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What Data Will Say When You're Willing To Listen With Scott Taylor

Clock Icon 62 minutes

About this episode you hear that? It's the Data Whisperer. And he's here to tell a story: A data story. He'll disclose some hard-won truths. He'll philosophically approach the idea of truth before meaning. There may even puppets.

im Gasper [00:00:01] Hello everyone. It's time once again for Catalog and Cocktails. It's your honest, no- BS, non- salesy conversation about enterprise data management, with tasty beverages in our hands. I'm Tim Gasper, longtime data nerd, product guy, customer guy at data. world, joined by co- host, Juan Sequeda.

Juan Sequeda [00:00:17] Hey, Tim, I'm Juan Sequeda, principal scientist at data. world. And as always, it's a pleasure to spend middle of the week, towards the end of your day, and just chat about data. And I am super, super excited to finally have the one and only Scott Taylor, the Data Whisperer. If you don't know who Scott is, I think you've been living underneath a rock or something like that. But Scott, how are you doing, finally?

Scott Taylor [00:00:41] I am wonderful guys. Great to see both of you. Thrilled to be here with a tasty beverage and ready for it. Ready for the show today.

Juan Sequeda [00:00:48] All right. Well, I do want to start off, I want some quick shout- outs, because we've been seeing a lot of people posting some stuff about things that have been happening in the podcasts out there. So Araly Roland, thank you so much for listening to our podcast and writing some really interesting positions about data product ownership. We had our previous guest, Eva Nahari, who also wrote some really cool posts on Substack. Jenna Jordan also was posting some stuff around it. So thank you so much. We really, really are so appreciative of all our podcast listeners and who followed us all our content around that. You're one of the reasons why we do this. I also want to do a shout- out, one of our former guests, Wendy Turner- Williams, if you've been following her, she is an amazing data leader. She's been going through some tough times, and I reached out to Wendy saying, hey, let's give a shout- out to Wendy, so people know, " Hey, go see Wendy, and let's support our folks in our community."

Tim Gasper [00:01:46] All about the data community.

Juan Sequeda [00:01:48] All about the Wendy community. So Wendy, we're all thinking about you, so let's go support our community. And with that, what are we drinking? And what are we going to go toast for, Scott?

Scott Taylor [00:01:57] Am I starting? We're starting with a toast?

Juan Sequeda [00:02:00] Yeah, you start.

Scott Taylor [00:02:00] Cheers to better data, always. Better data and peace. And not necessarily in that order, but here's to better data.

Tim Gasper [00:02:09] Cheers to that.

Juan Sequeda [00:02:09] Cheers. And what are you drinking?

Scott Taylor [00:02:12] I just have a Corona Premier in this little slender can, in a lovely little cozy brought to me by Alteryx. Hopefully, that's not a nice little shout- out for them. Thanks for the swag. But this keeps it nice and chill.

Tim Gasper [00:02:26] That's actually a really nice looking cozy. It's like tall and it's skinny.

Scott Taylor [00:02:29] Yeah, it fits right in there. Fits those slim back. I've got a spare, depending on how long we go, so we'll see what happens here.

Tim Gasper [00:02:38] Excellent.

Juan Sequeda [00:02:39] Well- prepared man.

Scott Taylor [00:02:40] With the big fish, as you can tell. The big fish.

Tim Gasper [00:02:43] Yeah, I know. That reminds me, you know there's those talking fish, where it'll turn its head or whatever, but that one looks like it's static. It's not going to turn, right?

Scott Taylor [00:02:49] Yeah, it does not turn, but it has a light. The guy lit it. I'm in an Airbnb here, and today is World Storytelling Day. So here's to all you storytellers out there, keep it going. And that's what we're going to talk about today. But yes, in fact, it is World Storytelling Day today.

Tim Gasper [00:03:04] I love that. I'm going to cheers to World Storytelling Day. And the drink that I'm drinking actually is a gin elderflower daiquiri. So it's got some of this HENDRICK'S Flora Adora in it. So I've got some of that. It's a very floral flavor, so you got to pick what it goes with. But it goes really well with elderflower. So that's my drink today.

Juan Sequeda [00:03:27] And I'm having a cucumber vodka. It's actually with EFFEN vodka, which is cucumber flavored, and I actually have some really nice cucumbers in it. So for those who are looking, watching the video, you can see all my nice cucumbers.

Tim Gasper [00:03:40] That is a very nice presentation.

Scott Taylor [00:03:42] You're on like over 200 podcasts, right? Have you ever had the same drink twice? I know you guys always have these very sophisticated...

Juan Sequeda [00:03:49] Sadly, we have to say yes.

Scott Taylor [00:03:52] I'm just wondering if you've ever...

Juan Sequeda [00:03:53] No, sometimes, we get a bit lazy over last minute, because as people know, we record this live. I think it's like 95% of the time we've been able to do this Wednesdays at 4: 00 PM Central. So sometimes, it's really quick, and then, we're like, " Oh..."

Tim Gasper [00:04:07] When we do have a cocktail, we try to mix it up as much as we can, although I think that both Juan and I have done repeats on old fashioneds, because that's like the go- to.

Scott Taylor [00:04:17] It's impressive. I just wanted you to know. It's impressive.

Juan Sequeda [00:04:19] Thank you.

Scott Taylor [00:04:19] Your potable range is very impressive.

Tim Gasper [00:04:21] Thank you.

Juan Sequeda [00:04:22] All right, so warmup question. What's your favorite saying?

Scott Taylor [00:04:28] A favorite saying I have, I learned it from my father, who is one of the greatest storytellers and greatest salespeople of all time. And he told me, " When you get the yes, shut up. Shut up." So it doesn't mean you have to stop talking, but it does mean you have to stop trying to convince somebody of what you're trying to convince them of. And I learned it the hard way, because I had this sales call, my background's sales obviously, and I blew it. I lost the sale, because I was talking to this person, and I said something along the lines of, okay, she said yes, and I said, " That's great, it's going to be the same as last year." And she said something along the lines of, " Well, I didn't like last year. We're going to have to think about this some more." And the whole thing just evaporated in front of me. I go back, and I was working for my father at the time and I tell him this, and he's like, " Son, when you get the yes, shut up."

Tim Gasper [00:05:25] That's some good advice. That's good advice. I feel like a very similar thing is, " Don't sell past the sale."

Scott Taylor [00:05:31] Yeah, yeah, that's definitely another way to put it. And those of us out there trying to, again, convince people of the benefits of what we have to offer, why is what we're representing or talking about going to bring business value, that's what we all have to try and do in the data space. We're always talking to these folks in the business side, trying to get them to engage, get them to support what we're doing, get them to align what we're trying to do, understand their challenges and whatever. But people tend to overtalk past the sale, past the point. You made the point. Keep moving forward.

