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EDUCAUSEEDUCAUSE Live! – Findings from the 2019 Enterprise Summit: Analytics

Thursday, June 20, 20191:00PM – 2:00PM Eastern

EDUCAUSE Live!June 20, 2019

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EDUCAUSEEDUCAUSE Live! – Findings from the 2019 Enterprise Summit: Analytics

Thursday, June 20, 2019

>> Betsy Reinitz: Welcome to EDUCAUSE Live! Everyone, this is Betsy Reinitz, and I'll be your moderator for today's e-live webinar. Wed like to thank Quest for their sponsorship of the 2019 webinars. Quest is a global solution help institutions and schools better move, manage, and secure their Microsoft infrastructure. You're particular familiar with the interface for our webinar, but here are a few reminders. Use the chat window on the left side of the screen to submit questions and to share resources and comments. We'll save some time at the end for questions, but please put questions in the chat box as you think of them. We'll keep an eye on those questions and come back to them at Q&A at the end, if we don't get them addressed before then. If you're tweeting, use the tag #EDUlive, or any of the tags on the slides, "EDUCAUSE enterprise." If you have audio issues, click on the link in the lower left-hand corner in the screen and direct a question to the technical help. A drop-down menu will appear, where you can select start chat with and hosts. Session recording slides will be available later today on the EDUCAUSE live website. The 2019 enterprise summit, a collaboration of EDUCAUSE and NACUBO focus on higher education. We invited several presenters from the summit to be with us today to talk about the ideas and recommendations that emerged from this year's summit, and I'd like to introduce them to you now. We're delighted to be joined by Rebecca Barber, the senior director of management analysis at Arizona State University, a clinical state professor in the Mary Lou Fulton teacher's college. Prior to ASU, Dr. Barber worked in a variety of positions at the University of Phoenix, Cornell University, SUNY-Albany, as well as taught statistics in the Maricopa Community College district. Also joined by Sandra Cannon, associate vice Provost for data governance for the University of Rochester. Prior to joining the University of Rochester, San worked for the Federal Reserve Bank of Kansas City, where she worked for the center of data and research economics and worked 20 years at the federal reserve board in Washington, D.C. We also have Christopher Gill, chief information technology officer for Drake University. Prior to assuming the CITO role at Drake, Chris was the chief information office at Gonzaga University for more than ten years and a member of the Gonzaga information technology team for 24 years. He's served on a number of EDUCAUSE committees, including the 2007 national conference program committee and on the EDUCAUSE professional development committee. And finally, we're joined by Gina Johnson, senior associate of the national center for higher education management systems. Gina has more than 20 years of experience in the field of education including teaching, policy analysis, association leadership, and institutional relationship. At NCHEMS, Gina's focus is on expanding the capacity of data and analytics functions in higher education through collaboration with higher education associations and organizations focused on improving the use of data and information to inform decision making. Thanks very much, Rebecca


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Barber, Sandra Cannon, Christopher Gill, and Gina Johnson for joining us today. And with that, let's begin.

>> Gina Johnson: Hello, everyone, this is Gina Johnson. As Betsy said, I'm senior associate at NCHEMS, and I was very pleased this year to serve on the program committee for the Enterprise Summit Analytics and asked to moderate a panel, and I'm going to talk about the panelists on that session, because that's one of the things I think is really important, and I'm also going to share what kind of takeaways we came up with as presenters on that panel. But first we want to have our first opportunity to get a bit interactive, and so we are going to open up a poll for all of you, and what we'd like you to answer is -- and you'll have to be sort of -- practice your brevity skills, but we're going to ask you in one or two words, what skills or characteristics do you look for when hiring and developing analytics staff? So next slide, please, and we can go ahead and open up that poll. And I'm going to pause a little bit. I don't want to talk over folks as they are coming up with ideas for what's going to fit in the poll. We'll see some answers starting up already, and one of the things that we talked about that I will share, and we'll see if this comes true in our poll answers from all the individuals listening in on this webinar, is we were thinking of technical skills and we were also thinking of people skills and how do you find that in individuals is sometimes a challenging thing. And some of the things that we train for and look for aren't necessarily things that we knew, and that's why we titled our session "the future of the higher education workforce." But I'm seeing things like critical thinking, thoughtfulness, communication skills, curiosity, and I'm highlighting that one, because that's definitely one that came up in our session and one I've been thinking about, seeing as I just hired a new data analyst at my organization. And curiosity is one thing that I was very cognizant of when we were doing the interviews. So I also see technical knowledge, inference skills, business knowledge, which is critical, as well. Lots of great ideas in there, and I think I see lots of things that came up in our conversation, as well. So we're going to switch back out of the poll now, so thank you for participating with us, and the next thing I want to show you is the -- pictures of the folks who served on this panel with me, because this is very important, in particular, I think, one of the things I really like about the Enterprise Summit Analytics is it brings folks together across institutions thinking about analytics, and with EDUCAUSE co-hosting it with NACUBO and AIR, it highlights the fact analytics isn't done in one office or function, it's something that's done across institution, and I.T. and IR and business and finance professionals are particularly essential to these roles. So I want to let you know I am representing the other members of this panel on this webinar today, and the three folks pictured on the slide that's up right now, Ellen Peters, Mac McIntosh, and Sherri Newcomb, are all board members of organizations that co-hosted the event, and they all had a very varied experience, because Sherri had some IR background before she became a CFO. Mac serves on both the board of NACUBO and EDUCAUSE and is a CEO, and Ellen has vast experience working in IR assessment and other areas. So they are really people who think about things globally. The topics that we came up with when we were


