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Hi, and welcome to the Data Strategy Gurus podcast. What if everything we think about data is just noise? I'm here today with Scott Taylor, the Data Whisperer, who believes data is the new [especially in the age of GenAI]. Stick around as we strip away the buzzwords and discover how data storytelling can win executive minds, secure funding, and change how organizations act.
Scott, welcome.
Hi, Eve. Great to see you. Thanks for having me.
Yeah, it's been a long while. Finally, we get virtually face-to-face again, but uh, it's great to have you on the show. Scott, it always intrigued me. Uh, your tagline is the Data Whisperer. What does that mean to you, and where did that persona come from?
I put it on a badge one day at a conference, and I got so much positive feedback I never looked back again. But it comes from this idea of helping people calm data down, similar to, let's say, the Horse Whisperer, the Dog Whisperer—you know, we're whisperers of things. And so, that, I think, is a nice way to articulate data management. But anybody who's followed me for any period of time knows—big spoiler alert here—I don't do a lot of whispering. I'm out there selling, yelling, and telling about the power and value of proper data management. And we need to do that at all levels, especially in this time of AI. We need to make sure people remember you need that data management piece as well.
Yeah, you say calming down your data. What do you mean by that? I find it intriguing to think it through and understand what you exactly mean by calming it down.
I see everybody jumping, but nobody is always putting the exact data management or the foundations in place. Uh, we've been screaming data quality for the last 20 years. Nobody really cared about it. We say yes, it's valuable. You need to put that in place—data stewards and data governance—but still, we jump just on AI without having the foundations. So, being data well, AI-ready, and then having that in place—how do you suggest calming down your data?
Uh, just one point—I think we've been screaming about it for a few more decades than that. I know at least I'm in my third decade of screaming about data management. And "calming" was just kind of a nice emotional way to characterize the concept of structuring, governing, curating, stewarding, mastering the core entity information that an enterprise has about really what I believe is the most important things in any company—which are their relationships and their brands and their assets, you know, the things they do, the people they work with, the entities they collaborate with, and the products and services that they make. And the data about those needs to be, you know, pretty calm or pretty well structured or pretty well organized to be able to unleash the power of—what are we talking about these days? Oh yes, AI.
Well, do you see that there are still too many companies not paying attention to the real data management, data governance, how you call it?
I see that we're switching more from pure data management and foundations to the governance, which feels to me kind of just having it in place—your structure, compliance. That's why governance feels more to me than purely data management, data quality kind of stuff. But maybe because I'm getting a bit too gray and, like you, already three decades in the data space as well. So, I'd like to hear what you see when you're working with companies.
I think this idea—one point I want to elaborate on on your comment there—is I bifurcate the entire space for the sake of simplicity between, you know, data management and business intelligence or analytics or AI, you know, where data starts—which is data management, data governance, data stewardship, master data, reference data, metadata, MDM, RDM, PIM, RIM, DAM.
All those foundational activities that we do. And when you get into the expertise conversation, then those differences make you know, distinct set. Okay, is it data governance, is it data management, is it data stewardship, is it data enablement? But when you're talking to business leaders, they don't care about those differences, and I think a lot of them don't even recognize this bifurcation I'm talking about. They just think about data as whatever I'm supposed to be getting the value out of that people are just keep screaming and asking me for more money, and we never seem to get there.
But I always feel that literally every enterprise organization has these data whisperers, these data champions who are really trying to reinforce the value of that work despite all the rest of the noise. And that was part of the inspiration for me to write my book. I will plug it now and show you: "Telling Your Data Story: Data Storytelling for Data Management." It says right on it, "99% buzzword-free." So wow.
To over-promise and be able to articulate why managing data is important—even the term data storytelling—you know, I feel like in these decades I've worked with analytics or business intelligence or whatever is in front of the business, it's the stuff that gets the attention and the focus and a disproportionate amount of the funding. Yet the behind-the-scenes, classically looked at as kind of clerical, mundane, boring, back-office data management never gets the credit that it's due.