Juan Sequeda [00:06:07] That was something that actually my PhD advisor told me. We started a company together, and that was one of his first sales advice for me is like, " They agreed, shake your hand, and then, shut up. Probably even leave, and let's go."

Scott Taylor [00:06:26] That's it. The other one my dad told me was, " The sale isn't closed until the check clears." Remember that one.

Juan Sequeda [00:06:34] Tim, how about you?

Scott Taylor [00:06:35] Cheers to my father.

Tim Gasper [00:06:36] Yeah, cheers. The one that I'll say is, actually, I always think about, how do you keep a clear head? How do you handle the challenges of the day, of work or anything with a clear mind? And so, the saying, I'm going to actually pick from my role model, maybe not so much, Frank Costanza from Seinfeld, when he said, " Serenity now."

Juan Sequeda [00:07:03] "Insanity later." That's how the saying goes. Well, I've got so many, I'll probably go it a little bit more philosophical, but one is, " Those who don't know their history are doomed to repeat it." I think that's one that I'm constantly always reminded about. And also, one of my favorite ones is, " Don't boil the ocean." And actually one that always comes to mind, and just because we really love Sanjeev Mohan, so shout-out to Sanjeev. Funny guy. He has some little quirky things he comes up. He goes, " I never met a data I didn't like."

Scott Taylor [00:07:41] "I never met a data," yes. I don't know if he came up with that one, by the way, but we'll give him credit for it.

Juan Sequeda [00:07:47] That's where I heard him from. All right, all right, let's kick this off now.

Scott Taylor [00:07:51] "Never met a data I didn't like," yes.

Juan Sequeda [00:07:54] All right, honest, no- BS, " truth before meaning," what do you mean by that?

Scott Taylor [00:07:59] That is how I boiled down my entire data philosophy done to three words. I couldn't get it any smaller than that. Truth before meaning, determine the truth in your data, before you derive meaning from it. So that determining the truth part, master data, reference data, MDM, RDM, PIM, RIM, DAM, all those foundational activities that we all know have to be done to build that data foundation, before you start to derive meaning out of it through analytics, AI, BI, whatever you want to call it these days. And it's not chicken or egg here. It is egg and omelet. If you do not have the truth in your data, you are not going to get the meaning you want out of it. Obviously, I'm in the truth team, hence the meaning of my truth hat here. But for me, that is my data philosophy. And it's also an example of my work, in terms of helping people talk about data in a business accessible way. An exercise I go through often is, what's the fewest number of words you can use to express your point? And so, it's truth before meaning. It was a lot longer when I started, but I got right down to it. Bumper sticks. Talking bumper stickers.

Tim Gasper [00:09:12] Yeah, both concise and compelling. That is a very interesting way to say it, so it's kind of the fundamental idea here, garbage in, garbage out. So you got to address the front part of this equation.

Scott Taylor [00:09:28] So we learned that on the first day of data, GIGO. Unfortunately, that doesn't resonate enough, because it's the laws of physics. It's like gravity. What goes up must come down. What goes in must come out. And what you put into something is going to have an effect on what comes out of it. And for some reason, people in the data space keep rediscovering that, with the latest greatest thing. So GenAI, that needs very well governed data going into it, otherwise, those LLMs are going to hallucinate. It's like, " Okay, thank you. Duh." It's the same thing, but for some reason, we have to continue to pound on that. And GIGO is basically all I ever riff on. That's my whole space is reminding people of that in different ways, so we get this message through.

Tim Gasper [00:10:24] Go ahead, Juan. Go ahead.

Juan Sequeda [00:10:26] Again, going back to my saying, it's about history. Like, " Oh, we need to have well governed data for AI." And we're like, " No shit, Sherlock, tell me something new." But why do we have to continue to tell this kind of obvious message? This is also a theme that I see here a lot on the podcast. A lot of the folks who come to the podcast, we have this conversation like, this kind of seems obvious, but we're still in business ourselves talking about this stuff, because apparently, it isn't. Why isn't this that seems obvious, it's not obvious?

Scott Taylor [00:10:57] I wish I had an easy answer, because my whole life's work has been around this conundrum that we keep facing in the data space, but it's similar that everybody knows, if you want to be healthy, you got to eat right and exercise. But we don't all do that, do we? Buy a Peloton, and it's a great place to hang your clothes. Or, "No, I'm going to have that snack," or whatever. And we always cheat a little bit. So there's something around that that people still don't adhere to. And also, the stuff that gets the most attention is the super sexy stuff. Data stewardship is boring. Data governance is like, " Oh really? Do I have to go through that?" These are not the sexy parts of the data space unfortunately. And that's what I felt and that's part of what inspired me in my work was like, " Okay, I want to be a loud voice out there." Even though I'm the Data Whisper, spoiler alert, I don't do a lot of whispering, in case you haven't noticed yet. But we've got to be able to shout and sell and tell and yell about the power and value of data management, while all this other cool, sexy stuff gets a disproportionate amount of attention and interest and sizzle and frankly funding and support, when we know that none of it works. It's just, again, a law of physics. None of that stuff works without doing the hard work beforehand. It just doesn't happen. It doesn't happen.

Tim Gasper [00:12:29] What things would you say fall into the truth bucket versus things that fall into the meaning bucket? Whether you're talking about practices or technologies, interpret that however you will.

Scott Taylor [00:12:42] So I bifurcate the space, good question, into these two, for the sake of simplicity, these two giant buckets, truth versus meaning, data management versus business intelligence. Data actually versus analytics is like at the phylum level, if you will. But those foundational activities, master data, reference data, metadata, data governance, data stewardship, data quality, data management, RDM, M, RIM, DAM, versus analytics, AI, BI, data science, data visualization. And frankly, most forms of data storytelling are really about analytics. I wish it had been called analytic storytelling, because that's really what it is. And that's part of what inspired me to do my book, Telling Your Data Story. Oh, just happen to have a copy of it here, Telling Your Data Story: Data Storytelling for Data Management. Says right on the cover, " 99% buzzword free."

Juan Sequeda [00:13:42] Wait, what's the 1%?

Scott Taylor [00:13:44] I did not want to overpromise, so I left a little gap there.

Tim Gasper [00:13:50] That's no- BS. That's no- BS.

Scott Taylor [00:13:52] That's no- BS. Right there, it's no- BS. And I felt every enterprise has a story to tell about why managing data is of strategic importance to their company. And data management folks that I dealt with my entire career had this dual emotional state. They were really passionate and focused and determined about what data management, again, that high level heading, data catalogs are in that, all that foundational stuff, what data management could bring to their organization. But they were really frustrated, because people didn't listen to them. " Let's set a data standard." That sounds like it's fun. You want to clear a room of stakeholders? Start talking about how we're going to go on a data hygiene program. They'll go running for the door. These things don't have...

