1 00:00:00,000 --> 00:00:01,680 Welcome to Making Data Matter. 2 00:00:01,680 --> 00:00:04,720 We have conversations about data and leadership 3 00:00:04,720 --> 00:00:08,160 at mission-driven organizations with practical insights 4 00:00:08,160 --> 00:00:11,400 into that intersection of nonprofit mission strategy 5 00:00:11,400 --> 00:00:12,640 and data. 6 00:00:12,640 --> 00:00:14,760 I'm your host Sawyer Nyquist. 7 00:00:14,760 --> 00:00:16,920 And I'm your co-host Troy Dewek. 8 00:00:16,920 --> 00:00:21,040 And today we're joined by guest Olivia Koufikis. 9 00:00:21,040 --> 00:00:22,880 Welcome to the show, Olivia. 10 00:00:22,880 --> 00:00:24,540 Thank you so much both for having me today. 11 00:00:24,540 --> 00:00:25,960 It's a real pleasure to be here. 12 00:00:25,960 --> 00:00:26,880 Absolutely. 13 00:00:26,880 --> 00:00:28,840 Olivia, for people just meeting you for the first time, 14 00:00:28,840 --> 00:00:30,800 give us a little background of who you are 15 00:00:30,800 --> 00:00:32,280 and what do you do. 16 00:00:32,280 --> 00:00:35,520 I am the Chief Data Officer at Vanderbilt University. 17 00:00:35,520 --> 00:00:39,360 Vanderbilt is a private research university 18 00:00:39,360 --> 00:00:40,880 based in Nashville, Tennessee. 19 00:00:40,880 --> 00:00:43,040 We have about 13,000 students. 20 00:00:43,040 --> 00:00:44,760 About half of them are undergraduates, 21 00:00:44,760 --> 00:00:46,440 almost all residential. 22 00:00:46,440 --> 00:00:50,000 And half are graduate and professional students. 23 00:00:50,000 --> 00:00:54,680 And I oversee the data team, as the name suggests. 24 00:00:54,680 --> 00:00:58,360 And we work with both the academic side of the shop 25 00:00:58,360 --> 00:01:00,720 and also with all of our administrative partners 26 00:01:00,720 --> 00:01:02,240 across the university. 27 00:01:02,240 --> 00:01:04,040 Now, correct me if I'm wrong, 28 00:01:04,040 --> 00:01:07,240 but when people think of higher education 29 00:01:07,240 --> 00:01:08,480 and data in higher education, 30 00:01:08,480 --> 00:01:09,960 they might just think that you're kind of keeping track 31 00:01:09,960 --> 00:01:12,320 of what students are enrolled and what classes. 32 00:01:12,320 --> 00:01:14,840 And is that about the extent of it? 33 00:01:14,840 --> 00:01:19,000 Or correct my perception of how complex data is in higher ed. 34 00:01:19,000 --> 00:01:20,600 Sure, no problem. 35 00:01:20,600 --> 00:01:23,040 So what I think people don't understand always 36 00:01:23,040 --> 00:01:25,360 about a university, particularly a research university 37 00:01:25,360 --> 00:01:28,320 like Vanderbilt is we're basically a small town. 38 00:01:28,320 --> 00:01:30,280 So yes, we have students. 39 00:01:30,280 --> 00:01:31,320 They're really important. 40 00:01:31,320 --> 00:01:32,680 They're the reason we're there. 41 00:01:32,680 --> 00:01:36,240 We also have huge research labs. 42 00:01:36,240 --> 00:01:40,080 We have a huge dining facility. 43 00:01:40,080 --> 00:01:41,840 We have a huge residential facility. 44 00:01:41,840 --> 00:01:44,120 We maintain a campus. 45 00:01:44,120 --> 00:01:46,320 We're an arboretum. 46 00:01:46,320 --> 00:01:51,320 We have a huge kind of energy plant that supports us. 47 00:01:54,320 --> 00:01:56,720 A lot of shared services around that. 48 00:01:56,720 --> 00:01:59,320 We obviously have a lot of kind of classroom 49 00:01:59,320 --> 00:02:02,440 and conferencing facilities, lots of buildings. 50 00:02:02,440 --> 00:02:06,040 And we do a lot of work out in the community. 51 00:02:06,040 --> 00:02:09,040 So we have all our engagements with our alumni. 52 00:02:09,040 --> 00:02:09,880 We're embedded. 53 00:02:09,880 --> 00:02:11,160 We're in the middle of Nashville. 54 00:02:11,160 --> 00:02:12,920 So we're embedded in the city of Nashville. 55 00:02:12,920 --> 00:02:17,280 We engage with the community and we're a huge employer. 56 00:02:17,280 --> 00:02:19,600 So we have thousands of staff. 57 00:02:19,600 --> 00:02:21,880 We have a big HR facility. 58 00:02:21,880 --> 00:02:25,120 So we do kind of all sorts of stuff. 59 00:02:25,120 --> 00:02:27,760 And that's what I love about working in the universities 60 00:02:27,760 --> 00:02:31,240 because the data is actually really, really diverse. 61 00:02:31,240 --> 00:02:34,880 And there's always something new to get involved with. 62 00:02:34,880 --> 00:02:36,600 My team's just getting involved with working 63 00:02:36,600 --> 00:02:38,160 with our athletics department. 64 00:02:38,160 --> 00:02:41,560 And so that's a whole kind of different ball game, 65 00:02:41,560 --> 00:02:42,720 if you'll forgive the pun. 66 00:02:42,720 --> 00:02:49,240 And it's just a lot of fun, but it is, as you say, 67 00:02:49,240 --> 00:02:51,600 it's not just counting the students 68 00:02:51,600 --> 00:02:53,200 and making sure they get to class on time. 69 00:02:53,200 --> 00:02:55,400 Well, when you said you had an arboretum, 70 00:02:55,400 --> 00:02:58,000 I was half expecting you to say, and the data swamp 71 00:02:58,000 --> 00:03:00,120 is right next door or something like that. 72 00:03:00,120 --> 00:03:02,600 So that's amazing. 73 00:03:02,600 --> 00:03:06,320 So the chief data officer role, would you 74 00:03:06,320 --> 00:03:11,320 see that as more of a technical role, more of a strategic role? 75 00:03:11,320 --> 00:03:13,400 A lot of people listening to this 76 00:03:13,400 --> 00:03:16,880 may not have heard of a CDO role before. 77 00:03:16,880 --> 00:03:19,160 And the only thing maybe you think about is 78 00:03:19,160 --> 00:03:21,680 a manager of analytics or manager of data. 79 00:03:21,680 --> 00:03:25,800 How does a CDO function, technical, strategic, 80 00:03:25,800 --> 00:03:27,440 somewhere in between? 81 00:03:27,440 --> 00:03:30,480 So CDO role, as it is evolving, and this is not just 82 00:03:30,480 --> 00:03:34,280 at Vanderbilt, this is across the world, really, 83 00:03:34,280 --> 00:03:38,520 because CDOs are some of the fastest growing new roles 84 00:03:38,520 --> 00:03:39,200 right now. 85 00:03:39,200 --> 00:03:43,240 It really is an intermediary role. 86 00:03:43,240 --> 00:03:47,160 When done right, I think a CDO role spans everything 87 00:03:47,160 --> 00:03:51,960 from deeply strategic and very, very embedded 88 00:03:51,960 --> 00:03:54,480 in what does the business want to do. 89 00:03:54,480 --> 00:03:57,600 And I'm going to use business, even though it's a university, 90 00:03:57,600 --> 00:04:01,400 it's just shorthand, all the way through to needing 91 00:04:01,400 --> 00:04:03,200 to understand the data stack, needing 92 00:04:03,200 --> 00:04:05,680 to understand how those pieces fit together, 93 00:04:05,680 --> 00:04:10,040 and what are the dependencies between those two things. 94 00:04:10,040 --> 00:04:12,640 And so again, I think it's a role 95 00:04:12,640 --> 00:04:14,000 you can come at from either side. 96 00:04:14,000 --> 00:04:17,280 There's lots of CDOs who have a technical background. 97 00:04:17,280 --> 00:04:18,480 I am not one of those. 98 00:04:18,480 --> 00:04:20,480 I've come at it from the strategy side. 99 00:04:20,480 --> 00:04:24,160 My previous role was as director of strategic planning 100 00:04:24,160 --> 00:04:25,480 at a different university. 101 00:04:25,480 --> 00:04:29,560 And so I have had to learn a lot of the technical stuff. 102 00:04:29,560 --> 00:04:33,880 And I'm not a technologist. 103 00:04:33,880 --> 00:04:38,120 I'm not going to go out and code for you. 104 00:04:38,120 --> 00:04:39,640 That's not my wheelhouse. 105 00:04:39,640 --> 00:04:42,720 But I do have a pretty good understanding 106 00:04:42,720 --> 00:04:44,440 of how all the pieces fit together. 107 00:04:44,440 --> 00:04:46,440 And I find that just fascinating. 