1 00:00:00,000 --> 00:00:12,080 Welcome to Making Data Matter, where we have conversations about data and leadership at mission-driven organizations with practical insights into the intersection of nonprofit mission strategy and data. 2 00:00:12,600 --> 00:00:14,240 I'm your host, Sawyer Nyquist. 3 00:00:14,640 --> 00:00:16,040 I'm your co-host, Troy Dueck. 4 00:00:16,440 --> 00:00:19,320 And today we're joined by guest, Philip Wallace. 5 00:00:19,520 --> 00:00:20,840 Philip, welcome to the show. 6 00:00:21,320 --> 00:00:22,480 Hey, thank you for having me. 7 00:00:22,480 --> 00:00:22,840 Thank you. 8 00:00:23,000 --> 00:00:28,640 And for folks just meeting you for the first time, Philip, give us a little bit of background about who you are and what you do. 9 00:00:28,640 --> 00:00:32,840 Well, for background, first off, I'm incredible and believe everything I say. 10 00:00:33,080 --> 00:00:35,080 No, I'm joking, but only halfway. 11 00:00:35,080 --> 00:00:43,960 No, I currently, currently, I serve as the director of knowledge management at UNCF, the S.United Negro College Fund. 12 00:00:44,840 --> 00:00:45,840 Context about that. 13 00:00:46,320 --> 00:00:53,000 UNCF is an 80-year-old organization that was created to support private HBCUs, historically Black colleges and universities. 14 00:00:53,000 --> 00:00:55,240 I realize there are a lot of acronyms in my description. 15 00:00:55,240 --> 00:00:58,600 Now, my team is ICB, known as the Institute for Capacity Building. 16 00:00:58,960 --> 00:00:59,920 We're not a fund. 17 00:00:59,920 --> 00:01:02,360 We're not focused on fundraising or student programs. 18 00:01:02,360 --> 00:01:05,960 We're focused on building capacity at a lot of HBCUs around the country. 19 00:01:06,440 --> 00:01:14,640 A lot of data show that HBCUs, when you look at various metrics, they have been punching above their weight when it comes to supporting Black students in higher education. 20 00:01:15,040 --> 00:01:18,840 And many times, in other words, HBCUs have been doing more with less. 21 00:01:19,160 --> 00:01:24,160 Part of what the Institute for Capacity Building, part of what we focus on is the capacity building. 22 00:01:24,160 --> 00:01:29,040 Part of what we focus on is how do we support HBCUs so that they can do more with more. 23 00:01:30,120 --> 00:01:31,520 My whole career has been in higher ed. 24 00:01:31,520 --> 00:01:33,280 Those are like 17, 18 years in higher ed. 25 00:01:33,640 --> 00:01:40,080 And private, private university, public universities, and then now here with the nonprofit, UNCF. 26 00:01:40,560 --> 00:01:45,240 So already, one of those words sticks out to me, capacity building. 27 00:01:45,400 --> 00:01:47,400 And that's not a phrase I've heard thrown around a lot. 28 00:01:47,400 --> 00:01:49,840 So give me a little more context around what does it look like? 29 00:01:49,840 --> 00:01:54,520 How do you define capacity in the context of higher ed and what does it look like to build capacity? 30 00:01:55,040 --> 00:01:56,400 Yeah, that's a great question. 31 00:01:57,440 --> 00:01:57,920 Period. 32 00:01:58,120 --> 00:02:09,600 No, I mean, I say they have jokingly, but because the capacity is so broad and we are a fully grant funded organization. 33 00:02:09,600 --> 00:02:13,240 So there are certain things where we want to help build capacity. 34 00:02:13,240 --> 00:02:22,000 But if we're honest, there are certain levels of capacity that we can't really practically reach in it or build in a sustainable way. 35 00:02:22,280 --> 00:02:27,440 So, for example, if you have an institution where, hey, look, they're understaffed by 15, 20 people. 36 00:02:27,640 --> 00:02:30,680 OK, well, we're kind of limited there. 37 00:02:30,880 --> 00:02:33,840 We don't have infinite funds to do that. 38 00:02:33,840 --> 00:02:46,880 But in our specific context, we have a set of transformation partners and we will look at various functions in the university and engage and say, what are your needs? 39 00:02:48,080 --> 00:02:49,560 Where are you right now, basically? 40 00:02:49,760 --> 00:02:50,880 Where are you trying to get to? 41 00:02:50,880 --> 00:02:51,480 What's the gap? 42 00:02:51,480 --> 00:02:52,960 You know, it's doing a gap analysis. 43 00:02:52,960 --> 00:03:03,280 And then let's partner with you to see how do we identify some solutions to get us from where we are to where we want to be, or at least somewhat closer there so we can be bridging that gap. 44 00:03:03,280 --> 00:03:11,440 And then from there, we will we have one of our verticals is primarily focused on providing that transformation support. 45 00:03:11,720 --> 00:03:29,920 My vertical is focused on really part of is measuring the impact of and the progress of transformation efforts, but also building partnering with HBCUs to help shape their data strategy, to cultivate data communities of data fluency and to facilitate data maturity. 46 00:03:29,920 --> 00:03:35,440 There is another vertical that's focused on focus on digital innovations and things of that nature. 47 00:03:35,440 --> 00:03:41,320 We have another one that's focused on engaging executive leaders and boards, another one that's focused on strategic finance. 48 00:03:41,520 --> 00:03:42,360 So how do we partner? 49 00:03:42,360 --> 00:03:47,080 We're trying to do things at scale because it's we could just we could do more. 50 00:03:47,080 --> 00:03:49,960 We're trying to basically multiply our efforts instead of saying a one on one. 51 00:03:49,960 --> 00:04:03,560 But how do we focus along these areas to say, what are some scalable, sustainable areas and opportunities to help bridge the gap from where you are to where you need to be to go from surviving to thriving? 52 00:04:03,880 --> 00:04:03,960 All right. 53 00:04:03,960 --> 00:04:13,360 So that already probably spans a lot of different areas, even just think about the complexities of a university system and how do you impact change in different areas? 54 00:04:13,360 --> 00:04:16,760 So give me details around like what kind of data are you looking at? 55 00:04:16,760 --> 00:04:19,160 What sort of data are you managing? 56 00:04:19,160 --> 00:04:22,160 And reviewing to measure progress and success? 57 00:04:22,160 --> 00:04:24,160 Is that student outcomes? 58 00:04:24,160 --> 00:04:25,160 Is that admissions? 59 00:04:25,160 --> 00:04:31,160 Is that like academic performance or financing of those programs? 60 00:04:31,160 --> 00:04:33,160 Give me some more ideas about that. 61 00:04:33,160 --> 00:04:34,160 Yes, all of it. 62 00:04:34,160 --> 00:04:35,160 All of the above. 63 00:04:35,160 --> 00:04:40,160 Now, the issue is we're at where I'm the first director in this role. 64 00:04:40,160 --> 00:04:44,160 So that means we're at the beginning of our data collection and just our data strategy. 65 00:04:44,160 --> 00:04:51,160 So if we're talking about particularly USCF works with the we focus primarily on the private HBCUs. 66 00:04:51,160 --> 00:04:54,160 My team endeavors to work with all of them. 67 00:04:54,160 --> 00:04:58,160 However, I'm not sure if you know this, but so we'll talk about the public HBCUs. 68 00:04:58,160 --> 00:05:00,160 Many of them have been underfunded. 69 00:05:00,160 --> 00:05:02,160 They're supposed to be getting their land grant institutions. 70 00:05:02,160 --> 00:05:04,160 Land grant, you get a certain amount from federal dollars. 71 00:05:04,160 --> 00:05:06,160 Your state is supposed to match it. 72 00:05:06,160 --> 00:05:13,160 For decades, many of them, if not most of them have been, I'll say most, if not all rather, have been underfunded. 