Tim Gasper [00:14:47] "Hey, we've got a data janitorial initiative. Who's ready? Who's excited?"

Scott Taylor [00:14:52] "Yeah, come on in. We're going to teach you how to spell, " street," the same way twice." These are not business building value added stuff, but luckily, for corporations that are good at data, those folks are there. So I really felt like, " Okay, let me help by bringing a voice to that community, give them some techniques, give them some frame, and let them see somebody out there just as loud and as crazy as possible as the analytics side is." And there's a lot, again, data storytelling, there's like 30 books from great friends of mine around how to put analytics in a business context to drive action. There's a book out there about why you should put together a business accessible narrative for the value of data management. How do you sell in data management to your CEO? You actually don't talk about anything about how. That's one of my other headlines is like you got to focus, I focus, and that's my space, I focus on the why, not the how, why it's important, not how it gets done. And another problem, we'll probably answer your original question, Juan, throughout this whole show here, data people love to talk about how to do stuff. Actually, they love to talk about all the things they did to try and get to the point where they really were successful. " Look at this and look at this." And business people could care less. They don't care. I never met a CEO who cares how something is going to get done until they understand why it's important for their organization. And I've spent more than my share of time in front of senior leaders, even classically, like at the cocktail party situation. Again, I came from Nielsen, I came from Dun& Bradstreet, I came from these service providers, and I talked to every enterprise, at every category, at every level of maturity all over the world. That limits my scope. And so, you have to get hissy, you got to get right to it. And so, how do you explain it in a way that, frankly, you got to take people in this journey from, " I have no idea what you're talking about," to, " Gosh, how do we live without this?" And you got to bring them along.

Tim Gasper [00:17:08] I love the way that you're positioning this and the right way to sell it. I want to unpack a little bit more the selling piece in a second. But before I do that, one thing that astounds me, and I'm sure you face this a lot in the folks that you talk with, I know that we certainly face it in the catalog space, is I hear, all the time, folks say, " Oh," catalog, master data management, even things like reference data management, they say, " Oh, well, we're still building out our data lake," or something like that. " We're not ready for that yet, but we will be soon." And I go, " Whoa, wait, wait a second, aren't those things foundational?" I'm curious if you have any thoughts around that or how you deal with that.

Scott Taylor [00:17:51] Yeah, we don't want to focus on the food and the ingredients. We just want the meal. And we're setting the table for the meal, and meanwhile, nobody's cooking anything. It's some analogy like that. So the place you're going to put this data, if that's what you're focusing on, first, odds are pretty good that the data that's going to go into that is going to suck, and at some point, it's going to come to a head. And you're just delaying it. So data lake, data warehouse, data lakehouse, isn't that where my data goes on vacation? These things are just, technology's whatever's happening lately. It doesn't focus on the content itself, and the quality of that content, the dependability of the content, the truth, the trust, the reliability, nobody disagrees that they don't want that stuff. It's just the hard work. Again, we'd all like to be fit and lose 10 pounds and look better, and got to do the work. There's no magic panacea easy magic wand solution here.

Juan Sequeda [00:19:02] We got our T- shirt now, " Data lakehouse, isn't that where my data goes on vacation?"

Scott Taylor [00:19:06] There you go.

Juan Sequeda [00:19:06] Beautiful, beautiful. All right.

Scott Taylor [00:19:09] Sanjeev will probably steal that line from me.

Juan Sequeda [00:19:14] I'm going to text Sanjeev to join us live right here. Okay, so this is...

Scott Taylor [00:19:18] Oh inaudible.

Juan Sequeda [00:19:21] Actually Ramona, I could see her here. Hello, Ramona. It's not just data people, it's tech people, abstract in a way is not a strong suit. And so, I want to unpack this a little bit, because everything that you're seeing right now, who should be talking about more on the why, not the how? Do tech people, the data people, they need to upskill themselves around this? Do they need to bring in a colleague, a champion around this? Do they need to go hire around this? So I'm curious what your thoughts are on this. And I just want to bring up, last week, at Gartner, I was talking to a former guest, Krystin Kim, and she's at Post. And she was telling us that she actually hired, has a position of a mix of ENT education and training, but also marketing. And it's like we need to educate people about all this stuff that we're doing with our data, how they can use it, but we also need to be celebrate our wins and celebrate what we're doing. And I need somebody to be able to go tell that story. So they actually have this position. I found that exceptional. I'm like, how many people are doing this? Their background is in marketing, this person. So I thought that was a fantastic idea. So my question to you is, is it the data people technically need to upskill themselves? Or should we just actually be hiring and this should be part of your team?

Scott Taylor [00:20:46] That's the first I've heard of that kind of position. That's great. I'd love to follow up and find out who that is, because it is marketing your capabilities internally. That's what my whole book is about, putting that story, that internal marketing program, together to educate and get more stakeholder engagement, to get funding for the work you're trying to do. If you're a data leader of any sort, CDO on down, and you have aspirations to be a leader, then you got to be able to communicate. And that's all we're talking about here at its essence, can you communicate to folks? And I feel like everybody in the data space could get better at communicating at one level or another. The soft skills are really hard, and our discipline is built on hard skills, programming, analysis, data governing. These are hard skills that you've got to be able to master to be effective. And then, when you look at marketing folks, they're mostly soft skills, right? Storytelling, imagination, creativity, not that we're not imaginative and creative in the data space, but just where it comes from is from a different area. And I counsel data leaders, if you're going up for funding, which again, it's all about the money, how do we get the money? How do we get the support? And you're a CDO and you're explaining your data program and the investments you're looking for and you're up against the CMO and the chief sales officer, those two roles are based on storytelling. Marketers know how to spin a tail and put positioning together and convey the message that they're looking for. Salespeople, if they can't tell a story, they don't make quota. That's just bottom line. So you're up against people who inherently are better storytellers than you as part of their position. So if this makes anybody feel edgy and nervous and wondering if they have enough skills in the audience, good. You should feel uncomfortable about it, because you should get better at it. And if you want to be a leader, no matter what, you got to be able to communicate. And so, for me, who doesn't do better if they can communicate better?

Juan Sequeda [00:23:09] This is the honest, no- BS take, and I really appreciate how you're being very explicit about this. Because people need to feel uncomfortable about that. People need to realize that they're not doing a good enough job. And basically, the message here is, if you consider yourself a data leader, but you are complaining that you're not getting the support you need, it's your fault.

