Is your organization struggling to convert raw data into actionable insights? In today's landscape, being data-driven is a basic requirement for survival, yet persistent roadblocks like fragmented collaboration and ambiguous ownership plague most businesses.

This third episode, continues our discussion into the Insight Supply Chain Framework, a structured approach for organizing your data professionals and teams to ensure insights flow seamlessly throughout your organization.

This discussion focuses on a critical component of the framework: the Data Organization Matrix (DOM). The DOM helps leaders answer a strategic, fundamental question: Is data a core competency of your organization?

Explore how this strategic decision impacts your organizational structure, building upon the previous episode's debate on centralized vs. decentralized data teams. Learn about the four quadrants of the DOM and how they address the need for specialized vs. generalized skills among your data staff.

Tune in to discover:

1. The primary challenge organizations face in leveraging their data.
2. The role of the DOM in organizational strategy.
3. How different business needs may lead to different organizational models within a single company.
4. Why the question of data as a core competency is more important than ever, especially with the rise of AI and data.

This is essential listening for anyone looking to build robust data ecosystems and gain a competitive edge.

Being data-driven has moved from a competitive differentiator to basic survival requirement, yet most organizations struggle to convert raw data into actionable insights with fragmented collaboration and ambiguous ownership, creating persistent roadblocks. Why Dr. Duru Ahanotu, the leader of Tag1's data strategy team, has created the Insight Supply Chain Framework.

It brings structure to how you organize your data professionals and teams to leverage your data and enable insights to flow throughout your organization. This is the third episode in our series on the framework. In our first episode, we provided a general overview, and then the second we explored the critical decision between centralized and decentralized data teams.

Today we're diving into the Data Organization Matrix or DOM, and the strategic question as to whether or not data is or should be a core competency of your organization and how that's gonna impact the centralization versus decentralization decision. In future episodes, we're gonna talk about how AI has been impacting Duru's thinking, and do a deep dive on the Data Maturity Curve and how your organization fits into it today, and how you progress along it over time.

Thanks for joining us. Let's get started

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[00:01:40] Dr. Duru Ahanotu: Thank you, Michael.

It's good to be here

And today we're gonna highlight some of those real world experiences and provide you with some examples as to how he's applied this Framework across his career. Before we jump into the details on the supply chain, I wanna give you a little bit of insight into Tag1. Tag1 is the number two all time contributor to Drupal, which is the world's second most popular content management system.

For nearly 20 years now, we've been the architects of the open web, leading the collaboration of the software and best practices is 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 collaboration of the software and best practices that power millions of websites 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 platform.

We're trusted by in industry leaders, including Google, Sumitomo, NTT Data and the European Patent Office to solve mission critical challenges and build lasting solutions. Check out Tag1.com to learn more about how we can help you. So, uh, Duru, just to catch people up on episode one and two, where we talked about, you know, the Framework as a whole, just like a high level overview and they got more into the Centralization, you know, versus Decentralization.

Um, can you just give us a, a quick summary, you know, again, um, what is the, the, the challenge that organizations are facing and how does your, uh, framework address those challenges? And, you know, just really high level, why does it matter that, Decentralization versus Centralization?

Help solve is the challenge of how to best organize data professionals, um, both along their career paths and how to organize them as teams and groups in a way that will, um, facilitate the best mode of collaboration amongst themselves and collaboration with the rest of the company in the service of delivering value through products and services to a marketplace.

Um, and what I saw in my career is that there often wasn't a lot of structured thinking about how to do this. And so the insight supply chain came about from my own career experience in how I thought. Um, what I thought was as the best framework for doing all of these things in a very effective way and in an efficient way that would allow people, um, data professionals in particular to, um, you know, best enjoy their work and also, uh, have strong and robust career paths.

Um, and so this came out of a lot of my experiences and it's been constantly updated because this field, uh, changes a lot. And so I've learned also to make this, uh, robust through flexibility. And so it's been timeless in that sense and it's, you know, been, I've been able to apply it, um, in my own work and also my theoretical, um, outlook on what's to come in the future with AI .

Is that this Framework has a, a big impact, right? Like mm-hmm. Making decisions arbitrarily, not thinking them through using a framework, you know, taking brute force approach. Um, you know, none of these models come close to being, you know, enabling you to be anywhere as effective, you know, and taking the data and turning into decisions then what this framework enables you to do.

