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Welcome to the Data Strategy Gurus podcast. In this show, we bring together the brightest minds in the world of data strategy, data management, artificial intelligence, and disruptive technologies.
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Thought leaders and experts share their insights, knowledge, and experience on how to stay ahead of the game in an ever-evolving data landscape.
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Whether you're a data professional, a business leader, or simply someone who is passionate about the power of data, this podcast is for you.
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So sit back, relax, and join us on a journey to explore the world of data, analytics, artificial intelligence, tech, and beyond. [upbeat music] Hi, and welcome to the Data Strategy Gurus podcast.
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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 bullshit, especially in the age of, uh, GenAI.
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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, Yves. Great to see you. Thanks for having me.
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Yeah. It's been a, it's been a long while. Finally, we get virtually face to face again, but, uh, it's great to have you on this, on the show. Scott, it always intrigued me. Uh, your tagline is the data whisperer.
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What does that mean to you, and where did that persona come from? I, I put it on a, a badge one day at a conference, and I got so much positive feedback I, I never looked back again.
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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.
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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- [laughs]...
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selling, yelling, and telling about the power and value of proper data management, and we need to do that at all levels.
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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 with that?
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I, I find it intriguing to think it through and understand what you exactly mean with calming it down. I see everybody jumping, but nobody is always putting the exact data management or the foundations in place.
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Uh, we've been screaming data quality for the last 20 years. Nobody really cared about. We say, "Yes, it's valuable. You need to put that in place," and, and data stewards and data governance.
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But still we jump just on AI without having the foundations. So being data r- well, AI ready, and then having that, that in place, how do you... Can you suggest how to calm down your data? Uh, just one point.
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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, uh, screaming about data management.
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[laughs] And calming was just kind of a nice emotional way to characterize the concept of structuring, governing, curating, stewarding, mastering
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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, and the products and services that they make.
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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.
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[laughs] Well, do you see that are, are still too many companies are not paying attention to the real data management, data governance, how you call it?
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I see that we're switching more from data, pure data management and foundations to the governance, which feels for me kind of just having it in place, your structure, uh, compliancy.
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That's why governance feels more to me than purely data management, data quality kind of stuff.
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But maybe because I'm getting a bit too gray and, like you, already t- three decades [laughs] in, in the data space as well, so. I, I like to hear what, what you see when you're working with companies.
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I think this, this idea of... One point I wanna elaborate on, on, on your comment there is I bifurcate the entire space for the sake of simplicity
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between, you know, data management and business intelligence or analytics or AI. You know,
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where data starts, which is data management, data governance, data stewardship, master data, reference data, metadata, MDM, RDM, PIM, RIM- [laughs]... DAM, all those foundational activities that we do.
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And when you get into the expertise conversation, then those differences make, you know, distinct sense. Okay, is it data governance? Is it data management? Is it data stewardship? Is it data enablement?
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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 un- don't even recognize this bifurcation I'm talking about.
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They just think about data is 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, I always...
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I, I feel that literally every enterprise organization has these data whisperers, these data champions, who are really trying to
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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. Might as well plug it now and show you.
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Telling Your Data Story, Data Storytelling for Data Management. Says right on there, "99% buzzword free." So- Wow... didn't wanna overpromise. And, uh, is, is being able to articulate why managing data is important.
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And even the term data storytelling.
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You know, I feel like in, in these decades I've worked with analytics or business intelligence or whatever Is in front of the business is the stuff that gets the attention and the focus and a disproportionate amount of the funding, yet the behind the scenes kind of
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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 analytic storytelling.
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When it, when it came out, how to use an, uh, some sort of KPI or, or, or research and put it in some kind of business context to drive action, you know, with visualization, with a story around it.
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That's all super important. I'm not saying it's not important. But where's the story about the data?
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Where's the story about why starting with standards, why having these, you know, governance guidelines, why it's important to structure and have, you know, proper hierarchies and taxonomies? Where's that story?
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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
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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, they deserve.
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So you focus a bit on where you say the d- data storytelling, not on the end part.
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Yes, you f- you focus on that as well, but the data storytelling on, on the end part is, uh, explaining what the data means to the business impact driving decisions.
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But you find it very important as well to do the data storytelling on the standards, the beginning of why do 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 se- concepts and entities, but really tying that together and starting with that part in the beginning in your data sto- storytelling.
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That's, that's how I understand what you say, bringing in what we think is important as, as data management, uh, practitioners, uh, but nobody really cares about because they care about the business, but it's important to drive your business.
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So that's, that's how I understand it if you explain, okay. So but your keynote- Perfectly. It's where data starts. Where data starts, yes. Yeah, yeah. And you're right, most data storytelling is where data ends up.
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I want us to be also reinforce, you know, where data starts. 'Cause if you don't start the right way, you're not gonna end up where you wanna go. You know, you don't need a quote from Yogi Berra or Yoda to- [laughs]...
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to, to reinforce that, but we all know that. We gotta start the right way. And if you fix data, if you manage data closer to where it starts, and I don't wanna get technical here- Mm-hmm...
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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, uh, your data quality at the source.
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Uh, that's what I recall what we've been telling for the last 20 years. Right. Your keynote, Data is the New Bullshit, uh, the Gen AI Edition, it cuts through the buzzwords.
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What, what is your biggest frustration with how people talk about data today? I've, I've actually updated that keynote, so it's no longer the Gen AI edition, it's the Agentic AI edition with vibe coding.
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[laughs] So I wanted to make sure I was au courant.
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But the, it really, the, the subtitle of it is what I think is even more important, which is the w- 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- Yeah...
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reinforcing the importance of soft skills, of being able to understand h- what you do in data helps enable the strategic intentions of your enterprise.
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Explaining to your CEO why data management's important to help them achieve the goals and objectives that they've probably published in the annual report and don't realize they need good data quality to get there.
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It's those kind of themes that I'm, 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.
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Uh, can you walk us through a, 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.
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Uh, we, we understand well, well when we talk about taxonomies and entities, but it's n- again, just the geeky part of us, but not the business related part.
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Maybe that helps people understand better why it's so important just to start the data storytelling at the beginning. And I, and I'll even start with that word why.
