IT Forum

Hallmarks of the Data-Driven

University
Preview of Our Data-Driven Enterprise Research

Laura Whitaker Kevin Danchisko

IT Forum IT Forum
Practice Manager Research Analyst

eab.com
 Road Map for Today’s Discussion
2

1 Welcome

2 Our Directive from Members

3 Roadblocks to Sustainable Data Governance

Hallmarks of Data-Driven Enterprises in Higher

4 Education: Focus on Data Governance

5 Diving Deeper: One Example Tactic

6 What to Look Forward to at Our National Meeting

©2014 The Advisory Board Company • eab.com

 EAB’s IT Forum
3
90 Members and Growing
Armstrong State University McGill University University of Alberta University of Oregon

Ball State University Metropolitan State University of Denver University of Arkansas University of Ottawa

Baylor College of Medicine Northern Arizona University University of British Columbia University of Pittsburgh

California State University, Northern Illinois University University of Buffalo University of Rochester
Dominguez Hills
Northern Kentucky University University of California, Berkeley University of Saskatchewan
California State University, Fullerton
Old Dominion University University of California, Davis University of South Carolina,
California State University, Columbia
Northridge Oregon State University University of California, Irvine
University of South Florida
Carnegie Mellon University Pepperdine University University of Cincinnati
University of Texas at Arlington
Central Michigan University Prairie View A&M University University of Colorado, Boulder
University of Texas at Tyler
College of William & Mary Purdue University University of Colorado, Colorado
Springs University of Toronto
Colorado State University Queen’s University
University of Connecticut University of Utah
Dartmouth College Ryerson University
University of Florida University of Wisconsin, Madison
East Carolina University San Jose State University
University of Georgia University of Wisconsin, Milwaukee
Eastern Illinois University Southern Illinois University at
Carbondale University of Guelph University of Wisconsin, Stout
Eastern Kentucky University
Southern Polytechnic State University University of Illinois at Chicago Virginia Polytechnic Institute and
Elon University State University
Stony Brook University University of Kentucky
Gallaudet University Washburn University
Syracuse University University of Massachusetts System
Georgia College and State University Washington State University
Tennessee Technological University University of Memphis
Georgia State University Washington University
The George Washington University University of Michigan
Indiana University Webster University
Towson University University of Nevada, Las Vegas
Lafayette College Western Illinois University
Tulane University University of New Hampshire
Longwood University West Virginia University
University of Alabama University of North Carolina,
Louisiana State University Charlotte Winthrop University
University of Alabama at Birmingham

©2014 The Advisory Board Company • eab.com

 What Should We Research?
4
What Our Members Told Us

Data-Driven Student Transforming Rightsizing

Enterprise Success Administrative Security
Road Map Services Strategy

Sustainable Data Governance Accountability for Data Management

Access and End User Support BI Organization and Strategy

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Our Methodology
5
Process for Our Data-Driven Enterprise Research

Discovering Best Practices for the Data-Driven Enterprise

Comprehensive Assessment of practices Roundtables in

literature review of for results, against root Washington, DC,
news updates and causes, and for replicability to present
scholarly publications research findings

May-June July-Sept Oct-Nov Dec-March

75+ interviews with

CIOs, directors of BI, Creation of a best practice study
and private industry
thought leaders

©2014 The Advisory Board Company • eab.com

 Why Now?
6
Data an Increasingly Necessary Element of Decision-Making

Increasing Competition for Gut Decisions No Longer

Vital Resources Making the Cut
 Widespread funding  Decisions based on
shortfalls and budget cuts assumptions lead to sub-
 Rise of performance-based optimal allocation of
funding and budgeting resources
models
 Changing national
demographics, with the “We’re missing opportunities to improve
number of domestic high the university. We’re missing
school students forecasted to opportunities to increase our retention
decrease over the next rates; we’re missing opportunities to get
decade students graduated in four years; we’re
missing opportunities to understand
where we need to be recruiting.”
CIO, Master’s University

©2014 The Advisory Board Company • eab.com Source: EAB interviews and analysis.
 The Data-Driven University Already Exists…
7
The Concept of Business Intelligence Is Nothing New

When Did You Start Your BI Initiative?