Scott Taylor [00:23:36] I agree. Because every company can do better with their data, and every company's data can help that company do better. And the opportunity we have in the data space is we provide horizontal value across the company. And give me another department that can do that. So we touch finance, we touch operations, we touch sales, we touch marketing, we touch executive level. Every single part of the company can do better if they have the right data in the right place at the right time with the right kind of analysis around it and can operationalize it. So we have this tremendous horizontal opportunity across all those different silos. And so, there's no excuse. And if you can't explain it, it's your fault. It's not that you don't have the opportunities. Because you can talk to anybody at an enterprise level, and you guys deal a lot with enterprises, there's exceptions here or there, but enterprises, the nature of an enterprise is that it wants to scale, it has multiple silos, it may have grown by acquisition, it has disparate kinds of sources, the go- to market is complicated, depending on the category. It might be global, it might be local. These dynamics are in every type of enterprise, and data can help. When I say, " data," there, I mean the whole thing, data, analytics, BI, AI, all the rest of it. And help the whole company. And the transformational things that we see out there come from that data or come from data, hardware, software data, those three things in terms of technology for our organization.

Tim Gasper [00:25:23] Yeah, well said. I think one thing that hurts data people and data organizations and data leaders is this lingering perspective that data is IT. Because then, what that does is it puts you in that mindset of like, " Oh, I'm a data person," or, " I'm a data leader," or something like that. And what's our function? Well, you think of IT, you think of like, " Oh, well, my phone is broken. Well, I need to go to IT, and they're going to help me fix my phone." Or, " Oh, we need a SharePoint site or something like that to host our wiki." Like, oh, well, they're not going to fill out the wiki, they're just going to manage the SharePoints and it's an enabling technology, but data is not like that. It's not just an enabling technology. It's integrated, it's horizontal, it's vertical, it's centralized, it's decentralized. I think that's confusing for a lot of people and makes them move away from some of those softer skills or that storytelling that they need to get good at.

Scott Taylor [00:26:23] I think breaking away from IT, which has happened, that's certainly a dynamic I've seen, since I go back to pre 2K, in the data space. So I've seen when it was just IT.

Tim Gasper [00:26:32] Purely IT, yeah.

Scott Taylor [00:26:32] This is like data's in the computers and those folks handle the computers, but if you think about it, one of the biggest misnomers at the C level is the chief information officer, that I. It became more technology officer. And so, pulling data out of just strictly IT, I think there's plenty of data out there proving that that helps people get better value out of it.

Juan Sequeda [00:27:04] This is an interesting comment here. Do you think parenting helps to conceptualize? Parents are experts at, " Explain it to me like I'm five."

Scott Taylor [00:27:14] Oh, I guess, yeah, parenting. Yeah. Okay, I think ask your kids at this point. I guess so. Is that a lightning round question? Yes.

Tim Gasper [00:27:30] Parents out there, you've had to get better at storytelling, just a different kind of story.

Scott Taylor [00:27:33] Yeah, you got to manage priorities, you got to be able to get to the point.

Juan Sequeda [00:27:38] Okay, so one of the things we were talking before, the three Vs, right? Not the three Vs of big data, but the three Vs...

Scott Taylor [00:27:49] inaudible my three Vs of data storytelling for data management. Yes. With a knowing link to the three Vs of big data, coined by one of my favorite guys in the data space, Doug Laney. And you watch that too. I talked to him, first time I had dinner with him, we were just joking about, " How many Vs are there now? 42 Vs." People just started adding. Was there ever a construct anywhere where you could only describe it with words that began with a certain letter? That just never happened. But if you look at it in the data, people just went crazy. It was like, " Oh, what about value? What about viscosity?" It just went way off the deep end. So mirroring at least the original three Vs, volume, velocity, variety, mine are vocabulary, voice, and vision. And this is the framework that I have in the book that I do a lot of talks on, but just a simple approach for people to get their thoughts in order. " How do I start to organize it?" So you start at the bottom, vocabulary, the words you use are important. Stay away from the buzzword, stay away from the technical terminology. Use the language of your business to talk to your business. How hard is that to figure out? But people need to be reminded of that. So make sure you use the kinds of words that your audience already understands. Voice, " How does this thing sound?" What's your tone and tenor? What's your positioning? Does your whole department, do they all get it? Can they all say it the same way? What metaphors are you using? What similes are you? These figures of speech are really important. Good metaphors, good similes, good figures of speech shorten the distance to understanding. Bad ones confuse you. My favorite bad one is, " Data's the new oil." What do we mean by that? Right? Luckily, that has gone down, but that had its run for a while. And it was infuriating, especially watching people in LinkedIn debate, " Well, it's like oil, because it needs to be refined," " No, it's not like oil, because it's not sustainable." And you just go, " That proves my point. The fact that we don't have a clear understanding of what, "Data is the new oil," means means it's terrible poetry. Like stop." So that's been gone. My name of my keynote though is Data is the New Bullshit, which people get right away. So get the words right, get the tone right, and then, the most important one is vision, the third one, vision. Everything you do with data should enable the strategic intentions of your enterprise. Where is your company trying to go? And why does data help you get there? It is not about what you think the use case is. It is about where, again, to my point of horizontal value, where is your company trying to go. And if you don't know that, go find out, because your company has a direction, it has a strategy, it has intentions that you need to understand deeply in the data space, so you can show how data's going to help. That's how you get into the CEO's office. You go up and you say, " See this five things you wrote in the annual report? Three of them mentioned customer, and our customer data sucks. Two of them mentioned brand, and our brand hierarchies are messed." These are foundational things. We're not talking about analytics. We're talking about data management here. And every business is trying to bring value to their relationships through their brands at scale. That's my definition of digital transformation, if you will. Every business has relationships, every business has brands. How is your data about those two types of entities? Because if that isn't right, you're not going to get the scale, you're not going to be able to fulfill those kinds of initiatives. One thing for sure your CEO's not talking about is better data quality. They're not talking about that. And when you talk about data quality, most people sound like they start whining. It's like, " Our data quality is really bad. We got all these duplicates of hierarchy." And it's just like, " Shut up, who cares?" And frankly, they'll go, " Why? Why is data quality important?" You need to know the answer to that question, because the answer to that question is the reason you're in that office.

Juan Sequeda [00:32:13] Thank you so much for this right now.

Scott Taylor [00:32:14] This is helping.

Juan Sequeda [00:32:14] And this is actually, Malcolm Hawker brings this up all the time too, it's like it is embarrassing saying all these things about data quality. That's not the way to go talk about this. And I think you nailed it right there. " I don't care about this stuff." I'm like, " Poor you." Like, " No, you got to tie it directly to how this company is making money and saving money directly." So this is so, so...

