1 00:00:00,000 --> 00:00:05,600 Welcome to Making Data Matter, where we've got conversations about leadership and data 2 00:00:05,600 --> 00:00:10,520 at mission-driven organizations with practical insights into that helpful intersection of 3 00:00:10,520 --> 00:00:13,160 nonprofit mission strategy and data. 4 00:00:13,160 --> 00:00:15,720 I'm your host today, Sawyer Nyquist. 5 00:00:15,720 --> 00:00:18,000 I'm your co-host, Troy Dueck. 6 00:00:18,000 --> 00:00:21,360 And today we've got a friend of ours on the show. 7 00:00:21,360 --> 00:00:22,760 His name is Matt Brody. 8 00:00:22,760 --> 00:00:23,760 Matt, welcome. 9 00:00:23,760 --> 00:00:24,760 Hey, thanks. 10 00:00:24,760 --> 00:00:26,220 Really glad to be here. 11 00:00:26,220 --> 00:00:29,960 And Matt, for people meeting you for the first time, tell us a little bit about who you are, 12 00:00:29,960 --> 00:00:33,160 what you do, and maybe why you're here on the show. 13 00:00:33,160 --> 00:00:34,640 What do you have to do with data? 14 00:00:34,640 --> 00:00:35,720 Yeah, sure. 15 00:00:35,720 --> 00:00:42,080 So I get to call home this particular spot in Canada, which is a bit west of Toronto. 16 00:00:42,080 --> 00:00:43,640 It's called Guelph. 17 00:00:43,640 --> 00:00:45,480 I get to live and work here. 18 00:00:45,480 --> 00:00:50,040 And I also get to call myself a data storyteller and department co-manager. 19 00:00:50,040 --> 00:00:56,440 I'm at one specific spot in a small corner of an organization, a nonprofit, and it's 20 00:00:56,440 --> 00:00:59,940 kind of a weird space between formal leadership and just playing around, trying to make use 21 00:00:59,940 --> 00:01:02,800 of useful things for our organization is the main thing. 22 00:01:02,800 --> 00:01:03,800 Yeah. 23 00:01:03,800 --> 00:01:05,200 And I'm about data. 24 00:01:05,200 --> 00:01:11,920 I'm passionate about using tools to design and build stuff. 25 00:01:11,920 --> 00:01:16,560 Actually using tools to design and build tools that are data-based that will help our organization 26 00:01:16,560 --> 00:01:18,200 in some way, shape, or form. 27 00:01:18,200 --> 00:01:20,960 I'm very much at the beginning of figuring out how to do that, but that's probably going 28 00:01:20,960 --> 00:01:22,840 to be part of what we talk about. 29 00:01:22,840 --> 00:01:27,780 Tell me about where the title or why you call yourself a data storyteller. 30 00:01:27,780 --> 00:01:29,760 That's not a common job title. 31 00:01:29,760 --> 00:01:33,120 There's data analysts, there's maybe data architects. 32 00:01:33,120 --> 00:01:36,000 Tell me what a data storyteller is or how you adopted that as a title or were given 33 00:01:36,000 --> 00:01:37,000 that. 34 00:01:37,000 --> 00:01:38,000 Yeah. 35 00:01:38,000 --> 00:01:41,560 It was me adopting it and asking for permission and they kindly gave it. 36 00:01:41,560 --> 00:01:43,320 I kind of worked myself into this job. 37 00:01:43,320 --> 00:01:48,680 I saw the opportunity of like, oh, I think that this could be a really valuable thing 38 00:01:48,680 --> 00:01:52,040 and it seems like a thing I could do, provide value in. 39 00:01:52,040 --> 00:01:56,520 So I put my hand up and said, I think I'd like to move my job in this direction. 40 00:01:56,520 --> 00:01:57,520 And they said, yes. 41 00:01:57,520 --> 00:02:03,760 And the storyteller part, I was casting around for something and I defined my criteria first, 42 00:02:03,760 --> 00:02:04,760 kind of. 43 00:02:04,760 --> 00:02:08,120 I said, I want something that communicates at least to some people. 44 00:02:08,120 --> 00:02:09,880 I can't control what everybody thinks about me. 45 00:02:09,880 --> 00:02:11,600 So I just let go of that to start. 46 00:02:11,600 --> 00:02:19,240 But I want something that communicates a combination of technical chops to some extent, whatever 47 00:02:19,240 --> 00:02:22,720 technical chops are needed and artistic pen. 48 00:02:22,720 --> 00:02:28,160 Like goes back to my university degree, we put together like all the disciplines and 49 00:02:28,160 --> 00:02:32,760 we're like, the idea was you use whatever you need to design and build things for actual 50 00:02:32,760 --> 00:02:33,760 people. 51 00:02:33,760 --> 00:02:34,880 That was a bread and butter. 52 00:02:34,880 --> 00:02:40,520 So I've taken that with me and I want to use whatever scientific and or artistic methods, 53 00:02:40,520 --> 00:02:45,360 creative and or technical that's going to do what I need to help people. 54 00:02:45,360 --> 00:02:46,360 That's great. 55 00:02:46,360 --> 00:02:52,880 I think I've seen so many times out there, whether on LinkedIn or just presentations, 56 00:02:52,880 --> 00:03:00,360 even where there's this illustration of Legos, where you've got the mess of them and that's 57 00:03:00,360 --> 00:03:02,040 just data in the raw. 58 00:03:02,040 --> 00:03:07,000 And then you've got another layer where it's color coordinated and they start to cleanse 59 00:03:07,000 --> 00:03:08,000 it. 60 00:03:08,000 --> 00:03:11,400 And by the end of that illustration, it's the Lego house is put together. 61 00:03:11,400 --> 00:03:13,120 All the colors are in the right place. 62 00:03:13,120 --> 00:03:16,800 And I think it's an illustration that says that's data storytelling. 63 00:03:16,800 --> 00:03:24,200 Well, as neat as that illustration is, give us an example of like successful data storytelling 64 00:03:24,200 --> 00:03:26,200 from your perspective, Matt. 65 00:03:26,200 --> 00:03:27,200 Sure. 66 00:03:27,200 --> 00:03:28,200 Can do. 67 00:03:28,200 --> 00:03:33,120 I'll talk about some recent projects I've done slash am doing. 68 00:03:33,120 --> 00:03:34,280 Some of them are kind of iterative. 69 00:03:34,280 --> 00:03:36,160 We do them a couple of times a year. 70 00:03:36,160 --> 00:03:40,460 So one example is snapshot survey is what we call it. 71 00:03:40,460 --> 00:03:45,400 It's just a survey of everybody involves like it's a national organization with a lot of 72 00:03:45,400 --> 00:03:47,520 local chapters, you could say. 73 00:03:47,520 --> 00:03:52,280 And so everybody who's leading in those local chapters wants to know, Hey, what's going 74 00:03:52,280 --> 00:03:56,720 on on the grounds right now around me that I maybe can't see. 75 00:03:56,720 --> 00:04:01,660 So we survey all the students we can gather stuff together and people want to know, Hey, 76 00:04:01,660 --> 00:04:03,360 what did the survey say? 77 00:04:03,360 --> 00:04:05,480 And so that's been my job. 78 00:04:05,480 --> 00:04:10,560 I was the first person to do this as we were just trying out the survey thing. 79 00:04:10,560 --> 00:04:14,560 And it's nothing impressive on the tech stack. 80 00:04:14,560 --> 00:04:16,720 It's Google Sheets. 81 00:04:16,720 --> 00:04:20,720 That's we live in Google and I know my way around some Google formulas. 82 00:04:20,720 --> 00:04:24,000 Again, as I've needed to learn them, I've learned them. 83 00:04:24,000 --> 00:04:29,400 And I designed and built super simple Google Sheet that can be exported to PDF because 84 00:04:29,400 --> 00:04:31,200 that's what people wanted. 