I don't have any much to add on that one.

[00:06:17] Dr. Duru Ahanotu: I know

[00:06:19] Dr. Duru Ahanotu: Yeah, I thought about that. Sorry, it was a quick sidebar.

I thought about that because I first learned about the DOM way back when working with an internet company, and I said, ah, well, I'm just gonna claim it as my own domain specific acronym.

[00:06:55] Dr. Duru Ahanotu: Um, of course.

[00:06:57] Dr. Duru Ahanotu: Of course. So. The, the, the running joke from business schools and folks in consulting is that to explain any concept you need a two by two matrix. Um, and in working with actually a friend of mine a while back in terms of make, figuring out how to best communicate these concepts, the two by two matrix just serve the role.

Um, so that's where the m comes from, from the matrix. Then the data org part, of course is I wanted to speak to organizational principles. Um, the Insights Supply Chain started as a way of just describing a flow of data through an organization from raw data to the insights, uh, that again, deliver value through products and services.

But what was missing was, well, what are the implications for strategy in a company? What are the implications for organization? And so. The Data Org Matrix came about to serve all of those roles, to put it all in a neat summary, two by two summary, using the, again, decentralized, the decision of decentralized versus centralized as the core narrative.

And then looking at how those two simple things, two simple pivot points, I should say, um, drive a very rich set of outcomes in your organization. So that's what my goal and intention was in building this matrix. And it just serves as a nice meme, uh, in a sense in terms of communicating, you know what this is all about.

[00:08:26] Dr. Duru Ahanotu: yes,

original two by two, um, you know, which, uh, we, we've talked a lot about. Um mm-hmm. How does, you know, we're, we're digging a level deeper, you know, uh, you know, the, the initial matrix, you know, the fundamental thing is whether or not you are decentralized versus centralized, and whether or not you have, uh, specialized or, or generalized resources that you're working with across your organization.

Uh, we're going a level deeper here. You know, walk me through this matrix, you know, um, how, how does each section apply and, and how do I, you know, leverage this?

Mm-hmm. So it's centralized on the left, decentralized on the right, but then on the vertical axis, you have generalized versus specialized. And so that's the skillset. And so this is where the inter interaction intersection is, is. Um, how are you going to hire or staff your people? Is it gonna be a broad, you know, people are gonna have broad skill sets or very specialized, and then again, how are you going to organize them?

Are you gonna organize them in one place? Are you gonna distribute them across the organization? So now if we want to see how this plays out, we can pick any part of this matrix. So let's take one of the simpler ones, which is the, uh, upper left, which is the centralized interdependent business units. So, uh, what that means is that you're gonna make a centralized team, uh, centralized, sorry, set of, uh, data professionals.

And then the business units that they serve are gonna be interdependent through their need for the centralized data team services. Mm-hmm. Then the skillset, when it's generalized, well, that means what you have is a, is a business that you know, may be very mature. Um, it's maybe the business doesn't change that much.

And so the standardization that you need is just making sure the data pipelines are running and the robust, and when people are doing their weekly, monthly periodic reporting, the reports are ready. Right? So that's like the basic bread and butter data service model, right? So again, centralized data team, and you have generalized skill sets because you're just focused on a business that just needs the basics of, of out of their data.

Um, and then if you want, I could go through all the ma all the, all the different parts of the quadrants, or maybe you want, um, there's a specific flavor that you want to address. It can go either way.

You know, like, um, is there a logical next place you would be, or is it just. You know, dependent upon where you're at as an organization.

It's not directly integrated in here because. The data maturity curve kind of is like this environmental variable, um, in the background. And you could be in any one of these quadrants and be going up the data maturity curve. So it's not a direct dependency in a sense, but, so for instance, if I'm taking the next deck in the complexity, this is where this question of, Hey, is data gonna be a core competence for your business comes in?

Mm-hmm. So the next stage of complexity would be, yes, I'm centralized in terms of my data professionals, but now I need specialists in, I need specialized data professionals because data is going to be, must be a core competence of my business. And it's because, um, either I'm delivering data as a service or product or data, uh, um, is an integral part of making my services and products successful.

Mm-hmm.