And so even the term data storytelling—that's really about analytics storytelling when it came out—how to use a KPI or research and put it in some kind of business context to drive action, you know, with visualization, with a story around it. That's all super important. I'm not saying it's not important. But where's the story about the data? Where's the story about why starting with standards, why having these governance guidelines, why it's important to structure and have proper hierarchies and taxonomies? Where's that story?
And so I think that story is trying to be told at every enterprise, and I just felt through my work I could help unleash that story and help those data experts, those data management experts, put it in a more business-accessible fashion to get the attention and support and funding that I believe they deserve.
So you focus a bit on where you say the data storytelling not on the end part. Yes, you focus on that as well, but the data storytelling on the end part is explaining what the data means to the business impact, driving decisions. But you find it very important as well to do the data storytelling on the standards, the beginning of why we need to have architecture but in the business context. If we have standards, how does that help with marketing and sales, that we understand what is a client, that we don't have these endless discussions of aligning on concepts and entities, but really tying that together and starting with that part in the beginning in your data storytelling?
That's how I understand what you say—bringing in what we think is important as data management practitioners, but nobody really cares about because they care about the business, but it's important to drive your business. So that's how I understand it if you explain okay. So but your keynote—perfectly. It's where data starts. Where data starts, yes, telling where data ends up. I want us to also reinforce where data starts because if you don't start the right way, you're not going to end up where you want to go. You don't need a quote from Yogi Berra or Yoda to reinforce that, but we all know that we've got to start the right way. And if you fix, then you manage data closer to where it starts, and I don't want to get—
Technical here. Mhm. Then those downstream problems don't exist anymore. You know, you reduce so many issues down the line if you are starting the right way. Exactly. That's that's fix your data quality at the source. That's what I recall what we've been telling for the last 20 years.
Your keynote, data is the new the GenAI edition. It cuts through the buzzwords. What what is your biggest frustration with how people talk about data today? I've actually updated that keynote so it's no longer the GenAI edition, it's the agentic AI edition with vibe coding. So I wanted to make sure I was au courant. But the really the the subtitle of it is what I think is even more important which is the why the way we talk about data is holding the industry back and what you can do about it. And that gets right into this storytelling aspect of reinforcing the importance of soft skills of being able to understand how what you do in data helps enable the strategic intentions of your enterprise. Explaining to your CEO why data management's important to help them achieve the goals and objectives that they probably published in the annual report and they'll realize they need good data quality to get there. It's those kind of things that I'm I'm trying to to to elaborate on.
Yeah, you've coined the the three V's of data storytelling, vocabulary, voice and vision as well. Uh can you walk us through a few of these and and give a few of examples maybe that's that's more tangible for people instead of we just talk a bit in in the wild. We understand well well when you we talk about taxonomies and entities, but it's again just a geeky part of us, but not the business related part. Maybe that helps people understand better why it's so important just to start the data storytelling at the beginning.
And I'll even start with that word why. The whole overall framing of it is focus on the why, not the how. As data people, we love to explain how something is going to get done, how we tried this, and how we tried that. And I never met a CEO or a CFO or anybody with a business budget that cares about how you're going to do it until they understand why it's important. So, focusing on that why. And then using this really light framework that I've got in this book, as you mentioned, the three Vs of data storytelling, obviously a little knowing wink to the three Vs of big data. Shout out to Doug Laney there. But mine are vocabulary, voice, and vision.
