Turning Data into Decisions: The Insights Supply Chain Framework

[00:00:00] Michael Meyers: Why is it that at the very moment being data-driven is shifting from competitive advantage to survival requirement? Most organizations are still struggling with it. They're stuck low on the data maturity curve. The reality is that few organizations today have anything close to a complete ecosystem where data flows across an organization from raw information into insights that drive business value, decisions and actions, even fewer can measure their success and optimize their approach for continuous improvement.

[00:00:29] Why? Because the way data and tools have proliferated across organizations has created a fundamental problem, short-term gains that lead to long-term limitations. And the process of transforming data into actionable insights has become fragmented, hindered by ad hoc collaboration and unclear ownership.

[00:00:48] While individuals may have more insight than ever before, there's a hard limit to what they can achieve on their own. Most systems don't talk to each other, and even if they could, it's difficult or impossible to correlate [00:01:00] information across them. And the paradox, as everyone in your organization becomes a data analyst.

[00:01:05] At least to some degree. The need for data professionals only becomes more critical, not less. Now you might be thinking AI capabilities like conversational analytics will solve all of your problems. You'll just type in questions in natural language, you'll get answers. No data cleanup's gonna be needed.

[00:01:24] No data specialists are gonna be needed. Yeah, it is another step forward. It too has its limits. For now at least. It's the latest version of the Holy Grail that people have been chasing since SQL was invented over 50 years ago, to be that non-technical query language and regardless, it can't solve the fundamental underlying problem.

[00:01:45] If you wanna reposition your organization to be truly data-driven, you have to start with the foundations. Today we're exploring the Insight Supply Chain Framework, a structured approach to organizing your data and teams and systems that enables collaboration and drives genuine business value.

[00:02:02] Created by Dr. Duru Ahanotu, leader of Tag1's data science team. The Insight Supply Chain Framework gives you a strategic blueprint for making the fundamental decisions that determine whether your data drives maximum value or remains untapped potential. Should your data teams be centralized or distributed across business units?

[00:02:21] Do you need specialized roles or can generalists handle all the work? How should teams be structured to foster collaboration that turns your data into action? By answering these and other questions strategically, not arbitrarily, you create a clear pathway for data to flow through your organization. You establish career paths for data professionals, which is critical to attracting and retaining scarce talent, and you build an ecosystem that evolves as your data maturity grows.

[00:02:49] It's not gonna solve all of your problems, but this is the foundation that enables you to move from not being as successful as you want with your data to being truly data-driven. There's a lot to unpack here. So this is the first in a series of podcast episodes on the Insight Supply Chain Framework.

[00:03:06] Today, you'll hear the origin story and get an overview of the framework. In future episodes, we'll go deeper into critical topics, including the centralization versus decentralization decision, the DOM or data org matrix, and how it plays into that, how AI adoption factors into the framework, and we'll provide more insight into the data maturity curve.

[00:03:27] Let's dive in.

[00:03:33] Michael Meyers: Hello and welcome to Tag 1 Team Talks.

[00:03:34] The Tag1 Consulting podcast. Business Intelligence, BI has been the standard approach for decades, but it's outdated. Today we're talking about what's replacing it, the insights supply chain framework. I'm Michael Meyers, managing director at Tag1, and today I'm joined by Dr. Duru Ahanotu. The creator of the Insights Supply Chain Framework and the leader of Tag1's Data Strategy Team. Duru, welcome.

[00:04:00] Dr. Duru Ahanotu: Thank you, Michael. It's great to be here.

[00:04:03] Michael Meyers: Duru is one of those people who embodies everything I love about working at Tag 1. Uh, forgive me Duru, but you're, you're brilliant. You're incredibly insightful and you're really easy and fun to talk to. Um, your, your background is, is fascinating and really diverse.