73 00:05:13,160 --> 00:05:18,160 The states have not been matching the land grants for the public HBCUs. 74 00:05:18,160 --> 00:05:23,160 However, they have been matching the land grants for the non-HBCUs in those same states. 75 00:05:23,160 --> 00:05:25,160 So at the beginning, you see inequity. 76 00:05:25,160 --> 00:05:26,160 You see problems there. 77 00:05:26,160 --> 00:05:32,160 But to go further, the private HBCUs don't even get they don't qualify for the land grants. 78 00:05:32,160 --> 00:05:33,160 So there's even more. 79 00:05:33,160 --> 00:05:35,160 So that's the first part. 80 00:05:35,160 --> 00:05:43,160 I want to say like just for context, not to set a context of deficit, but just here's the reality. 81 00:05:43,160 --> 00:05:47,160 We can look at these systemic realities that impact their operation. 82 00:05:47,160 --> 00:05:55,160 I'm a Georgia Tech alumnus, and you can see the impact of continued investment in an institution over time. 83 00:05:55,160 --> 00:06:02,160 And so likewise, you can therefore see the impact of when institutions don't have that investment over time. 84 00:06:02,160 --> 00:06:03,160 How does that apply to data? 85 00:06:03,160 --> 00:06:05,160 Well, people want to get data. 86 00:06:05,160 --> 00:06:06,160 Everyone wants to have data. 87 00:06:06,160 --> 00:06:13,160 But then you say you have a team of maybe one or two people who are actually doing three to five jobs each. 88 00:06:13,160 --> 00:06:15,160 And we're saying, hey, can you get these data? 89 00:06:15,160 --> 00:06:16,160 It's not that simple. 90 00:06:16,160 --> 00:06:18,160 It's not that simple. 91 00:06:18,160 --> 00:06:20,160 Or hey, here's these certain things we've been doing. 92 00:06:20,160 --> 00:06:21,160 Let's switch and let's pivot. 93 00:06:21,160 --> 00:06:23,160 It's not that simple. 94 00:06:23,160 --> 00:06:28,160 Just the very capacity to retrieve the data may be a concern. 95 00:06:28,160 --> 00:06:39,160 And so part of my perspective coming from Higher Ed, having worked at institutions, is to engage with the folks, the data practitioners and leaders, to say, hey, look, I get where you all are. 96 00:06:39,160 --> 00:06:40,160 I've been there, done it. 97 00:06:40,160 --> 00:06:43,160 I'll be it not at HBCUs, but I've done it in universities. 98 00:06:43,160 --> 00:06:49,160 And so let's understand where things are from your place, from your perspective. 99 00:06:49,160 --> 00:06:54,160 And how do we find ways that are mutually beneficial to get us to where we need to be? 100 00:06:54,160 --> 00:06:58,160 And so we're at the beginning stages of just understanding a lot of these things. 101 00:06:58,160 --> 00:07:06,160 It's crazy how going from just the data for decision making, it very quickly takes you back into data governance and data quality. 102 00:07:06,160 --> 00:07:09,160 It takes you right back into the processes. 103 00:07:09,160 --> 00:07:11,160 And then pretty much now you're back into the politics. 104 00:07:11,160 --> 00:07:16,160 You're like, how am I even having this conversation when I thought we were just trying to build a dashboard? 105 00:07:16,160 --> 00:07:19,160 But a lot of these things are all tied together. 106 00:07:19,160 --> 00:07:24,160 And I think that brings up a question about focus, like data is ubiquitous. 107 00:07:24,160 --> 00:07:26,160 It's everywhere. 108 00:07:26,160 --> 00:07:37,160 There's, you know, just in our own personal devices, there are thousands of data points that as we pull up our smartphones and we're going places, so much data. 109 00:07:37,160 --> 00:07:51,160 So how do you put on a grid or a decision matrix in knowing what data is important to the particular initiatives that you're going after? 110 00:07:51,160 --> 00:07:58,160 How do you get that strategic alignment with your senior leaders and saying this is what we want to focus on in data? 111 00:07:58,160 --> 00:08:00,160 What's that process look like for you, Philip? 112 00:08:00,160 --> 00:08:03,160 It's a bit of a mix because, as I said before, we're grant funded. 113 00:08:03,160 --> 00:08:09,160 So some of our funds, I mean, our grants, rather, and the funders, they already have ideas of what they'd like to see. 114 00:08:09,160 --> 00:08:12,160 And so, bam, that's kind of there. 115 00:08:12,160 --> 00:08:25,160 What I try to do in those conversations is when I see that the metrics that they're pulling, or that they're requesting rather, are they're kind of narrowly defined or they're lacking context. 116 00:08:25,160 --> 00:08:38,160 I try to where I have opportunity to speak to the funders is to say, hey, I realize you're trying to get this, but do you realize if you get this information is going to tell this story that's automatically going to make most universities, not just the black ones. 117 00:08:38,160 --> 00:08:42,160 Most universities will probably look bad in light of what you're saying here. 118 00:08:42,160 --> 00:08:53,160 And by the way, I don't know if you all know that most of the metrics by which we tend to be judging higher education, they really only work for fewer than 100 universities out of 5,000, 5,000 plus. 119 00:08:53,160 --> 00:09:08,160 And so you can think about that. You're like, oh, huh, we really define higher ed and success based on what works well at the IVs, the IV plus and flagship public. 120 00:09:08,160 --> 00:09:12,160 Oh, well, that doesn't seem to make a whole lot of sense now, does it? 121 00:09:12,160 --> 00:09:17,160 And so, so if I have opportunities to speak with the funders, I try to have those conversations internally. 122 00:09:17,160 --> 00:09:25,160 And as far as our team, I try to focus and say, hey, look, let's begin by engaging with the institution. Say, what are your strategic plan priorities? 123 00:09:25,160 --> 00:09:27,160 Because that's where value that's that's what the value like. 124 00:09:27,160 --> 00:09:34,160 So I want to have that now. Thankfully, it's not as if it's just Philip by himself saying, hey, strategic plan and everyone else is like, why would you do that? 125 00:09:34,160 --> 00:09:42,160 Our broader team understands that. So we're already aligned with that reality and saying, so our transformation process begins by him. 126 00:09:42,160 --> 00:09:47,160 So let's engage you all to understand your strategic priorities and also let's do a needs analysis. 127 00:09:47,160 --> 00:09:56,160 So and see how these things align, because that's where you're going to get the most lift, because the whole purpose of data is to inform and optimize decision making. 128 00:09:56,160 --> 00:10:10,160 And it's and it should be driving value. And because this is a nonprofit working with HBC use in my mind, the it should first and foremost drive value and inform decisions for the institutions. 129 00:10:10,160 --> 00:10:19,160 And on that point, Philip, just for our audience, give us some examples of what value would be in these contexts. 130 00:10:19,160 --> 00:10:29,160 And so as you're working with those historically black colleges and universities, what is value in their minds and how do they measure that? 131 00:10:29,160 --> 00:10:32,160 So give us a few examples. Oh, that's that's a huge thing. 132 00:10:32,160 --> 00:10:41,160 So there's a lot of value. So obviously, there's the basic things we want students to be enrolled. We want them to stay enrolled and we want them to graduate. 