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

Most organizations have data. Systems are in place, dashboards exist, and reports are generated. Yet when it comes to making decisions, teams still struggle to see the full picture.

In this episode of Lean by Design, Oscar Gonzalez and Lawrence Wong explore why fragmented data systems and disconnected architectures create more confusion than clarity. Despite heavy investment in digital tools, organizations often operate with incomplete or inconsistent views of reality — leading to delays, misalignment, and poor decision-making.

The conversation reframes a common assumption: the issue isn’t a lack of data — it’s the lack of a coherent structure that allows data to flow, connect, and create shared understanding across teams.

Oscar and Lawrence unpack how data silos, inconsistent definitions, and weak system integration quietly undermine operational efficiency. They also explore why simply adding more tools or even AI — doesn’t solve the problem if the underlying data foundation is fragmented.

This episode is not about technology selection or architecture frameworks. It’s about recognizing when your systems are preventing you from seeing clearly and why better decisions start with better data flow, not more data.

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Transcript

SPEAKER_00 1:01

Here we are. This will be uh we're gonna take a little break after this episode, but I think we have an important topic today that we um we don't spend enough time thinking about when we are developing systems within our organizations, when we are bringing in new technology, we're bringing in new uh ways of working. And you know, this can look like you know, from the scientific side of like benching and and rippling, and you have other systems that pull out articles, and you have other systems for project management, and you have other systems to manage legal, other systems to manage drug dispensation and and clinical trials. There's a lot. There's a lot of software that's available, and when we pull these things in and we fail to connect them or even see how they can be connected, we start to start to struggle in seeing what is what is the sum of all that we're doing. What are the gaps that we need support? We're talking about our digital operations and data flow, how information flows from one space into another, how we receive it, how we bring context together to deliver reports to leadership. So a huge, you know, I think in 2026 it's huge for us to think more critically about how we are doing these things in our organizations and within our teams, and the impact of ignoring it or just going on our own sort of adventure to uh, you know, jump from one system to another. I feel like we've been at, you know, such an eye-opening junction here from our early days in pharma, in biotech, uh, to where we are now where we're integrating AI and we're using agents and bots to help complete tasks, et cetera, that sort of create just another avenue where data can sit. Well, let's talk about that today.

SPEAKER_01 3:17

Yeah, I think there's a lot of tools out there now that you can leverage that not only help you analyze the data and make decisions, but it actually sits on top of those applications so that you can actually gather more insight across multiple applications, right? So I I think we used to have even when we started in the industry, you certain data types would be siloed and you couldn't like move them essentially, and you'd have to maybe download you know data from this thing and then put it into a uh a Google Drive or OneDrive folder and then share point, SharePoint, whatever it is, and and you know, all these different data types and try to make sense of what it is that you're trying to do. But I think now, like you said, we have all these like different terminology and and tools that we can use, like a data lake where you can put different data types in there and then you can actually pull out of it to create the things that you need to do. But all of those fancy tools are they're only good if you're, I guess, you know, like we were saying, digital operations. It's it's really the the data flow and the architecture of how those pathways are created, so you can actually do those things. If you don't have that set up, you cannot harness the full capability of all the tools that we have currently.

SPEAKER_00 4:31

So I think there's you hanging your hat on that just because you have a lot of data does not mean that you can just layer something on top and it'll tell you what's happening.

Facilities Decisions Need Connected Data

SPEAKER_01 4:44

Yeah. If if you can't connect it, then how do you uh how do you make sense of it, right? And I think it's it's rare in in 2026 where you're only making decisions for your team or your business based on one application. The business is so much more complicated these days that you know you'd have to look at, especially in the realm of facilities management, you know, we're looking at not only the maintenance data for how you manage specific buildings or pieces of equipment, but you're also looking at the utilization of the equipment itself, right? Very every year companies go through this exercise where they have to allocate uh funding for purchasing new equipment. And it used to be the case where you would justify, hey, I have a business case, like you know, I have so-and-so change in the pipeline, and so we need to buy additional equipment to increase our capacity. But where are they coming up with the projections on what the current capacity is, right? If you don't have the data to tell you. And so you'd have to, you know, do some calculations in one system and then move it over to the other, then you'd have to go to your maintenance system, pull more data on that. And so that's just a very simple use case. That's not even considering any of the things that you have in your quality management system. Like if you have any deviations and problems that come up that are actually product impacting, you might want to take a look at that and decide, yeah, let's not buy that piece of equipment anymore because it sucks.