And what does specialized mean? So if we think about the Insights Supply Chain and the, the very basic, you know, roles, the three part roles, the data engineering. The data analysts and the insights analysts. So when I say specialize, I'm saying I'm gonna hire people who specifically are experts, deep experts in data engineering.

They're deep experts in data analytics, and they're deep experts in insights, um, analytics. And let me just talk a little about the insights analytics 'cause people, this is a term that came out of my work some time ago, so people probably haven't heard this term before, but think of insights analysts as folks who are really, um, expert in the business and know how to take the work of data analysts, whether it's their own or someone else's, and really evangelize

a particular decision based on the data and the evidence that's been, um, produced. And so they will, they can talk directly to executives. They can make the data plain and clear. They don't use data professional jargon to get their point across right. They can do that translation layer very quickly and they're very good at promoting results.

Right? And they can do visualizations in dramatic and coherent, clear, clear and crisp ways. Mm-hmm.

So that's the insights analysts. So if I have data as a core competence, so you could think of, let's just talk about that last part of insights, uh, supply chain, which is the insights analyst that person could be, for instance, the person who has a crystal clear vision of how this product or service, um, speaks to the marketplace.

And I'm going to take the data, um. That supports this product or service, and I'm going to make it plain. I'm gonna make it plain in the customer's language, or I'm gonna make it plain in the company leadership's, uh, language. Um, and so that's the next level of complexity because now I have these specialists.

I'm gonna have to think about career paths for different types of data professionals. You know, again, I always come back to this self-interested thing, which is I wanna make sure data professionals are being taken care of, and I have to think about how they collaborate together. Mm-hmm. You know, to be an effective team.

Because again, this is the centralized model, so they are collaborating across specialties and I have to make sure that they're able to communicate with each other in very effective ways.

[00:15:17] Dr. Duru Ahanotu: Well, okay. So, you know, there's a long history to this term, even core competence, right? I remember it was really big in the 1990s that hey, every company needs to have a core competence and they need to focus in on what that is and then cut the rest of the organization off, right?

Um, so I think now we have a more nuanced understanding of what core competence means. So, so for instance, back in the nineties, if I told you my company's core comp data is a core competence in my company, you might advise a company, okay, get rid of all the other functions and then focus on creating data or data products and data service.

But that's not quite what we mean today. What we mean is that data as a core competence means that if you don't take, if you don't professionalize in the sense. This, um, your data functions, then you are not gonna be successful, um, in the marketplace. And you may be undercut by competitors who do take data seriously, as a strategic, you know, object or I don't wanna use the word weapon.

What a strategic lever, right? For the business.

That's another way of thinking about it, is the data as a core compete doesn't mean it's the only thing you do. It means that it is a very important strategic lever, uh, for the success of your business. So yes. Now today we say, well, Duh!, of course data is a core competence because data is everywhere, right?

We have big data. Well, we went through there big data, and now it's ai. I think AI in particular is going to make this question very salient and more than ever, just about every organization will have to say yes, because you're not gonna just be able to sprinkle AI on your business and then make magic happen.

You actually have to make sure that your data's well governed, high quality, it's structured or unstructured with intention, such that the AI can make a good sense of it. And there's, you know, that that's a whole discipline in of itself, but still, the inside supply chain still speaks to that, um, that necessity.

Right. Again, with the different roles and functions along the, along the path.

Mm-hmm. Here to. Related problems, like the fact that you adopted all of these SaaS tools and they're sprinkled across your organization and they don't talk to each other, and you can't really leverage your data in a way that you want. And AI Yeah. Isn't yet fixing that and, and may not for some long, you know, so it's like, you know, it's, is not just, you know, and I guess that that brings me, like something else that's in the back of my mind with respect to this.

You know, I'm a, you know, eat my cake and have it too kind of personality. You know, I want it all. Um, I'm looking at this saying, well, you know, that upper left quadrant, like that seems like table stakes. Like, I gotta have those pipelines in place for my business to enable people to function, but I also wanna, you know, move more towards, you know, um, that lower left, you know, if I'm up there, you know, is it one or the other?

You know, do, do you, you know, certainly you might shift over time, but can you exist in a world where you have both of these in place?