And vocabulary, very simple. Start with the words. The words you use, the terminology you use, the lexicon you leverage needs to be business-friendly. It needs to be the language of the business. If you want to speak to the business, you got to speak in the language of the business. And it's not about, you know, entities and hierarchies and tech. You know, hierarchy they probably understand, but just that a lot of that, you know, you don't talk about the latest analytics graph up fabric mesh that you're going to implement. It's more about we have relationships. We're trying to manage those relationships. We're trying to bring value to those relationships through our brands at some sort of scale. Those are things that I think align with every enterprise objective, trying to bring value to our relationships through our brands and scale. And so, using the vocabulary you have about relationships. Every company's got relationships. You know, you don't have relationships, you don't have a business, but what we call them is different. I just spoke with a pharmaceutical company, they talk about providers and um patients, while a consumer packaged goods company will talk about consumers and customers. Those are all still relationships, but we have different terminology there. And the business understands the terminology about relationships in their own organization.
The second V, voice, the way you sound. How do you position this? Do you talk in a in a simple business-oriented fashion? Do you...
Know, get rid of the buzzwords? And does your team understand it, too?
When I say voice, it's one of the things I suggest is you want to harmonize your story to a common voice. And harmony doesn't mean everybody sings the same notes, but it does mean they sound good together. So, does your head of data architecture and data management and data steward—do they all sing from that same tune? Do they all have a similar tone about what they are talking about, and do they all have a similar understanding about why it's important?
Within that voice, too, are some marketing tricks. Essentially, you've got to put a marketing program together for your data management. That's what we have to do. It doesn't have to be fancy. It doesn't have to go on TV in the Super Bowl, and you're not going to run ads. But find some little twist of phrase or some analogy that makes sense to your business.
The biggest recommendation I have is: use your business as an analogy for why data is important. For example, a healthcare company—a healthy business runs on healthy data. Data's the lifeblood of our organization. Really simple things like that. I worked with a shipping company, and we talked about how their data pipeline and their data process was similar to their shipping process. Everybody in the organization understood shipping. Things go from here to there. Who are they from? Where are they going to? What's the provenance? Do we have the lineage? Are we tracking it? What's it used for? Those kinds of things.
Don't use "data as the new oil" unless you're in the oil business. And even that, it's an overused cliché. Hence my title—data's the new... Sorry, running off of that.
So, vocabulary—the words you use. Voice—the way you talk. And then finally, vision—why it's important. Where's your company going? What are those key objectives? And why is managing data—not why is AI, but why is managing data—going to help your leadership achieve those objectives?
If you speak to that, you will earn a seat at the table. You will earn the 15 minutes you want in front of your CEO to pitch this idea. You'll earn that trust from the business because you're not talking about implementation of a system for the sake of the system. You're talking about what that enables in the organization.
A trick I learned from being on the vendor side for almost 30 years is: if you use the words of the CEO to prove your point, very rarely is anybody going to argue with you. So, go into your annual report. Listen to your strategy discussions of your leadership. Find those phrases, find those terms, find the objectives the way they articulate them. And then show, in the end, that if you don't manage the data properly, you're not going to hit those objectives.
Great words, Sam. I think you bridged it very well. It's going to be hard to play for the technical people implementing data measures and data fabrics and telling that in the story or the language of the CEO. But it's a good analogy as well, where you say using those metaphors to translate our geeky environment and why it's important and translate it into the business.
So, in the search of AI adoption, how do you think that organizations ensure their data management actually supports generative AI initiatives? Not the... but just the readiness. And maybe to come back to agency AI initiatives, if you want to have it like that—any type of AI, whatever the flavor of the week is.
I mean, AI is built off of data. And if you don't have data that's not structured or managed well, then the AI is not going to work. You know, we don't have to... We can beat the GIGO—garbage in, garbage out, rubbish in, rubbish out.
Cliche to death, but it is still true. It's as irrefutable as gravity. What goes up must come down. What goes in must come out. We learn it on the first day of data, but it doesn't really resonate the way we want it to. But you got to continue to prove it.
You know, people think, all right, with agentyc AI, is that... Some people think, all right, AI is actually an Ozempic for data management. It's going to fix all these things that we used to have to do all this hard work for. Maybe, maybe not. Maybe around the edges, maybe not. But you still, you know, if you're not feeding AI the right thing, it's not going to solve the problem you're looking for, whether it's cleaning up street addresses or predicting the next best offer or whatever it happens to be.
