1 00:00:00,000 --> 00:00:02,160 Welcome to Making Data Matter, 2 00:00:02,160 --> 00:00:07,460 where we have conversations about data and leadership at mission-driven organizations, 3 00:00:07,460 --> 00:00:12,940 with practical insights into the intersection of nonprofit mission, strategy, and data. 4 00:00:12,940 --> 00:00:15,280 I'm your host, Sawyer Nyquist. 5 00:00:15,280 --> 00:00:17,360 I'm your co-host, Troy Dewek. 6 00:00:17,360 --> 00:00:20,440 Today, we're joined by guest, Joe Squire. 7 00:00:20,440 --> 00:00:22,480 Joe, welcome to the show. 8 00:00:22,480 --> 00:00:25,680 Thanks, Sawyer. Thanks, Troy. Glad to be here. 9 00:00:25,680 --> 00:00:28,440 For folks just meeting you for the first time, Joe, 10 00:00:28,440 --> 00:00:31,600 could you share just a little bit about your background and what you do? 11 00:00:31,600 --> 00:00:38,520 Yeah. My background actually came from a nursing background and transitioned into data, 12 00:00:38,520 --> 00:00:43,800 where I worked as a clinical data analyst for multiple years, 13 00:00:43,800 --> 00:00:47,720 and I currently sit as director of analytics and data science for 14 00:00:47,720 --> 00:00:52,760 the Heart and Vascular Institute at UPMC out of Pittsburgh, Pennsylvania. 15 00:00:52,760 --> 00:00:59,440 So very background coming from the clinical area, but still working in healthcare now. 16 00:00:59,440 --> 00:01:03,320 One of the things that's always fun about the data world is the backgrounds that people come from. 17 00:01:03,320 --> 00:01:06,280 We were talking about this a little bit off-camera beforehand, 18 00:01:06,280 --> 00:01:08,560 about Troy and I's backgrounds aren't data, 19 00:01:08,560 --> 00:01:11,120 and then you came from a nursing background, 20 00:01:11,120 --> 00:01:14,520 which I don't think is a common background for data professionals, right? 21 00:01:14,520 --> 00:01:19,000 No. If there's any nurses listening to this, 22 00:01:19,000 --> 00:01:20,800 which I highly doubt is probably the case. 23 00:01:20,800 --> 00:01:29,600 But I would say a majority of nurses are just very data illiterate in some senses. 24 00:01:29,600 --> 00:01:32,280 Well, I shouldn't say data, I should say tech illiterate. 25 00:01:32,280 --> 00:01:38,640 So they shy away from working with any raw data. 26 00:01:39,320 --> 00:01:43,280 They're not data illiterate because every day they go out and they're making 27 00:01:43,280 --> 00:01:48,320 interpretations of various data that comes into from the patient, 28 00:01:48,320 --> 00:01:53,280 whether it's labs or physiological data like blood pressures or something. 29 00:01:53,280 --> 00:01:55,760 So they are not data illiterate, 30 00:01:55,760 --> 00:02:03,840 but technically you run into a lot of nurses and they don't want to deal with that kind of work. 31 00:02:03,840 --> 00:02:08,360 So they shy away from it and they dismiss it as soon as they see it. 32 00:02:08,360 --> 00:02:11,800 So I think that's why you don't see many nurses in that role. 33 00:02:11,800 --> 00:02:16,320 But there is a very big need for nurses to transition out of 34 00:02:16,320 --> 00:02:21,360 that clinical care setting and into leveraging data and helping 35 00:02:21,360 --> 00:02:24,720 healthcare organizations become what they strive to be, 36 00:02:24,720 --> 00:02:28,000 and that's providing the best care to the patients at the right time. 37 00:02:28,000 --> 00:02:33,560 So Joe, what got you to cut your teeth on raw data like that? 38 00:02:33,560 --> 00:02:37,920 Did you just have someone send you an Excel spreadsheet and you nerded out one day? 39 00:02:37,920 --> 00:02:41,000 What was that journey like to cut your teeth on that? 40 00:02:41,000 --> 00:02:45,440 Well, it was completely on accident to be honest with you. 41 00:02:45,440 --> 00:02:51,520 So I was working in a cardiovascular OR sharing call, 42 00:02:51,520 --> 00:02:54,240 24-7 call with one other individual. 43 00:02:54,240 --> 00:02:59,680 So we saw like 2004, the call time of year. 44 00:03:00,400 --> 00:03:05,560 We just had our first, my wife and I just had our first kid and I needed some work-life balance 45 00:03:05,560 --> 00:03:10,680 and ended up finding a position that at the time I knew dealt with data. 46 00:03:10,680 --> 00:03:14,800 But it was really just a glorified data entry position that needed 47 00:03:14,800 --> 00:03:18,080 like clinical knowledge to do. 48 00:03:18,080 --> 00:03:20,480 But as soon as I got into that, 49 00:03:20,480 --> 00:03:25,400 I became bored with the data entry portion of it and asked the question, 50 00:03:25,400 --> 00:03:27,800 okay, we're collecting this data, 51 00:03:27,800 --> 00:03:29,440 what are we doing with it? 52 00:03:29,440 --> 00:03:30,640 How do we use it better? 53 00:03:30,640 --> 00:03:33,000 How do we make a difference with it? 54 00:03:33,000 --> 00:03:40,560 That's where you get your gateway drug of Excel and you start messing around with it often, 55 00:03:40,560 --> 00:03:43,640 and then everything snowballs from there. 56 00:03:43,640 --> 00:03:48,320 So that's really my career progression was just the natural curiosity 57 00:03:48,320 --> 00:03:53,480 that stemmed from an accidental move that was out of no other reason 58 00:03:53,480 --> 00:03:57,920 other than wanting to find a better work-life balance. 59 00:03:57,920 --> 00:04:04,680 So one follow-up question to that is many of us probably were cutting our teeth on Excel. 60 00:04:04,680 --> 00:04:08,880 Like you said, that's the gateway drug into the data world. 61 00:04:08,880 --> 00:04:10,480 But at some point, 62 00:04:10,480 --> 00:04:16,960 our thinking changes, there's this journey of you might be in that space of doing 63 00:04:16,960 --> 00:04:21,160 your pivot tables or you've got some VLOOKUP formulas that you're 64 00:04:21,160 --> 00:04:25,880 doing to try to mesh data together and then at some point, 65 00:04:25,880 --> 00:04:31,320 we're introduced to databases or dimensional data modeling and data warehousing. 66 00:04:31,320 --> 00:04:35,400 So just tell us a little bit about that part of your journey to go from 67 00:04:35,400 --> 00:04:41,440 the basics of data in Excel to starting to think bigger, 68 00:04:41,440 --> 00:04:44,520 broader picture design patterns and how we can 69 00:04:44,520 --> 00:04:50,280 actually make this an enterprise solution for the workplace. 70 00:04:50,280 --> 00:04:57,560 Yeah. So I think the biggest thing that comes down to is just the mindset of making data useful. 71 00:04:57,560 --> 00:05:06,480 And that concept guides you and drives you to try to understand basically, 72 00:05:06,480 --> 00:05:07,920 how do we automate things? 73 00:05:07,920 --> 00:05:09,600 How do we make things more efficient? 74 00:05:09,600 --> 00:05:15,400 And that's what leads you from the basics of Excel. 75 00:05:15,400 --> 00:05:18,000 And I'm not saying you have to move out of Excel, 76 00:05:18,000 --> 00:05:23,040 but there's a lot of people that their entire lives are based around the Excel environment. 77 00:05:23,040 --> 00:05:25,320 It's a very powerful environment. 78 00:05:25,320 --> 00:05:28,880 And you can connect directly to databases with it and everything. 