0304. When Data Exists but No One Sees the Full Picture

1 00:01:01,149 - > 00:01:01,710 SPEAKER_00: Here we are. 2 00:01:01,869 - > 00:01:05,549 This will be uh we're gonna take a little break after this 3 00:01:05,549 - > 00:01:10,829 episode, but I think we have an important topic today that we um 4 00:01:11,390 - > 00:01:14,590 we don't spend enough time thinking about when we are 5 00:01:14,590 - > 00:01:19,310 developing systems within our organizations, when we are 6 00:01:19,789 - > 00:01:26,349 bringing in new technology, we're bringing in new uh ways of 7 00:01:26,349 - > 00:01:26,990 working. 8 00:01:27,549 - > 00:01:32,030 And you know, this can look like you know, from the scientific 9 00:01:32,030 - > 00:01:36,750 side of like benching and and rippling, and you have other 10 00:01:36,990 - > 00:01:40,829 systems that pull out articles, and you have other systems for 11 00:01:40,829 - > 00:01:44,750 project management, and you have other systems to manage legal, 12 00:01:45,069 - > 00:01:48,829 other systems to manage drug dispensation and and clinical 13 00:01:48,829 - > 00:01:49,310 trials. 14 00:01:49,549 - > 00:01:50,109 There's a lot. 15 00:01:50,270 - > 00:01:54,509 There's a lot of software that's available, and when we pull 16 00:01:54,509 - > 00:01:59,229 these things in and we fail to connect them or even see how 17 00:01:59,229 - > 00:02:04,109 they can be connected, we start to start to struggle in seeing 18 00:02:04,270 - > 00:02:07,389 what is what is the sum of all that we're doing. 19 00:02:08,110 - > 00:02:12,349 What are the gaps that we need support? 20 00:02:13,229 - > 00:02:17,469 We're talking about our digital operations and data flow, how 21 00:02:17,710 - > 00:02:22,270 information flows from one space into another, how we receive it, 22 00:02:22,909 - > 00:02:28,349 how we bring context together to deliver reports to leadership. 23 00:02:28,590 - > 00:02:33,710 So a huge, you know, I think in 2026 it's huge for us to think 24 00:02:34,349 - > 00:02:38,270 more critically about how we are doing these things in our 25 00:02:38,270 - > 00:02:42,349 organizations and within our teams, and the impact of 26 00:02:42,669 - > 00:02:48,830 ignoring it or just going on our own sort of adventure to uh, you 27 00:02:48,830 - > 00:02:51,629 know, jump from one system to another. 28 00:02:51,870 - > 00:02:57,069 I feel like we've been at, you know, such an eye-opening 29 00:02:57,069 - > 00:03:02,349 junction here from our early days in pharma, in biotech, uh, 30 00:03:02,430 - > 00:03:06,349 to where we are now where we're integrating AI and we're using 31 00:03:06,349 - > 00:03:11,949 agents and bots to help complete tasks, et cetera, that sort of 32 00:03:11,949 - > 00:03:15,709 create just another avenue where data can sit. 33 00:03:15,870 - > 00:03:16,909 Well, let's talk about that today. 34 00:03:17,150 - > 00:03:19,389 SPEAKER_01: Yeah, I think there's a lot of tools out there 35 00:03:19,389 - > 00:03:24,590 now that you can leverage that not only help you analyze the 36 00:03:24,590 - > 00:03:27,789 data and make decisions, but it actually sits on top of those 37 00:03:27,789 - > 00:03:31,150 applications so that you can actually gather more insight 38 00:03:31,150 - > 00:03:33,150 across multiple applications, right? 39 00:03:33,389 - > 00:03:36,430 So I I think we used to have even when we started in the 40 00:03:36,430 - > 00:03:39,389 industry, you certain data types would be siloed and you couldn't 41 00:03:39,469 - > 00:03:43,629 like move them essentially, and you'd have to maybe download you 42 00:03:43,629 - > 00:03:47,870 know data from this thing and then put it into a uh a Google 43 00:03:47,870 - > 00:03:52,349 Drive or OneDrive folder and then share point, SharePoint, 44 00:03:52,509 - > 00:03:54,590 whatever it is, and and you know, all these different data 45 00:03:54,590 - > 00:03:57,150 types and try to make sense of what it is that you're trying to 46 00:03:57,150 - > 00:03:57,310 do. 47 00:03:57,469 - > 00:04:01,469 But I think now, like you said, we have all these like different 48 00:04:01,469 - > 00:04:04,430 terminology and and tools that we can use, like a data lake 49 00:04:04,590 - > 00:04:06,909 where you can put different data types in there and then you can 50 00:04:06,909 - > 00:04:08,989 actually pull out of it to create the things that you need 51 00:04:08,989 - > 00:04:09,229 to do. 52 00:04:09,389 - > 00:04:14,509 But all of those fancy tools are they're only good if you're, I 53 00:04:14,509 - > 00:04:17,230 guess, you know, like we were saying, digital operations. 54 00:04:17,389 - > 00:04:20,750 It's it's really the the data flow and the architecture of how 55 00:04:20,750 - > 00:04:23,629 those pathways are created, so you can actually do those 56 00:04:23,629 - > 00:04:24,189 things. 57 00:04:24,430 - > 00:04:28,269 If you don't have that set up, you cannot harness the full 58 00:04:28,269 - > 00:04:30,910 capability of all the tools that we have currently. 59 00:04:31,069 - > 00:04:35,870 SPEAKER_00: So I think there's you hanging your hat on that 60 00:04:37,069 - > 00:04:40,590 just because you have a lot of data does not mean that you can 61 00:04:40,590 - > 00:04:43,709 just layer something on top and it'll tell you what's happening. 62 00:04:44,430 - > 00:04:44,750 SPEAKER_01: Yeah. 63 00:04:44,910 - > 00:04:50,430 If if you can't connect it, then how do you uh how do you make 64 00:04:50,430 - > 00:04:51,389 sense of it, right? 65 00:04:51,629 - > 00:04:56,509 And I think it's it's rare in in 2026 where you're only making 66 00:04:56,509 - > 00:04:59,389 decisions for your team or your business based on one 67 00:04:59,389 - > 00:04:59,870 application. 68 00:05:00,110 - > 00:05:02,750 The business is so much more complicated these days that you 69 00:05:02,750 - > 00:05:05,550 know you'd have to look at, especially in the realm of 70 00:05:05,550 - > 00:05:08,670 facilities management, you know, we're looking at not only the 71 00:05:08,829 - > 00:05:13,550 maintenance data for how you manage specific buildings or 72 00:05:13,870 - > 00:05:17,469 pieces of equipment, but you're also looking at the utilization 73 00:05:17,469 - > 00:05:19,469 of the equipment itself, right? 74 00:05:19,790 - > 00:05:22,990 Very every year companies go through this exercise where they 75 00:05:22,990 - > 00:05:26,430 have to allocate uh funding for purchasing new equipment. 76 00:05:26,829 - > 00:05:30,269 And it used to be the case where you would justify, hey, I have a 77 00:05:30,269 - > 00:05:33,230 business case, like you know, I have so-and-so change in the 78 00:05:33,230 - > 00:05:36,430 pipeline, and so we need to buy additional equipment to increase 79 00:05:36,430 - > 00:05:37,310 our capacity. 80 00:05:37,709 - > 00:05:41,149 But where are they coming up with the projections on what the 81 00:05:41,149 - > 00:05:42,670 current capacity is, right? 82 00:05:42,750 - > 00:05:44,750 If you don't have the data to tell you. 83 00:05:44,990 - > 00:05:47,949 And so you'd have to, you know, do some calculations in one 84 00:05:47,949 - > 00:05:50,110 system and then move it over to the other, then you'd have to go 85 00:05:50,110 - > 00:05:52,589 to your maintenance system, pull more data on that. 86 00:05:52,750 - > 00:05:55,069 And so that's just a very simple use case. 87 00:05:55,149 - > 00:05:58,029 That's not even considering any of the things that you have in 88 00:05:58,029 - > 00:05:59,629 your quality management system. 89 00:05:59,870 - > 00:06:03,550 Like if you have any deviations and problems that come up that 90 00:06:03,550 - > 00:06:06,750 are actually product impacting, you might want to take a look at 91 00:06:06,750 - > 00:06:09,069 that and decide, yeah, let's not buy that piece of equipment 92 00:06:09,069 - > 00:06:10,349 anymore because it sucks. 93 00:06:12,110 - > 00:06:14,430 SPEAKER_00: We've had to repair it every six months, you know, 94 00:06:14,589 - > 00:06:15,310 big repairs. 95 00:06:15,469 - > 00:06:17,870 Like that's that's you know, and there's a lot of options out 96 00:06:17,870 - > 00:06:17,949 there. 97 00:06:18,110 - > 00:06:18,829 That's exactly right. 98 00:06:18,990 - > 00:06:22,750 I think that's a great example of you know how you can use that 99 00:06:22,750 - > 00:06:25,709 data to better inform those future decisions. 100 00:06:25,870 - > 00:06:29,310 And, you know, you mentioned I I I've also heard the terms, you 101 00:06:29,310 - > 00:06:32,269 know, such as the data late, data warehouse. 102 00:06:32,509 - > 00:06:36,430 I think now where we're finding ourselves is sort of in this, 103 00:06:36,670 - > 00:06:40,029 you know, persona of we have plenty of data, we're collecting 104 00:06:40,029 - > 00:06:43,149 stuff all the time, we have slides everywhere, but we are 105 00:06:43,149 - > 00:06:47,149 struggling to create a shared picture of what's happening with 106 00:06:47,149 - > 00:06:50,670 our projects, what's happening with our organization, how our 107 00:06:50,670 - > 00:06:54,509 resources are actually being allocated, you know, and some 108 00:06:54,509 - > 00:06:57,870 folks uh, you know, they they make the uh the attempt to say, 109 00:06:58,029 - > 00:07:02,189 let's I used to have to put by project how what percentage of 110 00:07:02,189 - > 00:07:02,430 time. 111 00:07:02,589 - > 00:07:05,230 And that doesn't have anything to do with how many hours or 112 00:07:05,230 - > 00:07:07,709 what part of the project was it, you know, the project was. 113 00:07:07,870 - > 00:07:09,310 And all of these things matter. 