133 00:10:41,160 --> 00:10:49,160 Wonderful. And we want them to graduate and move on to have to be to be satisfactorily employed and paid. 134 00:10:49,160 --> 00:10:53,160 Wonderful. Those are certain things where there that's where where there's value there. 135 00:10:53,160 --> 00:10:59,160 But there's also a value that and that's the value that most people talk about when we're talking about higher education. 136 00:10:59,160 --> 00:11:04,160 But then there's a value that often gets understated. Now, you two, I think, both went to Wheaton. 137 00:11:04,160 --> 00:11:09,160 You all both went to Wheaton. Moody. Moody. Moody. Just kidding. 138 00:11:09,160 --> 00:11:13,160 See, I was testing you. You both passed the test. 139 00:11:13,160 --> 00:11:21,160 Speaking of rivalry, is that exactly what you went to Wheaton and vandalized it? 140 00:11:21,160 --> 00:11:44,160 With pages of scripture. No. So there at Moody, rather you at that Moody Bible Institute, you're if you if they're judging Moody based on how much its alumni were paid or or how quickly this person was fully was fully employed doing X, Y, Z or the number of Moody grads who are working at Fortune 500 institutions. 141 00:11:44,160 --> 00:11:52,160 You'd be like, oh, what is it? Number is not looking good. Yeah. Yeah. So how do we define value for Moody? Well, we look at what Moody, what do you exist to do? 142 00:11:52,160 --> 00:11:59,160 Do you remember the memorize Moody's mission statement? Don't say if you don't, don't say it on the recording. 143 00:11:59,160 --> 00:12:07,160 But if you look at Moody's mission statement, it'll tell you what Moody values. If you look at Moody's strategic plan, hopefully they have a valuable. 144 00:12:07,160 --> 00:12:13,160 Some people just come up with strategic plans to make one. But we'll just assume Moody actually has one that tells you what they value. 145 00:12:13,160 --> 00:12:26,160 Many times it's going to be producing some it's going to be some variation of thinking and thinking Christians who are actively participating, reflecting Christ well in society and everything they do. 146 00:12:26,160 --> 00:12:30,160 Some version of that right there. Sure. So how well does Moody do that? 147 00:12:30,160 --> 00:12:38,160 How well is Moody equipping its graduates to be to be effective Christians in whatever their walk of life may be? 148 00:12:38,160 --> 00:12:47,160 And there are people who are paying I don't know how much it goes to cost to go there, but they're not going for free. So they're paying money to go there, which tells you they value what Moody values. 149 00:12:47,160 --> 00:12:56,160 Likewise with the HBC use you have you look at the mission, largely speaking, I've worked at a Christian college and work at HBC use and we work working for HBC use. 150 00:12:56,160 --> 00:13:05,160 And one thing that they have in common is that they both represent the smaller subgroups of people who don't see themselves represented in higher education. 151 00:13:05,160 --> 00:13:12,160 And not only do we not see ourselves represented, we actually see who and what we represent actually actively attacked in higher education. 152 00:13:12,160 --> 00:13:19,160 I spoke with some philosophy instructors who are just like I can't tell people that I'm a Christian because I lose all credibility in this world here. 153 00:13:19,160 --> 00:13:25,160 Black folks, I mean, hey, everyone people, people automatically assume we don't even belong in higher education. 154 00:13:25,160 --> 00:13:35,160 Like the affirmative action conversation is stupid, y'all, because I don't know what side of it y'all on. We just if we are on opposite sides and you know, we're probably I might be starting to fight, but I'm not trying to. 155 00:13:35,160 --> 00:13:41,160 But the reason why I'll tell you why I say it's stupid. So it first started I was from California born and raised. 156 00:13:41,160 --> 00:13:54,160 And the so I was in high school when the California first banned affirmative action and the conversation was always based on, oh, these under qualified blacks and Latinos are taking our place in the UCs. 157 00:13:54,160 --> 00:14:09,160 And I first heard it and I was like, well, first of all, your mama. And then second of all, how would you even know that I'm under qualified as a kid as a 15 year old I didn't quite have all of the language to say that to really articulate it well as I thought over time it came to 158 00:14:09,160 --> 00:14:24,160 it was able to get it but the thing that's really crazy is that no one ever considers that the possibility of a white person that doesn't deserve to be there, the existence of an under qualified white person is like, they all they all we all made it. 159 00:14:24,160 --> 00:14:48,160 But my whole point is being black higher education has made it very, very clear that it's not for us. So the value of HBC use is not just having a space, but now how do you maintain your dignity and walk in this in this excellence that is inherent in putting your blackness I'm going to say, I'm going to add that is it's inherent in how God made us part because we're made in the image of God, how do we how do we walk this out there. 160 00:14:48,160 --> 00:15:06,160 Now that's a part of it that's harder to measure. That was my question. That's the part that's hard to measure salaries or fortune 500 placements but how do you measure those kind of results and it's riddled with bias as you're talking about here you might measure it by the percentage of this ethnicity 161 00:15:06,160 --> 00:15:25,160 versus that ethnicity, but it is riddled with bias about what that ethnicity actually means which isn't measured in a concrete way. So how do you deal with that, you know, pre supposition that people bring to the table about race and ethnicity and how that ties to qualification and then the 162 00:15:25,160 --> 00:15:39,160 Sawyers point how do you then provide the alternative solution of here's some concrete ways to measure that. And that's the thing people don't have patience for processes that's why we have proxies. That wasn't unintentional alliteration, however, it's there. 163 00:15:39,160 --> 00:15:54,160 And we most of our measurements are proxies as proxies on proxies on proxies that are measurements for salary those aren't even good. When we look at ROI for colleges and universities, and it's based off of salary career day that's not even good people look at, oh, these folks got this job 164 00:15:54,160 --> 00:16:09,160 out of straight out of college, they're looking at the best source of data for that is going to be the NACE first destination survey is a voluntary survey of alumni. So, so, I don't know if you all like me, you get stuff from from the school alumni survey is typically through some third party like I don't know who you are 165 00:16:09,160 --> 00:16:24,160 how'd you got my address, believe me alone. And then, if you get something I might I'll be more inclined to vote to respond to something that's straight from the school perhaps but alumni surveys, you don't get 100% of the alumni base participating. And then it's self reported, 166 00:16:24,160 --> 00:16:40,160 it's self reported, and then it's, you know, there's so many different things, but NACE is still the best that we have in this brain, especially if you're looking at private universities and people who are doing all kinds of things, which might not even be on on the IRS may never really know 167 00:16:40,160 --> 00:16:55,160 all these other things that someone is doing. And so I when I was working at the Christian University, the violin I was working at Biola University, and I was looking at some of the data from the NACE server once again it's good. Once you understand those nuances and the gaps. 168 00:16:55,160 --> 00:17:08,160 But as I began to look at it I saw certain things like, wait, what's going on here what's happening here, and I looked into it I was like, Oh, this looks bad. And as I looked into it I said, Oh, this makes sense when you understand the culture of the institution, and the students 169 00:17:08,160 --> 00:17:14,160 and the value, you're in you look at the like, they're actually very satisfied with their outcomes. 