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co•he•sion noun kō-ˈhē-zhən 1 : the act or state of sticking together tightly; especially: unity

Providing Metrics for
De...
Session Rules of Etiquette
Please turn off your cell phone/pager
If you must leave the session early, please do so
as disc...
Introduction

Data Driven Decision Making Comes to
Higher Education!
•
•
•
•
•
•
3

An Anecdotal Data Story
Drivers & Barr...
A Data Request Story …

The “New”
Chancellor’s/President’s First
Request for “Information”

4

CoHEsion Summit
Lessons Learned from our Story?
•

•

•

•

•
5

Leadership doesn’t always know where to go to
ask the question.
They don’...
DRIVERS AND BARRIERS TO
BIG DATA

6

CoHEsion Summit
Drivers to Big Data
Include:
•

•
•
•

Market related factors (e.g. competition)
Consumer demand (e.g. quality, completion...
NCHEMS Recommendations to TBR
(Accepted at June 20th 2010 Board Meeting)

8

CoHEsion Summit
Barriers (Issues & Obstacles)
•

•

•

•

9

How “data driven/influenced” is your
institution’s leadership?
Do you have th...
TBR’s Challenges

10

CoHEsion Summit
THE TBR APPROACH
Collaboration on development, costs, and
maintenance of 3 repositories.

11

CoHEsion Summit
The TBR Report Repository
≈ 400 reports identified
Being examined for duplication & overlap
Categorized into:
Institution ...
The TBR KPI Repository
Numerous key performance metrics have been defined using the following
factors:
Metric
Category
Met...
Single Database (Oracle)

The TBR Common Data Repository

14

BI Development
Board Office
APSU
ChSCC
ClSCC
CoSCC
DSCC
ETSU...
A Two Phase Approach
PHASE I

PHASE II

APSU
ETSU
MTSU
TSU
TTU
UoM

15

ChSCC
CoSCC
DSCC
ClSCC
TBR
MSCC
PSCC

STCC
VSCC
RS...
Institutional Performance
Management Beta Negotiations
http://bit.ly/1cfB2VX

16

CoHEsion Summit
OPPORTUNITIES
Additional Collaboration in Big Data and BI

17

CoHEsion Summit
KPI Repository Development
KPI Examples - Graduation Rates with Sub-populations
ACADEMIC_OUTCOME
academic_period
person_ui...
Awareness, Education, Training
President
VP

Dean – AVP

Director - Department Head

Faculty Member
19

CoHEsion Summit
Taking It To The Next Level
“Predictive” models as they relate to producing concrete, tangible, and
useful results.

20

C...
CLOSING THOUGHTS

21

CoHEsion Summit
The Gartner “Hype” cycle

Source: Gartner, Inc.

22

CoHEsion Summit
Crossing the “Chasm” – Big Data
Analytics

Source: Stefan Groschupf | December 19, 2012 | Big Data Analytics

23

CoHEsion...
Questions & Discussion?
Thank You!
Thomas Danford
Tennessee Board of Regents
http://www.linkedin.com/in/tdanford
http://tw...
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Providing Metrics for Decision Makers CoHEsion13

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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.

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Providing Metrics for Decision Makers CoHEsion13

  1. 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. 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! 2 CoHEsion Summit
  3. 3. Introduction Data Driven Decision Making Comes to Higher Education! • • • • • • 3 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 CoHEsion Summit
  4. 4. A Data Request Story … The “New” Chancellor’s/President’s First Request for “Information” 4 CoHEsion Summit
  5. 5. Lessons Learned from our Story? • • • • • 5 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? CoHEsion Summit
  6. 6. DRIVERS AND BARRIERS TO BIG DATA 6 CoHEsion Summit
  7. 7. Drivers to Big Data Include: • • • • 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 7 CoHEsion Summit
  8. 8. NCHEMS Recommendations to TBR (Accepted at June 20th 2010 Board Meeting) 8 CoHEsion Summit
  9. 9. Barriers (Issues & Obstacles) • • • • 9 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? CoHEsion Summit
  10. 10. TBR’s Challenges 10 CoHEsion Summit
  11. 11. THE TBR APPROACH Collaboration on development, costs, and maintenance of 3 repositories. 11 CoHEsion Summit
  12. 12. The TBR Report Repository ≈ 400 reports identified Being examined for duplication & overlap Categorized into: Institution specific Potential system-wide 12 CoHEsion Summit
  13. 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 13 CoHEsion Summit
  14. 14. Single Database (Oracle) The TBR Common Data Repository 14 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) CoHEsion Summit
  15. 15. A Two Phase Approach PHASE I PHASE II APSU ETSU MTSU TSU TTU UoM 15 ChSCC CoSCC DSCC ClSCC TBR MSCC PSCC STCC VSCC RSCC WSCC JSCC NaSCC NeSCC CoHEsion Summit
  16. 16. Institutional Performance Management Beta Negotiations http://bit.ly/1cfB2VX 16 CoHEsion Summit
  17. 17. OPPORTUNITIES Additional Collaboration in Big Data and BI 17 CoHEsion Summit
  18. 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 18 CoHEsion Summit
  19. 19. Awareness, Education, Training President VP Dean – AVP Director - Department Head Faculty Member 19 CoHEsion Summit
  20. 20. Taking It To The Next Level “Predictive” models as they relate to producing concrete, tangible, and useful results. 20 CoHEsion Summit
  21. 21. CLOSING THOUGHTS 21 CoHEsion Summit
  22. 22. The Gartner “Hype” cycle Source: Gartner, Inc. 22 CoHEsion Summit
  23. 23. Crossing the “Chasm” – Big Data Analytics Source: Stefan Groschupf | December 19, 2012 | Big Data Analytics 23 CoHEsion Summit
  24. 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 24 CoHEsion Summit

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