108 00:04:46,440 --> 00:04:48,640 It's a constant challenge. 109 00:04:48,640 --> 00:04:51,360 Give us a day in the life of Olivia 110 00:04:51,360 --> 00:04:55,040 as she bridges those two worlds of the strategy 111 00:04:55,040 --> 00:04:56,000 and the technical. 112 00:04:56,000 --> 00:04:59,920 So think of maybe a project that you worked on, 113 00:04:59,920 --> 00:05:02,920 just something to give us more insight into what that 114 00:05:02,920 --> 00:05:07,240 looks like in an earthy, nitty gritty kind of a way. 115 00:05:07,240 --> 00:05:08,880 So we're working on a project right now 116 00:05:08,880 --> 00:05:11,120 about research administration. 117 00:05:11,120 --> 00:05:18,520 And research administration is, at Vanderbilt, 118 00:05:18,520 --> 00:05:20,720 we collaborate with Vanderbilt University Medical Center. 119 00:05:20,720 --> 00:05:22,440 They're a separate entity to us. 120 00:05:22,440 --> 00:05:25,000 But together, we have over a billion dollars worth 121 00:05:25,000 --> 00:05:27,320 of research activity going on every year 122 00:05:27,320 --> 00:05:29,920 between Vanderbilt University and the University Medical 123 00:05:29,920 --> 00:05:30,800 Center. 124 00:05:30,800 --> 00:05:33,720 And so managing all of that money flowing 125 00:05:33,720 --> 00:05:36,880 through the organization is not a small task. 126 00:05:36,880 --> 00:05:38,360 Much of it is federal money. 127 00:05:38,360 --> 00:05:40,640 And so there's a lot of requirements around that. 128 00:05:40,640 --> 00:05:46,560 And just helping the individual researchers, what's 129 00:05:46,560 --> 00:05:49,320 called the principal investigators, the PIs, 130 00:05:49,320 --> 00:05:52,360 to understand how much money they have in their grants, 131 00:05:52,360 --> 00:05:54,920 how much has been spent, how much will be spent, 132 00:05:54,920 --> 00:05:57,600 how much is still free and unallocated, 133 00:05:57,600 --> 00:05:59,360 and they can spend. 134 00:05:59,360 --> 00:06:03,520 Are they aligning with the requirements of what they 135 00:06:03,520 --> 00:06:05,520 were given by the federal government 136 00:06:05,520 --> 00:06:07,240 in terms of what they're putting in there? 137 00:06:07,240 --> 00:06:08,960 That's a huge task. 138 00:06:08,960 --> 00:06:13,200 And we've been working to pull together all of that information. 139 00:06:13,200 --> 00:06:17,040 And so those conversations for that project 140 00:06:17,040 --> 00:06:21,280 will go anywhere from meeting with the provost 141 00:06:21,280 --> 00:06:24,680 so that she explains to us why this is so important, what 142 00:06:24,680 --> 00:06:27,840 is the demand coming into her from her deans 143 00:06:27,840 --> 00:06:30,720 and from the PIs in the different schools 144 00:06:30,720 --> 00:06:33,080 as to what they're saying they're not getting, 145 00:06:33,080 --> 00:06:36,920 through to understanding what the research administrators, 146 00:06:36,920 --> 00:06:40,040 the people who are sitting there day to day doing that work, 147 00:06:40,040 --> 00:06:44,680 supporting the PIs need, through to getting into what 148 00:06:44,680 --> 00:06:48,560 are the source data systems, where the information is held. 149 00:06:48,560 --> 00:06:52,200 We actually have multiple systems that data is held in. 150 00:06:52,200 --> 00:06:54,760 How can we bring that together? 151 00:06:54,760 --> 00:06:57,040 And then how can we create them? 152 00:06:57,040 --> 00:07:00,840 We're using Snowflake as our foundation there. 153 00:07:00,840 --> 00:07:04,520 And then how can we create a front end interface 154 00:07:04,520 --> 00:07:07,880 that the PIs will be able to use and their administrators 155 00:07:07,880 --> 00:07:11,720 will be able to use easily that pulls on all that information 156 00:07:11,720 --> 00:07:13,880 and can collect new information from them? 157 00:07:13,880 --> 00:07:17,600 So they can model, OK, if I hire this person, 158 00:07:17,600 --> 00:07:20,160 will I have enough money then to also buy 159 00:07:20,160 --> 00:07:22,400 this particular piece of equipment I need to buy? 160 00:07:22,400 --> 00:07:25,600 Or will that throw all of my other metrics out? 161 00:07:25,600 --> 00:07:28,040 And so we're thinking about data sources. 162 00:07:28,040 --> 00:07:29,600 We're thinking about platforms. 163 00:07:29,600 --> 00:07:31,280 We're thinking about warehousing. 164 00:07:31,280 --> 00:07:33,200 We're thinking about data modeling. 165 00:07:33,200 --> 00:07:36,520 And we're thinking about what do the end users need? 166 00:07:36,520 --> 00:07:39,000 How are we going to get that in front of them? 167 00:07:39,000 --> 00:07:41,080 How are they going to be trained? 168 00:07:41,080 --> 00:07:45,000 And of course, they want it all six months ago. 169 00:07:45,000 --> 00:07:47,720 And at a scale of a billion dollars of research, 170 00:07:47,720 --> 00:07:50,800 which is not the scale that a lot of organizations operate at. 171 00:07:50,800 --> 00:07:54,360 And so those are problems that people solve at small levels, 172 00:07:54,360 --> 00:07:56,400 but you're solving them at a scale 173 00:07:56,400 --> 00:07:57,800 that's different than most places. 174 00:07:57,800 --> 00:07:59,920 Now, I think we need to pause, though, 175 00:07:59,920 --> 00:08:02,040 and just clarify something. 176 00:08:02,040 --> 00:08:06,080 So Olivia, do you have the IT personnel 177 00:08:06,080 --> 00:08:07,960 right directly on your staff? 178 00:08:07,960 --> 00:08:10,520 Or is there a separate IT organization 179 00:08:10,520 --> 00:08:11,680 that you're partnering with? 180 00:08:11,680 --> 00:08:13,920 And what does that relationship look like? 181 00:08:13,920 --> 00:08:16,840 So my office is located in the office of the chancellor. 182 00:08:16,840 --> 00:08:18,600 That's the president's office. 183 00:08:18,600 --> 00:08:23,040 And that is to recognize that data is fundamentally 184 00:08:23,040 --> 00:08:26,640 about supporting the needs of the business 185 00:08:26,640 --> 00:08:29,720 rather than a technical item. 186 00:08:29,720 --> 00:08:33,120 And so we have in my office a lot of the people 187 00:08:33,120 --> 00:08:34,880 who work closely with leaders. 188 00:08:34,880 --> 00:08:37,040 They work closely with data users. 189 00:08:37,040 --> 00:08:38,760 They work with our data stewards. 190 00:08:38,760 --> 00:08:40,000 We run our data governance. 191 00:08:40,000 --> 00:08:43,240 But the IT stack, the data engineering stack, 192 00:08:43,240 --> 00:08:45,400 sits within our IT department. 193 00:08:45,400 --> 00:08:48,320 And we have a Vanderbilt culture of what 194 00:08:48,320 --> 00:08:49,920 we call radical collaboration. 195 00:08:49,920 --> 00:08:53,680 And so we work really closely with them in partnership, 196 00:08:53,680 --> 00:08:58,160 in daily, weekly partnership, to make sure they understand 197 00:08:58,160 --> 00:09:01,400 what we need and that we understand 198 00:09:01,400 --> 00:09:03,200 what are the issues they're facing 199 00:09:03,200 --> 00:09:06,360 and how we prioritize things, how we bring things together, 200 00:09:06,360 --> 00:09:09,000 how we make sure we've got the right technology. 201 00:09:09,000 --> 00:09:12,320 And that's just constant communication. 202 00:09:12,320 --> 00:09:14,200 I know you think a lot about strategy. 203 00:09:14,200 --> 00:09:16,800 And you mentioned earlier on, you came from the strategy side 204 00:09:16,800 --> 00:09:18,920 and you've mentioned how strategy is kind of what 205 00:09:18,920 --> 00:09:20,760 holds these pieces together. 206 00:09:20,760 --> 00:09:23,280 Tell us a little bit about what does data strategy 207 00:09:23,280 --> 00:09:25,040 look like at Vanderbilt. And I think 208 00:09:25,040 --> 00:09:27,040 there's a few more questions we'll go to after that. 209 00:09:27,040 --> 00:09:28,480 But I want to start there, I guess. 