How Data Can Benefit Colleges & Universities: Part 1

HOST: Hello, everyone, and welcome to this episode of Digital Journeys.

HOST: Today on our show, we will have Jim Holman and Anthony Pember. Jim and Anthony are both directors in our Digital Advisory Practice. They are going to discuss how data can help higher education make better decisions.

HOST: Without delay, I'll turn it over to Jim.


JIM HOLMAN: Thanks, Jerry, for the intro. Now let's get started with Anthony Pember.

JIM HOLMAN: Anthony, with regard to how data can benefit colleges and universities, what challenges and barriers exist for traditional colleges and universities in becoming more data-driven?


ANTHONY PEMBER: Thanks, Jim. Interesting question. I think there are a lot of challenges these days for institutions, both colleges, community colleges, and traditional universities.

ANTHONY PEMBER: I've seen a number of challenges at different institutions. A couple that come highest to mind: available expertise is one. I often find that universities either don't have personnel with the skills to help with data-driven decisions, or if they do have personnel with those skills, they are already consumed with something else.

ANTHONY PEMBER: I think that's one of the challenges that a lot of institutions have, particularly smaller institutions.

ANTHONY PEMBER: Another challenge is the higher education business model. There are many aspects of higher education that are inherently difficult to manage and measure. For example, how do you measure research efficiency or teaching efficiency?

ANTHONY PEMBER: Because of these difficulties in measuring some things in higher education, it's difficult to find data and information that can be used to make more informed decisions, particularly data-driven decisions.

ANTHONY PEMBER: That being said, I still think there are plenty of opportunities available for higher education to take advantage of. It's just sometimes difficult for them, because of their business model, to find the right information to help drive decisions with data.

ANTHONY PEMBER: The other challenge is data availability. I would say that most institutions I've worked with have lots and lots of data. So I think it's probably more of a perception of data availability.

ANTHONY PEMBER: A lot of institutions think their data is really poor and that they can't use that data to help drive decisions. My experience is actually the opposite, but my experience is less important here. It's the perception of the institution that thinks they don't have good data.

ANTHONY PEMBER: So they're creating a challenge that they then have to overcome to convince the constituents within the institution that their data is useful, helpful, and can be used to drive decisions and become more data-driven.


JIM HOLMAN: Even if it is good, it's the perception that's causing the problem here.

JIM HOLMAN: So, it's interesting. It's a mixture of a higher education business model that is a bit nebulous and hard to understand and manage. But then you also mentioned smaller entities and community colleges. Perhaps there are opportunity costs where they just don't have the resources to manage their data, let alone produce analytics. Is that fair?


ANTHONY PEMBER: Yes, absolutely. Couldn't agree more.


JIM HOLMAN: Given that there are also compliance-driven metrics, so mandates for educational entities, universities, and community colleges, they're required by state and local governments to produce certain types of metrics and analytics.

JIM HOLMAN: Do you see that there's potentially competition between the resources that have to produce compliance-driven metrics, which are mandated, versus ones perhaps that they want to do?


ANTHONY PEMBER: Absolutely. I think the answer to that is yes.

ANTHONY PEMBER: I've seen a lot of institutions where, as I said earlier, the bandwidth is pretty difficult. You've got people who are already taxed in terms of being able to create analytics and data and use data to drive decisions. They're busy having to check a box, maybe that's the wrong phrase, but check a box to fill in a compliance metric of some sort, whether that be state, local, or even federal in some cases.

ANTHONY PEMBER: That lack of bandwidth creates a capacity issue for many institutions.

ANTHONY PEMBER: In an ideal world, I think the required metrics would also be useful for the institution to manage internally. I know the state of Florida, for example, has some really interesting metrics that they collect and require institutions to provide.

ANTHONY PEMBER: They use those metrics to help develop or deliver funding to the institution. They're looking at effectiveness of educational delivery. There are some challenges with those metrics, and I don't necessarily want to go into those right now, but I have seen Florida institutions use some of those metrics internally, as well as reporting them centrally to Florida.

ANTHONY PEMBER: That's the ideal situation, where they're able to be used internally as well as externally.

ANTHONY PEMBER: But I think in most cases, the metrics that institutions are being asked to provide to the state or federal government aren't necessarily ones they can use to internally manage. That's when you suddenly start to get a bandwidth issue.


JIM HOLMAN: It's interesting that you talk about the competition for resources and bandwidth, which is the professionals and data professionals able to produce the content. Ultimately, they have to do what they're required to do. Only then can they do what they would perhaps like to do.

JIM HOLMAN: The thing that makes me think about the resource constraints is what you said earlier, given how many universities have data and analytics degree offerings.

JIM HOLMAN: Should colleges and universities consider data in ways they aren't already, or perhaps consider resources that are non-traditional within their own degree programs?


ANTHONY PEMBER: Yes. I think it's interesting. The plumber always has the worst bathroom, and the builder has the worst house because they're too busy doing other people's stuff to focus on their own.

ANTHONY PEMBER: It's similar here. You've got degrees offering data analytics and data science. You've got a load of students, both undergraduate and graduate, who have a lot of expertise in this.