114 00:07:09,389 - > 00:07:13,389 Like you said, the complexity of the of the projects are just 115 00:07:13,389 - > 00:07:18,269 increasing, you know, now and and it's not just because of the 116 00:07:18,269 - > 00:07:20,990 science that we're doing, it's also because of the guardrails 117 00:07:21,069 - > 00:07:23,709 that we have to put on some of these, you know, new 118 00:07:23,709 - > 00:07:27,550 technologies or new therapies that, you know, may not have 119 00:07:27,550 - > 00:07:29,550 precedent, you know, from the past. 120 00:07:29,629 - > 00:07:32,110 So we're sort of developing those things as we go. 121 00:07:32,269 - > 00:07:36,189 So, you know, we're we're we're really we've talked about why 122 00:07:36,189 - > 00:07:39,469 it's important and why we care about data flow and digital 123 00:07:39,469 - > 00:07:42,670 operations, and we're sort of bucketing a lot of these things 124 00:07:42,670 - > 00:07:47,790 that I think many of us have experienced where there's this 125 00:07:47,790 - > 00:07:53,310 abundance of information, but we don't know what's real, we don't 126 00:07:53,310 - > 00:07:54,670 know what the source is. 127 00:07:54,829 - > 00:07:58,430 Sometimes we don't even know who provided it, who came up with 128 00:07:58,430 - > 00:07:59,069 this numbers. 129 00:07:59,230 - > 00:08:02,990 How did it, how did it come to be that this becomes now our 130 00:08:02,990 - > 00:08:03,870 benchmark? 131 00:08:04,029 - > 00:08:04,990 Who created that? 132 00:08:05,149 - > 00:08:06,430 Was it somebody from this group? 133 00:08:06,589 - > 00:08:07,949 Was it somebody from a different group? 134 00:08:08,110 - > 00:08:09,709 Was there an analysis that was done? 135 00:08:09,870 - > 00:08:13,949 And it creates this sort of ambiguous feeling in terms of 136 00:08:13,949 - > 00:08:15,469 the data that you're collecting. 137 00:08:15,629 - > 00:08:19,230 Well, we're just going through the motions and then we just 138 00:08:19,230 - > 00:08:21,629 respond based on what leadership is asking for. 139 00:08:21,790 - > 00:08:23,870 Then we try to find out what it is. 140 00:08:24,189 - > 00:08:28,189 But I think the power and the way that we can leverage this 141 00:08:28,189 - > 00:08:32,590 data is anticipating what those questions are and developing 142 00:08:32,590 - > 00:08:35,389 that structure, that architecture to say these are 143 00:08:35,389 - > 00:08:38,190 the things that are most important to this organization. 144 00:08:38,590 - > 00:08:42,190 Probably has something to do with funding, probably has 145 00:08:42,190 - > 00:08:46,269 something to do with timing, how long things take, you know, the 146 00:08:46,269 - > 00:08:47,310 phase of development. 147 00:08:47,550 - > 00:08:50,909 Where are these projects or where, you know, are you 148 00:08:50,909 - > 00:08:52,110 starting a new facility? 149 00:08:52,269 - > 00:08:53,790 Are you maintaining a new facility? 150 00:08:53,950 - > 00:08:55,230 Are you moving a new facility? 151 00:08:55,389 - > 00:08:57,870 Are you integrating new, you know, new equipment? 152 00:08:57,950 - > 00:09:01,310 You know, how are all these things going to flow as opposed 153 00:09:01,310 - > 00:09:03,310 to, well, we're just gonna set it over there and then it'll 154 00:09:03,310 - > 00:09:05,070 just have a dedicated computer? 155 00:09:05,470 - > 00:09:08,909 I mean, that that's great, but then when you try to, you know, 156 00:09:08,990 - > 00:09:12,509 collect the data to develop insights, you're gonna find that 157 00:09:12,509 - > 00:09:15,550 you're you're gonna end up with a real struggle because what 158 00:09:15,550 - > 00:09:20,590 ends up happening is these metrics that we really covet are 159 00:09:20,590 - > 00:09:21,470 in silos. 160 00:09:21,710 - > 00:09:25,230 Well, that data is just in that computer that's not connected, 161 00:09:25,310 - > 00:09:28,190 uh, you know, any of our other digital spaces. 162 00:09:28,350 - > 00:09:33,950 We'll find conflicting reports where you know you might have 163 00:09:33,950 - > 00:09:35,070 timing shifts. 164 00:09:35,230 - > 00:09:38,830 Well, the research group says that this is gonna take about 165 00:09:38,830 - > 00:09:42,990 this long, but we got things from the portfolio team that it 166 00:09:42,990 - > 00:09:44,590 should be six months longer than that. 167 00:09:44,750 - > 00:09:46,590 Like, how are we so far apart? 168 00:09:46,830 - > 00:09:49,230 Well, the data that we're working with is not the same 169 00:09:49,230 - > 00:09:51,790 data that we're, you know, we don't have the same baseline 170 00:09:51,790 - > 00:09:54,830 here of information to move forward. 171 00:09:55,070 - > 00:09:57,310 And it continues to cycle. 172 00:09:57,390 - > 00:10:00,670 And I'm sure you've been in a position where you know you get 173 00:10:00,670 - > 00:10:04,590 a request and it becomes leadership getting a request 174 00:10:04,590 - > 00:10:07,150 from that group, you know, sending a request to that group, 175 00:10:07,550 - > 00:10:11,230 sending another one to, you know, this team here, and 176 00:10:11,230 - > 00:10:14,830 sending another one to, you know, the the whatever the 177 00:10:14,830 - > 00:10:17,310 portfolio of the project management team over here. 178 00:10:17,629 - > 00:10:22,430 And they're spending the time to collate what am I trying to see 179 00:10:22,430 - > 00:10:22,670 here? 180 00:10:23,550 - > 00:10:28,990 You know, we are in an age where you know knowledge, you know, 181 00:10:29,150 - > 00:10:30,110 knowledge is power. 182 00:10:30,190 - > 00:10:35,310 We talk that much is is said, but the data cannot become 183 00:10:35,310 - > 00:10:37,950 knowledge if we can't create any context out of it. 184 00:10:38,190 - > 00:10:41,710 So even though we've done a lot of experiments, we've done a lot 185 00:10:41,710 - > 00:10:45,629 of tracking, we've done a lot of collating of the information and 186 00:10:45,629 - > 00:10:50,830 the data that we're producing, if you're not creating the flow, 187 00:10:51,070 - > 00:10:53,230 it becomes challenging. 188 00:10:53,550 - > 00:10:57,790 It becomes an arduous task that you have to give probably four 189 00:10:57,790 - > 00:11:04,430 or five people that are not there to compile, to synthesize, 190 00:11:04,830 - > 00:11:06,670 you know, all day, every day. 191 00:11:06,750 - > 00:11:09,230 They're probably running from one meeting to the next, and you 192 00:11:09,230 - > 00:11:12,110 start to find delays and you start to create, you start to 193 00:11:12,110 - > 00:11:17,470 make decisions that are not based on the data that you have. 194 00:11:17,710 - > 00:11:21,790 It's based on a piece of data that people could see. 195 00:11:22,190 - > 00:11:24,030 And that's when we start to have problems. 196 00:11:24,190 - > 00:11:26,750 That's when we start to, you know, our timeline slow down, 197 00:11:26,830 - > 00:11:30,830 our budgets start to wither away, the you know, onboarding 198 00:11:30,830 - > 00:11:36,590 of assets starts to become uh a daunting task as opposed to a 199 00:11:36,590 - > 00:11:38,430 nice flow of operations. 200 00:11:38,590 - > 00:11:39,950 And that's what we're talking about here. 201 00:11:40,190 - > 00:11:43,070 SPEAKER_01: Yeah, I I think um the there's a there's definitely 202 00:11:43,070 - > 00:11:47,629 a correlation between digital operations and I'm just gonna 203 00:11:47,629 - > 00:11:49,629 call them physical operations because it's things that you're 204 00:11:49,629 - > 00:11:52,350 gonna you're gonna see throughout work, right? 205 00:11:52,510 - > 00:11:55,870 I I think a really good example of this is um like you you were 206 00:11:55,870 - > 00:11:58,910 talking about before, especially in in the lab environment where 207 00:11:58,910 - > 00:12:01,310 you have certain experiments that are done for certain 208 00:12:01,310 - > 00:12:01,950 programs. 209 00:12:02,190 - > 00:12:05,390 I think when you don't have enough information, especially 210 00:12:05,390 - > 00:12:07,470 around inventory management, supplies, right? 211 00:12:07,550 - > 00:12:10,430 Just we'll we'll just focus on lab supplies, is that when you 212 00:12:10,430 - > 00:12:13,390 fluctuate and do these different types of experiments, it's gonna 213 00:12:13,390 - > 00:12:16,430 increase or decrease the amount of materials that you need for 214 00:12:16,430 - > 00:12:17,230 certain things. 215 00:12:17,470 - > 00:12:20,590 And so if you don't have the right systems in place to 216 00:12:20,590 - > 00:12:23,870 collect the information on how much are you buying of this 217 00:12:23,870 - > 00:12:28,190 particular pipette or you know, these particular plastics and 218 00:12:28,190 - > 00:12:31,070 things like that, it creates a real disruption in how people 219 00:12:31,070 - > 00:12:32,910 physically work with one another. 