Build Resilient Architecture For Change

SPEAKER_00 6:12

We've had to repair it every six months, you know, big repairs. Like that's that's you know, and there's a lot of options out there. That's exactly right. I think that's a great example of you know how you can use that data to better inform those future decisions. And, you know, you mentioned I I I've also heard the terms, you know, such as the data late, data warehouse. I think now where we're finding ourselves is sort of in this, you know, persona of we have plenty of data, we're collecting stuff all the time, we have slides everywhere, but we are struggling to create a shared picture of what's happening with our projects, what's happening with our organization, how our resources are actually being allocated, you know, and some folks uh, you know, they they make the uh the attempt to say, let's I used to have to put by project how what percentage of time. And that doesn't have anything to do with how many hours or what part of the project was it, you know, the project was. And all of these things matter. Like you said, the complexity of the of the projects are just increasing, you know, now and and it's not just because of the science that we're doing, it's also because of the guardrails that we have to put on some of these, you know, new technologies or new therapies that, you know, may not have precedent, you know, from the past. So we're sort of developing those things as we go. So, you know, we're we're we're really we've talked about why it's important and why we care about data flow and digital operations, and we're sort of bucketing a lot of these things that I think many of us have experienced where there's this abundance of information, but we don't know what's real, we don't know what the source is. Sometimes we don't even know who provided it, who came up with this numbers. How did it, how did it come to be that this becomes now our benchmark? Who created that? Was it somebody from this group? Was it somebody from a different group? Was there an analysis that was done? And it creates this sort of ambiguous feeling in terms of the data that you're collecting. Well, we're just going through the motions and then we just respond based on what leadership is asking for. Then we try to find out what it is. But I think the power and the way that we can leverage this data is anticipating what those questions are and developing that structure, that architecture to say these are the things that are most important to this organization. Probably has something to do with funding, probably has something to do with timing, how long things take, you know, the phase of development. Where are these projects or where, you know, are you starting a new facility? Are you maintaining a new facility? Are you moving a new facility? Are you integrating new, you know, new equipment? You know, how are all these things going to flow as opposed to, well, we're just gonna set it over there and then it'll just have a dedicated computer? I mean, that that's great, but then when you try to, you know, collect the data to develop insights, you're gonna find that you're you're gonna end up with a real struggle because what ends up happening is these metrics that we really covet are in silos. Well, that data is just in that computer that's not connected, uh, you know, any of our other digital spaces. We'll find conflicting reports where you know you might have timing shifts. Well, the research group says that this is gonna take about this long, but we got things from the portfolio team that it should be six months longer than that. Like, how are we so far apart? Well, the data that we're working with is not the same data that we're, you know, we don't have the same baseline here of information to move forward. And it continues to cycle. And I'm sure you've been in a position where you know you get a request and it becomes leadership getting a request from that group, you know, sending a request to that group, sending another one to, you know, this team here, and sending another one to, you know, the the whatever the portfolio of the project management team over here. And they're spending the time to collate what am I trying to see here? You know, we are in an age where you know knowledge, you know, knowledge is power. We talk that much is is said, but the data cannot become knowledge if we can't create any context out of it. So even though we've done a lot of experiments, we've done a lot of tracking, we've done a lot of collating of the information and the data that we're producing, if you're not creating the flow, it becomes challenging. It becomes an arduous task that you have to give probably four or five people that are not there to compile, to synthesize, you know, all day, every day. They're probably running from one meeting to the next, and you start to find delays and you start to create, you start to make decisions that are not based on the data that you have. It's based on a piece of data that people could see. And that's when we start to have problems. That's when we start to, you know, our timeline slow down, our budgets start to wither away, the you know, onboarding of assets starts to become uh a daunting task as opposed to a nice flow of operations. And that's what we're talking about here.

