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USING BI TO IDENTIFY
REVENUE AND RESOURCEREALLOCATION
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Donna RohlferDirector Budgeting and
Business TransformationFinance and Business Services
Phyllis Wykoff Director,
Business Intelligence Center
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•University is one of the oldest public
institutions in the country. It waschartered in 1809 and opened itsdoors to students in 1824.
• Located in Southwest Ohio
• Named for the Miami Indian Tribethat inhabited the area now knownas the Miami Valley Region of Ohio
Miami University
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Total Enrollment 2012 23,390
Undergraduates
Oxford: 15,081
Hamilton: 3,615Middletown: 2,092
Masters & Doctoral
Oxford: 2,476
Voice of America Learning Center: 126
Faculty & Staff (with GA’s) 5,000Faculty & Staff (excluding GA’s) 4,198
College & Schools 6
Miami University
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Recognition
Kiplingers (2012):Once again, Miami University has been
identified as a bestvalue 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 anduniversities." Recognized for strongprograms in architecture, business, andmusic.
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Recognition
• 2012 President's Higher Education Community ServiceHonor Roll
One of five recipients out of 641 eligible schools inthe nation to Miami's honor recognizes service
programs in the area of early childhood education.
Forbes 2012In the magazine's 2012 list of America's Top Colleges,
Miami ranks 35th in the nation and 1st in
Ohio among public universities
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Our Environment
•Ellucian Banner ERP
•Primarily an Oracle shop
•Business Intelligence tool is
•Oracle Business Intelligence Enterprise Edition(OBIEE)
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Miami’s BI History
• 2008 - Limited funding to build a BI program
• Got creative• Found and used extra SQL Server Licenses
• Found unused hardware
• Created small model and cube to show thepotential of BI
• With Miami University data
• Used Excel as presentation tool
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Miami’s BI History
• 2009 – Summer Profitability Project
• Create „Profit and Loss‟ statement for eachcourse section
• First production use of BI at Miami University
• Utilized „found‟ hardware and software
• Used Excel as presentation tool
• Did write some report using SQR
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Miami’s BI History
• 2010 Released RFP for Enterprise BI tool
• 2011 – Purchased Oracle Business IntelligenceEnterprise Edition
• Implemented in 2011
• First dashboards in late 2011
• 2013 – Still learning!
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The Situation!
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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
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Funding Sources per StudentFY1990-FY2012
(1990 Inflation Adjusted $)
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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 tosolutions
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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…”
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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
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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
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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
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Considerations in Developing a New Budget Model
• Impact of governance on planning and decision-making
• Historical clustering of academic programs
• Industry and customer (student) connections occur at the unit level
• Successful change requires strong unit supportdriven by clear incentives and rewards
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New Budget Model
• More effective measure of financial performancefor 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 andresource allocation decisions
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New Budget Model (cont‟d)
• Better assures financial issues are considered inacademic 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?
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Help!
We need data!
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They asked for data—
But we deliveredInformation!
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Cross Functional Core Team
Cross Functional Core Team
Provost
Academic Affairs
Vice President
IT Services
Asst DirectorContinuing
Education
Assoc. VP Finance
& Business
Assoc. VP Budget &
AnalysisAsst Registrar
Controller
Budget DirectorDirector,
Institutional
Research
Sr. Director,
Enterprise
Information Systems
Director, Business
Intelligence
Business
Intelligence Team
Members and
Analysts
Director, Financial
Assistance Bursar
Vice President
Finance and Business
Services
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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
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Registration data
•Student based data•Course data•Section data•Registration data
•Instructor •Credit hours•Headcounts
This is not an enrollment view of registration data butrather a Bursar‟s view of registration
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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)
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Financial Aid
•External Financial Aid
•Institutionally Funded Financial Aid•Expense to University
•Institutional discount rate
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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
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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
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Classification of Bursar Codes
Created a five level hierarchy for EACHdetail 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
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Convert Fees/Waivers to be Course-based
• Miami University‟s fee structure is based on credithours 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
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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
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Derived fields = Information
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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 BusinessIntelligence to address strategic needs
• Demonstrated the value of eliminating datasilos
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Project issues and challenges
• Testing
• Categorizing the same data for different user needs and applications
• Merging the need for:
• Exclusive and exhaustive data granularity
• Analytics
• Executive dashboards
• Management level reportingFrom ONE data source!
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Project issues and challenges
•BI requires leadership, commitment, input and work from both ITand 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
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Learnings
• Testing
• Metadata management
• Data visualization – Design and Testing
• Training – BI concepts, new tools
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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 andparticipating
−Moves the project forward•Sub-groups address specific issues and topics
•Document, document, document
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Where are we now?
• Data Warehouse developed and deployed usingOBIEE
• 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
• Value increased communication and client/ITteamwork
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Where are we now?
• Revenue model contains:
• 19 Dimensions
• 3 Fact tables
• Revenue – Over 555,000,000 rows
• Course registrations – Over 6,300,000 rows
• Updated nightly
• Capture Point in Time (PIT) Snapshots on strategicdates for historic analysis
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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?
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Clients view of solution
Miami University's investment in institutional analytics has provided hugedividends 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 analyticalcorps across the university.
And, last but not least, it is supporting strategic conversations aroundbudgeting and finance that were not possible without this capacity. Thepromise 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
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Executive Dashboards
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Questions?