Scott Taylor [00:32:37] That's it. Malcolm and I, if you don't know our history, we were brothers together at Dun& Bradstreet, and we literally traveled the world doing a road show on D& B. And I was the why and he was the how, and he's just blossomed into this huge... I saw him at Gartner, and it was like, I have one word for you, my friend, gravitas. He's just like nails it on stage and I'm so proud of him. But yeah, so obviously, we became fast friends, because philosophically, we have the same. But he can talk the how stuff, he can talk the tech stuff, like nobody. And we'd be in meetings, and literally, he would go, " Okay Scott, we're going to lose you here for a minute, but I'm going to talk to these IT people." And they're like, " Wah, wah, wah, wah, wah." And I'm just wait until he is done, and then, I get back into a normal conversation. But he can talk it all.

Juan Sequeda [00:33:25] Well, this is a good... Everybody's listening, just go back to, we have, I don't know what episode it was, but Malcolm has been on the podcast, for sure. Oh, I do remember. Malcolm's podcast last year was actually the most popular one. I remember that.

Scott Taylor [00:33:39] Oh, nice. Good for him.

Juan Sequeda [00:33:40] Is MDM dead? I think that was the question. Anyways, and also just call out here, John Mercknies says, " Hey, you're pretty good at this. I think you should speak about data for a living. Just FYI."

Scott Taylor [00:33:51] Thank you. I appreciate the career advice, John. My question to John is, how did I not find him at Gartner? How did I not see him? How did I not see him there? Who I did find at Gartner, this guy.

Juan Sequeda [00:34:05] Yeah, so okay, there's another thing. What the heck are these data puppets? Tell us more. And that seems like a little owl inaudible

Scott Taylor [00:34:13] What's this guy? What's his name? What's this guy's name?

Tim Gasper [00:34:16] Sparkle.

Scott Taylor [00:34:17] Sparkle the Owl. Sparkle, unfortunately, I came by your booth, because I had my analysts from Gartner who was doing interviews and interviewed 20 different... There's Dremio. We had a bunch of different... I interviewed everybody who had a little toy, and I kept coming back, Juan, and you were deeply engaged in value- based conversations. So I did not want to interrupt you, but I did data puppets. If you don't know what they are, Google the term, " data puppets." You will find only my stuff. It's like an SEO marketer's dream. It's a hundred percent organic reach here. Google has never seen the words, " data," and, " puppets" together before. So I lucked into that one. But I have anchored by my star here, the CDO, the chief dog officer, the ITB, who speaks only in buzzwords. They hire a cat consultant from Meow- Kinsey. And I have this whole series I've been working on. You can find, I've got a preview, Tim, I know you saw that today, a preview of a multi- part series called Journey to the Center of the Single Version of the Truth: the Greatest Data Story Ever Told. And it follows the adventures of a CDO and the ITB, and they wander off and make every possible mistake. And I did these puppets, a couple years ago, I did one of them, and it was called Too Much Tech Talk. And it was just like joke after joke after joke, the bee and this monkey from the business called Monkey Business and the Dog. And they were struggling to try and explain SQL and Spark. And I had all this ridiculous language in there. And the reaction I got was just overwhelming. People were like, " I just left this meeting. This is just like my company. How did you know this? This is so funny, but this is so true." And I'm like, " These are puppets. You realize that, don't you?" So inspired by that, I've extended it off into a couple different areas, one doing this massive, creatively, it's some of the wildest work I've ever done and it's a ton of work. I'm not complaining at all, but it's so fun. And so, I've got this series coming out sometime later in the year. I've been working on it for a while. I have a Simpsons- like casting approach. So Malcolm's in it, and he plays an antalyst from Gartner. Kate Strachnyi's in it. She plays the CEO, the chief elephant officer. There's a mouse, who's the CMO. CFO's a fish. It's like, how am I the only one to ever think of this? I've never thought of this. Nobody else has done this whole animal through the sea level. So I've established that whole conceit, and they review a bunch of software. So Sanjeev plays Sales Fork, or no, he plays Micro Spoon. And I have somebody else play Sales Fork.

Tim Gasper [00:37:17] That was Sanjeev?

Scott Taylor [00:37:19] Yeah, Sanjeev plays Micro Spoon, yeah.

Tim Gasper [00:37:21] I broke down laughing when I saw that part of the trailer. I didn't realize that was Sanjeev.

Scott Taylor [00:37:27] And then, the knife comes out as Chopped GPT. So for me, this stuff just comes out of me. And then, I went to a Salesforce conference, and it just struck me, I wasn't prepared. So I spontaneously decided, " Okay, I'm going to go do these interviews with these little stuffed animals," because all these people were giving away these stuffed animals. And so, I couldn't find a fork. So I found a spoon. And at the end, he gets kicked out of the conference, and they're like, " You can't be in here, because you're not a fork." And he's like, " Well, I'm a spoon, but I identify as a fork." inaudible and he's a runcible spoon. So I went around and interviewed all these folks, and it was a blast. And so, I did it at Gartner. I did 20 interviews with this antalyst, and he gets kicked out by the Gartner whale at the end. So I've decided that's a motif that, every time my character gets kicked out of the conference, he goes, " No, no, I'm really an antalyst. Look me up. My name is Antdrew White." You know Andrew?

Tim Gasper [00:38:31] Yeah.

Scott Taylor [00:38:31] "I wrote the 7 Circles of MDM Hell. Look it up. It's a classic." He's like, " No, get out of here." You'll see that. I'm just riffing on this, but that's coming. So more data puppets are coming. I love it. It's so much fun. It's so much work.

Juan Sequeda [00:38:46] Don't you have to have a swan and a dolphin too, because we're at the Gartner Swan and Dolphin...

Scott Taylor [00:38:52] Yeah, there was a dolphin something. I didn't have a swan. I also did an actual... I'm commercializing this, so any of the brands out there who want to work with me on create puppet content to break through the clutter, I did one for Databand, IBM brand Databand. And again, the creative challenges, I had to use a really analog form of storytelling to explain a very digital situation. So I had to explain data observability very quickly with these characters, and the dog and the bee are arguing about it. And then, the hero that comes in is this raccoon who's played by the CMO of Databand, a guy named Ryan Yackel. And there's a band, we actually created a little band that plays music every time there's a data problem. And there's these pipes with ping pong balls that represent data, and look it up. It's on the internet, it's on YouTube, it's on my LinkedIn profile. But check out the Databand. I forget, what is it called? STRIKE UP THE DATA BAND is what we called it. So simple stuff, funny stuff, and a little bit of Muppets, a little bit of triumph, the insult dog. But just apply it all to data. We got plenty of room in the space.