SPEAKER_01 11:40

Yeah, I I think um the there's a there's definitely a correlation between digital operations and I'm just gonna call them physical operations because it's things that you're gonna you're gonna see throughout work, right? I I think a really good example of this is um like you you were talking about before, especially in in the lab environment where you have certain experiments that are done for certain programs. I think when you don't have enough information, especially around inventory management, supplies, right? Just we'll we'll just focus on lab supplies, is that when you fluctuate and do these different types of experiments, it's gonna increase or decrease the amount of materials that you need for certain things. And so if you don't have the right systems in place to collect the information on how much are you buying of this particular pipette or you know, these particular plastics and things like that, it creates a real disruption in how people physically work with one another. And it's it's all because you you're you don't have enough context to understand when we make these decisions for the rest of the company, is there something within our digital operations side that can inform us so that we know the impact to how physically it's going to influence our not only employees, but how things are going to get done uh across not one function, but multiple functions, right? And so I I think to your point before, it's uh it's often something that nobody looks at with the right lens just because in it's it's not easy to tell when things are siloed, like when you're looking at it on the computer, but when you talk to people, it's very, very clear that people are complaining, uh, well, I didn't find this, or this information's not here, or this system doesn't talk to this system. But when you look at the application, it it's not going to speak back to you. It's it's when we talk to our coworkers, when we talk to our teams, it's this thing like, where the hell am I gonna get this information? Because it's just not here.

SPEAKER_00 13:31

So back to my previous point, um, we've in some cases we can be a little lazy when trying to point somebody to where where this information lives. Might say, oh, go and SharePoint. Have you ever been inside of a SharePoint for a company that's been in business for you know five, 10 years? There are thousands of places that you can wander in there and still not even come close. And and then your initial understanding of what you were expecting to see doesn't always match up what you end up seeing. So it just becomes this vicious cycle of great, I spent all this time and it doesn't actually show me anything. You know what we're when so so what happens when we don't prioritize responsible digital operations? These are the things that ends up happening where we're searching for data, we can't quite create the context. We may have to transcribe over and over again, like you said, different systems have different naming, different nomenclature that don't align with how we've presented, whether to leadership, whether into, you know, um, you know, connecting it across systems. And these are where it's it's it's real critical to have that unified data structure, even as things come in. Okay, now that things are coming in, let's make sure that we have some baselines. Here's the projects, here's roles or functions that are responsible for these things so that we can now start to create different views across your portfolio because that's ultimately what leadership is going to be looking for. Yes, the science is important, yes, the the location that you're at is important, the resources are important, but they cannot be viewed in a bubble. They need to be viewed together to have the right context to make the right decisions because you can always come back and say, well, what about the number of people that are on that project? Well, how long did this project take? And what were the resource needs for that, for that type of project or for that type of initiative? And you know, it's I go back to the frequency that I've seen in even in leadership discussions where seemingly questions that we should already have answers to are followed up with, I'll get back to you. I'll need to get back to you. It's a complex question, absolutely. But there should be a bare minimum to what we can just pull right up because we have the right architecture, we have the right data flow and the right people managing those systems, because that's the reality of it too. This is not any one person's responsibility. You know, we talk about in the biopharma Nexus part of you know, these first principles is that we have to own our of these shared themes, is that we have to own our software. If you are the main user of that software, you should know it more than anybody else at that organization and start reaching across the aisle to understand, hey, who's creating the architecture over there? I want to look at how you guys are naming things so that as we're building things out, we're gonna be able to connect it in the future. You know, these are things that unfortunately are not prioritized. It's more of a, you know, let's get it done as fast as we can. But we're, you know, these things are key for us to responsibly transform the operations and our day-to-day in these digital formats successfully. We just have to do them.

SPEAKER_01 18:07

Yeah, I think you and you know, your point about management asking questions. I'm almost certain that most of the time they're asking the same questions. Where is the timeline? What are the risks?

SPEAKER_00 18:17

Like that's how that's how I build most of my dashboards. Let me sit in one of your meetings for about two or three meetings. Okay, here's the eight questions that keep coming up. Let's just build things that answer those questions so that we start to get better questions. You nailed it. Absolutely.