210 00:09:28,480 --> 00:09:30,360 Tell us about data strategy at Vanderbilt. 211 00:09:30,360 --> 00:09:33,160 So when I came to Vanderbilt five years ago, 212 00:09:33,160 --> 00:09:37,080 I was actually hired into what's called institutional research, 213 00:09:37,080 --> 00:09:41,480 which is the, it's seen as largely being around the, what 214 00:09:41,480 --> 00:09:44,720 we started with, counting the students, sometimes counting 215 00:09:44,720 --> 00:09:47,040 the faculty, doing all of that thing, 216 00:09:47,040 --> 00:09:49,640 doing some basic reporting to the federal government, 217 00:09:49,640 --> 00:09:51,080 sometimes to state governments. 218 00:09:51,080 --> 00:09:56,360 It's, institutional research has been around a long time. 219 00:09:56,360 --> 00:09:59,480 And I was hired, I had already been in a broader role 220 00:09:59,480 --> 00:10:00,000 than that. 221 00:10:00,000 --> 00:10:01,640 And I said to them when they hired me, 222 00:10:01,640 --> 00:10:02,840 what do you want from this? 223 00:10:02,840 --> 00:10:05,200 And they said, Olivia, make it strategic. 224 00:10:05,200 --> 00:10:08,320 And I remember meeting during my recruitment process 225 00:10:08,320 --> 00:10:11,480 with a couple of the vice chancellors, the vice presidents 226 00:10:11,480 --> 00:10:13,320 who were from non-academic areas. 227 00:10:13,320 --> 00:10:15,720 And they said, we really just, we 228 00:10:15,720 --> 00:10:19,560 want this team to really engage across the university. 229 00:10:19,560 --> 00:10:22,240 So I came in with a brief to say, OK, 230 00:10:22,240 --> 00:10:25,440 what does it mean to make data strategic at Vanderbilt? 231 00:10:25,440 --> 00:10:29,080 And my first year, I spent doing a lot of listening 232 00:10:29,080 --> 00:10:30,360 in order to understand that. 233 00:10:30,360 --> 00:10:32,320 And what I heard was the same as you hear 234 00:10:32,320 --> 00:10:34,920 a lot of organizations. 235 00:10:34,920 --> 00:10:37,000 We don't know where the data is. 236 00:10:37,000 --> 00:10:38,280 We can't get to it. 237 00:10:38,280 --> 00:10:40,680 We're not sure who to ask for for it. 238 00:10:40,680 --> 00:10:45,560 When we do get it, we don't really know what it means. 239 00:10:45,560 --> 00:10:49,280 It was nothing unusual, but it was clear 240 00:10:49,280 --> 00:10:52,120 that there was a real desire to create something 241 00:10:52,120 --> 00:10:53,240 that was bigger than that. 242 00:10:53,240 --> 00:10:56,160 So the first part of what was data strategy at Vanderbilt 243 00:10:56,160 --> 00:10:58,760 was, in fact, moving my office to be not 244 00:10:58,760 --> 00:11:01,480 institutional research, but to be data, 245 00:11:01,480 --> 00:11:04,920 and to be all sorts of data. 246 00:11:04,920 --> 00:11:07,120 And so we were moved into the president's office 247 00:11:07,120 --> 00:11:09,840 at that point. 248 00:11:09,840 --> 00:11:11,920 The next challenge you face at a university 249 00:11:11,920 --> 00:11:16,080 is that you have a lot of systems. 250 00:11:16,080 --> 00:11:18,800 There's the ones you would think you have, the big ones, 251 00:11:18,800 --> 00:11:21,440 the student system, for instance, the HR system, 252 00:11:21,440 --> 00:11:23,120 the finance system. 253 00:11:23,120 --> 00:11:25,080 Those are your fundamentals. 254 00:11:25,080 --> 00:11:27,000 But there are so many little systems. 255 00:11:27,000 --> 00:11:30,040 There's a different system that does admissions than the one 256 00:11:30,040 --> 00:11:32,360 that holds student information. 257 00:11:32,360 --> 00:11:36,520 We have a system that just handles 258 00:11:36,520 --> 00:11:40,480 what we call immersion, which is the students, 259 00:11:40,480 --> 00:11:44,360 they kind of extracurricular, or they're doing a piece, 260 00:11:44,360 --> 00:11:46,080 they're doing a special project, or they're 261 00:11:46,080 --> 00:11:48,160 doing service learning, or something like that. 262 00:11:48,160 --> 00:11:49,720 They study abroad. 263 00:11:49,720 --> 00:11:52,440 We have a system that handles the immersion work. 264 00:11:52,440 --> 00:11:55,600 We have a system that handles internal scholarships. 265 00:11:55,600 --> 00:11:57,360 So when somebody's come, if they want 266 00:11:57,360 --> 00:11:59,640 a grant to do something else, we have a system 267 00:11:59,640 --> 00:12:02,360 that kind of does that. 268 00:12:02,360 --> 00:12:04,480 Mental health has its own system. 269 00:12:04,480 --> 00:12:05,920 Housing has its own system. 270 00:12:05,920 --> 00:12:08,920 I mean, there's just dozens of them. 271 00:12:08,920 --> 00:12:12,120 And so one of the real challenges in a university 272 00:12:12,120 --> 00:12:13,840 is how do you pull that together? 273 00:12:13,840 --> 00:12:16,520 And so we started exploring. 274 00:12:16,520 --> 00:12:19,280 We already had a good on-prem data warehouse 275 00:12:19,280 --> 00:12:21,560 for our student information system. 276 00:12:21,560 --> 00:12:25,840 But we said we need to be able to warehouse and pull together 277 00:12:25,840 --> 00:12:29,640 and read across a lot more systems than just these ones 278 00:12:29,640 --> 00:12:30,800 that we already have. 279 00:12:30,800 --> 00:12:33,880 And so it was obvious we needed to go cloud. 280 00:12:33,880 --> 00:12:36,880 We ended up going with Snowflake for that. 281 00:12:36,880 --> 00:12:40,360 And we are in that process now of slowly moving things 282 00:12:40,360 --> 00:12:43,640 into Snowflake, starting to be able to integrate them much 283 00:12:43,640 --> 00:12:46,160 more easily than we could before. 284 00:12:46,160 --> 00:12:48,760 And then, so really, the first kind 285 00:12:48,760 --> 00:12:50,800 of stage of our data strategy was 286 00:12:50,800 --> 00:12:52,720 about building those foundations, 287 00:12:52,720 --> 00:12:54,800 the technical foundations, and then also, 288 00:12:54,800 --> 00:12:57,920 like in many organizations, the data governance foundations. 289 00:12:57,920 --> 00:13:02,000 So we had a strand of activity around data governance. 290 00:13:02,000 --> 00:13:04,960 And then, and I shouldn't really have left this till last, 291 00:13:04,960 --> 00:13:06,560 you've got the people foundations. 292 00:13:06,560 --> 00:13:09,840 So we had our team. 293 00:13:09,840 --> 00:13:12,560 And I was really lucky to inherit 294 00:13:12,560 --> 00:13:14,560 a really, really good team. 295 00:13:14,560 --> 00:13:18,360 We had our IT team, also really strong. 296 00:13:18,360 --> 00:13:23,760 But what's happened is that we found different units 297 00:13:23,760 --> 00:13:25,680 around the university have wanted 298 00:13:25,680 --> 00:13:27,920 to hire in a data person or have wanted 299 00:13:27,920 --> 00:13:31,360 to tap more professional data expertise. 300 00:13:31,360 --> 00:13:33,400 And so we slowly built up a team, 301 00:13:33,400 --> 00:13:35,360 some people who are located in my office, 302 00:13:35,360 --> 00:13:37,600 some people who are located in other units 303 00:13:37,600 --> 00:13:40,400 around the university, who are increasingly having 304 00:13:40,400 --> 00:13:42,640 data as their primary role. 305 00:13:42,640 --> 00:13:45,160 And we're building a community of practice around that, 306 00:13:45,160 --> 00:13:47,480 trying to build up their skills. 307 00:13:47,480 --> 00:13:49,080 And so that's been super exciting. 308 00:13:49,080 --> 00:13:52,000 And what I will say about our data strategy now 309 00:13:52,000 --> 00:13:54,160 is that I feel like we've built the foundations. 