220 00:12:33,070 - > 00:12:36,190 And it's it's all because you you're you don't have enough 221 00:12:36,190 - > 00:12:40,430 context to understand when we make these decisions for the 222 00:12:40,430 - > 00:12:43,310 rest of the company, is there something within our digital 223 00:12:43,310 - > 00:12:47,070 operations side that can inform us so that we know the impact to 224 00:12:47,230 - > 00:12:50,750 how physically it's going to influence our not only 225 00:12:50,750 - > 00:12:54,030 employees, but how things are going to get done uh across not 226 00:12:54,030 - > 00:12:55,950 one function, but multiple functions, right? 227 00:12:56,190 - > 00:13:00,190 And so I I think to your point before, it's uh it's often 228 00:13:00,190 - > 00:13:04,670 something that nobody looks at with the right lens just because 229 00:13:04,750 - > 00:13:09,230 in it's it's not easy to tell when things are siloed, like 230 00:13:09,310 - > 00:13:12,110 when you're looking at it on the computer, but when you talk to 231 00:13:12,110 - > 00:13:15,390 people, it's very, very clear that people are complaining, uh, 232 00:13:15,470 - > 00:13:17,710 well, I didn't find this, or this information's not here, or 233 00:13:17,710 - > 00:13:19,310 this system doesn't talk to this system. 234 00:13:19,470 - > 00:13:21,950 But when you look at the application, it it's not going 235 00:13:21,950 - > 00:13:22,750 to speak back to you. 236 00:13:22,830 - > 00:13:25,550 It's it's when we talk to our coworkers, when we talk to our 237 00:13:25,550 - > 00:13:29,150 teams, it's this thing like, where the hell am I gonna get 238 00:13:29,150 - > 00:13:29,710 this information? 239 00:13:29,790 - > 00:13:30,830 Because it's just not here. 240 00:13:31,629 - > 00:13:36,030 SPEAKER_00: So back to my previous point, um, we've in 241 00:13:36,030 - > 00:13:39,790 some cases we can be a little lazy when trying to point 242 00:13:39,790 - > 00:13:42,270 somebody to where where this information lives. 243 00:13:42,510 - > 00:13:44,350 Might say, oh, go and SharePoint. 244 00:13:45,310 - > 00:13:48,670 Have you ever been inside of a SharePoint for a company that's 245 00:13:48,670 - > 00:13:51,629 been in business for you know five, 10 years? 246 00:13:51,870 - > 00:13:58,030 There are thousands of places that you can wander in there and 247 00:13:58,030 - > 00:13:59,470 still not even come close. 248 00:13:59,629 - > 00:14:04,030 And and then your initial understanding of what you were 249 00:14:04,030 - > 00:14:07,950 expecting to see doesn't always match up what you end up seeing. 250 00:14:08,190 - > 00:14:11,629 So it just becomes this vicious cycle of great, I spent all this 251 00:14:11,629 - > 00:14:14,030 time and it doesn't actually show me anything. 252 00:14:14,270 - > 00:14:19,790 You know what we're when so so what happens when we don't 253 00:14:19,790 - > 00:14:22,990 prioritize responsible digital operations? 254 00:14:23,150 - > 00:14:25,870 These are the things that ends up happening where we're 255 00:14:25,870 - > 00:14:29,790 searching for data, we can't quite create the context. 256 00:14:30,030 - > 00:14:33,629 We may have to transcribe over and over again, like you said, 257 00:14:33,790 - > 00:14:37,470 different systems have different naming, different nomenclature 258 00:14:37,629 - > 00:14:41,550 that don't align with how we've presented, whether to 259 00:14:41,550 - > 00:14:45,070 leadership, whether into, you know, um, you know, connecting 260 00:14:45,070 - > 00:14:46,430 it across systems. 261 00:14:46,590 - > 00:14:50,110 And these are where it's it's it's real critical to have that 262 00:14:50,110 - > 00:14:52,830 unified data structure, even as things come in. 263 00:14:52,990 - > 00:14:56,350 Okay, now that things are coming in, let's make sure that we have 264 00:14:56,350 - > 00:14:57,390 some baselines. 265 00:14:57,550 - > 00:15:01,310 Here's the projects, here's roles or functions that are 266 00:15:01,310 - > 00:15:04,990 responsible for these things so that we can now start to create 267 00:15:04,990 - > 00:15:07,710 different views across your portfolio because that's 268 00:15:07,710 - > 00:15:10,430 ultimately what leadership is going to be looking for. 269 00:15:10,670 - > 00:15:14,750 Yes, the science is important, yes, the the location that 270 00:15:14,750 - > 00:15:18,750 you're at is important, the resources are important, but 271 00:15:18,750 - > 00:15:21,150 they cannot be viewed in a bubble. 272 00:15:21,230 - > 00:15:24,990 They need to be viewed together to have the right context to 273 00:15:24,990 - > 00:15:27,870 make the right decisions because you can always come back and 274 00:15:27,870 - > 00:15:31,390 say, well, what about the number of people that are on that 275 00:15:31,390 - > 00:15:31,790 project? 276 00:15:32,030 - > 00:15:34,830 Well, how long did this project take? 277 00:15:34,910 - > 00:15:39,310 And what were the resource needs for that, for that type of 278 00:15:39,310 - > 00:15:41,710 project or for that type of initiative? 279 00:15:42,030 - > 00:15:48,750 And you know, it's I go back to the frequency that I've seen in 280 00:15:49,230 - > 00:15:54,910 even in leadership discussions where seemingly questions that 281 00:15:54,910 - > 00:15:58,750 we should already have answers to are followed up with, I'll 282 00:15:58,750 - > 00:15:59,310 get back to you. 283 00:15:59,470 - > 00:16:00,590 I'll need to get back to you. 284 00:16:00,990 - > 00:16:03,629 It's a complex question, absolutely. 285 00:16:04,430 - > 00:16:08,190 But there should be a bare minimum to what we can just pull 286 00:16:08,190 - > 00:16:11,390 right up because we have the right architecture, we have the 287 00:16:11,390 - > 00:17:08,660 right data flow and the right people managing those systems, 288 00:17:08,900 - > 00:17:10,740 because that's the reality of it too. 289 00:17:10,980 - > 00:17:13,860 This is not any one person's responsibility. 290 00:17:14,019 - > 00:17:17,779 You know, we talk about in the biopharma Nexus part of you 291 00:17:17,779 - > 00:17:22,180 know, these first principles is that we have to own our of these 292 00:17:22,180 - > 00:17:25,299 shared themes, is that we have to own our software. 293 00:17:25,539 - > 00:17:29,539 If you are the main user of that software, you should know it 294 00:17:29,539 - > 00:17:33,380 more than anybody else at that organization and start reaching 295 00:17:33,380 - > 00:17:36,420 across the aisle to understand, hey, who's creating the 296 00:17:36,420 - > 00:17:37,460 architecture over there? 297 00:17:37,700 - > 00:17:40,259 I want to look at how you guys are naming things so that as 298 00:17:40,259 - > 00:17:43,700 we're building things out, we're gonna be able to connect it in 299 00:17:43,700 - > 00:17:44,340 the future. 300 00:17:44,500 - > 00:17:47,220 You know, these are things that unfortunately are not 301 00:17:47,220 - > 00:17:48,019 prioritized. 302 00:17:48,100 - > 00:17:51,779 It's more of a, you know, let's get it done as fast as we can. 303 00:17:52,100 - > 00:17:56,819 But we're, you know, these things are key for us to 304 00:17:57,140 - > 00:18:02,259 responsibly transform the operations and our day-to-day in 305 00:18:02,259 - > 00:18:05,059 these digital formats successfully. 306 00:18:05,220 - > 00:18:06,819 We just have to do them. 307 00:18:07,619 - > 00:18:10,339 SPEAKER_01: Yeah, I think you and you know, your point about 308 00:18:10,339 - > 00:18:11,619 management asking questions. 309 00:18:11,779 - > 00:18:14,740 I'm almost certain that most of the time they're asking the same 310 00:18:14,740 - > 00:18:15,379 questions. 311 00:18:15,699 - > 00:18:16,819 Where is the timeline? 312 00:18:16,980 - > 00:18:17,779 What are the risks? 313 00:18:17,859 - > 00:18:20,259 SPEAKER_00: Like that's how that's how I build most of my 314 00:18:20,259 - > 00:18:20,980 dashboards. 315 00:18:21,139 - > 00:18:24,659 Let me sit in one of your meetings for about two or three 316 00:18:24,659 - > 00:18:25,059 meetings. 317 00:18:25,619 - > 00:18:28,579 Okay, here's the eight questions that keep coming up. 318 00:18:28,740 - > 00:18:32,179 Let's just build things that answer those questions so that 319 00:18:32,179 - > 00:18:34,179 we start to get better questions. 320 00:18:34,500 - > 00:18:35,859 You nailed it. 321 00:18:36,019 - > 00:18:36,659 Absolutely. 322 00:18:37,139 - > 00:18:40,099 SPEAKER_01: Yeah, asking the same questions over and over 323 00:18:40,099 - > 00:18:40,339 again. 324 00:18:40,500 - > 00:18:43,699 But for some reason, when when new projects or initiatives come 325 00:18:43,699 - > 00:18:46,740 up, we we tend to get very creative in certain ways if 326 00:18:46,899 - > 00:18:51,139 there is no standardized process to create these things, is we 327 00:18:51,139 - > 00:18:54,099 start to customize them, and then now you get this one-off 328 00:18:54,419 - > 00:18:57,619 setup for something and it's not similar to something else. 329 00:18:57,699 - > 00:19:02,179 And now the the flow used to be flowing into a data lake, but 330 00:19:02,179 - > 00:19:04,579 now it's like these different data streams that are just you 331 00:19:04,579 - > 00:19:07,699 know going in separate directions, and it's hard for 332 00:19:07,699 - > 00:19:11,219 anybody to compare apples to apples if if things are going to 333 00:19:11,219 - > 00:19:12,419 be moving that way. 334 00:19:13,059 - > 00:19:15,379 SPEAKER_00: And how do you approach that when you come from 335 00:19:15,379 - > 00:19:16,499 the outside? 336 00:19:16,979 - > 00:19:22,899 It's so difficult to get up to speed when there's no real rhyme 337 00:19:22,899 - > 00:19:25,379 or reason with how things are connected. 