25% 25%

21%

18%

11%

Have yet to start, Just starting now 1 to 3 years ago Between 3 and 5 More than 5
but have a plan years ago years ago

n = 28, with 3 skipped

©2014 The Advisory Board Company • eab.com Source: EAB interviews and analysis.
 …And Everyone’s Doing It
8
All Corners of the Campus Already Using Data for Decision Making

Distributed Analytics Staff Appearing across Campus

In Schools In Business Units

VP of Enrollment
Dean
Management

Data Analysis AVP of Enrollment

15
Distributed Cognos licenses
Division Analytics
discovered at one research
university at the start of a
coordinated BI effort
Director of Reporting Reporting Reporting
Data Analyst Analyst Analyst
Analytics

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 So What’s Broken?
9
Roadblocks to Effective Data-Driven Decision Making

Data Definitions Data Collection Data and Systems

Architecture
 No standard definitions  Data fields not collected
 Static system structure not
 No access to data  Open field entries not
aligned to the institution
definitions defined
 Variations in existing  Place-holder data used  Improper system
definitions implementation
 Fields misappropriated
 Existence of suboptimal
 No central staff to resolve
 No checks on data entry shadow systems
inconsistencies
quality
 No standardized data
practices

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Key Elements of the Data-Driven University
10

Data Governance Structure Institutional Strategy

Structure data governance with long- Establish BI as an all-in, enterprise
term sustainability as a top priority effort

Data Management Disciplined BI Software Spend

Make data quality problems visible to Eliminate redundant and inefficient
procure participation in data distributed BI spend
management efforts

Organizational Structure End User Support

Centralize BI leadership efforts to Empower end users through self-
expand the abilities of distributed service BI
data users

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Today’s Focus
11
Hallmarks of Sustainable Data Governance

Data Governance Structure

Structure data governance with long-term sustainability as a top priority

1 2 3 4
Invest inbetter data Put one full-time staff Structure data Make attendance at
governance before member in charge of governance into two data custodian
expanding the BI tool data governance main committees: 1) a committee meetings
portfolio. efforts. prioritization voluntary for all but the
committee of data governance
university executives, director and the unit
and 2) a with the most
definition/access responsibility for the
committee of data data in question at
custodians who are each meeting.
aligned with functional
units and understand
the organizational use
of data.

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 1: Prioritize Investment in Data Governance

A Culture Problem, Not a Tech Problem

12
Don’t Invest in More Technology Before Investing in Data Governance

Data Problems New ERP System Data Problems

 Administrators at a private  University purchases  Same data management

research university in the Workday, thinking it problems persist
Northeast frustrated with will solve the data
data problems across the management problems
university

“If Cognos has 100 “A data-driven institution is very transparent, very

functions, we’re using two open, and sharing is everywhere. And that
or three.” particular value set is not part of our institution at
Chief Data Officer, Public this moment in time.”
Research University CIO, Public Research University

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 1: Prioritize Investment in Data Governance

Setting up for Success

13
BI Maturity vs. Data Governance Maturity
Characteristics Fragmented: zero or Focused: within Enterprise
Determining BI Maturity few processes a narrow terrain Perspective:
govern the input, (e.g., reporting) common policies
 Our data resides in collection, policies, and standards in
departmental silos definitions, usage, definitions and effect, centrally-
and access of data processes exist managed KPIs
 Institutionally, data is
viewed as a shared 10
asset
5
 Decisions are validated
with data from central
0
BI Maturity Index

sources 0 1 2 3
 We align BI initiatives -5
with institutional
priorities -10

-15

-20

-25
Data Governance Index
n = 28, with 3 skipped
Source: EAB interviews and analysis.
©2014 The Advisory Board Company • eab.com
 Hallmark 2: Dedicated Data Governance Director

Follow the Leader

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Staffing for Data Governance Often Nonexistent

Typical Failure Points of Data Governance Related to Leadership

Staff Responsibility Changes No Shepherd of the Flock

Person with side responsibility for No leader exists to hold committee
data governance moves to another members accountable to complete
position; data governance follow-up tasks from meetings.
responsibilities not reassigned. Committee disbands due to frustration
among members.

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 2: Dedicated Data Governance Director

Rise of a New “Chief” Position

15
Chief Data Officers Appearing in Higher Education

…But Uncertainty Abounds

US Higher Global about the CDO’s Role
Education Organizations

8 ~250
CDOs found in Estimated CDOs across “If someone today tells
higher education the world by end of 2014
you they know how to do
the chief data officer's
function, they're lying to
Example Institutions: Example Organizations: you.”
 Cornell University  Cambia Health Solutions Richard Wang, Director of
 Kennesaw State University  City and County of San Francisco MIT’s CDO and Information
 Purdue University  IBM Quality Program
 Savannah State University  Nationwide Insurance
 University of South Carolina  ShopAdvisor
 University of Wisconsin  State of Colorado
 University System of Georgia  TD Bank
 Wichita State University  Wells Fargo

Source: “Talent Map 2014,” Chief Digital Officer Summit; Jeff Bertolucci, “Chief Data
Officer: Do You Need One?” Information Week, 2014; EAB interviews and analysis.
©2014 The Advisory Board Company • eab.com
 Hallmark 2: Dedicated Data Governance Director

Who’s Needed Right Now?