85 00:04:31,200 --> 00:04:38,480 And it's got the stuff that hopefully they need and I won again, maybe sticking point 86 00:04:38,480 --> 00:04:42,240 as I go through this process every single time is I want to hear what people actually 87 00:04:42,240 --> 00:04:47,560 need and not just build the stuff that I think is cool or I think the data is saying I tend 88 00:04:47,560 --> 00:04:48,560 to lean toward. 89 00:04:48,560 --> 00:04:52,080 I know you can start from the data and what does it say and explore that that's a valid 90 00:04:52,080 --> 00:04:53,200 way to do it. 91 00:04:53,200 --> 00:04:57,200 I'm learning, but I tend to lean in the other direction and start with, Hey, what are the 92 00:04:57,200 --> 00:04:58,880 people actually need? 93 00:04:58,880 --> 00:05:00,360 What are they expecting? 94 00:05:00,360 --> 00:05:02,680 Out of that, what can we do with the data? 95 00:05:02,680 --> 00:05:05,760 So that's a bit more about posture, but with one concrete example. 96 00:05:05,760 --> 00:05:10,960 And I think you're hitting on like we can always start from different ends of the spectrum 97 00:05:10,960 --> 00:05:18,840 and arrive at conclusions and inevitably there's going to be bias that enters into that process, 98 00:05:18,840 --> 00:05:23,240 especially if you start with asking the question, well, what do we need? 99 00:05:23,240 --> 00:05:31,680 How do you protect against confirmation bias where, okay, this is what I needed to know. 100 00:05:31,680 --> 00:05:35,560 So I went out and I found exactly what I needed to know and it just told us what we already 101 00:05:35,560 --> 00:05:37,920 thought we wanted it to say. 102 00:05:37,920 --> 00:05:40,880 So how do you guard against that? 103 00:05:40,880 --> 00:05:41,880 It's important. 104 00:05:41,880 --> 00:05:46,200 I think you're bringing up a really valid thing where you want to be human centric, 105 00:05:46,200 --> 00:05:50,960 that you want to understand what is the business need and back your way into the right kind 106 00:05:50,960 --> 00:05:54,000 of data solutions that will answer those. 107 00:05:54,000 --> 00:05:58,840 But how do you guard against that bias that can easily creep in if you're focused on data 108 00:05:58,840 --> 00:06:04,400 points that you know will confirm what you already know to be true or want to have be 109 00:06:04,400 --> 00:06:06,680 true versus looking at all the data? 110 00:06:06,680 --> 00:06:07,680 Yeah. 111 00:06:07,680 --> 00:06:08,680 Oh, great question. 112 00:06:08,680 --> 00:06:09,920 And I'm smiling a bit though. 113 00:06:09,920 --> 00:06:16,240 You can't see it as you're listening to this because just recently a senior leader in our 114 00:06:16,240 --> 00:06:19,900 organization has a lot of wisdom and experience. 115 00:06:19,900 --> 00:06:23,800 He philosophized a bit and said, you know, confirmation bias is kind of off the rails 116 00:06:23,800 --> 00:06:25,800 in our kind of organization. 117 00:06:25,800 --> 00:06:27,280 And I can see how that's the case. 118 00:06:27,280 --> 00:06:29,400 So yeah, we've got to watch out for that. 119 00:06:29,400 --> 00:06:30,880 A couple of things that come to mind. 120 00:06:30,880 --> 00:06:35,400 Number one is having people around me who think differently than me and you have more 121 00:06:35,400 --> 00:06:42,440 technical chops in like the science or statistics area because I've in that survey project had 122 00:06:42,440 --> 00:06:48,080 somebody alongside me who took some basic research courses in university and I said, 123 00:06:48,080 --> 00:06:50,080 hey, we should maybe go about things this way. 124 00:06:50,080 --> 00:06:52,160 And he's like, no, that's the wrong way. 125 00:06:52,160 --> 00:06:56,200 So have somebody who can tell you, no, that's the wrong way is one thing. 126 00:06:56,200 --> 00:07:02,800 But another is going about trying to I tried to design the presentation of the data in 127 00:07:02,800 --> 00:07:05,800 a like relatively neutral way. 128 00:07:05,800 --> 00:07:09,840 I know things are always going to be subjective, but they don't have to be arbitrary. 129 00:07:09,840 --> 00:07:13,640 That's another opinion point that I picked up as I go along. 130 00:07:13,640 --> 00:07:19,880 Since I try to make it intentionally, so not arbitrarily say, hey, this is just what's 131 00:07:19,880 --> 00:07:20,880 going on. 132 00:07:20,880 --> 00:07:24,640 Like this is it's always going to be imperfect because it's always a model of reality. 133 00:07:24,640 --> 00:07:26,240 It's not reality itself. 134 00:07:26,240 --> 00:07:35,400 But here are some like graphs with scales that represent things with the whole perspective. 135 00:07:35,400 --> 00:07:39,360 I don't crop the graphs to make them show only part. 136 00:07:39,360 --> 00:07:44,160 Like there's a lot of this probably sounds like people are not along, nodding along going 137 00:07:44,160 --> 00:07:45,760 like yeah, duh. 138 00:07:45,760 --> 00:07:50,960 But it's being intentional in all the choices along the way and keeping in mind like, oh, 139 00:07:50,960 --> 00:07:53,080 how is somebody going to interpret this? 140 00:07:53,080 --> 00:07:54,180 What might they think? 141 00:07:54,180 --> 00:07:59,280 And also finally thought final thought is actually talking to people after they've used 142 00:07:59,280 --> 00:08:00,280 the thing. 143 00:08:00,280 --> 00:08:06,640 And as much as I know it can be painful to get or to give feedback after you just use 144 00:08:06,640 --> 00:08:13,520 the thing, I try to coax that out of people and always ask like, hey, what was good? 145 00:08:13,520 --> 00:08:15,280 What was not so good? 146 00:08:15,280 --> 00:08:16,640 And how can we get better? 147 00:08:16,640 --> 00:08:19,520 So I picked up a couple frameworks for doing that. 148 00:08:19,520 --> 00:08:23,560 And I make sure to do it as much as I can and just hope that people don't find me super 149 00:08:23,560 --> 00:08:28,480 annoying as man, he's the feedback guy because then you take what they said and you make 150 00:08:28,480 --> 00:08:30,880 something useful out of it. 151 00:08:30,880 --> 00:08:31,880 And that's good. 152 00:08:31,880 --> 00:08:34,000 Anyways, I drifted a lot in that answer. 153 00:08:34,000 --> 00:08:38,320 I'm curious about something in there you mentioned because storytelling and then also presenting 154 00:08:38,320 --> 00:08:43,080 data in a neutral way and even how those go together because sometimes I'll hear data 155 00:08:43,080 --> 00:08:48,660 storytelling where you're crafting a specific narrative that has key points and then a conclusion 156 00:08:48,660 --> 00:08:53,120 that someone should get to along the way, or at least that's one way to craft storytelling, 157 00:08:53,120 --> 00:08:59,220 what you're describing though, you also mentioned presenting it in a neutral way or as subjective 158 00:08:59,220 --> 00:09:02,640 as possible without it being to avoid a sense of bias. 159 00:09:02,640 --> 00:09:06,980 So tell me how those kind of blend together having a neutral opinion and presentation 160 00:09:06,980 --> 00:09:09,360 and trying to craft a storytelling element to it. 161 00:09:09,360 --> 00:09:13,860 Yeah, I'm definitely just starting to figure this out too, along with everything else. 162 00:09:13,860 --> 00:09:17,580 But I've heard enough to know that, oh, this is a thing that people talk about a lot. 163 00:09:17,580 --> 00:09:21,120 So probably a good props to you for asking the question. 