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having our conversations before the event were four takeaways that we wanted folks to think about before they listened to our conversation on site and as they left, so we want to share those with you, as well, today. And those four were the fundamentals, leadership, and the role it plays in thinking about the workforce and how we pick those people for those positions and train those people, collaboration, which is really, as I mentioned, kind of the theme of the event, and how we think about collaboration in our workforce, as well. And, of course, not surprisingly, talent investment. And a fifth piece came up on site that I'll just preview right now and say you'll watch for that when I share that at the end of my comments, but really these four were the fundamentals, and I'm going to touch mostly on fundamentals, leadership, and talent investment. So related to fundamentals, we were thinking about what are the knowledge, skills, and abilities that are needed for data fundamentals and ensuring institution-wide data literacy, and those are things that came up in our poll. What are fundamentals, things people need to know in order to work in the higher education analytics workforce, especially the future of that, because things are ever changing and we have new tools and new ideas coming at us all the time. So one of the things in fundamentals that came up that's the second bullet point there that you can see is data literacy across the institution. So we had conversations about not just what do we look for in the folks that consider themselves part of the data analytics team at an institution, but also how do those folks help everyone across the institution really understand data and information and how it can help them have a more data-informed decision making process. And so some of the skills we're looking for, thinking about, are really those people skills, because we need folks who can actually do the data and analytics and all that's involved in that, and we also need folks that can help others, as well, so it's a bit about communication and education and really being able to communicate with people. We talked about leadership and the importance there, and that top leaders support a culture of inquiry, and it's not just does your president do that or does your provost do that, but also unit leaders. So that could be deans, it could be people in the C-suite that help lots of folks reporting to them. It could be directors of whatever it is across your institution and thinking about how they are involved in data access and transparency and thinking about how they then access those core data and analytics professionals and how they also encourage their own -- their own staff to embrace the data literacy that's coming across an institution. I want to mention briefly collaboration, because that's something another presenter is going to touch on more, but we did have a conversation within the session about cross-functional conversations about skills and positions. So if, for instance, an institution decides they are ready to hire a data scientist, one unit doesn't just have that conversation and hire a data scientist and another unit thinks they need one, as well. Maybe only one is needed at a institution, so having cross-functional conversations, and if you're brave enough, inviting people from across the organization related to that position to be in on the conversations and the process for developing a job description and interviewing, so that you know people are going to be working across the institution once they are hired, because they've been involved in that process to begin with. And then finally, talent investment. And this is what we were


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really thinking of. It's one thing to write a job description and do a good job on your interview process and think you've got, you know, a brand new person who has all the characteristics of the future of the higher analytics workforce, but it's also about taking a look at who you've got already and the skills that they have and the skills that might be missing across your institution, as well, and who is it that you want to have learn those skills so that you've got it, because not everybody can have every skill, and that's fine. So this is where we talked about, again, with collaboration, having a variety of people or unit on your search committee, so you have that broader view in thinking about how we can connect those folks once they are hired, as well. So also the importance of personal skills and networking, so being able to kind of sus that out in the interview process, and then again when you're thinking about the folks who are at your institution already, how can we teach networking skills? How can we teach those personal skills? Because those don't tend to be the ones we have training for. We tend to train towards the technical. Finally, last point here, focus on inquiry and commitment to higher education. We feel that in our conversation that if you've got somebody who's really curious to pull that word for the fold that someone chaired and thinking about what's new and what do the data say and how can we find this information and who has a passion for education and thinking about higher ed, some of those other technical skills we can train for, so really finding and focusing on that in your interviewing process. And then my final slide is that one I previewed a little bit to say this kind of came up on site, and we hadn't necessarily talked about it in advance, but it's really why now, why are we worried about this, and why did we title it the future of the education analytics workforce? So two things that we highlighted in this session, and one that as many of you know from reading of the chronicle of higher education, inside higher ed, some of these news organizations, the value of higher education is being challenged right now, so it's very important for us to be thoughtful about how we use our data and information to make data-informed decisions so that we can share the value of higher education and really, you know, reconnect with the public in general and with our students in particular. And on that note with students, the last point I'll make is, it really is fundamentally about the students, so we have an imperative right now to use data and information to make sure that the students we have in our institutions right now have the best experience possible that they can. So we might be spending time building a data warehouse and data infrastructure, but right now we have to be using the data information we have in order to make decisions to make their lives and their institutions experience the best it can possibly be. And now I'm going to turn it over to our next presenter. So thank you.

