Episode 204: Using Data to Create Success with Lindsay Wayt

Success, to me, looks like continuous improvement. And I know sometimes those those words "continuous improvement" get tired or overused, but that really is what a successful analytics program would look like at a college or university. Welcome to Focus! A podcast dedicated to the business of higher education. I'm your host, Heather Richmond, and we will be exploring the challenges and opportunities facing today's higher learning institutions. In today's episode, I'm talking with Lindsay Wayt, NACUBO's director of analytics. We'll be discussing the ways higher education can use data to help them pave the way to a successful future. Well thank you for being here today, Lindsay. Yeah, thank you for having me. I'm really excited to be able to talk about analytics with you. Great, so can you share a little bit about your role at NACUBO? Yeah, I would love to. So I'm NACUBO's director of analytics. And so, in short, that means I am focused on NACUBO's strategic priority number five. Which is to help institutions lead higher education's integration of analytics to achieve their institutional strategic goals. So I used to be a researcher at NACUBO, and so data has always been a part of my job. And now the exciting part is I get to talk about data with our members, and plan professional development opportunities, and create resources to help them be able to harness the power of data and analytics at their institutions. So that's the the short version of what I do now. Well it sounds like the perfect role for you then, being a data girl and all. Yes, yes. Well, and it's definitely important in higher ed as well that we use data. I mean, I think like any professional - whether you've grown up as a data professional or not in higher ed - I think the way the world seems to be going, everybody's going to have to be able to use data and analytics to some degree. And so when we think about, "Wel, why does NACUBO have a strategic priority around data? Isn't that just something for IR or IT?" But it's not. You know, our members really are facing increasingly complex challenges. I mean, if we just look at, you know, some of the context even before the pandemic that we're facing now, we already saw that state funding hasn't really recovered since the great recession. we know student aid continues to be a concern. You know, we see the Pell grant hasn't kept pace with the price of higher education. We see lots of individuals in the public - whether it's students, their family members, policy makers, questioning the value of higher ed. And so I think our our industry, our sector, is really facing quite a few challenges and they're going to have to use data to navigate all those. And so it's not just an IR or an IT thing. It really is an entire campus program, or at least it should be at institutions. And business officers really need to be involved with analytics. Yeah, you're absolutely right. And so I'm assuming that that's why last year 2019 NACUBO launched the study of analytics and this was a new survey, right? Yes, yes, it's a new survey. So we launched the survey itself last year in July. So it's been a year since we collected the data, and then the report was published right before our integrating analytics forum in 2019. So that was in November. So the data are still new, but as you could imagine, i'm sure there are questions now that we all find ourselves in the midst of a pandemic. But I would say findings, though, are still valid. Like, it's still important to know where were we with the use of data and analytics before the pandemic, and now that all of those concerns I just mentioned about affordability, the value of higher ed, concerns about funding cuts, it's even more important that we use data now. You're absolutely right. And so curious: speaking of that, so even though the study kicked off in 2019, when was the research actually completed? So did you get a little bit of pre- and post- COVID results in there? No, so it was a one-time survey. So we collected the data in July of 2019 and the report came out in November of 2019. And we are not currently in a phase of collecting data again on that same survey. Okay, that makes a lot of sense. The answers would be completely different now anyway, wouldn't it? Yeah, well yeah to some degree, I think, honestly, our business officers - I mean, as we kind of dive into some of these findings, I'm sure your listeners will agree - I think a lot of what you would expect is maybe just a little more heightened sense of need, or a heightened sense of purpose, for using data. Although, I mean it already was evident in the results as they were in November, so before the pandemic. Yeah, that makes a lot of sense. So when it comes to analytics for higher education, what type of data are business officers and university leaders looking for? That's a really great question. So I will say before we launched the the survey itself, NACUBO's staff conducted a series of focus groups with our members at our different regional meetings, as well as at a NACUBO annual meeting, really trying to get a sense of, you know, if we have this, you know, idea about data and analytics. But what do you need? What do business officers really want to do? How do they want to be able to use data? And the qualitative responses from those focus groups - this is all before the survey itself - they pointed to three big buckets of