12123

1© 2015 ELLUCIAN. CONFIDENTIAL & PROPRIETARY | Session 12123
Writing the Right Reports:
Establishing a Reporting Strategy
Henry DeVries, Ellucian
Digi Edwards, Ellucian
April 15, 2015
Session 12123

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!

Introduction

A little about us –
Dr. Henry DeVries
• Management Consultant, Principal
Targeted services for Business Analytics
• Ph.D. Cornell University
40 years in higher education
• Faculty member, administrator, researcher
o 16 years as CIO at Calvin College in Grand Rapids, MI
o 13 years (concurrently) as CFO
o Active in NACUBO, CACUBO, EDUCAUSE, CCCU

A little about us --
Ms. Digi Edwards, CMC
• Management Consultant, Principal
• M.S. Software Systems Engineering, George Mason University
25 years of higher education experience
• Faculty member, ERP developer, application trainer
o CIO at Edison State College in Ft. Myers, FL
o Banner HR/Payroll/Student upgrade
o Luminis implementation
o ODS/EDW implementation

1 What is the Reporting Review Service?
2 Developing a Reporting Infrastructure
3 Case Study: Montclair State University
Agenda

Objectives
• We will explore the various components of an institutional
reporting review program
• We will explore some ways to align your business analytics /
reporting infrastructure into a framework that supports your
reporting requirements
• We will review a case study of a recent Reporting Review
engagement at Montclair State University (NJ)

What is the Reporting Review Service?

The Infrastructure Model
People
Policies Tools
TechnologyProcess

Business Analytics Strategy
Improve Information for Decision Making
Information
Infrastructure (people, process, policies, tools, technology)
Client
Need
Ellucian
Offer
Ellucian
Delivers
Client
Outcome
Determine Key
Performance
Measures
Performance
Metric
Services
Balanced
Scorecard
Strategy Map
Information to
Measure and
Monitor
Improve Data
Quality
Data
Governance
Services
Data
Standards
Mechanism to
Govern
Institutional
Data
Ensure
Infrastructure
Readiness
Reporting
Review
Services
Understand
Current State
Future State
Roadmap
Sound
Infrastructure
to Build Upon

The “Elevator Speech”
The desired outcome of implementing a reporting infrastructure
is to get “improve information for decision making”.
The Reporting Review Service attempts to do just that -- by
helping organizations understand if they have the necessary
business intelligence infrastructure (people, policies, process,
tools, technology) in place and, if not, determining what else they
would need to get there.
Based on 3 guiding principles –

#1 - Standardization
When the same tool is widely used throughout a department or
college, it simplifies both the support requirements for IT and
leverages community resources among the end-users.
Corollary #1
Standardization also delivers an attendant economy for the
institution because it requires fewer resources to license and
support one tool rather than many.

#2 - “A Single Source of the Truth”
All reports should derive from a single agreed-upon central data
source that is universally endorsed as correct and trustworthy.
Corollary #1—
This will insure consistency of results and reporting to better
support data-driven decision-making
Corollary #2—
Given appropriate role-based security, this will enable self-
service to information / data by end users

#3 - “The right information, to the right people, at the
right time.”
If institutional data and the reports derived from it are to be
considered as the grounds for data-driven decision-making, the
reporting environment must provide timely, relevant, and
appropriate data for the context of the decision-maker, whether a
departmental knowledge worker or an institutional executive.
Corollary #1–
Data is an institutional asset, not a departmental asset.
Corollary #2—
As a general principle, institutional data should be shared among
those employees whose work can be done more effectively by
knowledge of such information.

