How to Tame Data Sprawl

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0:00:11.8 S1: Hello and welcome to Office Hours with EAB. Today our experts examine business intelligence tools that help university leaders access and gain useful insights from data trapped in disparate repositories and IT systems. They discuss the relative scarcity of chief data officers in higher ed and reasons why effective data management and governance, regardless of how you resource that function, has become more critical than ever to colleges and universities. So give these folks a listen and enjoy.

0:00:48.0 Matt Logan: Hello and welcome to Office Hours with EAB. My name is Matt Logan. I serve as our Director of Partner Development. And one of the things I spend a lot of time doing day in and day out is talking to higher education leaders, particularly about how to leverage data to better inform strategic and tactical decisions really across all areas of university and college operations. And let’s face it, despite the urgent call for data-informed decisions making to survive in today’s competitive landscape, many institutions candidly still struggle with basic data challenges such as unifying disparate data systems or providing leaders with timely access to the right information. So today I am absolutely thrilled to be joined by a wonderful colleague, new to the podcast, who has sat next to me in a lot of these conversations. And so Dr. Jason Browning, would you mind introducing yourself, telling us a little bit about your role at EAB, and ultimately anything else you think our listeners should be hearing from you?

0:01:55.7 Jason Browning: Absolutely, Matt, and great to be with you. Jason Browning, I am a senior director of data and analytics here at EAB. So I work exclusively in our analytics and warehousing space helping different colleges and universities address these data related challenges that you’re mentioning, Matt. But prior to my time at EAB, I spent most of my career on the institutional side of the house, built the Office of Institutional Effectiveness at Utah Tech University. Was AVP of institutional research for a community college district in Wyoming. And so I’ve had a lot of experience on both sides of the coin. Interestingly, shortly I will be moving to Montana and have accepted the Chief Data Officer job at Montana State University, and I know we might want to discuss that as we go through here as well.

0:02:42.5 ML: I am eager to. It feels like we are sneaking this in right underneath the deadline, but Jason, maybe once you’re at Montana we can bring you back for a special guest episode.

0:02:53.1 JB: It’d be a pleasure.

0:02:53.2 ML: Jason, thanks for introducing yourself. Obviously, we’re gonna be talking a lot today around data-informed decisions, and I know data-informed is a particular verbiage that you like rather than data-driven decisions. These are not new concepts within higher ed, but certainly we’re hearing them more and more often as of late. What do you make of that? How do you think this movement is playing out on college campuses across the country? What are some of the challenges that are now creating a call for better data utilization? And why do you think this is happening now in higher ed versus the shifts we’ve seen across corporate America across the last decade or so?

0:03:33.0 JB: Yeah, and you know, Matt, that’s an interesting question with a lot of loaded pieces in it that I could pontificate on forever. Data and…

0:03:41.6 ML: And you know you can…

0:03:43.7 JB: Thank you. Data and data challenges aren’t new to higher education. They’ve been there. In fact, the late 1870s is when the Department of Education started tracking data and started asking for reporting from different colleges and universities. So this has gone on forever. However, we’re in an era where higher education is under scrutiny from all sides, whether that’s accreditors asking for student assessment work, looking to make sure that the college is adding that value that they claim to add, as well as the general public. There’s these continued conversations around making sure that higher education is still a value proposition, as well as legislatures as they turn more and more to performance-based funding and conversations around accountability. And all of this gives rise to the need for more and more data. So what we’ve seen historically is colleges focus a lot on operational data, right? How to keep those trains running on time, how to keep classes full.

0:04:40.5 JB: Where we’re going and what we see more and more is a focus on strategic data, and how we can use, whether that’s predictive analytics, whether that’s looking at long-term trends, whether that’s doing benchmark comparisons with other institutions, more and more there’s this call to really engage data to help move strategy forward. And your question, the second part which I love, is some of the challenges that we run into. And some of those challenges are pretty broad and, are encountered across a variety of institutions as well as a variety of organizations outside of higher education. Things like lean budgets, things like having difficulty finding those qualified data and analytics staff. That is often amplified, there was a recent article in The Chronicle of Higher Education just this past week that was talking about looking at how higher education has this continual salary problem, and how that salary problem is becoming more and more magnified. And you see that in certain corners, particularly things like data and analytics where you need that specialized skill set. Those broad things, though, there are specific challenges in higher education.