interest for business officers for how they wanted to use analytics. One is not going to be a surprise: that's finance. So when we think about institutional finances, business officers want to be able to deploy those limited resources the most efficiently and as effectively as they can. So it's not really a surprise that finance is one, you know, bucket area, or one thematic area where business officers want to use analytics. The next bucket - and I'm not listing these in order of size, I'm saving the biggest one for last - but the next one is facilities. And that makes perfect sense, too. Especially pre-pandemic. You know, campuses have a really large footprint and it makes sense that business officers would want to make sure that they're optimizing the use of their space. And then the third bucket - and this one maybe would be a surprise to those, especially if you're outside of the business officer world, but I don't think other business officers find this as a surprise - but the third big area where they really want to see analytics used is student success. And I will say one of, there's a NACUBO member - he happens to serve on the analytics advisory group - and he always uses this phrase. He said, "It's not just about return on investment, it's about return on mission investment." And I think that really shows the tie about why business officers are so interested in analytics as it relates to student success, because they want to know that those resources that they're allocating - which we know are limited - are having an impact. And an impact for students, and students are able to achieve the outcomes that they want to. Yeah, that makes a lot of sense. And it's interesting when you say student succes,s because that really kind of is two-fold. And we talk a lot about having success outside the classroom in addition to inside the classroom, and those really being separate. And so obviously from a business officer perspective, we hear a lot of our schools, too, saying that they're really looking at that success outside the classroom. Yes, well, and that's definitely important, too. And I would, you know, point your readers to NACUBO's the study of analytics, where we show the results of this survey, and where we really break down some of those items about how analytics is currently being used to support institutions and where business officers are really seeing it make an impact. And we do break down that student success piece into maybe not necessarily the inside/ outside of the classroom, but we look at the whole spectrum of, you know, how are you using it for, you know, for enrollment, or admissions? And then, you know, you think about student progress, you think about retention. And then, you know, as you think about after completing a degree, you know, how are students - how are we using analytics to show that the success for the post-graduation outcome. So maybe we look at it slightly differently, but it's the same thing we, you know, obviously we have believed that the experiences in the classroom, there's definitely opportunities for learning analytics from the faculty side, from the provost side, and there's also lots of opportunities for understanding, you know, how do different success programs influence student outcomes, you know? How are we making that case for investing into student support services outside of the classroom? And, you know, we've heard from talking to members that there are some interesting ways that that they're working on, you know, addressing both the inside and outside of the classroom to make sure that students are being successful. Yeah really all builds on each other, right? so there's not really this or that, it's a combination and really one impacts the other to have success across the board. So yeah, that's great. Well since this study is relatively new to NACUBO, can you talk more about your approach - the NACUBO's higher ed analytics framework? Yeah, yeah definitely. I will say, so the framework itself was built kind of around, or an extension of, the Gartner Framework on Analytics. And I'm not sure if your your listeners are familiar with that, So I'll kind of just describe what the the Gartner Framework looks like. It's basically a visual that shows progression so that, you know, if it's a little line graph that's going up and to the right, and it's showing the the different levels of analysis that you could conduct. So it goes from hindsight, to insight, to foresight. And what we're showing using both the X and the Y axis in that framework is you see that as you move across - so from hindsight, to insight, to foresight - the level of difficulty of the analysis increases, but so does the value. And what we did - and when I say we, I mean the analytics advisory group at NACUBO - we tried to use that as a framework for understanding our own programming. However when you think about higher ed and you want to contextualize all of that within a, you know, a framework that business officers would relate to, we added a few elements to the Gartner Framework. And the three elements that we added to it are: 1) to the far side of the framework we added return on investment. And so really encouraging our members to think through, you know, as you increase in difficulty and value of your analyses that you're conducting, you know, what is the return on investment that you're getting for that? And always look for how are you actually improving things at your