170 00:17:14,160 --> 00:17:28,160 And so, part of the issue is that it is hard, but we tend to go I have a job to do. And so, at the end of the day people want to see results. So if I say, hey look, someone says hey we want to know the value of this and I go, well, you know it's pretty difficult 171 00:17:28,160 --> 00:17:38,160 to answer. So, I'm like, well, you know, I'm going to do a 20 year longitudinal study with full of qualitative data to answer the question that you asked today. 172 00:17:38,160 --> 00:17:51,160 It's like, well, that's not going to cut it. So sometimes there are some legitimate reasons why we're looking at these proxies because to really be thorough it would take a lot of time. And between now and then so many things will have changed. 173 00:17:51,160 --> 00:18:03,160 Were iPods even out 20 years ago? I don't know. I forget but you seem like so much can change within 20 years, let alone 50, that you do need to have something with a shorter turnaround. 174 00:18:03,160 --> 00:18:12,160 But then some data just aren't available. Some things like I said are very qualitative. How do you measure spiritual growth? Like every pastor in America wants that. 175 00:18:12,160 --> 00:18:25,160 Some of them do. Some of them would rather actually rather not have that. But the, get out of here. You're going to make the rules go lower. But you see it so some things are just difficult. And so there are some legitimate difficulties. 176 00:18:25,160 --> 00:18:39,160 So I don't want to make it seem like you were just lazy and greedy. But I think the legitimate things also tie together with impatience and just like give me now results results results now now now now now it creates this thing that we have. 177 00:18:39,160 --> 00:18:52,160 It's difficult and it's hard to really find a good way forward. But I do know that big part of the way forward really involves knowing each other and knowing people. And that takes us back to difficult. 178 00:18:52,160 --> 00:19:04,160 One quote and I'll say it's like data problems are people problems. And that's I get that from Harvard Strategic Data Project. And that's something they often say data problems or data issues are people issues. 179 00:19:04,160 --> 00:19:17,160 And we think that we could just solve it by this analysis of this tool. But really the hardest work is dealing with the people to get the data together to understand the data that people that the data are talking about. It's it's wild. 180 00:19:17,160 --> 00:19:27,160 A lot of data people are really comfortable in the quantitative side of analytics where we're talking about numbers, dollar figures or test scores or whatever. 181 00:19:27,160 --> 00:19:42,160 You've been talking a lot about the qualitative side of that. And can you speak a little bit about I guess how much work you do on both sides of that quantitative and qualitative and even some of the differences in how you approach analytics or decision making with two different sorts of data types like that. 182 00:19:42,160 --> 00:19:48,160 Yeah, maybe how you try to think through comparing those and using those and leveraging those well. 183 00:19:48,160 --> 00:20:00,160 I think qualitative data don't get enough respect. They don't once again probably going back to because it's harder. It's like a lot you look at it's no words. Come on. 184 00:20:00,160 --> 00:20:20,160 So, but it's I want to say that it is so like that the two are. They play into each other. You know you may you know so I've done a lot of survey analysis, done some things with with with the with focus groups and interviews things that nature, and so that the survey is good self is going to be. 185 00:20:20,160 --> 00:20:33,160 You're going to get some stuff which are basically the analysis is going to be quantitative because you're looking at the sign of that number number to each point in the scale whatever. But let's say you do your survey and they are at least some interesting results, the numbers don't tell me enough. 186 00:20:33,160 --> 00:20:43,160 I need to talk with you all to understand this. Okay, let's talk now we're having this qualitative part. Okay, that's really interesting. You know what this thing came up, we need to do some research into this topic that came up here. 187 00:20:43,160 --> 00:20:57,160 And as you're doing that there may be some, some more quantitative things that are coming up in your research, and also, and it's a huge. It's a huge undertaking. And I think I like to, I like to partner with the researchers. 188 00:20:57,160 --> 00:21:13,160 I have I have certain things. I have research proclivities, but not necessarily research responsibilities. And so I like to, I like to stay really close to the researchers and say hey, here's what's going on so and some of the conversations that we've been having it in my current 189 00:21:13,160 --> 00:21:23,160 role, people are asking me a lot of questions because I'm the one in front like if you can make a cool dashboard everyone wants to talk to you. And so, you know, so I'm in the front as I look at I'm hearing these questions I'm like you know what I need to go as his research 190 00:21:23,160 --> 00:21:36,160 team Frederick D Patterson research Institute, they're incredible researchers have a whole they've done a depth of work on HBC us and the value of HBC us that you can look they have some something talking about the HBC you effect, punching above their weight that's 191 00:21:36,160 --> 00:21:48,160 all their work. And if someone asked me a question about HBC is like, that's a good question. Let me go ask this team over here. And the more I do that I say, you know what you guys just stop asking me this question, you should ask them this question. 192 00:21:48,160 --> 00:22:00,160 And I'm just pointing people to them, and not because I'm trying to, you know, compete one way or the other but I feel like it has to be in strong partnership. And so, behind the scenes, I'm going to have conversations with them and say how do we actually partner together 193 00:22:00,160 --> 00:22:15,160 to answer this question well, because it doesn't need to people aren't looking for quants what what people don't need quantum call, they need answers, and our answers need to be thorough and holistic so if I can engage with this team well and say, excuse me, 194 00:22:15,160 --> 00:22:30,160 and say well from this angle and this angle I'm informing what they're saying and they're informing what I'm saying, and then we come together and say here is a bigger picture, that's, I feel like that's really the best way to do it is rare that you have an individual who is going deep on all the quantitative 195 00:22:30,160 --> 00:22:44,160 and deep on, you know, doing a whole lot of research just because the research is can be so intensive. So the end result of that is people want answers. What are those answers look like from a decision making standpoint so you want people to do things with this data 196 00:22:44,160 --> 00:22:48,160 what is data that drives decision making look like. 197 00:22:48,160 --> 00:22:56,160 You mean okay when we actually let me ask for clarification do you mean what are what types of data elements, or you talk about data products and deliverables. 