Presented by: Thomas Danford Tennessee Board of RegentsMonday, November 4 11:15

Course ID 237

Providing Metrics for Decision

Makers

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

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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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IntroductionData Driven Decision Making Comes to Higher Education!

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

A Data Request Story …

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Lessons Learned from our Story?

• 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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DRIVERS AND BARRIERS TO BIG DATA

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Drivers to Big DataInclude:

• 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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NCHEMS Recommendations to TBR(Accepted at June 20th 2010 Board Meeting)

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Barriers (Issues & Obstacles)

• 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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TBR’s Challenges

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THE TBR APPROACHCollaboration on development, costs, and maintenance of 3 repositories.

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The TBR Report Repository≈ 400 reports identifiedBeing examined for duplication & overlapCategorized into:Institution specificPotential system-wide

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The TBR KPI Repository

Source/KPI Document

Documents where the recommendation for the metric came from

Metric OwnerExplains who at institution is responsible for hitting the metric's target

Department

States the department of the person at institution who is responsible for hitting the metric's target

Dimensions

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)

Frequency

States how often the metric should be reported (Most are reported by semester or annually)

Related Objective Maps the metric to an institution metric

Metric Category

Type of metric (e.g. Admissions, Development)

Metric IDUnique identifier assigned to each metric

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 Defines how to calculate the metricUnit of

MeasureExplains the form the metric will be in (e.g. $, %)

Numerous key performance metrics have been defined using the following factors:

≈180 reportable out of Banner with an additional 12 added from CCTA

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The TBR Common Data Repository

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CDR

UMWSCCVSCCTTUTSU

STCCRSCCPSCC

NeSCCNaSCCMSCCMTSUJSCCETSUDSCC

CoSCCClSCCChSCCAPSU

Board OfficeBI Development

Sin

gle

Data

base

(O

racl

e)

Multiple Entities (MEP)

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PHASE I PHASE II

A Two Phase Approach

ChSCCCoSCCDSCCClSCCTBRMSCCPSCC

STCCVSCCRSCCWSCCJSCCNaSCCNeSCC

APSUETSUMTSUTSUTTUUoM

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Institutional Performance Management Beta Negotiations

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http://bit.ly/1cfB2VX

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OPPORTUNITIESAdditional Collaboration in Big Data and BI

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KPI Repository Development

KPI Examples - Graduation Rates with Sub-populations

ACADEMIC_OUTCOMEacademic_periodperson_uiddegreedegree_awarded_ind

PERSONperson_uidprimary_ethnicitygenderbirth_date

AID_DISBURSEMENT aid_yearperson_uidpell_eligible_indpell_calculatedtotal_disbursed

f((fp)+(f0))=graduate

f((fp)+(f0)+(fd))=Pell graduate

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Awareness, Education, Training

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Faculty Member

Director - Department Head

Dean – AVP

President

VP

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

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CLOSING THOUGHTS

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The Gartner “Hype” cycle

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Source: Gartner, Inc.

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Crossing the “Chasm” – Big Data Analytics

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

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Thank You!Thomas DanfordTennessee Board of Regents

Please complete the session evaluation formCourse ID 237

http://www.linkedin.com/in/tdanfordhttp://twitter.com/tdanfordthomas.danford@tbr.edu

Questions & Discussion?

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