institution. And then the other two things we added that I think are are of critical importance to our members as they think about analytics and how to have a successful analytics program at their campus are culture and capacity. So when we added capacity to the framework, we're really thinking about asking folks who are using the framework to question, "Are we investing the right amount of time, resources, and staff?" You know, really the institutional capabilities. And then the other part of the framework is culture. So you can hire all of the analytics experts that your heart desires. You can buy all the latest and greatest tools, but if you don't have a culture that, you know, values being data informed, then you're not gonna have the action. And that's the whole point of doing all the analysis and using analytics is to change things for the better at your campus. Absolutely. I think that's great. And like anything, we talk a lot about really being tailored for higher education. So it's great to be able to look at some of these other frameworks and how is it happening in other business worlds. But at the end of the day, higher ed is different and unique and I think those are three really good additions that you made to the framework. Yeah, thank you. So can you go into specifically the - I know that we talked about having the six guiding principles to use analytics - so can you talk about what those six guiding principles are? Yeah so the six guiding principles that are in a publication that NACUBO wrote together with EDUCAUSE, which is a professional association for IT professionals in higher education, and also with the Association for Institutional Research. So our three associations have partnered, you know, on professional development programs or, you know, in other capacities for the past six years if we're thinking EDUCAUSE, and I think we're at at least three years now working with AIR. So our organizations had already been working together in analytics. And it was probably, it was November of 2018, there was a group of us meeting, and we were thinking that it might be good, you know - we keep collaborating, but we really need to get the message that it needs to be our members and it also needs to extend beyond just the members of our three organizations - but we need to really have this call to action. So an idea was born. And the three associations work together to write the joint statement on analytics. And the joint statement on analytics really does two things: first it's a call to action for everyone in higher ed to really start prioritizing analytics. Because we know all the challenges that institutions are facing, and they're really going to have to start using data as an asset. And then the other part is these six principles that you're asking about. And so there are six things that our three organizations thought all institutions - so all colleges and universities - should use these principles to guide them. Whether they're just starting an analytics program, or whether they're already well on their way. These are guiding principles that everyone should consider throughout their work. And so the six principles - and I'll be brief about them. but in no particular order - one of them simply says, "Go big." And we're really asking for institutions to make a commitment to analytics. And we think that that commitment needs to come both from the top top down, and also bottom up. But there definitely has to be leadership buy-in. If you don't have buy-in from your president and your cabinet level-leaders, then it's really difficult to maintain any kind of data-informed culture. The second: analytics is a team sport. And maybe I should have said that one first, but clearly collaboration is important. We know in higher ed, a lot of folks operate in really siloed environments. We also know that that means there's probably an Excel spreadsheet on one person's computer, in one department, that nobody else knows about, but it really could add value to to the institution to have access to that information. So we think it's important to break down silos and to collaborate. And that's beyond, you know, IR/IT and the business office, but also into student affairs, the provost office, the president, etc. The third principle is to be prepared. That one is more timely now than maybe any of us ever could have predicted it would be. But in short, we're calling on folks to prepare for detours, or challenges, or, you know, we're saying your first attempt at using analytics, or developing a campus-wide analytics program, probably not going to be perfect - I mean I can't think of an example that's been perfect - but it's prepared for detours is the the principle because we know it's hard for higher ed to change. And, you know, whether it was a pandemic, or or something else, there are going to be other challenges that your campus is going to experience. But that doesn't mean you should let your efforts to use analytics be derailed. So keep your focus, because ultimately you're serving students, which is one of the next principles. So analytics has real impact on real people. We talked earlier about how higher ed as a sector is very different than other businesses. And that's because we're human-focused. So everything is about students, or faculty, or staff or, you know, hopefully a combination of all of those. And so behind every piece of data, behind those numbers, are real people. And so we