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The whole overall framing of it is focus on the why, not the how.
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As data people, we love to explain how something is gonna 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 gonna do until they understand why it's important.
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So focusing on that why, and then using this really light framework that I've got in this book, as you mentioned, the three V's of data storytelling, obviously a knowing wink to the three V's of big data.
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Shout out to Doug Laney there. [laughs] But mine are vocabulary, voice, and vision. And vocabulary, very simple. Start with the words. The words you use,
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the terminology you use, the lexicon you leverage needs to be business friendly. It needs to be the language of the business.
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If you wanna speak to the business, you gotta speak in the language of the business, and it's not about, you know, entities and hierarchies and te- You know, hierarchy they probably understand, but just that a lot of that, you know, you don't talk about the latest analytics graph hub fabric mesh that you're gonna implement.
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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.
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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.
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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.
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They talk about providers and, um, patients, while a consumer packaged goods company will talk about consumers and customers.
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Those are all still relationships, but we have different terminology there, and the business understands the terminology about relationships in their own organization.
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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, you know, get rid of the buzzwords? And does your team understand it, too? What is the...
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When I say voice, it's a, it's a... One of the things I suggest is you wanna 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.
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So does your head of data architecture and data management and data steward, do they all, are they all singing from that same tune?
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Do they all have a similar tone about what they are talking about and all have a similar understanding about why it's important? And within that voice, too, are some marketing tricks.
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Essentially, you've got to put a marketing program together for your data management program. That's, that's the, what we have to do. It doesn't have to be fancy.
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It doesn't have to go on TV in the Super Bowl, and, you know, you're not gonna run ads, but find some little twist of phrase or some analogy that makes sense to your business. And the biggest recommendation I have is
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use your business as an analogy for why data's important. So an example. Uh, you know, a healthcare company, a healthy business runs on healthy data.
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Data's the lifeblood of our organization, you know, really simple things like that.
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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 knew in the organization, everybody understood shipping.
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Things go from here to there. Who, where are they from? Where are they going to? What's the provenance? Do we have the lineage? Do we, are we tracking it? What's it used for? You know, those kinds of things.
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Don't use data's the new oil unless you're in the oil business, and even then, it's an overused cliché, hence my title, Data's the New Bullshit. It's already, you know, running off of that.
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So vocabulary are 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,
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and why is managing data, not why is AI, but why is managing data going to help your leadership achieve those objectives? And you speak to that, you will earn a seat at the table.
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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.
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You're talking about what that enables in the organization.
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And 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 anybody's gonna argue with you.
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So [laughs] go into your annual report, listen to your strategy discussions of your leadership.
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Find those phrases, find those terms, find that, the, the objectives the way they articulate it, and then show in the end that if you don't manage the data properly, you're not gonna hit those objectives.
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Great words, Sam. Uh, I think, uh, you, you bridged very well. It's gonna be hard to play for, for the technical people implementing data measure and data fabrics and telling that in the story or the language of the CEO.
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But, but it's a, it's a good analogy, uh, as well, where you say using those metaphors to translate our geeky environment and why it's important and translate that into, uh, really the business.
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So in the search of AI adoption, how do you think that organizations ensure their data management actually supports generative AI, uh, initiatives, not the bullshit, but just the readiness, and maybe to come back to agentic AI initiatives, uh, if you want to [laughs] have it like that?
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Any, 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 gonna work. You know, we don't have to...
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We can, we can beat the GIGO, garbage in, garbage out, rubbish in, rubbish out cliché to death, but it is still true. It's as irrefutable as gravity. What goes up [laughs] must come down. What goes in must come out.
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We learn it on the first day of data, but it doesn't really resonate the way we want it to, but you gotta continue to prove it. You know, people think, all right, with agentic AI, you know, is that...
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Some people think, all right, AI's actually, you know, an Ozempic for data management. It's gonna fix all these things that we used to have to do all this hard work for.
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You know, maybe, maybe not, maybe around the edges, maybe not, but you still...
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You know, if you're not feeding AI the right thing, it's not gonna 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.
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So it always goes around... You know, I always come back to
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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 [laughs] sat through.
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You know, big data came out. Okay, well, what made big data valuable? Little data, structured data. Enterprise systems were implemented in the,
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you know, 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.
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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. Yeah. So how is that...
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I think, I think all of that is the same situation fundamentally at its core as what we're dealing with, with agentic AI and data governance. Same relationship. Yeah. It's an information management in, in the essence.
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If you look at libraries, they, they are doing that already for, for, for centuries- Right...
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organizing their information, the knowledge, uh, but we think we, we need to do that differently in, uh, in the digital world with, uh, with computers.
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But exactly, it's, it's good to reflect back and say, "Hey, why are we doing this? Why are we standardizing?" Good analogies.
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So for leaders just entering in the field, CEOs, and aspiring CDOs or data practitioners, what's, what's the first practical step that they can do to calm down their data and align it with the, with the business outcomes?
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Uh, something else that you can add to that?
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You know, making sure you've got, you know, formalized data governance programs in place and standards, and I'm not a organizational expert in terms of, you know, here's the five next steps.
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But one piece of advice I always give everybody, whether they're a, you know, just graduating data scientist or a seasoned CDO, is learn how to tell a story. Understand that there's structures to stories.
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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.
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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.
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Similarly to how you would use the hard skills you've got to manage data. And, you know, these soft skills- Right... it's iron- always that irony that soft skills are so hard.
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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?
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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,
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you know, it's, Aaron Sorkin, who's the, uh, you know, great screenwriter, said that the best delivery system for an idea is a story, and stories can sh- cut through all kinds of processes.
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They can cut through hierarchy. That was my career when I was wor- You know, I was able to sit with CEOs, and more than my sh- fair share of them,
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at a cocktail party, in an informal gathering, and just be able to just, in a couple of sentences, explain the value of what I was talking about, and have them go, "Have..."
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Have them go, and it's a miraculous thing, Yves, to see somebody do this, to 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.
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So yeah, really getting that aha. Aha. This is something I never thought about it. [laughs] Great, great one.
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So and how do you see with, with all the AI that, that data or data management landscape evolves in the next three to, to five years, especially, uh, in the light of AI and shifting executive expectations?