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Two Roles for Advancing the Use of Institutional Data

Comparing the CDO to the Data Governance Director

Chief Data Officer Data Governance Director

Core  Leads data definition creation  Leads data definition creation

Responsibilities  Coordinates data governance meetings  Coordinates data governance meetings
 Oversees data quality processes  Develops data governance policies
 Develops data management policies  Advises campus members on data
 Oversees the design of the data management and data use
warehouse and data integration  Maintains the data dictionary
 Encourages use of BI for decision-
making and strategic planning

Desired  Experience with data architecture, data  Broad understanding of higher education
Attributes and management, and development of data operations
Skill Sets governance  Experience with higher education data
 Strong communication skills for both (from one or more functions)
executive-level and technical  Respected among colleagues on campus
implementation discussions  Project management skills

Estimated Salary $145-165K $80-110K

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 3: Bicameral Data Governance Committees

Perils of the Single Committee Structure

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Committees Doomed to Fail from the Start

Roadblocks to Data Governance Committee Success

Committee turns into a Committee lacks the Committee members

prioritization committee, appropriate level of staff to consider the group a one-
disagreeing on what to do think strategically about time project, not a long-
next (and never getting to it) data assets across the term process
institution

Committee turns into a No arbiter exists to resolve No show of support from

group of delegates, as disputes as there is no true institution executives leads
members aren’t held leader of the committee to loss of interest
accountable to anyone

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 3: Bicameral Data Governance Committees

Separate Strategy from Operation

18
Two Committee Structure Best for Achieving Execution

Data Strategy Committee Data Governance Committee

 Role/purpose: Direction setting (the “what”)  Role/purpose: Execution (the “how”)

 Seniority: VP- to AVP-level  Seniority: AVP- to director-level
 Composition: Cross-functional data trustees  Composition: Cross-functional data
(IT, Provost’s office, CBO’s office, Registrar’s custodians (IT, Provost’s office, CBO’s office,
office, etc.) Registrar’s office, etc.)
 Size: 5-10  Size: 12-20
 Time commitment: Minimal (one hour per  Time commitment: High (one hour per week
quarter or semester) or month)
 Agenda:  Agenda:
 What areas of the university may benefit  What should the definition and security level
most from better data? for these terms be?
 What has the data governance committee  What standard terms do we not have that
done since the last meeting, and what are causing problems?
should they focus on until our next meeting?  Who across campus should be a data
custodian?
Source: EAB interviews and analysis.
©2014 The Advisory Board Company • eab.com
 Hallmark 4: Opt-in Definition Creation

Why Am I Here?
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Typical Data Governance Meetings Fail to Maintain Members’ Interests

Representative Data Governance Committee Meeting Results

Low Engagement
among Members

“I’m the AVP of Research. Drop-off in

Why am I talking about Attendance
building codes?

Members Send
“When are we going to get Delegates
to the terms I actually care
about defining?
Committee Stops
Meeting

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 4: Opt-in Definition Creation

Inclusive Doesn’t Have to Mean Big

20
Process for Term Definition at the University of Notre Dame

Project Identification Role Selection Term Definition

 BI strategy committee  Data governance  Data governance

determines project priorities committee members committee members who
self-select their roles in opt-in to definition creation
 Data governance director
definition creation meet to define terms
and relevant unit directors
determine terms necessary  Committee meets once per
to define week for 60-75 minutes

Data Governance Committee Member Roles: RACI

Responsible: Owns the definition and Consult: Participates in the
leads the effort to accurately develop it development of the definition
Accountable: Answers for the Inform: Is kept informed on the
completeness and correctness of development of definitions. Provides
definitions across the institution tacit agreement to the term’s definition

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 4: Opt-in Definition Creation