338 00:19:25,459 - > 00:19:30,579 You know, there's I recently, I I think maybe earlier today, I 339 00:19:30,819 - > 00:19:35,299 scheduled a post, but it was on this idea that we need to get 340 00:19:35,299 - > 00:19:39,379 comfortable sort of building and archiving, pinning and 341 00:19:39,379 - > 00:19:43,379 unpinning, favoriting and unfavoriting, like to make our 342 00:19:43,379 - > 00:19:45,299 work more appeal more to us. 343 00:19:45,379 - > 00:19:50,659 Like it's okay to build some of these systems and then fold them 344 00:19:50,659 - > 00:19:52,659 away as the business evolves. 345 00:19:52,899 - > 00:19:56,819 I think we find ourselves now in a place where we're always 346 00:19:56,819 - > 00:19:59,859 trying to like, can we just stay with something for a long time 347 00:19:59,859 - > 00:20:01,539 to get really good at it? 348 00:20:01,779 - > 00:20:03,299 In some cases, yes. 349 00:20:03,459 - > 00:20:06,979 But if your company is growing and expanding into areas that 350 00:20:06,979 - > 00:20:11,459 you can't foresee, you have to expect that the system that 351 00:20:11,459 - > 00:20:16,099 you're building might change, but that architecture can very 352 00:20:16,419 - > 00:20:17,939 much stay the same. 353 00:20:18,179 - > 00:20:20,739 And that's how you build resilient systems. 354 00:20:20,899 - > 00:20:23,859 You build them based on an architecture because no matter 355 00:20:23,859 - > 00:20:27,539 what you're doing, or no matter what system you're you're you're 356 00:20:27,539 - > 00:20:32,739 migrating to, you still want the information to tell you the 357 00:20:32,739 - > 00:20:37,059 story that you've been trying to come up with, you know, and 358 00:20:37,059 - > 00:20:41,699 instead of looking at, you know, having you know seven, eight, 359 00:20:41,859 - > 00:20:45,299 nine, ten PowerPoint slides open, and you're now trying to 360 00:20:45,299 - > 00:20:48,499 take little pieces from all of them so that you can craft a 361 00:20:48,499 - > 00:20:48,899 story. 362 00:20:49,139 - > 00:20:52,899 These things should be somewhat available through how your data 363 00:20:52,899 - > 00:20:53,619 is connected. 364 00:20:53,779 - > 00:20:56,099 And I'm not saying that everything is going to be in the 365 00:20:56,099 - > 00:20:59,859 same place because that's the likelihood of that is very slim. 366 00:21:00,019 - > 00:21:03,619 You know, some things are more text heavy, some things are more 367 00:21:03,619 - > 00:21:07,299 data heavy with, you know, with mainly integers, some things may 368 00:21:07,299 - > 00:21:09,139 be contextual, like a CRM. 369 00:21:09,219 - > 00:21:11,379 You know, this is the conversation I had, and it feels 370 00:21:11,379 - > 00:21:14,739 like this, and we should come up, you know, with another uh, 371 00:21:14,819 - > 00:21:15,939 you know, conversation. 372 00:21:16,099 - > 00:21:17,699 So data is gonna look differently. 373 00:21:17,779 - > 00:21:20,259 I'm not saying these things should all live in the same 374 00:21:20,259 - > 00:21:24,739 place, but I think as your organization moves and matures, 375 00:21:24,899 - > 00:21:27,699 you have to think that your systems may do that as well. 376 00:21:27,939 - > 00:21:31,859 But if we maintain that architecture, if you have in the 377 00:21:31,859 - > 00:21:36,019 back of your mind, or even you know, static as a lucid chart 378 00:21:36,019 - > 00:21:42,179 flow or something, that creates the strength and resilience for 379 00:21:42,179 - > 00:21:43,699 when change does happen. 380 00:21:43,859 - > 00:21:48,179 That we know how the data needs to connect, we know who needs 381 00:21:48,179 - > 00:21:49,539 what kind of information. 382 00:21:49,619 - > 00:21:52,339 And I think those are really critical for when there's any 383 00:21:52,339 - > 00:21:56,419 kind of digital transformation for people to be able to develop 384 00:21:56,419 - > 00:22:01,699 something that can move without disrupting how people work. 385 00:22:02,739 - > 00:22:05,459 SPEAKER_01: Yeah, I think there's um a few different ways 386 00:22:05,459 - > 00:22:06,019 to look at it. 387 00:22:06,099 - > 00:22:09,139 And the way that I prefer to look at it, especially for 388 00:22:09,699 - > 00:22:13,219 things that uh these larger companies use, is you look at 389 00:22:13,219 - > 00:22:15,859 the application and you have to understand like what the 390 00:22:16,019 - > 00:22:19,299 different end users are engaging with, right, on the particular 391 00:22:19,299 - > 00:22:20,579 platform or application. 392 00:22:20,659 - > 00:22:22,739 And how does this impact their work day-to-day? 393 00:22:22,899 - > 00:22:26,099 I think a really good example is looking at like a what we would 394 00:22:26,099 - > 00:22:28,339 call a computerized maintenance management system. 395 00:22:28,499 - > 00:22:30,979 So this is where you have all your records for your equipment, 396 00:22:31,059 - > 00:22:32,019 your work orders. 397 00:22:32,259 - > 00:22:35,939 Some of it will have inventory, so you know, your parts that you 398 00:22:35,939 - > 00:22:37,619 need for certain work orders, right? 399 00:22:37,859 - > 00:22:41,139 So for your technician, it'll be really helpful to know, you 400 00:22:41,139 - > 00:22:43,939 know, what is it that I have to do today, you know, what's 401 00:22:43,939 - > 00:22:46,739 coming in the week so I can, you know, create my schedule and 402 00:22:47,219 - > 00:22:50,499 come up with um a plan for how I'm going to do all these things 403 00:22:50,499 - > 00:22:51,459 throughout the week. 404 00:22:51,699 - > 00:22:57,059 I think a another stakeholder that would be a part of that 405 00:22:57,059 - > 00:23:03,379 would be if you have a uh a QA group that has to review um 406 00:23:03,379 - > 00:23:08,419 certain changes that impact uh quality impacting systems, you 407 00:23:08,419 - > 00:23:12,099 would want to have a setup where they can quickly go in there and 408 00:23:12,099 - > 00:23:15,379 view what it is that they need to review and approve so that 409 00:23:15,379 - > 00:23:16,659 certain changes can be made. 410 00:23:17,859 - > 00:23:21,539 And there's, you know, understanding how the the data 411 00:23:21,539 - > 00:23:25,859 is input into the system allows you to construct the report or 412 00:23:25,859 - > 00:23:28,739 the dashboard and the or the information needed for that end 413 00:23:28,739 - > 00:23:29,059 user. 414 00:23:29,219 - > 00:23:32,899 And thinking about it from the the end user perspective, they 415 00:23:32,899 - > 00:23:35,619 don't want to have to go into the application and then click a 416 00:23:35,619 - > 00:23:37,779 thousand different windows to figure out what it is that they 417 00:23:37,779 - > 00:23:38,179 need to do. 418 00:23:38,419 - > 00:23:38,739 Right. 419 00:23:38,979 - > 00:23:41,539 I think they would, you know, for their job, they they want to 420 00:23:41,539 - > 00:23:43,939 know, you know, these are the things that I, you know, the top 421 00:23:43,939 - > 00:23:46,899 three things I need to know to to get through my day. 422 00:23:47,139 - > 00:23:50,419 And I I think you know, one layer above that is gonna be 423 00:23:50,419 - > 00:23:52,179 your your management layer, right? 424 00:23:52,259 - > 00:23:54,739 Where they're looking at, okay, you know, they were. 425 00:23:55,379 - > 00:23:57,939 X number of work orders that were scheduled for the month. 426 00:23:58,019 - > 00:23:58,659 Are we on track? 427 00:23:58,819 - > 00:23:59,379 Are we not? 428 00:23:59,539 - > 00:24:02,419 And so they might go, hey, you know, it looks like we're 429 00:24:02,419 - > 00:24:03,299 struggling a little bit. 430 00:24:03,379 - > 00:24:06,499 So let's let's deploy some more resources to help out this 431 00:24:06,499 - > 00:24:09,859 particular team in resolving those work orders. 432 00:24:10,019 - > 00:24:12,499 Or it might be, hey, it looks like QA is backlog. 433 00:24:12,819 - > 00:24:16,499 Let's figure out how we can help them prioritize the list so that 434 00:24:16,499 - > 00:24:19,459 they can go through the most critical changes first, and then 435 00:24:19,459 - > 00:24:21,699 the other ones can kind of go in the back burner. 436 00:24:21,779 - > 00:24:24,659 But looking at the application and how the different end users 437 00:24:24,659 - > 00:24:26,659 engage with it, I think is really important. 438 00:24:27,699 - > 00:24:31,059 And I think again, this depends on the context, right? 439 00:24:31,219 - > 00:24:35,139 So when you look at a like a portfolio or project management 440 00:24:35,139 - > 00:24:37,859 group, they're looking at a whole bunch of different things 441 00:24:37,859 - > 00:24:39,619 where you have all these different systems, right? 442 00:24:39,779 - > 00:24:42,979 And so chances are they're probably not pulling 100% of all 443 00:24:42,979 - > 00:24:46,019 the different data points that you have in all these systems, 444 00:24:46,099 - > 00:24:49,219 but there's probably select things that they need in order 445 00:24:49,219 - > 00:24:51,699 to report back and for them to make decisions, right? 