[00:04:17] Like a lot of people who work here, uh, three degrees from Stanford, including a PhD in management, science and engineering. You built expert systems, diagnosing, manufacturing robots. You worked in data science at Yahoo, dictionary.com. You've worked in the startup acquired by Microsoft, uh, where your team worked on pricing methodologies and optimization solutions that earned a patent.

[00:04:41] Um, but I think what I love the most, um, is how you think about problems and how clearly you're able to communicate those ideas. Like when we were preparing for this podcast, it was just, it was just so easy. Uh, but before we get into that insight supply chain, uh, I have to give everyone a little bit of insight into Tag1.

[00:05:02] Tag1 is the number two all time contributor to Drupal, the world's second most popular content management system. For nearly 20 years, we've been the architects of the open web, leading the creation of the software and the best practices that power millions of websites.

[00:05:15] And hundreds of thousands of organizations worldwide. We're a full service strategic partner applying that same architectural expertise across technologies and throughout your organization. From discovery and design to building and scaling complex applications, we lead AI strategy and implementation, design and manage infrastructure and architect large scale web applications across a wide range of platforms.

[00:05:38] We're trusted by industry leaders including Sumitomo, NTT Data, the European Patent Office, and the American Federation of Teachers. To solve mission critical challenges and build lasting solutions, check out tag1.com to learn how we can help you too.

[00:05:54] So Duru, let's start from the beginning. Can you tell me uh, the origin story, where did this concept of the Insight Supply Chain first come from?

[00:06:04] Dr. Duru Ahanotu: So it came from my days working at Yahoo, where I first got into management and um, and leadership. And what happened is that I found myself in a position where I had to grow. Uh, my SVP, uh, the data team, I was in a centralized data team, asked me to grow out a full service data analytics, um, and data science team to service our product teams globally.

[00:06:32] It took me about a year. I grew it out to about 20 people. Uh, it included what we would call business analysts, what we call at that time insights analysts. And you'll see in my framework I've included that. A title or role. And it also had, uh, data scientists at the end there. And so together that team create, we created a centralized data analytics, or sometimes we might call it a data team to service, um, Yahoo's product teams and marketing actually, um, across the globe.

[00:07:03] Shortly after I finished that, um. The CEO decided, you know what? I don't think we should have a centralized data analytics team. We should have a decentralized one. And it was actually a unique restructuring process because the decision of where we went didn't come from the top. We actually got to decide where to go and, uh, where I decided to go was over to marketing, partially because it was the, one of the last organizations in the company that still had a centralized role.

[00:07:35] So I could still touch pretty much every aspect of the business. Now, in the middle of that, you know, I had gone through several restructurings at Yahoo. I mean, I think this was maybe year four, uh, at Yahoo. And this was. The one of many reorganizations, and it just struck me that why, you know, the question struck me in my mind, why do organizations constantly go through this cycle of centralizing, and decentralizing, and then back again?

[00:08:01] Because to me, from my perspective, where I sat in the organization, it seemed almost capricious. It almost seemed like it was just a preference of whoever happened to be in leadership. And I decided, you know what? I'm gonna create a framework that will help, um. Help model this in a structured, uh, framework.

[00:08:21] And that's how the Insights Supply team came about. It came about from my experience in building out a large team for a global organization, but then also trying to wrestle with how would I guide future executives on how to decide when it's actually good to centralize your data team versus decentralizing it.

[00:08:38] And then I grew the concepts from there.

[00:08:42] Michael Meyers: Why the metaphor, uh, the supply chain?

[00:08:47] Dr. Duru Ahanotu: Yes. So that's kind of my bias. I was, uh, uh, manufacturing, um, sorry, not a me mechanical engineer as an undergrad, and I'd always wanted to, uh, be part of the revitalization of American manufacturing. So the insight, the, the supply chain metaphor just came naturally to me because of that.

[00:09:06] But also because of the way we organized our work. And back then things were much more sequential than they are now. Um, now it's a little bit more complicated than just a sequential step-by-step process of, you know, going from data to insights. But I was looking for a metaphor that would just resonate that was very easy to explain, easy to visualize, right?