0:05:49.4 JB: I mentioned kind of salaries and that whole discussion. But there’s also structural challenges. There’s things like this reluctance to adopt new tools. And that comes from organizational considerations, where we think about implementation is difficult. Colleges are large and sprawling enterprises, even the smallest colleges have a lot of different departments that they need to bring on board. So as you think about things like shared governance, as you think about things like buy-in, the structural things really slow folks down. As well as the technological challenges, where you have these fragmented systems, with pockets of data all over campus. Systems have been acquired over time, a lot of times not thinking about integrating those different systems together. So those are just some of the challenges that come to mind and I could go on and on.

0:06:36.5 ML: Yeah, well, and I will jump in here. But I think Jason, what I’m hearing is a rise in complexity, right?

0:06:42.7 JB: Absolutely.

0:06:43.0 ML: A rise in some external factors that are making really higher ed leaders’ jobs fairly tough in this day and age, as well as just the idea that, gosh, we’re in an era of digital transformation to throw another data buzzword at you. And inevitably, that means the digitization of processes of our data, of whatever else it is that we’re sort of relying on day in and day out., I think is also representative, and this is something that you and I talk about fairly often, the shift from sort of a traditional institutional research office, that is maybe more focused on compliance, on accreditation, into the idea of an office of institutional effectiveness, right? So we’re seeing more of that push for strategic analytics. And I ultimately think that that means the shift in the way that colleges are positioning their talent. So I’m gonna lead us into another topic that I know that you could speak ad nauseam about as a result of the fact that it’s your topic of your dissertation. But we’re also seeing sort of, with that shift from IR to IE, the rise of the Chief Data Officer. And so can you sort of give us some more context around what you’ve seen there? And again, knowing that’s a pretty tall task to synthesize your whole dissertation in a two to three-minute response?

0:08:06.0 JB: No, absolutely. I’d love to. As Matt mentioned, as you mentioned, I completed my PhD in higher education administration at the University of Wyoming, and really tried to consider a variety of topics, but I kept coming back to this topic because it is important to me with my institutional experiences, this concept of the Chief Data Officer. And so my dissertation really was focused on exploring what is this? What does it mean? What does it do and what should it do? And there were a few key takeaways I’d love to share, one of those being that there aren’t a lot of chief data officers. At the time of the dissertation in 2021, there were less than 70 chief data officers in higher education, which is so interesting because it’s become a very prevalent role in organizations outside of higher education. Actually got its start in banking. But what we’re seeing is more and more institutions are looking toward the functions of the Chief Data Officer. Even if it really isn’t a full-fledged CDO role, it might be an Executive Director of Institutional Research. It might be an AVP of Institutional Effectiveness, that also has responsibility for supporting the data environment on campus. And I’d love to talk a little bit about what I mean when I talk about the data environment. What is that?

0:09:21.1 JB: Historically, we’ve really focused, as I mentioned, on that compliance reporting, on getting some enrollment dashboards out the door perhaps, but focusing on IPEDS, focusing on state reporting, those kinds of things. And what we really need to do as a discipline, as an institution, is move towards this idea of employing data more strategically. And that’s where this Chief Data Officer really has a role to play. There’s making sure that the data is accurate. There’s making sure that the data is governed, and we can talk a little bit about that in a minute. But what’s the end game? And there are a few things that really emerged in my research and that I know, Matt, you and I have come up again and again in conversations with different colleges. One is looking at all those reams of data and helping our institution to understand what data is usable, what data is here that really can help us make better decisions, what data is here to help us steer the institution forward.

0:10:16.9 JB: Once we identify that data, it’s thinking about a couple of different things. One of those things is reframing ownership of data. A lot of times we have conversations with campuses where enrollment folks are glad to share some enrollment data, but financial aid hangs on really tightly to their data. They say, “That’s my data. You can’t have that.” It’s really important as a Chief Data Officer that you reframe the institutional approach to data. The institution owns the data, and there has to be a willingness to share data across these different silos to help the institution make better decisions instead of keeping data locked in these individual departments. So the CDO can help facilitate that conversation both from an organizational perspective, in terms of breaking down those silos, but also paying consideration to security access those kinds of considerations as well. I think the last main role of that Chief Data Officer is to really help enhance access to the data, help build data systems so that folks can take information and use it. Whether that’s dashboarding and reporting tools, whether that’s building models using different predictive modeling applications, that can look like a variety of things, but it’s the CDO’s job to help connect those questions that exist all across campus, take those questions, connect them to the data.