310 00:13:54,160 --> 00:13:56,800 And so it's not that we haven't done anything, 311 00:13:56,800 --> 00:13:58,240 it's not that we haven't delivered anything, 312 00:13:58,240 --> 00:14:01,560 but we're now at a place where we're ready to do takeoff. 313 00:14:01,560 --> 00:14:03,880 And so we're really trying to understand, 314 00:14:03,880 --> 00:14:05,280 OK, what's the next stage? 315 00:14:05,280 --> 00:14:09,080 What can we do that will really be transformational? 316 00:14:09,080 --> 00:14:13,080 Now, my conversations are shifting quite rapidly 317 00:14:13,080 --> 00:14:18,480 to leaning into talking to leaders and saying, OK, 318 00:14:18,480 --> 00:14:21,200 what do you want data to do for you? 319 00:14:21,200 --> 00:14:23,680 And they often start by being like, well, 320 00:14:23,680 --> 00:14:25,120 just get it all available to me. 321 00:14:25,120 --> 00:14:28,600 And it's like, no, what do you actually want to do with it? 322 00:14:28,600 --> 00:14:31,560 It sounds like that difference between the descriptive 323 00:14:31,560 --> 00:14:35,480 and diagnostic to, can you be a little more forward thinking 324 00:14:35,480 --> 00:14:38,680 and, OK, how do you want this to forecast for you? 325 00:14:38,680 --> 00:14:43,080 Is that what you mean by these conversations? 326 00:14:43,080 --> 00:14:43,960 It is. 327 00:14:43,960 --> 00:14:48,280 And so I'll give you an example. 328 00:14:48,280 --> 00:14:51,560 We have done an awful lot of work with our alumni team. 329 00:14:51,560 --> 00:14:55,480 And they moved to Salesforce system 330 00:14:55,480 --> 00:14:57,280 two and a half, three years ago. 331 00:14:57,280 --> 00:15:00,000 And at that point, that was a real opening for us 332 00:15:00,000 --> 00:15:02,280 to come in and work with them. 333 00:15:02,280 --> 00:15:04,520 And they were some of our first tenants, 334 00:15:04,520 --> 00:15:06,280 if you will, in our Snowflake environment. 335 00:15:06,280 --> 00:15:08,360 We were able to pull the data out of Salesforce. 336 00:15:08,360 --> 00:15:11,760 We did it really easily, put it into Snowflake, 337 00:15:11,760 --> 00:15:15,080 started to see how they needed to be thinking 338 00:15:15,080 --> 00:15:18,080 about some of their business processes differently. 339 00:15:18,080 --> 00:15:22,080 They brought in some really data forward leaders 340 00:15:22,080 --> 00:15:23,760 around the same time. 341 00:15:23,760 --> 00:15:28,640 And so we've been able to build out some great dashboards 342 00:15:28,640 --> 00:15:31,000 that are embedded in their business processes. 343 00:15:31,000 --> 00:15:33,040 And so when we go in, we can see that they're 344 00:15:33,040 --> 00:15:36,720 using these on a regular basis to make decisions. 345 00:15:36,720 --> 00:15:40,240 First of all, first they started being just monitoring. 346 00:15:40,240 --> 00:15:42,560 OK, can we see what's happening? 347 00:15:42,560 --> 00:15:45,720 But now we've built for them dashboards that, for instance, 348 00:15:45,720 --> 00:15:47,560 we have some great mapping dashboards. 349 00:15:47,560 --> 00:15:49,680 So if somebody is saying, OK, I've 350 00:15:49,680 --> 00:15:51,680 got a development officer going to St. Louis 351 00:15:51,680 --> 00:15:54,160 to visit this person, you can actually then 352 00:15:54,160 --> 00:15:55,240 put in the parameters. 353 00:15:55,240 --> 00:15:57,680 And you can see within five miles of where you're going, 354 00:15:57,680 --> 00:15:59,040 there might be three other people 355 00:15:59,040 --> 00:16:00,280 it would be worth going to see. 356 00:16:00,280 --> 00:16:02,960 And so now you spent your money to go. 357 00:16:02,960 --> 00:16:04,320 You were going to go anyway. 358 00:16:04,320 --> 00:16:07,120 But now in that one day or day and a half, 359 00:16:07,120 --> 00:16:09,600 you've identified people you can reach out to, 360 00:16:09,600 --> 00:16:11,280 hopefully set something up. 361 00:16:11,280 --> 00:16:13,800 And so they're doing that daily. 362 00:16:13,800 --> 00:16:15,840 As they plan their travel, they're going in 363 00:16:15,840 --> 00:16:19,440 and they're using that data. 364 00:16:19,440 --> 00:16:21,840 And they continue to want more and more. 365 00:16:21,840 --> 00:16:28,000 So it's kind of like, OK, how can we think about how do we 366 00:16:28,000 --> 00:16:32,600 model some of our alumni so that we understand which alumni 367 00:16:32,600 --> 00:16:35,280 might be really interested in Vanderbilt, 368 00:16:35,280 --> 00:16:38,200 but they've given, but they haven't given a lot. 369 00:16:38,200 --> 00:16:41,280 But we've not really developed a proactive relationship 370 00:16:41,280 --> 00:16:42,240 with them. 371 00:16:42,240 --> 00:16:45,040 How can we reach out to them, proactively start 372 00:16:45,040 --> 00:16:46,960 to build those relationships? 373 00:16:46,960 --> 00:16:49,520 How can we understand people who are younger, who won't be 374 00:16:49,520 --> 00:16:52,080 necessarily giving, but who are really engaged? 375 00:16:52,080 --> 00:16:54,560 How can we find them, figure out what turns them on, 376 00:16:54,560 --> 00:16:57,240 gets them excited, so that we can keep them engaged? 377 00:16:57,240 --> 00:17:00,360 Because our goal with our alumni is 378 00:17:00,360 --> 00:17:02,640 to maintain a lifelong relationship. 379 00:17:02,640 --> 00:17:06,960 We're not trying to just sell them something tomorrow. 380 00:17:06,960 --> 00:17:10,800 We want them to be engaged with Vanderbilt 20, 30 years from now. 381 00:17:10,800 --> 00:17:15,040 So those are some of the things that we found 382 00:17:15,040 --> 00:17:16,880 that it is an evolution. 383 00:17:16,880 --> 00:17:19,800 That you start by, yes, just exposing some of the data, 384 00:17:19,800 --> 00:17:22,640 giving people insight into things they didn't have before. 385 00:17:22,640 --> 00:17:25,960 And then they start to see how they can use the data. 386 00:17:25,960 --> 00:17:27,720 And they also start to see how they 387 00:17:27,720 --> 00:17:30,320 need to improve their business processes so that their data 388 00:17:30,320 --> 00:17:31,480 quality will be better. 389 00:17:31,480 --> 00:17:33,440 Because the more they want to rely on it, 390 00:17:33,440 --> 00:17:35,840 the more they realize, oh, hang on a second. 391 00:17:35,840 --> 00:17:36,800 It wasn't that great. 392 00:17:36,800 --> 00:17:41,000 And we need to change something so that our data gets better. 393 00:17:41,000 --> 00:17:43,680 And that was what happened with our maps. 394 00:17:43,680 --> 00:17:46,640 We had to completely update our addresses 395 00:17:46,640 --> 00:17:48,360 so that we could map them. 396 00:17:48,360 --> 00:17:49,840 That's what I was going to ask is, 397 00:17:49,840 --> 00:17:54,000 how did you get them to actually realize the business process was 398 00:17:54,000 --> 00:17:56,760 broken based on the data that was coming in? 399 00:17:56,760 --> 00:18:01,080 That seems like an age-old battle where the data person 400 00:18:01,080 --> 00:18:03,120 says, well, you're giving me garbage data 401 00:18:03,120 --> 00:18:04,320 through your business process. 402 00:18:04,320 --> 00:18:05,400 And they're like, no, we're not. 403 00:18:05,400 --> 00:18:06,640 You just have to do this and this, 404 00:18:06,640 --> 00:18:08,140 and it'll make total sense to you. 405 00:18:08,140 --> 00:18:09,880 So how did you convince them that they 406 00:18:09,880 --> 00:18:11,680 could identify their own brokenness 407 00:18:11,680 --> 00:18:14,480 in their business processes? 408 00:18:14,480 --> 00:18:17,240 Well, it's not necessarily that the business processes 409 00:18:17,240 --> 00:18:18,400 were broken. 410 00:18:18,400 --> 00:18:20,840 Sometimes it is about, we just need 411 00:18:20,840 --> 00:18:23,080 to raise the profile of something. 