So, it always goes around. You know, I always come back to building this foundation, making sure the relationship data you have and the brand data you have is well structured and standardized. Having a common language for those kinds of things in the organization are all absolutely critical. And I don't see those things as different from the last 22 technological revolutions we've sat through.
You know, big data came out. Okay, well, what made big data valuable? Little data, structured data. Enterprise systems were implemented in the late '90s when I started off in the business, and people started talking about harmonizing our legacy data. And you know, the concepts of silos came up. I mean, if you go all the way back—let's go all the way back before computers, before even electricity, to the concept of a general ledger—you still need a chart of accounts.
So, how is that? I think all of that is the same situation fundamentally at its core as what we're dealing with with agentyc AI and data governance. Same relationship. Yeah, it's in information management in the essence. If you look at libraries, they are doing that already for centuries, organizing their information, the knowledge. But we think we need to do that differently in the digital world with computers. But exactly, it's good to reflect back and say, "Hey, why are we doing this? Why are we standardizing?" Good analogies.
So, for leaders just entering in the field, CEOs and aspiring CDOs or data practitioners, what's the first practical step that they can do to calm down their data and align it with the business outcomes? Something else what you can add to that?
Making sure you've got formalized data governance programs in place and standards. And I'm not an organizational expert in terms of, you know, here's the five next steps. But one piece of advice I always give everybody, whether they're just graduating data scientist or a seasoned CDO, is learn how to tell a story. Understand that there's structures to stories. Know that you already know how to tell a story.
So, if they're at home over the weekend and something happened, they know how to explain that to their parents, to their spouse, to their kids, whatever. We all know as humans know how to communicate and know how to tell stories. But realize it's a formal... There's lots of resources out there, mine included, to help you kind of formalize that approach. Similarly to how you would use the hard skills you've got to manage data.
And, you know, these soft skills are in always that irony that soft skills are so hard. These soft skills are important for anybody who wants to be a leader and anybody who's currently a leader. How do you communicate to your organization ways that you can explain what the importance of certain things are, ways to inspire and bring to light things that might be behind the curtain that folks don't realize. All that comes through storytelling.
And there's, you know, it's Aaron Sorkin, who's the, you know, great screenwriter, said that the best delivery system for an idea is...
A story. And stories can cut through all kinds of processes. They can cut through hierarchy. That was my career when I was—you know, I was able to sit with CEOs and more than my fair share of them at a cocktail party, in an informal gathering, and just be able to, in a couple of sentences, explain the value of what I was talking about. And have them go—have them go—and some miraculous thing, you just see somebody do this. They go from, "I have no idea what you're talking about," to, "How do we live without this?" That's the magic a story can bring. So yeah, really getting that aha—"This is so—I never thought about it." Great, great one.
So, how do you see, with all the AI, that data or data management landscape evolves in the next three to five years, especially in the light of AI and shifting executive expectations? I see that we use AI to improve the data, but we need the data to improve the AI. So it's a kind of loop—a bit of a chicken and egg thing there.
And I—I'm not much of a trends identifier. What I tend to do is wait for the folks who say, "This is what's coming next," and look for what that thing is and link the importance of data management to it. So I look at that one. But obviously, you know, tools are getting bigger—but it's the same—you know, and better and more sophisticated and faster. These are all kind of cliche ways to look at it, 'cause I don't have any specifics around, "Okay, what's happening? Where?" But I am confident that companies still want to—no matter what, since the beginning of commerce—want to try and bring value to their relationships through their brands at scale.