>> Christopher Gill: Thanks, Gina. Chris Gill, I'm the chief information technology officer at Drake University, and I'd like to begin my section with a poll. So I think what I'm trying to do here for all of us is give some context, some perspective. This is really about level setting for me. So what I think the purpose of this poll is to give us a sense of where people are on their journey to a mature data governance environment. And as I reflected on what I heard at the summit, what I was repeatedly reminded was that we're


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all at varying levels of maturity in our analytics endeavors, our analytics activities, and looking at the poll, as I look through it here, obviously, many of us feel like we have a long way to go. If I'm honest both with myself and for my institution, the many of you who have indicated that your current data governance environment is ad hoc is reflected here at Drake. That's really what we are trying to get our arms around next, is what does good data governance look like? So let's move on to our next slide here. Maybe given that so many of us are feeling like our maturity levels are at Charlie Moran's quote makes even more sense, it brings a pretty great image to mind. I can't get the image of a goat rodeo out of my head, but I love Charlie's comment, because I think it speaks to what happens in an ad hoc data governance environment, and at the same time, stealing from the work that San Cannon has done at the University of Rochester, who's going to speak later in this session, I think that San's quote gets at something that's at the very heart of what good data governance is about, and that's a sense of partnership. I interpret this quote as really being about all of us understanding that our data governance journey is about what's in the best interest of our institution. What can we do as individuals who have some responsibility for data stewardship and trusteeship to make sure that we're getting the biggest bang for the buck out of our institutions? So another perspective on this is really kind of at the heart of my journey through analytics maturity or towards analytics maturity, and I find myself relating so strongly to this cartoon, because, really, we're all feeling that sense of the need to move faster, the need for data to be an inherent part of what goes into decision making at our institutions, but at the same time, the struggle with how to actually make that happen and in an efficient and effective way. Our journey at Drake really was a journey that was in three phases, and the first of those phases was to fail. And, frankly, that failure occurred for myself both at Gonzaga and for our -- my partner here, who experienced the same thing at Drake, our experience was one of initial failure. Stage two for us was recovering. Creating the space and foundation for true organizational change based on data-informed decision making. And then stage three, which I would say that we're looking towards in a hopeful fashion, but not arrived yet, is applying data analytics as a lever for institutional change and accelerating our maturity journey. Along the way, we learned through what I described as three epiphanies. That sense of all of a sudden understanding something true that you hadn't seen before, and for myself and Kevin Saunders, who is the director of institutional research and assessment at Drake, we found that out through three spots where all of a sudden we were in a different place in terms of our understanding of what data maturity, what analytics maturity, and what effective data governance might look like for us. First of those epiphanies for us was an aha moment with the director of institutional research that occurred for me at Gonzaga, and it was really understanding what she really needed from a data warehouse. And her response made clear to me that her immediate need wasn't complicated. It wasn't sophisticated. It was really basic trend information that could be easily accessed, filtered, framed for different audiences on the fly, and then grouped together to make access easy and to make day-to-day decision making easier. So those key points of ease of access, instantaneous filtering, available on demand, and