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I see that, that we use AI to improve the data, but we need the data to improve the AI. So it's, it's a kind of loop what is happening.
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Yeah, it's a bit of a chicken and egg thing there, and I, I'm not much of a trends, uh, identifier. What I tend to do is wait for the folks who say, "This is what's coming next,"
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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.
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But it's the same, you know, a- and better and more sophisticated and faster. These are all kind of, you know, cliché [laughs] ways to look at it 'cause I don't have any specifics around, okay, what's happening where.
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But I am confident that companies still wanna, no matter what, since the beginning of commerce, want to try and bring value to their relationships through their brands at scale.
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Modern enterprises that wanna 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.
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So for me, that's kind of the shortest way to cut through it and say, "It's not going away." And
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what I sort of smirk at is, you know, the latest thing, one of the latest things I'm tracking in LinkedIn are people saying, "You know, structured data's not needed anymore 'cause unstructured data is so valuable.
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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? You put some kind of structure on it. And- I read this whole
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white paper somebody wrote about, like, data management going away, and I kind of scan it because I don't really understand a lot of the technical details.
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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, you put a label on so- Where did those come from?
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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, that's the feeling I get as well.
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I'm working a lot with, 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, uh, on the markets, and trying to spot those early trends, uh, and, and communicate that.
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But what I see exactly, I'm using the large language model to extract some structure, some standards out of that context, and then grouping that together, and exactly what you're doing, creating hierarchies, because I can't, I can't understand 34,000 themes which are very detailed themes.
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So I have to level that up to, as a human, understand the 40 categories, and then I'd say, "Okay, this one is right," and then deep dive to understand, and comes down to, to your data storytelling again, what, what you say
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Uh, in your communication with, with, uh, your, with data leadership, what are the personal values, integrity, clarity, creativity, and humor that you mostly apply and, and help you get that better communication, uh, in place?
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For me, yeah, I mean, you've listed a bunch of them, so obviously I like to be entertaining in my presentations, inspiring,
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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.
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And I, 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,
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and, and I just would like people to think about too, that too. Don't spend half the time prepping on delivery.
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I do it because I'm, you know, essentially to a great extent, a performer when I [laughs] get out there on stage in a keynote in front of a couple thousand people, you gotta be able to deliver.
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But most folks don't even, I don't even think about delivery at all. You know, have they stood up before and practiced this?
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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?
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So there's, 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 me.
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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.
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But you know, a professional athlete or professional actor or professional anything that doesn't practice, rehearse, train, do drills, y- you know, it doesn't just...
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Th- th- this idea that you'll just, you're just gonna personally come through when you need it, that's not something you can bank on. [laughs] No.
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Makes me think back of the days when I was starting on the, on the pirate radio stations, and first time in front of a mi- in, in a mic, uh, well, not even having an audience in front of you was kind of [gasps].
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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.
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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- Exactly. Yeah... the same thing where you say, just prepare.
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If you have to do the, the, the presentation, uh, for the board, just prepare it upfront. Try to understand what would be the, the remarks, the questions you will get and, and, and everything like that.
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That's classic pr- And the type of story, again, 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.
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It's not a romantic novel. It, 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.
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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.
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You are trying to have them commit to an action based on what you are sharing, and, and, and that's the key. It's not just okay, and they lived happily ever after. It's, we need to...
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Okay, you like this, we need these three steps to happen, or whatever that, that close is that gets people to, to take action on what you're sharing with them. Great one.
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Scott, we're coming closer to, to the end of our discussion. Uh- Already? God, we've probably- Yeah... barely started here. [laughs] We'll do a wrap up afterwards as, if you want to.
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But, uh, anyway, I got some few questions where you have to think fast. So- Okay... what do you think? All right. In- what, influencer or thought leader? I go with, I go with influencer,
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but I think they're, they're somewhat interchangeable. I feel like influencer's just sort of the modern version of thought leader, but it seems a little,
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I don't know, hipper, which is not really a hip word, but these days. But I go with that, [laughs] especially since brands have influencer relation departments, and they have budgets for influencers.
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And since what I'm doing is providing brand content, you know, as a, as a service, so call me whatever you want, whatever the budget's called, yeah. [laughs] Exactly. Data mesh or data mess? Data [laughs] mess, really.
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The mesh, I just... I found that an example, I think it's Zhamak who came up with the data mesh, is one of the greater storytellers in our business. And I, I'm not technical, so I never looked under the hood.
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I don't know the difference between data mesh, data fabric, data Spanx, whatever you want to call it.
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[laughs] But I really feel a lot of the success was in her ability to share this vision, and people just got all wrapped up in them.
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Remember, it was before, you know, AI wiped the whole data mesh, data fabric story off the map there when it came in, but there was a lot going on.
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That was a primary focus, and it was just hysterical to watch this battle of the storytellers between,
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I articulate it as, you know, data mesh represented by Queen Z and data, data fabric represented by, you know, Big Blue G, Gartner, and the Fabriconians.
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And they were just, like, fighting each other over, like, terminology that was, when you take even a half a step back, makes no sense to anybody else in the room. It's like, what's the point here?
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You're fighting over analogies and, and, and, uh, and metaphors. But, um, anyway, lo- very long-winded, rambly answer for that, but, uh- [laughs]... I think we're more data mesh, mess than data mesh. [laughs] Yeah.
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I think we had about five or six, uh, definitions of data mesh, and nobody really nailed it down what it exactly was. But I think you have a good, uh, expression for that.
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It's really the, the, the storytelling and selling it, uh, that's the data mesh. It's decentralized and everything like that. Not, not close to technical stuff. It's, it's more, more an approach. But whatever.
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[laughs] Uh- Data busy or data-driven? Data busy, did you say? Yeah, data busy. Data busy? I would say data driven. I, I haven't even heard data busy before, but that sounds- That's-...
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just like, kinda, for me it sounds like busy work. Like, get data busy. [laughs] That's exactly what it is. I mean- Yeah...
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people are doing a lot of stuff with data, but they're not looking at the data drivenness or just using it to, uh, decision-making. Oh, okay. All right. And they're, they're- Then that makes sense. [laughs]...
