Today, we need to see further into the future more than ever before. Life for universities, whether large or small, public or private, has become increasingly complicated. Keener competition, rigorous performance-based funding models and seemingly whimsical legislative and federal mandates have contributed to the angst. That’s why it’s absolutely critical for Big Data and analytics to be part of—if not central to—the effective management of the modern university.
Big Data works in much the same way as the old-school model of collecting feedback from small faculty-to-student ratios. Smaller classes would allow professors the ability to make quick judgment calls about their students. The intimate settings revealed who was in trouble, who was missing class, etc. We would then respond based on those observations. Big Data has the same impact. It allows us to make quick judgment calls and act on those observations.
Leveraging analytics also enables institutional leaders to make better-informed decisions for long-term sustainability. Predictive models can yield a splendid competitive advantage. The ability to connect the dots helps us improve the students’ academic experiences, become more efficient, and more accountable.
How We Leverage Data at UWF
We use data in a few different ways at the University of West Florida. We struggled for years to create a system of continuous feedback in order to evaluate the effectiveness of our decisions and financial investments. With the use of Big Data analytics, we have made good progress, but have had to and continue to overcome a number key issues.
For example, our enrollment staff has been tasked to admit students who will persist and succeed to graduation. Test scores and grade point averages alone are not sufficient predictors of success. Instead, with sufficient data, our people can look at historic yield rate, geographic data, and social media use to narrow recruitment campaigns.
Our aim to keep students on track for graduation is greatly assisted by Big Data analytics. Through early warning systems, we are able to determine if a student’s grades are starting to slip, and run appropriate interventions. This results in cost savings for both the institution and the student. We are also able to assist students in selecting majors by correlating their status in specific courses with the likelihood of success in specific majors. Helping students avoid wrong choices in selecting a major (and the ensuing career) early enough is a gift that keeps on giving. Not only are students likely to find a career choice for which they are well suited, they will also be more likely to find employment and job satisfaction for a lifetime.
Overcoming the Roadblocks to Data-Driven Decision Making
There are a few key issues that we faced in getting our Big Data analytics off the ground. Funding was one issue we had to solve. After all, money is always scarce and the investment in data management support is not cheap. Unless you have good buy-in on campus as to its value, you will have faint support for the investment.
Other smaller but nettlesome issues include consistent definitions of terms, identification of a single office that is designated as the official data steward, the maintenance of proper control over the reporting aspect, and mechanisms to manage data so as not to become overwhelmed with data. You also need a highly educated staff who can provide analysis as well as reports.
My provost often points out the need for finding a balance between what he calls data democracy (access for all to reports and data) and data fascism (centralized control of data) since data and access thereunto is power.
A Cultural Change, Not A Structural Change
At the heart of things, the change to the Big Data world for modern universities is a cultural change—not a structural one. It doesn’t come in a box where you just add water and voila, you have it. You can’t buy it. There has to be a universal willingness to change and an appreciation for its value.
Once done, however, better begets more, which is bigger data. The bigger it gets the less you have to fix, and the results can be simply grand.
For example, as we have progressed through better analytics, we have begun to identify outliers. We found a lot of things we didn’t expect to correlate, like a specific course that may be making the difference in student progress. It feels like we have opened the door on a whole new world and the journey is exhilarating.
Ultimately, I’ll leave you with just one cautionary tale. Try to avoid losing ground when someone leaves. At too many institutions, there’s just this one guy who has all the “magic numbers.” Often he has been around a while, and is perceived as the authority figure. In truth, the university has relied for years on his hunches instead of solid data. Then poof, he retires, and you’re left at square one.
Making Big Data a reality on a university campus requires conscious effort from everyone—from the senior leadership all the way across the institution. It cannot be a purely top-down exercise. It must be well supported and structured. Once the institutional culture is focused around data-driven decision making, there’s no stopping you.
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