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multiperiod or trend information were the real need. And those were all of the things that a data warehouse can do well. We were trying to drain the ocean with everything that we were trying to do. We also made a decision to start giving keys of population easy access and flexible access and treat data as a shared resource. We weren't going to put significant restrictions on that data. So the lessons learned for the first epiphany for us were start somewhere, start small and deliver value, and solve real business problems for your university. The second epiphany started with a sketch on a piece of paper. The interesting thing about this sketch is this is, in fact, a dashboard. It just happens to be a dashboard that we hadn't created nicely for our provost, she created on her own based on the data that we were giving her and reformatted it to meet her needs. It was a decision support model that this dashboard was really a decision support model that our provost used to determine how to allocate funding for faculty replacements and new hires. She used the dashboard to move from judgment-based decision making on where to apply dollars to data-informed decision making, and what this dashboard essentially shows is how are these programs doing in terms of using their available capacity? Is the demand trend increasing, staying flat, or decreasing? Did they submit information requesting a new line? A whole bunch of pieces of information here that she then used to make decisions and allocate dollars based on information and what happened out of that was dramatic change. All of a sudden the data warehouse became very important to deans and department chairs. They established an expectation that data would be used to evaluate hiring and replacement, but maybe most importantly, it opened our eyes to the way that the provost wanted to use data strategically and her approach, which caused us to realign our work with hers. So here are the lessons learned, to be opened to new ways of using data, pay attention to decision support pain points, and look for places where data with empower decision makers. The third epiphany was in the acceleration phase for us. We really needed to take a step back and think about using data less from an analytics-centric perspective and more from a strategic decision making perspective. Our goal was to mock up a set of views on some data related to academic financial performance and to provide stuff -- provide examples of dashboards that our provost could use to accelerate her use of data. And what we did, as you can see, is set up a bunch of wire frames that she looked through and evaluated and provided feedback on. And we went through two or three iterations of this document. Out of that, what happened was the provost moved completely away from a dashboard approach to one focused on departmental improvement. Out of this work, the provost decided rather than using a dashboard, she was going to move to a model where she was meeting routinely with departments and building their capacity for ongoing strategic or operational performance on an ongoing basis, and the data was going to be secondary to that process. So we are not even releasing a dashboard until next year. So the lessons learned for us here were don't fall in love with the presentation, engage with institutional leaders regularly and strategically, and be prepared to change your approach and responses to changes and strategy. All of this leaves me reflecting on what I experienced at the summit, and these five conclusions that I drew and that we heard repeatedly from participants. First of all, we have to focus


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on people and their challenges. Secondly, we have to remember that collaboration means we're in partnership. We are all in this together. It doesn't mean that we always get our way, but ideally it means that everybody gets an opportunity to participate in the discussion. Ultimately, analytics maturity is about culture, and culture follows leadership, intention, and value to the organization. So our focus needs to be on being leaders, being intentional about what we're doing, and constantly driving value. Failure is not a bad thing. We need to be prepared to fail. In fact, often, failure is our path to enlightenment, rather than the path away from it. And being open to trying something that doesn't work and learning from that has been at the heart of our success strategy. Finally, you need to start somewhere. Don't be afraid to pick a place that's in the middle, solve a real problem, move from crawling to walking, and, hopefully, eventually, running. And at this point in time, I'm going to turn it over to San.

>> Sandra Cannon: Thanks a lot, Chris. So as was mentioned, my name is San Cannon, associate vice provost for data governance and chief data officer at University of Rochester, and I have the distinct honor of being the first person in that position, and, therefore, I'm going to tell you about how I see the role of chief data officer and the context of supporting analytics and working with data and higher education. So complete disclosure, this was not really the focus of my topic at the summit, but this is sort of a piece of it. The challenge that I want everybody to understand is talking about data governance, and data governance is foundational for analytics and all things data related. You really need to make sure that you understand and know what you're reporting on, where it came from, and what it means. And so I generally tend to use cheese math and Barbie dolls to illustrate this point, but that doesn't work well in a webinar, so you'll have to use your imagination. And I want to point out the notion of data governance and all the benefits that come with it don't require a chief data officer, but I do want to give you a little bit of a sales pitch for why there is a great benefit to having a point person to handle these things and to think about data activities and insights. A little bit of history. Before I was a named chief data officer, I was that person that everybody called about the data. If you knew or you had a question about something data related, the answer was almost call San, but because I was a staff member on a particular business line, I could only be so helpful. So for a lot of instances, there needs to be a little more visibility for a point person to be really helpful. Now, the level and the placement for that point person are going to be affected by the maturity of your data governance program, and given the last poll results, we saw that we still have a lot of people who consider their data environment, analytics or governance, to be in an ad hoc or sort of reactive type of state. So does that really mean that you're ready for a CDO? And so now we have another poll to say what does it look like on your campus? Do you have someone who is like a CDO, whether named or not, in a role, strategic or executive role, at your institution? And I'm going to put a caveat on that and say if your CIO is doing this function, I want you to answer no. And I'll explain why in a minute. And so we give everybody a few seconds to weigh in, and you can see why I'm not the only person at an institution where this is not a