0:11:37.3 ML: It makes a ton of sense. And Jason, I’m grateful that you were able to synthesize that, ’cause there’s a lot to unpack there, right? You talked about tools, you talked about data governance, best practices, right? As we talk about accessible data, as we talk about governed data, truthful data. And now of course, we know that with however many different contexts we have to look at data, whether it is for IPEDS or it’s thinking strategically, you are gonna need different definitions of those data sources. I think lastly, in your response, I’m worried that you’re gonna have some financial aid folks showing up with pitchforks outside of your home there saying, “No, it’s really our data.”

0:12:17.8 JB: Yeah. Yeah.

0:12:20.8 ML: Jason, I want to dig in a little bit more, I guess, on the tool side of things, right? We talked a little bit about how there’s emerging roles from a human capacity perspective. We’re also seeing that burgeoning of tech solutions, not only thinking about the CRM space, things like Navigate360, EAB’s really approach to supporting students from first touch to last touch, but you’re also hearing all sorts of things about business intelligence tools, you talked about dashboards, hearing things about data lakes, data warehouses, data lake houses, which I know, again, is not a term that you like, but I certainly want some beachfront properties, so explain to us in layman’s terms, what are all of these systems and why are they important?

0:13:08.2 JB: Yeah, that’s another very broad question. And I want to approach it picking up, first, I should apologize to our financial aid friends. Human resources, finance, everyone plays the, “It’s my data,” game. And that’s just an important thing to overcome. But I want to approach your question by kind of picking up where we left off, which is the mention of data governance. Data governance is a conversation that’s existed in a fashion for many years on campuses, but it’s traditionally been looking at data and assigning data definitions. And true data governance now has to move beyond that. It has to look at who can access the data. It has to look at what does the data mean, and what data is appropriate for those different applications. It has to look at how that data is accessed. And that’s where we get to this conversation around tools. There’s a proliferation of data on campus. There are emails from vendors every day, EAB included, that offer to solve your problems if you add this additional system. And those additional systems solve a lot of great problems, but they often don’t talk to each other as well as they could to have a consolidated data source on campus.

0:14:18.9 JB: And that’s where a lot of these tool conversations come in. So thinking about a data warehouse, that’s a really common place to start and a really great place to start on campus. A data warehouse’s primary function is to bring your data from these disparate sources, so from your student information system, like Banner, from your learning management system like Canvas or Blackboard, from your CRM, like Slater Salesforce, bring your data from award-winning student success tools like Navigate. Put all of that data into one central location. What this does is a few different things. It gives us a place that our end users can come and access data. It gives us a place to govern data. It lets us make sure that the data reflect what we intend for them to reflect. I know we offer a data warehouse product at EAB called Edify that does a lot of this work. And what you see is that when bring data in, you’ll see data that is occasionally in conflict with one another.

0:15:16.2 JB: You think about a first-generation student. That could come from the financial aid application, the FAFSA. That could come from an admissions application. We work with campuses where that comes from an orientation course, where they report it in a survey. It can come from a lot of different sources. By bringing those sources into a data warehouse, we can set a priority order and say, if we know this information from the FAFSA, for example, let’s use that. If we don’t, let’s use the admissions application. So on and so forth. And what that does is it makes the data environment on campus more trustworthy, more reliable, and gives us a really good place to move forward, as you think about reporting tools, there are tools like Edify that have a reporting tool built into the application. But there are reporting tools like Tableau, like Power BI, like RStudio for predictive modeling, there’s a lot of great tools that are out there. Those tools can all generally connect to a data warehouse on campus, freeing that data, making it more accessible.

0:16:19.2 JB: You mentioned the data lake, and I’ll nod to that as well, which is really the next stage evolution of that warehouse. When you think about a data warehouse, it’s a very conventional database in a lot of ways. Tables and rows, very relational structure. So a table of enrollments is related to a table of students. The next evolution are things like data lakes and lake houses, where you have room for that unstructured data. As you have students who are submitting video, pieces of video with their applications, as you have students that are submitting artifacts, as you as an institution are creating various planning documents and these kind of things, they may not be tied directly to something that’s happening in your SIS, or even something that’s in your warehouse, but you want to maintain that data, and a data lake is a place that that data can go to live, so that you can use that data going forward. So there’s been a great evolution in data storage. I think for a lot of campuses, data warehousing is a great place to start.