Matching the Right People to the Right Terms

21
Opt-in Process Promotes Engagement and Sustainability
Opt-in Survey Data Governance RACI Matrix (excerpt)

Campus Strat.
Stud.
Faculty Status Term Data Regis. Plann
Aff.
Athl.
Steward ing
Indicates whether an individual
holds a current appointment to Course A R C
the faculty and, if so, whether Category
they hold an appointment to the Course A R C
Regular faculty, as defined in Category
Article III, Section 1, Subsection Banding
(e) of the University of Notre
Dame Academic Articles. Course A R C
Number
 Responsible Level
 Accountable GIA Athlete A C C I R
Status
 Consult
 Inform Satisfactory A C C I
Academic
 No Stake Progress

Sport A C C I R

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Hallmark 4: Opt-in Definition Creation

A Win-Win-Win
22
Seeing Results in Engagement, Efficiency, and Effectiveness

Number of People Time Needed to Define a Terms Requiring

Attending Meeting Term (in Minutes) Revisions

10%
20 15

8 5%
6

Typical Opt-in Typical Opt-in Typical Opt-in

Process Process Process Process Process Process

300+ 600+
Terms defined by Notre Director-level and above staff
Dame’s process hours saved per year
(conservative estimate)

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 What to Look Forward to
25
More Hallmarks of Data-Driven Universities

Data Governance Structure Institutional Strategy

Structure data governance with long- Establish BI as an all-in, enterprise
term sustainability as a top priority effort

Data Management Discipline BI Software Spend

Make data quality problems visible to Eliminate redundant and inefficient
procure participation in data distributed BI spend
management efforts

Organizational Structure End User Support

Centralize BI leadership efforts to Empower end users through self-
expand the abilities of distributed service BI
data users

Source: EAB interviews and analysis.

©2014 The Advisory Board Company • eab.com
 Services Available to IT Forum Members
26
Helping Your Team Work Smarter and Faster

Insight Centers Webconferences Onsites

 Immediate access to web-  Hour-long educational  One-to-two hour formal
based resources for sessions that focus on presentations with Q&A or
members of your team select case studies from half-day facilitated
trying to get smart on an best practice research or interactive sessions on
issue quickly emerging issues selected issues
 Example insight centers:  Example webconferences:  Example uses:
– Data-Driven Enterprise – IT Security Breach – Strategic planning
Preparation & Response
– Academic Information – Consensus building
Services – Rationalizing University
– Board education
Technology Investments
– Innovations in Delivering
Quality Instruction at
Scale

©2014 The Advisory Board Company • eab.com

 Services Coming Down the Road
27
Teaching You Our Identified Best Practices

Roundtable Meeting Best Practice Study Toolkits and Resources

 A concise presentation of the  Innovative ideas—all  Implementation guides
year’s most promising proven and put into and diagnostic tools to
research, reserved for CIOs. practice at other colleges help you put best
The roundtable meeting is and universities— practices in place at your
limited to 25 to 35 attendees addressing the data institution (e.g., hallmark
to ensure robust discussion governance and BI maturity diagnostic, data
and sharing of ideas challenges facing IT quality problem
leaders across the country identification survey)
 Multiple roundtables will be
held to accommodate busy
member schedules
– Dec 2-3, Washington (at
capacity)
– Jan 13-14, Washington
– Feb 24-25, Washington
– Mar 17-18, Chicago
©2014 The Advisory Board Company • eab.com
 Thank You
28
Contact Information for IT Forum Team Members

Laura Foster Whitaker

Practice Manager
EAB Strategic Research Education Advisory Board
lwhitaker@eab.com Website
202-568-7483
 www.eab.com
 Open to all members of your
Kevin Danchisko institution with an @___.edu or
Analyst @____.ca email address
EAB Strategic Research
kdanchisko@eab.com
202-568-7509

©2014 The Advisory Board Company • eab.com

 Existing EAB Data-Driven Research
29
Perspectives from our CBO-focused research

Developing a Data-Driven
University
Research study with strategies and best Webinar Sessions on Developing a
practices for increasing reporting and Data-Driven University
analytical capacity to improve institutional
effectiveness. Part One:
This webinar provides an overview of how
 Forum: Business Affairs Forum progressive institutions have liberated
scarce analytical resources to focus on
 Audience: CBOs, CIOs,
internal assessment versus external
BI Directors
accountability requirements.
 Year: 2010
 Learn How To: Part Two:
The session profiles the dashboards, key
– Overcome data denial performance indicators, and business
– Increase analytical and reporting intelligence capabilities that are emerging
capacity as the new gold standard for university
decision support.
– Drive awareness of data and
analytics
– Inspire use of data in decision-
making
©2014 The Advisory Board Company • eab.com