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always working on the data, so they're more data busy than data driven. Right. [laughs] Yeah. So you touched briefly- Get data busy out there. Yeah... yeah, you touched briefly on the next one.
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Um, center of excellence or silos? Well, what a, what... Those are two very different things, right? So silo, I look at as a department, you know.
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And departments are important, you gotta have departments and, you know, we've kind of articulated that as silos based on the system that drives that.
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Um, center of excellence I've always found to just be a little bit too, arrogant is a bit of a strong word,
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but I remember going into a company that was just starting a master data product positioning, and they said, "Well, we're, we're setting up a center of excellence."
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I said, "I don't know if we're that excellent right now. That's a little overpromise there." I mean, you know,
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and center of mediocrity doesn't sound too good, so let's just call it a department for now and, [laughs] you know, get the work done and name it later.
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But I always kinda feel like center of excellence, that whole framing, that whole term just is a little off-putting and, you know, "We're excellent," and it just, I don't know. So I never liked that. [laughs] Yeah.
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You, you help me- Seriously... you help me think it, think it through in a certain way. It should help people, support them, but it's more driving them away because you say excellence and the expectations are high.
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This is what you do. Yeah, it's a little intimidating. It's like, all right, you know, or like I said, kind of off-putting and...
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Yeah, but it's the movement what we saw, uh, 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.
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So it's moving [laughs] back and forth and trying- That, that's been swinging back and forth, yeah, so. Exactly. Data experiments or super governance data value chain?
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[laughs] Super governance data value chain to the rescue. Yes, I love that. Hilarious. [laughs] We were looking for these extra buzzwords you, you can exclude from, from your book and from your keynotes.
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That's, that's why I put it in, so. Yeah, that, that, that might end up in my, one of my puppet vi- data puppet videos there, so it's, that, that's a hilarious term.
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[laughs] And then the last one, data driven or data informed? I think people end up being more data informed, but data driven, again, is such a, I mean, it is a buzzword, but at least it has action to it.
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What I like about data driven is that it has action. You know, it's driving somewhere. Although my, you know, I posted a few times, are you, do you wanna be data driven but you can't find a place to park?
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Just 'cause I think it's funny to connect cars with parking in a nonsensical way there. But, uh, you know, people are striving to be data driven.
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I think spending too much time on ide- on really defining a lot of those terms that we just went through, Yves, distract, you know, how many of those are things you wanna explain to the business?
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And, you know, that would be a serious exercise for, for me if you went through, like, the, you know, the 12 terms even that you just mentioned there.
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And, and then when you pick the ones you wanna really explain to the business, cut those down again, because do they really need to...
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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?
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Most, first of all, most of your meeting's over, it's really boring, and you also haven't gotten to the point, which goes back to, all the way back to my first V, vocabulary. Get the words right.
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Speak the language of the business. Yeah, and that is already, uh, a multi-year project, uh, most of the time [laughs] if you try to rely on that, so. Uh, Scott, data connects us all, but, but music connects us as well.
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So can you tell me what is your favorite band, artist, or genre, uh, in this marvelous scene? Oh, genre for sure is jazz. I'm a jazz baby from way back. I love jazz, bebop, kind of Miles Davis, Coltrane.
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That, and Miles Davis is my go-to all the time. Uh, his ballads, early Miles Davis, if you're a Miles Davis fan. I, I stop at Bitches Brew. Past that, it's just too, too much for me. It gets a little too fusionist.
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Mm-hmm. But, uh, before that, I love a melody.
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I love the structure of it, too, where somebody establishes, uh, 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.
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We play off each other. We've got a point, but, you know, one goes one way, and one goes the other way. We bring it back around.
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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.
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I've 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. [laughs] That's, that's a great one.
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Scott, this, this was such a great conversation. You know, we can keep on going for a few other hours as well. We can, yes. If you like, yes. Yeah.
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[laughs] Especially, so if people wanna find you online or wanna hire you as a, as a speaker, where can they find you? Something else, uh, your marvelous books you wanna share with the audience?
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What they, uh, want to keep, uh, take away from this conversation? Easiest way to get ahold of me is on LinkedIn, Scott Taylor, The Data Whisperer.
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If you Google The Data Whisperer, it now comes up with a little, 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?
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And it was, you know- Yeah... now it's, it's working after seven years of this. Um, my website is metametaconsulting.com, or just search for Scott Taylor, The Data Whisperer, and all that stuff will come up.
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I have another content stream I call The Data Puppets, if you haven't seen those, uh, starring the CDO, the Chief Dog Officer, and the ITB, and they hire a cat-sultant from Meow Kinsey, and they deal with every kind of data problem going on, and with disastrous yet hilarious effects.
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And, uh, 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."
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And I'm like, "These are puppets just babbling, with j- " And s- it struck a chord in folks, so I built out a whole series.
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There's a couple episodes on LinkedIn, but I'm working, 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, the CDO, who's a dog.
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I put all this together even, I'm like, "How did nobody else come up with this before?" Right? [laughs] We're, we're- A CEO is an elephant. It works perfectly. Um We're, we're taking things too serious, I think, yeah.
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Yeah. Yeah, yeah. So, uh, that's great, but I'm on YouTube, on LinkedIn, and, you know, those are the two platforms I focus the most on. And my book is on Amazon, Telling Your Data Story.
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It's half price on Amazon, so there's a good deal for you.
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And love to hear from anybody who's, you know, inspired, who has a question, who's interested in having me come and speak or do content for their brand, all that kind of stuff I'm open to. Scott, amazing.
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Thanks for having you on the show. Great to have you. Uh, well, let's keep the conversation going, uh, maybe do another episode, uh, and going more into depth. Thanks for, uh, for joining me. Super.
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This was great fun, Yves. Thanks for having me. [outro music] Thank you for joining us on this awesome podcast.
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As senior executives, data and analytics architects, and AI professionals, your time is valuable and we appreciate you choosing to hang out with us.
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If you liked what you heard, please give us a thumbs up, hit the subscribe button, and leave a comment. We love hearing from our audience.
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Don't forget to spread the word on social media, and let's continue to drive innovation in the industry together. Thanks for listening, and we'll catch you on the next episode.