446 00:24:51,859 - > 00:24:54,179 So that's coming at it from a different lens where you're 447 00:24:54,179 - > 00:24:56,739 looking at your role and then pulling from multiple data 448 00:24:56,739 - > 00:25:00,259 sources versus the initial example where you're an end 449 00:25:00,259 - > 00:25:02,979 user, but you're pulling multiple data sources from the 450 00:25:02,979 - > 00:25:03,939 same application. 451 00:25:04,099 - > 00:25:08,499 And so these things can get really complicated, convoluted, 452 00:25:08,579 - > 00:25:11,219 whatever you want to say, but it's it's good to have that 453 00:25:11,219 - > 00:25:14,499 mapping out so that you can configure the application, the 454 00:25:14,499 - > 00:25:18,259 data flow so that it matches the need for what their job or role 455 00:25:18,259 - > 00:25:18,419 is. 456 00:25:18,659 - > 00:25:19,139 SPEAKER_00: I love that. 457 00:25:19,219 - > 00:25:22,819 You know, I I have here written um, you know, when we talk about 458 00:25:22,819 - > 00:25:25,939 what some of these primary risk factors are, you can end up with 459 00:25:25,939 - > 00:25:28,979 fragmented data structures when you don't take the approach that 460 00:25:28,979 - > 00:25:29,859 you're talking about. 461 00:25:30,019 - > 00:25:31,059 And what does that mean? 462 00:25:31,219 - > 00:25:32,339 I think we've heard it a lot. 463 00:25:32,659 - > 00:25:33,779 We're building it as we go. 464 00:25:34,019 - > 00:25:38,979 You know, I understand that from you know the standpoint of 465 00:25:39,219 - > 00:25:42,979 building a new group or you know, starting a new arm of the 466 00:25:42,979 - > 00:25:46,419 organization, um, or even just starting up a new organization. 467 00:25:46,579 - > 00:25:52,259 But I I think what it sort of becomes this like badge of honor 468 00:25:52,339 - > 00:25:55,139 that, like, oh yeah, you know, we're just we keep working and 469 00:25:55,219 - > 00:25:57,779 you know, we don't know how to prioritize anything, we're just 470 00:25:57,779 - > 00:25:59,539 gonna keep on moving through. 471 00:25:59,779 - > 00:26:02,979 And we have to ask ourselves like, is that really the best 472 00:26:02,979 - > 00:26:07,219 way for us to move this giant shit that we're trying to, which 473 00:26:07,219 - > 00:26:10,499 is the organization, all of its assets, all of its people, into 474 00:26:10,499 - > 00:26:12,179 that successful next stage? 475 00:26:12,419 - > 00:26:18,179 You know, we're not taking a pause to ask ourselves, what are 476 00:26:18,179 - > 00:26:20,819 those things that we need to have happen in the future? 477 00:26:21,059 - > 00:26:23,619 What's the data or the information that is going to 478 00:26:23,619 - > 00:26:28,579 influence that at these various stages so that we can start to 479 00:26:28,579 - > 00:26:34,579 at least, you know, conceptually understand how these things need 480 00:26:34,579 - > 00:26:38,019 to be linked so that we can make those better decisions. 481 00:26:38,179 - > 00:26:40,019 And what do I mean by better decisions? 482 00:26:40,179 - > 00:26:44,659 More informed decisions, as opposed to taking one or two 483 00:26:44,659 - > 00:26:48,659 data points and the comments from a person and say, okay, 484 00:26:48,739 - > 00:26:50,259 we're gonna go in that direction. 485 00:26:50,419 - > 00:26:54,259 We actually have data to back up why we're making the decisions 486 00:26:54,259 - > 00:26:57,459 that we're making, you know, that are the best at that point 487 00:26:57,539 - > 00:27:00,339 for point in time for the for the organization. 488 00:27:00,739 - > 00:27:04,739 So, you know, we're we've seen where there's no single source 489 00:27:04,739 - > 00:27:07,459 of truth, and that sort of becomes this like wild, wild 490 00:27:07,459 - > 00:27:07,779 west. 491 00:27:08,099 - > 00:27:10,259 You're struggling to know more. 492 00:27:10,419 - > 00:27:14,419 So it's just this constant reconciliation of how else can I 493 00:27:14,419 - > 00:27:14,899 frame this? 494 00:27:15,059 - > 00:27:17,459 How else can I get the information to tell me more 495 00:27:17,459 - > 00:27:18,659 about what I'm looking at? 496 00:27:18,899 - > 00:27:22,099 When you don't have this very thoughtfully put together, you 497 00:27:22,099 - > 00:27:25,139 start to increase that risk where you're making decisions 498 00:27:25,139 - > 00:27:28,659 that are misinformed, because you may have a data point that 499 00:27:28,659 - > 00:27:33,539 is pointing you to choose, you know, the red pill, but you have 500 00:27:33,539 - > 00:27:36,499 two other data sets that are telling you, no, this is 501 00:27:36,499 - > 00:27:39,699 definitely the green pill, but you don't know how to access it. 502 00:27:39,859 - > 00:27:41,939 You didn't even know that they were relevant to the 503 00:27:41,939 - > 00:27:44,179 conversation or to the question you're trying to answer. 504 00:27:44,419 - > 00:27:51,139 Points here that um I think we feel in the day-to-day, and it 505 00:27:51,299 - > 00:27:55,939 becomes this sort of, yeah, you know, we're we're we're we're 506 00:27:55,939 - > 00:27:56,739 flying the plane. 507 00:27:56,819 - > 00:27:57,139 What is it? 508 00:27:57,219 - > 00:28:00,099 We're flying the plane, I don't know, something like while we're 509 00:28:00,499 - > 00:28:02,499 trying to change the uh the engine at the same time. 510 00:28:03,219 - > 00:28:04,179 Yeah, yeah, yeah. 511 00:28:04,339 - > 00:28:07,139 You know, we're driving the train and and laying down the 512 00:28:07,139 - > 00:28:07,939 tracks. 513 00:28:08,579 - > 00:28:12,179 But if you don't know where those tracks are supposed to 514 00:28:12,179 - > 00:28:16,499 take you, then how do you even know that you're building it in 515 00:28:16,499 - > 00:28:17,459 the right direction? 516 00:28:17,699 - > 00:28:21,699 You know, your compass is a little bit broken, but if you're 517 00:28:21,699 - > 00:28:25,299 not thoughtfully thinking about where those tracks need to be 518 00:28:25,299 - > 00:28:28,979 laid and in what direction, you're gonna end up off the mat 519 00:28:29,299 - > 00:28:33,939 very quickly and struggling to make decisions for that next 520 00:28:33,939 - > 00:28:34,579 step. 521 00:28:34,899 - > 00:28:38,259 SPEAKER_01: Yeah, there's a good uh quote from his name is Naval 522 00:28:38,659 - > 00:28:41,059 Ravakant, and uh, I've been following him for a while. 523 00:28:41,139 - > 00:28:43,619 And he hates being called a modern-day philosopher, but he 524 00:28:43,619 - > 00:28:45,859 was really successful in tech, and he has this really good 525 00:28:45,859 - > 00:28:50,019 definition of success, which is it's basically two things 526 00:28:50,179 - > 00:28:52,899 getting what you want and wanting the right things, right? 527 00:28:52,979 - > 00:28:56,739 And I think this is very applicable to data, is you have 528 00:28:56,739 - > 00:29:02,019 to know what the right data is that you want for whatever 529 00:29:02,019 - > 00:29:04,819 you're trying to do, and then enabling people so that they can 530 00:29:04,819 - > 00:29:05,779 actually give it to you, right? 531 00:29:05,859 - > 00:29:08,659 And that this is where it comes into that like user interface 532 00:29:08,659 - > 00:29:11,379 aspect of how data flows are set up. 533 00:29:11,619 - > 00:29:14,659 If if you want certain data points so you can make a certain 534 00:29:14,659 - > 00:29:18,819 decision, you have to make it easy or somewhat easy for the 535 00:29:18,819 - > 00:29:20,819 person to give you that information, right? 536 00:29:20,979 - > 00:29:24,739 Don't make it some super complicated thing where people 537 00:29:24,739 - > 00:29:28,019 have to again click through a bunch of windows and then they 538 00:29:28,019 - > 00:29:31,859 have to, you know, enter through you know one avenue to go 539 00:29:31,859 - > 00:29:32,499 somewhere else. 540 00:29:32,659 - > 00:29:35,139 And that navigation nightmare, right? 541 00:29:35,219 - > 00:29:37,699 And where it's this thing where people get frustrated, and then 542 00:29:37,699 - > 00:29:42,579 that's when you have this um lack of uh people just give up, 543 00:29:42,659 - > 00:29:42,819 right? 544 00:29:42,899 - > 00:29:45,539 They they don't want to have to go through all these hoops to 545 00:29:45,539 - > 00:29:46,739 give you that information. 546 00:29:46,979 - > 00:29:50,739 If if it's something simple, it increases the the likelihood of 547 00:29:50,739 - > 00:29:52,739 somebody giving you accurate information. 548 00:29:52,979 - > 00:29:57,939 If you make it incredibly difficult, it's it makes it very 549 00:29:57,939 - > 00:30:03,539 hard to um make sure that they do that consistently, I guess is 550 00:30:03,539 - > 00:30:04,819 what I'm saying, right? 551 00:30:06,499 - > 00:30:09,219 You're already asking for this information that may or may not 552 00:30:09,219 - > 00:30:12,819 be in the scope of their role, and so you might as well make it 553 00:30:12,819 - > 00:30:14,819 easier for them to give you that information. 554 00:30:14,979 - > 00:30:17,059 And I think it's I don't think this is rocket science. 555 00:30:17,139 - > 00:30:19,299 I think a lot of people just don't really think about that 556 00:30:19,299 - > 00:30:19,619 aspect. 557 00:30:19,699 - > 00:30:22,739 They just go, oh, I need this information for this report or 558 00:30:22,739 - > 00:30:24,259 whatever deck I'm putting together. 559 00:30:24,419 - > 00:30:27,379 And then they don't think about like, well, how how would this 560 00:30:27,379 - > 00:30:28,499 person actually put this information? 