S1:E12 John Felushko – Using existing data to answer new questions

Tyler Jacobson  0:00

Welcome to LabChats, a podcast from the team at LabStats. I’m Tyler Jacobson, your host for today’s episode. Each week, we’ll sit down with technology leaders in higher education to get the latest buzz and insights while we discuss current events, trends, problems and solutions. Now let’s get into it.

Tyler Jacobson  0:19  

Joining us today we have John Felushko, who is the Product Manager here at LabStats. And the reason that I wanted to have you join us, John was you have done an extensive amount of speaking with universities and colleges about how they’re using data and the value they’re getting out of data. I wanted to get some of the insights that you’ve received [from] that. Let’s start with a big vision of: What should we be doing with the data that universities are collecting? 

John Felushko  0:52  

Whatever you want! Data is a tool that helps us make better decisions. And the more we have of it, the more easily accessible it is, the more it’s combined, the more interesting questions we can have. And the more quickly, we can see the impacts of our decisions and make the decisions. So we have this concept in software of failing fast. The idea is…really being humble as decision makers and saying, “We don’t know what the impact of our decisions are going to be when we make a change. We don’t know what’s going to happen. So let’s make sure we’re watching it using the scientific method, having a hypothesis and seeing what changes.”

Tyler Jacobson  1:40  

So basically treating every change that you make in the infrastructure, everything you do on the university as an experiment, and then capturing what impact it has. 

John Felushko  1:51  

Absolutely. That’s been proven in many different businesses across a lot of time, to help you get to your goals more quickly, at a lower cost with less risk.

Tyler Jacobson  2:06  

One of the things that I have often had conversations with [school staff about is] they would like to use data to make data-driven decisions. However, it costs a lot to collect the data. And so a logical first step is, what are they collecting now? And how are they using it now?

John Felushko  2:26  

Yeah, I think when I talk to schools…schools have a huge range of their data literacy, both between organizations and across organizations. But a lot of people are incredibly surprised about how much data exists in their organization already, going back for [a long time], and how easy it is with modern tools, to combine it and provide the context they need to make decisions.

Tyler Jacobson  2:54  

What data streams are they collecting? Now, give me some examples of what they have to work with without any additional expense. 

John Felushko  3:01  

Sure. So you have your student management system. So you have a lot of information, first of all on your students, right, and your students and how they’re doing and how they’re performing are tied to many, many of the goals that learning organizations have. You have your financial system. [It] contains all the records of what you spent, all the bills that got paid. You have your feed from your learning management system that tells you about how students are interacting with resources. You have your library’s computer system that tells you who checked out and who looked into and who accessed what resources and when. Those things were on every campus I’ve ever talked to. And they provide a huge amount of data. And they can be combined with stuff you probably don’t think is related, like your network system. Who logged in? What’s our login rate per Wi-Fi? How many devices are logged in? That tells you a huge amount of information about who’s where in space. What spaces are getting used? And I’ve seen these combined. I was at a campus last year where these were combined…to know whether students were in class or not. Without any new tools. Just new analysis of the data that they already had.

Tyler Jacobson  4:22  

Some of those data streams that you had mentioned, seem like they’re very department specific. For instance, the library data, how is that valuable outside of the library? And how do you get it to people that are outside the library in a usable manner?

John Felushko  4:42  

So, [a] really simple question a lot of people have is: “What students are succeeding?”, “What [are] students going to do well?” and “What students [aren’t doing] well?”. For a lot of programs, there’s a pretty strong association between what library resources the students are accessing and how they perform in the course. So you can use library data as a leading indicator of students performance in their program, if they are in history or political science, and then they’re not showing up in the library system on a regular basis and accessing stuff from the library on a regular basis, [that’s] a pretty good indicator [that] they’re not going to get their research papers done.

Tyler Jacobson  5:28  

How can a university share that data interdepartmentally as well as up and down the chain of administration.

