Data Governance and Adoption of Analytics

163 Pages·2015·1.96 MB·English

by  Starchak, Matt

A Common Currency
Achieving Excellence in Data Governance and
Adoption of Analytics
IT Forum
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Table of Contents
Advisors to Our Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Understanding Your Current Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Analytics as an Enterprise Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Data Governance for Performance Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Hallmark 1: Institutional Ownership of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Hallmark 2: Bicameral Data Governance Committees . . . . . . . . . . . . . . . . . . . . . . . . 34
Hallmark 3: Fast-Cycle Decision Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Hallmark 4: Pop-Up Data Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Data Quality Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Hallmark 5: Accountable Data Stewards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Hallmark 6: Automated Data Quality Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Hallmark 7: Fast-Turnaround Error Check Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Hallmark 8: Unit-Level Data Quality Scorecards . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Hallmark 9: User-Friendly Data Entry Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Hardwiring Data Access Provision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Hallmark 10: Data Sensitivity Classification Framework . . . . . . . . . . . . . . . . . . . . . . . 81
Hallmark 11: Role-Based Data Access Rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Hallmark 12: HRIS Access Rights Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Maximizing Report Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Hallmark 13: BI Merchandising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Hallmark 14: Personalized Recommendation Engine . . . . . . . . . . . . . . . . . . . . . . . . 108
Hallmark 15: Demand-Driven Report Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 113
Hallmark 16: Unit-Level BI Road Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Hallmark 17: Crowdsourced Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Hallmark 18: Low-Cost Bridge Workarounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Organizational Continuity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Hallmark 19: Centralized BI Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Hallmark 20: Chief Data Steward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Implementation Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
©2015 The Advisory Board Company • 30820 3 eab.com
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A Common Currency Toolkit
Visit the online toolkit to download resources to assist
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include job descriptions, organizational charts, role-
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About the IT Forum
Our Parent Firm: The Advisory Board Company
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Advisors to Our Work
The IT Forum is grateful to the individuals and organizations that shared their insights, analysis, and
time with us. We would especially like to recognize the following individuals for being particularly
generous with their time and expertise.
With Sincere Appreciation
Leah Lommel Mona Isaacs Brian Parish
Arizona State University Eastern Kentucky University IData Incorporated
Tempe, AZ Richmond, KY
Beth Ladd
John Rome Rumy Sen Illinois State University
Arizona State University Entigence Corporation Normal, IL
Tempe, AZ
Douglas Robertson Erik Bowe
Phil Repp Florida International University Kennesaw State University
Ball State University Miami, FL Kennesaw, GA
Muncie, IN
Ronald Lane Ed Clark
Trish White George Washington University Minnesota State University,
Belmont University Washington, DC Mankato
Nashville, TN Mankato, MN
Margaret Roldan
Phil Adams George Washington University Charlie Moran
Blackboard Analytics Washington, DC Moran Technology Consulting
Shyla Kennedy Jelena Roljevic Mark Mooney
Booz Allen Hamilton George Washington University MyEdulife
Washington, DC
Daniel Riehs Tom Redman
Boston College Robert Goodman Navesink Consulting Group
Chestnut Hill, MA Georgetown University
Washington, DC Michael Rappa
Jeff Gold North Carolina State University
California State University Office Bob Orr Raleigh, NC
of the Chancellor Georgia College and State
Long Beach, CA University Harper Johnson
Milledgeville, GA Northern Arizona University
Courtney Carpenter Flagstaff, AZ
College of William and Mary Sharon Blanton
Williamsburg, VA Hawaii Pacific University Lois Brooks
Honolulu, HI Oregon State University
Stephen Vieira Corvallis, OR
Community College of Rhode Fred Friedrich
Island Higher-Ed Solutions Michael Hansen
Warwick, RI Oregon State University
Vijay Thiruvengadam Corvallis, OR
Peter Aiken Higher-Ed Solutions
Data Blueprint Jim Hook
Steve Adler Portland State University
Skip Kirby IBM Portland, OR
East Carolina University
Greenville, NC Scott Flory Alison Nimura
IData Incorporated Portland State University
Portland, OR

©2015 The Advisory Board Company • 30820 6 eab.com
Ellen Weeks Sam Scozzafava Hank Childers
Portland State University Syracuse University University of Arizona
Portland, OR Syracuse, NY Tucson, AZ
Jay Dominick Ellie Fields Simran Nihr
Princeton University Tableau University of Arizona
Princeton, NJ Tucson, AZ
Reid Christenberry
Brent Drake Tennessee Tech University Viji Murali
Purdue University Cookeville, TN University of California, Davis
West Lafayette, IN Davis, CA
Mark Hamner
David Robledo Texas Woman’s University Jerry Singer
Purdue University Denton, TX University of California, San Diego
West Lafayette, IN La Jolla, CA
Phil Ice
Aaron Walz The American Public University Vince Kellen
Purdue University System University of Kentucky
West Lafayette, IN Charles Town, WV Lexington, KY
Jim Anderson Stephanie Bond Huie Laura Patterson
Rowan University The University of Texas System University of Michigan
Glassboro, NJ Austin, TX Ann Arbor, MI
Peg Kuman Jessica Shedd Anne Fay
Relevate Group The University of Texas System University of Pittsburgh
Austin, TX Pittsburgh, PA
Bill Anderson
Saint Michael’s College Jeff Schmidt Bill Rupp
Colchester, VT Towson University University of Pittsburgh
Towson, MD Pittsburgh, PA
Pat McQueen
Salesforce Johan Bergstrom Michael Dillon
Umeå University University of Maryland,
Lisa Helmin Foss Umeå, Sweden Baltimore County
St. Cloud State University Baltimore, MD
Saint Cloud, MN Henrik Stenwall
Umeå University Arnold Foelster
Henry May Umeå, Sweden University of Maryland,
St. Cloud State University Baltimore County
Saint Cloud, MN Laura Duvall Baltimore, MD
United States Department of
David Belanger Homeland Security Mike Glasser
Stevens Institute of Technology University of Maryland,
Hoboken, NJ Angela Baldasare Baltimore County
University of Arizona Baltimore, MD
Tucson, AZ