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[outro music]
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Welcome to the Data Strategy Gurus podcast. In this show, we bring together the brightest minds in the world of data strategy, data management, artificial intelligence, and disruptive technologies.
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Thought leaders and experts share their insights, knowledge, and experience on how to stay ahead of the game in an ever-evolving data landscape.
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Whether you're a data professional, a business leader, or simply someone who is passionate about the power of data, this podcast is for you.
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So sit back, relax, and join us on a journey to explore the world of data, analytics, artificial intelligence, tech, and beyond. [upbeat music] Hi, and welcome to the Data Strategy Gurus podcast.
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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 bullshit, especially in the age of, uh, GenAI.
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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, Yves. Great to see you. Thanks for having me.
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Yeah. It's been a, it's been a long while. Finally, we get virtually face to face again, but, uh, it's great to have you on this, on the show. Scott, it always intrigued me. Uh, your tagline is the data whisperer.
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What does that mean to you, and where did that persona come from? I, I put it on a, a badge one day at a conference, and I got so much positive feedback I, I never looked back again.
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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.
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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- [laughs]...
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selling, yelling, and telling about the power and value of proper data management, and we need to do that at all levels.
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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 with that?
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I, I find it intriguing to think it through and understand what you exactly mean with calming it down. I see everybody jumping, but nobody is always putting the exact data management or the foundations in place.
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Uh, we've been screaming data quality for the last 20 years. Nobody really cared about. We say, "Yes, it's valuable. You need to put that in place," and, and data stewards and data governance.
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But still we jump just on AI without having the foundations. So being data r- well, AI ready, and then having that, that in place, how do you... Can you suggest how to calm down your data? Uh, just one point.
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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, uh, screaming about data management.
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[laughs] And calming was just kind of a nice emotional way to characterize the concept of structuring, governing, curating, stewarding, mastering
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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, and the products and services that they make.
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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.
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[laughs] Well, do you see that are, are still too many companies are not paying attention to the real data management, data governance, how you call it?
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I see that we're switching more from data, pure data management and foundations to the governance, which feels for me kind of just having it in place, your structure, uh, compliancy.
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That's why governance feels more to me than purely data management, data quality kind of stuff.
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But maybe because I'm getting a bit too gray and, like you, already t- three decades [laughs] in, in the data space as well, so. I, I like to hear what, what you see when you're working with companies.
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I think this, this idea of... One point I wanna elaborate on, on, on your comment there is I bifurcate the entire space for the sake of simplicity
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between, you know, data management and business intelligence or analytics or AI. You know,
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where data starts, which is data management, data governance, data stewardship, master data, reference data, metadata, MDM, RDM, PIM, RIM- [laughs]... DAM, all those foundational activities that we do.
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And when you get into the expertise conversation, then those differences make, you know, distinct sense. Okay, is it data governance? Is it data management? Is it data stewardship? Is it data enablement?
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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 un- don't even recognize this bifurcation I'm talking about.
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They just think about data is 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, I always...
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I, I feel that literally every enterprise organization has these data whisperers, these data champions, who are really trying to
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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. Might as well plug it now and show you.
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Telling Your Data Story, Data Storytelling for Data Management. Says right on there, "99% buzzword free." So- Wow... didn't wanna overpromise. And, uh, is, is being able to articulate why managing data is important.
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And even the term data storytelling.
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You know, I feel like in, in these decades I've worked with analytics or business intelligence or whatever Is in front of the business is the stuff that gets the attention and the focus and a disproportionate amount of the funding, yet the behind the scenes kind of
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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 analytic storytelling.
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When it, when it came out, how to use an, uh, some sort of KPI or, or, or research and put it in some kind of business context to drive action, you know, with visualization, with a story around it.
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That's all super important. I'm not saying it's not important. But where's the story about the data?
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Where's the story about why starting with standards, why having these, you know, governance guidelines, why it's important to structure and have, you know, proper hierarchies and taxonomies? Where's that story?
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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
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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, they deserve.
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So you focus a bit on where you say the d- data storytelling, not on the end part.
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Yes, you f- you focus on that as well, but the data storytelling on, on the end part is, uh, explaining what the data means to the business impact driving decisions.
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But you find it very important as well to do the data storytelling on the standards, the beginning of why do 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 se- concepts and entities, but really tying that together and starting with that part in the beginning in your data sto- storytelling.
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That's, that's how I understand what you say, bringing in what we think is important as, as data management, uh, practitioners, uh, but nobody really cares about because they care about the business, but it's important to drive your business.
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So that's, that's how I understand it if you explain, okay. So but your keynote- Perfectly. It's where data starts. Where data starts, yes. Yeah, yeah. And you're right, most data storytelling is where data ends up.
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I want us to be also reinforce, you know, where data starts. 'Cause if you don't start the right way, you're not gonna end up where you wanna go. You know, you don't need a quote from Yogi Berra or Yoda to- [laughs]...
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to, to reinforce that, but we all know that. We gotta start the right way. And if you fix data, if you manage data closer to where it starts, and I don't wanna get technical here- Mm-hmm...
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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, uh, your data quality at the source.
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Uh, that's what I recall what we've been telling for the last 20 years. Right. Your keynote, Data is the New Bullshit, uh, the Gen AI Edition, it cuts through the buzzwords.
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What, what is your biggest frustration with how people talk about data today? I've, I've actually updated that keynote, so it's no longer the Gen AI edition, it's the Agentic AI edition with vibe coding.
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[laughs] So I wanted to make sure I was au courant.
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But the, it really, the, the subtitle of it is what I think is even more important, which is the w- 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- Yeah...
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reinforcing the importance of soft skills, of being able to understand h- what you do in data helps enable the strategic intentions of your enterprise.
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Explaining to your CEO why data management's important to help them achieve the goals and objectives that they've probably published in the annual report and don't realize they need good data quality to get there.
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It's those kind of themes that I'm, 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.
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Uh, can you walk us through a, 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.
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Uh, we, we understand well, well when we talk about taxonomies and entities, but it's n- again, just the geeky part of us, but not the business related part.