Modern enterprises that want to do things at scale must use technology. Technology is hardware, it's software, it's data. And if you have data, you need data management. So for me, that's kind of the shortest way to cut through it and say it's not going away. And what I sort of smirk at is—you know, the latest thing—one of the latest things I'm tracking on LinkedIn are people saying, "You know, structured data is not needed anymore 'cause unstructured data is so valuable. That's where the real value is." And there's lots of value in all that unstructured data. But you know—and I know—how do you bring value to it? Put some kind of structure on it.
I read this whole white paper somebody wrote about, like, data management going away, and I kind of scanned it because I don't really understand a lot of the technical details. But then at the end, the summary was like, "Well, tags and labels." I go, "What's that? That's structured data, isn't it? You put a tag on something, put a label on something. Where do those come from? Where's your list of tags? Where's your list of labels?" You know, it always comes back to the same thing.
Yeah, exactly. That's the feeling I get as well. I'm working a lot with the large language models and extracting information out of a lot of articles, building that intelligence platform to understand what is happening on the marketing and on the markets and trying to spot those early trends and communicate that. But what I see exactly—I'm using the large language model to extract some structures, some standards out of that context, and then grouping that together. And exactly what you're doing, creating hierarchies, because I can't understand 34,000 themes which are very detailed themes. So I have to level that up to, as a human, understand the 40 categories, and then I say, "Okay, this one is right," and then deep dive to understand.
And it comes down to your data storytelling again—what you say. In your communication with your data leadership, what are the personal values—integrity, clarity—
Creativity and humor that you mostly apply and help you get that better communication in place?
For me, yeah, yeah, I mean you've listed a bunch of them. So obviously, I like to be entertaining in my presentations, inspiring. I try to be funny where I can, try to be, you know, very serious around certain areas to get people, you know, focused on it.
And I will tell you for me—and because I'm a professional speaker, so that's mostly what I do—I tell people I have a fear of not public speaking. That's my attitude. Probably half of my time I work on delivery. And I just would like people to think about that too. Don't spend half the time prepping on delivery. I do it because I'm, you know, essentially doing great things and a performer when I get out there on stage in a keynote in front of a couple thousand people. You got to be able to deliver.
But most folks don't even think about delivery at all. You know, have they stood up before and practiced this? I don't know how many data conferences I go to that I feel like, has this person even said this before one time? Have they even practiced this once? So there's a lot of technique out there that can help people better articulate what they want to do, and it's not all from, you know, join a Toastmasters club, stand up and practice, get in front of the mirror, whatever it is.
And people are always nervous about that and feel like they could do a shortcut and not do it. But you know, a professional athlete or professional actor or professional anything that doesn't practice, rehearse, train, do drills—it doesn't just... This idea that you'll just personally come through when you need it, that's not something you can bank on.
No, makes me think back of the days when I was starting on the private radio stations and first time in front of a mic—well, not even having an audience in front of you was kind of "ah." And then after a while it becomes just genuine and it's natural, and you jump in front of the mic, no problem, no fear. And then you get your audience in front of you and you block again. So until you get used to that. And then you play with the energy, and that's when the magic starts to happen.
And it's exactly. Yeah, where you say just prepare. If you have to do the presentation for the board, just prepare it up front, try to understand what would be the remarks, the questions you will get, and everything like that. That's classic.
And the type of story you can—I come from the vendor side, so I mean, but the type of story we're trying to tell about data management in an organization is a pitch. It's not, you know, some epic saga, it's not a romantic novel. The story is a pitch. Here are the benefits of what we have to offer. Here's the challenge, here's the solution, here's the next steps. Whatever framework you want to use, use whatever you want. There's plenty of sales pitch frameworks out there.
But you are trying to sell somebody something. You are trying to have them commit to an action based on what you are sharing. And that's the key. It's not just, "Okay, and they lived happily ever after." It's, "We need to—okay, you like this, we need these three steps to happen." Whatever that closes, that gets people to take action on what you're sharing with them.
Great one. Scott, we're coming closer to the end of our discussion. Already? We barely started here.