Mm-hmm. Uh, in the organization any one time. And yes, you could have different parts of the organization at different levels of maturity because you just have different needs that are across the business, right? So your business may not be a big monolith. You may have the cash cow. In fact, let's talk about it in terms of.

Um, uh, in terms of like a cash cow, you know, a legacy business that does move slowly, everyone understands exactly what needs to be done. You just turn the crank, turn the products out, and boom, you make sales. And then you have other parts of the business. And you know, companies like Google of course, and Facebook or meta platforms understand this.

You have other parts of business that are much more innovative and they are moving fast. They have to create and destroy products and ideas at a rapid pace that can exist in the same company as, let's just say, um, I think, you know, Google's case, the cash cow would be the search business, right? And not to cast any aspersions.

I'm not calling Google search slow and, and Plotty, I, I, I don't work there. So I don't know, we're just using as example. But you know that Google, in fact, they went through that restructure and called themselves Alphabet because they recognized that they had this cash cow. But then they have to be purposeful and intentional.

About all these other businesses and opportunities that they're incubating. Um, and I can imagine in those other smaller organizations that they're incubating that they have a lot of, um, um, specialists that are data specialists that are working hard to advance up the, uh, data maturity curve as fast as possible and making data a core competence of these new emerging, um, new emerging businesses.

And so, yeah, you can have different parts of the business moving at different speeds because you have different product lines and different services and different marketplaces that you're, you're serving. It's not a one size fits all when you have a very diverse portfolio. Right.

[00:20:50] Dr. Duru Ahanotu: Alright. I, you can see a distinct difference between our, our personalities. You see, um, the data maturity curve, uh, you called it an environmental variable, uh, a few times now. I would call it an existential crisis.

That's fine too.

[00:21:10] Dr. Duru Ahanotu: uh, and as a ex existential crisis, right? That's where our opportunity is to help organizations succeed and, and do better, right? Um, when we can see where those crises are and the pressure points that are creating those crises. Yeah.

I think it's, uh, a really interesting model. Uh, you know, if people aren't familiar with it, uh, it would really help them to understand it better. Um, so we've covered the, the top left, the bottom left, um, mm-hmm. What's the next step or where do you wanna go from here?

And this is the, this is the case where we, and we're in the bottom right, and this is the case where we are now decentralizing, um, and we have specialists and data is, we've decided data is a core competence. Now, in this case, what you may have is a lot of data scientists, for instance, in various parts of the company, and these data scientists, think of them again in terms of the insights supply chain are serving, like the analytics, uh, sorry, the insights analysts who speak the business, but also can organize the data, um, sorry, who know how to analyze the data and build models and algorithms and so on.

Um, but they do it in the language of whatever particular vertical or product and service offerings that they're, that they're working on. And that, um, a term that I've, I've come to learn recently that I really love is this artisanal. Um, aspect to the work. Right? Um, and for those who aren't familiar, and artisan is very specialized, you could think about in the old days they were the craftspeople who had very specialized skills that took years to hone.

They may have been an served as an apprentice, right? And they learned by doing. And so they have very specific, sorry. Their skill sets are very specifically tuned to solve problems in a particular domain. And in this case, when we're talking about the business, they're solving problems specific to a set of products or, uh, services.

And so in this case, they are, uh, specialized because they're data scientists. But then they also, we may specialize, sorry, we may have specialists with data engineers. Because we don't want the data, uh, sorry, the data scientists to spend too much time building pipelines and doing the data governance and all that kind of stuff.

Now, the fantastic thing about this new world is that there are more and more tools that are blurring some of these lines and enabling data engineers to, you know, function as data scientists and data scientists to function more as data engineers. So that's how we come up with terms now, like analytics engineers who, you know, are kind of bridges along this line.

But anyway, long story short, um, this is where you're specifically trying to monetize your data. So data's a COCOMs. You're trying to monetize your data in a specific way by creating data products. Again, all of our big tech firms, we know them well. We know that they are creating, using, collecting data as a specific product.

Um, and their customers are very specialized sometimes, um, in non-overlapping verticals. Um, uh, and, oh, sorry. And they have to move rapidly. They have these individual verticals have to move fast because they have competitors that are constantly, uh, innovating. Um, and when you have that fast-paced environment, having people have generalized skills may not help because you know, you need specialists to, sorry, let me tell you.

