providing metrics for decision makers cohesion13
DESCRIPTION
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.TRANSCRIPT
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/[email protected]
Questions & Discussion?
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