How we Deliver This
Preliminary information gathering
• Questionnaire
On-site Interviews / Focus Groups
• Leadership
• Technical Staff
• Functional Departments
Deliverables
• Executive Summary
• Findings and Recommendations

Developing a Reporting Infrastructure

Visibility, Insight, and Foresight:
Equipping People with the Information They Need to Be Successful
17

Two Different, but Related, Responsibilities
Run the Institution:
- Recruit & Admit Students
- Teach Students
- Invoice Students, Pay bills
- Operate Auxiliaries
- Hire & Pay Faculty & Staff
Manage the Institution:
- Who are the best admissions
candidates?
- Who is most likely to enroll?
- What academic programs are
popular / profitable?
- Projecting deferred maintenance
- Prepare regulatory reports
Process Transactions:
ERP, CRM, Advancement, LMS
Answer Questions:
Business Analytics

Two Related Categories of Reporting
Tactical Reporting (Culture of Assessment)
• Operational metrics
• Activity summaries
• Annual Reports
• Compliance
Strategic Reporting (Culture of Performance)
• Student Success analytics
• Strategic goals
• Key Performance Indicators

Culture of Assessment Culture of Performance
Data collected end of term
or year
Data collected quarterly,
monthly, weekly
Data is a means unto itself Data is a means to an end
Data used for accreditation
or end-of-year reports
Data used for decision-
making and improvements
Many measures Few key measures
Aligned with professional
interests
Aligned with strategic
priorities
Reactive / Inactive Proactive
Assessment vs. Performance

Some Changes with a Culture of Performance
Moving From:
Lagging indicators
• Quantify the results of past efforts
• Track stakeholder benefits
• Focus on results of actions
already taken
• Guide resource availability
Moving To:
Leading indicators
• Specific how outcomes will be
achieved
• Provide early indicators of
success
• Reflect the organization’s unique
strategy
“Use of institutional data to illustrate institutional
effectiveness and institutional success”

There are lots of questions to answer
What happened?
Why did it happen?
What’s happening now?
What will happen?
What do we want to happen?
There is a logical progression here –
it’s aligned with the reporting maturity

BA Maturity in Higher Education
Data Sophistication & Institutional Value
Data-driven
planning and
forecasting
MONITORING
WHAT is trending
NOW?
Ability to model,
manage, and
adapt
OPTIMIZING
What do we WANT
to happen?
Primarily batch and
some ad hoc
reports
Increase in
ad hoc
reporting and
Information
on demand
ANALYZING
WHY
did it happen?
INFORMING
WHAT
happened?
Dashboards
for
managers
and analysts
PREDICTING
WHAT do we THINK
will happen?
Managed Reporting
Ad Hoc Reporting & Queries
Operational Dashboards
Scorecards, KPIs & Benchmarking
Predictive Analytics
BusinessAnalyticsMaturity
ODS
EDW
PM
ERP
EDW +
Statistical

What Else Happens with Evolving Business Analytics?
Data volume grows
Number of users grows
Depth of analysis grows
Query complexity grows
Need to visualize grows
Expectations grow
We hope that:
Data-driven decision-making grows

The institution as “organized anarchy”
Decentralized academic and administrative units
Vague/non-existent objectives, weak reward
structures, few systems for executions
Limited analytical expertise, practice and tools;
coupled with inconsistent data standards
Barriers to Data-Informed Decision-Making
in Higher Education

The Reporting Infrastructure needs to Deal With --
People
• Leadership
• Culture of Evidence
• Alignment
Process
• Data Governance
• Performance Metrics
Policies
• Operating Goals
• Strategic Plan KPIs
Tools
• Change Management
• Communications Strategy
Technology
• BA stack
• Reporting tools
• Technical support

A Recent Case Study:
Montclair State University

The Montclair State University Project - OneMontclair
Initial Implementation
Early Stage
of BI Maturity
Leverage & Expand
Existing BI Resources
Maximize Investment
of Banner BI Platform
(ODS/Cognos/RAP/SRP)
Empower Staff
With Tools for
Data-Driven
Decisions
Best of Breed –
Consolidate Complementary
Systems

Presenting Issues
• Interest in moving to a mature BI scenario, dashboards, scorecards
• Desire for Institutional Analysis to provide institution-wide reporting,
not just the student system
• Distributed reporting to the campus
• Improved data governance
• Retire “shadow” databases

Desired Outcomes for Reporting Review
• Assess your needs in the area of information reporting
• Confirm your priorities for data and reports
• Identify the strategic drivers in your environment
• Begin to think functionally and operationally about your data and reports
• Consider strategic approach for using reports to enhance performance and
accountability
• Prepare you to work effectively with business intelligence consultants to
create your standard reports
• Develop recommendations for achieving your reporting goals
• Identify key objectives and outcomes for Montclair State University’s
reporting strategy
30

Step 1 - Preliminary Questionnaire
Four areas
Data Collection
Data Analysis
Data Utilization / Reporting
Data-Driven Decision-Making

Data Collection
Q1. We place a high priority on effectively collecting data.
Q2. We are successful in collecting data which leads to
improving performance.
Q3. Staff members understand how data leads to improved
performance.