SPEAKER_01 18:37

Yeah, asking the same questions over and over again. But for some reason, when when new projects or initiatives come up, we we tend to get very creative in certain ways if there is no standardized process to create these things, is we start to customize them, and then now you get this one-off setup for something and it's not similar to something else. And now the the flow used to be flowing into a data lake, but now it's like these different data streams that are just you know going in separate directions, and it's hard for anybody to compare apples to apples if if things are going to be moving that way.

Resource Models Break Without Definitions

SPEAKER_00 19:13

And how do you approach that when you come from the outside? It's so difficult to get up to speed when there's no real rhyme or reason with how things are connected. You know, there's I recently, I I think maybe earlier today, I scheduled a post, but it was on this idea that we need to get comfortable sort of building and archiving, pinning and unpinning, favoriting and unfavoriting, like to make our work more appeal more to us. Like it's okay to build some of these systems and then fold them away as the business evolves. I think we find ourselves now in a place where we're always trying to like, can we just stay with something for a long time to get really good at it? In some cases, yes. But if your company is growing and expanding into areas that you can't foresee, you have to expect that the system that you're building might change, but that architecture can very much stay the same. And that's how you build resilient systems. You build them based on an architecture because no matter what you're doing, or no matter what system you're you're you're migrating to, you still want the information to tell you the story that you've been trying to come up with, you know, and instead of looking at, you know, having you know seven, eight, nine, ten PowerPoint slides open, and you're now trying to take little pieces from all of them so that you can craft a story. These things should be somewhat available through how your data is connected. And I'm not saying that everything is going to be in the same place because that's the likelihood of that is very slim. You know, some things are more text heavy, some things are more data heavy with, you know, with mainly integers, some things may be contextual, like a CRM. You know, this is the conversation I had, and it feels like this, and we should come up, you know, with another uh, you know, conversation. So data is gonna look differently. I'm not saying these things should all live in the same place, but I think as your organization moves and matures, you have to think that your systems may do that as well. But if we maintain that architecture, if you have in the back of your mind, or even you know, static as a lucid chart flow or something, that creates the strength and resilience for when change does happen. That we know how the data needs to connect, we know who needs what kind of information. And I think those are really critical for when there's any kind of digital transformation for people to be able to develop something that can move without disrupting how people work.

SPEAKER_01 22:02

Yeah, I think there's um a few different ways to look at it. And the way that I prefer to look at it, especially for things that uh these larger companies use, is you look at the application and you have to understand like what the different end users are engaging with, right, on the particular platform or application. And how does this impact their work day-to-day? I think a really good example is looking at like a what we would call a computerized maintenance management system. So this is where you have all your records for your equipment, your work orders. Some of it will have inventory, so you know, your parts that you need for certain work orders, right? So for your technician, it'll be really helpful to know, you know, what is it that I have to do today, you know, what's coming in the week so I can, you know, create my schedule and come up with um a plan for how I'm going to do all these things throughout the week. I think a another stakeholder that would be a part of that would be if you have a uh a QA group that has to review um certain changes that impact uh quality impacting systems, you would want to have a setup where they can quickly go in there and view what it is that they need to review and approve so that certain changes can be made. And there's, you know, understanding how the the data is input into the system allows you to construct the report or the dashboard and the or the information needed for that end user. And thinking about it from the the end user perspective, they don't want to have to go into the application and then click a thousand different windows to figure out what it is that they need to do. Right. I think they would, you know, for their job, they they want to know, you know, these are the things that I, you know, the top three things I need to know to to get through my day. And I I think you know, one layer above that is gonna be your your management layer, right? Where they're looking at, okay, you know, they were. X number of work orders that were scheduled for the month. Are we on track? Are we not? And so they might go, hey, you know, it looks like we're struggling a little bit. So let's let's deploy some more resources to help out this particular team in resolving those work orders. Or it might be, hey, it looks like QA is backlog. Let's figure out how we can help them prioritize the list so that they can go through the most critical changes first, and then the other ones can kind of go in the back burner. But looking at the application and how the different end users engage with it, I think is really important. And I think again, this depends on the context, right? So when you look at a like a portfolio or project management group, they're looking at a whole bunch of different things where you have all these different systems, right? And so chances are they're probably not pulling 100% of all the different data points that you have in all these systems, but there's probably select things that they need in order to report back and for them to make decisions, right? So that's coming at it from a different lens where you're looking at your role and then pulling from multiple data sources versus the initial example where you're an end user, but you're pulling multiple data sources from the same application. And so these things can get really complicated, convoluted, whatever you want to say, but it's it's good to have that mapping out so that you can configure the application, the data flow so that it matches the need for what their job or role is.