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big thing. I'm the first chief data officer here. I'm still trying to figure out exactly what my job is, and I know I'm not alone in that. Looking at this, the vast majority of you seem to not have someone in that position at your institution. So it seems like this is a good time to end the poll before I get depressed about never having friends to play with. So I do want to spend a couple of seconds to talk about what I see as the benefits, so if you can use this as a sales pitch at your university to have people start thinking about this, I'm really happy to lend this to you, along with my collaboration saying, which I will sell T-shirts for at some point. So one thing to keep in mind is when you're talking in the data world, education is really far behind corporate in having this sort of strategic, centralized focus for data. And I've figured out this is likely due to the cultural differences we have with many other corporate entities. Because universities, and to some extent health care, tend to be mission driven rather than profit driven, it's often harder to make the case for these executive roles, since it's really hard to show hard dollar returns in our investment world, but I still think that there's great benefit, especially given all of the challenges that the first two presenters have outlined. So one of the first benefits that I do want to mention is a CDO has a full-time data focus, which is why I mentioned leaving the CIO out of the yes vote in the poll, because chief information officers have enough on their plate worrying about the technology and other aspects of how data need to work in order for you to think about them as being somebody who does data full time. CDOs have anything else to distract us. It's all data, all the time, and even when there's a lot going on, and there is always a lot going on, it's always about data. Now, my personal opinion is data are a business issue, and that's where the value is for this position, to sit in the business and work with the technology. I see the data as being the what that needs to be managed and the technology folks as being the how in order to be able to do it, and so one of the benefits about having a full-time data focus is that I want to talk to you about the data you need and not the tools that you use. Okay? I see the relationship between data and IR. I see that's one of the questions we have as being similarly symbiotic. I see that the analytics is something that is born out of the data governance work that a chief data officer does. They might live together, they may not. I am a colleague of the head of IR at my institution. And because I have a full-time data focus, I have a strategic view. CDOs are charged with and should be able to think big, as well as little. Thinking about and understanding everything at the university level and not just for a single business line, okay? And I have the ability to, and I think a chief data officer is important to be able to focus on being able to translate the strategic to the tactical. So how do you actually get the leaders to understand the cultural role that they have in order to have that inquiry-driven viewpoint? How do you actually translate what the executives need to understand to the data work that needs to get done by people who are not at that level? To understand how to help them think about getting the data stuff right. Another really important thing that I do, serving as a connector and helping to connect people across silos to prevent duplication of investment and effort. I drink a lot of coffee. It's all decaf, or else I'd talk faster than I am right now, but it's really important to understand all of the data use and the data needs across the various silos that are inevitably in every institution,


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and I want to share that information to build communities. So a recent example is I was able to connect our facilities folks with our librarians, because there was a need for them to be able to discuss geo location software, and it's not usually the case folks that do our maintenance would think about talking to the librarians, but I happen to know one of them have the need to understand geolocation data and the other had the expertise, so being able to fill that role is very important. I don't necessarily want to herd the goats, as Chris mentioned, but it's really important I be able to help connect the people to support the kind of collaboration that is necessary and is one of the focuses that both Chris and Gina had mentioned. In addition, I feel like a chief data officer is uniquely positioned to play evangelist. In a lot of organizations, including my own, governance is seen as a negative thing. I don't want to be the data police. Now, there's going to be times when I have to actually broker discussions about who is not getting their way, even though they are having their say, but focusing not on the what can't you do, but focusing more on the what you could do if we did things differently. So it's really important to stay positive, especially because being able to come up with the hard dollar ROI is challenging. So being able to talk in terms of what the possibilities are instead of what the downside and the risks are if we don't do things a particular way. I like to think of this as being sort of the light side of the force rather than the dark side of the force. And so being able to talk about enablement, which is critical to meeting the challenges that Gina's ticking clock pointed out, is really going to be important. And it's a critical foundation for analytics. It enables confidence in data usage and results. And so being the evangelist I equate to sort of being like Dorothy in "The Wizard of Oz," where everyone wants to get to the Emerald City and I'm trying to show them how to get there, even though we have different reasons for wanting to get there, and the idea things will all be better in the end, but if I can protect you from the flying monkeys and the evil trees, that's great, even though I might have to occasionally tell you have to do things in a particular way in order for things to work. So not only are there benefits for having a chief data officer, there's also certain responsibilities that can be given to a chief data officer for that role to be successful. And often are not necessarily being filled with current roles that are not chief data officers. So for example, recognize data management work. So the session that I presented in was a power talk session that had three different presentations in it besides my own, and one of the other presentations actually mentioned this, that it needs to be in somebody's job description. It's really this notion that managing data is really an important job, and somebody is doing this, whether it's listed as their job description or not. Somebody's playing data steward or data custodian, whatever your roles are, and it might be 10% of their job, 15%, it might even be 50% of their job, but if it's not actually part of the job description and something they are being evaluated against, then it doesn't rise to the level of importance to actually get things done. And so it's just another thing that gets heaped on their plate that they may or may not have time to do effectively. And so really being able to champion and encourage people to recognize this and make changes across the institution to recognize how important data work really is, is one of the responsibilities that I see as part of my role. And I know we've already talked about