need to make sure that we take data privacy, data security, very very seriously at our institutions. And the next principle is invest what you can - you can't afford not to. I love when I get to talk about that one. Especially being from, you know, NACUBO. We always assume people are meaning, "Oh, yep, you're the business officer, you're talking dollars and cents." And we are, and if you read the statement it does say using analytics will require, you know, investments in terms of like a monetary investment. You might have to buy new software, or focus on your infrastructure. But I would say one of the most important aspects of that investment is investing in your staff. So it's investing by taking the time to offer professional development, to ensure that your campus has data literacy skills that help them be able to use the data so that we're actually translating everything to action. So that's really what we mean by investment. It's more than just the dollars. It's all the time the staf,f the professional development, the data literacy, etc. I'm so glad you said that, because I talk often about as we bring new tools or new systems to the table, you have to also - not just buy the tool, but you have to invest your time, and understanding it, and making sure you're getting the value. And I think the same thing is true of data, right? And data, I'll say sometimes too, data is only as powerful as it is manageable. And so if you don't know how to manage it then it's just gonna be a bunch of numbers. Yes, yes, oh no, I agree. Yes numbers or, you know, some folks they want that, you know, we see those commercials on tv about some kind of magic button, or there's like a button you can click on your computer, and it tells you what to do. That's not really how analytics works. It's definitely a resource and you should use it especially when our institutions are, you know, so financially constrained. But but it's not magic, it definitely requires effort. You have to be able to understand the context around the data. There's no magic answer for higher education, unfortunately. And then I would say the the last of the six principles that we outlined in the joint statement is tick tock tick tock. What we really mean by that is the time to act is now. And we were saying that, you know, before the pandemic, so this came out, you know, some time ago. And it was already imperative for institutions to really start to leverage the power that data can bring to support them. And I would say you know if we could use a bigger, bolder font for that particular principle maybe we should. Because I do think the the pandemic has highlighted, you know, all of the things that we've just been talking about. It's made them seem, you know, even more stark. And so I think, you know, if we haven't already started using data at institutions, we definitely should be thinking about it and thinking quickly. Because every day that passes is another day lost. You're absolutely right and I've talked to some schools recently that they were thankful that they felt a little bit ahead of the curve, if you will, by already starting to track some data. And so then when they were asked all of a sudden like, "Hey, do you have this information? Do you have this information?" they were able to say yes on there. So how have you then talked with some other schools and, you know, how do you see other business officers using analytics? Is this, you know, again we talked a little bit about servicing their students, but are you seeing them start to trend towards some different ways with using analytics? Yeah, so actually I'll share a couple examples. I have two I'm thinking of in my head right now, and they're both examples from members of my analytics advisory group, so they're both business officers who are clearly very involved with analytics. You know, being in part of a volunteer group at NACUBO, focused on our efforts on that. So I'll share a couple examples. The first one is from Mike Gower and he's the executive vice president for finance and administration and is also the treasurer at Rutgers University in New Jersey. And I'd also say Mike is a co-author of a chapter in a book that's going to be coming out this November. I think it comes out November 3rd of 2020. The book is called Big Data on Campus: Data Analytics and Decision Making in Higher Education. And there's a chapter that was co-authored by a couple NACUBO staff - myself included - and a couple NACUBO members, and Mike is one of them. And in that chapter, he writes about a story about Rutgers using analytics to solve a problem that they've been trying to tackle. So in short, Rutgers University surrounds the city of New Brunswick, and so for students to get from class to class or as they travel - you know, quote unquote - across campus, they had to go through a city to do it - to get to the, you know, the other side of campus. And that was creating a lot of transportation issues. When you think about the busing routes or students trying to get from class to class, and what the university saw in terms of the challenge is that students were spending so much time on buses, that it made it difficult for them to get to the courses that they needed, ultimately, to be able to graduate. So it was delaying some students' ability to complete a degree and they even said the campus lore was you'll spend more time on buses than in the classroom. So Rutgers had a problem and the way they