[00:09:26] So in the inside supply chain, you start with the raw data. Just like you know, in a manufacturing system, there's the raw data. Data engineers doing data mining, you know, it just seemed to fit that whole metaphor. And then they are the ones responsible for supplying that data over to the next step, which is the, uh, data analyst who organizes, who looks for structure, who does queries on the data.

[00:09:53] And then from that mode. Or node move over to what, again, in this framework is called the insights analysis, which speaks the language of the business. So understands how to take the data that's been organized by the data analyst and put it in a way that the business folks can understand and also can advocate, can evangelize, and can, can promote those ideas throughout the organization.

[00:10:18] Um, so that's the sequential. You know, metaphor that I developed. And then of course from there, there are all sorts of flavors in terms of how you mix and match or organize those different modes.

[00:10:30] Michael Meyers: I, I love the name. Um, but why not? Like, you know, we started out the show by saying, you know, business intelligence is an outdated term.

[00:10:40] Yeah. Um, why not just like, work within the existing framework, you know, why, why create a new framework? Why did you want to distance yourself from the concepts and, and frameworks behind BI.

[00:10:53] Dr. Duru Ahanotu: Yes. So. Another reason why I came up with this framework is because, um, I've always been concerned with organizations' inability to provide clear career paths for data professionals.

[00:11:05] Um, and to me in particular, the term Business Intelligence does not offer a clear career path for data professionals. Not only does it, and it also doesn't describe a whole ecosystem, right? So I wanted to come up with a framework that speaks to both an ecosystem where data is moving from one form into insights that drives business value and decisions and action, but also a place where people can see themselves moving as well in this system and progressing in their careers.

[00:11:36] So business intelligence made sense when it came out in the eighties and nineties. Data was kind of a monolith, right? Um, IT reigned supreme over all things data. But now as we sit here in this more modern, in this era, you know, IT

[00:11:53] almost is not always even in charge of the data ecosystem. It's, it could be a whole data organization in of itself.

[00:12:01] So to me, business intelligence as a term just is too confining and not to mention. In, in my coming up, uh, in becoming a data leader, business intelligence or business, um, uh, business analysts were one part of the data ecosystem. So to me it was just confusing to elevate that term, to go from one part of our ecosystem to basically be an all-encompassing term, a really a new term, a new way of thinking seemed appropriate.

[00:12:33] Michael Meyers: It seems like a very holistic picture, how information, you know, yeah. Uh, goes across the organization, how you capitalize on that information and turn it into something you can execute on and results career paths of individuals. You know how you should and shouldn't, you know, decentralize, which is something that's frustrated me on the engineering side.

[00:12:52] I can't tell you the number of organizations I've been through that, you know, we're gonna be siloed, we're not gonna be siloed, we're gonna be in matrix, we're gonna be, you know, and it is capricious,.

[00:13:00] Dr. Duru Ahanotu: Sorry, and I can't speak to that one. Sorry.

[00:13:04] Michael Meyers: I, I know we're focused on, on data engineering here, but it is a, it is a common parallel.

[00:13:09] Um mm-hmm. Can you, you know, let's dig in more to the, the framework itself, right? Like, you know, gimme a better sense of, you know, what it is and how it works and all these different components.

[00:13:22] Dr. Duru Ahanotu: So, uh, one thing I learned from my marketing folks and from actually my studies in, um, I did some classes in the business schools that every framework needs a two by two.

[00:13:32] Particularly, you know, management consultants always have a two by two matrix. Um, and so if I were to show that matrix, and, sorry, I'm gonna be waving my hands a little bit on the axes of my two by two matrixes. On one side there's the specialization versus generalization. Um. Spectrum. And then the others other axis, let's just call it the X axis, is the centralized versus decentralized question.

[00:13:58] And then each of the quadrants then is a particular strategy, a particular way that you wanna run your business, and a particular way in which people are gonna be offered career paths. So let's take one of those quadrants. Um, the one that really applied most directly when I was building out the 20 person team, which is it was a centralized.