Tim Gasper [00:40:08] I love this. It's so good. For everybody who's listening, you got to Google data puppets, check out the trailer. It is so hilarious. And this is a perfect medium to not just have a laugh, but also surface a little truth and meaning around data.

Juan Sequeda [00:40:26] Thank you so much. I will say, this is the funniest episode we've ever done.

Scott Taylor [00:40:32] Good. All right. That's what I'm looking for. My partner's applauding over there. The wonderful, the lovely, the brilliant, the sexy Marianne, the Data Whisperer whisperer. We all got to have our own whisperer there. She calms me down.

Juan Sequeda [00:40:45] So not only the funniest, but the funniest and I think one of the best honest, no- BS truths coming out there, so thank you.

Scott Taylor [00:40:55] All right, cool.

Juan Sequeda [00:40:55] You knew exactly what we needed.

Scott Taylor [00:40:56] I earned a shirt. I want one of your shirts.

Juan Sequeda [00:40:59] Yes, we need to get shirts back. We're going to open...

Tim Gasper [00:41:03] I just ordered another batch, so we'll make sure we give you one.

Juan Sequeda [00:41:06] We need to start a T- shirt business, Tim, I don't know, one day. You do puppets, Tim and I want to do T- shirts.

Tim Gasper [00:41:12] We've been talking about T- shirts for so long now, now I feel like we have to never make T- shirts, because the joke must continue.

Juan Sequeda [00:41:20] Somebody else will do it, right? So anyways, let's go to our lightning round questions, because I'm sure we're going to continue this banter here. All right, number one, we talked about truth and meaning. Is AI part of meaning? Or both?

Scott Taylor [00:41:36] Part of meaning.

Juan Sequeda [00:41:37] Oh, that was a very serious answer. I thought you were going to...

Scott Taylor [00:41:42] That's it.

Juan Sequeda [00:41:43] All right, perfect.

Scott Taylor [00:41:44] You put bad data into AI, you get AS, artificial stupidity.

Tim Gasper [00:41:52] Yes. All right, second question. Governance traditionally has been more defensive and regulatory oriented, right? Like the police, right? I'm seeing some companies start to call it data enablement and make some shifts there, or data governance and enablement. Do you feel like the phrase, " data governance," ultimately is doomed?

Scott Taylor [00:42:16] No. it's a discipline that's going to be there. You could dress it up and call it enablement. Just personally, I find the word, " enablement," a little squishy. And does that really focus on the grunt work that's got to get done to fix the data? Or is it more about helping people use the data? I don't know. And my challenge with some of these new terms is explaining them to everybody and then, getting to internalize it and then, working on it. And it was just like, by then, the meeting's half done. So do we need to come up with a new way to talk about the stuff we've always dealt with? Sometimes, you want to brush it up, but I just feel like there's maybe a little too much effort in there in that world trying to just call something that exists something else.

Juan Sequeda [00:43:12] And push it into telling the value story, the why.

Scott Taylor [00:43:17] Again, I don't mind picking on them, because I was at the Gartner conference, we're at the keynote. And the first thing was collective intelligence, right? Gartner's where buzzwords are born. That's like the black hole that just births out new buzzwords, and you just go, " Okay, yeah." First of all, John Thompson told me afterwards that he actually coined that a while ago, and I give him props for it. But just regardless of the term, do you think anybody's going to go back from that conference and then, go to their business leaders and say, " You know what we need to do is work on collective intelligence, and here's what it is and here's 19 slides?" And it's like, are you really doing the work?

Juan Sequeda [00:44:01] But how many people actually do that? How many people are actually doing that?

Scott Taylor [00:44:06] Hopefully, nobody. AI radar, did you catch that one?

Tim Gasper [00:44:10] I didn't catch that one. What was that one?

Scott Taylor [00:44:12] That was another one they put up. I collect these. Nexus of forces, that was a couple of years ago. Technocracy, that was a good one.

Tim Gasper [00:44:23] Technocracy, that sounds interesting.

Scott Taylor [00:44:26] I think it's technology and democracy, and I don't know what it is. But you come out of there like, " Okay, this is great." And then, you walk into another room and you have to explain all this stuff and it's not even yours. And people are just, " Did you get..."

Tim Gasper [00:44:39] That's actually a good litmus test, right? If you had to explain that word, would you look like an idiot? If so, maybe it's not a good phrase.

Scott Taylor [00:44:49] Yeah, maybe it's not. Yeah, exactly. That's a good litmus test.

Tim Gasper [00:44:52] All right.

Juan Sequeda [00:44:56] Don't look like an idiot, please. All right, next question. Can we teach good data analytics storytelling?

Scott Taylor [00:45:04] Yes. You teach storytelling. Yes, absolutely. You learn it. I learned it. I'm natural story. I've been doing storytelling since it was two words. That's how long I go back. But yes, you can teach storytelling, and people tell stories all the time. And it's not unheard of to look at consumer stories. Watch a great movie that you love. Why do you love that movie? Is it the special effects? No, it's the story. And when people spend a hundred million dollars on a movie and it bombs, why does it bomb? Because the story isn't any good. It's just so important. So learning how to tell a story is super important for, again, everybody in life.

Tim Gasper [00:45:53] The special effects could be lousy, and you can fault it, but if the story was good, you probably still enjoyed the movie.

Scott Taylor [00:45:59] Yeah, yeah.

Tim Gasper [00:46:01] All right. Fourth question, final question here. If we shifted or any company shifted 80% of their analytics people and budget to data management and foundation instead, would that be net good?

Scott Taylor [00:46:21] Yes, absolutely. I don't even think you have to go 80%. Something. Get them at the table. Get them in the room. Get these data management people in the room, because they're going to help you. Andrew Ng, great data scientist, huge thinker in the space. Came out with a breakthrough statement last year. " Oh, it's not about the models. It's about the inputs." So my riff on it was like, " Data scientist discovers GIGO." There it is again. This is not a new story. Oh yeah. Shift something.

Tim Gasper [00:47:09] Love it.

Scott Taylor [00:47:10] That's right. It's reminding me of, it's the last page in my book, I'm going to give away the ending right here. Where is it? " Data management work is never done. Business is never finished. Hardware comes and goes, software comes and goes. Data remains. The end."

Juan Sequeda [00:47:37] That's also one of my favorite books over here, these two, Software Wasteland by Dave McComb, and then, the Data- centric Revolution. And it's all about applications come and go. Your data is at the center.

Scott Taylor [00:47:56] Yeah. Yeah.

Juan Sequeda [00:47:58] We're all doing this.