79 00:05:28,880 --> 00:05:38,880 But if you're thinking and you want to automate things that may not be as functional in Excel, 80 00:05:38,880 --> 00:05:41,320 then you're going to be looking to move out of that, 81 00:05:41,320 --> 00:05:43,440 looking to see what else is out there. 82 00:05:43,440 --> 00:05:47,880 And so that automation concept is really what drove me to be like, 83 00:05:47,880 --> 00:05:49,120 okay, what else is out here? 84 00:05:49,120 --> 00:05:50,200 What else can we find? 85 00:05:50,200 --> 00:05:53,040 And that's where you start understanding, 86 00:05:53,040 --> 00:05:55,880 okay, this is where the data is coming from. 87 00:05:55,880 --> 00:05:57,000 These are databases. 88 00:05:57,000 --> 00:05:58,760 This is how it's structured and everything. 89 00:05:58,760 --> 00:06:02,560 And you start looking at, 90 00:06:02,560 --> 00:06:06,200 okay, now I can actually plug in, say, a BI tool on top of this. 91 00:06:06,200 --> 00:06:11,240 Now I can automate that report that I had to do manual manipulations on to send out. 92 00:06:11,240 --> 00:06:16,000 And then you start learning your coding languages, 93 00:06:16,000 --> 00:06:21,600 whether it's SQL to bring that out or whether it's Python to do some more advanced manipulation 94 00:06:21,600 --> 00:06:23,840 or advanced analytics. 95 00:06:23,840 --> 00:06:27,120 And that's really where you get your... 96 00:06:27,120 --> 00:06:29,840 For me, that's where my curiosity juices get going. 97 00:06:29,840 --> 00:06:37,040 And I'll be honest, ever since the whole generative AI explosions occurred, 98 00:06:37,040 --> 00:06:44,520 it really helps you iterate a lot faster and explore more avenues more quickly 99 00:06:44,520 --> 00:06:50,440 than in the past whenever you were sorting through Stack Overflow or Googling things. 100 00:06:50,440 --> 00:06:54,400 Because now you can just type in, hey, I have this thought, 101 00:06:54,400 --> 00:06:58,400 how would this be possible if you were to do it in Python? 102 00:06:58,400 --> 00:07:01,520 And it would say, these are the basic steps. 103 00:07:01,520 --> 00:07:03,560 There's a skeleton of code. 104 00:07:03,560 --> 00:07:06,080 And then you can just pull it out and start manipulating it 105 00:07:06,080 --> 00:07:09,880 and work on what you're doing a lot quicker. 106 00:07:09,880 --> 00:07:12,880 And this is kind of going down a rabbit hole, 107 00:07:12,880 --> 00:07:16,600 but as someone in a leadership position, that's very helpful 108 00:07:16,600 --> 00:07:20,920 because it exponentially helps your efficiency. 109 00:07:20,920 --> 00:07:28,720 And since your scope of what you're doing is much broader and it's not as technical, 110 00:07:28,720 --> 00:07:33,120 you naturally have to move away from the technical aspect of your work 111 00:07:33,120 --> 00:07:36,760 because if your head's down, you're not looking up and out. 112 00:07:36,760 --> 00:07:42,240 But with Gen. AI, that efficiency gain really lets you also keep developing 113 00:07:42,240 --> 00:07:46,240 in some capacity to help your team out, whether it's offloading work 114 00:07:46,240 --> 00:07:50,240 or generating new ideas on how to look at the data 115 00:07:50,240 --> 00:07:55,000 or how to bring the data in from a different source. 116 00:07:55,000 --> 00:08:02,280 That's really some of the largest benefits I've seen with that aspect of AI augmentation, 117 00:08:02,280 --> 00:08:04,880 especially as a leader in the data field. 118 00:08:04,880 --> 00:08:07,840 But yeah, coming back to your original question, 119 00:08:07,840 --> 00:08:13,280 Zoe, go from Excel to everything else. 120 00:08:13,280 --> 00:08:15,720 Now, OK, so Joe, you opened the AI box. 121 00:08:15,720 --> 00:08:18,240 So we're going to go there, I guess. 122 00:08:18,240 --> 00:08:21,320 So first of all, tell me a little bit about your team 123 00:08:21,320 --> 00:08:25,600 and then even how you've thought about Gen. AI for how your team works 124 00:08:25,600 --> 00:08:29,200 or what they're empowered to do with Gen. AI or ChatDBT 125 00:08:29,200 --> 00:08:30,560 or what type of tools you've done there. 126 00:08:30,560 --> 00:08:35,680 So yeah, as a leader with team members, what does Gen. AI look like for you guys? 127 00:08:35,680 --> 00:08:40,880 So my team, we sit within the Heart and Vascular Institute. 128 00:08:40,880 --> 00:08:43,960 So that's a service line within the hospital. 129 00:08:43,960 --> 00:08:47,880 And healthcare, like larger health systems, have these service lines, 130 00:08:47,880 --> 00:08:52,800 which are generally dedicated to specific procedures and disease processes. 131 00:08:52,800 --> 00:08:59,280 Ours happens to be cardiovascular, so heart, the veins and arteries, 132 00:08:59,280 --> 00:09:02,600 those types of procedures and diseases. 133 00:09:02,600 --> 00:09:09,680 And so much of the work that we do is centered around outcomes, 134 00:09:09,680 --> 00:09:13,240 like quality outcomes, research. 135 00:09:13,240 --> 00:09:20,320 We have a pretty big research lean where we help basically use the EHR data 136 00:09:20,320 --> 00:09:24,000 that we have to answer research questions and publish. 137 00:09:24,000 --> 00:09:29,360 And we also support the operational side in some capacity. 138 00:09:29,360 --> 00:09:34,440 In terms of generative AI, UPMC itself is a large Microsoft shop. 139 00:09:34,440 --> 00:09:42,840 So our approved AI, generative AI, is Microsoft, so copilot. 140 00:09:42,840 --> 00:09:46,120 And currently right now, they're trialing that across the system 141 00:09:46,120 --> 00:09:48,880 with certain individuals. I'm part of that trial. 142 00:09:48,880 --> 00:09:53,720 And copilot's fine. I think it has a ways to go. 143 00:09:53,720 --> 00:09:57,360 I think that some of the integrations are neat. 144 00:09:57,360 --> 00:09:59,720 I haven't found it extremely useful. 145 00:09:59,720 --> 00:10:05,760 Like it's integrated into Excel, but I haven't really needed to use it a whole lot in there 146 00:10:05,760 --> 00:10:08,120 because I don't work super heavily in there. 147 00:10:08,120 --> 00:10:11,560 It's mostly just simple things like so. 148 00:10:11,560 --> 00:10:16,760 But in terms of empowering my team, it's difficult. 149 00:10:16,760 --> 00:10:21,040 So there's individuals. 150 00:10:21,040 --> 00:10:26,240 So I would say there's experts in the field and they've been doing it for a long time. 151 00:10:26,240 --> 00:10:34,360 And so there's not a whole lot of need to look at, like use gen AI to do the work that they do 152 00:10:34,360 --> 00:10:39,760 because they either have scripts that they've used in the past, they can easily modify it. 153 00:10:39,760 --> 00:10:44,240 They know exactly what they're going to do as soon as they go into a script. 154 00:10:44,240 --> 00:10:48,960 And I would also say there's an age factor in there. 155 00:10:48,960 --> 00:10:56,560 And this is generalizing, but from my experience that if somebody's older and they're set in their ways 156 00:10:56,560 --> 00:11:03,120 and they have that expertise, they're less likely to adopt generative AI than somebody who's younger 157 00:11:03,120 --> 00:11:08,360 and who's not as set in their ways or they don't have the extensive experience. 158 00:11:08,360 --> 00:11:12,480 And I would put myself in that bucket with a lot of the individuals. 