198 00:22:56,160 --> 00:23:14,160 Yeah, how do you communicate with data or how do you do the research in a way that you can deliver something that will drive action or drive somebody inform somebody to build and make a decision, as opposed to maybe we present a dashboard of information we present a report with lots of information. 199 00:23:14,160 --> 00:23:29,160 So you're making a lens, when you're either doing the analysis, or when you are communicating that analysis. Okay, yeah, so I think the first part is just to think just as you're doing it think this is not about my analysis, it's about what is the research question. 200 00:23:29,160 --> 00:23:45,160 What is the decision that needs to be made and just really have that focus, and even above that, what are the higher strategic priorities that are in place. I have recently been doing a well I did a presentation for first with the Alabama Association for institutional 201 00:23:45,160 --> 00:24:06,160 research and it's just called beyond waffle house, shifting from transit from transactional data providers to strategic partners, and in it I highlight four personas that reflect their flick paradigms that are ultimately limiting the power of the value of data teams 202 00:24:06,160 --> 00:24:16,160 and the value of the house, which is basically the ad hoc champion high volume ad hoc stuff. Second is the vending machine. Now the vending machine is trash, just to call you just to call it out what it is. 203 00:24:16,160 --> 00:24:25,160 It's just you get a vending machine because you don't want to deal with people. And when the vending machine gets on your nerves, what do you do you punch you hit it you shake it is like no respect give me what I want right now, or the 204 00:24:25,160 --> 00:24:39,160 other way around you treat it poorly. That's doesn't provide much value at all. But then there's the glorified Alexa, you know that's like the underutilized sophistication capacity to take us to the, we've had AI in our homes for years, and we're asking 205 00:24:39,160 --> 00:24:55,160 you to do that. And then we've and then there's the Butler, you know the the invisible workhorse. Now the Butler is interesting because I think that the Butler kind of really gets to what we want from all four of these it's kind of like we really want the 206 00:24:55,160 --> 00:25:09,160 the Butler, what do you get a Butler for you get a Butler to do all the things that you really don't want to do like no one hires a Butler says I want you to do the most important and enjoyable things in my life. It's just like, here's all the things I don't want to do have 207 00:25:09,160 --> 00:25:25,160 time to do typically that in the day in an organization, the person who says I'm not a data person, and the person who says I don't have time to do the data, they just like the solution is the data Butler. And so they give all the stuff to the data Butler, which is fine because sometimes you're just at that place and you need that 208 00:25:25,160 --> 00:25:40,160 and you just absolutely do the, and there's nothing wrong with that. The problem is, can you imagine I don't know if you need to have butlers, I mean I don't I don't you don't seem like, but no, you got a butler. 209 00:25:40,160 --> 00:26:01,160 You got a livery, black tie affairs, you know, I'm working on my kids see if they can fill some of that role. Exactly, exactly. You need a butler for those butlers. But like, so the, if you imagine having a butler to help with all the things around the house, and then you sit back and you realize this butler is really talented, they can do so much more. 210 00:26:01,160 --> 00:26:16,160 Sure, the person, the part of you that loves people is going to be like, I really want to encourage them to leave and go do better things their lives, but the party that hired them is going to say, no, I need you to stay, I don't want to, I don't want people will not be willing to get rid of having a butler 211 00:26:16,160 --> 00:26:28,160 once you have a butler, you want to keep that but same thing with the data Butler. So in other words, that they very inertia that that led to the need for data Butler will lead to that butler never transcending that state. 212 00:26:28,160 --> 00:26:43,160 The ideal scenario, the ideal persona is the navigator like not like GPS but the crew member. And so that navigator is what helps to guide you to reach to your to your destination helps identify obstacles threats all these things along the way the navigator does not does not control 213 00:26:43,160 --> 00:26:55,160 the vessel, but the navigator partners with the pilot to get it navigator has a strong understanding of the of the mission objectives, the navigator has learned the crew and the pilot, and or the, excuse me, or the captain. 214 00:26:55,160 --> 00:27:08,160 And so they know, okay, here's how to and how not to talk here's what they value. And sometimes here's why I just need to stand firm and push on these things. Here's where I need to relent, because it's a partnership, you see that the navigator is a strategic partner, 215 00:27:08,160 --> 00:27:23,160 those first four they were all transactional and reactive this strategic the navigator. It is strategic, and it is proactive and so you have to be proactive, going back to what I said before, so much of this is relational, these are people issues and that's really, when you get down to the 216 00:27:23,160 --> 00:27:39,160 decision making, you can't skip past the, the people part. And I think many times those of us who are the analysts and data scientists, we skip past that which is why there's that stat that says up to the highest I saw was 87% of data science initiatives 217 00:27:39,160 --> 00:27:56,160 failed to produce any ROI. I love those personas I think that's really powerful and it provides more of that humaneness to what it means to be a technical person, and you know we've all heard the stereotypes of, oh well we put the tech people in the closet 218 00:27:56,160 --> 00:28:14,160 because they're not good with people but data more than any other, I think position within tech is calling tech people to a higher position of you need those people skills you need the soft skills to be able to relate and Philip, you shared with us earlier, before we 219 00:28:14,160 --> 00:28:36,160 started recording that your preacher that that your communicator and that there's some real parallels between what you do to prepare a message to communicate and the same thing you would do to prepare say a presentation on a dashboard or data to tell us more about those 220 00:28:36,160 --> 00:28:46,160 parallels that you've seen between those two things. Yeah, one of my colleagues call me, call me the daywalker like blade Wesley Snipes. 221 00:28:46,160 --> 00:29:00,160 So, because I've been walking both worlds is, and I'm gonna say that's a that's that's a grace from God to, while I was at, while I studied computer engineering, people like you sure you're an engineer, because you like people to. 222 00:29:00,160 --> 00:29:07,160 I think that's a compliment. I think, but really big closet. 223 00:29:07,160 --> 00:29:09,160 Everyone can fit in there. 