Well, do a wrap-up afterwards if you want to, but anyway, I got some few questions where you have to think fast. So okay. Yeah. Influencer or thought leader? I go with... I go with influencer.
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But I think they're somewhat interchangeable. I feel like "influencer" is just sort of the modern version of "thought leader," but it seems a little—I don't know—hipper (which is not really a hip word these days, but I'll go with that).
Especially since brands have influencer relations departments, and they have budgets for influencers. And since what I'm doing is providing brand content as a service, call me whatever you want—whatever the budget's called.
Data mesh or data mess? Data mess. The mesh—I just found that as an example. I think it's Zhamak who came up with the data mesh. She's one of the greater storytellers in our business. And I'm not technical, so I never looked under the hood. I don't know the difference between data mesh, data fabric, data lakes—whatever you want to call it. But I really feel a lot of the success was in her ability to share this vision.
People just got all wrapped up in it—I remember it was before AI wiped the whole data mesh/data fabric story off the map when it came in. But there was a lot going on. That was a primary focus, and it was just hysterical to watch this battle of the storytellers between—I articulate, as you know—data mesh represented by Queen Z and data fabric represented by, you know, Big Blue G (Gartner) and the Baberconians.
They were just fighting each other over terminology that, if you take even half a step back, makes no sense to anybody else in the room. It's like, "What's the point here? You're fighting over analogies and metaphors." But anyway—long-winded, rambling answer for that—but I think we're more data mesh mess than data mesh.
Yeah, I think we had about five or six definitions of data mesh, and nobody really nailed down what it exactly was. But I think you have a good expression for that. It's really the storytelling and selling it—that's the data mesh. It's decentralized and everything like that. No, no—close to technical stuff. It's more an approach, but whatever.
Data busy or data driven? Data busy? Did you say? Yeah, data busy. Data busy? I would say data driven. I haven't even heard "data busy" before. That sounds just like kind of—for me, it sounds like busy work. Like, "Get data busy?" That's exactly what it is. I mean, people are doing a lot of stuff with data, but they're not looking at the data-drivenness or just using it for decision-making.
Oh, okay. All right, then—always working on the data, so they're more data busy than data driven.
You talked briefly on the—yeah, you touched briefly on the next one: center of excellence or silos? Well, they're two very different things, right? So, a silo I look at as a department. You know, departments are important—you've got to have departments. And we've kind of articulated that as silos based on the system that drives that.
Center of excellence—I've always found it to be a little bit too arrogant (that's a bit of a strong word). But I remember going into a company that was just starting a master data product positioning, and they said, "Well, we're setting up a center of excellence." I said, "I don't know if we're that excellent right now. That's a little over-promise there. I mean, 'center of mediocrity' doesn't sound too good. So, let's just call it a department for now, get the work done, and name it later."
But I always kind of feel like "center of excellence"—that whole framing, that whole term—is just a little off-putting. And, you know, "We're excellent"—I don't know. So, I never liked that.
Yeah, you helped me think it through in a certain—
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Way. It should help people support them, but it's more driving them away because you say excellence, and expectations are high. Yeah, it's a little intimidating, and all right, you know, or like I said, kind of off-putting.
Yeah, but it's the movement what we saw. I'm moving people from the business, the experts into the center of excellence, centralizing it, and now again, we take the people from the center of excellence and put them back in the business. So, it's moving back and forth, and that's been swinging back and forth, yeah. So. Exactly.
Data experiments or super governance data value chain? Super governance data value chain to the rescue. Yes, I love that. Hilarious.
We were looking for these extra buzzwords that you can exclude from your book and from your keynotes. That's why I put it in, so. That might end up in one of my puppet data puppet videos there. So, that's a hilarious term.
And then the last one, data driven or data informed? I think we'd wind up being more data informed, but data driven again is—I mean, it is a buzzword, but at least it has action to it. What I like about data driven is that it has the action. You know, it's driving somewhere.