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Maybe that helps people understand better why it's so important just to start the data storytelling at the beginning. And I, and I'll even start with that word why.
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The whole overall framing of it is focus on the why, not the how.
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As data people, we love to explain how something is gonna 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 gonna do until they understand why it's important.
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So focusing on that why, and then using this really light framework that I've got in this book, as you mentioned, the three V's of data storytelling, obviously a knowing wink to the three V's of big data.
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Shout out to Doug Laney there. [laughs] But mine are vocabulary, voice, and vision. And vocabulary, very simple. Start with the words. The words you use,
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the terminology you use, the lexicon you leverage needs to be business friendly. It needs to be the language of the business.
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If you wanna speak to the business, you gotta speak in the language of the business, and it's not about, you know, entities and hierarchies and te- You know, hierarchy they probably understand, but just that a lot of that, you know, you don't talk about the latest analytics graph hub fabric mesh that you're gonna implement.
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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.
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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.
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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.
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They talk about providers and, um, patients, while a consumer packaged goods company will talk about consumers and customers.
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Those are all still relationships, but we have different terminology there, and the business understands the terminology about relationships in their own organization.
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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, you know, get rid of the buzzwords? And does your team understand it, too? What is the...
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When I say voice, it's a, it's a... One of the things I suggest is you wanna 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.
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So does your head of data architecture and data management and data steward, do they all, are they all singing from that same tune?
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Do they all have a similar tone about what they are talking about and all have a similar understanding about why it's important? And within that voice, too, are some marketing tricks.
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Essentially, you've got to put a marketing program together for your data management program. That's, that's the, what we have to do. It doesn't have to be fancy.
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It doesn't have to go on TV in the Super Bowl, and, you know, you're not gonna run ads, but find some little twist of phrase or some analogy that makes sense to your business. And the biggest recommendation I have is
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use your business as an analogy for why data's important. So an example. Uh, you know, a healthcare company, a healthy business runs on healthy data.
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Data's the lifeblood of our organization, you know, really simple things like that.
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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 knew in the organization, everybody understood shipping.
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Things go from here to there. Who, where are they from? Where are they going to? What's the provenance? Do we have the lineage? Do we, are we tracking it? What's it used for? You know, those kinds of things.
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Don't use data's the new oil unless you're in the oil business, and even then, it's an overused cliché, hence my title, Data's the New Bullshit. It's already, you know, running off of that.
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So vocabulary are 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,
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and why is managing data, not why is AI, but why is managing data going to help your leadership achieve those objectives? And you speak to that, you will earn a seat at the table.
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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.
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You're talking about what that enables in the organization.
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And 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 anybody's gonna argue with you.
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So [laughs] go into your annual report, listen to your strategy discussions of your leadership.
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Find those phrases, find those terms, find that, the, the objectives the way they articulate it, and then show in the end that if you don't manage the data properly, you're not gonna hit those objectives.
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Great words, Sam. Uh, I think, uh, you, you bridged very well. It's gonna be hard to play for, for the technical people implementing data measure and data fabrics and telling that in the story or the language of the CEO.
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But, but it's a, it's a good analogy, uh, as well, where you say using those metaphors to translate our geeky environment and why it's important and translate that into, uh, really the business.
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So in the search of AI adoption, how do you think that organizations ensure their data management actually supports generative AI, uh, initiatives, not the bullshit, but just the readiness, and maybe to come back to agentic AI initiatives, uh, if you want to [laughs] have it like that?
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Any, 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 gonna work. You know, we don't have to...
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We can, we can beat the GIGO, garbage in, garbage out, rubbish in, rubbish out cliché to death, but it is still true. It's as irrefutable as gravity. What goes up [laughs] must come down. What goes in must come out.
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We learn it on the first day of data, but it doesn't really resonate the way we want it to, but you gotta continue to prove it. You know, people think, all right, with agentic AI, you know, is that...
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Some people think, all right, AI's actually, you know, an Ozempic for data management. It's gonna fix all these things that we used to have to do all this hard work for.
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You know, maybe, maybe not, maybe around the edges, maybe not, but you still...
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You know, if you're not feeding AI the right thing, it's not gonna 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.
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So it always goes around... You know, I always come back to
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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 [laughs] sat through.
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You know, big data came out. Okay, well, what made big data valuable? Little data, structured data. Enterprise systems were implemented in the,
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you know, 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.
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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. Yeah. So how is that...
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I think, I think all of that is the same situation fundamentally at its core as what we're dealing with, with agentic AI and data governance. Same relationship. Yeah. It's an information management in, in the essence.
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If you look at libraries, they, they are doing that already for, for, for centuries- Right...
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organizing their information, the knowledge, uh, but we think we, we need to do that differently in, uh, in the digital world with, uh, with computers.
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But exactly, it's, it's good to reflect back and say, "Hey, why are we doing this? Why are we standardizing?" Good analogies.
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So for leaders just entering in the field, CEOs, and aspiring CDOs or data practitioners, what's, what's the first practical step that they can do to calm down their data and align it with the, with the business outcomes?
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Uh, something else that you can add to that?
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You know, making sure you've got, you know, formalized data governance programs in place and standards, and I'm not a organizational expert in terms of, you know, here's the five next steps.
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But one piece of advice I always give everybody, whether they're a, you know, just graduating data scientist or a seasoned CDO, is learn how to tell a story. Understand that there's structures to stories.
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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.
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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.
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Similarly to how you would use the hard skills you've got to manage data. And, you know, these soft skills- Right... it's iron- always that irony that soft skills are so hard.
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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?
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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,
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you know, it's, Aaron Sorkin, who's the, uh, you know, great screenwriter, said that the best delivery system for an idea is a story, and stories can sh- cut through all kinds of processes.
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They can cut through hierarchy. That was my career when I was wor- You know, I was able to sit with CEOs, and more than my sh- fair share of them,
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at a cocktail party, in an informal gathering, and just be able to just, in a couple of sentences, explain the value of what I was talking about, and have them go, "Have..."
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Have them go, and it's a miraculous thing, Yves, to see somebody do this, to 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.
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So yeah, really getting that aha. Aha. This is something I never thought about it. [laughs] Great, great one.