[00:14:18] Data team, but I had very specialized data professionals. So in that quadrant, what the strategy of the company is, is that, uh, people are gonna grow their careers as specialists. So I'm gonna have those specific roles that I talked about earlier, and we are gonna be a service shop across business units.

[00:14:40] Now, say you want to have a decentralized system, which is what we went to when the CEO flipped the switch. Well, in that different model now. You, you have the experts and the specialists, but now they are scattered across the organization and they don't service the whole organization. They service their particular business unit.

[00:14:59] That's a very different orientation. It's a greater challenge to offer the data professionals, uh, career paths because they're going into organizations that. They may be the only data professional, maybe they're one of several. I just happened to be fortunate that when I went to marketing, there were lots of, uh, data professionals.

[00:15:18] I actually brought a portion of my team as well, and I built out what eventually got called the data science and data engineering team in, um, marketing analytics. So yeah, and I forgot that that component of the. But my whole story is that when I went to marketing, I actually took on data engineering for the first time and that was really what also helped crystallize my concept.

[00:15:39] Because for the first time, you know, I was managing engineers at that time. You know, data engineering wasn't really a thing. There were people who were doing data engineering, were. We're typically computer, uh, sorry, software developers who just had a shine for data and decided to go down that path. And so I was actually managing engineers and doing the whole, um, you know, the whole thing, uh, learning how to, how to do that management side.

[00:16:02] So, but anyway, so in those quadrants, those quadrants then define the strategy, um, that the company wants to apply. How do you think about your data? Are you gonna make data, your core competence? So are, and, and Yahoo had to make data, its core competence, or is data just sort of a, uh, it's a byproduct of what you're doing, and if it says a byproduct of what you're doing, then for instance, you don't need data experts.

[00:16:27] Maybe you just need to, you need a generalized set of skills. You can decentralize folks and so on. So that's, uh, that's the beginning of, of the framework, and it allows a company to decide right up front, make a very conscious decision. Okay, what is. My orientation, my company's orientation towards data.

[00:16:46] Okay. I belong in this particular quadrant if I want to effectively apply that strategy.

[00:16:53] Michael Meyers: And do organizations move across the spectrum with some fluidity, like, you know, is, you know, um, is there a reason why an organization would switch from decentralized to centralized? Is it really up to the executive? Like, how, how should that work?

[00:17:09] Dr. Duru Ahanotu: Well, you can imagine my biases. I would like not to leave it up to the executive and I would like them to consult with me and, and, and we'll, we'll, we'll apply the, the two by two matrices of their situation. Um, yes, so I don't. It, it, this, this framework is not meant to be rigid, but it is meant to be structured and I think you, you might appreciate the difference, right?

[00:17:32] Dr. Duru Ahanotu: Structure allows you to make very rational moves. It allows you to explain to your people why it makes sense to go from this state to another state. So for instance, let's just take a company's lifecycle or its trajectory. Where data is just a byproduct of what they're doing, um, at some point they may realize, oh my goodness, actually this data stuff is actually a strategic advantage for us in a competitive advantage for us in the marketplace.

[00:18:00] We better get more serious about how we think about, um, the specialists that we need to deliver data products. Right? Um, so you may decide that if data's gonna be a competitive advantage for me, I want to have. Uh, I want to move from, and I'm just, of course making up this scenario. I wanna move from this place where I have generalists.

[00:18:22] And generalists might be, Hey, my finance people are doing data stuff. My marketing people are doing data stuff. My salespeople are doing data stuff. And instead I realize, you know what? I want to hire a data engineer, a data analyst, a data scientist, and I want this team of specialists. To create my data products that I go to market or I accompany my services or products, uh, with this, uh, specialized data, um, application.

[00:18:49] So that's one example of how a company may decide to move from one state to another or one con, uh, configuration of an, of the inside supply chain, uh, to another one.