Say it a different way. Um, innovation and creativity moves fastest when you have deep set of skills in a particular domain, and that's what we call specialists. And, but you need to have a structure around them such that they can communicate. They're innovative and creative ideas effectively so that they make it out into the organization.

So it's, it's no good if you're very good at a particular thing, but you can't integrate it into the rest of the business. Um, and so that's why in this case, the decentralized model exists because you need to integrate those specialists with the business folks and they can work well together and move fast.

Um, so sorry that was a long answer to your original, uh, question, but there's a lot here. You know, we stepped up complexity. Um, and the more and more complex you get, the more fluid fluidly you have to think about how you're organizing the teams, the data professionals.

Yes. Uh, but I, I love the way you described it. You know, my, my background in, in startups, you know, I, I think about how you drive innovation in environments. Mm-hmm. And this quadrant speaks directly to that, right? You want to have right specialists, you know, throughout your organization to drive innovation, not to be, you know, held back by, you know, the organization as a whole.

Um, it makes a lot of sense to me why this model, like, to me, this helps illustrate, you know, from, from my background and context, why it's so important to follow a model like this and how it can clearly define success and failure, or you need to brute force things. Um, you know, you really do fit into an aspect of this for, you know, parts of your business at least,

Exactly. Yeah.

[00:26:40] Dr. Duru Ahanotu: I know, I've just love that word and it's a great way of, of honoring the past in terms of how people, you know, develop skills, um, in the workplace.

Uh, yeah.

[00:26:57] Dr. Duru Ahanotu: Yeah. So the upper right, you know, you notice I was going, what I was saying is sort of this linear increase in complexity. And so the upper right is not necessarily the increase in complexity, um, because it's, again, the, the folks in this model are generalized, but they're decentralized in business units and data is not a core competency, but the complexity here is generalists to move fast enough with the business.

Right. So, um, the emphasis for these individual business units will be, um, they need to address certain business problems, marketplace problems as fast as possible. Mm-hmm. Um. They don't need to innovate in the data realm per se, but they do need folks who know data and can offload the work of data from the other people in the businesses.

You know, we talked about earlier, the sales, marketing operations, finance, HR and whatnot. Um, so the generalists know enough to help folks, but they don't have to be specialists in a particular data domain because that's not, the data's not a core confidence in this particular case. Now, as we discussed earlier or inferred earlier, this upper right quadrant is probably gonna become less and less relevant over time.

Right. Uh, just because in some form or fashion data is going to be a core competence of any fast moving organization.

You know, 'cause another thing we talked about is you can just as easily extrapolate and make bad decisions from data. You know, the idea that

[00:28:53] Michael Meyers: Yeah. So empowering everybody to make decisions based on data is both, uh, great. But, you know, something that needs to be checked and, and having data professionals, you know, generalize support to

You know, help a broader army of, of analysts, you know, that could be the mainstay, uh, you know, the bedrock of every organization, you know, that has to have that. I don't know.

You know, we earlier, uh, talked about data users. Um, my favorite example, um, is finance, because in. In a startup situation and in a big company situation, I often found the folks in finance to be the most savvy of the data user in the data user class who were not themselves data professionals, um, but because they have to, you know, they're all about the books and making sure the money's flowing and all that kind of stuff.

They don't have time to, to build out pipelines and make data, you know, a big part of their job. But they are savvy enough because they deal with numbers all the time. They, understand the concepts of trends and, and correlations and all that kind of stuff, statistics, all that kind of stuff that we do data professionals deal with.

And so, uh, in that case, you have a very savvy data user, so you could just serve as a data coach. Trust that they can run with the tools that you give them, you know, that make their life easier. Whereas, again, I'm speaking from my own experience, I won't mention any specific companies, but whereas in marketing for instance, you have data users who, um, they don't have time, they don't have the compunction so to speak, to crunch numbers.

Right. And to go through and, and figure out. Um, and, and I'm not talking about marketing analysts, so all you marketing analysts out there are not talking about you of course. 'cause that's again, a very specialized role. I'm talking about the general marketing organization. So the general marketing organization may have marketing analysts.