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So and how do you see with, with all the AI that, that data or data management landscape evolves in the next three to, to five years, especially, uh, in the light of AI and shifting executive expectations?
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I see that, that we use AI to improve the data, but we need the data to improve the AI. So it's, it's a kind of loop what is happening.
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Yeah, it's a bit of a chicken and egg thing there, and I, I'm not much of a trends, uh, identifier. What I tend to do is wait for the folks who say, "This is what's coming next,"
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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.
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But it's the same, you know, a- and better and more sophisticated and faster. These are all kind of, you know, cliché [laughs] ways to look at it 'cause I don't have any specifics around, okay, what's happening where.
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But I am confident that companies still wanna, no matter what, since the beginning of commerce, want to try and bring value to their relationships through their brands at scale.
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Modern enterprises that wanna 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.
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So for me, that's kind of the shortest way to cut through it and say, "It's not going away." And
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what I sort of smirk at is, you know, the latest thing, one of the latest things I'm tracking in LinkedIn are people saying, "You know, structured data's not needed anymore 'cause unstructured data is so valuable.
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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? You put some kind of structure on it. And- I read this whole
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white paper somebody wrote about, like, data management going away, and I kind of scan it because I don't really understand a lot of the technical details.
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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, you put a label on so- Where did those come from?
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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, that's the feeling I get as well.
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I'm working a lot with, 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, uh, on the markets, and trying to spot those early trends, uh, and, and communicate that.
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But what I see exactly, I'm using the large language model to extract some structure, some standards out of that context, and then grouping that together, and exactly what you're doing, creating hierarchies, because I can't, I can't understand 34,000 themes which are very detailed themes.
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So I have to level that up to, as a human, understand the 40 categories, and then I'd say, "Okay, this one is right," and then deep dive to understand, and comes down to, to your data storytelling again, what, what you say
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Uh, in your communication with, with, uh, your, with data leadership, what are the personal values, integrity, clarity, creativity, and humor that you mostly apply and, and help you get that better communication, uh, in place?
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For me, yeah, I mean, you've listed a bunch of them, so obviously I like to be entertaining in my presentations, inspiring,
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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.
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And I, 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,
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and, and I just would like people to think about too, that too. Don't spend half the time prepping on delivery.
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I do it because I'm, you know, essentially to a great extent, a performer when I [laughs] get out there on stage in a keynote in front of a couple thousand people, you gotta be able to deliver.
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But most folks don't even, I don't even think about delivery at all. You know, have they stood up before and practiced this?
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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?
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So there's, 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 me.
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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.
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But you know, a professional athlete or professional actor or professional anything that doesn't practice, rehearse, train, do drills, y- you know, it doesn't just...
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Th- th- this idea that you'll just, you're just gonna personally come through when you need it, that's not something you can bank on. [laughs] No.
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Makes me think back of the days when I was starting on the, on the pirate radio stations, and first time in front of a mi- in, in a mic, uh, well, not even having an audience in front of you was kind of [gasps].
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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.
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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- Exactly. Yeah... the same thing where you say, just prepare.
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If you have to do the, the, the presentation, uh, for the board, just prepare it upfront. Try to understand what would be the, the remarks, the questions you will get and, and, and everything like that.
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That's classic pr- And the type of story, again, 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.
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It's not a romantic novel. It, 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.
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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.
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You are trying to have them commit to an action based on what you are sharing, and, and, and that's the key. It's not just okay, and they lived happily ever after. It's, we need to...
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Okay, you like this, we need these three steps to happen, or whatever that, that close is that gets people to, to take action on what you're sharing with them. Great one.
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Scott, we're coming closer to, to the end of our discussion. Uh- Already? God, we've probably- Yeah... barely started here. [laughs] We'll do a wrap up afterwards as, if you want to.
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But, uh, anyway, I got some few questions where you have to think fast. So- Okay... what do you think? All right. In- what, influencer or thought leader? I go with, I go with influencer,
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but I think they're, they're somewhat interchangeable. I feel like influencer's just sort of the modern version of thought leader, but it seems a little,
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I don't know, hipper, which is not really a hip word, but these days. But I go with that, [laughs] especially since brands have influencer relation departments, and they have budgets for influencers.
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And since what I'm doing is providing brand content, you know, as a, as a service, so call me whatever you want, whatever the budget's called, yeah. [laughs] Exactly. Data mesh or data mess? Data [laughs] mess, really.
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The mesh, I just... I found that an example, I think it's Zhamak who came up with the data mesh, is one of the greater storytellers in our business. And I, I'm not technical, so I never looked under the hood.
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I don't know the difference between data mesh, data fabric, data Spanx, whatever you want to call it.
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[laughs] But I really feel a lot of the success was in her ability to share this vision, and people just got all wrapped up in them.
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Remember, it was before, you know, AI wiped the whole data mesh, data fabric story off the map there when it came in, but there was a lot going on.
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That was a primary focus, and it was just hysterical to watch this battle of the storytellers between,
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I articulate it as, you know, data mesh represented by Queen Z and data, data fabric represented by, you know, Big Blue G, Gartner, and the Fabriconians.
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And they were just, like, fighting each other over, like, terminology that was, when you take even a half a step back, makes no sense to anybody else in the room. It's like, what's the point here?
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You're fighting over analogies and, and, and, uh, and metaphors. But, um, anyway, lo- very long-winded, rambly answer for that, but, uh- [laughs]... I think we're more data mesh, mess than data mesh. [laughs] Yeah.
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I think we had about five or six, uh, definitions of data mesh, and nobody really nailed it down what it exactly was. But I think you have a good, uh, expression for that.
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It's really the, the, the storytelling and selling it, uh, that's the data mesh. It's decentralized and everything like that. Not, not close to technical stuff. It's, it's more, more an approach. But whatever.
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[laughs] Uh- Data busy or data-driven? Data busy, did you say? Yeah, data busy. Data busy? I would say data driven. I, I haven't even heard data busy before, but that sounds- That's-...
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just like, kinda, for me it sounds like busy work. Like, get data busy. [laughs] That's exactly what it is. I mean- Yeah...