159 00:11:12,480 --> 00:11:17,640 Like a lot of the individuals underneath me have much more, their technical chops are much better. 160 00:11:17,640 --> 00:11:20,840 They're much more experienced than I am. 161 00:11:20,840 --> 00:11:26,600 I mean, we have analysts and biostatisticians and some of these individuals have PhDs. 162 00:11:26,600 --> 00:11:29,480 It's just they're much more experienced. 163 00:11:29,480 --> 00:11:32,960 So I am all for using generative AI. 164 00:11:32,960 --> 00:11:37,160 And if anybody ever wants to adopt it, I am more than happy to help them, 165 00:11:37,160 --> 00:11:41,480 like understand it and introduce it to them. 166 00:11:41,480 --> 00:11:46,000 But I'm not going to force it on them, because if you force it on them, 167 00:11:46,000 --> 00:11:51,560 they don't understand that idea of how can I leverage this to be more efficient kind of goes away. 168 00:11:51,560 --> 00:11:55,400 And they just see it as nonsense. 169 00:11:55,400 --> 00:12:05,480 And that's probably one of the biggest stumbling blocks I see with the analytics world adopting generative AI. 170 00:12:05,480 --> 00:12:12,160 But there is like using GitHub Copilot. 171 00:12:12,160 --> 00:12:17,560 It's basically like autocorrect and autocomplete for codes. 172 00:12:17,560 --> 00:12:27,720 And it's very efficient because you can basically start typing in your SQL and it will bring up different ideas on what it thinks you are going to do. 173 00:12:27,720 --> 00:12:34,000 And it reduces a lot of redundancy of copy and pasting certain variables or statements and stuff. 174 00:12:34,000 --> 00:12:40,960 And so that's really like one of my analysts really found that extremely helpful and up in their efficiency. 175 00:12:40,960 --> 00:12:46,600 And it was a newer analyst who was straight out of, I mean, they were still finishing their masters, 176 00:12:46,600 --> 00:12:50,440 but it was basically their first analytics position. 177 00:12:50,440 --> 00:12:52,440 And they really loved it. 178 00:12:52,440 --> 00:12:59,520 And they were very gung ho on the whole gen AI and how I can use it to work into my workflow. 179 00:12:59,520 --> 00:13:07,680 And so, again, it just reiterates that divide that I see in terms of people using it to augment their workflows. 180 00:13:07,680 --> 00:13:10,200 I think it's again, I think it goes back to experience. 181 00:13:10,200 --> 00:13:22,440 Like if people and I don't know why it is this way, but people with lesser experience tend to see it as this is very helpful versus those that are older where they it's not necessarily they don't see as helpful. 182 00:13:22,440 --> 00:13:25,400 They just don't see the use case for it yet. 183 00:13:25,400 --> 00:13:28,520 And I think that's I think maybe that comes down to workflow. 184 00:13:28,520 --> 00:13:38,800 So once you get products out there that inherently have it built into the workflow, like AutoCorrect has been in workflows for years and years and years, 185 00:13:38,800 --> 00:13:42,320 people get used to it and people will adopt it and understand it. 186 00:13:42,320 --> 00:13:49,960 And that's understanding is almost the entirety of that battle adopting new technology. 187 00:13:49,960 --> 00:13:57,120 Recently, I've been testing the copilot features specifically for Power BI. 188 00:13:57,120 --> 00:14:10,240 And I've found that I like the more developer side of the copilot tools where it might enhance my semantic model by auto populating the descriptions for all the fields. 189 00:14:10,240 --> 00:14:19,160 And I'm like, man, that saves me so much time not having to sit there and type these out, especially for any of the DAX measures and calculated columns. 190 00:14:19,160 --> 00:14:26,960 I'm like, man, if it can pick up some natural language from the code and populate that in there for my report builders, I'm loving it. 191 00:14:26,960 --> 00:14:37,640 But then the other side of that is some of the new copilot features in the Power BI service for the report consumers and for dashboards, 192 00:14:37,640 --> 00:14:45,480 where you could have smart narratives or you could have a conversation with copilot about the visuals they're seeing in the report. 193 00:14:45,480 --> 00:14:50,080 I'm just curious, is that an area that your team has gone yet? 194 00:14:50,080 --> 00:14:53,640 Is that I mean, it's it's really new on the Power BI side. 195 00:14:53,640 --> 00:15:01,120 And I'm not sure if you're using Power BI, but I'm thinking about those report consumers and where the role of Gen. 196 00:15:01,120 --> 00:15:10,640 AI fits into the report consumers that that feels a little more concerning to me than the developer enhancements and efficiencies. 197 00:15:10,640 --> 00:15:18,360 What are your thoughts on that, Joe? So as part of our trial, we don't have the Power BI copilot initiated. 198 00:15:18,360 --> 00:15:26,160 Like, we're not allowed to use it, which I'm very bummed about because I think I'd probably be heavily using it. 199 00:15:26,160 --> 00:15:30,120 No, I I think that is a big white space. 200 00:15:30,120 --> 00:15:40,680 And in the day in the analytics community is that, you know, the the ultimate thing that we kind of aspire to is that I shouldn't say it's the ultimate thing. 201 00:15:40,680 --> 00:15:47,360 But like the promise of self-service analytics has been out there for years and it's largely kind of falling on its face. 202 00:15:47,360 --> 00:15:49,600 But I think that's an area where Gen. 203 00:15:49,600 --> 00:15:57,840 AI can kind of meet those expectations if you model the data correctly, where you can like, OK, here's the use case. 204 00:15:57,840 --> 00:16:01,040 So you send that that report out to the executive. 205 00:16:01,040 --> 00:16:03,720 It's 8 a.m. The executive logs into their email. 206 00:16:03,720 --> 00:16:07,160 They look at the report. They have some they have some top of mind questions. 207 00:16:07,160 --> 00:16:15,320 Well, rather than firing email to so and so, then cascades it to so and so and so and so until finally it's something that can answer the question. 208 00:16:15,320 --> 00:16:24,080 You have that ability to do natural language querying where it can mean that ideally it would bring back a valid answer. 209 00:16:24,080 --> 00:16:29,000 That's not incorrect. But, you know, that's that's where I see that going. 210 00:16:29,000 --> 00:16:46,960 And I think that's there's a lot of promise in that and being able to search across the whatever data model you have or even the entirety of data like a warehouse in terms of trying to find cool answers to questions. 211 00:16:46,960 --> 00:16:52,440 And really. And that's what analytics is, is empowering the business to make decisions. 212 00:16:52,440 --> 00:17:01,840 And if you can do that in a way that people are better able to find those answers, then we should go for it. 213 00:17:01,840 --> 00:17:04,520 I think that people have been working in that area for a while now. 214 00:17:04,520 --> 00:17:09,200 Like you see, like the narrative, the smart narrative has been around for a while. 