SPEAKER_00 25:18

I love that. You know, I I have here written um, you know, when we talk about what some of these primary risk factors are, you can end up with fragmented data structures when you don't take the approach that you're talking about. And what does that mean? I think we've heard it a lot. We're building it as we go. You know, I understand that from you know the standpoint of building a new group or you know, starting a new arm of the organization, um, or even just starting up a new organization. But I I think what it sort of becomes this like badge of honor that, like, oh yeah, you know, we're just we keep working and you know, we don't know how to prioritize anything, we're just gonna keep on moving through. And we have to ask ourselves like, is that really the best way for us to move this giant shit that we're trying to, which is the organization, all of its assets, all of its people, into that successful next stage? You know, we're not taking a pause to ask ourselves, what are those things that we need to have happen in the future? What's the data or the information that is going to influence that at these various stages so that we can start to at least, you know, conceptually understand how these things need to be linked so that we can make those better decisions. And what do I mean by better decisions? More informed decisions, as opposed to taking one or two data points and the comments from a person and say, okay, we're gonna go in that direction. We actually have data to back up why we're making the decisions that we're making, you know, that are the best at that point for point in time for the for the organization. So, you know, we're we've seen where there's no single source of truth, and that sort of becomes this like wild, wild west. You're struggling to know more. So it's just this constant reconciliation of how else can I frame this? How else can I get the information to tell me more about what I'm looking at? When you don't have this very thoughtfully put together, you start to increase that risk where you're making decisions that are misinformed, because you may have a data point that is pointing you to choose, you know, the red pill, but you have two other data sets that are telling you, no, this is definitely the green pill, but you don't know how to access it. You didn't even know that they were relevant to the conversation or to the question you're trying to answer. Points here that um I think we feel in the day-to-day, and it becomes this sort of, yeah, you know, we're we're we're we're flying the plane. What is it? We're flying the plane, I don't know, something like while we're trying to change the uh the engine at the same time. Yeah, yeah, yeah. You know, we're driving the train and and laying down the tracks. But if you don't know where those tracks are supposed to take you, then how do you even know that you're building it in the right direction? You know, your compass is a little bit broken, but if you're not thoughtfully thinking about where those tracks need to be laid and in what direction, you're gonna end up off the mat very quickly and struggling to make decisions for that next step.

SPEAKER_01 28:34

Yeah, there's a good uh quote from his name is Naval Ravakant, and uh, I've been following him for a while. And he hates being called a modern-day philosopher, but he was really successful in tech, and he has this really good definition of success, which is it's basically two things getting what you want and wanting the right things, right? And I think this is very applicable to data, is you have to know what the right data is that you want for whatever you're trying to do, and then enabling people so that they can actually give it to you, right? And that this is where it comes into that like user interface aspect of how data flows are set up. If if you want certain data points so you can make a certain decision, you have to make it easy or somewhat easy for the person to give you that information, right? Don't make it some super complicated thing where people have to again click through a bunch of windows and then they have to, you know, enter through you know one avenue to go somewhere else. And that navigation nightmare, right? And where it's this thing where people get frustrated, and then that's when you have this um lack of uh people just give up, right? They they don't want to have to go through all these hoops to give you that information. If if it's something simple, it increases the the likelihood of somebody giving you accurate information. If you make it incredibly difficult, it's it makes it very hard to um make sure that they do that consistently, I guess is what I'm saying, right? You're already asking for this information that may or may not be in the scope of their role, and so you might as well make it easier for them to give you that information. And I think it's I don't think this is rocket science. I think a lot of people just don't really think about that aspect. They just go, oh, I need this information for this report or whatever deck I'm putting together. And then they don't think about like, well, how how would this person actually put this information?