0:17:16.6 ML: Yeah, and certainly a lot there, Jason, as well. I hope that we have not lulled our listeners to sleep as we discuss so much of this data talk, but inevitably, this is what you and I get really excited about. I know, though, talking to campus leaders day in and day out, there are folks who don’t know that these are worthwhile investments. Data warehouse is not necessarily the shiny, sexy thing to buy. So Jason, you came to us because you had bought Edify when you were on the campus side. Tell us a little bit more about how did you build engagement and stakeholder buy-in when it came to either pushing forward this idea of better data governance, or actually truly carving away budget for something like a data warehouse. I know we’re going a little off script here, but would be really curious to get your insights.

0:18:11.0 JB: No. That’s, and you make a great point about lulling our audience to sleep. There’s a quote, and when I was writing my dissertation, there’s a quote from the Management Consultant, Arthur Slaven. It’s a quote from the late ’80s. And he said, “Many institutional initiatives follow a general pattern; early enthusiasm, widespread dissemination, subsequent disappointment, and eventual decline.” And I love that quote because it does describe so often what we see on campus, there’s real enthusiasm that the system is gonna come, it’s gonna fix things, we distribute it broadly across campus, it doesn’t fix things, and it fades into the graveyard of other failed systems. And it’s really important as we think about this call for data and this call for accountability that we don’t let that happen in a data environment. And so you think about things like a data warehouse and infusing that across your campus, ’cause you’re right, I don’t think there’s a lot of presidents that are out there that are really excited about warehousing. At Utah Tech University, as you mentioned, I did bring Edify to campus as our data warehousing solution. And I had a lot of those conversations with the president, with cabinet, who might be a little bit more removed from those infrastructure conversations.

0:19:22.2 JB: And so I think it’s really important to understand there is a technological component, a significant one, but really to focus on what those outcomes are gonna be. That accessible data, that data that we can trust as we make these decisions moving forward. And by creating these new tools that make data easier to access, your Provost, for example, can enhance that academic review process, that program review process, make that a more meaningful tool. Make assessment data more meaningful, as you think about curricular decisions. Your Career Development Office can access a labor and program market data, use that to make decisions related to where we’re placing graduates. Let’s look at that data and see how that’s changed over time. So, so many different ways that you can go with the data, and I think it’s important to help paint that picture. You mentioned budgets, which I mentioned in challenges, and if this weren’t an audio podcast, we’d surround it in exclamation points, budget is always a challenge.

0:20:20.2 JB: I was fortunate at Utah Tech to tie business intelligence, to tie data warehousing, into the strategic planning process. So there’s actually a goal in the strategic plan focused on enhancing institutional effectiveness. And a key way to enhance that effectiveness is access to data. So I think it’s very important. And my dissertation research will support this as well, it’s very important when it’s possible that data gets that attention at the highest level. It is a presidential imperative, it is a cabinet-level imperative. If it can be tied to strategic planning, if it can be tied more practically to an upcoming accreditation review, an upcoming self-study or an upcoming site visit, those can be great times to have these conversations around what’s making data so important, and why it’s a necessary investment. There’s also the more practical allocation of sometimes their strategic planning dollars, or accreditation dollars, that can be pointed toward that goal. So I think there has to be some real creativity from time to time, but I think my overarching message is just focusing on the importance of using this data as a strategic asset.

0:21:28.7 ML: Yeah, it’s such a great push, Jason, and I hope that there are listeners out there in the data terrain that this inspires them to say, “Okay, there is a path forward here.” Inevitably, I know you and I both just believe that this work has a true return on investment to it as well, whether it’s on the time savings, right? We have partners that indicate, “Gosh, this reduced our time to make a change in a system, or to submit our IPEDS report by 75%.” I’m really glad to shout out John Carroll University who talked about the idea that a sort of leveraged agile integration system like Edify can create that reduction in time to change.

0:22:12.4 JB: Absolutely.

0:22:13.6 ML: But you also truly see either on, gosh, we can actually apply for more grants. We can ensure that we don’t miss reporting deadlines, right? We have Northampton Community College, a college that I’ve worked very closely with, who managed to avoid a $950,000 state funding loss by being sure that they could hit those reporting metrics when they needed to. So there is true ROI here, right, Jason? And I don’t know if this is something that you then had to sort of prove out as you invested in these tools, or something that you just consistently have an eye on as somebody who is both financially and data oriented?

0:22:55.7 JB: No, that’s absolutely true, Matt, and you know, if we go back further in my career, which starts to date myself, I was an accountant in my first life, accounting and finance is my background. And so I bring that cement to our conversations.

0:23:09.2 ML: You’re gonna be in trouble ’cause we all know this is coming out during tax season, so I might have you do some review for me.