164 00:09:21,120 --> 00:09:26,720 And my best responses, as usual, come from stuff other people have said. 165 00:09:26,720 --> 00:09:32,560 One is to harken back to my speech communication course in university. 166 00:09:32,560 --> 00:09:35,560 Actually, and they like traditional rhetoric. 167 00:09:35,560 --> 00:09:37,720 There's a few different things you can do with communication. 168 00:09:37,720 --> 00:09:41,360 You can inform, you can entertain, you can persuade. 169 00:09:41,360 --> 00:09:45,700 And it's good to just be conscious of which one you're trying to do and try to do that 170 00:09:45,700 --> 00:09:48,240 as ethically as you can, in short. 171 00:09:48,240 --> 00:09:50,560 And there's going to be a mix, obviously. 172 00:09:50,560 --> 00:09:56,280 But if you come out of the gate and say like, hey, this is the conclusion, then for one 173 00:09:56,280 --> 00:09:59,180 thing you better have a lot of evidence to back that up. 174 00:09:59,180 --> 00:10:01,680 And then you can have somewhere else for people to dig into the data. 175 00:10:01,680 --> 00:10:04,920 That's something I'm playing with now is conclusion, then evidence, then data. 176 00:10:04,920 --> 00:10:08,440 I can give you the citation for where I got that if that's helpful. 177 00:10:08,440 --> 00:10:15,400 And then another thing is knowing what you're trying to do and being upfront about that. 178 00:10:15,400 --> 00:10:16,800 Like don't be too sneaky. 179 00:10:16,800 --> 00:10:17,800 It's okay. 180 00:10:17,800 --> 00:10:23,600 Actually, one way of serving people, according to a LinkedIn post I saw recently, is to give 181 00:10:23,600 --> 00:10:26,560 them what they need in as little time as possible. 182 00:10:26,560 --> 00:10:30,040 And they don't have time to wade through everything. 183 00:10:30,040 --> 00:10:33,860 So sometimes you're going to choose to give the conclusion upfront. 184 00:10:33,860 --> 00:10:35,760 It's going to be short. 185 00:10:35,760 --> 00:10:40,440 But make sure you've got the evidence to back it up if and when people want to dig in deeper. 186 00:10:40,440 --> 00:10:44,720 That sounds like a little bit of the artistic side to this and a technical side to this. 187 00:10:44,720 --> 00:10:49,120 You said you're approaching problems with some sort of mixing of those two and how you 188 00:10:49,120 --> 00:10:53,000 order the equation of whether the conclusion is coming first or whether the data is coming 189 00:10:53,000 --> 00:10:54,160 first. 190 00:10:54,160 --> 00:10:58,040 Sounds like you maybe would start from more of an artistic and a design perspective rather 191 00:10:58,040 --> 00:11:00,040 than the technical. 192 00:11:00,040 --> 00:11:04,600 How do you make those decisions about I'm approaching this from a technical lens first 193 00:11:04,600 --> 00:11:09,800 or I'm approaching this from a design and artistic lens first? 194 00:11:09,800 --> 00:11:10,800 Yeah. 195 00:11:10,800 --> 00:11:11,800 Good question. 196 00:11:11,800 --> 00:11:15,920 And so far, my response has been to lean on processes that other people have already 197 00:11:15,920 --> 00:11:16,920 figured out. 198 00:11:16,920 --> 00:11:21,920 Generally, what I try to do is figure out how people make these decisions and how they've 199 00:11:21,920 --> 00:11:22,920 already done it. 200 00:11:22,920 --> 00:11:28,120 And then if I can learn that how, I can go apply it in different appropriate settings. 201 00:11:28,120 --> 00:11:30,600 You got to know when to use the tool and when not to use the tool. 202 00:11:30,600 --> 00:11:35,860 So one tool I found helpful in this area comes from a book called Good Charts by Scott Baranato. 203 00:11:35,860 --> 00:11:42,400 And it's talk, sketch, prototype, three-step process where first you talk about the need 204 00:11:42,400 --> 00:11:48,060 and what you're trying to do with data, the thing that you're trying to make, like some 205 00:11:48,060 --> 00:11:50,020 kind of chart presumably. 206 00:11:50,020 --> 00:11:54,480 And then you sketch out, hey, it could look like this or it could look like this or it 207 00:11:54,480 --> 00:11:56,320 could look like this. 208 00:11:56,320 --> 00:12:00,940 And you talk ideally with the people who are going to be using it, but at least with people 209 00:12:00,940 --> 00:12:06,680 who kind of know the basics of the situation, say which one looks good. 210 00:12:06,680 --> 00:12:12,040 And then you can build a very basic prototype and go test it and validate or invalidate 211 00:12:12,040 --> 00:12:13,760 certain parts of what you were thinking. 212 00:12:13,760 --> 00:12:16,400 And you kind of repeat it, talk, sketch, prototype. 213 00:12:16,400 --> 00:12:20,520 And I don't know if it even fits in either technical or artistic. 214 00:12:20,520 --> 00:12:22,000 It's just been useful. 215 00:12:22,000 --> 00:12:26,640 And it has a lot of similarities to another approach I default to a lot, which is design 216 00:12:26,640 --> 00:12:33,080 thinking as things like inspiration, ideation, implementation from firms like IDEO, IDEO. 217 00:12:33,080 --> 00:12:34,560 You can go look that up. 218 00:12:34,560 --> 00:12:39,120 And yeah, it seems to have a good marriage of both. 219 00:12:39,120 --> 00:12:42,360 Because at some point, you got to know some technical stuff and you got to know what's 220 00:12:42,360 --> 00:12:45,040 possible and what's not possible. 221 00:12:45,040 --> 00:12:47,480 But I try to center it again on humans. 222 00:12:47,480 --> 00:12:52,640 So technical comes up, I guess, if I had to pick one, maybe it's artistic, but it's just 223 00:12:52,640 --> 00:12:53,880 a process. 224 00:12:53,880 --> 00:12:59,800 So design thinking, that's something I hear about in the world outside of data. 225 00:12:59,800 --> 00:13:02,880 And I don't have the background in design thinking. 226 00:13:02,880 --> 00:13:06,760 So how would you, and probably some of our listeners don't have a design thinking background 227 00:13:06,760 --> 00:13:08,580 or they're technical nerds like me. 228 00:13:08,580 --> 00:13:11,760 How would you orient me to that topic of design thinking? 229 00:13:11,760 --> 00:13:16,360 Or maybe like some of the core principles I would want to have if I were trying to practice 230 00:13:16,360 --> 00:13:18,280 that into my daily life? 231 00:13:18,280 --> 00:13:19,280 Yeah. 232 00:13:19,280 --> 00:13:20,280 Thanks for asking. 233 00:13:20,280 --> 00:13:21,280 This is fun. 234 00:13:21,280 --> 00:13:22,320 I like talking about it. 235 00:13:22,320 --> 00:13:23,880 And like I said, using it. 236 00:13:23,880 --> 00:13:29,600 I'll point, I'll pause and note that you mentioned like people may not have a background in this, 237 00:13:29,600 --> 00:13:34,040 but I'll point out people do have a background in other stuff too. 238 00:13:34,040 --> 00:13:39,560 And I'll just advocate, don't be afraid to, in fact, maybe lean into using other areas 239 00:13:39,560 --> 00:13:42,740 of your life to inform the quote technical stuff. 