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people are doing a lot of stuff with data, but they're not looking at the data drivenness or just using it to, uh, decision-making. Oh, okay. All right. And they're, they're- Then that makes sense. [laughs]...
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always working on the data, so they're more data busy than data driven. Right. [laughs] Yeah. So you touched briefly- Get data busy out there. Yeah... yeah, you touched briefly on the next one.
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Um, center of excellence or silos? Well, what a, what... Those are two very different things, right? So silo, I look at as a department, you know.
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And departments are important, you gotta have departments and, you know, we've kind of articulated that as silos based on the system that drives that.
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Um, center of excellence I've always found to just be a little bit too, arrogant is a bit of a strong word,
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but I remember going into a company that was just starting a master data product positioning, and they said, "Well, we're, we're setting up a center of excellence."
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I said, "I don't know if we're that excellent right now. That's a little overpromise there." I mean, you know,
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and center of mediocrity doesn't sound too good, so let's just call it a department for now and, [laughs] you know, get the work done and name it later.
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But I always kinda feel like center of excellence, that whole framing, that whole term just is a little off-putting and, you know, "We're excellent," and it just, I don't know. So I never liked that. [laughs] Yeah.
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You, you help me- Seriously... you help me think it, think it through in a certain way. It should help people, support them, but it's more driving them away because you say excellence and the expectations are high.
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This is what you do. Yeah, it's a little intimidating. It's like, all right, you know, or like I said, kind of off-putting and...
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Yeah, but it's the movement what we saw, uh, 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.
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So it's moving [laughs] back and forth and trying- That, that's been swinging back and forth, yeah, so. Exactly. Data experiments or super governance data value chain?
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[laughs] Super governance data value chain to the rescue. Yes, I love that. Hilarious. [laughs] We were looking for these extra buzzwords you, you can exclude from, from your book and from your keynotes.
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That's, that's why I put it in, so. Yeah, that, that, that might end up in my, one of my puppet vi- data puppet videos there, so it's, that, that's a hilarious term.
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[laughs] And then the last one, data driven or data informed? I think people end up being more data informed, but data driven, again, is such a, I mean, it is a buzzword, but at least it has action to it.
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What I like about data driven is that it has action. You know, it's driving somewhere. Although my, you know, I posted a few times, are you, do you wanna be data driven but you can't find a place to park?
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Just 'cause I think it's funny to connect cars with parking in a nonsensical way there. But, uh, you know, people are striving to be data driven.
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I think spending too much time on ide- on really defining a lot of those terms that we just went through, Yves, distract, you know, how many of those are things you wanna explain to the business?
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And, you know, that would be a serious exercise for, for me if you went through, like, the, you know, the 12 terms even that you just mentioned there.
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And, and then when you pick the ones you wanna really explain to the business, cut those down again, because do they really need to...
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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?
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Most, first of all, most of your meeting's over, it's really boring, and you also haven't gotten to the point, which goes back to, all the way back to my first V, vocabulary. Get the words right.
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Speak the language of the business. Yeah, and that is already, uh, a multi-year project, uh, most of the time [laughs] if you try to rely on that, so. Uh, Scott, data connects us all, but, but music connects us as well.
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So can you tell me what is your favorite band, artist, or genre, uh, in this marvelous scene? Oh, genre for sure is jazz. I'm a jazz baby from way back. I love jazz, bebop, kind of Miles Davis, Coltrane.
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That, and Miles Davis is my go-to all the time. Uh, his ballads, early Miles Davis, if you're a Miles Davis fan. I, I stop at Bitches Brew. Past that, it's just too, too much for me. It gets a little too fusionist.
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Mm-hmm. But, uh, before that, I love a melody.
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I love the structure of it, too, where somebody establishes, uh, 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.
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We play off each other. We've got a point, but, you know, one goes one way, and one goes the other way. We bring it back around.
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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.
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I've 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. [laughs] That's, that's a great one.
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Scott, this, this was such a great conversation. You know, we can keep on going for a few other hours as well. We can, yes. If you like, yes. Yeah.
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[laughs] Especially, so if people wanna find you online or wanna hire you as a, as a speaker, where can they find you? Something else, uh, your marvelous books you wanna share with the audience?
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What they, uh, want to keep, uh, take away from this conversation? Easiest way to get ahold of me is on LinkedIn, Scott Taylor, The Data Whisperer.
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If you Google The Data Whisperer, it now comes up with a little, 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?
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And it was, you know- Yeah... now it's, it's working after seven years of this. Um, my website is metametaconsulting.com, or just search for Scott Taylor, The Data Whisperer, and all that stuff will come up.
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I have another content stream I call The Data Puppets, if you haven't seen those, uh, starring the CDO, the Chief Dog Officer, and the ITB, and they hire a cat-sultant from Meow Kinsey, and they deal with every kind of data problem going on, and with disastrous yet hilarious effects.
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And, uh, 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."
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And I'm like, "These are puppets just babbling, with j- " And s- it struck a chord in folks, so I built out a whole series.
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There's a couple episodes on LinkedIn, but I'm working, 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, the CDO, who's a dog.
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I put all this together even, I'm like, "How did nobody else come up with this before?" Right? [laughs] We're, we're- A CEO is an elephant. It works perfectly. Um We're, we're taking things too serious, I think, yeah.
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Yeah. Yeah, yeah. So, uh, that's great, but I'm on YouTube, on LinkedIn, and, you know, those are the two platforms I focus the most on. And my book is on Amazon, Telling Your Data Story.
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It's half price on Amazon, so there's a good deal for you.
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And love to hear from anybody who's, you know, inspired, who has a question, who's interested in having me come and speak or do content for their brand, all that kind of stuff I'm open to. Scott, amazing.
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Thanks for having you on the show. Great to have you. Uh, well, let's keep the conversation going, uh, maybe do another episode, uh, and going more into depth. Thanks for, uh, for joining me. Super.
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This was great fun, Yves. Thanks for having me. [outro music] Thank you for joining us on this awesome podcast.
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As senior executives, data and analytics architects, and AI professionals, your time is valuable and we appreciate you choosing to hang out with us.
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Don't forget to spread the word on social media, and let's continue to drive innovation in the industry together. Thanks for listening, and we'll catch you on the next episode.
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