Scott Taylor [00:47:59] As much focus as we could, we can't put enough focus on it, because it's always an uphill battle.

Juan Sequeda [00:48:05] I agree.

Scott Taylor [00:48:06] It just is. And I think people who are really, truly love the data management side know that. That's our challenge. They're not talking about data... What happens is people talk about it without talking about it. So if you watch CNBC and all these business leaders come on and they talk about AI and they talk about stuff, and then, behind the scenes, you realize, oh, somebody at some point goes, " But the data that we focused on put into these models actually gave us the result."

Tim Gasper [00:48:37] Yeah.

Juan Sequeda [00:48:38] What do you think about this one, " Data is renewable energy?"

Scott Taylor [00:48:42] Okay, sure.

Juan Sequeda [00:48:44] I am impressed.

Tim Gasper [00:48:48] I'll take it. Just before we go to the takeaways, I'll express one hope. My hope is that, as AI really captures the attention of everybody, which it has, and I think that's just going to keep on amplifying and rolling forward, that it breaks some of the problem that we find ourselves in, that we actually finally break the mold and say like, " Oh, wow. The more we invest in our data foundation, the better the AI gets," in a way that maybe hasn't happened in the past. Because people, you invest in your foundation, like, " Oh, it makes it easier to do an analysis and things like that." But you can always lean on the analyst to say like, " Oh, well, they'll interpret it. They'll do it for me. They'll do the hard work." But hopefully, now, with AI, I'm hoping it shifts the tides.

Scott Taylor [00:49:39] So will AI, will it replace data management? I say no, because somewhere, at some point, there is data management. Even if it's baked into the AI process, it still happens. It still happens. If you have more than one data source, you're going to need data management. And everybody's got more than one data source about everything. So it might be called something different. It might be put somewhere else. It might have different technology on top of it, but it still happens. It's like, again, back to the cooking analogy, which is my favorite set of analogies, if you want to describe data in any organization, use food, if you can't think of anything else. If you make a meal, you didn't replace the need for ingredients. It's still there.

Juan Sequeda [00:50:23] I would love to get Scott and Krystin together, because Krystin also works in food, and she had this food analogy. We need to get former guests together and inaudible. But anyways.

Scott Taylor [00:50:36] That's a good one.

Juan Sequeda [00:50:37] Takeaway time. Tim, take us away with takeaways.

Tim Gasper [00:50:40] Let's do some takeaways. So we started with honest, no- BS, what does it mean, " Truth before meaning?" What is this data philosophy which we're uncovering here, which the Data Whisperer has revealed to us? Well, it was that you said it's to determine the truth of the data. That is the foundation. And that has to happen before deriving analytics. And we said the garbage in garbage out needs to become goodness in, goodness out. I love that. GIGO, flip the GIGO to the good times.

Scott Taylor [00:51:14] Exactly, yes.

Tim Gasper [00:51:15] You said it's not a chicken and egg situation. Egg is the truth, and then, you make an omelet, which I love it. So think of it differently. It's not like, " Oh, it's both the parallel..." No, the one helps the other. Fewest numbers of words to explain things is really important. And that's how you got this truth before meaning. And what is truth? The truth is the foundational data management. It's the data. It's the master data management. It's the metadata management, the reference data management, the data integration. It's that which binds and identifies that truth. The meaning is the business intelligence, the analytics, the data science, the data visualization, most forms of data storytelling's on the meaning side. And you mentioned, in our lightning round, that a lot of the AI stuff, in terms of how people are trying to apply the AI, really, is more on this meaning side. It needs that truth. It needs that foundation to be effective. And when we talked about storytelling and data storytelling and analytics versus the data foundation, you mentioned, when trying to sell data management and truth to the business, focus on why, not on the how. Focus on the why. Data people like to talk about how they did stuff, the tech, and how they tied it together and all the nerdy stuff that goes right over the CEO and COO and CFO's head. Focus on the why. Focus on the ROI. I'm ad- libbing a little bit here, but we focus so much on the tech and not enough on the value. We should not, as an industry, be suffering in terms of figuring out what the value is of this work that we're doing on the data management side. It is incredibly valuable. And I thought what was really funny is we started to talk about data lakehouses and people investing in their data lakehouse before investing in their metadata management and things like that. And you said, " Data lakehouse, isn't that where my data goes on vacation?" T- shirt coming soon to a store near you. Juan, what about your takeaways?

Scott Taylor [00:53:18] That was super impressive, Tim. By the way, I didn't want to interrupt you. That was amazing, really. Right, hun? Wasn't that a good... Really.

Juan Sequeda [00:53:28] We're paying attention here.

Tim Gasper [00:53:30] Appreciate it. Well, that was just your appetizer. Juan, what are your takeaways?

Scott Taylor [00:53:37] Okay, sorry, Juan, I don't want to disturb the flow here.

Tim Gasper [00:53:40] No, you're good.

Juan Sequeda [00:53:41] This notion, what I called out here, this data marketing, I brought up a former guest, Krystin Kim from Post who's like they have this role of education and training and marketing. And you're saying you've actually never heard about that. And you think that's great. And I think I'm seeing this now as like there's opportunity for not just innovation, but also innovation in the space in your teams. But it's a requirement, because we need to communicate. You need stakeholder engagement. You need to drive funding for everything you're doing. If you want to be a leader, you and your team, you need to communicate. Everyone in data can do better at communicating. So if you're going out for funding and you are the CDO explaining your programs and your investment needs and you're up against the chief marketing officer, the chief of sales officer, well, you better be freaking good at storytelling, because they are. And if you aren't, you're screwed. So does this make you feel uncomfortable? Good. You need to get better at this. So if you're the one complaining that, " Oh, I can't get my money," well, guess what? It's your problem. Go figure out how to go communicate better. If you can't explain it, it's your fault. Then we go to your other framework, the three Vs, vocabulary, voice, and vision. Vocabulary, don't use weird words. Don't use buzzwords. Use the language of your business to talk to the business. Voice, " How does this sound? Is everybody getting it?" What metaphors are you using? How good are you at that telling part? Because the bad ones actually confuse people like, "Data is the new oil." And then, finally, the vision. That data enables that strategic vision of your company. How are you thinking of data? And how is that going to help you get there? And if you don't know where that vision is or where you need to go to, you need to find out. And that's the most important part. I like what you said, every business has relationships and brands. How is your data doing with respect to those things? The CEO really doesn't care about better data quality. They care about, " Oh, we have issues. Our goal is to improve customers, and we have these issues about brands. Well then, let's talk about that. Let's talk about data quality." And then, oh man, data puppets. I want to meet this chief dog officer, the ITB, the antalyst, the Journey to the Center of the Single Version of the Truth. Sounds like a novel I want to read tonight. Scott, how did we do on our takeaways?