561 00:30:30,739 - > 00:30:32,979 SPEAKER_00: You know, I think it when we talk about it in that 562 00:30:32,979 - > 00:30:34,899 sense, we don't know what we don't know. 563 00:30:35,059 - > 00:30:38,419 So I think there's also this part where leadership or 564 00:30:38,419 - > 00:30:41,539 managers, like maybe they're not in these systems very often. 565 00:30:41,699 - > 00:30:45,219 So they're just gonna ask you a question, hey, can you tell me 566 00:30:45,219 - > 00:30:48,659 the last time that this happened where we spent you know X amount 567 00:30:48,659 - > 00:30:54,819 of money on a particular project and a modality of a therapy? 568 00:30:54,899 - > 00:30:59,539 Of I mean, that could take days depending on who you're asking. 569 00:30:59,699 - > 00:31:03,859 And it doesn't really give you clarity in the resolution. 570 00:31:04,019 - > 00:31:06,019 Like, are you looking for one big number? 571 00:31:06,179 - > 00:31:08,259 Are you looking for like a trending graph? 572 00:31:08,419 - > 00:31:12,419 And either way, if the data is not really positioned to answer 573 00:31:12,419 - > 00:31:17,059 a question like that, it is just gonna be a manual burden to 574 00:31:17,059 - > 00:31:17,699 create that. 575 00:31:18,019 - > 00:31:24,019 And even with the use of AI, if your data has different names 576 00:31:24,019 - > 00:31:27,859 and located in in disparate systems, like and you don't have 577 00:31:27,859 - > 00:31:30,739 this idea of like a data lake or something that you can sort of 578 00:31:30,739 - > 00:31:33,379 query, who knows what kind of information you're gonna be 579 00:31:33,379 - > 00:31:33,779 getting. 580 00:31:34,019 - > 00:31:38,499 So, you know, now again, making decisions on inaccurate 581 00:31:38,499 - > 00:31:42,259 information or you know, uh falsified context, you know, 582 00:31:42,419 - > 00:31:46,659 hallucin hallucinated context in other in other ways. 583 00:31:46,899 - > 00:31:53,539 So it's I've seen it and it's uh even now in 2026, you it's still 584 00:31:54,019 - > 00:31:56,819 present in in the workplace. 585 00:31:57,299 - > 00:32:05,379 And it does take uh a person, a lead, a guide, a sherpa to be 586 00:32:05,379 - > 00:32:10,179 constantly thinking about this because we are making decisions 587 00:32:10,339 - > 00:32:15,219 directly from data, not just from the voices of those that 588 00:32:15,219 - > 00:32:18,019 gather, not just from how we feel about things. 589 00:32:18,259 - > 00:32:22,179 We need data to feel confident that the things that we're 590 00:32:22,179 - > 00:32:26,739 deciding on are based or are rooted in some factual 591 00:32:26,739 - > 00:32:27,219 information. 592 00:32:27,539 - > 00:32:29,539 That's really what this boils down to. 593 00:32:29,859 - > 00:32:34,179 So we need to, and as these companies and these projects 594 00:32:34,179 - > 00:32:41,859 grow, the data volume goes up and the clarity goes down, 595 00:32:42,019 - > 00:32:44,739 especially when you don't consider what that architecture 596 00:32:44,739 - > 00:32:49,219 looks like, what the data flow, how your operations digitally 597 00:32:49,219 - > 00:32:51,139 are going to be talking to each other. 598 00:32:51,379 - > 00:32:54,819 And that's and this is where you feel that, oh, these are growing 599 00:32:54,819 - > 00:32:56,819 pains, growing pains, growing pains. 600 00:32:56,899 - > 00:32:58,979 How long are growing pains supposed to last? 601 00:32:59,859 - > 00:33:04,419 And growing pains to me just says we did not think about how 602 00:33:04,419 - > 00:33:06,019 this would look as we grew. 603 00:33:06,179 - > 00:33:08,819 SPEAKER_01: Yeah, and then you you get to the point where if if 604 00:33:08,819 - > 00:33:12,179 you do have if you've amassed a lot of data for, let's say, 605 00:33:12,419 - > 00:33:13,859 resource projections, right? 606 00:33:13,939 - > 00:33:16,979 This is a very tough thing that a lot of companies deal with is 607 00:33:17,139 - > 00:33:21,859 how many resources do I need to support a particular set of 608 00:33:22,419 - > 00:33:23,619 programs, right? 609 00:33:23,939 - > 00:33:26,899 And so you go through this exercise of, okay, well, let's 610 00:33:26,899 - > 00:33:29,459 look at this particular program and then has this set of like 611 00:33:29,459 - > 00:33:31,699 metadata associated with attributes, right? 612 00:33:31,939 - > 00:33:35,619 It might be for this particular modality, and it, you know, the 613 00:33:35,619 - > 00:33:39,299 complexity level was medium, and you know, the the amount of that 614 00:33:39,299 - > 00:33:42,339 we needed was for however many patients. 615 00:33:42,499 - > 00:33:47,219 And so you're relying on people providing you their time of how 616 00:33:47,219 - > 00:33:49,619 long they spent on certain things, but then you're not 617 00:33:49,619 - > 00:33:52,739 thinking about, okay, when the person's entering the time, what 618 00:33:52,739 - > 00:33:54,259 does half an hour mean? 619 00:33:54,339 - > 00:33:57,139 Or what does one hour actually mean here if they're working on 620 00:33:57,139 - > 00:33:59,139 something that impacts multiple programs? 621 00:33:59,299 - > 00:34:02,339 Like, how do you, how do I, how do I actually build that into my 622 00:34:02,579 - > 00:34:03,459 resource model? 623 00:34:03,699 - > 00:34:06,739 And I think it's important, like you, like you said, right? 624 00:34:06,899 - > 00:34:10,659 Designing the uh the art, like the architecture of the data 625 00:34:10,659 - > 00:34:13,940 flow so that we understand from the moment that we allow people 626 00:34:13,940 - > 00:34:16,900 to enter things in, this is what this means. 627 00:34:17,139 - > 00:34:19,699 You can't get to the end of it and then you have a bunch of 628 00:34:19,699 - > 00:34:22,179 data and then you start moving the goalpost, and then people 629 00:34:22,179 - > 00:34:25,380 start losing confidence in the actual data and the insights 630 00:34:25,380 - > 00:34:27,380 that you're getting from it because they go, oh, well, 631 00:34:27,460 - > 00:34:29,940 that's not accurate because when they were working on this, that 632 00:34:29,940 - > 00:34:31,300 actually counted for multiple programs. 633 00:34:31,460 - > 00:34:33,619 Okay, well, why don't you say that in the beginning when you 634 00:34:33,619 - > 00:34:35,940 were asking people to input that information? 635 00:34:36,099 - > 00:34:39,380 Because now you're you're having to make these pretty risky 636 00:34:39,380 - > 00:34:43,059 decisions on do I need five scientists or do I need 50? 637 00:34:43,139 - > 00:34:46,900 I don't I don't know because the information that you provided me 638 00:34:46,900 - > 00:34:49,860 on the these couple of assets are telling me this thing, and 639 00:34:49,860 - > 00:34:51,940 then something is is telling me something else. 640 00:34:52,179 - > 00:34:55,380 And that's you're and that's a situation where you're looking 641 00:34:55,380 - > 00:34:56,900 to increase capacity, right? 642 00:34:57,059 - > 00:35:00,500 If you go the other way around, and now it's saying, and 643 00:35:00,500 - > 00:35:03,300 actually, based on you know the the change in the pipeline, we 644 00:35:03,300 - > 00:35:05,060 actually need less people in your department. 645 00:35:05,220 - > 00:35:07,540 And now they go, oh no, the model is actually really 646 00:35:07,540 - > 00:35:07,940 accurate. 647 00:35:08,020 - > 00:35:10,900 I think I think it's I think we actually need that many people, 648 00:35:10,980 - > 00:35:11,220 right? 649 00:35:11,300 - > 00:35:14,180 Because people don't want to lose headcount and they don't 650 00:35:14,180 - > 00:35:15,140 want to lose budgets. 651 00:35:15,460 - > 00:35:15,700 SPEAKER_00: Yeah. 652 00:35:15,780 - > 00:35:18,980 They they want to have more, they want to save, they want to 653 00:35:18,980 - > 00:35:21,380 protect what was allocated to them initially. 654 00:35:21,540 - > 00:35:21,780 Absolutely. 655 00:35:22,020 - > 00:35:22,180 Right. 656 00:35:22,260 - > 00:35:24,580 SPEAKER_01: It's funny how the data tries to change meaning 657 00:35:24,580 - > 00:35:27,540 when it impacts the the headcount. 658 00:35:28,580 - > 00:35:33,140 SPEAKER_00: Long ago, we I was in a situation where this became 659 00:35:33,140 - > 00:35:37,300 uh new at the organization, and we started getting a lot of 660 00:35:37,300 - > 00:35:41,460 people that said, what if we work more than 40 hours and the 661 00:35:41,460 - > 00:35:43,380 goal here was percentage? 662 00:35:43,700 - > 00:35:46,980 Sure, I mean, I I think you're pointing out something too that, 663 00:35:47,060 - > 00:35:53,380 you know, even uh one FTE as best as we can do it, one FTE we 664 00:35:53,380 - > 00:35:57,300 think is 40 hours a week, but we don't know how well people are 665 00:35:57,300 - > 00:35:59,140 leveraging their 40 hours a week. 666 00:35:59,300 - > 00:36:01,700 We don't know really where they're spending their time or 667 00:36:01,700 - > 00:36:05,140 what that actually means in the context of a project that is 668 00:36:05,140 - > 00:36:09,220 early in the process, in the middle, or somewhere towards the 669 00:36:09,220 - > 00:36:09,380 end. 670 00:36:09,860 - > 00:36:15,300 And I think that's an important distinction that we can't just 671 00:36:15,540 - > 00:36:20,420 base our needs on how much time has been spent on a specific 672 00:36:20,420 - > 00:36:20,820 project. 