John Felushko  5:35  

So there’s a lot of great tools out there. Generally, they’re called business intelligence tools, BI tools, I think, because they have the word business in them. And intelligence. People think they’re the specialist domain of analysts, but I’ve been in a lot of schools where they’re being used across the organization, I was at a major customer that in their IT organization had over 50 databases, streaming into their business intelligence system. And when I first saw that, I thought, well, this is years of work. This is a huge amount of effort. This is beyond my capability to understand. And going out and playing with those tools, we found that they’re very, very easy to use. So the most popular one in our research universities is Power BI, the next most popular is Tableau. If you’ve interacted with your state, or country’s COVID dashboards, in the last year or so you’ve probably interacted with a Tableau dashboard, or a Power BI dashboard. And every single school I’ve been at, in the last two years, has had those capabilities somewhere on campus and specialists [or] analysts for the summer on campus. And very often they’re paid for already. The[se tools are] free to add users [and] free to download.

Tyler Jacobson  6:58  

So how would somebody identify who on campus is their data analyst, because a lot of the people that we speak with, they’re working with the data within their department, what would be the first step for them to get some of this bigger picture data analysis?

John Felushko  7:18  

Just start talking. If you ask…when I get a couple people in a room, and I ask, “Where’s an analyst?”. Usually somebody knows. Very often they report to the CIO, or the CFO. There’s some department somewhere in your school that has a bunch of analysts. There’s some of the most posted jobs we see from universities. They’re out there. But you don’t need them. Honestly, you can just go download, figure out what your school’s using. If you’re a Microsoft brand school, go download Power BI onto your desktop, start playing with it. And there are links to most major applications built into Power BI. Like our devs here, say, it’s mostly a matter of knowing what to Google.

Tyler Jacobson  8:03  

How easy is it for departments to share their data streams with each other?

John Felushko  8:10  

if you have the permissions…and this is where the politics of your organization work out, but if you have the permissions? Minutes. It’s minutes to get the data into systems. So an example would be: “I want to understand how people use my computer labs who aren’t logged into the machines…How many people are in my computer labs that aren’t logged into computers?” I can take my data from something like LabStats that has a record of everybody who logs into computers. And then I talk to my network guys and figure out where the data exists for logging into WiFi. Most places I’ve talked to have what’s called an API, an “application programmer interface”. It’s a place you can access that data online and Power BI and Tableau have hooks to those. And you can get the list of who logged into the Wi Fi in line, who log on to your lab computers. And when and by comparing those two sets of data, you can see how many more people were in your computer labs than are logged into your machines.

Tyler Jacobson  9:22  

How does that help? What additional insights is that giving? 

John Felushko  9:26  

Well, we see very often when we go to a conventionally set up computer labs, [we see] rows of desks, for example. You know, next to machines [and] next to each other, that all the seats are in use or close to all the seats are in use. But less than half the machines are in use because people are working as groups together on projects. You might be getting a very false impression from only looking at one data source of how many people are using that space. And if you find out: “Hey, wow, there’s like…for every log in to our machine, there are six or seven devices logged into the local wireless network in that space, we know that the average student has…2.7, something like that wireless devices with them at any given time. So that number, you can kind of divide that by two and a half or whatever, and say, okay: this many people, we have 90 people logged into the wireless in that lab, we only have 20, people using computers, oh, there’s a bunch of groups in there. And when you go to say, reorganize that space, you can reorganize that space [so] each computer is at a separate desk, or you have a bunch more [space per computer]. And we’ve seen this on a lot of campuses, where there’s station computers with big screens, and four or five chairs, around the perimeter of the lab. So people can come in and do group projects on those machines. Some schools call those pods, for example. And by looking at that kind of data that supports how many pods doing, how many group projects spaces do I need? Maybe I don’t need more machines, I need those same machines spread out over more space. So I can accommodate more group work. Here’s the data that supports that. And that’s really simple, just combining two sets of data, your wireless login with your LabStats, you could then further combine that with your student management system data to figure out who’s using it. One of the things we see anecdotally is that [it] tends to be a much higher percentage of foreign students, first generation students and disadvantaged students using those public spaces and those computers to do work. Because they might be living in dorms, they might not have big computers and big scre