[00:19:03] Michael Meyers: Is it how organizations are like predominantly organized. Um, and, and what I mean is. We sort of live in a world where everybody is, or should be a data analyst to some degree, right?

[00:19:14] Mm-hmm. You do need your finance people to be doing things you, like. Everybody in the organization needs to be doing it to some degree. But I am not a data scientist. I am not a specialist. Um, you know, it's not my focus area. Um, you know, uh, is there some sort of hybrid model or is it based on more, the, you know, the, the main classification.

[00:19:37] Dr. Duru Ahanotu: So there's a way you can do a hybrid model, which is, um, let's again just go back to the centralized model where you have a team of data experts and they are servicing different bus business units. Well, within those different business units, there may be data people, right? Um, and those data people don't have the full set of expertise in a particular area.

[00:19:57] And what you're doing is supplementing, their capabilities. So finance is a great example actually, both finance, marketing, and sales. All of those are great examples. Finance people, of course, are very great with numbers. Uh, they, some, they oftentimes can do queries, but maybe they can't do data pipelines to, you know, uh, really well and they can't standardize the data flows that they need to have a consistent, robust set of numbers, you know, for their reporting. Well then call on the centralized data team and they supplement the data skill sets in that particular, uh, business unit, uh, for example. So yeah, we are of course moving to a world, you know, with, um, with a, with, uh, generative AI and no code. And conversational analytics and all these great fancy things that AI is allowing us to do, where more and more people are going to be their own, they're going to have, uh, greater power to do data analysis themselves.

[00:20:55] Um, but I will always contend, uh, that the, the specialist, the specialized knowledge of the data expert or the data professional is not gonna go away, uh, anytime soon.

[00:21:07] Michael Meyers: Definitely. And, you know, highly beneficial. Um. For, you know, I, I, I think of some of the organizations that we work with, they don't have specialized data teams.

[00:21:17] Michael Meyers: Uh, they, they, they might need them, but they don't have them yet. You know, if, if you're an organization that you know, is, looking at your framework and model, you know, what, what's the best place to get started, right? Like, I, you know, these are great ideas. This framework makes a lot of sense. Um, how do I start to put it into practice?

[00:21:38] Dr. Duru Ahanotu: So, uh, almost like with every project there would be a discovery phase, right? So who and where are all your data assets right now in terms of the people and the pipelines, and how are you using that data? It may be a big spaghetti mess, which is the worst case scenario, or maybe it's very well thought out, but it's in the wrong places.

[00:22:04] Dr. Duru Ahanotu: Right. So that's the discovery phase, like what is your current state, and then let's compare that current state to where you think your business should be.

[00:22:25] Dr. Duru Ahanotu You know, I mentioned again, is data going to be a byproduct of what you do as a business or is it your competitive advantage? You have to make that call, um, uh, where you are.

[00:22:23] And so is there a fit then between, and I'm just gonna take the data as a competitive advantage case. Is there a fit between that orientation and the folks that you have and where the data sits and how you structured your whole data ecosystem to service those people and the products and the services?

[00:22:41] If there's a mismatch, then yes, a reorientation needs to be, uh, done. Um, again, according to the. The principles of the inside supply chain. So it's just like almost every other project, right? It's the gap analysis, um, framework that you would go, uh, that you would use to figure out how to proceed.

[00:23:05] Michael Meyers: We talked about how, uh, you know, BI is something that has become outdated. Um mm-hmm. We live in a, uh, a fast moving world, right? Where, you know, new, new terms every day, new technologies, you know, dare I say it, ai, um, you know, is, is driving the way. How does your framework accommodate? Rapid change in evolution, you know, uh, what are you thinking?

[00:23:30] Michael Meyers: Where is this going? And, and can this avoid the fate of, you know, BI.

[00:23:36] Dr. Duru Ahanotu: Yes. Well, you never know, right? A hundred years from now, there'll be something else, uh, that we haven't even, we can't even contemplate right now. But in this moment that we're in, I have been heartened to see that my Insights Supply Chain actually still applies because I've identified.