215 00:17:09,200 --> 00:17:15,800 But, you know, the underlying model really changed at this point since it's been out. 216 00:17:15,800 --> 00:17:19,000 And I think I think that there's still work to do. 217 00:17:19,000 --> 00:17:28,000 And obviously, it all depends on the data underneath it. But it's definitely an area that has a lot of promise, in my opinion. 218 00:17:28,000 --> 00:17:32,280 So you talked about data consumers. 219 00:17:32,280 --> 00:17:43,640 Ideally, that's the space where we can grow is that self-serve analytics, they can consume data on their own more effectively than having to send 12 emails to find an answer to the question they saw in a report. 220 00:17:43,640 --> 00:17:48,960 Tell me a little bit about your the people who consume data from you, who are the stakeholders that your team interacts with? 221 00:17:48,960 --> 00:17:55,840 What's their perspective or culture around data? How do they how data literate are they? 222 00:17:55,840 --> 00:17:59,960 Do they love it or are they resistant? I don't know. What kind of interaction do you have with stakeholders? 223 00:17:59,960 --> 00:18:03,920 What's kind of their what's their what's their role? What do they do? 224 00:18:03,920 --> 00:18:07,280 Yes. I mean, there's a couple of different stakeholders. 225 00:18:07,280 --> 00:18:18,760 The research size a little different. It's kind of niche. So we won't talk about that. But the terms in terms of the clinic, let's let's do clinical outcomes. 226 00:18:18,760 --> 00:18:31,600 So we have an interesting model with clinical outcomes, because a lot of it a lot of it is geared around the clinical registries, which are still largely manually abstracted. 227 00:18:31,600 --> 00:18:42,640 And so you have a person actually goes into the medical record and they look for certain variables and those variables are then put into a data collection form. 228 00:18:42,640 --> 00:18:49,240 And basically, the purpose of these registries is to look at specific procedures or disease processes. 229 00:18:49,240 --> 00:18:58,120 And it's usually a national or international registry that then can risk adjust and compare all these sites and how you're doing to each other. 230 00:18:58,120 --> 00:19:08,840 But with those clinical outcomes, the registries in particular, we the way we have it structured is we actually have the abstraction outsourced to a third party. 231 00:19:08,840 --> 00:19:16,200 And then for each registry, we have a subject matter expert who does data QAQI. 232 00:19:16,200 --> 00:19:21,200 And then they also serve as basically like the educator. 233 00:19:21,200 --> 00:19:30,200 So they're the ones that are meeting with different teams, clinicians, administrators to to go over the data, 234 00:19:30,200 --> 00:19:36,480 tell people, understand the metrics, what goes into the metrics, what levers to pull to change those metrics. 235 00:19:36,480 --> 00:19:41,280 And so it's we were doing data literacy before data literacy literacy was cool. 236 00:19:41,280 --> 00:19:47,280 But that's all right. But I it's it's an interesting model. 237 00:19:47,280 --> 00:19:54,160 And even though you have that set up, there's still people that will give you a hard time saying, oh, the data is not right. 238 00:19:54,160 --> 00:19:57,240 This metric isn't right. And so on and so forth. 239 00:19:57,240 --> 00:20:05,280 But it's it's something that you the way that I see it is if somebody is engaging with you that way, they're at least looking at the data. 240 00:20:05,280 --> 00:20:15,360 Right. And so you want and that's that means that you're actually making it meaningful for them in some way, whether they find it useful or not. 241 00:20:15,360 --> 00:20:18,240 They're at least looking at it, trying to understand it. 242 00:20:18,240 --> 00:20:21,280 And maybe they're engaging with you in a way that's negative. 243 00:20:21,280 --> 00:20:32,560 But, you know, it's still engagement. It's still trying to understand, trying to make things work to get to that point where we can use that data to make those decisions. 244 00:20:32,560 --> 00:20:40,520 And so I think it's it's no matter how you have it structured, there's always going to be people that don't like what you show them, 245 00:20:40,520 --> 00:20:47,760 whether it's because the data is negative, reflects negatively on them or because it's it's actually incorrect. 246 00:20:47,760 --> 00:21:02,280 So I think I think in terms of consumers and your engagement with them, it's really just a matter of being willing to listen, being willing to eat some humble pie and being willing to admit that maybe there is a mistake. 247 00:21:02,280 --> 00:21:05,240 Going back to the drawing board, taking a look at it. 248 00:21:05,240 --> 00:21:12,840 But it's also being willing to put your foot down at some point and say, look, this is how this is legitimately correct stuff. 249 00:21:12,840 --> 00:21:23,600 And you need to, you know, kind of partner with us here and figure out how we can change the direction of the boat and make it make this metric go the right direction. 250 00:21:23,600 --> 00:21:27,720 That's a great conversation. And I love that you've mentioned two things. 251 00:21:27,720 --> 00:21:37,720 You were talking a good bit about data literacy and sort of how those executives and whatever stakeholders you're working with, they do need some level of data literacy. 252 00:21:37,720 --> 00:21:42,440 But you mentioned things like listening well and understanding them. 253 00:21:42,440 --> 00:21:52,240 So there is a role that we as data professionals need to be willing to learn the business literacy in order to bridge that gap. 254 00:21:52,240 --> 00:21:57,880 Otherwise, it feels like an impasse or stalemate in that, well, this is the data. 255 00:21:57,880 --> 00:22:07,480 Well, I don't like the data. And then you ended so well in saying, how do we partner together to turn that ship around to actually make a difference? 256 00:22:07,480 --> 00:22:10,320 The data isn't the end all be all. 257 00:22:10,320 --> 00:22:19,520 It's it's got to lead to something where we're going to make changes based on the data to help this organization or this business. 258 00:22:19,520 --> 00:22:22,560 So I love hearing about success stories. 259 00:22:22,560 --> 00:22:30,160 So, Joe, I'm wondering if you could tell us about one project or something that was like this was a really good success. 260 00:22:30,160 --> 00:22:33,800 There might have been bumps in the road along the way to get there. 261 00:22:33,800 --> 00:22:44,480 But what was it like to see the light bulbs go on, see data being used in a way that whether it was positive or negative, that stakeholder was like, you're right. 262 00:22:44,480 --> 00:22:46,640 Let's get on board. Let's make a change. 263 00:22:46,640 --> 00:22:53,720 So I'm wondering if you have one of those examples where you could get a little more into the weeds and details of what that was like. 264 00:22:53,720 --> 00:22:57,560 Yeah, I don't want to be a wet blanket, but I don't know. 265 00:22:57,560 --> 00:23:05,520 I don't know if I've ever actually experienced that sitting in a room and you're telling people all this stuff and all these light bulbs go on. 266 00:23:05,520 --> 00:23:07,560 I don't I don't know. 267 00:23:07,560 --> 00:23:11,280 Like if there's some, yeah, we may not have a video. 