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Key Dashboard Metrics Universities Should be Keeping an Eye On

Recently, Lee Huang, President and Principal of ESI and head of ESI’s Higher Education Practice Area, caught up with some of our thought leaders in the higher ed space to ask them some of the most critical questions facing the industry. This exert includes Elmore Alexander’s full response.


Elmore, you’ve consistently stressed the importance of data-informed decisions. Amid so much fluidity inside and outside of our institutions, what are the key dashboard metrics universities should be keeping an eye on?

Lee, universities should be looking at data-informed decisions from two different perspectives.

Looking at revenue and cost data

First, universities should be looking at revenue and cost data by programs and units. For non-revenue units, the cost metrics are relatively simple—salaries, operating budgets and overhead allocations.  Outcome metrics for such units are much more complicated. While it is possible for some units to identify cost savings and service impact of their operations, these numbers are very subjective. It can be done. I’ve seen some good examples by the IT units at Bridgewater State University and U Mass-Boston.

For revenue-producing units especially within academics, the process is reasonably straightforward. How much revenue does the unit/program produce and what are the costs associated with the unit/program? In practice, however, this process can become complicated. The real costs of teaching a program (faculty salaries and benefits, administrative overhead, and instructional costs such as databases and specialized software) can readily be identified. But if instruction is provided by adjunct faculty members, the cost is different than if it is provided by full professors. Allocations are easier looking retrospectively where the courses have been taught and the students and professors are a matter of record than they are prospectively. Thus, using data to assess current programs is easier than evaluating the future prospects of proposed programs.

Determining revenue is also less than straightforward.  Several important issues must be considered:

  • Data must be built up from the student perspective. While it is convenient to simply multiply the number of students by tuition, each student is not paying the same amount. Discounting, institutionally funded scholarships, and other such factors must be taken into account in determining the appropriate revenue generated by a particular course or program. When looking at potential programs, pro forma budgets must include tuition discounts and expectations for student attrition.
  • It is easier to calculate the revenue from a program or course containing solely students enrolled in that program—all the students and all the revenue associated with them counts. Blended programs that provide instruction to students from multiple programs and the general education program of the university are more complicated. The English department, for example, teaches large numbers of students as part of the general education program. Those students are not pursuing a degree in English. They are at the university to study business or education or science.  For that reason, some part of the revenue they generate by taking a general education course should be allocated to the unit in which they are majoring. Responsibility/Revenue Centered Management (RCM) budgeting systems address this issue by allocating revenue to the program of a student’s major and then having that program negotiate a payment to the general education department for the instruction that they provide. If you are going to effectively evaluate programs, you must figure out ways to recognize the complicated nature of academic revenues. William Massy’s book, Reengineering the University, is an excellent resource for understanding the cost of teaching and how to develop a rational financial planning and budgeting system.

Ultimately, the institution should be looking at a calculation of the profitability or return on investment of each program or unit. Academicians from the humanities often see such analysis as a strategy for eliminating humanities education from universities. In reality, some humanities programs are among the most profitable within universities.


Benchmarking data against comparable universities

Second, universities should be benchmarking data about the university and their units to data from other comparable universities. Whatever the cost of instruction for your MBA program is, you cannot understand whether or not it is appropriate without being able to compare it to the costs at other institutions. If your development unit is staffed by 10 professionals and generates $10 M a year from the annual fund, you cannot determine if this is good or bad without being able to compare your results and staffing to other institutions. Benchmarking is the process of making such comparisons.  There are many types of benchmarking—for example, operational benchmarking (the structures and processes that your institution uses) and strategic benchmarking (leading indicators within higher education such as rankings in national surveys). Getting comparative data is sometimes a challenge. There are many sources, however, that can provide data for benchmarking—for example, IPEDS (Integrated Postsecondary Education Data System), AGB (Association of Governing Boards) Benchmarking Services, Delaware Study of Instructional Costs and Productivity, NACUBO (National Association of College and University Business Officers), and Moody’s Municipal Financial Ratio Analysis. Additionally, some universities have formed consortia to share data—Higher Education Data Sharing consortium (HEDS), the Association of American Universities Data Exchange (AAUDE), Southern Universities Group (SUG), and the Lower Cost Models for Independent Colleges Consortium (LCMC). What is critical is that the university identifies comparable institutions and assess the reasonableness of its processes and outcomes.

One final and important caveat—whatever data you choose to emphasize, they must be shared.  During my career, I’ve worked in institutions where I had large amounts of data to consider in my decision-making processes; and I’ve worked at others where I was totally “flying blind.” My most frustrating experience was at an institution that spent my entire tenure hoarding the data and arguing that it was working to make sure that the data was “right.” Decision-makers (from deans and department chairs to faculty leadership) need to see the data that is driving top-level decisions. Vice presidents cannot understand program-level data without interpretation from department chairs and faculty members.  The faculty leadership cannot understand the workings of the university if they do not see the data that links courses to programs to the university.  Shared governance needs to be about shared data as well as shared decision making.


Elmore Alexander

Elmore Alexander is a Senior Advisor at Econsult Solutions, Inc. with expertise in higher education. Mr. Alexander is currently Dean Emeritus of the Louis Ricciardi College of Business at Bridgewater State University. Prior to that, he served in high-level posts in business schools of Marist College, Johns Hopkins University, American University, and the University of Memphis.

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