240 00:13:42,740 --> 00:13:44,260 Don't keep it all walled off. 241 00:13:44,260 --> 00:13:45,700 The best creative stuff. 242 00:13:45,700 --> 00:13:48,740 And even if you think of yourself as technical, I think you are creative. 243 00:13:48,740 --> 00:13:53,360 Best creative stuff comes from making connections across different disciplines or across different 244 00:13:53,360 --> 00:13:54,360 areas of your life. 245 00:13:54,360 --> 00:13:58,580 Have some fun and play with that, which is actually, I think also a tenant of design 246 00:13:58,580 --> 00:14:01,480 thinking is creative play. 247 00:14:01,480 --> 00:14:08,280 So one way to talk about it is again, the process of inspiration, defining a problem 248 00:14:08,280 --> 00:14:10,120 is usually one part of the process. 249 00:14:10,120 --> 00:14:14,960 And this is one area where maybe you can just skip ahead a bit and do a Google search and 250 00:14:14,960 --> 00:14:18,880 it might be a bit, make a bit more sense to you, but this is how I've experienced it. 251 00:14:18,880 --> 00:14:23,560 You define the problem, you figure out like who you're trying to serve exactly what they 252 00:14:23,560 --> 00:14:27,280 need and then you use some divergent thinking. 253 00:14:27,280 --> 00:14:31,240 So like think up a whole bunch of possibilities of how you could solve that problem. 254 00:14:31,240 --> 00:14:35,440 And then you converge to like, okay, actually it seems like this one's going to be good. 255 00:14:35,440 --> 00:14:38,560 We're going to actually test it out and you prototype it. 256 00:14:38,560 --> 00:14:42,840 And from that you can decide, persevere or pivot. 257 00:14:42,840 --> 00:14:47,640 So this is also getting into lean startup methodology, which is a really similar process. 258 00:14:47,640 --> 00:14:52,600 If that makes more sense to you, as you look it up, go use that, like just go use something 259 00:14:52,600 --> 00:14:54,040 and try it. 260 00:14:54,040 --> 00:14:58,960 But it's that idea of define the problem, then try something, think of ideas, narrow 261 00:14:58,960 --> 00:15:04,340 them down to one, try something out, repeat until you have made something valuable for 262 00:15:04,340 --> 00:15:05,720 some other person. 263 00:15:05,720 --> 00:15:10,520 And there is that company, I think you mentioned it already, Matt Ideo. 264 00:15:10,520 --> 00:15:15,440 And they've got some videos that you can kind of watch how they've done their design thinking 265 00:15:15,440 --> 00:15:21,220 and they've come up with how they designed grippers for toothbrushes or, you know, grocery 266 00:15:21,220 --> 00:15:23,160 carts for the grocery store. 267 00:15:23,160 --> 00:15:25,220 And they follow this design thinking. 268 00:15:25,220 --> 00:15:30,520 It's a neat pattern and it's cool to hear, Matt, how you're bringing that into the data 269 00:15:30,520 --> 00:15:31,520 world. 270 00:15:31,520 --> 00:15:35,440 And usually I've thought of design thinking as something when I'm going to design something 271 00:15:35,440 --> 00:15:43,120 very concrete, very like just a daily tool that I might use in toothbrush, like a toothbrush. 272 00:15:43,120 --> 00:15:44,120 Exactly. 273 00:15:44,120 --> 00:15:49,880 And yet here you are, like you said, taking those ideas and concepts and building a bridge 274 00:15:49,880 --> 00:15:58,160 into these less concrete tools in that we're talking about systems of data and relationships 275 00:15:58,160 --> 00:16:03,760 of data and how to then wireframe that into a report and get feedback on a wireframe so 276 00:16:03,760 --> 00:16:08,040 that I've got the right chart to display to my end users. 277 00:16:08,040 --> 00:16:09,040 Really awesome stuff. 278 00:16:09,040 --> 00:16:13,720 I'd like to piggyback on that to talk about some strategic objectives. 279 00:16:13,720 --> 00:16:20,560 You know, that is a core of what we talk about as these mission driven, non-profit, private 280 00:16:20,560 --> 00:16:23,820 sector type of data leaders. 281 00:16:23,820 --> 00:16:31,120 What are some of the strategic objectives that you have had to aim towards and think 282 00:16:31,120 --> 00:16:32,120 through? 283 00:16:32,120 --> 00:16:35,320 Okay, I heard that strategic objective. 284 00:16:35,320 --> 00:16:37,320 Now what's that problem I'm trying to solve? 285 00:16:37,320 --> 00:16:42,080 So just give us a few more examples, maybe even related to that survey project you talked 286 00:16:42,080 --> 00:16:43,080 about earlier. 287 00:16:43,080 --> 00:16:47,160 What was the strategic objective in that and any others if you've got some that come to 288 00:16:47,160 --> 00:16:48,160 mind? 289 00:16:48,160 --> 00:16:50,400 A note on what you said is getting in the way of my thinking of that. 290 00:16:50,400 --> 00:16:54,680 So I'll just say that you're also not just designing like data and stuff, you are designing 291 00:16:54,680 --> 00:16:56,640 an experience for a person. 292 00:16:56,640 --> 00:17:00,400 And that's the reality that I think we should keep in front of mind. 293 00:17:00,400 --> 00:17:04,520 The data is there to serve the people, not the other way around. 294 00:17:04,520 --> 00:17:06,040 Let's design an experience well. 295 00:17:06,040 --> 00:17:08,360 Okay, onto your question, strategic objectives. 296 00:17:08,360 --> 00:17:09,920 I might actually need to clarify. 297 00:17:09,920 --> 00:17:16,640 Are you talking about strategic objectives for like how we're going to chart a course 298 00:17:16,640 --> 00:17:24,040 for our data use or strategic objectives of the organization that we use data to accomplish? 299 00:17:24,040 --> 00:17:26,120 Right, I'm thinking of the latter. 300 00:17:26,120 --> 00:17:28,020 It's the mission of the org. 301 00:17:28,020 --> 00:17:32,360 And so how does your data help meet one of those objectives for the org? 302 00:17:32,360 --> 00:17:33,360 Yeah, thanks for clarifying. 303 00:17:33,360 --> 00:17:34,360 Yeah, sure. 304 00:17:34,360 --> 00:17:35,360 Okay. 305 00:17:35,360 --> 00:17:41,480 Yeah, if we start with the survey example, the objective of the survey is basically get 306 00:17:41,480 --> 00:17:48,000 a pulse on our population, the people who are benefiting from our services. 307 00:17:48,000 --> 00:17:55,080 And specifically, it comes from some really strong strategic leadership that our national 308 00:17:55,080 --> 00:17:58,060 leadership team did within our org. 309 00:17:58,060 --> 00:18:04,440 And they set out a like playbook of here are our strategic anchors, here are our values, 310 00:18:04,440 --> 00:18:08,480 and here's like where we're going in some really concrete terms. 311 00:18:08,480 --> 00:18:12,360 So then survey project was kind of birthed from saying like, hey, how about we check 312 00:18:12,360 --> 00:18:15,840 whether we're doing this with the people we're working with? 313 00:18:15,840 --> 00:18:20,280 So the survey was basically transferring a good bulk of it. 314 00:18:20,280 --> 00:18:22,280 Like it's used for a few different purposes. 