0:23:15.9 JB: Yeah, I know, I know, it’s, but it really does angle… It really angles and skews your perspective, and so I always operate with ROI in mind. And I think that’s important… I think that’s another important consideration as we talk about getting support, whether it’s financial or just support generally on campus for data and data warehousing initiatives. I think it’s important to keep that focus on ROI. If we are an IR or IE leader, and we sit in our office and generate reports, that’s great, but it’s gonna be really hard to quantify that impact. It’s gonna be really hard to say, this was a worthwhile investment for us. And so I think having that ROI lens is really critical. I think you can do that in a few different ways, you mentioned the Northampton example, which is a fantastic one. That oversight could have led to a significant loss.

0:24:06.1 JB: There’s the loss prevention component. There’s making sure that IPEDS and your state reporting and things are completed in a timely fashion. Beyond that, we have campuses that are dealing with these very real staffing concerns, and there are situations where you can automate a lot of that compliance reporting so that you can use your limited staff to focus on those strategic ads. They can focus on those analyses that are gonna help you make the right programmatic decisions, or the right enrollment decisions, or the right financial decisions, depending on the health of the institution. And so there’s real ROI there as well I think it’s important to capture.

0:24:43.6 ML: Yeah. And Jason, I think that that truly is the crux of what we are trying to drive at. How do we start to shift our time away from those monotonous tasks, the compliance-oriented things, and start to shift them into much more strategic predictive analysis, right? Move up that data maturity curve. Before we do that though, you have brought IPEDS into the fold and thinking about easing that process, I’m sure you’ve piqued listeners interest, how can you do that? As well as, Jason, for folks who are sort of listening in and might be looking for some compliance support, anything that the IPED changes that are sort of been announced for this year that are catching your attention or just any general advice you have there.

0:25:30.9 JB: Yeah, yeah, that’s a great question. And there are tools in the market like EAB’s Edify that have those IPEDS templates for lack of a better term, where we’re using a normalized data model that we provide, we ingest all of your data, it’s available in this data warehouse. There’s the warehousing benefit. But there’s also the benefit of these templates or accelerators. And so IPEDS is a set of… There are a set of those accelerators for IPEDS that can automate a lot of that reporting. So as you think about the enrollment survey, the outcome measure survey, those kind of things, a lot of that can be generated from that normalized data model with minimal requirements from your staff. Again, letting them focus on that value add. Outside of those templated tools, even being able to write those queries against a normalized data warehouse is gonna help speed that up as you go year to year, there are often not a lot of substantive changes in certain portions of IPEDS.

0:26:26.2 JB: However, you alluded to upcoming and proposed changes that are pretty significant. One of those, which I personally think is ill-guided, is eliminating the libraries component, eliminating the academic libraries component of IPEDS. I think there’s some very real data there. I think it provides some valuable benchmarking data. If that component of IPEDS were to disappear, there would need to be an alternate benchmarking source. I think that’s a useful tool for academic libraries as they’re looking at their costs, as they’re looking at their operations, to have those reference points. I think there’s an interesting societal discussion to be had on the elimination of the academic libraries component, but we’ll save that for another day. Another interesting piece is this discussion of a total cost component, almost akin to a cost survey. And I think there’s some very real data to be gained from that exercise. That also though is going to necessitate a lot more data. There’s a lot of data there to collect. And so partnering with a tool like Edify where those templates are created on mass, gives you some value add there as well.

0:27:32.6 ML: Yeah, so every reporting cycle, there’s gonna be some level of anxiety about what’s new, what new data needs to be collected, what has changed entirely that we can get out of the way. And so Jason, I think really helpful to have your thoughts there. I’m sure folks will certainly want to carry along the dialogue and discussion around the academic libraries with you, so we can give you your contact information and follow up, or we can just invite you back to the podcast.

0:27:58.8 JB: I’d be glad to.

0:28:06.8 ML: There you go. Perfect. Well, outside of the compliance things, what are some of the analyses that you think are truly moving the needle forward at institutions? Whether that’s predictive analytics, it’s simply descriptive, these are the things that institutions can and likely should be tracking. Is there anything that as you go back to the institutional side, you’ll say, “Okay, this is gonna be a major priority for me, ensuring that our institution has access to X, Y, or Z type of data?