412 00:18:23,080 --> 00:18:28,040 But sometimes it is that they had historic data that 413 00:18:28,040 --> 00:18:29,760 had been put in a long time ago. 414 00:18:32,880 --> 00:18:34,920 And fixing that is actually going 415 00:18:34,920 --> 00:18:37,240 to be a significant amount of work. 416 00:18:37,240 --> 00:18:40,640 It's just walking through it with them. 417 00:18:40,640 --> 00:18:45,480 In that case, we had all the data in Salesforce. 418 00:18:45,480 --> 00:18:47,560 It was all data that had been ported over 419 00:18:47,560 --> 00:18:50,280 from a previous system or almost all of it. 420 00:18:50,280 --> 00:18:53,960 We were able to bring the US Postal Service address 421 00:18:53,960 --> 00:18:56,880 database together with that. 422 00:18:56,880 --> 00:18:58,960 And that was not a small lift. 423 00:18:58,960 --> 00:19:04,240 And my team walked alongside them going through identifying 424 00:19:04,240 --> 00:19:08,240 addresses that weren't making sense, things like that. 425 00:19:08,240 --> 00:19:10,040 And we've more or less got there. 426 00:19:10,040 --> 00:19:12,240 Now we're turning to international addresses. 427 00:19:12,240 --> 00:19:15,160 How can we fix our international addresses, many of which 428 00:19:15,160 --> 00:19:17,360 have been put in more or less as a string? 429 00:19:17,360 --> 00:19:21,480 And so how can we start to do that? 430 00:19:21,480 --> 00:19:25,160 And how can we work with our Salesforce team 431 00:19:25,160 --> 00:19:27,760 so that we can force some of those things 432 00:19:27,760 --> 00:19:30,840 like drop down menus or auto filling 433 00:19:30,840 --> 00:19:33,520 rather than just people continuing 434 00:19:33,520 --> 00:19:35,960 to put in garbage data that we continue to have to clean up? 435 00:19:35,960 --> 00:19:38,840 A labor of love, that's what I heard. 436 00:19:38,840 --> 00:19:40,080 It is. 437 00:19:40,080 --> 00:19:40,600 It is. 438 00:19:40,600 --> 00:19:41,680 It's step by step. 439 00:19:41,680 --> 00:19:43,800 And it takes a lot of resource from them. 440 00:19:43,800 --> 00:19:45,400 So they have to figure out what they're 441 00:19:45,400 --> 00:19:46,760 going to prioritize as well. 442 00:19:46,760 --> 00:19:50,160 And that's just one sliver we're talking about of alumni 443 00:19:50,160 --> 00:19:51,520 and really donor relations. 444 00:19:51,520 --> 00:19:53,720 And I imagine you could go down through all 445 00:19:53,720 --> 00:19:55,320 the different pillars of Vanderbilt 446 00:19:55,320 --> 00:19:57,760 and talk about different use cases and different ways 447 00:19:57,760 --> 00:20:00,960 data can be activated. 448 00:20:00,960 --> 00:20:02,760 Is there a way the data strategy helps 449 00:20:02,760 --> 00:20:05,160 you to think about prioritizing which 450 00:20:05,160 --> 00:20:08,240 of those pillars and silos are you going to dive into next? 451 00:20:08,240 --> 00:20:13,120 And how do you handle the competing needs of a small city 452 00:20:13,120 --> 00:20:15,040 that you have at your hands? 453 00:20:15,040 --> 00:20:17,000 It's a great question because it's 454 00:20:17,000 --> 00:20:20,560 something we grapple with on a regular basis. 455 00:20:20,560 --> 00:20:25,320 We started by working where we had enthusiasm. 456 00:20:25,320 --> 00:20:31,240 And as it happened, we did all this work with alumni. 457 00:20:31,240 --> 00:20:35,280 And without really realizing it at first, 458 00:20:35,280 --> 00:20:37,400 but it became apparent pretty quickly 459 00:20:37,400 --> 00:20:39,880 that the thing about the alumni relations 460 00:20:39,880 --> 00:20:44,520 is they have dollar goals that they're 461 00:20:44,520 --> 00:20:45,520 trying to meet. 462 00:20:45,520 --> 00:20:49,000 And so we can actually measure our effectiveness really 463 00:20:49,000 --> 00:20:51,040 easily in that area. 464 00:20:51,040 --> 00:20:54,520 And this is something that a lot of not-for-profits 465 00:20:54,520 --> 00:20:55,960 will recognize. 466 00:20:55,960 --> 00:20:59,200 As you move out from that, a lot of your other areas 467 00:20:59,200 --> 00:21:02,760 don't have easy dollar goals. 468 00:21:02,760 --> 00:21:06,880 And so with our student data, and especially 469 00:21:06,880 --> 00:21:10,240 at a place like Vanderbilt where we're not looking to grow, 470 00:21:10,240 --> 00:21:12,640 particularly our undergraduate student population, 471 00:21:12,640 --> 00:21:15,400 it's all about how do we serve them better? 472 00:21:18,480 --> 00:21:23,600 Doing that better is not going to result in more dollars, 473 00:21:23,600 --> 00:21:24,600 for the most part. 474 00:21:24,600 --> 00:21:26,080 We have very low dropout rates. 475 00:21:26,080 --> 00:21:27,640 We have very high graduation rates. 476 00:21:27,640 --> 00:21:30,840 There are other institutions where they can do that better 477 00:21:30,840 --> 00:21:33,640 and they can see a dramatic increase in their retention 478 00:21:33,640 --> 00:21:35,760 rates or their graduation rates or whatever. 479 00:21:35,760 --> 00:21:37,920 That's not the case at Vanderbilt. 480 00:21:37,920 --> 00:21:41,200 And so we have to really think hard 481 00:21:41,200 --> 00:21:43,840 about how are we going to show that return on investment? 482 00:21:43,840 --> 00:21:45,280 What is that going to look like? 483 00:21:45,280 --> 00:21:47,040 What is success going to look like? 484 00:21:47,040 --> 00:21:50,640 And we've had some where it looks 485 00:21:50,640 --> 00:21:53,600 like we weren't getting very far. 486 00:21:53,600 --> 00:21:55,760 And then a year or two years later, 487 00:21:55,760 --> 00:21:58,120 we suddenly see a decision being made. 488 00:21:58,120 --> 00:22:00,880 And we're like, oh, they're doing that because the data was 489 00:22:00,880 --> 00:22:01,400 better. 490 00:22:01,400 --> 00:22:02,680 OK, that's good. 491 00:22:02,680 --> 00:22:04,360 What are some of those things then 492 00:22:04,360 --> 00:22:06,640 that you do start to look at and measure? 493 00:22:06,640 --> 00:22:09,080 If it's not student retention, which is really strong, 494 00:22:09,080 --> 00:22:11,240 graduation rates, which is really strong, what 495 00:22:11,240 --> 00:22:13,360 are other things that are important to the student 496 00:22:13,360 --> 00:22:15,360 experience that Vanderbilt wants to enhance 497 00:22:15,360 --> 00:22:18,400 and that maybe you can use data to get insights into? 498 00:22:18,400 --> 00:22:23,480 One of the major challenges that we've had is graduate outcomes. 499 00:22:23,480 --> 00:22:26,720 So where do our students go after they graduate? 500 00:22:26,720 --> 00:22:28,600 A lot of people will think about that and say, well, 501 00:22:28,600 --> 00:22:30,120 of course you know that, don't you? 502 00:22:30,120 --> 00:22:32,480 But if you then think about how is the data 503 00:22:32,480 --> 00:22:34,240 flow going to work for that? 504 00:22:34,240 --> 00:22:36,800 Who's going to tell us where our students have 505 00:22:36,800 --> 00:22:39,040 gone after they've graduated? 506 00:22:39,040 --> 00:22:39,840 The students are. 507 00:22:39,840 --> 00:22:42,160 Yeah, self-reported. 508 00:22:42,160 --> 00:22:46,760 And so you can often get a good slice 509 00:22:46,760 --> 00:22:48,840 of what we call first destinations 510 00:22:48,840 --> 00:22:50,320 before they graduate. 511 00:22:50,320 --> 00:22:54,920 And we do hit them up with all sorts of surveys about that 512 00:22:54,920 --> 00:22:56,640 before they graduate. 513 00:22:56,640 --> 00:22:59,240 But there will be some who don't know at the point 514 00:22:59,240 --> 00:23:01,200 that they graduate where they're going to go. 515 00:23:01,200 --> 00:23:05,400 And there will be some for whom that first year or so out 516 00:23:05,400 --> 00:23:07,840 is not where they're headed. 