decided to, you know, to try to better understand the problem and ultimately develop a solution was to use data and analytics. And so they analyzed bus routes, they analyzed schedules of both the buses and the students, they analyzed travel patterns. And so they they pulled together all of these data to determine, you know, are there patterns, are there better ways that we could support students? And it turns out there was. So they used all of that data to reduce the amount of student travel. So they travel from course to course while having to get all the way through the city of New Brunswick. They also used that information to create an app. And the app was very much student- focused and allowed students to optimize their course schedules with the consideration of travel built in. And what they've started to see is that this does help students reduce their time to degree, because they're able to get to the courses that they need. And so I would say that's one example, and I promised you a second. So the second example is from Sherri Newcomb, who's also a member of NACUBO's analytics advisory group, and she's the the senior vice president and chief operating officer at Queensborough College, which is part of the the City University of New York. And, you know, I tried to pick a couple, you know, diverse examples. So we just heard about a big research university, and now we'll hear about a community college. I'll also point out that Sherri's story will eventually be released as one of the resources in NACUBO's the solution exchange, where she's talking about planning and budgeting, and how you can really use analytics to support your planning and budgeting efforts - especially in a time of crises. So Sherri's story actually starts only a few months ago. So at the height of the pandemic, she was working with staff at Queensborough College to develop a schedule for this upcoming fall, so fall 2020. And I'm sure all of your listeners know that has been a huge challenge, for not just our members, not just business officers, but for really everybody in higher ed. And some of that is there's a lot of unknowns. So even as, you know, we want to say we're going to be data-informed, it's difficult to do when your enrollment projections are, you know, we might have 40 percent fewer students, or we might have 25 percent fewer students, or 5 percent fewer students. So all of that modeling has been a challenge. But I will say what Sherri did as she was working, is they actually pulled a lot of historical data and looked at patterns about course enrollment. So you know, from everything from, you know, when a course goes live and students can register for it, which ones fill faster. And you can analyze that by time of day, you know, by what courses they need to complete their degree, you know, what satisfies what requirements, etc. And so they looked through all of their data to try to understand what courses filled the fastest that they they knew would be most likely that students would need to have for their degree, regardless of if enrollment was, you know, expected to be 40 percent lower, or 25 percent lower, than what it had been the year prior. And so they they dove into that, and they also looked at the data from the faculty perspective. So they wanted to look at these patterns ultimately to figure out how can we make sure that we offer the courses students need to have to satisfy their graduation requirements. How can we make sure we're offering them, you know, at the right time of day or, you know, any of those things that you would look at. And on the other side of that, because we do know institutions are going to be financially constrained because tuition revenue is an important revenue source for all institutions, not just private ones, but how can we also ensure that we have full-time faculty who have full schedules? You know, and I know at Queensborough College they have a tiered system for how they will hire adjuncts, you know, how do we have a way to fill in adjuncts as we need for the courses that our students really need. So really coming up with a way to marry student needs and faculty needs together. And that's how they were able to develop their schedule. So those are just a couple examples of using analytics in the business office world. Those are great examples! And I'm sure a lot of insight of thinking, you know, just thinking differently, right? And a lot of times being able to have access to the data to analyze it, allows you to think differently. And see some, like you said, some patterns and some trends to help you shift during that time. Yeah, actually, you know, you saying that made me think of something else about Sherri's story, or really the story at Queensborough College. But one of the things that i know she did is, you're right, it is important that as soon as you see data, and you see the patterns, and you can start to understand what's behind them, and how we can use this data to to make informed decisions. Sherri didn't make those decisions in a vacuum and I would say one of the most important things she did is she collaborated with academic staff at their institution, and you know, didn't go in with like, "Here's what I think we should do. This is what the data say," but brought the data and used it to have a real conversation. A conversation where there was, you know, trust and transparency. And I really think that that's the real value when we think about cultural