268 00:23:11,280 --> 00:23:17,960 Let us know if that's a real thing or not, if you've ever experienced it. 269 00:23:17,960 --> 00:23:31,640 But I would say that with projects that drive meaningful benefit and my perspective is biased because I'm from health care and things generally move slower in health care. 270 00:23:31,640 --> 00:23:39,000 But projects that drive meaningful benefit generally take there's generally a long horizon on them. 271 00:23:39,000 --> 00:23:47,360 And so and this leads to one of the benefits of staying at a company for more than two years gives you that perspective, 272 00:23:47,360 --> 00:23:53,440 especially if you're working on projects of, OK, this is what it takes to deliver a project. 273 00:23:53,440 --> 00:23:56,040 This is what happens after the project's delivered. 274 00:23:56,040 --> 00:23:58,080 This is how you sustain a project. 275 00:23:58,080 --> 00:24:08,560 And so I think one of the along those lines, one of the big things that we did was use these clinical outcomes to basically turn the ship around. 276 00:24:08,560 --> 00:24:15,160 And so we did that at our flagship hospital, UPMC Presbyterian for cardiac surgery programs. 277 00:24:15,160 --> 00:24:20,880 I'll be candid, the program, the cardiac surgery programs there were floundering. 278 00:24:20,880 --> 00:24:33,960 There wasn't the outcomes were very poor and it wasn't just like one source of data that led to that change generation. 279 00:24:33,960 --> 00:24:36,520 There are multiple things that went on. 280 00:24:36,520 --> 00:24:42,280 We started with the clinical outcomes where people said, OK, something's wrong. 281 00:24:42,280 --> 00:24:44,440 We need to make a change. 282 00:24:44,440 --> 00:24:54,120 They actually did a lot of qualitative research to get that small data from interviews with individuals in terms of trying to understand is there a cultural aspect to what's occurring. 283 00:24:54,120 --> 00:25:05,280 And you put all that together at the end and you drive a path forward using that data to help guide as long as as well as pairing it with the business stakeholders and their input. 284 00:25:05,280 --> 00:25:08,480 So there were leadership changes that occurred. 285 00:25:08,480 --> 00:25:11,480 There were practice changes that occurred. 286 00:25:11,480 --> 00:25:30,720 And it took it took a total of probably close to five years, really, to see a significant turnaround where we went from being the bottom quartile basically of clinical outcomes in our program across the country to the top 15 percent in the country. 287 00:25:30,720 --> 00:25:39,560 So, you know, it's not it's not always going to be like you, the data master, standing up there delivering a grand speech. 288 00:25:39,560 --> 00:25:41,040 It's going to be a partnership. 289 00:25:41,040 --> 00:25:47,880 And that's that's really the important aspect of it is partnering with individuals, getting leadership by it is huge. 290 00:25:47,880 --> 00:25:52,760 If you don't have leadership by and you should just stop your project, basically, it's my opinion. 291 00:25:52,760 --> 00:26:04,280 Like if you if you can afford it as many times as you want, but if that leader doesn't say, yes, let's do that, it's I'd say your failure rate is probably going to be like 98 percent. 292 00:26:04,280 --> 00:26:17,160 But yeah, part of people getting that leadership by and using that data in tandem with the business knowledge and setting clear, definitive goals on what you want to accomplish. 293 00:26:17,160 --> 00:26:28,800 That's that's really the big things in terms of having a successful project that is data driven and that actually succeeds and accomplishes something at the end. 294 00:26:28,800 --> 00:26:33,680 So questions around the you talked about outcomes a few times and like quality outcomes. 295 00:26:33,680 --> 00:26:40,080 And I'm guessing there's a large aspect of that that's that's quantitative, like that's data based. 296 00:26:40,080 --> 00:26:48,480 And how can you develop it more, put more texture on that for me in terms of how do you measure outcomes or what is a good outcome? 297 00:26:48,480 --> 00:26:51,160 What is a negative outcome in this world? 298 00:26:51,160 --> 00:26:57,720 Is it like did the patient have a successful recovery or not successful recovery or like what are the types of outcomes that you're measuring? 299 00:26:57,720 --> 00:27:02,000 Because it seemed like those were a linchpin for outcomes are down. 300 00:27:02,000 --> 00:27:05,560 Let's we need to start transitioning to something different and making changes. 301 00:27:05,560 --> 00:27:08,120 So tell me about those outcomes that you were measuring and what that looked like. 302 00:27:08,120 --> 00:27:16,960 Yeah, so there's there's a lot of different ways, like any any type of outcome is so outcomes can be measured with anything in health care. 303 00:27:16,960 --> 00:27:24,440 So you would have some some type of patient contact, whether that's like an office visit or whether it's a surgical procedure. 304 00:27:24,440 --> 00:27:26,640 And you can measure obviously an outcome from that. 305 00:27:26,640 --> 00:27:32,760 It'd be the same thing as somebody visiting your website and seeing, OK, did they make it to the cart? 306 00:27:32,760 --> 00:27:34,920 Did they leave stuff in the cart? Basically. 307 00:27:34,920 --> 00:27:40,080 Now, here is this person had a procedure. Did they have a surgical infection? 308 00:27:40,080 --> 00:27:42,920 Did they die? So it's very similar. 309 00:27:42,920 --> 00:27:47,000 That's just we call them outcomes in in health care. 310 00:27:47,000 --> 00:27:50,800 And so like we'll use we use the cardiac surgery as an example. 311 00:27:50,800 --> 00:28:01,200 So there's outside of like the national bodies actually risk adjust everything and not rank you, but they they don't like to say rank. 312 00:28:01,200 --> 00:28:08,720 But essentially, they they assign you basically different quartiles in your performance. 313 00:28:08,720 --> 00:28:15,080 And and so you can see that like where you're sitting nationally or internationally. 314 00:28:15,080 --> 00:28:18,640 But for cardiac surgery, the predominant ones are obviously mortality. 315 00:28:18,640 --> 00:28:21,360 Did the patient die after the surgery? 316 00:28:21,360 --> 00:28:25,720 And there's certain parameters around that. OK, did they did they die within 30 days? 317 00:28:25,720 --> 00:28:30,600 If they did not die within 30 days or the hospitalization where the procedure occurred, 318 00:28:30,600 --> 00:28:37,720 then it counts as a surgical mortality versus if they were discharged and they died at 45 days, it's not a surgical. 319 00:28:37,720 --> 00:28:44,840 And, you know, that's that whole you can get into the whole idea of whether that's accurate or not accurate, 320 00:28:44,840 --> 00:28:52,120 because literally the patient could get hit by a bus at day twenty nine and that'd be a surrogate mortality. 321 00:28:52,120 --> 00:28:55,760 But, you know, chances are probably pretty low. 322 00:28:55,760 --> 00:29:03,840 So like surgical mortality and then major, major issues like we talked about, like infection or whether they, 323 00:29:03,840 --> 00:29:14,160 you know, were on the breathing tube for too long or they developed some kind of like developed kidney failure or they had a stroke or things like that. 324 00:29:14,160 --> 00:29:16,600 And so those are the big outcomes that you measure. 