0:28:32.7 JB: Yeah, and I’m glad you asked that question, Matt, and I’m glad you mentioned predictive analytics in there as well. There for the past several years there’s been this real fascination with predictive analytics, and it is a very useful tool, an excellent tool. But I think my primary advice as I head back towards the institutional side of the house, my primary advice for any institution would be, make sure you have a handle on your situation now. So as you mentioned, descriptive analytics, and you often see these in a continuum where descriptive is kind of at the bottom of the barrel. That’s not necessarily a bad thing, it’s really critically important to have a handle on where you are. And particularly as you think about predictive analytics, as you think about AI, as you think about things that are coming at us fast and furious every day, all of those things require clean, reliable, curated data.

0:29:22.9 JB: If you feed unreliable data into those models, if you feed unreliable data into an AI chatbot or what have you, it’s gonna generate unreliable data, and that’s gonna move downstream, it’s gonna be more and more difficult to isolate the impact of that data. And so you wanna make really sure that you understand where you are, and that you start with that clean data. And so I think governance efforts are really important. I think warehousing efforts are really important. But I think those descriptive analytics, where we can look at enrollment, we can look at program counts, we can look at that basic data, and make sure we’re near where we need to be before we move on to those exciting next steps is really critical.

0:29:58.8 ML: And…

0:30:00.4 JB: Beyond a… I’m sorry, Matt.

0:30:07.1 ML: You’re totally fine. I was just gonna say that we have to walk before we can run analogy, and with all of these new shiny tool, well, I guess, right? AI being one of them, that I think a lot of our listeners and leaders that we talk to are candidly very excited abou, and I’ll be honest, I’m excited for as well, I’ll join the AI hype train, but that whole idea of garbage in, garbage out, continues to be a very real one across higher education, and so I love the continued push throughout our conversation to know that data governance is foundational work, to be able to do anything strategic moving forward.

0:30:43.8 JB: And that would be my closing thought more broadly is really an extension of what you’re offering there, which is, beyond data and the mechanics of data and the warehousing of data and the analysis of data, there’s this broader conversation that I think is really critical, which has two prongs. One is, being willing to examine your current IR structure, your current IE structure, your current data structure. Making sure that that structure is giving you the analysis that you need, making sure that structure is managing your data in the way you need. But then also making sure that your Chief Data Officer, whether that’s a true CDO, whether that’s an IR or IE executive, making sure that they take the time to build these relationships across the institution and collaborate across the institution. These relationships are what are really critical to making sure that the data’s at its best coming in, but also that the data is so useful going out, being able to help folks think about data questions, being able to help folks think about things they’re already thinking about day to day and saying, “Have you thought about data point A, B, and C? Have you thought about modeling X, Y and Z?” Having those relationships that allow those conversations to manifest organically is gonna help take you really far. So I think it’s important to emphasize those soft things as well.

0:31:56.9 ML: Yeah, Jason, that makes a ton of sense. And again, I would love to just pepper you with questions all day long, I think that we are though likely hitting a time limit here, and I wanna sort of wrap us up with a bit of a summary of what we’ve talked through today and then get some parting words from you. Feels like we have talked broadly about the role that data is playing and the evolution of data across the higher ed sphere, ultimately then sort of mirroring that through both the rise of the Chief Data Officer as well as then all of these tools from BI tools, data warehousing, data lakes, data catalogs, data dictionaries, all these different buzzwords and solutions. And then ultimately the idea that regardless of which direction you want to go analytically and strategically, the heart of the work is really rooted in that data governance component. Does that feel like a fair synopsis? Anything you think I have missed?

0:33:03.5 JB: That does feel like a fair synopsis, Matt, and you gave my half hour of conversation in a two-minute synopsis and I think that’s right, I think that really is the takeaway, Matt, is an effective data governance program is critical, an effective business intelligence program is critical. But all through this is the importance of connecting technology and connecting these initiatives back to our mission, our institutional mission, our focus on students and student success, and helping just really paint that picture how data supports those initiatives and supports the reason that we exist as an institution. It’s just really mission critical.

0:33:38.4 ML: Yeah. I totally hear you. Well, Jason, you… I made you pare down your dissertation into two minutes, so it felt only fair that I was able to pare down our conversation today in about two minutes.

0:33:49.4 JB: Fair enough.

0:33:51.2 ML: I am forever grateful for your partnership and for all the learnings that you have provided for me during your time at EAB, and so I know that our listeners will get the same from you. Jason, it has been a pleasure. Thank you so much for your time.

0:34:05.6 JB: The pleasure is mine, Matt. Thank you.

0:34:08.8 ML: And I can’t wait to see all of the great things you do over at Montana State, we’ll be rooting for you.

0:34:12.0 JB: Thank you. I appreciate it.

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