SPEAKER_00 30:30

You know, I think it when we talk about it in that sense, we don't know what we don't know. So I think there's also this part where leadership or managers, like maybe they're not in these systems very often. So they're just gonna ask you a question, hey, can you tell me the last time that this happened where we spent you know X amount of money on a particular project and a modality of a therapy? Of I mean, that could take days depending on who you're asking. And it doesn't really give you clarity in the resolution. Like, are you looking for one big number? Are you looking for like a trending graph? And either way, if the data is not really positioned to answer a question like that, it is just gonna be a manual burden to create that. And even with the use of AI, if your data has different names and located in in disparate systems, like and you don't have this idea of like a data lake or something that you can sort of query, who knows what kind of information you're gonna be getting. So, you know, now again, making decisions on inaccurate information or you know, uh falsified context, you know, hallucin hallucinated context in other in other ways. So it's I've seen it and it's uh even now in 2026, you it's still present in in the workplace. And it does take uh a person, a lead, a guide, a sherpa to be constantly thinking about this because we are making decisions directly from data, not just from the voices of those that gather, not just from how we feel about things. We need data to feel confident that the things that we're deciding on are based or are rooted in some factual information. That's really what this boils down to. So we need to, and as these companies and these projects grow, the data volume goes up and the clarity goes down, especially when you don't consider what that architecture looks like, what the data flow, how your operations digitally are going to be talking to each other. And that's and this is where you feel that, oh, these are growing pains, growing pains, growing pains. How long are growing pains supposed to last? And growing pains to me just says we did not think about how this would look as we grew.

SPEAKER_01 33:06

Yeah, and then you you get to the point where if if you do have if you've amassed a lot of data for, let's say, resource projections, right? This is a very tough thing that a lot of companies deal with is how many resources do I need to support a particular set of programs, right? And so you go through this exercise of, okay, well, let's look at this particular program and then has this set of like metadata associated with attributes, right? It might be for this particular modality, and it, you know, the complexity level was medium, and you know, the the amount of that we needed was for however many patients. And so you're relying on people providing you their time of how long they spent on certain things, but then you're not thinking about, okay, when the person's entering the time, what does half an hour mean? Or what does one hour actually mean here if they're working on something that impacts multiple programs? Like, how do you, how do I, how do I actually build that into my resource model? And I think it's important, like you, like you said, right? Designing the uh the art, like the architecture of the data flow so that we understand from the moment that we allow people to enter things in, this is what this means. You can't get to the end of it and then you have a bunch of data and then you start moving the goalpost, and then people start losing confidence in the actual data and the insights that you're getting from it because they go, oh, well, that's not accurate because when they were working on this, that actually counted for multiple programs. Okay, well, why don't you say that in the beginning when you were asking people to input that information? Because now you're you're having to make these pretty risky decisions on do I need five scientists or do I need 50? I don't I don't know because the information that you provided me on the these couple of assets are telling me this thing, and then something is is telling me something else. And that's you're and that's a situation where you're looking to increase capacity, right? If you go the other way around, and now it's saying, and actually, based on you know the the change in the pipeline, we actually need less people in your department. And now they go, oh no, the model is actually really accurate. I think I think it's I think we actually need that many people, right? Because people don't want to lose headcount and they don't want to lose budgets.

SPEAKER_00 35:15

Yeah. They they want to have more, they want to save, they want to protect what was allocated to them initially. Absolutely. Right.

SPEAKER_01 35:22

It's funny how the data tries to change meaning when it impacts the the headcount.