315 00:18:22,280 --> 00:18:25,800 You imagine lots of people want to know lots of different things. 316 00:18:25,800 --> 00:18:30,440 But one core part of it that we have kept the same over a few years now is basically 317 00:18:30,440 --> 00:18:36,320 just asking, are we doing what we're doing according to our strategic anchors and our 318 00:18:36,320 --> 00:18:37,320 values? 319 00:18:37,320 --> 00:18:41,360 We're asking people who are actually on the ground and asking them in an anonymous way 320 00:18:41,360 --> 00:18:43,840 like, hey, what else can you tell us? 321 00:18:43,840 --> 00:18:46,800 What else are like, are we on track or not? 322 00:18:46,800 --> 00:18:50,920 And we've gotten some good info from that that has led to some concrete decisions of 323 00:18:50,920 --> 00:18:55,200 like, hey, we should try this, or maybe we need to pay attention to that. 324 00:18:55,200 --> 00:18:59,280 And we pay attention to that at a national and local scale. 325 00:18:59,280 --> 00:19:00,840 That's one thing. 326 00:19:00,840 --> 00:19:03,080 And there are a few others. 327 00:19:03,080 --> 00:19:07,240 A lot of the rest is kind of a bit more scattershot in that, A, we're trying to figure it out. 328 00:19:07,240 --> 00:19:11,680 And B, it covers a lot of different sections of the organization, which I'm still just 329 00:19:11,680 --> 00:19:12,680 starting to figure out. 330 00:19:12,680 --> 00:19:14,760 But we can talk about those too, if you want. 331 00:19:14,760 --> 00:19:21,840 Matt, as I'm sure you described the surveys and working within leadership, I expect there's 332 00:19:21,840 --> 00:19:26,480 a probably an orientation around data that's newer for leadership. 333 00:19:26,480 --> 00:19:30,080 I think about like data literacy or being able to understand and interact with data 334 00:19:30,080 --> 00:19:31,080 effectively. 335 00:19:31,080 --> 00:19:35,400 And even you don't come from as technical of a data background, more of a design background. 336 00:19:35,400 --> 00:19:40,880 I'm curious how you've helped yourself grow in maybe your ability to understand and interpret 337 00:19:40,880 --> 00:19:45,040 data or how you've had to help leadership and people you've been presenting data to 338 00:19:45,040 --> 00:19:46,900 help them to understand. 339 00:19:46,900 --> 00:19:52,080 When you present data to them in like an objective way or a neutral way, there's going to be 340 00:19:52,080 --> 00:19:54,040 some interpretation that they're doing on that. 341 00:19:54,040 --> 00:19:56,880 How have you helped people navigate that, either yourself or other people? 342 00:19:56,880 --> 00:19:58,360 Yeah, great question. 343 00:19:58,360 --> 00:20:02,720 So I'm hearing about how do I get there and how to help other people get there. 344 00:20:02,720 --> 00:20:05,840 Start with the first in short, I get there by reading a whole lot. 345 00:20:05,840 --> 00:20:08,280 I'm a nerd in lots of areas of life. 346 00:20:08,280 --> 00:20:12,960 So I've got a bunch of books and we can like throw them all in the show notes or something 347 00:20:12,960 --> 00:20:15,640 because I don't know that everybody wants to hear me list all of them. 348 00:20:15,640 --> 00:20:20,800 But yeah, and I find that reading has helped me, especially the stuff that I don't really 349 00:20:20,800 --> 00:20:21,800 understand. 350 00:20:21,800 --> 00:20:26,220 Like I've got a copy here of the Data Warehouse Toolkit by Kimball on recommendation by somebody 351 00:20:26,220 --> 00:20:27,220 very wise. 352 00:20:27,220 --> 00:20:31,000 And I don't understand a whole lot of what's in there. 353 00:20:31,000 --> 00:20:33,600 And that I've learned since the beginning of my undergrad. 354 00:20:33,600 --> 00:20:38,360 That's really valuable, being able to know, like, I don't yet know this, I don't yet know 355 00:20:38,360 --> 00:20:42,640 that, but this is kind of the lay of the land of what is possible. 356 00:20:42,640 --> 00:20:48,000 And I try to get to know the stuff that I need to know for the task at hand while also 357 00:20:48,000 --> 00:20:51,760 paying attention to what else is out there, what else is possible. 358 00:20:51,760 --> 00:20:56,380 Another book I have is a bunch of interviews of data viz professionals. 359 00:20:56,380 --> 00:21:01,360 And I took copious notes through that and I'm like, okay, so this is how you do this. 360 00:21:01,360 --> 00:21:03,360 Oh, this is a way to think about it. 361 00:21:03,360 --> 00:21:08,640 And so I try to take from all sources I can of like different mental models, different 362 00:21:08,640 --> 00:21:10,840 ways of thinking about the world. 363 00:21:10,840 --> 00:21:15,300 And then I can use those again as tools in the appropriate situation. 364 00:21:15,300 --> 00:21:20,000 So listening to lots of people have already figured it out and trying to take the 20% 365 00:21:20,000 --> 00:21:24,800 as far as I can tell of what they do that can give me 80% or more of the results. 366 00:21:24,800 --> 00:21:30,640 As for other people, it's been first trying to understand where they are and what's typical 367 00:21:30,640 --> 00:21:31,640 for them. 368 00:21:31,640 --> 00:21:36,240 So I have a list of questions I tend to ask at the beginning of a data project, which 369 00:21:36,240 --> 00:21:37,720 is taken from yet another book. 370 00:21:37,720 --> 00:21:42,520 And that list of questions includes things like what kind of charts are people used to 371 00:21:42,520 --> 00:21:43,520 seeing? 372 00:21:43,520 --> 00:21:45,040 What's going to be typical for them? 373 00:21:45,040 --> 00:21:46,040 What's going to be atypical? 374 00:21:46,040 --> 00:21:49,760 Because especially if it's people I haven't interacted with, I have no idea. 375 00:21:49,760 --> 00:21:54,800 So I try to ask people if I've done some kind of internal consulting work, like working 376 00:21:54,800 --> 00:21:59,080 with people who I don't work with usually and producing some one-off reports. 377 00:21:59,080 --> 00:22:01,280 And I ask them, hey, what's typical? 378 00:22:01,280 --> 00:22:02,400 What's not? 379 00:22:02,400 --> 00:22:05,480 And then I can gauge whether I want something typical or not. 380 00:22:05,480 --> 00:22:08,320 But if I don't even know that, I guess that gets back to helping me. 381 00:22:08,320 --> 00:22:13,380 I'll say one more thing about the people, like helping people understand. 382 00:22:13,380 --> 00:22:19,680 And it's kind of defaulting to the simplest and or the, yeah, again, most typical if you 383 00:22:19,680 --> 00:22:20,680 understand that. 384 00:22:20,680 --> 00:22:22,360 But simplest can have a lot of value. 385 00:22:22,360 --> 00:22:26,640 Like I read a line in one of these books that said, like, why design a chart if you don't 386 00:22:26,640 --> 00:22:27,780 have to? 387 00:22:27,780 --> 00:22:32,120 If you can just convey stuff in words, then maybe that's best. 388 00:22:32,120 --> 00:22:37,040 So right now I'm playing with a Google Sheet that reports on one of our KPIs. 389 00:22:37,040 --> 00:22:40,400 And the display is not a scorecard or a chart or graph or anything. 