©2015 The Advisory Board Company • 30820 7 eab.com
Jack Suess Bill Yock
University of Maryland, University of Washington
Baltimore County Seattle, WA
Baltimore, MD
Jason Fishbain
Christina Drum University of Wisconsin
University of Nevada, Madison, WI
Las Vegas
Las Vegas, NV Roberta Bell
Vanderbilt University
Mike Ellison Nashville, TN
University of Nevada,
Las Vegas Daniel Kirby
Las Vegas, NV Vanderbilt University
Nashville, TN
Mike Chapple
University of Notre Dame David Wright
Notre Dame, IN Wichita State University
Wichita, KS
Augie Freda
University of Notre Dame James Hammond
Notre Dame, IN Winthrop University
Rock Hill, SC
Amy Miller
University of Pennsylvania
Philadelphia, PA

Mike Kelly
University of South Carolina
Columbia, SC
Anja Canfield-Budde
University of Washington
Seattle, WA
Jim Kresl
University of Washington
Seattle, WA
Steve Majeski
University of Washington
Seattle, WA
Aaron Powell
University of Washington
Seattle, WA

©2015 The Advisory Board Company • 30820 8 eab.com
Executive Summary
Intuition-Driven Decision Making No Longer Sufficient
Increases in higher education costs and greater competition for students, donations, research grants,
and other revenue sources have necessitated data-informed decision making at many institutions.
Pressure to be more analytical comes from many places, from state legislatures to boards of
trustees, and often winds up at the CIO’s door because business intelligence is perceived by campus
members to be a technical capability.
Flattening Revenues, Rising Costs
The Recession The Decade Ahead
Administrative Looking for Growth
Belt-Tightening
2000 2009 2015 2020
2000 2003 2006 2009 2012 2015 2018 2020
Costs Revenue
Data Governance Is Crucial to BI Success, but Often Low in Maturity
Data governance, the process of creating standards for data elements (e.g., data definitions,
potential values, security levels), promotes consistency that enables reliable data comparison across
an organization—a fundamental input to BI. Many organizations, however, suffer from low
accountability for data governance responsibilities and poor campus engagement in related
discussions. Institutions with successful data governance efforts have created sustainable models for
ongoing data governance efforts, achieved consistently defined performance metrics across the
institution, and created more mature BI efforts.
Enterprise Data Governance Indicative of Mature BI
n=46
Data Governance Maturity Index
Fragmented Focused Enterprise
20

x
e
d 10
n
I

y
t
ri 0
u
t
a
M
-10
I
B
-20
Source: EAB IT Forum 2014 BI Survey; EAB interviews and analysis.
©2015 The Advisory Board Company • 30820 9 eab.com
Data Quality Everyone’s Problem, No One’s Job
Higher education institutions collect large amounts of data, such as student grades, swipe card
records, faculty service activity, and procurement purchases, but much of the data is too low quality
to be useful in data analysis. Poor quality data may impede analyses or guide campus members to
misinformed conclusions. Few campus members are incented to fix data problems (e.g., missing
data, incorrect data, misplaced data) in source systems, so data cleaning occurs only (if at all) in
frozen sets outside source systems. Institutions that have increased data quality in source systems
have achieved gains in BI staff productivity and campus member efficiency.
One Valid and Accurate Value, Many Chances for Mistakes
Data stored in shadow Local workarounds used Open fields lead to
system, not central to bypass required fields inconsistently entered
system of record data
Data collected Data input standards
systematically only when vary across the
required immediately or institution
for federal reporting
Role-Based Access Holds the Solution for Secure, Just-in-Time Data Privileges
Users must have access to data for analysis, but typical case-by-case access provision practices fail
to balance access, efficiency, and privacy. Institutions, trying to balance security with openness,
typically create processes in which individuals request access on a case-by-case basis. However,
desire for data (and more objective security) has outpaced the adequacy of one-off access requests.
Institutions have turned to models of role-based security to provide quicker and more principled data
access to categories of campus members.
Case-by-Case Access Determinations Not Making the Cut
Data Access the New Not Quite Just-in-Time Lack of Standards =
Password Reset Data Access Lack of Security
• One FTE may be dedicated to • Time to provision at a typical • Subjective and inconsistent
access privilege granting, in university: 2-4 weeks privilege granting across
an already overworked and data stewards
• Loss of productivity while
understaffed environment
waiting for data access:
priceless
Source: EAB interviews and analysis.
©2015 The Advisory Board Company • 30820 10 eab.com