224 00:29:09,160 --> 00:29:18,160 Yeah. So basically when you're when you're preparing a sermon, you know, three general steps observation interpretation application if you want to make it simple. 225 00:29:18,160 --> 00:29:30,160 You're looking at, you're observing all these things not just in the text but the context, you know that the little bit of historical literary cultural context, you're reading some like the chapter before and after you might be reading some of the other works, you've 226 00:29:30,160 --> 00:29:46,160 got the commentaries, all these different things you might be getting some some people are Greek if you're into that sort of thing. I really am. Oh my goodness, I like it, but the, the, you want to make sure that you're following certain processes your methodology is on point. 227 00:29:46,160 --> 00:30:01,160 There's just some key hermeneutical methods that you want to employ to make sure that you're not messing things up I love had in Robinson at a whole article talking about the heresy of application, and you just going straight from, hey, here's this to Ben 228 00:30:01,160 --> 00:30:17,160 and you're going to go through the process. However, the process of all of that exegetical hermeneutical processing is not an end up into itself. No one cares. No one wants to see all that you'll put everyone to sleep and they'll turn the sawdust. 229 00:30:17,160 --> 00:30:31,160 So what you want to do is like the whole point of that is to is to communicate it and some of us we go so some of us will sit back and they have all this stuff, only the other scholars want to see the scholarly work, and there is a place for that to be sure, but 230 00:30:31,160 --> 00:30:44,160 that's not really the primary focus even the scholars who are doing the scholarly work together. There has to be a place besides the scholarly discussions otherwise this is pointless, there has to be a spiritual and lived out impact, but even then, the next step between 231 00:30:44,160 --> 00:30:56,160 communicating it from the scholarly to you know like typically we'll say in the in the sermon, but for many people, they stop there. And they say the whole point is that create this wonderful crafty sermon. 232 00:30:56,160 --> 00:31:08,160 And here's this this bomb sermon with all these things. And it's just like yeah I could connect with this and this and this, you know I gotta, you know, Kendrick Lamar was hot this this year, so I'm going to drop in some, they not, they not like us we talk about holiness baby. 233 00:31:08,160 --> 00:31:24,160 And we have a great opportunity to tie it all together all this cultural relevance. Oh wow that's so cool. And then, but that's not really the end point. The end goal is to communicate it, not so that I could get the glory not so I can be big, but so that people who hear it, they 234 00:31:24,160 --> 00:31:39,160 receive it, they can understand it, and they can, they can, they be faced with this decision. What do I do with this information that's been given to me, and do I choose to, how does it affect my daily decision, do I choose to abide by it or not. 235 00:31:39,160 --> 00:31:55,160 You, in my preaching, I realized everybody has a decision, I want to inform the decision as much. No, I'm saying inform not drive, because that's, you know, in church culture that's how you to be that's a that's a element of unhealthy cultures, we'll say sinful cultures 236 00:31:55,160 --> 00:32:09,160 that are trying to force behavior, and because I'm doing it by now it's not the same thing on the data side but that's like a quick, quick church hurt thing we don't want we're not trying to force behavior but we do want to inform decisions, want to inform behavior, 237 00:32:09,160 --> 00:32:17,160 same thing with data. No, we're not taking all these data and looking at them, so that people can look at all these incredible fascinating statistical methodologies. 238 00:32:17,160 --> 00:32:29,160 I think that's why scholars need to see that sure and it's important it has its place, but that's not the whole point. And I think that's part of the reason why many of these initiatives failed, because people are doing things based off the base off of some sort of intellectual 239 00:32:29,160 --> 00:32:40,160 curiosity, but it's not actually tied to the decisions that need to be made. There's some people that they get it and they go, hey look I great these awesome dashboards, I made these great deliverables, and they think like that's the end goal. 240 00:32:40,160 --> 00:32:49,160 No it's not the end goal is not your dashboard or deliverable. That's why people are saying dashboards are dead dashboards aren't dead, they're not dead. 241 00:32:49,160 --> 00:33:03,160 They're just overused, and people are stopping there. The whole point is, how do I inform these decisions and by the way a great preacher is going to be a great pastor, what I mean by what I mean by that is you understand the people to whom you're 242 00:33:03,160 --> 00:33:14,160 speaking. You're part of the community in which you're communicating, you are living it with them. And so now because you've taken time to understand your audience or your stakeholders. 243 00:33:14,160 --> 00:33:30,160 Now when it comes to going from these things, even the nerdiest, so like I've seen some of the nerdiest people in the world who have zero, like cultural similarities to their audience, because they've taken time to understand and actually, and to understand 244 00:33:30,160 --> 00:33:46,160 the people, they've been able to communicate things in a way that had the greatest impact, because they understood and care for the people and they understood. Here's what you all are wrestling with. So how do I did the biggest stretch is to take all of this information and tie it to what people are wrestling with 245 00:33:46,160 --> 00:34:00,160 both in data and in sermon preparation, even Bible study. That's like that navigator role of I am looking out I'm seeing the terrain I realize what the where the curves are where the bumps are where the trouble is and where the smooth path is and I am navigating us to a 246 00:34:00,160 --> 00:34:13,160 destination. And like the navigator role is alongside on the journey, looking and and navigating it with the people on the on the ship or the plane or whatever we're, whatever we're navigating. Yeah, can you imagine a navigator who gets so annoyed when the captain doesn't listen 247 00:34:13,160 --> 00:34:17,160 and they said just move I'll take over. 248 00:34:17,160 --> 00:34:20,160 We call that mutiny my friends. 249 00:34:20,160 --> 00:34:34,160 Can you imagine, like sometimes us and data teams we feel that way. But sometimes in church I'm sure people feel that way, but it's, there's a little bit of a difference so because in church is typically the, the preacher has some sort of authority, whereas in our organizations, 250 00:34:34,160 --> 00:34:43,160 the data team typically doesn't have that authority. And so that's where the metaphor begins to break down like I said, but it's still interesting thing to even realize within ourselves. 251 00:34:43,160 --> 00:35:02,160 Yeah, and I love that you brought up the fact that there's a lot that goes behind research, studying this mining the data, trying to figure out the mess of it all going through that exegetical process before you communicate the conclusions, and we have to be able to put that 252 00:35:02,160 --> 00:35:18,160 upfront time into building that conclusion and I had a, I had a professor who said, now in the context of preparing for a sermon, but it was, you know, people don't want to see that it's like your underwear you need it to gird your loins but don't go showing it to nobody. 