Risk Assessment And Next Steps

SPEAKER_00 35:28

Long ago, we I was in a situation where this became uh new at the organization, and we started getting a lot of people that said, what if we work more than 40 hours and the goal here was percentage? Sure, I mean, I I think you're pointing out something too that, you know, even uh one FTE as best as we can do it, one FTE we think is 40 hours a week, but we don't know how well people are leveraging their 40 hours a week. We don't know really where they're spending their time or what that actually means in the context of a project that is early in the process, in the middle, or somewhere towards the end. And I think that's an important distinction that we can't just base our needs on how much time has been spent on a specific project. We have to think about what are those activities, what are those demands at these different stages, and then start to query not just, oh, we went up or down. Does this make sense? We can't accept all these things at face value. We have to also question whether, you know, question the validity of whatever model that you're deciding to propose for your business. Does this make sense that we're spending that, you know, this function is spending 70% of their time on this project that is at this phase of development? Let's go have a conversation to understand what that actually means. Because resourcing is not about increasing or decreasing per se. I think it is more about you have this bucket of elements that is your systems, your computers, your people, the training, the technology, skill sets. How do you launch this to run 20 programs, to run a facility that does eight different medications or 25 different types of synthesis, et cetera? Finding out not just these whole numbers and saying, let's respond based on this, but also looking at that and saying, in what context? You know, start to develop those guardrails and those rules for like, you know, what are we, what are we putting into the system in terms of how much we're working on something, what we're actually doing, where that effort is being done so that you can actually have data that supports any resourcing, any budget conversation, any partnership conversations. It's critically important when that doesn't show, that also shows when you're having those discussions and you're you're sort of in your mind thinking like these people have no idea what it will take to do this, they have no clue what to expect in the future. And you might have the data, but if you haven't pieced it together, it's a nightmare. You know, you talked about getting those requests, most of them are ad hoc. You know, you'll get a few of the same questions in the same in those similar meetings, and then you get those emails. Hey, can you help me figure out one, two, three, four, five? Now we're looking at fragmented data. Now we're looking at data that doesn't even have the same language, and and you're spending this time to translate it to develop an insight. AI is not gonna help craft the narrative of your story if your characters have different names than what in one group versus another. If you know, and we can go on and on and on, I think, I think on that. But what we're trying to get to is that there are risks that we can identify in these organizations that go from our system to system connectivity, how manual the data burden has become, the ownership, who's owning the data that enters into these spaces and the source of truth, who's managing that? And you're talking about timelines, you're talking about research data, you're talking if there's a particular machine that somebody is the expert in, they should also own the data that is happening within there. Um, you know, the the data to decision flow. What's the data that we need to create, you know, to come up with decisions at each of these different layers? And then your integrity, your data integrity and the risk exposure. Are we training? Are we giving our workforce the capacity to be successful in all of these digital operations? That we're giving them constant reminders of how we need the work to transform from conversations and post-it notes and sticky notes to things that live in a digital architected, in a in a well-architected digital space that will decrease your exposure to making wrong decisions, to delays, to risk of misinformation. Um, you know, and all of these things, luckily, Lawrence, for our listeners and and those that have been following us, we have taken this whole topic that we've talked about today, and we've put it into one of our risk assessments to help people identify where is their biggest issue? Is it that there's no integrity? Is it that the decision, the data to decision flow is broken? There's no real source of truth, the manual data burner, each of these pieces requires a different approach to fix. So, how do you focus on the right one so that you're creating a more mature, healthier digital operations? And we have that in our risk assessments.

SPEAKER_01 41:21

Yeah, and you know, to your point, the the assessment itself, it's it's not, you know, whether you have this or that. It's it's really gauging the how the people on your team perceive or interpret how strong your your digital operations are, right? Because certain users might be really great at it, and then other users are maybe they don't have enough context, right? You know, like we were saying before, you know, one hour of Sally is not equivalent to one hour of Bob because Sally is way more adept at accessing all the information. But should that be the case? Shouldn't everybody have the right access level or or have the means to access the data so that they can, you know, do their certain roles and jobs. And I think the the beauty of the assessment, it's it's a it's an opportunity for the team to reflect and really gauge how well things are set up and and where you need improvements. Because, like you said, unless people enjoy the frustrations that go along with having an unorganized, unstructured workflow and you like doing these ad hoc tasks, then maybe you don't want to take the assessment and you should leave things as is. But I think we're we're getting to a point where the capability of some of these new AI-powered tools are only as good as the inputs that you feed it, right? And so the better you are at organizing those inputs, the more likely you'll be able to capitalize on some of the gains that you will have from using these tools. If you leave it with gaps, things not structured correctly, it's just gonna give you garbage. It's not gonna tell you what you need to know to make the right decision.