Although, my—you know, I posted a few times, "Are you data driven, but you can't find a place to park?" Just 'cause I think it's funny to connect cars with parking in a nonsensical way there. But you know, people are striving to be data driven.
I think spending too much time on really defining a lot of those terms that we just went through—Eeb, distract—you know, how many of those are things you want to explain to the business? And you know, that would be a serious exercise for me if you went through like the—you know, the 12 terms even that you just mentioned there. And then when you pick the ones you want to really explain to the business, cut those down again, because do they really need to?
When you're trying to explain something to somebody, if you give them a whole set of new terminology that they're not familiar with, and then have to have them be fluent in that before you can even get to the point, guess what? Most—first of all, most of your meeting's over. It's really boring, and you also haven't got to the—which goes all the way back to my first V, vocabulary. Get the words right. Speak the language of the business.
Yeah, and that is already a multi-year project most of the time if you try to align on that, so.
Uh, Scott, data connects us all, but music connects us as well. So, can you tell me what is your favorite band, artist, or genre in this marvelous scene?
Oh, the genre for sure is jazz. I'm a jazz baby from way back. I love jazz. Bebop, kind of Miles Davis, Coltrane. That Miles Davis is my go-to all the time. His ballads, early Miles Davis. If you're a Miles Davis fan, I—I stop at "Brew." Past that, it's just too much for me. Gets a little too fusionist.
But before that, I love a melody. I love the structure of it, too, where somebody establishes a—some form of, you know, theme, and then people riff off that. And I always felt like sales and data conversations and conversations with folks are really a form of jazz. We play off each other. We've got a point, but you know, one goes one way, one goes the other way. We bring it back around. So, jazz for sure.
And when it comes to rock and pop, I'm a Dave Matthews fan as well. So, he's the one—been to 20-something of his concerts, well more than anybody else according to my setlist.fm data, which tracks all the concerts I go to. That's a great one.
Scott, this was such a great conversation, you—
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We can keep on going for a few other hours as well. Especially, so if people want to find you online or want to hire you as a speaker, where can they find you? Something else, where your marvelous books—you want to share with the audience what they want to take away from this conversation?
The easiest way to get a hold of me is on LinkedIn—Scott Taylor, the Data Whisperer. If you Google "the Data Whisperer," it now comes up with one of those little boxes that talks about me, which I'm very proud of after doing—I mean, I met you when I first started, right? And now it's working after 7 years of this.
My website is metamedaconsulting.com, or just search for Scott Taylor, the Data Whisperer, and all that stuff will come up. I have another content stream I call the Data Puppets. If you haven't seen those, starring the CBO, the Chief Dog Officer, and the ITB, and they hire a cat consultant from Meowkinsey. They deal with every kind of data problem going on with disastrous yet hilarious effects.
I did one of these puppet videos a couple years ago, and the number one reaction I got from people was, "That sounds just like my organization." And I'm like, "These are puppets just babbling." But it struck a chord with folks, so I built out a whole series. There's a couple episodes on LinkedIn, but I was working yesterday on kind of doing the introductory board meeting series between the CEO, who's an elephant, the CMO, who's a mouse, the CFO, who's a fish, and the CBO, who's a dog.
I put all those together and thought, "How did nobody else come up with this before, right?" CEO is an elephant. We're taking things too seriously, I think. Yeah, yeah, yeah. So, that's great. But I'm on YouTube, on LinkedIn, and those are the two platforms I focus the most on.
My book is on Amazon—*Telling Your Data Story*. It's half price on Amazon, so there's a good deal for you. I'd love to hear from anybody who's inspired or has a question or is interested in having me come and speak or do content for their brand—all that kind of stuff. I'm open to that.
Scott, amazing. Thanks for having you on the show. Great to have you. Well, let's keep the conversation going—maybe do another episode and go more into depth. Thanks for joining me.
Super. This was great fun. Thanks for having me.