[00:23:52] Dr. Duru Ahanotu: The key components. Now whether, you know, I talked a bit again about data engineering, data analytics, and insights, analytics as key roles, but there is nothing so strict in this framework that says each of those roles has to be a specific person or a specific set of people. You can have one person that covers all of those roles.

[00:24:14] Dr. Duru Ahanotu: Like for instance, there's this term out there now called the Data Artisan. So the data artisan is this person who, who is so expert but also so broad in their expertise that they can, they actually can. Can operate along the entire insight supply chain. But in doing their work, if you were to break down what they do, you would still see the insight supply chain at work, right?

[00:24:39] Dr. Duru Ahanotu: You would still see that artisan. figuring out how to get the data, figuring out whether they need to craft it, their own specialized pipeline or a standardized pipeline. And then they will do their data analysis. They'll do sql. They might decide there's a particular model or machine learning has to be applied, and then they're gonna do the insights, uh, um, analysis, which is then to convince the business to act on the role on the results that they've come up with.

[00:25:06] Dr. Duru Ahanotu: Those to me, are the fundamentals and the basics of, a data team or, or of moving data to insights. I don't think that's gonna change anytime soon. The technologies and the ways in which insights gets delivered into the organization based on data, driven by data, um, can change.

[00:25:24] Dr. Duru Ahanotu: But that basic fundamental flow, I think is going to be pretty sticky, uh, for some time. And that's gonna be my claim. For this podcast, we, we can check this, check in a few years and see how that's going. So you mentioned AI, so let me just specifically address this and I have a very, I, I'm developing more and more pet peeves.

[00:25:46] Dr. Duru Ahanotu: The more I see people talk about how AI is going to undermine data, uh, analytics. Oh, data science is done. Oh, you know, on and on and on it goes. And one of the examples I have, uh, is. Um, and I guess I shouldn't name the vendor here, but their data analytics platform is uh, or has rolled out conversational analytics and one of the, one of the memes that they created is they show this.

[00:26:13] Dr. Duru Ahanotu: Organization where there are all these fraught data professionals and they don't have time to do anything. It's like, Hey, we've got a solution. Your data professionals, and I think they call them data analysts, they never have time to get done everything that you executives want to do. Well, we have a magic solution, which is conversational analytics.

[00:26:33] Dr. Duru Ahanotu: All you have to do, I mean this is kind of the marketing message, right? All you have to do is sit down at your computer, type in natural language, you wanna see the sales results for the last 12 months. Boom. There. You don't have to go ask any data experts. You don't have to get a data scientist to run a regression model.

[00:26:50] Dr. Duru Ahanotu: You just type out your request and boom, there it is. So major pet peeve for me because one, I have been through this, um, many times in past data teams, um, you know, leading data teams, which is this tension. There's always gonna be this tension between the, um, executive or somebody who needs some data answer right now versus the data team's interest, which is in creating standardized products like dashboards, like pipelines that can answer these questions.

[00:27:22] Dr. Duru Ahanotu: Without there, uh, without someone having to, to, you know, ask for something special to be done. So what you want to do, of course, is over time you can see the pattern in the questions that people ask. And then you just create the standard dashboards that they can go and ask those questions or find out the update.

[00:27:39] Dr. Duru Ahanotu: So when I see that meme that shows, oh, the data professionals, they're so, you know, they have no time. One, I say, well, why not think about reorganizing the way you work? Maybe it's because you are bombarding them with all these nitpicky questions and not giving them the time to build out the standardized tools that would answer your questions.

[00:28:00] Dr. Duru Ahanotu: Trying to solve that problem with conversational analytics is going to lead, uh, organizations to trouble. I will almost guarantee it.

[00:28:06] Dr. Duru Ahanotu: But, um, here's the, here's the problem. I look at conversational analytics as an attempt to just try to shortcut the entire inside supply chain, right? That somehow the, the, the data will just magically be there available in a condition that will answer every question that you have. Magically and with ease.

