For decades, leaders at colleges and universities of all types across the country have defended themselves from being seen as, or operating as, a business. The very idea seemed counter to the goals and purpose of the postsecondary institution. However, it has become increasingly clear that some practices from the business world—from making sure students succeed to generating enough revenue to keep the lights on—are critical to the successful operation of an institution of higher education. In this interview, Nate Johnson shares his thoughts on the value effective data leveraging can bring to the table and reflects on the roadblocks standing in the way of its wider adoption.
The EvoLLLution (Evo): Why have higher education administrators become interested in the potential for Big Data and analytics to support their work?
Nate Johnson (NJ): There are two main reasons higher education administrators are interesting in the potential of Big Data. One is the same reason that every business has taken an interest in Big Data: because it can give you a competitive advantage if you’re good at it. The more other institutions are using it, the more it becomes a defensive measure where you have to be able to do it in order to compete.
The other reason has to do with the learning objectives of higher education. Data presents us an opportunity to do things in terms of student learning and student success that weren’t possible without the kinds of data tools that are available now. Some Big Data applications serve the business side of the house and others serve the academic side, and both are crucial.
Evo: What are a few of the most common examples of how Big Data is being leveraged by colleges and universities?
NJ: Big Data is really being leveraged right now on the business side of house because, as with most innovations, advances tend to happen where revenue is being generated. That money is then used to develop the tools, and only after that do we see those tools being used for non-revenue-generating applications. What’s more, some of the academic applications of Big Data are built out of business or financial applications.
The institutions that are best at this are the big for-profit companies both because they have the most students and they have the biggest marketing budgets, and because they’re typically online, collecting a lot more data inputs from their online courses. The University of Phoenix or any similarly large for-profit school—from recruitment funnel down to student enrollment and retention—are using data at every point in that process. The targeting of recruitment ads to produce leads is, in some ways, no different from what any other business is doing with online advertising. It’s just that we often don’t think of higher education as a business in the same sense except when it’s obvious, as it is with the for-profit institutions. They’re using Big Data for that targeting and figuring out what platforms and what strategies they can use to get the most bang for their advertising and recruiting buck.
Once they have the students, they need to retain them, especially in ways that are aligned with their business interests. They’re using Big Data to figure out what patterns are indicators either that students will continue to enroll and then pay for more online courses or that they are at risk of leaving. Again, those are very similar strategies in some ways to what a credit card company, a cable company or a cell phone company are also doing in terms of customer retention. It’s just that the input to the models are coming from participation in online courses rather than a different kind of customer activity. Once you have those kinds of databases built up and ways of identifying patterns of student success and retention, you start to have people and software tools and ways of thinking about student success and retention that you can apply outside of the context of purely financial interest.
However, smaller institutions may not have the resources of the bigger schools to develop these tools, since they lack the size and resources available to major institutions. As a result, smaller institutions could actually end up losing money on these kinds of innovations, at least in the short term.
Evo: Do you think for public institutions and non-profit private institutions that there needs to be a shift in mindset before they’ll be able to maximize the value the data could bring?
NJ: To some extent there has already been a shift among all institutional stakeholders towards understanding the value data can bring to the table. It will get easier as the tools become more accessible and the number of people who can run the models and have Big Data literacy proliferate in the workforce. But the challenge is that it’s still pretty expensive to do—and to do well. Additionally, people who are good at it are in extremely high demand both inside and outside the higher education industry. A lot of the tools are the same, just tweaked for higher education applications, and higher education institutions are competing with everybody else for the workforce. It’s not going to be something that’s affordable for every institution without sufficient scale or until the tools are a lot cheaper and easier to use.
Evo: What are some common misconceptions higher education leaders tend to have around the potential or value of data analytics?
NJ: The biggest misconception is that data is a button that you can press and that it’s primarily a technology tool. I think the real secret sauce is in the application of critical thinking to the data, the persistence and the systematic follow-up of an iterative use of data to improve models over and over again. This is true whether you’re using the most high-tech, latest data-mining tools and statistical methodologies or whether you are doing calculations on a ledger with a pen and paper and monitoring where students are going and trying to observe patterns.
You need to follow up, you need to use the data when you have it, you need to have the right people in place to act on insights that come from the data and you need to be willing to fail in order to identify things that aren’t working and to actually move your organization in response to what you’re finding in the data. Those are areas where a lot of for-profit companies both inside and outside of higher education tend to be much more responsive than traditional higher education institutions.
Evo: Is there anything you’d like to add about the potential for Big Data and analytics to really transform the way institutions operate?
NJ: Big data is one tool in the toolbox and if used well it can really help students and institutions both. But, like with any tool, if it’s used in an undisciplined or uninformed way it is probably going to be a big waste of time and money.
This interview has been edited for length and clarity.
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