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this a couple of times already, but the whole notion of building data literacy. It was mentioned, and I think it was part of Gina's panel, where this is a really important thing to do, but who should be the one charged with doing it? And I think if you have someone that's a chief data officer, that's a really good opportunity to have something and have someone to focus on this. So one of the top things that keeps me up at night is the notion that people are now being expected to have data skills in jobs where it didn't used to be an issue, and they are not ready to do that. So how can we do a better job of helping them? One of the other presentations in the Power Talk session focused on making investments in current staff and giving business folks of actually being able to use their data. So it's not just the I.T. people or IR people expected to do analytics, and I put that in air quotes, but everybody really needs some level of help and some level of knowledge of how to do things effectively, even if it is in spreadsheets and is not as part of a bigger technology or technical platform. So the bottom line from the community college presentation was to maximize the use of existing talent, they achieved a lot without any new hires, because they were able to increase data literacy. And so that was a place where actually showing some ROI was able to do that in hindsight and be a good lesson learned for people to take away. Another thing that I see as an important responsibility of the chief data officer, and again, harkens back to some of the foundational things that have already been mentioned, is this notion of building a community. Another presentation, I think it was Charlie Moran's presentation in the Power Talk session, talked about the benefits of working together to increase resources. So when you act as one, you wind up with shared resources and can reduce costs in terms of hard dollar costs or costs on the staff. When you don't have that community focus, you'll wind up buying multiple technology licenses for the same product. Because a vendor has no reason to tell you so and so at your university already has a license for that. You can also invest a lot more in terms of time and resources for staff if people are inventing the wheel in various silos. If people see themselves in the work that others across campus do, then you can build a community where people know they have somebody they can ask about how to do things more efficiently or more effectively, rather than having to make things up and do it themselves. And so the costs of doing things can be decreased when people are able to rely on others for other feedback. So that's all part of the community building part. And then the number one thing that I see as my responsibility is this notion of communication. Communicate, I can't stress it often enough, data governance, whether be in search of improved analytics or done for other reasons, it always means change for someone. And having somebody like a chief data officer to lead that change means that you've got someone who can take the flag and run with it and talk to everybody about everything data all the time. And so I feel like often I am saying the same thing over and over again. Sometimes I am saying it ten or 20 times, but to different people at different context and sometimes I have to make sure the message is worded differently, so the people that are receiving it are hearing words that make sense to them. Because having conversations about data with the people trying to manage equipment are not the same as having conversations about data with the people who are trying to purchase data products from vendors. And so being able to


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actually reach out and talk to people about all the things that you need and how anything that gets done in the data governance world can potentially affect their business process, their bottom line, their activities, is really, really important. Now, the other benefit that I see, and it's a role that I take on and I take very seriously is the notion of making sure that communication is clear and is using a common language. One of the things that I find is a challenge with all of this new data activity that happens outside of the traditional places like I.T. and IR, where data are discussed, is that people don't have the right language to be able to understand and communicate with each other about data. And so one of the things that I spend a lot of time doing is making sure that we do have a common data language. Not everyone lives and breathes data the way that I do and some of my colleagues do, and so the things and the terms and the definitions that are obvious and second nature to me are not necessarily obvious and second nature to others. And so having the conversations and communicating with people about how to communicate is really, really important. And there's a whole nother presentation that we could go into about how to actually do that with a common language, but I think that my time is up, and it is time for me to pass it on to the next presenter for future use cases.

>> Rebecca Barber: Thanks very much, San. So as with everyone else, I want to start off with a bit of a survey. Many of you have seen this Analytics Maturity Curve in the past, where it moves from ad hoc reports, forecasting, predictive modeling, up to optimization, so I'd like to get a sense from the audience of where you think your institution is at this point and what -- where you would say you are on the level. And what we're looking for here is the things you feel you're doing well and are solidly part of your operational process, not necessarily the pilot predictive model that maybe one group off in a corner is doing. And what I'm seeing is something very common throughout higher education. We're getting to a point we have data available for people, the more advanced techniques, we're really only seeing that in pockets. Even here at Arizona state, there are areas using predictive models and areas where we are not, things we could improve, but just haven't had the bandwidth to start developing. So let's set that off to the side, and I want to talk a little bit about some of the things that came up at the forum that I thought were particularly important. One of the things you've already heard mentioned is that there is this very common idea across our industry that things are not the way they used to be. It's not business as usual anymore. We're not only getting questions about the value of higher education from papers like the chronicle, but "The Wall Street Journal," "Forbes," questions about every aspect what we're doing. All of these people are asking how is the money being spent on universities and how is the money that students are paying resulting in usable outcomes for those students, and then how do we communicate that information? There's good research that shows that there is still a premium for having a degree, and yet at the same time that we know that research exists, it's not getting communicated out to the broader public, so how do we facilitate that? One of the interesting things about the conference is it was made up of people from three organizations. Obviously, there was EDUCAUSE, also the Association for Institutional