517 00:23:07,840 --> 00:23:10,400 We have some who take a break, they travel for a year, 518 00:23:10,400 --> 00:23:11,480 or they do something else. 519 00:23:11,480 --> 00:23:16,200 Or they might be, we have a music school, 520 00:23:16,200 --> 00:23:17,880 so we have many who are kind of jumping 521 00:23:17,880 --> 00:23:19,600 into these very unstable, they're 522 00:23:19,600 --> 00:23:23,840 not sure what that's going to look like gig type professions. 523 00:23:23,840 --> 00:23:26,600 And so those people look really weird in the data. 524 00:23:26,600 --> 00:23:33,240 And so we've spent a lot of time on data collection 525 00:23:33,240 --> 00:23:35,680 for first destinations, understanding where they 526 00:23:35,680 --> 00:23:37,800 go that first year out. 527 00:23:37,800 --> 00:23:42,760 And then we are now trying to grapple with where 528 00:23:42,760 --> 00:23:45,080 have they gone after that? 529 00:23:45,080 --> 00:23:49,440 And how do we understand what those trajectories look like? 530 00:23:49,440 --> 00:23:52,280 And do we think that what we at Vanderbilt 531 00:23:52,280 --> 00:23:55,760 gave them during their four years 532 00:23:55,760 --> 00:23:58,600 in undergraduate education with us, 533 00:23:58,600 --> 00:24:00,880 did that change their trajectory? 534 00:24:00,880 --> 00:24:03,040 Did that improve their trajectory? 535 00:24:03,040 --> 00:24:07,320 And so we've done a lot of data scraping, as you can imagine. 536 00:24:07,320 --> 00:24:10,480 We've worked with companies that scrape LinkedIn, 537 00:24:10,480 --> 00:24:13,400 and then tried to match that up to our students. 538 00:24:13,400 --> 00:24:17,880 That's a lot of fun, because that's name based matching. 539 00:24:17,880 --> 00:24:19,360 Wildly consistent, I'm sure. 540 00:24:19,360 --> 00:24:22,600 How many John Smiths are there in the world? 541 00:24:22,600 --> 00:24:23,400 Yeah, yeah. 542 00:24:23,400 --> 00:24:26,640 And the thing is, we tend to have some of their address 543 00:24:26,640 --> 00:24:28,320 information. 544 00:24:28,320 --> 00:24:31,560 Many people will put what their degree was, 545 00:24:31,560 --> 00:24:34,240 so they often get flagged as being Vanderbilt people 546 00:24:34,240 --> 00:24:36,280 from LinkedIn as well. 547 00:24:36,280 --> 00:24:38,160 But we also have a lot of people who 548 00:24:38,160 --> 00:24:41,120 go into medical professions, as you can imagine, 549 00:24:41,120 --> 00:24:42,960 at a place like Vanderbilt. Those people 550 00:24:42,960 --> 00:24:45,960 have a very long trajectory between when they leave us 551 00:24:45,960 --> 00:24:47,120 and when they start working. 552 00:24:47,120 --> 00:24:51,120 And a lot of doctors actually don't use LinkedIn heavily. 553 00:24:51,120 --> 00:24:53,640 And so we've got these kind of data gaps. 554 00:24:53,640 --> 00:24:56,040 So we've been working with, we've 555 00:24:56,040 --> 00:24:57,720 done a lot of work with third parties, 556 00:24:57,720 --> 00:24:59,680 both as I say, these data scraping firms, 557 00:24:59,680 --> 00:25:02,880 but also with some more sophisticated labor 558 00:25:02,880 --> 00:25:05,880 economist type people, trying to say, OK, what 559 00:25:05,880 --> 00:25:08,240 does a Vanderbilt education actually do? 560 00:25:08,240 --> 00:25:11,560 Because those are the kind of questions, 561 00:25:11,560 --> 00:25:13,920 even before we get to how much do they make, 562 00:25:13,920 --> 00:25:18,520 people seem to think we know how much our graduates make. 563 00:25:18,520 --> 00:25:21,720 Where would we get that information from? 564 00:25:21,720 --> 00:25:24,320 How many have founded a company? 565 00:25:24,320 --> 00:25:27,400 How many are chief executive officers? 566 00:25:27,400 --> 00:25:29,920 These are questions we get asked on a regular basis. 567 00:25:29,920 --> 00:25:33,200 And we kind of grope towards answers. 568 00:25:33,200 --> 00:25:35,680 But there's not good data sources for those. 569 00:25:35,680 --> 00:25:39,720 And so we're always playing a little bit of a scavenger 570 00:25:39,720 --> 00:25:40,640 hunt on that. 571 00:25:40,640 --> 00:25:42,680 But it is really important information. 572 00:25:42,680 --> 00:25:46,400 Because ultimately, if we want to convince people 573 00:25:46,400 --> 00:25:49,520 that it's worth coming to private university 574 00:25:49,520 --> 00:25:51,080 and spending the money that you might have to, 575 00:25:51,080 --> 00:25:52,520 although we have very good financial aid, 576 00:25:52,520 --> 00:25:53,440 spending the money you might have 577 00:25:53,440 --> 00:25:55,160 to spend to come to a private university, 578 00:25:55,160 --> 00:25:58,920 well, then you would hope that there's something better 579 00:25:58,920 --> 00:26:01,040 out the other end. 580 00:26:01,040 --> 00:26:02,720 That it's worth that investment. 581 00:26:02,720 --> 00:26:04,880 From the technology side, you've talked 582 00:26:04,880 --> 00:26:07,680 about a few of the challenges of just all the different source 583 00:26:07,680 --> 00:26:08,920 systems that come together. 584 00:26:08,920 --> 00:26:10,520 What are the other technologies that you 585 00:26:10,520 --> 00:26:11,800 found to be really useful? 586 00:26:11,800 --> 00:26:13,440 So I think you've mentioned Snowflake so far 587 00:26:13,440 --> 00:26:14,720 as one of the main pieces. 588 00:26:14,720 --> 00:26:16,880 What other pieces make up the technology stack 589 00:26:16,880 --> 00:26:19,840 that help you start to enable these use cases you're 590 00:26:19,840 --> 00:26:21,400 trying to get to? 591 00:26:21,400 --> 00:26:24,720 We have three technologies that we've invested in, 592 00:26:24,720 --> 00:26:26,520 significant technologies we've invested in 593 00:26:26,520 --> 00:26:29,480 since I've been at Vanderbilt. So Snowflake is one of them. 594 00:26:29,480 --> 00:26:31,160 We talk a little bit more about that. 595 00:26:31,160 --> 00:26:34,240 Tableau is another one. 596 00:26:34,240 --> 00:26:36,280 We already had Power BI. 597 00:26:36,280 --> 00:26:38,960 We had Oracle Analytics Cloud. 598 00:26:38,960 --> 00:26:43,840 But we wanted a tool that was really heavily visual. 599 00:26:43,840 --> 00:26:46,000 And so we went with Tableau. 600 00:26:46,000 --> 00:26:48,360 And it intersects as well, obviously, 601 00:26:48,360 --> 00:26:50,160 with our Salesforce environment really well. 602 00:26:50,160 --> 00:26:54,360 So that's a massive win for that environment. 603 00:26:54,360 --> 00:26:56,800 And then the last is our data catalog. 604 00:26:56,800 --> 00:27:00,120 And we have ended up going with a relatively small data 605 00:27:00,120 --> 00:27:00,640 catalog. 606 00:27:00,640 --> 00:27:03,200 It's a company called Data Edo. 607 00:27:03,200 --> 00:27:06,120 It is right sized for us. 608 00:27:06,120 --> 00:27:08,040 So a lot of the big data catalogs 609 00:27:08,040 --> 00:27:11,200 are extremely expensive and do a lot of things 610 00:27:11,200 --> 00:27:13,560 that when you're just starting out in data governance, 611 00:27:13,560 --> 00:27:15,760 you're probably not ready to do yet. 612 00:27:15,760 --> 00:27:17,600 And so we found Data Edo. 613 00:27:17,600 --> 00:27:23,520 We could kind of scale it desk by desk, basically. 614 00:27:23,520 --> 00:27:26,040 But it does a lot of data lineage work for us. 615 00:27:26,040 --> 00:27:29,160 It provides us with those foundations. 616 00:27:29,160 --> 00:27:33,280 It enables us to not have to do everything by hand, 617 00:27:33,280 --> 00:27:34,720 which was important. 618 00:27:34,720 --> 00:27:39,160 And so that data catalog is really important to us. 619 00:27:39,160 --> 00:27:41,280 So I'm going to bring this back to data strategy. 620 00:27:41,280 --> 00:27:43,360 Because in data strategy, you talked about people 621 00:27:43,360 --> 00:27:45,920 and kind of like the processes of how data is collected 622 00:27:45,920 --> 00:27:48,320 and managed and then the technologies. 