You've seen that role before, right? Because they are offloading a lot of the data user stuff that marketing has. Or if they don't have marketing analysts, then again they're relying on the data professionals. In a centralized, uh, in a centralized team or with the, whether it's within marketing or whether it's in the organization Anyway, all that to say is in this data coach example, you can dial up or dial down the amount of collaboration that you need to do, depending upon the savviness of the user you're dealing with.

Always cognizant though that, um, the organization should always be cognizant of what, again, career path are you creating? So do you, are you incentivizing your marketing, your sales, your finance folks in their performance reviews? Are you, are you evaluating how effectively they use data? Mm-hmm. You know, then the data coach will need to dial their support, uh, accordingly because this person's being judged on how well they use data, uh, and not just how well they use me or me, data professional as a resource.

It's, if anything mm-hmm. Increasing the need for professionals to make sure that you're, you know, quote unquote army of analysts, um, are effectively utilizing that data, making decisions with it that are, you know, yeah. Um, you know, going to lead to value and not, you know, serious problems.

[00:32:49] Michael Meyers: So I mean, one of the things that, uh, I'm wondering is I'm an organization that wants to use this framework.

Um, is it aspirational? And, and by that I mean, um, do I look at where I want to be or do I look at where I am today when I try and figure out how to organize my organization?

Mm-hmm. Um, and I think, again, as we were alluding to before. Almost no matter where you think you are in terms of data as a core competency, you can look ahead in the future and anticipate that to some extent, data is going to be a core competency. And then, then this entire matrix, sorry, the bottom of this matrix is what's going to apply to you.

So you may be at the top part of the matrix, but you should be thinking about over the time, you're gonna have to move yourself to the bottom. Again, whether it's the centralized organizational model or the decentralized model, um, you are going to need some level of specialization. Um, when data's the core competence, and again, I know we'll talk about this in the future episode.

AI adds another interesting layer to this question because what people are going to try to do is make AI the data specialist, and there are all sorts of trap doors if that's the approach you're going to make, um, in terms of, you know, creating a core competency in data. And there'll be all sorts of blind spots because you, you're not taking advantage of the experience and the training of data professionals who can spot uh, gaps.

They can do verification, they could do validation of error, you know, errors. And they can also help people use, again, use the tool effectively given the context of what the data ecosystem itself, um, looks like. Um, yeah. So that's the way I, I think about it. Yeah.

[00:35:10] Dr. Duru Ahanotu: Oh, I love that one. Yeah.

Um mm-hmm. But it just keeps coming back to, you know, the, the need to have, you know, real data professionals, you know, to, to think about the way you structure your organization, you know, to empower your, your larger teams to, to make sense of your data, um, and your systems. You know, that's another thing that we haven't talked too much about, but like.

You know, all these different systems and, and enabling you to, you know, uh, have them talk to each other, be able to leverage your data. You know, there are all sorts of problems that, uh, organizations face that data professionals are, are core to solving. Um.

I mean, every year people are pushing these new faddish terms and soon there'll be armies of agents, AI agents, and who in the world is actually going to be on top of managing how and whether these things are all collaborating effectively. Right? Um, I contend it's gonna be some flavor of a data professional that's gonna be tasked with making sure that these AI agents are collaborating effectively and effectively, um, uh, for the organization.

Um, I could see someone right now thinking, well, couldn't I just build the AI agent of all agents to manage all of the Army? I was like, no, don't do that. I think we're still, you can't just hand over the keys to the kingdom and then, you know, go away and expect profits to be piled up at the end of the year.

No, that's not. Um, again, if we talk about the holy grail, that's I think what is in the back of some people's minds, but, um, be very careful.

And, uh, mm-hmm. I really wanna dig into the data maturity curve. I think that's gonna turn into more than one episode because there's so much going on on there.

[00:37:35] Michael Meyers: Um, yeah, but I, uh, I really appreciate your, your digging more into the, um, document object model. I, I mean the, um,

brilliant slip, so to speak.

The, uh, the, the data org matrix and for talking more about the framework, um, for everybody who tuned in. Thank you so much for listening. You can check out, uh, past episodes on the Framework, uh, as well as other topics at Tag1.com/podcast. Uh, we'd love your input and feedback. If you have questions about the framework, we'd be happy to answer them.

Uh, you can reach us at info@tag1.com. Uh, and of course, please subscribe so you don't miss out on future conversations.

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.

Awesome.