[00:28:30] Dr. Duru Ahanotu: Even just saying that we know that's wrong, right? Because some data professional has to think about what are the possible questions that might be asked of the data and how am I going to, to create the data pipeline, the semantic layer, the metric definitions, all that stuff takes a lot of thinking and that's a data professional sitting there, in collaboration with the executives to figure out how best to structure the data, and then even after that part is done, you need to educate the users as to what are the scenarios. What is, you know, what is the sandbox in which you can play safely? Right? We all know that a system has its boundaries and there are some questions that some executive or someone may ask.

[00:29:16] Dr. Duru Ahanotu: That whose answer will lead you astray in ways that you won't even appreciate until somewhere down the line. Some businesses decision goes awry. When all it would've taken is if you just sat down with your data professional, talked to them about what you're trying to do. You know, you could have, you could have shortcut a major error.

[00:29:36] Dr. Duru Ahanotu: So all that to say, my major pet peeve in all of this is that no, data professionals, we're not going anywhere. You still need us as long as data exists. The, the insights supply chain will exist in some form or fashion, and it's gonna take data professionals to help make sure your insights supply chain runs smoothly, no matter what, how you shape the roles, no matter whether you centralize or decentralize, whether you have generalized skill sets or specialized skill sets, you're still gonna need us.

[00:30:04] Dr. Duru Ahanotu: Sorry. Um. Sorry for you. Sorry. For the folks who are still trying to, um, market, um, things like conversational analytics as a way to replace the need, um, for data professionals.

[00:30:19] Michael Meyers: The frustration is real. Um, the magic solutions. Yes, yes,

[00:30:27] Dr. Duru Ahanotu: yes. And of course, and that's time eternal, right? And software. There's always these magic solutions and the holy grail.

[00:30:33] Dr. Duru Ahanotu: What's funny is, and sorry, I'm gonna do a quick sidebar. I listened to this podcast. The, the, the, uh, founder. Of SQL, the guy who created sql, it was so fascinating to listen to him. The dreams that he had way back when, when SQL was created was that he could create a language such that it didn't require technical skills to query data, to get insights from data.

[00:30:57] Dr. Duru Ahanotu: So this has been the holy grail forever, and I know you're laughing because of course the way the SQL is now, there's so many different flavors. You know, there's so many different nuances and syntaxes. That SQL absolutely requires some deep technical knowledge and awareness. So even a tool that way back in the day was supposed to create this no non-technical data access for everybody.

[00:31:23] Dr. Duru Ahanotu: It didn't work. And we've had different stages of this along the way, and conversational analytics and whatever's coming next will just be another, uh, phase of that, that search for the holy grail.

[00:31:36] Michael Meyers: I mean, it's better than writing a query and assembly language, I guess, but it's not exactly. Well, yeah, it was de

[00:31:41] Dr. Duru Ahanotu: right.

[00:31:42] Dr. Duru Ahanotu: It, this is a, you know, it is a progression. So it was definitely better than what people had before. Uh, SQL was invented for sure. Yeah.

[00:31:50] Michael Meyers: Big step forward, but, but not exactly natural language. Um. Speaking of the professionals, I know that, you know, it's hard [00:32:00] to pinpoint this because lines are blurry, you know, organizations are structured differently.

[00:32:05] Michael Meyers: Um, but because mm-hmm. You know, people play a really important role in this, um, you know, at a high level, can you, you know, kind of give a sense of what you see as the key roles or players in an organization and where they fit, you know, engineers, analysts, um, you know, how does it typically break down in your mind?

[00:32:26] Dr. Duru Ahanotu: Yeah. So I like to think of this as, uh, right roles first, and then how do you assemble the, the folks or the expertise around those roles. Um, and you know, for instance, one of the interesting twists that we've had in recent years in the data. In the data world is the analytics engineer, which is supposed to be this person who bridges, uh, data engineering and data analytics.