623 00:27:48,320 --> 00:27:51,080 If somebody is just approaching data strategy 624 00:27:51,080 --> 00:27:53,240 for the first time, where do they kind of 625 00:27:53,240 --> 00:27:55,440 start on that paradigm? 626 00:27:55,440 --> 00:27:57,160 How do they approach the challenges that 627 00:27:57,160 --> 00:28:00,520 come from technology, processes, and people? 628 00:28:00,520 --> 00:28:02,680 I mean, it's the classic triumph, isn't it? 629 00:28:02,680 --> 00:28:04,200 People, process, technology. 630 00:28:04,200 --> 00:28:06,800 And you do need all three. 631 00:28:06,800 --> 00:28:09,760 You don't want to be driven by the technologies. 632 00:28:09,760 --> 00:28:12,640 Because if you don't have the people in the processes, 633 00:28:12,640 --> 00:28:14,240 the technologies are just money you're 634 00:28:14,240 --> 00:28:15,720 flushing down the toilet. 635 00:28:15,720 --> 00:28:21,800 And so I think you really want to start with the people first. 636 00:28:21,800 --> 00:28:24,360 You want to have that core of people who understand 637 00:28:24,360 --> 00:28:25,840 what you're trying to do. 638 00:28:25,840 --> 00:28:27,560 And as I say, when I came to Vanderbilt, 639 00:28:27,560 --> 00:28:30,480 I was lucky that that already existed. 640 00:28:30,480 --> 00:28:34,080 That not only did I have a good team in the team 641 00:28:34,080 --> 00:28:38,080 that I inherited, but also there was demand for data. 642 00:28:38,080 --> 00:28:39,560 There was interest in data. 643 00:28:39,560 --> 00:28:45,280 I had senior leaders who were saying, we want more data. 644 00:28:45,280 --> 00:28:49,880 I have not had anybody ever say to me, is this worth it? 645 00:28:49,880 --> 00:28:53,480 Which has been tremendously helpful. 646 00:28:53,480 --> 00:28:56,640 It doesn't mean that it's always easy, then, 647 00:28:56,640 --> 00:28:58,320 to get the resourcing you need. 648 00:28:58,320 --> 00:28:59,920 You're still in the resource. 649 00:28:59,920 --> 00:29:02,160 You're still in a resource-constrained environment. 650 00:29:02,160 --> 00:29:05,480 Even a lot of institutions will say, well, yeah, 651 00:29:05,480 --> 00:29:08,200 Vanderbilt, you've just got a big endowment. 652 00:29:08,200 --> 00:29:09,560 We're still resource-constrained. 653 00:29:09,560 --> 00:29:10,600 Everybody is. 654 00:29:10,600 --> 00:29:15,360 And so we're still having to make those decisions. 655 00:29:15,360 --> 00:29:17,520 But I would say you've got to start with the people. 656 00:29:17,520 --> 00:29:20,720 You have to have both some good people to work with to do 657 00:29:20,720 --> 00:29:21,360 the work. 658 00:29:21,360 --> 00:29:24,160 And you have to have enough leaders who understand 659 00:29:24,160 --> 00:29:26,560 what you're trying to do that they're going to at least 660 00:29:26,560 --> 00:29:28,920 support you in it. 661 00:29:28,920 --> 00:29:31,040 Olivia, what is it about higher education? 662 00:29:31,040 --> 00:29:33,200 You've spent a lot of your career in the higher education 663 00:29:33,200 --> 00:29:35,040 space. 664 00:29:35,040 --> 00:29:36,760 Why did you land your career there? 665 00:29:36,760 --> 00:29:40,480 And why have you stayed in higher ed and at Vanderbilt? 666 00:29:40,480 --> 00:29:43,800 What about the space is exciting or interesting to you? 667 00:29:43,800 --> 00:29:47,400 I started my career working internationally. 668 00:29:47,400 --> 00:29:51,920 When I finished college, I was really interested in going 669 00:29:51,920 --> 00:29:55,960 into international space of some kind. 670 00:29:55,960 --> 00:29:58,360 And I actually spent several years working in Ukraine. 671 00:29:58,360 --> 00:30:00,720 I worked on the USAID project. 672 00:30:00,720 --> 00:30:03,320 I learned Ukrainian. 673 00:30:03,320 --> 00:30:05,760 I've got this whole strand over there. 674 00:30:05,760 --> 00:30:09,520 And I realized that what I learned 675 00:30:09,520 --> 00:30:12,480 was that there were a lot of universities that were doing 676 00:30:12,480 --> 00:30:14,600 a lot of international stuff. 677 00:30:14,600 --> 00:30:17,480 And so when my USAID project time ended, 678 00:30:17,480 --> 00:30:20,000 I was looking for what the next step was. 679 00:30:20,000 --> 00:30:21,040 And I found myself. 680 00:30:21,040 --> 00:30:24,720 I had moved at that time as well, got married and moved. 681 00:30:24,720 --> 00:30:28,160 And I found myself working in one of the Cal State 682 00:30:28,160 --> 00:30:31,920 institutions, Cal Poly Pomona, doing international work. 683 00:30:31,920 --> 00:30:35,040 And I discovered that universities 684 00:30:35,040 --> 00:30:37,960 are just these fabulous, as I say, they're towns. 685 00:30:37,960 --> 00:30:40,120 So they are their own communities. 686 00:30:40,120 --> 00:30:41,440 Most of them have heft. 687 00:30:41,440 --> 00:30:45,240 They're big enough that you've got real good interaction 688 00:30:45,240 --> 00:30:46,080 of people. 689 00:30:46,080 --> 00:30:48,440 And they're smart people. 690 00:30:48,440 --> 00:30:50,480 People in universities are smart. 691 00:30:50,480 --> 00:30:55,280 And they're interested in doing good for the world. 692 00:30:55,280 --> 00:30:59,200 And there's very few people that go into universities 693 00:30:59,200 --> 00:30:59,800 to get rich. 694 00:31:03,240 --> 00:31:06,560 And so people have chosen that role 695 00:31:06,560 --> 00:31:11,040 because they do want to make the world a better place. 696 00:31:11,040 --> 00:31:14,240 I just it's endlessly fascinating. 697 00:31:14,240 --> 00:31:17,120 And although I spend a lot of time talking to people 698 00:31:17,120 --> 00:31:23,520 about data and just the hows of running a big organization, 699 00:31:23,520 --> 00:31:27,040 I always try and chat particularly 700 00:31:27,040 --> 00:31:31,080 with my academic colleagues about what do you really do? 701 00:31:31,080 --> 00:31:33,000 What's your research about? 702 00:31:33,000 --> 00:31:34,440 What's exciting to you? 703 00:31:34,440 --> 00:31:39,480 And I've talked to people about drilling ice cores in Antarctica 704 00:31:39,480 --> 00:31:43,080 and measuring the different debris in them, 705 00:31:43,080 --> 00:31:49,400 how you can improve your biofauna, 706 00:31:49,400 --> 00:31:52,400 your internal microfauna, whatever it is, 707 00:31:52,400 --> 00:31:55,160 the bacteria in your gut and stuff like that, 708 00:31:55,160 --> 00:32:00,040 right the way through to how children get language 709 00:32:00,040 --> 00:32:01,560 and how they think about it. 710 00:32:01,560 --> 00:32:04,120 And so these people are just fascinating. 711 00:32:04,120 --> 00:32:06,440 And they know all sorts of stuff. 712 00:32:06,440 --> 00:32:08,560 And they do like crazy stuff. 713 00:32:08,560 --> 00:32:10,560 I was talking to one researcher one day. 714 00:32:10,560 --> 00:32:12,320 He was giving me a ride somewhere. 715 00:32:12,320 --> 00:32:14,760 And he's like, yeah, and I use these mice to do this and that. 716 00:32:14,760 --> 00:32:16,640 And I'm like, where do you get your mice from? 717 00:32:16,640 --> 00:32:19,040 And he's like, I make them. 718 00:32:19,040 --> 00:32:20,240 You do what? 719 00:32:20,240 --> 00:32:23,160 These are bioengineered mice. 720 00:32:23,160 --> 00:32:28,840 So I just think you never can be bored in a university 721 00:32:28,840 --> 00:32:31,760 because you're always learning something new. 722 00:32:31,760 --> 00:32:33,520 You're always finding something different. 723 00:32:33,520 --> 00:32:37,040 So what is the future, maybe specifically 724 00:32:37,040 --> 00:32:42,400 for you and your team when there's so much excitement 725 00:32:42,400 --> 00:32:46,320 happening, like bioengineered mice and microfauna in your gut? 726 00:32:46,320 --> 00:32:49,640 I mean, sounds like there's so much to explore. 