325 00:29:16,600 --> 00:29:23,600 And those are the ones that these like these governing bodies, they have risk models that are run. 326 00:29:23,600 --> 00:29:28,320 These outcomes are run through and then you're stratified based on those risk models. 327 00:29:28,320 --> 00:29:35,280 And so that's those are like that's just an example of some of the outcomes we look at within within health care. 328 00:29:35,280 --> 00:29:41,880 But it's not any different than looking at how people interact with a website, like I said, or, you know, 329 00:29:41,880 --> 00:29:51,400 how people interact with a product or something like that, like you have what the downstream effects are of that interaction is basically what that prevents. 330 00:29:51,400 --> 00:30:03,680 Yeah. And you have the advantage of or suppose what's different is there's some kind of national established ways of measuring certain things and measuring outcomes where some businesses are kind of left to their own like, well, how do we want to measure this or what do we want? 331 00:30:03,680 --> 00:30:12,360 That's maybe one of the pros and cons, perhaps, of health care is you have a lot of established standards for what things look like in health care outcomes. 332 00:30:12,360 --> 00:30:20,240 Right. And that also like that comes to the discussion of sometimes you'll have people say, well, this metric isn't correct, even though it's a nationally established metric. 333 00:30:20,240 --> 00:30:24,960 It's like we don't do so much of it's actually established. 334 00:30:24,960 --> 00:30:39,120 But yeah, and now and there's and it's you know, that's you run into those issues and that's like sometimes it'd be nice to be operating in an industry where it's like we can actually tweak this metric and make it more accurate in our eyes. 335 00:30:39,120 --> 00:30:43,480 And so, yeah, you are in highly regulated environments. You are held to those issues. 336 00:30:43,480 --> 00:30:52,800 And that's that's one of those things like you you don't have much control over those aspects. And and there's there's an aspect of gamesmanship that comes in there. 337 00:30:52,800 --> 00:31:00,160 Right. So making sure that you are doing what you can to make that metric look as good as it can be. 338 00:31:00,160 --> 00:31:12,040 It's not not committing fraud, but it's OK. What's what is there like some documentation that needs to be tweaked that would make this metric better because it just needs to be in that. 339 00:31:12,040 --> 00:31:20,280 So and, you know, it's again, you can get into a whole conversation about administrative burden and health care and everything else. 340 00:31:20,280 --> 00:31:24,440 But that that is one of those I wouldn't say it's just limited to health care. 341 00:31:24,440 --> 00:31:31,160 I'd say like highly regulated environments like banking would be another one where you see some of that stuff going on. 342 00:31:31,160 --> 00:31:39,200 Yeah. What are the things that any any metric that you give and then there's when there's incentives wrapped around it, then you do end up with. 343 00:31:39,200 --> 00:31:47,720 Yeah, gaming or at least optimizing for metrics because there's potential incentives or potential opportunities tied to it. 344 00:31:47,720 --> 00:32:01,800 I'm curious about the next if you look ahead, six, 12 months, what are some exciting initiatives you are tackling or you see on the horizon for data or within your hospital or within the space, health care space in general? 345 00:32:01,800 --> 00:32:13,520 Yeah, so there's I actually have two big projects, one one still in the ideation phase, the one we're moving forward with, the one we're moving forward with is actually working with our clinical trial group. 346 00:32:13,520 --> 00:32:19,680 And so we have a group of research coordinators that part of part of what they do. 347 00:32:19,680 --> 00:32:30,920 And so these research coordinators, they get these clinical trials and there's long lists of inclusion, exclusion criteria for who would qualify to be recruited to the trial. 348 00:32:30,920 --> 00:32:44,200 And it's basically like a big, you know, a big logical if if statement, like so if they have this or if they have this, this and not if they have this, not if they have this. 349 00:32:44,200 --> 00:32:48,800 And so they're manually combing through charts, trying to find those people. 350 00:32:48,800 --> 00:32:53,080 And I was just working on some of the metrics recently. 351 00:32:53,080 --> 00:33:02,640 And some of these for the trials that we're looking at implementing, what I'll talk about is what I'll talk about here in a second. 352 00:33:02,640 --> 00:33:14,640 They had for every patient recruited, it was about 120 to 180 hours of chart review for a single patient. 353 00:33:14,640 --> 00:33:16,280 That's astronomical, right? 354 00:33:16,280 --> 00:33:19,320 Like that's. Yeah, yeah. 355 00:33:19,320 --> 00:33:27,480 So so the idea here is to take a company that was actually came through our enterprises arm at UPMC, 356 00:33:27,480 --> 00:33:33,440 which is basically like they they see fun startups and this group. 357 00:33:33,440 --> 00:33:43,000 What they do is they they take I think their underlying technology is based around NLP because it's it's not it was before the whole gen AI boom. 358 00:33:43,000 --> 00:33:50,400 But they take this NLP technology and they build different models in terms of what it goes out and looks for. 359 00:33:50,400 --> 00:33:56,520 And so what we want to do is take use this clinical trials group with the research coordinators, 360 00:33:56,520 --> 00:34:08,960 take this this product that this group has and then let it loose on our data to go out and basically call through the data to find people based on these inclusion, exclusion criteria. 361 00:34:08,960 --> 00:34:13,720 So rather than spending 120 hours looking for these patients, 362 00:34:13,720 --> 00:34:22,720 they get a preset group of patients brought back, which they can then screen versus going out into the mass and trying to find these patients. 363 00:34:22,720 --> 00:34:30,760 So the idea is to reduce that amount of screening time, be able to recruit more patients more quickly. 364 00:34:30,760 --> 00:34:38,520 And that leads to things like, you know, expanding access for patients that need access to these trials as well as revenue for us, 365 00:34:38,520 --> 00:34:42,800 because we get industry sponsorship for the patients that we recruit in. 366 00:34:42,800 --> 00:34:45,280 And so there's it's it's a huge benefit. 367 00:34:45,280 --> 00:34:56,120 And most people don't really think about like clinical trials and and matching patients to those trials until you're in a position where you actually need to be in that need to be matched. 368 00:34:56,120 --> 00:35:01,400 And so like a good a good instance, I didn't even really fully understand until recently. 369 00:35:01,400 --> 00:35:10,560 We had a a friend of ours whose two year old son had a rare cancer and, you know, when they get diagnosed, 370 00:35:10,560 --> 00:35:16,480 you're sitting in the hospital and you think about, well, I only have this one opinion. 