[00:32:53] Dr. Duru Ahanotu: So this is another flavor, uh, along the insight supply chain, right? So this is someone who kind of sits, um, upstream but not all the way upstream to data engineering. Um, and so the way I see the roles is I am always oriented insights first. So. I think there's been a greater awareness, and I see this with the various YouTubers and whatnot, that a data analyst can't just be all about crunching numbers.

[00:33:22] Dr. Duru Ahanotu: A data analyst needs to know how to talk the language of the business and how their work is going to translate into value and insights. Um, and so for those folks. Upskilling, you know, their career paths is indeed to make sure that they always know what is the business problem that they need to be solved.

[00:33:42] Dr. Duru Ahanotu: So ultimately, everyone in the inside supply chain. I say all the way, you know, upstream to downstream should understand what is the problem that's trying that we're trying to solve, and what is the insight that the business is trying to get at. And that should, um, that should structure all of the, all of the work for everybody in the inside supply chain.

[00:34:03] Dr. Duru Ahanotu: So if you talk about the key roles, I'm still gonna go back to it. It's still gonna be data engineering. It's gonna be data analytics, and it's going to be, uh, insights analytics. And I know from my data scientist. Um, colleagues out there who are wondering, why don't I, why where's there's no place for data scientists?

[00:34:19] Dr. Duru Ahanotu: Well, actually, to me, data science is an amalgamation of, you know, all those different roles. Some data scientists focus downstream, which is the insights, uh, analytics component. Most data a, uh, scientists, I think play in the data analytics plus insights analytics. Um, uh, realm. And then these artisans that I referred to earlier, those are the folks who do everything.

[00:34:45] Dr. Duru Ahanotu: They can do data engineering just as well as they can do insights, analytics. And, and, and this is another reason why I like my insight supply chain, is because you can take a role like data science and understand, you know, more [00:35:00] accurately what a data scientist does or what a data scientist can do and how you might fit it in different components of your, your strategy.

[00:35:08] Dr. Duru Ahanotu: Yeah.

[00:35:11] Michael Meyers: Duru. That was great. Uh, we're gonna do a series of follow up, uh, episodes on the insight supply chain. We wanna talk about tactical, we'll talk about strategic.

[00:35:22] Michael Meyers: We wanna talk about your future thinking. Uh, can you just give us, uh, some quick teasers, uh, what's coming up in these episodes? What do you wanna cover in each? Where is this framework going?

[00:35:34] Dr. Duru Ahanotu: Yeah, so first of all, um. AI is real, and I have big hopes and dreams for how I will incorporate the world of AI into the inside supply chain.

[00:35:45] Dr. Duru Ahanotu: And this will actually be an even more comprehensive model where I'm going to include elements of knowledge management. Cognitive science, and then AI as an orca, sort of an orchestration layer for helping to organizations to really [00:36:00] fine tune the way that the inside supply chain works according to the set of skills, uh, that an organization has.

[00:36:06] Dr. Duru Ahanotu: So that's, that's like my really moonshot future thinking. Um, and then, you know, there's. All sorts of, of additional nuances we can discuss about how to really get, you know, roll up your sleeves on the tactics of the inside supply chain. And then also how to take a step back and really think about the different ways in which the str uh, strategic framework plays out based on different, um, you know, case studies.

[00:36:35] Dr. Duru Ahanotu: So there's all sorts of places that we can go. Lots of teasers there for you and everybody in the audience.

[00:36:42] Michael Meyers: Awesome. Thank you so much. Uh so fun as always. Uh, thank you to all our listeners. Uh, you guys can send us, uh, feedback at [email protected]. Uh, please check out our past episodes at tag1.com/podcasts and, and remember subscribe so you, don't miss any future conversations.

[00:37:00] Michael Meyers: Uh, special thanks to Tracy Cooper and June Gregg for producing today's episode with input from Hank VanZile and Cassey Bowden. Until next time, take care.