253 00:35:18,160 --> 00:35:35,160 So, I had to share that. I appreciate that. That's hilarious. I mean, yeah, really, if you think about when I don't really need to see every grain that's been put into my bowl of cereal. I just want my cereal made, and I want to end up thing is I want to trust that you've done a good job. 254 00:35:35,160 --> 00:35:51,160 Now that the role. What I do want to know is that you've taken the care to to to select quality ingredients to take yet I know that I can trust you to not be sweating all over my food and you know now you know like you take care of what you're doing and you handle 255 00:35:51,160 --> 00:35:57,160 things. You handle things with care when you give it to me with care. I want to trust your preparation. 256 00:35:57,160 --> 00:36:14,160 But I don't need to know everything that's in it. Your preparation is important, but not, not right right here. Philip, you've got a, you've talked about just like the large source of challenges and complex problems that there are to solve with your work at UNCF and with HBCUs. 257 00:36:14,160 --> 00:36:28,160 What are you, what's kind of the vision or what are you hoping for the next 1218 months like what's the next step for you where are you headed with knowledge management and capacity building. What's your vision for kind of the direction you're headed and what you're hoping for. 258 00:36:28,160 --> 00:36:43,160 Yeah, one of the things I what I'm excited about doing is so I have a built a community of practice the HBCU data advisory group. And so that's the very thing I was saying many times we tend to start with intermediaries and funders and we get together in rooms 259 00:36:43,160 --> 00:36:56,160 and have great ideas we're really excited and get the contract signed and we go to the institutions and say, Hey, look, we got this great deal you want to get on board, come on there's money involved. And, and it's not like a UNCF and that's just like the nonprofit thing. 260 00:36:56,160 --> 00:37:08,160 And so what I'm trying to say is you know let's do this differently. Let me go and engage these these stakeholders and say, let me understand you on your own terms, let me understand your needs on your own terms. 261 00:37:08,160 --> 00:37:24,160 At the same time, I'm also because I understand these are these are data teams is typically from institutional research and effectiveness in higher ed, that will be your institutional researchers and or if and or effectiveness would be your core data team that's looking at 262 00:37:24,160 --> 00:37:38,160 most centralized look at data for understanding things about the university. So because these are data folks. I also have to realize Hey look, you guys are great at this part. How do I stretch you all to be thinking as don't think as don't basically don't just 263 00:37:38,160 --> 00:37:46,160 deliver data deliver value. And so there's a stretch for the data teams there, but there's also a part say now that let's understand this how do we make sure it's tied to that value. 264 00:37:46,160 --> 00:38:00,160 And then so my direct report, what are part of her projects is actually to go there just do this, do this gap analysis and understand some solutions, and then we want to engage funders we have some funders that that I'm excited about and working with who 265 00:38:00,160 --> 00:38:06,160 are also excited about this whole process they were just like you know this would be a great way for us to consider it. 266 00:38:06,160 --> 00:38:19,160 Just engaging nonprofits as a whole, and say, let's start with the stakeholders let's understand things from their perspective, let's, let's co create solutions with them. So now we're not spending so much time and effort trying to get folks to buy in for 267 00:38:19,160 --> 00:38:34,160 something that was made talking about them but not with them. So I'm really excited about about that and growing that I'm excited about developing partnerships that to increase that more I'm really excited about opportunities to to honestly to partner with 268 00:38:34,160 --> 00:38:50,160 the FTP ROI the Frederick D Patterson Research Institute that mentioned earlier to talk about hey how do we actually strengthen this community of practice, so that we are able to to better build people's capacity just in terms of networking opportunities, 269 00:38:50,160 --> 00:39:06,160 skilled skilled skilled upskilling opportunities, anything like that I just so many opportunities that could come just from engaging this community of practice and and scaling and in that to scale capacity building across several HBC is right now we already already have 270 00:39:06,160 --> 00:39:24,160 over. I think I've had over 5060 something individuals from over 40 institutions. There's 101 HBC us nationwide. So, that's a pretty good reach. And so just having that that level of influence and just really solidifying growing that there's huge 271 00:39:24,160 --> 00:39:38,160 opportunities with that, not once again, not for me, not so that I can, you know, build a my platform, but to actually help the institutions build up their capacity at the levels where they needed, and we're quick one reason why that's important. 272 00:39:38,160 --> 00:39:46,160 Almost all the funders are requesting data and I've often said to them you are demanding outputs which you have not invested in the inputs that makes zero sense. 273 00:39:46,160 --> 00:39:59,160 Yeah. Yeah. And so, let's now build if you, however, if you were to invest in these inputs, especially on the data side I had a conversation with one institution recently, and the president was saying well, yeah, here's these things and all these 274 00:39:59,160 --> 00:40:11,160 other levels that have advising advancement of course scheduling and registration enrollment. These, those are the core things. Those are that that's like the, the biceps and the chest and everything. 275 00:40:11,160 --> 00:40:22,160 But this person recognize that the data that's leg day, and this president she kept saying, Yeah, but we need this but we just don't have the data. Once we get the data we can make we can do better with this, if we have the data, it can do this or the data 276 00:40:22,160 --> 00:40:34,160 work, we're more aligned and integrated we could do this, she understood that the data are leg day. And what I'm trying to get is just everyone to understand. This is leg day. Now the data is not the whole thing some people think data is everything 277 00:40:34,160 --> 00:40:47,160 they know. This is leg day, and, and I want the funders head, let's get these things but I feel like having this this opportunities to build a coalition around data as leg day because then we realized there is no transformation without data. 278 00:40:47,160 --> 00:40:52,160 My workouts are never going to be the same now. 279 00:40:52,160 --> 00:41:06,160 Philip why have you chosen to invest your careers your skills your talent as a communicator. Why are you doing it in data and in higher education specifically what landed you here and why have you chosen to stay here. 280 00:41:06,160 --> 00:41:22,160 Oh man, okay so short story. I'm getting ready to graduate from Georgia Tech I'm at a NSBE conference as a national society black engineers, and they were a couple things happen, but one was I was at a session, they were talking about the digital divide this is 281 00:41:22,160 --> 00:41:36,160 about 10 years ago, and they were saying, which is crazy because we're still talking about the digital divide, and they were just talking about certain realities that I knew were true they were saying like a lot of black black schools, you may have one computer for every like 282 00:41:36,160 --> 00:41:44,160 100 or 200 students, where a lot of the white schools particularly the white schools that are in more affluent neighborhoods, you might have one computer for every 10 students. 