You may not think that business analytics as a concept applies to academics, but access to meaningful and timely information is critical to the proper function of a dean’s office.  Anyone who has been a dean or worked in the dean’s office knows that.  And, like in any other business, certain kinds of data seem to percolate to the top of the needs list, but that data is all too frequently not easily accessible.

Take, for example, the need to project student enrollment for the ensuing year. As new students arrive on campus, there must be an adequate number of required courses as well as qualified faculty to teach those courses.  If enrollment is low because of a glitch or error in the Admissions Office, or there is an unexpected fluctuation in applications, the number of courses can be adversely impacted.

Similarly, enrollment projections are required several years forward, suggesting the need for a strong analytics tool, and one that allows for “what if” eventualities. The dean’s office needs to be able to monitor the number of applications, the number of offers tendered, the number of offers accepted, the number of paid acceptances, the cumulative amount of financial aid and the average aid per applicant.

These are just a few of the desired “data intelligence” items to have available at one’s fingertips; each one of these belongs to its own “dimension” for measuring performance. These enrollment statistics and other metrics drive the university, and the university president regularly seeks to be updated, just like any other CEO who needs “business analytics” to run a company.  Of course, a graph showing how the current numbers compare to past numbers is necessary.

The challenge is delivering up this intelligence, as by nature it is “multidimensional,” a word one encounters in typical definitions of OLAP technology.

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For example, one obvious dimension is “school” or “college,” since typically colleges are subdivided into various schools while universities are subdivided into colleges.  Thus, university X might have a College of Medicine, College of Business, and the like.  And so all the metrics above might be required by each school or college as well.

Other dimensions might concern staff [faculty] allocation to courses as well as course availability for student selection. Tracking the number of students progressing through the colleges from year to year, especially at the undergraduate level, is desirable so that sufficient resources can be properly allocated in a timely fashion.  When faculty are absent from the university for the purpose of a sabbatical (another dimension), their classes and student advisees must be accommodated either through existing faculty movement or the hiring of adjunct faculty.  If adjunct faculty are to be hired, the problem of recruiting an academically or professionally qualified individual can be a formidable challenge, not to mention the impact on a budget that might need to be crafted two or three years in advance.  This means that access to appropriate historical and current data must be available.

To consider another factor impacting faculty allocation: students, especially undergraduates, change majors at least once and it is not altogether unusual for a student to change majors several times.  As students move from one major to another, frequently within the same college, demand for certain courses might unexpectedly increase or drop.  Having as much advance data intelligence as possible for the potential impact on staff teaching needs in turn would lessen any adverse effect on financial budget plans.

Of course, the need for financial information is forever present in higher education as it is in business.  The usual data such as major budget categories and current progress should be easily available along with a comparison with past budgets.  Of equal importance is the projection to end-of-year.  At a different level the projected liability of the university to support the scholarships granted is something the financial vice president would want to know.  And it is not uncommon for faculty to approach their dean with requests for additional travel funds so they can take advantage of some unforeseen opportunity. As in the business world, in the face of unavailable or incomplete data, the answer is typically in the negative!

The list of information needs continues with the number and kind of publication each faculty member writes, the total yield for each faculty member over the number of years necessary to meet accreditation requirements, the percentage of publications in high quality journals, and perhaps some ranking of faculty by academic production.

I have indicated these information needs not only to suggest that in the academic environment they can be vast and various, but also to suggest that it behooves us to look beyond products with limited single information source availability. A really productive solution would also provide modeling with 1) robust “dimensionality”, 2) “what if” capabilities, and 3) the ability to compare Current v. Historical v. various Planned scenarios. These are hallmarks of products featuring OLAP technology, which, though more widely adopted in the business community, are clearly well-suited and necessary to provide an application-oriented academic “business analytics” program as well.

(Professor Bill Tastle is a regular contributor to OLAP.com. At Ithaca College he teaches a course in Business Analytics, and uses PowerOLAP from PARIS Technologies for in-class exercises.)