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Researchers, and NACUBO. My role is in a business office, so I see these questions coming from our board of regents and from our legislature all the time. Because of that, I was paying particular attention to all of the different types of projects that were showing up at the seminar that were related to how we communicated these things better and how we moved everything forward. Now, having said that, there were a lot of really exceptional use cases going on. There was a lot of leading edge implementations that we can all learn from, but there were also some common things that we discovered as we worked our way through the days. And I want to talk a little bit about what those common lessons learned were, because it doesn't matter what type of advanced project you're trying to attempt, if you are unable to get it done because of some of the same problems that we see everywhere else, so here's some of the things that we were seeing. There was an overall call for us to build what we were going to use, and that meant a number of things. Almost everything in higher education, wide collaboration is necessary to ensure that what we're building mirrors both the process that we're trying to analyze and that it meets everyone's needs. And that means that collaborators are likely to be the ones that have to actually implement or change the process and maybe not the data people so much. These are also the people who will have some say into whether it truly gets adopted or not. Projects driven by only one group often fail, even if that group is institutional research or I.T. Often those types of isolated projects are reported as not going well and having to go back and really start from scratch in order to build something that would work. Data quality is critical. Michigan state built a system and came back and said the adoption only occurs if people actually trust the data, and that means that investing in data quality early and, frankly, often, is one of the most important things we can do to ensure success in our projects. The more complex the analysis that we're doing, the more important it is that people trust the underlying data that it's being built on. And that also means seeing data as an institutional asset. It's not something that belongs to one department. As with everyone here, I had a particular quote that I heard at the -- at the sessions that really stuck with me, and that was the concept of getting rid of, quote, cylinders of excellence. This is a combination of the concept of silos, which exist across higher education, with the concept of the center of excellence. So if you are a registrar or enrollment management organization sees themselves as the center of excellence about student data, they still can't make true advancements in analytics without having assistance from other groups. We have to break down those walls and really open it up. There was another comment from Hamilton College that nothing goes into the warehouse, the dashboards or systems for integration until it's gone through the data governance process, until there is some control of what is going in there, what the data looks like, making sure that we know what it means and how it's going to be used. Lastly, there is this concept of not trying to boil the ocean. Do one thing well first, and then expand it. Virginia Tech brought forward a very interesting project that they are using to create both a dashboard and a data analysis environment to look at their budgeting process. They wanted to make the data that was being used to determine resource allocations transparent to the institutions, so people could see what metrics were being


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used to drive their budget. And they started by focusing on this one area, and one area where people were particularly motivated to pay a lot of attention, namely, the thing tied to their budgets. They did not try to do everything at once. There's a lot of additional phases they have planned for the project, and that has allowed them to be successful at getting this first phase up and running and out in front of the people it needs to. Now, one of the other comments that came up a number of times was that technology is changing at light speed. We cannot keep up, particularly in higher education, with the speed of technology. I am probably not the only institution out there who has -- who still has an implementation of some piece of software that has been out of production, essentially, or unsupported for a very long time. I know of a lot of higher education institutions still running what used to be called Brio and was bought by oracle and now called Hiperion Reporting. Hardly the only institution running that, and I think that's a reflection of the fact that bringing on new tools all the time is expensive, and we just can't keep up with it. Institutions are often not ready for the technology that's coming up, and that results in projects that don't have the support or don't have the results that people want to see in order to justify the costs. Thing about that is that complexity alone doesn't necessarily yield the best and the largest returns. In many cases, starting with a simpler piece of the pie and focusing on doing that well will result in an institution being able to deliver something that's useful. Long Beach Community College talked at length at using exploratory data analysis techniques, not necessarily complex ones, to determine where their problem points were and then using, essentially, investments in high-touch interventions at those targeted spots. By doing that, they've seen substantial improvements in their ability to attract new students and then retain them and get them to graduation. Penn State advocated in favor of finding a low-risk area to experiment with predictive analytics. Predictive analytics is a really compelling possibility, but you don't want to try to sell it to your senior leadership as though it's going to fix every problem in your institution. Find some place that you can start with that type of technology and build from there. At Arizona State, one of our mantras has been to get student workers heavily involved. This is an untapped resource for many of us. There are a lot of students in our institutions that are studying data science, that are studying analytics in one way or another, and getting them involved allows for a lot less money to bring a lot of knowledge to the fore. It is important to pay attention to what leading institutions are doing in terms of their direction, but don't let them set the pace. You cannot approach a project by saying, well, such and such college has already accomplished this, so we need to get it done next week. If you rush a project, you are not going to be successful. Every institution has different budgets, different resources available, and different capabilities. You can use the leaders for inspiration, but not necessarily to define how fast you can go. And one thing that came from Virginia Tech's presentation was that you may be better off spending money on skilled people and not on software, because that money's going to go a lot further. Their implementations have used largely open-source and cloud-based technologies. They have then gone out and hired competent programmers to work on their implementations who were both programmer/analyst types and data types, and by


