Identifying Revenue
& Resource
Re-Allocation to
Grow the University
Donna Rohlfer
Director Budgeting and
Business Transformation
Finance and Business Services
Rohlfedm@MiamiOH.edu
Phyllis Wykoff
Director,
Business Intelligence Center
IT Services
Phyllis.Wykoff@MiamiOH.edu
• Miami University is one of the oldest
public institutions in the country. It was
chartered in 1809 and opened its doors
to students in 1824.
• Located in Southwest Ohio
• Named for the Miami Indian Tribe that
inhabited the area now known as the
Miami Valley Region of Ohio
Enrollment Fall 2011
Total Enrollment
23,240
Undergraduates
Oxford:
Hamilton:
Middletown:
14,936
3,672
2,172
Masters & Doctoral
Oxford:
2,299
Voice of America Learning Center: 161
Faculty & Staff (with GA’s)
4925
Faculty & Staff (excluding GA’s) 4122
College & Schools
5
Kiplingers (2012):
Once again, Miami University has been
identified as a best
value in Kiplinger's annual list of the
"100 Best Values in Public Colleges“
Fiske Guide to Colleges (2013)
The Fiske Guide to Colleges
2013 includes Miami in its list of the
nation's "best and most interesting
colleges and universities." Recognized
for strong programs in architecture,
business, and music.
2012 President's Higher Education
Community Service Honor Roll
One of five recipients out of 641 eligible
schools in the nation to Miami's honor
recognizes service programs in the area
of early childhood education.
Forbes 2012
In the magazine's 2012 list of America's
Top Colleges, Miami ranks 35th in the
nation and 1st in Ohio among public
universities
The Situation!
Financial Issues Facing Higher Education
• Price regulation
• Increasing price elasticity
• Negative demographic trends
• Increased competition
• Potential cuts in state funding
• Increased health care costs
• Rapidly rising commodity prices
Funding Sources per Student FY1990FY2012
(1990 Inflation Adjusted $)
Special Challenges for Miami University
• Limited revenue diversification
• Restrictive access to some high demand programs
• Slow development of new academic revenue
• Inability to measure performance of initiatives
• Cost cutting versus increased productivity
• One time versus permanent approaches to solutions
Strategic Initiatives and Forward Thinking
“...Given the very significant changes in the
economy and the competitive context of higher
education, more than ever it is critical that Miami
be forward-looking and purposeful in fulfilling our
mission...”
“…prioritize and align our strategic goals with the
new economic reality and competitive higher
education context by creating a long-term
sustainable baseline budget and identifying
strategic options for improving our resource
base…”
Strategic Initiative Recommendations
A. New Revenue Opportunities
B. Improve the Efficiency and Effectiveness of
University Administrative and Support
Functions
Included the recommendation to develop a
new budget model that will result in the
generation of new revenues, improved
resource reallocation and greater operating
efficiencies
Strategic Initiative Recommendations
(cont’d)
C. Improve the Efficiency and Effectiveness of
Our Core Educational Efforts
D. Improve the Efficiency and Effectiveness of
Student Services and Co-Curricular Activities
Current Budget Model
• Historical budget practices at Miami
• Centralized approach
• Stand-alone worksheet based model
• Focus on unit actual to budget performance rather
than institutional financial performance
• Lack of financial performance information for
academic programs or support centers
Considerations in Developing a New
Budget Model
• Impact of governance on planning and decisionmaking
• Historical clustering of academic programs
• Industry and customer (student) connections occur
at the unit level
• Successful change requires strong unit support
driven by clear incentives and rewards
New Budget Model
• More effective measure of financial performance
for every academic program
• Where are my revenues being generated?
• How are resources consumed to generate
these revenues?
• Who is enrolled in my courses?
• Decentralized system increases influence of
academic divisions over financial planning and
resource allocation decisions
New Budget Model (cont’d)
• Better assures financial issues are considered in
academic program decisions
• What is capacity?
• What does this cost to offer?
• What is net revenue?
• Provides very clear incentives for improved
financial performance
• How to determine the method of revenue
allocation?
Help!
We need data!
They asked for data—
But we delivered
Information!
Our Environment
• Ellucian Banner ERP
• Primarily an Oracle shop
• Business Intelligence tool is
• Oracle Business Intelligence Enterprise Edition (OBIEE)
Cross Functional Core Team
Vice President
Finance & Business
Affairs
Provost
Academic Affairs
Asst Director
Continuing
Education
Assoc. VP Finance
& Business
Assoc. VP Budget &
Analysis
Controller
Bursar
Asst Registrar
Director, Financial
Assistance
Director,
Institutional
Research
Vice President
IT Services
Sr. Director,
Enterprise
Information Systems
Director, Business
Intelligence
Budget Director
Subject Matter Experts
(SMEs) as needed
Working committees as
needed
Business
Intelligence Team
Members and
Analysts
Data Sources
• Registration data – Who is taking each course?