changes in higher ed, and making sure we're able to serve our communities and our students is the conversations that it facilitates. So I think that's a great point that you make. Yeah, that's a real key too, because it really shifts away from just, you know, anecdotal beliefs, to hard data saying here are the numbers, or here's the data, now let's figure out what this means together. And I like that it gives you buy-in on both sides, and so it's not an opinion or a belief, but it really is taking that together and working together to make a solution. That's great. Yes, yes, and I think - and that really is, you know, I know sometimes we're splitting hairs, you know, when I look at conference sessions, or you know, white papers that I read. But there seems to be almost a way where folks are, you know, interchangeably using the words data-driven and data-informed. And I do see there's value in being data-driven, I know when you can automate things and have the data say we're using too much heat, and this room's empty, have the power be turned off, or into something. Like, there's ways to have data-driven decisions, but when we're thinking higher ed and how it really is a service industry, like you know, when you think about the students, and the faculty, and the staff, and how many people are involved in all the real decisions that are made, being data-informed is much more valuable. Because we know our campuses hire experts, you know, experts in student affairs, you know, faculty who are experts in their fields, and we can empower them with data to make sure we're serving students the best we can and making the most efficient use of our resources. And everyone really is involved in that process. I think that's great, you know. Data-informed just even as a word - it's all about positioning sometimes, I say, right? And so just saying that we're being data-informed just gives the ability to want to bring that all together and collaborate, versus we're going to be data-driven. So I think there's just a tone that comes with. That's really good insight. But I'm betting that, you know, there's still, like you said, because of some of the silos and and just how some things have gotten done on higher ed before, there might be still some barriers to to using data in higher education. Do you want to talk a little bit about what some of those barriers might be? Oh yes, I do. This has been the focus of my work, and will probably be the focus of my work for a while to come. So when we launched the survey to business officers about the use of analytics at their institutions, we asked them about barriers. And now our efforts are to help them come up with ways to address those, or come up with resources to help them address those. But in short the barriers really fall into two different categories. So one category of barriers are all cultural. And so just a couple of examples of what I mean by cultural barriers are: we asked institutions, we listed a few different kinds of cultural barriers, and we asked them to indicate if they thought these barriers were never a barrier at their campus, if they were were a barrier but are no longer a barrier, if they are a contributing barrier, or if they're a pressing barrier. So a likert scale question. And I will share just a few of those findings. And I think some of them, your listeners might find surprising, and others perhaps not. So one of the cultural barriers that I know we hear quite a bit, whether it be in white papers, or we hear about it at conferences, is that there is a fear around analytics. That the data are going to be used to punish. You know, especially when we have business officers starting to talk about making data-informed decisions, I think folks - and it's, you know, fair to some degree - are worried that this information will be used to cut programs, or to cut courses, or to cut other kinds of costs. And so 53.8 % of our survey participants indicated that that was either a contributing or a pressing barrier. So over half of our business officers think that there's fear around the use of analytics. We also asked about the campus being siloed, if that was a contributing barrier. And 61.8 percent of business officers said that the lack of collaboration, or having a siloed campus, is a cultural barrier to using data to inform decisions. And one more in the cultural space I want to share is 50.4% of our our participants said that mistrust or misunderstanding about how analytics would be generated and/or used - so kind of hitting at that ethics piece that we were just talking about - that they saw that as a cultural barrier. And those are just a few of them that stood out to me as I was reading the results. Yeah, that's really interesting, especially the trust factor, and it's interesting that kind of relating that human characteristic of trust to data. Yeah, yes, definitely. Yeah and then if you want me to share some of the capacity barriers, again I've just pulled a few that the findings kind of stood out to me. One I'll list because I think it's kind of expected. We asked, you know, if cost would be a barrier, and we specifically asked about the cost to invest in the the skills, or the staff necessary in order to allow your institution to really leverage analytics. And 66.2 percent of our survey respondents said that the cost of investing in skills and staff was a barrier. And in addition to that 78.9 percent said that they really just didn't have