Departments across any institution, from finance to HR, enrollment to alumni, to student services et al., management is constantly looking for ways to improve the performance of their organizations and initiatives. Nevertheless, providing metrics to enable decision makers to align departmental goals with the mission of the institution is difficult. This presentation will chronicle what the Tennessee Board of Regents is doing to lower the barriers of cost, time, and quality in delivering actionable metrics to campus leaders across the system.

1. co•he•sion noun kō-ˈhē-zhən 1 : the act or state of sticking together tightly; especially: unity
Providing Metrics for
Decision Makers
Presented by: Thomas Danford
Tennessee Board of Regents
Monday, November 4 11:15
Course ID 237

2. Session Rules of Etiquette
Please turn off your cell phone/pager
If you must leave the session early, please do so
as discreetly as possible
Please avoid side conversation during the
session
Thank you for your cooperation!
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3. Introduction
Data Driven Decision Making Comes to
Higher Education!
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An Anecdotal Data Story
Drivers & Barriers to Big Data
The Tennessee Board of Regents (TBR)
Challenges
TBR’s Collaborative Approach
Opportunities to Partner
Closing Thoughts
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4. A Data Request Story …
The “New”
Chancellor’s/President’s First
Request for “Information”
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5. Lessons Learned from our Story?
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Leadership doesn’t always know where to go to
ask the question.
They don’t always know how to phrase the
question.
Even if they phrase the question correctly it isn’t
always interpreted correctly.
Though we don’t collect the data … someone
else might be.
Others?
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6. DRIVERS AND BARRIERS TO
BIG DATA
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7. Drivers to Big Data
Include:
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Market related factors (e.g. competition)
Consumer demand (e.g. quality, completion)
Technology inputs
Societal pressures (e.g. government regulation)
Complete College Tennessee Act of 2010 (CCTA)
TCA 49-8-101(c)
The National Center for Higher Education
Management Systems (NCHEMS) Report
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8. NCHEMS Recommendations to TBR
(Accepted at June 20th 2010 Board Meeting)
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9. Barriers (Issues & Obstacles)
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How “data driven/influenced” is your
institution’s leadership?
Do you have the infrastructure (data
warehouse) to support a big data project?
Do you have the funding and staffing for
a big data project?
How “on board” is everyone?
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10. TBR’s Challenges
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11. THE TBR APPROACH
Collaboration on development, costs, and
maintenance of 3 repositories.
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12. The TBR Report Repository
≈ 400 reports identified
Being examined for duplication & overlap
Categorized into:
Institution specific
Potential system-wide
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13. The TBR KPI Repository
Numerous key performance metrics have been defined using the following
factors:
Metric
Category
Metric ID
Type of metric (e.g. Admissions,
Development)
Unique identifier assigned to each
metric
Source/KPI
Document
Metric Owner
President’s
Dashboard
(Y/TBD/N)
Establishes whether the metric will or
will not be on the President's executive
dashboard
Metric Name
Name given to the metric
Metric
Description
Detail on what the metric measures
Calculation
Unit of
Measure
Defines how to calculate the metric
Explains the form the metric will be in
(e.g. $, %)
Department
Dimensions
Frequency
Related
Objective
Documents where the recommendation
for the metric came from
Explains who at institution is responsible
for hitting the metric's target
States the department of the person at
institution who is responsible for hitting
the metric's target
Explains all the categories in which the
metric will be reported (e.g. total
enrollment by race, gender, zip code race, gender, zip code are the
dimensions)
States how often the metric should be
reported (Most are reported by semester
or annually)
Maps the metric to an institution metric
≈180 reportable out of Banner with an additional 12 added from CCTA
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14. Single Database (Oracle)
The TBR Common Data Repository
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BI Development
Board Office
APSU
ChSCC
ClSCC
CoSCC
DSCC
ETSU
JSCC
MTSU
CDR
MSCC
NaSCC
NeSCC
PSCC
RSCC
STCC
TSU
TTU
VSCC
WSCC
UM
Multiple Entities (MEP)
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15. A Two Phase Approach
PHASE I
PHASE II
APSU
ETSU
MTSU
TSU
TTU
UoM
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ChSCC
CoSCC
DSCC
ClSCC
TBR
MSCC
PSCC
STCC
VSCC
RSCC
WSCC
JSCC
NaSCC
NeSCC
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16. Institutional Performance
Management Beta Negotiations
http://bit.ly/1cfB2VX
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17. OPPORTUNITIES
Additional Collaboration in Big Data and BI
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18. KPI Repository Development
KPI Examples - Graduation Rates with Sub-populations
ACADEMIC_OUTCOME
academic_period
person_uid
degree
degree_awarded_ind
PERSON
f((fp)+(f0))=graduate
person_uid
primary_ethnicity
gender
birth_date
AID_DISBURSEMENT
f((fp)+(f0)+(fd))=Pell graduate
aid_year
person_uid
pell_eligible_ind
pell_calculated
total_disbursed
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19. Awareness, Education, Training
President
VP
Dean – AVP
Director - Department Head
Faculty Member
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20. Taking It To The Next Level
“Predictive” models as they relate to producing concrete, tangible, and
useful results.
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21. CLOSING THOUGHTS
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22. The Gartner “Hype” cycle
Source: Gartner, Inc.
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23. Crossing the “Chasm” – Big Data
Analytics
Source: Stefan Groschupf | December 19, 2012 | Big Data Analytics
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24. Questions & Discussion?
Thank You!
Thomas Danford
Tennessee Board of Regents
http://www.linkedin.com/in/tdanford
http://twitter.com/tdanford
thomas.danford@tbr.edu
Please complete the session evaluation form
Course ID 237
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