390 00:22:40,400 --> 00:22:44,120 It's like some if statements that spit out a sentence. 391 00:22:44,120 --> 00:22:46,200 And so far people have said that's really helpful. 392 00:22:46,200 --> 00:22:47,460 That's all they need to know. 393 00:22:47,460 --> 00:22:49,360 So keep that in mind. 394 00:22:49,360 --> 00:22:54,480 Maybe you can just keep it incredibly simple and you don't even need all the fancy data 395 00:22:54,480 --> 00:22:55,480 wizardry. 396 00:22:55,480 --> 00:22:56,760 I know it's tempting. 397 00:22:56,760 --> 00:23:00,680 Use all the tools and stuff, but you might not need to. 398 00:23:00,680 --> 00:23:01,680 That's interesting. 399 00:23:01,680 --> 00:23:07,760 The textual data or textual visualization of data is what it is. 400 00:23:07,760 --> 00:23:11,600 Like hey, we're going to just scribe in paragraph or sentence format. 401 00:23:11,600 --> 00:23:17,760 In Power BI, they've always had charts, bars, graphs, pies, et cetera. 402 00:23:17,760 --> 00:23:20,860 And that was the way I always defaulted to doing visualization. 403 00:23:20,860 --> 00:23:24,700 And then they introduced with kind of the advent of generative AI, it's even more so, 404 00:23:24,700 --> 00:23:30,720 but some sort of like narrative summaries of like you can put a chart on the visualization 405 00:23:30,720 --> 00:23:35,640 on the canvas and then it will summarize in a couple of paragraphs what data is being 406 00:23:35,640 --> 00:23:36,640 visible there. 407 00:23:36,640 --> 00:23:42,000 So people have a paragraph way to consume the information in text. 408 00:23:42,000 --> 00:23:44,040 And that's just a different way of experiencing data. 409 00:23:44,040 --> 00:23:46,880 And it comes back to, I think what you're talking about where you started like with 410 00:23:46,880 --> 00:23:50,920 the human experience in mind of how is this human going to experience it? 411 00:23:50,920 --> 00:23:53,160 Some humans might interpret charts really well. 412 00:23:53,160 --> 00:23:57,200 Some humans might interpret paragraphs a lot better or more effectively. 413 00:23:57,200 --> 00:23:58,200 Yeah. 414 00:23:58,200 --> 00:24:01,440 So I'd like to ask a question around aspirations. 415 00:24:01,440 --> 00:24:08,200 So Matt, you've talked a bit about like where you and where the company is at in terms of 416 00:24:08,200 --> 00:24:12,520 some of this is new, some of this you're still figuring out or it's a bit scattershot. 417 00:24:12,520 --> 00:24:14,760 And that's okay, because I think we're all there. 418 00:24:14,760 --> 00:24:19,720 And so I think for our audience, love to hear some of your aspirations, whether they're 419 00:24:19,720 --> 00:24:24,120 personal for you as a data storyteller, but maybe even organizationally, where do you 420 00:24:24,120 --> 00:24:26,020 see the organization going? 421 00:24:26,020 --> 00:24:32,720 Where would you like to see the organization go around things like data literacy, maybe 422 00:24:32,720 --> 00:24:36,760 tech stack in terms of maturity and the tools you're using. 423 00:24:36,760 --> 00:24:42,160 Maybe it's even just the collaboration that you desire to see around data when let's be 424 00:24:42,160 --> 00:24:49,160 honest, I think we've all lived in siloed data realities where, well, this department 425 00:24:49,160 --> 00:24:54,360 has data for their department and they're not allowed to see data from another department. 426 00:24:54,360 --> 00:24:55,360 And that's hard. 427 00:24:55,360 --> 00:24:56,880 It's hard to break down those walls. 428 00:24:56,880 --> 00:25:04,080 So I'd love to hear just what are your aspirations to keep growing in data and maybe even some 429 00:25:04,080 --> 00:25:08,200 tips and tricks, maybe things you've used to actually see some of those aspirations 430 00:25:08,200 --> 00:25:09,200 become realities. 431 00:25:09,200 --> 00:25:11,800 Yeah, that's a good question. 432 00:25:11,800 --> 00:25:19,280 I think one thing I'm holding onto for now for myself is I want to design and build useful 433 00:25:19,280 --> 00:25:20,280 things. 434 00:25:20,280 --> 00:25:27,520 And honestly, even being in data, being again, relatively new to it, it's a tool and it seems 435 00:25:27,520 --> 00:25:28,920 like a really cool place to play. 436 00:25:28,920 --> 00:25:32,400 So I hope I'll be here for a while playing with data. 437 00:25:32,400 --> 00:25:38,960 But if I can help people help understand the reality as it actually is, ponder some possibilities 438 00:25:38,960 --> 00:25:44,040 of how we might make it better and then act on those to make reality better for some people, 439 00:25:44,040 --> 00:25:48,940 I'm pretty happy, especially if and when I get to learn stuff along the way, can the 440 00:25:48,940 --> 00:25:50,360 nerd piece comes in. 441 00:25:50,360 --> 00:25:55,260 So that's as long as I can keep doing that, I'm going to be relatively happy, I think. 442 00:25:55,260 --> 00:26:02,720 But I also would love to keep learning what is possible and then creatively building a 443 00:26:02,720 --> 00:26:07,220 creative practice actually, which might sound really foreign to nerdy people. 444 00:26:07,220 --> 00:26:11,040 It's still a bit weird to me, but I'm learning from the creative people in my life. 445 00:26:11,040 --> 00:26:13,880 I'm actually in a department that's full of them. 446 00:26:13,880 --> 00:26:20,400 And I'm learning that there's something about making stuff that is inherently creative. 447 00:26:20,400 --> 00:26:22,880 So you could even use that word for yourself. 448 00:26:22,880 --> 00:26:28,180 And when you make stuff, you should be intentional about getting better at that. 449 00:26:28,180 --> 00:26:31,400 So I want to be intentional at getting better at actually making stuff because I can live 450 00:26:31,400 --> 00:26:34,040 in my head a lot of like, oh man, this would be cool. 451 00:26:34,040 --> 00:26:37,100 But then at the end of the day, it's in my head, not in reality. 452 00:26:37,100 --> 00:26:40,080 So I want to get better at actually making stuff through a creative practice. 453 00:26:40,080 --> 00:26:44,560 So if that sounds like you, definitely get in touch because another thing, an aspiration 454 00:26:44,560 --> 00:26:49,520 is getting connected to people who want to do similar work even. 455 00:26:49,520 --> 00:26:53,200 I started off asking who is doing similar work and for all kinds of reasons we don't 456 00:26:53,200 --> 00:26:56,800 need to go into, there wasn't a good connection. 457 00:26:56,800 --> 00:26:59,160 And we tried real hard finding some people. 458 00:26:59,160 --> 00:27:03,620 So I defaulted back to, I'll just read a bunch of books and connect with people that way. 459 00:27:03,620 --> 00:27:08,580 But I would love to have somebody who's got a similar bent, either in the technical or 460 00:27:08,580 --> 00:27:10,200 in the creative artistic side. 461 00:27:10,200 --> 00:27:16,560 I think we could build a team that blends the two together and designs database stuff. 