-wide
framework for metrics and key indicators
• Framework provides a structure to which
activity at all levels of the organization can
be aligned

26. Goals: Mission and Capacity
Exceptional
Extraordinary Education – Recruit, educate, challenge, and
Students
graduate outstanding students who become highly motivated
lifelong learners, leaders, and global citizens.
Transforming the U Pillars
Breakthrough Research – Explore new ideas and
Mission
Exceptional
Innovation
breakthrough discoveries that address the critical problems
and needs of the state, nation, and world.
Dynamic Outreach and Service – Connect the Universityʼs
Faculty and Staff
academic research and teaching as an engine of positive
Exceptional
change for addressing societyʼs most complex challenges.
World-Class Faculty and Staff – Engage exceptional
faculty and staff who are innovative, energetic, and dedicated
Capacity
to the highest standards of excellence.
Organization
Exceptional
Outstanding Organization – Be responsible stewards of
resources, focused on service, driven by performance, and
known as the best among peers. 26

27. Strategies and Key Indicators: Education
Extraordinary Education – Recruit, educate, challenge, and
Goal
graduate outstanding students who become highly motivated lifelong
learners, leaders, and global citizens.
Recruit highly prepared Incoming student preparation
students from diverse
Student diversity
populations
Graduation and retention rates
Key Indicators
Challenge, educate and
Placement of graduates
Strategies
graduate students
Student learning outcomes (in process)
Ensure affordable
Internal support for scholarships
access for students of
all backgrounds Average net cost for students
Student engagement
Develop lifelong learners,
leaders and global Participation in study abroad and
citizens international experiences
Student development outcomes (in process)
27

28. Strategies and Key Indicators: Research
Breakthrough Research – Explore new ideas and breakthrough
Goal
discoveries that address the critical problems and needs of the state,
nation, and world.
Highly cited research publications
Foster an environment of
creativity that encourages
National academy members and other
evolution of dynamic faculty awards
fields of inquiry
Key Indicators
Major research awards, research center
Strategies
awards and centers of excellence
Research expenditures and competitive
ranking
Develop innovative
strategies to accelerate
Technology disclosures, licenses and
the efficient and effective
startups
transfer of knowledge for
the public good
28

29. Strategies and Key Indicators: Outreach
Dynamic Outreach and Service – Connect the Universityʼs
Goal
academic research and teaching as an engine of positive change for
addressing societyʼs most complex challenges.
Promote and secure the Longitudinal changes in communities
advancement of the most where the University is actively engaged
challenged communities (in development)
Key Indicators
Strategies
Build community
partnerships that
Active partnerships and assessments of
enhance the value and impact (in development)
impact of the Universityʼs
research and teaching
Be a knowledge,
information, and human
Faculty, staff, and student engagement and
capital resource for the community service (in development)
betterment of the state,
nation, and world
29

30. Strategies and Key Indicators:
Faculty and Staff
World-Class Faculty and Staff – Engage exceptional faculty and
Goal
staff who are innovative, energetic, and dedicated to the highest
standards of excellence.
Recruit and place
Quality of incoming faculty and staff
talented and diverse
faculty and staff to best Faculty and staff diversity
meet organizational
Key Indicators
needs
Supervisor and departmental support
satisfaction
Strategies
Mentor, develop, and
train faculty and staff to Employee training and development index (in
optimize performance development)
Recognize and reward Faculty and staff salary and total
outstanding faculty and compensation
staff
Faculty and staff awards and distinctions
Engage and retain Employee engagement index
outstanding faculty and
staff Employee retention30
index

31. Strategies and Key Indicators: Organization
Outstanding Organization – Be responsible stewards of resources,
Goal
focused on service, driven by performance, and known as the best
among peers.
Bond rating: resources and leverage; liquidity
Ensure the Universityʼs and operating cushion
financial strength
Private giving and endowment
Be responsible stewards of Carbon footprint
resources
Facilities Condition Needs Index (FCNI)
Strategies
Promote performance,
Key Indicators
External awards to units for performance,
process improvement, and quality, and innovation (in development)
effective practice
Research proposals and awards
Foster peer-leading
Technology commercialization agreements
research competitiveness,
productivity, and impact Research space productivity
Ensure a safe and secure Workplace injuries
environment for the
Crime and perceptions of safety
University community
31
Faculty and staff satisfaction with support
Focus on quality service services

32. Integrated Metrics Framework
University-Wide
U-Wide
Goals
U-Wide
Strategies
Unit-Level
U-Wide Unit-Level
Key Indicators Goals
Unit-Level
Strategies
Unit-Level
Measures
32

33. Criteria for decision-making
1. Centrality to mission
2. Quality, productivity, and impact
3. Uniqueness and comparative advantage
4. Enhancement of academic synergies
5. Demand and resources
6. Efficiency and effectiveness
7. Development and leveraging of resources
http://www1.umn.edu/systemwide/strategic_positioning/decision.html

34. Useful URLs
• Institutional Research
– http://www.irr.umn.edu/
• Management Reporting
– http://www.umreports.umn.edu/
• Enterprise Financial System
– https://www1.umn.edu/cco/PeopleSoft/v8/fs.shtml
• Data Warehouse
– https://dw.umn.edu/index.asp

35. More useful URLs
• Accountable to U
– http://www.academic.umn.edu/accountability
• OVPR Levels and Trends
– http://www.oar.umn.edu/trends/index.cfm
• Office of Measurement Services
– http://oms.umn.edu/oms/index.php
• Office of Classroom Management
– http://www.classroom.umn.edu