SPEAKER_00 43:01

You're gonna look at those responses and go, what in the world is this talking about? What a waste of time. And you're right. There's I think we've heard it a thousand times, you know, garbage in, garbage out. And that is it's very true in a lot of scenarios. And I think we've positioned something that is asking the questions that we sometimes fail to ask internally. And we've packaged it in a place for us to understand, you know, what does it feel like on our team? Are we all, you know, I I aliken the things that we're talking about now to being on a sports team. You know, your assessment is that tryout. Okay, we have a couple of different levels of athleticism in any work that you're doing. And it's not just that, like, oh, the senior directors are stronger than the people that are associate managers. That's not necessarily the case. Sometimes it's experience, seniority, etc. But if you're thinking in the context again of like a team, you know, hey, uh, let's get these guys over here. I want them to work on, you know, if I can take some baseball terms, you know, I want to work them to work on some fly balls. I want these guys to do, you know, sprints because these are the guys we're gonna get to steal bases. You know, these are the ones that we're gonna get, you know, our pitchers, let's get our pitchers warming up over there. Let's get some infielders because we want to do some strategy on the infield of how they're gonna work together. And this is this is a little bit of how we need to work with our teams too, is recognize the strengths and the weaknesses, you know, in the context of of how our data is organized and make sure that we are giving the opportunity to lift those people up and enhance their capabilities so that when they're delivering their technical role on that project, it is consistent with how the rest of your team is also delivering on their projects. Very important, very important. And I hope that everyone takes takes some time to think about what does it feel like, what does it feel like at your organization? Is the data accessible? Are you constantly searching where do these things exist? Are you constant are you pulling things out that you thought were sources of truth, and then you come down come to find out that there were drafts that were never continued? That were, you know, builds that happen in Monday in Trello and Smartsheet and Notion that just stopped. Has a lot of data, but everything stopped because the people that were inputting the data decided to go somewhere else. But you didn't know that. Why would you have that context? Are these the things that you're feeling? These assessments look to elucidate where those challenges are.

SPEAKER_01 45:45

Yeah, I I think a good question to ask um for for anybody is the flow of information across your company? Or your team enabling some of the dysfunction across multiple departments.

SPEAKER_00 46:02

Let's say that again. Is it enabling the dysfunction? I think a lot of us will say, yes, it is definitely adding to that dysfunction. But ask yourselves that. Well, thanks so much for spending your time with us today. Uh, we truly appreciate you being part of the Lean by Design community. If this conversation resonated with you, we invite you to check out my new book that just landed. We just had a launch on Wednesday that went fantastic. And we're at this, and I take a candid perspective on the patterns of operational friction that quietly slowed teams down, erode trust from leadership and erode morale and what to do about them. How do we approach these things so that we can create an organization that has that data operational structure, that has that flow, that has that system level thinking. And if you're ready to take the next step, we invite you to explore our operational risk assessments that are these are hybrid engagements where you'll actually work directly with Lawrence and I to identify, prioritize, and then be able to clearly communicate the friction points in your organization so that you can move forward with confidence and alignment. So don't forget to follow us on Instagram at SciGuy underscore insights. That's uh my braided page. And you can now watch our podcast on YouTube at Lean by Design Podcast. You can watch the whole episode. Me and Lawrence are blenders, probably, that Lawrence will stick into there to show people that we are real people and we mess up, but we keep on going and we make adjustments, and we are just very fortunate and feel blessed that you guys are taking this journey with us. All the links are in the show notes, and uh, we're gonna take a little hiatus. Lawrence, a lot of big things have been happening outside of the book. So I will be moving, I will be welcoming a new addition to our family. So a lot of things that are happening, and we will miss out on being able to give some more insights to our listeners, but we'll definitely be coming back a little bit in the springtime, as soon as the six foot tall piles of snow start to melt. Thanks, Lawrence.

SPEAKER_01 48:20

All right, bye.