371 00:35:16,480 --> 00:35:19,280 I mean, fortunately, they're in Pittsburgh. It's a good opinion. 372 00:35:19,280 --> 00:35:22,120 But there are so many other people out there. 373 00:35:22,120 --> 00:35:27,200 There could be trials out there that could be treating this rare form of cancer. 374 00:35:27,200 --> 00:35:28,880 But how do you actually find it? 375 00:35:28,880 --> 00:35:39,440 Nobody's actually look like the chances are that you need to make a decision fairly quickly and you need to be able to do it with confidence. 376 00:35:39,440 --> 00:35:47,920 And so that I mean, you've got to stress your feeling right now, too, as your child is going through something like hard position to even make a decision to. 377 00:35:47,920 --> 00:35:51,880 Right. Right. It's hard to make a decision and you don't know if it's the right decision. 378 00:35:51,880 --> 00:36:07,080 And so that concept of being able to more to expand access to clinical trials, to be able to recruit more people and potentially life saving treatments is a really, really important thing, in my opinion. 379 00:36:07,080 --> 00:36:19,800 And, you know, leveraging these analytic capabilities of people and their products and being able to do that and see if it actually can deliver is is really one of the things I'm really excited about this year. 380 00:36:19,800 --> 00:36:27,080 The other aspect, the other thing that I'm where this currently in the ideation phase deals more with gender of a. 381 00:36:27,080 --> 00:36:29,520 And so this goes back to the clinical registries. 382 00:36:29,520 --> 00:36:34,440 I do have a group underneath me that's still manually abstracts for these clinical registries. 383 00:36:34,440 --> 00:36:42,800 And, you know, there's going to be this retirement tsunami coming with this group because they're all in their late 50s or hitting their 60s. 384 00:36:42,800 --> 00:36:45,720 And I can say that because they said that in a meeting to me. 385 00:36:45,720 --> 00:36:56,360 So the and so the thought is, can we use like a use a augmentation? 386 00:36:56,360 --> 00:37:15,480 Can we put this into our HR be able to tell it here are the different here where we generally find the different data points and go out there, get those data points for the certain parameters that need to be entered into the questionnaire and bring it back with context. 387 00:37:15,480 --> 00:37:22,040 So then the abstract or themselves as they work through, they can then reinforce that learning. 388 00:37:22,040 --> 00:37:32,200 And essentially what they do is one, they increase their efficiency to their training, something that is basically a knowledge capture of what they know and understand. 389 00:37:32,200 --> 00:37:33,760 And three, it sets them up. 390 00:37:33,760 --> 00:37:48,000 It sets the person up who's going to take over for them as having a much, much not a much less steep learning curve to take on that work after they retire. 391 00:37:48,000 --> 00:37:51,720 And so that's it's still ideation. 392 00:37:51,720 --> 00:37:57,280 I know I'm talking with one company about the possibility of that. 393 00:37:57,280 --> 00:38:11,120 And so but that's like super exciting for me because I've always been big on like with these clinical registries, there's a lot of like single fail points because there's a very deep knowledge expert that sits with those registries. 394 00:38:11,120 --> 00:38:15,240 And to lose that knowledge is a huge knowledge loss. 395 00:38:15,240 --> 00:38:24,000 And so it's always been like a bug, like a thorn in my side in terms of how do we prevent that single failure point with this? 396 00:38:24,000 --> 00:38:29,200 And this looks to be possibly a promising way of doing that. 397 00:38:29,200 --> 00:38:45,680 Yeah, I love hearing about efficiencies that can get built into organizations like hospital or health care because of how that opens up potential for people to get better health care or the right people to get access to the right thing or for it to scale in a way that it wasn't before. 398 00:38:45,680 --> 00:38:59,320 And so those those outcomes and the people's lives that are actually affected by the work of health care are improved in meaningful, tangible ways through like technology efficiencies and data efficiencies in the background. 399 00:38:59,320 --> 00:39:13,000 I was thinking the same thing, like as you're talking about outcomes, Joe, I'm thinking there's still a clear outcome that is different from most of corporate America, where it's let's just up the profits in our organization. 400 00:39:13,000 --> 00:39:23,400 And there's nothing wrong with that. But there's a unique mission that health care has in the improvement of people's lives and well-being and all of those kinds of outcomes. 401 00:39:23,400 --> 00:39:39,720 And the fact that you do even have those benchmarks shows that there's concern for that aspect of someone's life that often gets missed in consumer goods and other kinds of services that are out there that aren't focused on human well-being. 402 00:39:39,720 --> 00:39:48,640 So I think that's a neat distinctive that certain nonprofit, not-for-profit organizations get to focus on. 403 00:39:48,640 --> 00:40:03,760 And it brings meaning to jobs that may be very energizing and exciting, as I'm sure you even relate to having been an RN and having that boots on the ground experience, caring for patients. 404 00:40:03,760 --> 00:40:08,240 And now you are translating that into your data experience. 405 00:40:08,240 --> 00:40:14,280 I think that's a really neat part of this conversation that I've enjoyed listening to you talk about. 406 00:40:14,280 --> 00:40:22,400 Yeah, and I would just temper that with and I 100% agree with you, but I would temper that with saying every business is financially driven. 407 00:40:22,400 --> 00:40:26,920 So always have defensive metrics set up and ready to go. 408 00:40:26,920 --> 00:40:37,680 For instance, my my biostatisticians and the research group, like that's it's amazing that it still exists because there's not it's not like we're generating revenue, right? 409 00:40:37,680 --> 00:40:41,880 Like a lot of what a lot of my group, there's no direct revenue generation. 410 00:40:41,880 --> 00:40:45,080 We have a few few projects that do generate revenue. 411 00:40:45,080 --> 00:40:50,880 But so far as my research group, a big thing is like papers that we publish from that group. 412 00:40:50,880 --> 00:40:53,280 It's not just we're publishing papers. 413 00:40:53,280 --> 00:40:57,600 It's how many papers are we publishing in top quartile journals? 414 00:40:57,600 --> 00:41:00,520 Right now, it's over 80 percent in the last year. 415 00:41:00,520 --> 00:41:06,560 How many people are getting eyes on this in terms of the citations from each paper? 416 00:41:06,560 --> 00:41:18,000 In terms of how many news outlets have picked up some of the publications, how many blog posts, how many mentions on X and, you know, formerly known as Twitter. 417 00:41:18,000 --> 00:41:27,760 And, you know, that those kind of metrics are super important because the bean counters aren't really thinking in that way. 418 00:41:27,760 --> 00:41:33,400 So being able to show them, being able to tell them that story of, hey, look, this is marketing. 