• Bursar data – How much was each student charged?
– Undergraduate, Graduate, Campus, Residency
• Bursar data – How much did each student pay?
– Waivers
• Financial Aid – Authorized Financial Aid
Registration data
•
•
•
•
•
•
•
Student based data
Course data
Section data
Registration data
Instructor
Credit hours
Headcounts
This is not an enrollment view of registration
data but rather a Bursar’s view of registration.
Bursar Data
Student Based Revenue processed through Bursar
•
Instructional Fee
•
General Fee
•
Course Specific (Equestrian courses)
•
Program Specific (Architecture students)
•
Lab Fees (Chemistry)
•
Divisional Fee (Business Surcharge)
Financial Aid
•
External Financial Aid
•
Institutionally Funded Financial Aid
• Expense to University
•
Institutional discount rate
Reality Sets In…
• Business practices have changed
• Some data was obsolete
• Some data usage changed
over time
• Some data was just wrong
• Some data was missing
Reality of Source Data
• 7500+ Detail (charge) Codes used since 1999
• 1300+ Detail Codes for Tuition
− No distinction between General, Out of State
or Instructional Fee
• No easy way to organize or analyze the data
• Charges by credit hour, not by course
Classification of Bursar Codes
Created a five level hierarchy for EACH detail code.
Detail Code 0106 “UG Instructional Fee – 1st Sem”
•
Level 1 – Revenue
•
Level 2 – Tuition and Fees
•
Level 3 – Instructional Regular
•
Level 4 – UG Instructional Regular
•
Level 5 - blank
Detail Code C11S “Class of 2011 Scholarship”
•
Level 1 – Expense
•
Level 2 – Tuition Aid
•
Level 3 – Scholarship
•
Level 4 – Internal
•
Level 5 – Instructional
Convert Fees/Waivers to be Course-based
• Miami University’s fee structure is based on credit hours
attempted, not per course
• Pro-rated fees, waivers, financial aid
– Calculated per credit hour rate and applied to course
by credit hours for each student
• $10,000 in instructional fee, 10 credit hours
− Calculated per credit hour rate of $1,000 per hour
− 3 credit hour course ‘price’ $3,000
A Star is Born
• Created a dimensional model (star schema) that:
• Combined the data from multiple data silos
• Added derived measures (fees/waivers
per student per class per term)
• Added derived fields (financial cohort)
• Added hierarchies for analytical capabilities
• OBIEE used to create Executive level dashboard
• And Management level reports
• And Ad hoc query tool for further analysis
Derived fields = Information
Additional Project Benefits
• Model provides single version of the data
• All analyses and queries provide the
same numbers
• Provided the information needed to move
forward with Responsibility Center
Management
• Demonstrated the power of Business
Intelligence to address strategic needs
• Demonstrated the value of eliminating data
silos
Project issue and challenges
• Testing
• Categorizing the same data for different user needs and
applications
• Testing
• Merging the need for:
• Exclusive and exhaustive data granularity
• Analytics
• Executive dashboards
• Management level reporting
From ONE data source!
Project issue and challenges
•
•
•
•
Testing
Metadata management
Data visualization – Design and Testing
Training – BI concepts, new tools
Learnings
• BI requires commitment, input and work from both IT
and from the business users.
• No one person understands all the data and implications
– Talk to a wide range of people to get the entire picture
• Validate, validate, validate
– New way to look at data
– Prove it ties to source data on a daily basis
• Data validation
• Immediate notification data issues
• Context is important
– Data cube is both powerful and dangerous
Best Practices
• Allow reasonable timeframe
• Use project management methodology
• Have the right people at the table
− Accountability
− Everyone contributes, everyone questions
• Commitment to the project from top down
− Implies regular attendance at meetings
− The project sponsor sets the tone by showing up and
participating
− Moves the project forward
• Sub-groups address specific issues and topics
• Document, document, document
Where are we now?
• Data Warehouse developed and deployed using OBIEE
• Learning curve – new technology, new processes
• Testing – took a huge amount of time for IT staff and
for functional staff
• Training – multiple tools to learn
The original questions
• Where are my revenues being generated?
• How are resources consumed to generate these
revenues?
• Who is enrolled in my courses?
• What is capacity?
• What does this cost to offer?
• What is net revenue?
• How to determine the method of revenue allocation?
Clients view of solution
Miami University's investment in institutional analytics has provided huge
dividends for our institution.
First, it has added a level of sophistication to our understanding of how our
business practices are reflected in our ERP.
Second, the design work has resulted in closer alignment of our analytical
corps across the university.
And, last but not least, it is supporting strategic conversations around budgeting
and finance that were not possible without this capacity. The promise of IA
going forward is that Miami University will have stronger management and
more insight into strategic implications of our decisions.
---David Ellis, Associate Vice President, Budget and Analysis
Executive Dashboards
Questions?