462 00:27:16,560 --> 00:27:20,520 And even if it's not a department, even just like projects, I actually would kind of prefer 463 00:27:20,520 --> 00:27:27,040 to take on stuff on a project basis rather than long-term, but that's getting into personal 464 00:27:27,040 --> 00:27:28,040 career stuff. 465 00:27:28,040 --> 00:27:31,640 One more thing about the bigger picture, I kind of touched on it already. 466 00:27:31,640 --> 00:27:34,760 I've been talking a lot about what reality actually is. 467 00:27:34,760 --> 00:27:38,180 And then you get maps that are representations of reality. 468 00:27:38,180 --> 00:27:40,400 My minor in university was in geography. 469 00:27:40,400 --> 00:27:45,280 So I think in maps, and I think it's actually really useful to A, recognize that reality 470 00:27:45,280 --> 00:27:50,120 is out there and your map is not reality, but B, maps can be really useful, especially 471 00:27:50,120 --> 00:27:52,240 when everybody's looking at the same map. 472 00:27:52,240 --> 00:27:59,680 So this is kind of off the cuff, but I would love to see some version of people in the 473 00:27:59,680 --> 00:28:06,200 organization, whatever chunk of the organization, because ours is a bit complex, multiple people 474 00:28:06,200 --> 00:28:09,800 looking at this from different parts of the organization, looking at the same map and 475 00:28:09,800 --> 00:28:12,920 saying, yeah, that's what we need. 476 00:28:12,920 --> 00:28:17,480 We're starting to get there somewhat in our little corner, but I'm not sure that our map 477 00:28:17,480 --> 00:28:19,760 is going to jive all that well with other people's maps. 478 00:28:19,760 --> 00:28:21,500 And so we'll have to figure that out. 479 00:28:21,500 --> 00:28:25,680 So actively figuring that out is definitely where I'd want to be as for tech stack and 480 00:28:25,680 --> 00:28:26,680 stuff. 481 00:28:26,680 --> 00:28:30,180 I'm starting to play with Tableau and we're leaning into it a bit. 482 00:28:30,180 --> 00:28:33,860 So sure, if it's useful, great. 483 00:28:33,860 --> 00:28:39,600 If not, if Google Sheets proves to be better for some stuff, I'm not above using that instead. 484 00:28:39,600 --> 00:28:43,680 You've mentioned this a couple of times of making things that are valuable or making 485 00:28:43,680 --> 00:28:48,920 things or getting better at making your art or creating or creativity. 486 00:28:48,920 --> 00:28:54,440 This might be too, I don't know, philosophical, but how do you know if something's more valuable 487 00:28:54,440 --> 00:28:59,200 or getting more valuable or if you're becoming more creative or getting better at being creative? 488 00:28:59,200 --> 00:29:01,240 What are even marketed that? 489 00:29:01,240 --> 00:29:05,480 Creativity is one of my words that I have held and embraced of like, I am trying to 490 00:29:05,480 --> 00:29:11,280 express myself creatively more in different ways, in challenging ways for me. 491 00:29:11,280 --> 00:29:15,620 And I'm trying to chew on what does that look like for me to be more creative or to be better 492 00:29:15,620 --> 00:29:17,240 at being creative? 493 00:29:17,240 --> 00:29:20,760 So I'll love that one and see where that takes you. 494 00:29:20,760 --> 00:29:22,240 Yeah, sure. 495 00:29:22,240 --> 00:29:23,720 So I think we share that Sawyer. 496 00:29:23,720 --> 00:29:27,680 I have seven ideas or values that I hold. 497 00:29:27,680 --> 00:29:34,840 I'm again, holding on to for now and holding on pretty solidly because I like my life values. 498 00:29:34,840 --> 00:29:35,840 Creativity is one of them. 499 00:29:35,840 --> 00:29:38,240 Curiosity is another, as you might have guessed. 500 00:29:38,240 --> 00:29:39,560 But you didn't ask about that. 501 00:29:39,560 --> 00:29:41,720 It's like, how do you get better about creativity? 502 00:29:41,720 --> 00:29:42,920 Because we both value that. 503 00:29:42,920 --> 00:29:49,720 I once again, I'm trying to pay attention to and pick up work from other people. 504 00:29:49,720 --> 00:29:55,840 And there are people like Austin Kleon, who's an artist and he talks about creativity. 505 00:29:55,840 --> 00:30:00,120 I can't think of the names of his books right now, but I've read one of them. 506 00:30:00,120 --> 00:30:07,040 And yeah, his like him and a couple other people who are doing this well, including 507 00:30:07,040 --> 00:30:11,380 a data person, Ali Torban, who wrote Charts Spark, her whole book. 508 00:30:11,380 --> 00:30:16,300 So if you want to get better at data visualization type of creativity, go read Charts Spark and 509 00:30:16,300 --> 00:30:20,880 do some of the stuff because there are actionable prompts in there, some of which I found really 510 00:30:20,880 --> 00:30:23,880 useful and others of which you might find useful. 511 00:30:23,880 --> 00:30:29,600 That's my short answer, but the theme I get from that book, from Austin Kleon stuff, from 512 00:30:29,600 --> 00:30:34,880 other designers that I'm learning to respect is, and the thing you can hold me accountable 513 00:30:34,880 --> 00:30:39,440 to if we talk again in a year or something, is you just got to actually make stuff. 514 00:30:39,440 --> 00:30:43,840 And then you got to keep making stuff and ideally share it with other people and get 515 00:30:43,840 --> 00:30:49,700 some idea of whether that stuff is useful to them with the idea in mind, with the keeping 516 00:30:49,700 --> 00:30:54,800 in mind that sometimes the use or the purpose is attractiveness. 517 00:30:54,800 --> 00:30:56,500 Sometimes it's utility. 518 00:30:56,500 --> 00:30:58,880 Sometimes it's soundness or reliability. 519 00:30:58,880 --> 00:31:05,320 Those are, I've learned three things that the ancient Romans used to assess their architecture 520 00:31:05,320 --> 00:31:08,520 or to guide their architectural doings. 521 00:31:08,520 --> 00:31:12,200 So you can take any of those purposes and be like, is it better at that purpose? 522 00:31:12,200 --> 00:31:14,900 And get an idea between yourself and others. 523 00:31:14,900 --> 00:31:19,720 But you also kind of just need to trust the process because not everything can be graphed 524 00:31:19,720 --> 00:31:22,200 perfectly, including creativity. 525 00:31:22,200 --> 00:31:27,440 And you got to know that as you continue making stuff, ideally in community, you're going 526 00:31:27,440 --> 00:31:29,460 to get better at it. 527 00:31:29,460 --> 00:31:32,200 Practice makes progress, not perfect. 528 00:31:32,200 --> 00:31:35,400 And part of creativity is also things that you're going to try things that don't work 529 00:31:35,400 --> 00:31:36,640 and fail. 530 00:31:36,640 --> 00:31:41,840 And so there's the, hey, did that serve one of those, that utility or beauty better? 531 00:31:41,840 --> 00:31:42,840 No, that one failed. 532 00:31:42,840 --> 00:31:45,520 I tried something different that didn't work. 533 00:31:45,520 --> 00:31:50,000 That's a hard part of what makes creativity hard for me. 534 00:31:50,000 --> 00:31:54,400 And it's like, oh, I'm going to try something and it's going to break and fail. 535 00:31:54,400 --> 00:31:58,920 No one's going to listen to my podcast and whatever. 