673 00:36:20,980 - > 00:36:23,540 We have to think about what are those activities, what are those 674 00:36:23,540 - > 00:36:28,900 demands at these different stages, and then start to query 675 00:36:29,300 - > 00:36:31,540 not just, oh, we went up or down. 676 00:36:31,780 - > 00:36:33,220 Does this make sense? 677 00:36:33,620 - > 00:36:36,660 We can't accept all these things at face value. 678 00:36:36,820 - > 00:36:40,980 We have to also question whether, you know, question the 679 00:36:41,220 - > 00:36:44,740 validity of whatever model that you're deciding to propose for 680 00:36:44,740 - > 00:36:45,540 your business. 681 00:36:45,860 - > 00:36:49,380 Does this make sense that we're spending that, you know, this 682 00:36:49,380 - > 00:36:54,260 function is spending 70% of their time on this project that 683 00:36:54,260 - > 00:36:56,100 is at this phase of development? 684 00:36:56,260 - > 00:36:59,140 Let's go have a conversation to understand what that actually 685 00:36:59,140 - > 00:36:59,540 means. 686 00:36:59,860 - > 00:37:04,980 Because resourcing is not about increasing or decreasing per se. 687 00:37:05,300 - > 00:37:10,580 I think it is more about you have this bucket of elements 688 00:37:10,820 - > 00:37:15,140 that is your systems, your computers, your people, the 689 00:37:15,140 - > 00:37:18,900 training, the technology, skill sets. 690 00:37:19,060 - > 00:37:25,940 How do you launch this to run 20 programs, to run a facility that 691 00:37:25,940 - > 00:37:30,420 does eight different medications or 25 different types of 692 00:37:30,820 - > 00:37:32,420 synthesis, et cetera? 693 00:37:32,580 - > 00:37:36,660 Finding out not just these whole numbers and saying, let's 694 00:37:36,660 - > 00:37:41,220 respond based on this, but also looking at that and saying, in 695 00:37:41,220 - > 00:37:42,180 what context? 696 00:37:42,340 - > 00:37:45,700 You know, start to develop those guardrails and those rules for 697 00:37:45,780 - > 00:37:49,380 like, you know, what are we, what are we putting into the 698 00:37:49,380 - > 00:37:53,540 system in terms of how much we're working on something, what 699 00:37:53,540 - > 00:37:57,300 we're actually doing, where that effort is being done so that you 700 00:37:57,300 - > 00:38:03,700 can actually have data that supports any resourcing, any 701 00:38:03,700 - > 00:38:07,220 budget conversation, any partnership conversations. 702 00:38:07,380 - > 00:38:12,340 It's critically important when that doesn't show, that also 703 00:38:12,340 - > 00:38:16,180 shows when you're having those discussions and you're you're 704 00:38:16,180 - > 00:38:19,060 sort of in your mind thinking like these people have no idea 705 00:38:19,460 - > 00:38:24,100 what it will take to do this, they have no clue what to expect 706 00:38:24,180 - > 00:38:24,980 in the future. 707 00:38:25,140 - > 00:38:28,340 And you might have the data, but if you haven't pieced it 708 00:38:28,340 - > 00:38:30,580 together, it's a nightmare. 709 00:38:30,980 - > 00:38:34,980 You know, you talked about getting those requests, most of 710 00:38:34,980 - > 00:38:36,100 them are ad hoc. 711 00:38:37,140 - > 00:38:40,420 You know, you'll get a few of the same questions in the same 712 00:38:40,500 - > 00:38:42,980 in those similar meetings, and then you get those emails. 713 00:38:43,700 - > 00:38:46,660 Hey, can you help me figure out one, two, three, four, five? 714 00:38:47,220 - > 00:38:49,300 Now we're looking at fragmented data. 715 00:38:49,460 - > 00:38:51,940 Now we're looking at data that doesn't even have the same 716 00:38:51,940 - > 00:38:54,980 language, and and you're spending this time to translate 717 00:38:54,980 - > 00:38:57,060 it to develop an insight. 718 00:38:57,380 - > 00:39:03,460 AI is not gonna help craft the narrative of your story if your 719 00:39:03,460 - > 00:39:06,900 characters have different names than what in one group versus 720 00:39:06,900 - > 00:39:07,140 another. 721 00:39:07,380 - > 00:39:11,060 If you know, and we can go on and on and on, I think, I think 722 00:39:11,140 - > 00:39:11,540 on that. 723 00:39:11,700 - > 00:39:16,340 But what we're trying to get to is that there are risks that we 724 00:39:16,340 - > 00:39:22,260 can identify in these organizations that go from our 725 00:39:22,260 - > 00:39:27,220 system to system connectivity, how manual the data burden has 726 00:39:27,220 - > 00:39:31,700 become, the ownership, who's owning the data that enters into 727 00:39:31,700 - > 00:39:34,980 these spaces and the source of truth, who's managing that? 728 00:39:35,140 - > 00:39:37,620 And you're talking about timelines, you're talking about 729 00:39:37,620 - > 00:39:41,060 research data, you're talking if there's a particular machine 730 00:39:41,060 - > 00:39:44,020 that somebody is the expert in, they should also own the data 731 00:39:44,020 - > 00:39:45,700 that is happening within there. 732 00:39:45,940 - > 00:39:49,700 Um, you know, the the data to decision flow. 733 00:39:49,940 - > 00:39:53,540 What's the data that we need to create, you know, to come up 734 00:39:53,540 - > 00:39:56,740 with decisions at each of these different layers? 735 00:39:56,820 - > 00:39:59,700 And then your integrity, your data integrity and the risk 736 00:39:59,700 - > 00:40:00,180 exposure. 737 00:40:00,420 - > 00:40:01,700 Are we training? 738 00:40:01,860 - > 00:40:08,100 Are we giving our workforce the capacity to be successful in all 739 00:40:08,100 - > 00:40:09,860 of these digital operations? 740 00:40:10,100 - > 00:40:13,940 That we're giving them constant reminders of how we need the 741 00:40:13,940 - > 00:40:18,740 work to transform from conversations and post-it notes 742 00:40:18,740 - > 00:40:23,380 and sticky notes to things that live in a digital architected, 743 00:40:23,700 - > 00:40:28,740 in a in a well-architected digital space that will decrease 744 00:40:28,740 - > 00:40:34,820 your exposure to making wrong decisions, to delays, to risk of 745 00:40:34,820 - > 00:40:35,940 misinformation. 746 00:40:36,180 - > 00:40:40,420 Um, you know, and all of these things, luckily, Lawrence, for 747 00:40:40,420 - > 00:40:44,420 our listeners and and those that have been following us, we have 748 00:40:45,060 - > 00:40:49,620 taken this whole topic that we've talked about today, and 749 00:40:49,620 - > 00:40:53,300 we've put it into one of our risk assessments to help people 750 00:40:53,300 - > 00:40:57,140 identify where is their biggest issue? 751 00:40:57,380 - > 00:40:59,460 Is it that there's no integrity? 752 00:40:59,620 - > 00:41:03,380 Is it that the decision, the data to decision flow is broken? 753 00:41:03,620 - > 00:41:06,820 There's no real source of truth, the manual data burner, each of 754 00:41:06,820 - > 00:41:09,860 these pieces requires a different approach to fix. 755 00:41:10,100 - > 00:41:13,380 So, how do you focus on the right one so that you're 756 00:41:13,700 - > 00:41:18,340 creating a more mature, healthier digital operations? 757 00:41:18,500 - > 00:41:21,140 And we have that in our risk assessments. 758 00:41:21,380 - > 00:41:24,020 SPEAKER_01: Yeah, and you know, to your point, the the 759 00:41:24,020 - > 00:41:26,820 assessment itself, it's it's not, you know, whether you have 760 00:41:26,820 - > 00:41:27,300 this or that. 761 00:41:27,380 - > 00:41:31,860 It's it's really gauging the how the people on your team perceive 762 00:41:31,860 - > 00:41:35,460 or interpret how strong your your digital operations are, 763 00:41:35,620 - > 00:41:35,860 right? 764 00:41:36,100 - > 00:41:39,540 Because certain users might be really great at it, and then 765 00:41:39,540 - > 00:41:42,500 other users are maybe they don't have enough context, right? 766 00:41:42,660 - > 00:41:45,860 You know, like we were saying before, you know, one hour of 767 00:41:45,860 - > 00:41:49,780 Sally is not equivalent to one hour of Bob because Sally is way 768 00:41:49,780 - > 00:41:52,260 more adept at accessing all the information. 769 00:41:52,500 - > 00:41:53,620 But should that be the case? 770 00:41:53,780 - > 00:41:57,780 Shouldn't everybody have the right access level or or have 771 00:41:57,780 - > 00:42:00,820 the means to access the data so that they can, you know, do 772 00:42:00,820 - > 00:42:02,100 their certain roles and jobs. 773 00:42:02,260 - > 00:42:05,860 And I think the the beauty of the assessment, it's it's a it's 774 00:42:05,860 - > 00:42:09,940 an opportunity for the team to reflect and really gauge how 775 00:42:09,940 - > 00:42:12,900 well things are set up and and where you need improvements. 776 00:42:13,060 - > 00:42:17,380 Because, like you said, unless people enjoy the frustrations 777 00:42:17,380 - > 00:42:20,420 that go along with having an unorganized, unstructured 778 00:42:20,580 - > 00:42:25,860 workflow and you like doing these ad hoc tasks, then maybe 779 00:42:25,860 - > 00:42:28,660 you don't want to take the assessment and you should leave 780 00:42:28,660 - > 00:42:29,380 things as is. 781 00:42:29,620 - > 00:42:34,660 But I think we're we're getting to a point where the capability 782 00:42:34,660 - > 00:42:40,100 of some of these new AI-powered tools are only as good as the 783 00:42:40,100 - > 00:42:41,460 inputs that you feed it, right? 