the workforce capacity. There simply just weren't the staff at their campuses to be able to do this work. And so that's why when I was talking about investment earlier when we talked about the the joint statement on analytics from AIR and EDUCAUSE and NACUBO, it really is quite a bit about the people. We asked about, you know, the the cost of the technology that was a barrier at institutions as well, but it's really the staff piece that I found interesting. And along with that, and I think you'll find this really interesting, too. And we categorized it as a capacity barrier, but it really could be part of the the cultural barriers, but we asked individuals to indicate if they thought it was a barrier to have end users - so those who you know aren't in IR or IT, or they're not the business analyst, or the financial analyst - but they're those who are seeing dashboards or other data visualization pieces, or they're getting the reports and they're supposed to act on them, but they're not able to. For some reason, the end users don't have the data literacy skills to translate data to action. And that almost 81% of our participants said that was either contributing or pressing as a barrier at their institutions. And so that's, I think it kind of just, you know, puts a finer point on what we were talking about earlier. That, you know, you can have all kinds of fancy tools, or buttons, or technology, but if people can't translate that information or that data and analytics into action to make a difference at their campuses, then it's almost, you know, moot to even try. Yeah that's really interesting. And I think, you know, that really goes to show and something I know that NACUBO does a really good job of is always educating and helping to make sure people understand what are some new, you know, new tools, or new methodologies. And I think that's really important, a really big piece to keep going these days, at least, is to keep your staff educated on all the tools that you've made investments on. Yes, definitely. Yeah we have ongoing professional development. You know, the live programming - although for this year it's online - but the programming where folks can come, and talk, and engage about data and analytics. I will say we do have a standalone workshop - the NACUBO integrating analytics forum - that folks can come to and learn about how business officers are using data, and the different kinds of analyses that we're doing. You know, NACUBO was also working to embed analytics content into all of our other workshops and other professional development offerings, because it's not really a standalone you know skill or activity it's really a part of everyone's job at institutions. And I would say, you know, to go along with that, and not just because, you know, it's a pandemic and it makes it difficult to offer, you know, face-to-face professional development. But in addition to the online programming, we're also working on developing tools and resources to support our members as they move forward. So it's not out in the world yet, but we have a series called accelerating analytics. Which is, you know, these brief explanatory documents that go over key key topics. For example, data governance, or academic cost modeling, or you know, some of these topics that business officers really need to understand. And these resources will give them, you know, here's the basics of what you need to know, and then here are some action items you should be thinking about at your institution. Oh that's great. So let's say when all this is in place, and we're trained, and we're using it, we all know we're doing, what does success look like? I love that question - very optimistic and definitely what keeps me going every day. Although, my honest answer - it might sound cliche at first - but success to me looks like continuous improvement. And i know sometimes those those words "continuous improvement" get tired or overused, but that really is what a successful analytics program would look like at a college or university. You know, so at a successful campus your administration, your faculty, your staff, your students, they all have data literacy skills. They all can understand, you know, the different kinds of analyses that your institution is putting out. And not only do they understand it, but your administration, your faculty, your staff ,they know how to use that data for their day-to-day roles and make a difference for students. And so I would say, you know, in my perfect world of continuous improvement, data really is used like an institutional asset. And analytics is leveraged to further an institution's mission. I think that's great. And to me that's really at the core of higher education, which is continuous learning. Yes, yes, that is exactly right. That is wonderful. Well thanks so much Lindsay, for all your insights today. So where's the best place to access all the resources you talked about today? Oh that's a great question. I would tell your listeners to go to NACUBO's topic page on analytics. And you can get to that page by going to NACUBO.org/topics/analytics and then from that page, there will be links to most of the things that we talked about today, the exception is the book which is not released until November. Thanks for tuning in to this episode of Focus! Don't forget to subscribe so you can stay up to date on the business of higher education. For more information check us out at touchnet.com.