536 00:31:58,920 --> 00:32:02,520 But those are the things that come up for me when I think about creating the max and 537 00:32:02,520 --> 00:32:05,060 realize they might fail or they might be valuable. 538 00:32:05,060 --> 00:32:08,640 And I won't know until I start trying and iterating on those things. 539 00:32:08,640 --> 00:32:15,280 Matt, as you've thought about these gifts and mindset and thinking that you have around 540 00:32:15,280 --> 00:32:20,360 design thinking and storytelling and creativity, as well as data, why have you decided to apply 541 00:32:20,360 --> 00:32:22,580 them in the organization you're in? 542 00:32:22,580 --> 00:32:25,080 Why does the organization matter to you? 543 00:32:25,080 --> 00:32:27,320 Why is that where your career has landed you now? 544 00:32:27,320 --> 00:32:30,800 So tell us a little bit more about where you're at and maybe why you choose to brought your 545 00:32:30,800 --> 00:32:32,400 gifts to that place. 546 00:32:32,400 --> 00:32:33,400 Yeah. 547 00:32:33,400 --> 00:32:39,920 I'm working at Power to Change Students, which is a Christian nonprofit here in Canada. 548 00:32:39,920 --> 00:32:45,040 And it's like, I was going to say loosely connected, but like medium strength connected 549 00:32:45,040 --> 00:32:49,280 to crew in the States and all around the world. 550 00:32:49,280 --> 00:32:54,700 So we get to better from and also hopefully serve people all over the world. 551 00:32:54,700 --> 00:32:59,720 But we're focused on Canada and it's about student experience like university or post 552 00:32:59,720 --> 00:33:04,360 secondary, I guess, college student experience across the country. 553 00:33:04,360 --> 00:33:10,040 And the why it matters to me is I got involved right in university and thought, this is a 554 00:33:10,040 --> 00:33:14,760 really meaningful place to apply the gifts that I have these mindset shifts I'm developing. 555 00:33:14,760 --> 00:33:17,040 I think that could be really useful over here. 556 00:33:17,040 --> 00:33:23,000 Another thing was my degree is kind of weird and isn't career focused by its very nature. 557 00:33:23,000 --> 00:33:24,800 It's called knowledge integration. 558 00:33:24,800 --> 00:33:29,440 And at the end of university on a practical note, I was like, what do I do with this? 559 00:33:29,440 --> 00:33:34,880 And one real possibility was join up with this organization I know I care about and 560 00:33:34,880 --> 00:33:36,880 pioneer into something new. 561 00:33:36,880 --> 00:33:41,200 At the time it was social media and then advertising and now it's data. 562 00:33:41,200 --> 00:33:43,000 But they were like, nobody's done social media. 563 00:33:43,000 --> 00:33:44,000 Want to come try it? 564 00:33:44,000 --> 00:33:46,200 I was like, this seems like it could be a good fit. 565 00:33:46,200 --> 00:33:50,760 Again, not because it was social media, but because it was the organization working towards 566 00:33:50,760 --> 00:33:52,480 something I really care about. 567 00:33:52,480 --> 00:33:56,440 And I've gotten to continue exploring since, which I've learned is a value of working in 568 00:33:56,440 --> 00:33:58,640 this particular nonprofit. 569 00:33:58,640 --> 00:33:59,640 That's cool. 570 00:33:59,640 --> 00:34:07,340 And talking about creativity, I have to ask you, when you put a data analyst together 571 00:34:07,340 --> 00:34:10,960 with someone who can play music, what do you get? 572 00:34:10,960 --> 00:34:14,760 Oh man, I don't know. 573 00:34:14,760 --> 00:34:16,560 A disk jockey. 574 00:34:16,560 --> 00:34:17,560 Okay. 575 00:34:17,560 --> 00:34:20,400 Took me a moment, but I think I got it. 576 00:34:20,400 --> 00:34:23,040 Sawyer, did you get that one this time? 577 00:34:23,040 --> 00:34:25,920 I mean, it was like a disk of data, like a hard drive. 578 00:34:25,920 --> 00:34:26,920 Yeah. 579 00:34:26,920 --> 00:34:29,000 Okay, I think that's probably where he's going. 580 00:34:29,000 --> 00:34:31,800 But like, I don't work with that anyway. 581 00:34:31,800 --> 00:34:33,600 I was trying to be as punny as possible. 582 00:34:33,600 --> 00:34:37,520 See, this is one of those things where like our data becomes so abstracted from physical 583 00:34:37,520 --> 00:34:40,880 hardware that we don't think about disk of data anymore. 584 00:34:40,880 --> 00:34:41,880 What is a CD? 585 00:34:41,880 --> 00:34:42,880 Those kind of things. 586 00:34:42,880 --> 00:34:43,880 I'm just old enough to- Yeah, yeah, yeah. 587 00:34:43,880 --> 00:34:44,880 Floppy disk? 588 00:34:44,880 --> 00:34:46,440 Troy, are we talking about floppy disk? 589 00:34:46,440 --> 00:34:47,440 Okay. 590 00:34:47,440 --> 00:34:48,440 Yep. 591 00:34:48,440 --> 00:34:49,440 That's exactly where I was going here. 592 00:34:49,440 --> 00:34:50,440 All right. 593 00:34:50,440 --> 00:34:51,440 Good. 594 00:34:51,440 --> 00:34:52,440 Got right to the disk. 595 00:34:52,440 --> 00:34:58,360 Matt, this has been a really fascinating conversation in a different direction than we have gone 596 00:34:58,360 --> 00:35:02,760 with other conversations in this space or that I've had in this space for a while. 597 00:35:02,760 --> 00:35:07,280 And I'm curious if people are interested in connecting with you more about these topics 598 00:35:07,280 --> 00:35:11,800 you've mentioned wanting to have dialogue partners for some of these things. 599 00:35:11,800 --> 00:35:18,360 Where can people find you online or find out more about your organization if they're north 600 00:35:18,360 --> 00:35:19,360 of the border here? 601 00:35:19,360 --> 00:35:23,600 Yeah, or if you know people heading north of the border, the organization I'll start 602 00:35:23,600 --> 00:35:31,400 with that is p2c.com slash students, like p numeral 2c.com slash students. 603 00:35:31,400 --> 00:35:33,560 And you'll find out about us. 604 00:35:33,560 --> 00:35:37,640 Maybe you'll even come after we launched the website, the new website that we're having 605 00:35:37,640 --> 00:35:40,200 in a while. 606 00:35:40,200 --> 00:35:43,640 And you can connect with me personally on LinkedIn is a good default. 607 00:35:43,640 --> 00:35:44,640 I'm just Matt A Brody. 608 00:35:44,640 --> 00:35:47,600 I hope to get in touch with some people. 609 00:35:47,600 --> 00:35:50,640 This has been I've got a couple of things I need to talk with you about after this, 610 00:35:50,640 --> 00:35:54,000 Matt, because you've sparked some things that I need to follow up with you on. 611 00:35:54,000 --> 00:35:58,160 I hope some people in our audience, I'm sure people in our audience have also taken away 612 00:35:58,160 --> 00:36:02,560 some core principles around design thinking and creativity and data. 613 00:36:02,560 --> 00:36:04,960 And I hope it's I know it's been useful to them. 614 00:36:04,960 --> 00:36:06,840 Thank you for listening, Matt. 615 00:36:06,840 --> 00:36:08,440 Thanks for joining us, Troy. 616 00:36:08,440 --> 00:36:10,580 Troy, always a pleasure. 617 00:36:10,580 --> 00:36:12,280 And thanks everybody for listening today. 618 00:36:12,280 --> 00:36:13,280 That's all for making data matter. 619 00:36:13,280 --> 00:36:18,280 We'll see you again next time.