Data Collection
0.0
1.0
2.0
3.0
4.0
5.0
Priority Success Understanding
Data Collection avg = 3.72

Q1. We place a high priority on effectively reporting data.
Q2. We are successful in sharing data with the appropriate staff
members.
Q3. Staff members understand how data reports lead to
improved performance.
Q4. All staff members have the basic data they need to improve
their performance.

35

Data Analysis
Q1. We place a high priority on effectively analyzing data.
Q2. We are successful in analyzing data so as to improve
performance.
Q3. Staff members understand how data analyses lead to
improved performance.
Q4. All staff members have the analyzed information they need to
improve their performance.

Data Analysis
37
0.0
1.0
2.0
3.0
4.0
5.0
Priority Success Understanding Staff access
Data Analysis avg = 3.20

Data-Driven Decision Making
Q1. We place a high priority on making data-driven decisions.
Q2. We utilize clear measures to indicate success in achieving
our goals.
Q3. We are consistently successful in using data and information
to improve our overall performance.

Data-Driven Decision Making
39

Survey Summary – by category

Step #2 – Interviews / Focus Group
Sample Questions --
• Describe your reporting environment and the tools you use.
• What pain points are you experiencing with regard to reporting?
• What is the current and ideal state of reporting?
• Tell me about the types of data you collect on a regular basis.
• What are your primary formats of reporting/sharing data?

Sample Questions --
• How would you describe your current methods/process for analyzing data?
• Discuss how well you are reporting/sharing data with the appropriate staff
members?
• Discuss how well you are able to analyze and translate your data/metrics
into useful information/knowledge,
• Describe to what extent decisions are made based on relevant data and
information?

Step #3 – Summary of Findings (major issues)
• Lack of confidence in current systems (data input/data output)
• Unaware of system functional components
• Reliance on shadow systems that produce inconsistent results
• IR support to institution
• Multiple reporting tools against various data sources
(Excel/Crystal/Focus)

Step #3 – Summary of Findings (major issues) – cont’d
• In-house developed ‘ODS’ (data mart)
• Difficulty in accessing data sets for decision-making
• Technical support requirements
• Lack of intra-departmental collaboration/coordination (redundancy of
requests)

Step #4 – Recommendations (from the Exec Summary)
• Standardize on the Banner Business Intelligence components that
will be implemented (Ellucian ODS/Cognos) as the data source of
choice for all institutional reporting – “Best of Breed” offers
additional challenges
• Implement a comprehensive Data Governance review and
assessment to educate members of Montclair State community
about Data Governance concepts

Change ManagementStaffing Resource
Develop a long-range
plan to provide the
resource requirements to
effectively support the
campus-wide deployment
of the Banner
Performance Reporting &
Analytics (BPRA) suite
Develop a clearly-defined
change management
support to assist the
campus in understanding
the reasons for the
changes in the reporting
infrastructure
Step #4 – Recommendations (from the Exec Summary) – cont’d
Knowledge Transfer
Develop a comprehensive
training plan to provide
the necessary knowledge
transfer to equip staff and
faculty to effectively utilize
the BI platform
components

The Role of Meaningful Metrics
“We waste data if we don’t use it to
make decisions and take action.”
Thomas Davenport, Keeping Up with the Quants:
Your Guide to Understanding and Using Analytics
47

Questions & Answers

49© 2015 ELLUCIAN. CONFIDENTIAL & PROPRIETARY | Session ID
Thank you!
henry.devries@ellucian.com
digi.edwards@ellucian.com
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