ANTHONY PEMBER: I've seen a lot of institutions with data science and data analytics programs, and I don't think they're necessarily using that pool of resources, whether undergraduate students, postgraduate students, or even faculty members, to help drive internal analytics and help them use data and analytics internally to drive the business of the university or institution.

ANTHONY PEMBER: I have seen a few examples where they do it. I had one particular client where I was doing work on cost and revenue analytics, and they employed a postgraduate student to do a lot of the report building, dashboard building, and resulting analysis.

ANTHONY PEMBER: That was really effective because the student was getting a lot of experience in real-world business analytics. It was helpful for them from an experience perspective and from a degree perspective, but it was also immensely useful to the institution to have a person who knew not just the institution, but also analytics, to help push the analytics program further.

ANTHONY PEMBER: In terms of data and different ways they should be considering data, I would probably put it into a couple of buckets.

ANTHONY PEMBER: One is longitudinal data, meaning collecting data that goes beyond the institution or precedes the institution. For example, collecting data on student success post-graduation: what salaries students are getting after graduation and tracking that at the student level so you can measure the effectiveness of the degree program to a certain extent.

ANTHONY PEMBER: Or measuring information before students enter the institution, such as GPAs in high school and SAT scores, and connecting that to the student to predict whether students of a certain type would be successful in the programs they're enrolled in.

ANTHONY PEMBER: That can extend to other non-institutional data like demographics, census information, and job information in the immediate area around the institution to understand whether there's demand for current or future programs.

ANTHONY PEMBER: It's a bit like the Internet of Things, but for institutions. If you're collecting non-institutional data, you can combine it with your institutional data to get a more enhanced understanding of the programs you're offering, how mission-centric they are, and whether they are beneficial to both the institution and the surrounding community.

ANTHONY PEMBER: The other one that is fairly obvious but a bit ignored is combining existing data more effectively.

ANTHONY PEMBER: There's a lot of information in institutions already, from student data to financial data to research data, but most institutions I've worked with really struggle to pull it together effectively.

ANTHONY PEMBER: Most institutions have an institutional research department that focuses on some of that work, but they often just pull in student information, and it relates to reporting some of those required metrics.

ANTHONY PEMBER: Very few of them start to pull non-student-related data, financial data, and combine it all together. But if they do that, institutions suddenly get more meaningful information.

ANTHONY PEMBER: For example, if you're pulling program information and student information about a degree without the financial perspective, it's hard, when you're doing a program review, to understand the real impact of that program. Is it making money? Is it losing money? How effective is it at delivering for the student?

ANTHONY PEMBER: Being able to combine different sets of data within an institution is really helpful. It's a fairly obvious one, but one that's surprisingly difficult to do.


JIM HOLMAN: That's very interesting. I would say that most people would not think that a public institution would consider whether a program makes money, but certainly understanding the cost side of the equation is probably something that's been a little closer than understanding that the money coming in and the money going out needs to line up.

JIM HOLMAN: Based on that thought, Anthony, how would you suppose universities, colleges, and community colleges might use data to improve the financial health and stability of the entity itself?


ANTHONY PEMBER: It's interesting, Jim. I was talking to a CFO several years ago at an Australian university who said, "We may be not-for-profit, but that doesn't mean we're for loss."

ANTHONY PEMBER: It's a good statement to keep in mind, even when you're talking about a public nonprofit institution. You don't need every program to make money. In fact, you expect programs to lose money in many cases.

ANTHONY PEMBER: But overall, when you look at all of your programs, you need to be making enough money so that you're financially sustainable.

ANTHONY PEMBER: A lot of institutions measure this at the macro level, so they'll know that a program is financially sustainable, particularly from a direct perspective. It's relatively easy to measure the direct cost of faculty and other things needed to support a program.

ANTHONY PEMBER: Very few institutions do a really good analysis of the indirect costs. Those are a little bit more tenuous, but they're important to keep a firm grip on.

ANTHONY PEMBER: The CFO is obviously very important with the indirect costs and the overall costs of the institution. But often a dean or department chair doesn't really factor those in because there's very little they can do to control those costs. But that doesn't mean they shouldn't be considered overall.

ANTHONY PEMBER: I think institutions need to start thinking about pulling in student information and combining it with financial information, both direct costs and indirect costs, to really analyze what's driving those costs.

ANTHONY PEMBER: Not because you want to cut a program or get rid of a program, but because you need to understand. To be able to measure it, you need to be able to understand it, in my opinion.


JIM HOLMAN: Thank you for an enlightening conversation. It's always good to talk to you. I learned quite a bit and enjoyed the conversation. I find myself drifting off topic quite a bit, just enjoying the experience.

JIM HOLMAN: Thank you very much, Anthony, for participating.


ANTHONY PEMBER: No worries, Jim. It's always a pleasure to talk to you, and I enjoy it. Thank you.


JIM HOLMAN: For the listeners out there, stay tuned for part two of this series. We'll look at how data can help with student success, as well as steps universities should consider when helping students with their career path.


HOST: Thanks again, Jim and Anthony, for your insight today. Be sure to like and share this podcast and tune in again.