784 00:42:41,540 - > 00:42:45,460 And so the better you are at organizing those inputs, the 785 00:42:45,460 - > 00:42:48,900 more likely you'll be able to capitalize on some of the gains 786 00:42:48,900 - > 00:42:50,580 that you will have from using these tools. 787 00:42:51,220 - > 00:42:55,060 If you leave it with gaps, things not structured correctly, 788 00:42:55,220 - > 00:42:56,580 it's just gonna give you garbage. 789 00:42:56,660 - > 00:43:00,020 It's not gonna tell you what you need to know to make the right 790 00:43:00,020 - > 00:43:00,820 decision. 791 00:43:01,220 - > 00:43:04,020 SPEAKER_00: You're gonna look at those responses and go, what in 792 00:43:04,020 - > 00:43:05,220 the world is this talking about? 793 00:43:05,380 - > 00:43:06,580 What a waste of time. 794 00:43:06,820 - > 00:43:07,780 And you're right. 795 00:43:07,940 - > 00:43:11,860 There's I think we've heard it a thousand times, you know, 796 00:43:12,020 - > 00:43:13,540 garbage in, garbage out. 797 00:43:13,620 - > 00:43:16,900 And that is it's very true in a lot of scenarios. 798 00:43:16,980 - > 00:43:21,140 And I think we've positioned something that is asking the 799 00:43:21,140 - > 00:43:24,340 questions that we sometimes fail to ask internally. 800 00:43:24,660 - > 00:43:29,140 And we've packaged it in a place for us to understand, you know, 801 00:43:29,380 - > 00:43:31,060 what does it feel like on our team? 802 00:43:31,220 - > 00:43:34,740 Are we all, you know, I I aliken the things that we're talking 803 00:43:34,740 - > 00:43:37,060 about now to being on a sports team. 804 00:43:37,220 - > 00:43:39,540 You know, your assessment is that tryout. 805 00:43:39,940 - > 00:43:44,660 Okay, we have a couple of different levels of athleticism 806 00:43:44,660 - > 00:43:46,260 in any work that you're doing. 807 00:43:46,420 - > 00:43:49,220 And it's not just that, like, oh, the senior directors are 808 00:43:49,220 - > 00:43:51,940 stronger than the people that are associate managers. 809 00:43:52,100 - > 00:43:53,540 That's not necessarily the case. 810 00:43:53,700 - > 00:43:56,660 Sometimes it's experience, seniority, etc. 811 00:43:56,980 - > 00:43:59,940 But if you're thinking in the context again of like a team, 812 00:44:00,020 - > 00:44:02,420 you know, hey, uh, let's get these guys over here. 813 00:44:02,500 - > 00:44:05,700 I want them to work on, you know, if I can take some 814 00:44:05,700 - > 00:44:08,580 baseball terms, you know, I want to work them to work on some fly 815 00:44:08,580 - > 00:44:08,900 balls. 816 00:44:08,980 - > 00:44:12,660 I want these guys to do, you know, sprints because these are 817 00:44:12,660 - > 00:44:14,740 the guys we're gonna get to steal bases. 818 00:44:14,900 - > 00:44:16,900 You know, these are the ones that we're gonna get, you know, 819 00:44:16,980 - > 00:44:19,300 our pitchers, let's get our pitchers warming up over there. 820 00:44:19,460 - > 00:44:22,260 Let's get some infielders because we want to do some 821 00:44:22,260 - > 00:44:24,740 strategy on the infield of how they're gonna work together. 822 00:44:24,900 - > 00:44:27,540 And this is this is a little bit of how we need to work with our 823 00:44:27,540 - > 00:44:31,060 teams too, is recognize the strengths and the weaknesses, 824 00:44:31,140 - > 00:44:35,220 you know, in the context of of how our data is organized and 825 00:44:35,220 - > 00:44:38,980 make sure that we are giving the opportunity to lift those people 826 00:44:38,980 - > 00:44:42,660 up and enhance their capabilities so that when 827 00:44:42,660 - > 00:44:47,220 they're delivering their technical role on that project, 828 00:44:48,340 - > 00:44:52,100 it is consistent with how the rest of your team is also 829 00:44:52,100 - > 00:44:53,940 delivering on their projects. 830 00:44:54,100 - > 00:44:55,860 Very important, very important. 831 00:44:55,940 - > 00:44:59,140 And I hope that everyone takes takes some time to think about 832 00:44:59,300 - > 00:45:03,700 what does it feel like, what does it feel like at your 833 00:45:03,700 - > 00:45:04,500 organization? 834 00:45:05,700 - > 00:45:07,220 Is the data accessible? 835 00:45:07,780 - > 00:45:11,780 Are you constantly searching where do these things exist? 836 00:45:12,020 - > 00:45:15,380 Are you constant are you pulling things out that you thought were 837 00:45:15,380 - > 00:45:18,340 sources of truth, and then you come down come to find out that 838 00:45:18,340 - > 00:45:21,380 there were drafts that were never continued? 839 00:45:22,260 - > 00:45:27,300 That were, you know, builds that happen in Monday in Trello and 840 00:45:27,300 - > 00:45:30,020 Smartsheet and Notion that just stopped. 841 00:45:30,420 - > 00:45:32,980 Has a lot of data, but everything stopped because the 842 00:45:32,980 - > 00:45:35,300 people that were inputting the data decided to go somewhere 843 00:45:35,300 - > 00:45:35,540 else. 844 00:45:35,620 - > 00:45:36,420 But you didn't know that. 845 00:45:36,580 - > 00:45:38,100 Why would you have that context? 846 00:45:38,580 - > 00:45:40,340 Are these the things that you're feeling? 847 00:45:41,380 - > 00:45:45,060 These assessments look to elucidate where those challenges 848 00:45:45,060 - > 00:45:45,220 are. 849 00:45:45,940 - > 00:45:48,900 SPEAKER_01: Yeah, I I think a good question to ask um for for 850 00:45:48,900 - > 00:45:54,820 anybody is the flow of information across your company? 851 00:45:55,540 - > 00:46:01,060 Or your team enabling some of the dysfunction across multiple 852 00:46:01,300 - > 00:46:01,860 departments. 853 00:46:02,180 - > 00:46:03,140 SPEAKER_00: Let's say that again. 854 00:46:03,300 - > 00:46:06,260 Is it enabling the dysfunction? 855 00:46:06,420 - > 00:46:10,900 I think a lot of us will say, yes, it is definitely adding to 856 00:46:10,900 - > 00:46:12,100 that dysfunction. 857 00:46:13,140 - > 00:46:15,060 But ask yourselves that. 858 00:46:15,300 - > 00:46:17,300 Well, thanks so much for spending your time with us 859 00:46:17,300 - > 00:46:17,540 today. 860 00:46:17,620 - > 00:46:20,340 Uh, we truly appreciate you being part of the Lean by Design 861 00:46:20,340 - > 00:46:20,900 community. 862 00:46:21,060 - > 00:46:25,060 If this conversation resonated with you, we invite you to check 863 00:46:25,060 - > 00:46:26,980 out my new book that just landed. 864 00:46:27,140 - > 00:46:30,340 We just had a launch on Wednesday that went fantastic. 865 00:46:30,500 - > 00:46:35,460 And we're at this, and I take a candid perspective on the 866 00:46:35,460 - > 00:46:40,580 patterns of operational friction that quietly slowed teams down, 867 00:46:40,980 - > 00:46:46,100 erode trust from leadership and erode morale and what to do 868 00:46:46,100 - > 00:46:46,580 about them. 869 00:46:46,740 - > 00:46:50,100 How do we approach these things so that we can create an 870 00:46:50,100 - > 00:46:55,140 organization that has that data operational structure, that has 871 00:46:55,140 - > 00:46:57,860 that flow, that has that system level thinking. 872 00:46:58,100 - > 00:47:01,220 And if you're ready to take the next step, we invite you to 873 00:47:01,220 - > 00:47:04,180 explore our operational risk assessments that are these are 874 00:47:04,180 - > 00:47:06,900 hybrid engagements where you'll actually work directly with 875 00:47:06,900 - > 00:47:10,260 Lawrence and I to identify, prioritize, and then be able to 876 00:47:10,260 - > 00:47:14,100 clearly communicate the friction points in your organization so 877 00:47:14,100 - > 00:47:17,460 that you can move forward with confidence and alignment. 878 00:47:17,620 - > 00:47:22,340 So don't forget to follow us on Instagram at SciGuy underscore 879 00:47:22,500 - > 00:47:23,300 insights. 880 00:47:23,460 - > 00:47:25,700 That's uh my braided page. 881 00:47:25,780 - > 00:47:30,500 And you can now watch our podcast on YouTube at Lean by 882 00:47:30,580 - > 00:47:31,540 Design Podcast. 883 00:47:31,620 - > 00:47:33,380 You can watch the whole episode. 884 00:47:33,620 - > 00:47:37,300 Me and Lawrence are blenders, probably, that Lawrence will 885 00:47:37,300 - > 00:47:41,620 stick into there to show people that we are real people and we 886 00:47:41,620 - > 00:47:45,700 mess up, but we keep on going and we make adjustments, and we 887 00:47:45,700 - > 00:47:49,060 are just very fortunate and feel blessed that you guys are taking 888 00:47:49,060 - > 00:47:49,860 this journey with us. 889 00:47:50,020 - > 00:47:52,820 All the links are in the show notes, and uh, we're gonna take 890 00:47:52,820 - > 00:47:53,860 a little hiatus. 891 00:47:54,020 - > 00:47:56,820 Lawrence, a lot of big things have been happening outside of 892 00:47:56,820 - > 00:47:57,460 the book. 893 00:47:57,700 - > 00:48:02,340 So I will be moving, I will be welcoming a new addition to our 894 00:48:02,340 - > 00:48:02,900 family. 895 00:48:03,140 - > 00:48:06,820 So a lot of things that are happening, and we will miss out 896 00:48:06,820 - > 00:48:10,740 on being able to give some more insights to our listeners, but 897 00:48:10,740 - > 00:48:14,340 we'll definitely be coming back a little bit in the springtime, 898 00:48:14,580 - > 00:48:18,180 as soon as the six foot tall piles of snow start to melt. 899 00:48:18,340 - > 00:48:19,460 Thanks, Lawrence. 900 00:48:20,180 - > 00:48:20,980 SPEAKER_01: All right, bye.