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doing that, they've gotten further and they've been able to customize their results to their audience. Finally, there are a lot of different techniques that we talk about in terms of the future of analytics, but these techniques should not be looked at in isolation. Almost every project should involve some level of exploratory data analysis, because as Long Beach found, that can really highlight a lot of potential opportunities and open the door to a lot of new ideas and new ways of thinking about how you do your business. The presentation that I brought forward combined predictive analytics and traditional spreadsheet-based forecasting to try to improve both the results of those processes, and in our results we saw some definite improvement. Was it perfect? No. But by putting them together, we were able to move the institution forward and to move our thinking about the problem forward. Keep in mind that this type of work is not the same as a formal research project where you have to define your methods first. It's important as an organization and as analysts that we follow our noses and see where both the data and the results are taking us. And finally, we need to remember that there's other data out there, not just the data generated by your institution, that can give insight into student success. The University of Texas system is incorporating census data in order to get information on post-graduation outcomes, and they have opened that door sufficiently that the Census Bureau is working with other institutions, and specifically other states, to execute -- to execute MOUs so that other states and other institutions can take advantage of this data. By thinking outside of just the data they had access to within their own organization, they have improved our ability to report on post-graduation outcomes, not just for their institution, but going forward, this is likely to improve it for all of our institutions. So I am going to stop at this point and hand it back to our moderator.

>> Betsy Reinitz: Thanks, Rebecca, and thanks everybody else. We just have time for a couple of really quick questions. And Gina, I have one for you, if you don't mind starting, and that is someone in the chat asked about examples of building data literacy and Chris responded with ideas from his institution, but I wondered if you could talk about data literacy and what that means when it's across institutional things, maybe tips or tricks to increase data literacy across the entire institution.

>> Gina Johnson: Sure, thanks for the question, Betsy. I'm going to use an example from when I was leading an IR office at the University of Denver with a creative way that we started to build and enhance data literacy across our institution. University of Denver is a fairly large institution with about -- more than 10,000 students, graduate and undergraduate, so lots of staff and faculty, as well, so really had to think about kind of how to get that message out, and so what we did is we created a core what we called the Information Measurement and Analysis Council, and they were core to analytics work, from core I.T., assessment, budget and finance, strategic planning, and these folks would meet regularly and do trainings, and they weren't just trainings for ourselves, they were also trainings to figure out how can those individuals go back into their settings and help the individuals that they work with on a daily basis better understand how to use data and


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information, and how to access either the data that they need to answer questions, or the people who can get them the data that they need. So it really, I guess, train the trainer is a term I hear sometimes, but I think that's really -- you can't just have -- or maybe you can, but we didn't feel that we could have a formal training for everyone, but we felt actually a better way to do it was to key in on those folks who were sort of the core data and analytics group, and then ask them to sort of go forth and multiply and help create some data literacy across the institution.

>> Betsy Reinitz: Thanks, Gina. And just one more, this will have to be a very quick answer, San, but there's also a question about where, basically, it's about the chief data officer role, and if you could just tell us where you sit in your organization, sort of where you report to within, and then what functions report to you. I think that would be interesting to hear.

>> Sandra Cannon: So I'm actually part of the provost's office, I report to the vice provost for research, who reports to the provost, the senior vice provost for research. And right now I am a staff of one, but I work very closely with both the deputy CIO in our I.T. organization and the AVP for IR. So we are colleagues that work closely together, but I don't have any -- because the program and my role is so new, I don't have any functions that I directly manage yet.

>> Betsy Reinitz: Great, thanks. And question also just appeared in the chat window about whether there are materials from the summit that are available for download. There are. Any of the presenters who did upload their presenter materials and what not, those are available on the summit site for download, so go ahead and check that out. Also you'll see on the screen right now the speaker information, contact information for everyone who spoke today, and we are out of time, but I do hope that if you have further questions, that you'll contact these people directly and engage them in conversation. And then I guess I'll call it done, and on behalf of EDUCAUSE and our speakers, this is Betsy Reinitz. I want to thank you all for joining us today for this really engaging session and conversation. Before you sign off, please click on the session evaluation link, which you'll find in the chat window. Your comments are very important to us. The session recording and presentation slides will be posted to the EDUCAUSE Live! Website, so share that with your colleagues later on. And thanks very much for joining us.

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