283 00:41:44,160 --> 00:41:56,160 So, the data might show that there's computers at the school, but it's not really showing how what how much things are accessible to the students. And I chose computer engineering on accident. 284 00:41:56,160 --> 00:42:05,160 I saw my uncle go to work with some flip flops shorts, basketball shorts and a tank top. And I knew back in the 90s he was getting paid a good amount of money and I said, he doesn't have to wear a suit. 285 00:42:05,160 --> 00:42:15,160 My parents told me you have to wear a suit if you want to make good money. What does he do. Well I know he works with computers. He works with computers and the word engineers in his title do that I guess. 286 00:42:15,160 --> 00:42:24,160 Now, just that little bit of exposure changed my life, and I just still didn't even understand what engineer was. So I said, you know what, I'm getting ready to graduate from Georgia Tech I'm graduating from the best engineering school in the country. 287 00:42:24,160 --> 00:42:40,160 Sorry, not sorry everybody else I said the best, like, the best engineering school in the country. And, and I really just thinking about what they said, they one thing they said is like, imagine if, if there will be no Michael Jordan, if no one ever showed him a 288 00:42:40,160 --> 00:42:41,160 basketball. 289 00:42:41,160 --> 00:42:54,160 And I'm thinking about how many other Michael Jordan's there were of engineering that never really saw it or saw themselves in it so I say, you know what, I'm going to go teach high school, and I have a lot of have enough friends who are still engineers I can bridge 290 00:42:54,160 --> 00:43:03,160 that gap there, but I don't have enough friends who are teachers. So I went to go teach high school. I did that for a couple of years. And then I had already been been preaching. 291 00:43:03,160 --> 00:43:14,160 Right before I graduated from college. And, and I was like, I want to also want to go to seminary. So then I went back to the west coast to to Talbot school theology at Biola to for seminary. 292 00:43:14,160 --> 00:43:28,160 And while I was there, I was like, man, I need a job to support my habit of school. And also slash I want to get married so I started working at the university. And so I was working there in advance and fundraising as a project research so once again, data, and I was 293 00:43:28,160 --> 00:43:41,160 actually getting ready to go back to robotics, and the opportunity came. I was literally getting ready to go and Laura's like, Philip pause just wait. So I was like, I don't wait but this way and stuff is dumb guys I know you, I know it's what you tell us to do with this 294 00:43:41,160 --> 00:43:52,160 is dumb. And I waited. And then someone came up talking about this thing is hey you know what I think maybe a good opportunity for you to really utilize your tech education I'm like everybody says that like, they're always like this would be a good thing 295 00:43:52,160 --> 00:44:04,160 and great Georgia Tech thing and, and they're like, Okay, what is it I look I gotta I get excited like, man, can you do some if in Excel, like, you don't go to Georgia Tech to do some ifs my friend. 296 00:44:04,160 --> 00:44:14,160 But it's hard to say that because you sound really cocky you know what I mean, but it's just like fine. So then I started being about those in strategic planning, and it was a whole new role and I was like, Whoa, this is quite about lucky. 297 00:44:14,160 --> 00:44:23,160 I was like, I can see myself doing this for the rest of my career. And that's really where it all started being able to be at the center of strategic decision making strategic conversations. 298 00:44:23,160 --> 00:44:33,160 That was my navigator role every single one of those four, those five personas I mentioned the four bad ones that a navigator, every single one of those have been in roles. 299 00:44:33,160 --> 00:44:39,160 They all came from experience corroborated with other folks within within the field nationally. 300 00:44:39,160 --> 00:44:54,160 And I feel it was very much experience driven. And so just being in that realizing how much I was able to, to, to be a part of a lot of important conversations and make a huge impact, and even going back to fulfill that thing that happened at NSBE, how I can help 301 00:44:54,160 --> 00:45:06,160 decision makers on this side, understand and make decisions that could really impact the students as they're coming in. That was, that was really the genesis of it and just been growing ever since then. 302 00:45:06,160 --> 00:45:17,160 And I'm going to reach across now 50 plus HBC use your opportunity to shape data strategy and data thoughtfulness and literacy is being able to touch larger and larger groups of people in larger institutions. 303 00:45:17,160 --> 00:45:28,160 So now, Philip, because you're a higher edge education guy, you got to get this one. All right. So, why did the professor take his class on to the airplane. 304 00:45:28,160 --> 00:45:33,160 Why, because he wanted them to get a higher education. 305 00:45:33,160 --> 00:45:38,160 I was going to say that but I was like, it's too obvious. I can't be it. 306 00:45:38,160 --> 00:45:46,160 All of his jokes are terrible. Right there. Right there. I want someone to get it once. 307 00:45:46,160 --> 00:45:51,160 Like I just knew it was, it was the first thing in my brain. I was like, no, he can't, he's not going to go for that one. 308 00:45:51,160 --> 00:45:54,160 He's not, he's reaching for something. 309 00:45:54,160 --> 00:45:57,160 I was reaching for something higher. It's true. 310 00:45:57,160 --> 00:46:01,160 Overestimate, right. 311 00:46:01,160 --> 00:46:04,160 I appreciate you, sir. I appreciate your commitment to this. Please keep it up. 312 00:46:04,160 --> 00:46:10,160 Philip Wallace, the flawless. This has been wonderful. Thank you so much for joining us. It was a pleasure to meet you. 313 00:46:10,160 --> 00:46:12,160 This is the first time we've gotten to really connect. 314 00:46:12,160 --> 00:46:19,160 As Jordan, hear your story, hear about the work you're doing. And yeah, just have your insights from higher education world. So, love it. 315 00:46:19,160 --> 00:46:26,160 If people want to reach out to you or connect with you, hear more about your work or UNCF, where's a good place to connect with you online? 316 00:46:26,160 --> 00:46:32,160 The best way is LinkedIn for me. Yeah, I have, I've kind of cut myself off from most other social media. 317 00:46:32,160 --> 00:46:40,160 So LinkedIn will be the best way. And if you email me to my work email address and I don't know you, it's going to be deleted. 318 00:46:40,160 --> 00:46:46,160 I have too much. I was like, no, I got to prioritize. What is this? So LinkedIn will be the best way to get in. 319 00:46:46,160 --> 00:46:51,160 Pleasure. Well, thank you so much for being here with us today. And listeners, thanks for joining us on this episode of Making Data Matter. 320 00:46:51,160 --> 00:46:56,160 Have a great day, everybody.