419 00:41:33,400 --> 00:41:39,200 Like, that's what this is like you. You cannot look at this purely as a cost center. 420 00:41:39,200 --> 00:41:44,920 This is something that actually gives meaning, whether it's marketing for the company, 421 00:41:44,920 --> 00:41:54,360 progressing the science or helping clinical outcomes and making things better for the patients because we're using that data to drive improvement. 422 00:41:54,360 --> 00:42:02,640 And so those those kind of things like you have to be able to talk about that and convey that to those people that come in and they're just looking at the bottom line. 423 00:42:02,640 --> 00:42:08,320 Because, yeah, like at the end of the day, it's a business and you have to be financially profitable. 424 00:42:08,320 --> 00:42:15,720 And we just saw this UPMC laid off over a thousand people last week because we're in financial trouble. 425 00:42:15,720 --> 00:42:24,040 And because of that, like you that's when those defensive metrics, when those discussions happen, you want to be at the table, be able to say, look, 426 00:42:24,040 --> 00:42:29,880 this is what the team is capable of doing, this is what they've done, this is why they need to stay. 427 00:42:29,880 --> 00:42:42,240 And that is regardless of the altruistic goals of your company that will ultimately be a discussion at some point in most careers. 428 00:42:42,240 --> 00:42:45,200 Like, how do we keep this company financially viable? 429 00:42:45,200 --> 00:42:50,320 And you need to look at your services and what you're offering, what makes sense and what doesn't make sense. 430 00:42:50,320 --> 00:42:56,400 And you need to be honest with yourself, too. Like, is what we're doing really worth keeping here? 431 00:42:56,400 --> 00:43:04,640 And so there's those are tough decisions and those are decisions that people that move into leadership will naturally have to make. 432 00:43:04,640 --> 00:43:08,880 But if you're an individual contributor, like you should be thinking about, OK, 433 00:43:08,880 --> 00:43:17,320 how do I measure what our team is doing and the output that we're putting out there and make sure that the person leading your team, 434 00:43:17,320 --> 00:43:21,120 either they're tracking that or you get them thinking about it? 435 00:43:21,120 --> 00:43:29,280 Because if someone comes to you and you don't have any way of saying, here's our productivity, here's what we're doing for the work, I'm sorry. 436 00:43:29,280 --> 00:43:33,480 You're not going to get much sympathy from people that want to cut services. 437 00:43:33,480 --> 00:43:37,560 Joe, this has been fun. And we're coming a little a little bit closer on time. 438 00:43:37,560 --> 00:43:41,560 But a couple of questions before we're wrapping up. 439 00:43:41,560 --> 00:43:48,440 Tell me tell me something you do for fun outside of work. Who is Joe behind director of science and analytics? 440 00:43:48,440 --> 00:43:53,240 You know, I'm a robot. I don't do anything fun. 441 00:43:53,240 --> 00:43:58,360 All day, people are just robots. Yeah. 442 00:43:58,360 --> 00:44:03,320 I'm actually Derek, you know. 443 00:44:03,320 --> 00:44:10,360 So I have a I have a wife and three kids, eight, six and three. 444 00:44:10,360 --> 00:44:14,040 And so a lot of my time is spent with them. 445 00:44:14,040 --> 00:44:20,800 And so, you know, like going to soccer games or going to ice skating, things like that. 446 00:44:20,800 --> 00:44:26,560 And, you know, for fun, most recently, I've been training for half marathon. 447 00:44:26,560 --> 00:44:30,800 So that's been some of my some of my stuff that I do for fun. 448 00:44:30,800 --> 00:44:38,400 I think it's debatable whether half marathons are fun or not, but that's fun for you. 449 00:44:38,400 --> 00:44:40,520 Yeah. I mean, those are the big things that I do. 450 00:44:40,520 --> 00:44:45,880 I don't have like I my my hobbies vary. It all depends on the day of the week. 451 00:44:45,880 --> 00:44:53,000 I was really big in a DIY for a couple of years into winemaking for a while. 452 00:44:53,000 --> 00:44:58,240 You know, like different random stuff that you just pick up and you start doing. 453 00:44:58,240 --> 00:45:08,120 And I think it's probably one of the big reasons why I got into data is because it lets you marry that creativity with the right sided brain. 454 00:45:08,120 --> 00:45:10,680 And that's also one of the reasons why I couldn't go into the arts. Right. 455 00:45:10,680 --> 00:45:14,240 Like I'm not left brain enough to just go like crazy. 456 00:45:14,240 --> 00:45:17,360 So I have that right brain like pulling you back constantly. 457 00:45:17,360 --> 00:45:25,720 And data gives me a nice balance with that where you can be creative, but also use the like the logical side of your brain as well. 458 00:45:25,720 --> 00:45:31,040 Excellent. Excellent. For sure. I think Troy and I both feel the same way about about the data world. 459 00:45:31,040 --> 00:45:37,680 So before we wrap up, tell me tell tell listeners about where they can find out more about you online. 460 00:45:37,680 --> 00:45:43,240 I know you do you do a lot of work around helping people get into data and you have some content stuff around there. 461 00:45:43,240 --> 00:45:47,160 So, yeah, people want to find out more about you or look up some of the content you put out. 462 00:45:47,160 --> 00:45:50,920 Where should they go? Where should they go? Yeah, I mean, I really just focus on LinkedIn. 463 00:45:50,920 --> 00:45:55,800 I don't have I haven't really fostered any other social outlets. 464 00:45:55,800 --> 00:46:00,280 But if you if you just look for Joe Squire on LinkedIn, you can find me. 465 00:46:00,280 --> 00:46:04,160 And that's a lot of the work that I'm doing there. 466 00:46:04,160 --> 00:46:10,960 The original content was more more on that, like like helping people get in and up skill and data. 467 00:46:10,960 --> 00:46:15,720 And I still do some of that stuff with the base, like the technical stuff like out there. 468 00:46:15,720 --> 00:46:22,800 It's shifted a little more with more toward like data leadership and those concepts. 469 00:46:22,800 --> 00:46:26,480 And it'll shift more. I'm going back for my master's in data science. 470 00:46:26,480 --> 00:46:32,000 So I think it'll be more on the advanced analytics side in the near term here. 471 00:46:32,000 --> 00:46:36,240 Good stuff. Excellent. Well, Joe, thanks so much for your time today and for joining us. 472 00:46:36,240 --> 00:46:40,080 One last question before you go. I've got to get this in there. 473 00:46:40,080 --> 00:46:47,840 I'm a huge dad joke fan. So I just have to ask you, what's the richest kind of heir? 474 00:46:47,840 --> 00:46:53,480 The richest kind. I don't know. 475 00:46:53,480 --> 00:46:59,040 Billionaire. Oh, painful, painful. 476 00:46:59,040 --> 00:47:03,400 All right. Thank you, Joe. Joe, thanks so much. 477 00:47:03,400 --> 00:47:11,320 Pleasure having you here. All right, folks, that's it for today. 478 00:47:11,320 --> 00:47:17,920 Thanks for joining us on Making Data Matter. I'm Sawyer Nyquist and this was Joe Squire and Troy Dueck. 479 00:47:17,920 --> 00:47:29,920 Thanks so much, everybody. We'll see you next time.