727 00:32:49,640 --> 00:32:52,600 What's the future for a CDO in that environment? 728 00:32:52,600 --> 00:32:55,440 Where do you strategically want to see your team go, 729 00:32:55,440 --> 00:32:58,600 say, in the next 5, 10 years? 730 00:32:58,600 --> 00:33:02,000 So we're currently really getting 731 00:33:02,000 --> 00:33:06,040 excited about some of the new technologies out there. 732 00:33:06,040 --> 00:33:08,920 Right now, I mean, I can't tell you 5, 10 years. 733 00:33:08,920 --> 00:33:11,640 That's a really long time, Troy, in data land. 734 00:33:11,640 --> 00:33:17,800 But right now, I am excited because I 735 00:33:17,800 --> 00:33:22,160 see a lot of these technologies moving from proof of concept 736 00:33:22,160 --> 00:33:24,440 to some real maturity. 737 00:33:24,440 --> 00:33:28,560 And I see part of my role as scanning the horizon 738 00:33:28,560 --> 00:33:31,000 and finding things that are exciting that I 739 00:33:31,000 --> 00:33:33,320 can bring back to Vanderbilt. So I 740 00:33:33,320 --> 00:33:36,680 need to understand what matters to senior leaders. 741 00:33:36,680 --> 00:33:39,480 What are some of the challenges they're facing? 742 00:33:39,480 --> 00:33:44,760 And then I need to be able to say, OK, that's interesting. 743 00:33:44,760 --> 00:33:49,360 Maybe this is something that we might be able to pick up 744 00:33:49,360 --> 00:33:52,400 and use at Vanderbilt. Now, I'll give you an example. 745 00:33:52,400 --> 00:33:55,480 Unstructured data. 746 00:33:55,480 --> 00:33:56,800 You've heard the statistics. 747 00:33:56,800 --> 00:33:57,320 What is it? 748 00:33:57,320 --> 00:34:00,920 80% of all information is unstructured. 749 00:34:00,920 --> 00:34:04,440 And that's true at Vanderbilt as well. 750 00:34:04,440 --> 00:34:07,040 But how do you actually get value out of that? 751 00:34:07,040 --> 00:34:10,280 And Gen.ai is obviously the answer somehow. 752 00:34:10,280 --> 00:34:13,560 But that's not as simple as just throw a chat GPT at it 753 00:34:13,560 --> 00:34:15,480 and get on with it. 754 00:34:15,480 --> 00:34:21,160 And so I'm currently looking at how do we start to have, 755 00:34:21,160 --> 00:34:24,040 I'll come back to our alumni example again. 756 00:34:24,040 --> 00:34:28,360 Our development officers, when they go out to meet with folks, 757 00:34:28,360 --> 00:34:31,640 they will take notes and they will log all their emails 758 00:34:31,640 --> 00:34:33,680 and stuff like that in their system. 759 00:34:33,680 --> 00:34:36,600 I want to figure out how we mine those notes to figure out 760 00:34:36,600 --> 00:34:39,000 what are those alumni actually interested in? 761 00:34:39,000 --> 00:34:40,160 What were they talking about? 762 00:34:40,160 --> 00:34:42,840 What are the patterns that we can fill from that? 763 00:34:42,840 --> 00:34:46,800 And I'm trying to figure out how we do something like that. 764 00:34:46,800 --> 00:34:51,120 How can we really bring those technologies to bear? 765 00:34:51,120 --> 00:34:53,520 How can we think about surveys differently? 766 00:34:53,520 --> 00:34:55,080 Finding out what people are thinking. 767 00:34:55,080 --> 00:34:56,960 We always want to know what people are thinking. 768 00:34:56,960 --> 00:35:00,040 How can we make sure that surveys are broken? 769 00:35:00,040 --> 00:35:02,920 The traditional way of doing surveys is broken. 770 00:35:02,920 --> 00:35:04,760 People are not responding anymore. 771 00:35:04,760 --> 00:35:07,640 How can we use these new technologies 772 00:35:07,640 --> 00:35:09,720 to help us do some of these things differently? 773 00:35:09,720 --> 00:35:13,640 And so to me, that's the future of where my role is going. 774 00:35:13,640 --> 00:35:14,760 We've got the foundation. 775 00:35:14,760 --> 00:35:16,520 Now let's find those exciting things 776 00:35:16,520 --> 00:35:21,360 that we couldn't do before and bring them back and plug them 777 00:35:21,360 --> 00:35:23,920 into real problems that we have. 778 00:35:23,920 --> 00:35:26,920 How do you do that in a resource-constrained environment? 779 00:35:26,920 --> 00:35:29,320 Next major question, right? 780 00:35:29,320 --> 00:35:29,840 Yeah. 781 00:35:29,840 --> 00:35:35,160 I mean, so I am increasingly, I started out 782 00:35:35,160 --> 00:35:38,040 doing a lot of time in higher education conferences. 783 00:35:38,040 --> 00:35:41,520 And I now spend quite a bit of time at conferences. 784 00:35:41,520 --> 00:35:43,760 But quite a few of them are not higher education. 785 00:35:43,760 --> 00:35:48,160 Because I'm trying to learn what other people are doing. 786 00:35:48,160 --> 00:35:52,840 Then I try and kind of grab those ideas. 787 00:35:52,840 --> 00:35:54,920 Often, a lot of those companies are not 788 00:35:54,920 --> 00:35:56,280 working with higher education. 789 00:35:56,280 --> 00:36:01,880 So sometimes I can kind of get some preferential relationships 790 00:36:01,880 --> 00:36:03,640 with them because they want to figure out 791 00:36:03,640 --> 00:36:05,680 how to work in this market. 792 00:36:05,680 --> 00:36:08,320 But sometimes it's just about, OK, 793 00:36:08,320 --> 00:36:10,040 I've got to work with a particular area 794 00:36:10,040 --> 00:36:11,160 of the university. 795 00:36:11,160 --> 00:36:13,360 And if this is going to happen, they're 796 00:36:13,360 --> 00:36:14,880 going to have to make some investment. 797 00:36:14,880 --> 00:36:17,760 It can't come from my budget because I don't have it. 798 00:36:17,760 --> 00:36:18,440 There you go. 799 00:36:18,440 --> 00:36:21,840 Just get the business to pay for it, right? 800 00:36:21,840 --> 00:36:25,520 I mean, that is, if they're going to get the value, 801 00:36:25,520 --> 00:36:28,360 they're going to have to have some skin in the game as well. 802 00:36:28,360 --> 00:36:31,080 We started this conversation talking about a CDO being 803 00:36:31,080 --> 00:36:32,200 strategic and technical. 804 00:36:32,200 --> 00:36:34,080 And I think as this conversation has wandered, 805 00:36:34,080 --> 00:36:37,280 we've pretty much been bouncing between those two worlds 806 00:36:37,280 --> 00:36:38,080 constantly. 807 00:36:38,080 --> 00:36:40,440 And so I really do see, it makes a lot of sense. 808 00:36:40,440 --> 00:36:42,920 You're sitting right in between those spaces. 809 00:36:42,920 --> 00:36:44,680 Olivia, this has been really fun. 810 00:36:44,680 --> 00:36:47,600 If people want to reach out and have questions about what 811 00:36:47,600 --> 00:36:50,800 it's like to be a CDO or your role at Vanderbilt, 812 00:36:50,800 --> 00:36:52,600 where could people find you online? 813 00:36:52,600 --> 00:36:55,040 Or how could people connect with you further? 814 00:36:55,040 --> 00:36:57,240 The best way to find me is via LinkedIn. 815 00:36:57,240 --> 00:36:59,320 And I am the world's easiest person 816 00:36:59,320 --> 00:37:01,720 to find on LinkedIn because I'm the only Olivia Q 817 00:37:01,720 --> 00:37:03,480 ficus there. 818 00:37:03,480 --> 00:37:07,200 Easy name match right there. 819 00:37:07,200 --> 00:37:09,520 Your alumni organization loves you. 820 00:37:09,520 --> 00:37:10,440 They absolutely do. 821 00:37:10,440 --> 00:37:12,000 They don't have a problem with me. 822 00:37:12,000 --> 00:37:12,520 All right. 823 00:37:12,520 --> 00:37:14,680 Well, thank you so much for joining us today, Olivia. 824 00:37:14,680 --> 00:37:17,000 And for listeners out there, thanks 825 00:37:17,000 --> 00:37:20,280 so much for tuning into this episode of Making Data Matter. 826 00:37:20,280 --> 00:37:20,960 Thank you, Troy. 827 00:37:20,960 --> 00:37:25,840 Thank you, Sawyer.