Scott Taylor [00:56:09] If you guys start doing puppets, you're going to put me out of business. So you nailed it all the way across the board there. That was...

Juan Sequeda [00:56:16] No, we're going to do T-shirts. We're going to do T-shirts. You do the puppets.

Scott Taylor [00:56:19] All right, good. Just let's keep our spaces there. We can collaborate.

Tim Gasper [00:56:22] Stay in our lanes. Stay in our lanes.

Juan Sequeda [00:56:24] You do the theater. We'll be the ones doing the merchandise at the end outside.

Scott Taylor [00:56:26] I get the puppet, guys. That's mine. That's my...

Tim Gasper [00:56:28] We'll bring the cocktails.

Juan Sequeda [00:56:30] There we go. All right.

Scott Taylor [00:56:32] That was beautiful, really. That was spectacular, both of you. I'm super impressed, especially since we've all been drinking.

Tim Gasper [00:56:39] Well, that was all you. It wasn't a whisper. It was a shout. It was a very good...

Scott Taylor [00:56:44] That was it. Well done.

Juan Sequeda [00:56:46] I filled this up already, so this is not my first. And the thing people don't know is I always go to the gym. So five minutes after the run out, I'm going to the gym after this. Anyways, let's wrap this up. Three questions, what's your advice? Who should we invite next? What resources do you follow?

Scott Taylor [00:57:03] My advice is, and I've already mentioned it already, learn how to tell a story, learn how to tell a story. And I've told that to other people. Like a data science student came up to me and was like, " What do you..." I said, " Learn how to tell a story, no matter what." A lot of ways to do that, but learn that. It's a craft. It takes practice. I practice all the time, rehearse, all that kind of stuff. Learn how to tell a story. Who should you have on? Probably had a whole bunch of my friends, but I thought of this guy, Jake Sanders. He works for a company called NinjaCat. He did one of the most enjoyable podcast interviews I ever had. This one, it's up there as well. But he comes from the marketing data space, and he also did a rock opera, Hamilton- like musical called Big Data Day. So we discovered each other. Obviously, super creative, music- oriented, writes about data. Tiankai Feng is another one, if you don't have him. He's super creative. He did the music for my puppet show. He did the Governors of Data rap song. So those folks, both of them, I'd definitely put top of my list there. I'm sure you've had Malcolm and Kate and George and all the data venture folks as well. Where do I learn? I'm going out there talking to you guys, listen to other podcasts. Went to Gartner last week. I'm going to Enterprise Data World next week. LinkedIn feeds. I like to watch webinars from brands, and I like to watch the first five minutes. How do they set this up? What do they talk about? Do they have any interesting twist to it? Because then they get into a demo or whatever. And again, I don't look under the hood, but I like to hear how people set this stuff up. And that's some advice too. Your first couple of minutes are probably the most important, in terms of whether you're going to engage an audience or not. And think about that. Think about those precious few minutes you've got right up in the beginning. When I do my keynotes and I do my presentations on stage, I don't start with a bunch of rambling, " Oh, how's everybody doing? Oh, here's me." I do what I call the cold open and just go right into it and then, I get to my other stuff. But it just grabs people's attention,

Tim Gasper [00:59:20] Is that your tip is like, " Hook, hook people at the beginning, do it?"

Scott Taylor [00:59:24] Yeah, yeah, yeah. If you ever start a session with, " I'm the last thing between you and lunch," I will be the one in the back of the room that just yells, " Then get out of the way, really, because I'd rather have lunch than listen to somebody who inaudible listening to, really."

Juan Sequeda [00:59:41] Oh yeah, I have...

Scott Taylor [00:59:42] "Between you and drinks and blah, blah, blah." It's almost as bad as, " Well, I looked up in ChatGPT what this meant," which is the current version of, " Wikipedia says," which replaced, " According to Merriam- Webster." It's like, these are lazy things. You got that first moment. Don't waste it.

Juan Sequeda [01:00:02] Thank you so much for... Yes. I'm a whole anti agenda. No, no, let's go through this stuff. And actually, I love this cold opening. People are not comfortable about that. Makes them feel uncomfortable.

Scott Taylor [01:00:17] They're not. No, no. The first time I did it, I was concerned. I was like, " All right, I'm going to try something really new here." Because I did the like, " Oh, how's everybody doing?" And I just started with a booming sentence. I think it was like, " Everything we do turns to data," which is actually the first line in my book, or, " Free enterprise has a data story to tell." And you just, my dramatic arts background helps with that. And you just see people just sit up all of a sudden.

Tim Gasper [01:00:45] The phones go down and the eyeballs come up, right?

Scott Taylor [01:00:48] Yeah, " Here's my background and here's all this stuff and blah, blah, blah." But you just start that way, and it's fairly, yeah, it is a little bit uncomfortable to start that way at first, but it works. It's beautiful.

Tim Gasper [01:01:02] It's part of good storytelling.

Juan Sequeda [01:01:04] Do you think everybody should go to some theater acting improv class?

Scott Taylor [01:01:09] Anything that gets you comfortable on your feet talking to people you don't know. I'm different. I'm wired differently. I like to say I have a fear of not public speaking, but most folks aren't like that. And again, I have dramatic arts training. I lean on that stuff every day. Listen to yourself, record yourself, listen to it. Get rid of your verbal tics. Get rid of your wasted language. Take the space. It's your space, it's your time. And people don't respond to folks who are wondering why they're there.

Juan Sequeda [01:01:49] It's very wise. Scott, thank you so much for such a phenomenal conversation.

Scott Taylor [01:01:54] This is fun.

Juan Sequeda [01:01:56] This is a unique episode, because we had fun, not that we don't usually have fun, but we really laughed. And I'm like, I got tears here of just laughing.

Scott Taylor [01:02:07] At my job today.

Juan Sequeda [01:02:08] You went very profound, very wise. You have practical advice to go to your day- to- day, but also practical advice for life. So thank you, Scott. I really, really appreciate this. This was fantastic.

Scott Taylor [01:02:23] Thank you, gentlemen. It was a pleasure.

Juan Sequeda [01:02:25] Just a quick reminder, next week, we are, Tim and I are going to be live from Data Council here in Austin, and we're going to try something different. Our next episode, we're just going to go off and just, we're going to sit in a corner at the conference and just anybody who wants to come by, just come by and say hello. And anybody who wants to be interviewed, one question, " What's your honest, no- BS take on the data world?" With that, Scott, thank you so much. And as always, thanks to data. world who lets us do this every week. Cheers, everybody.

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Scott Taylor The Data Whisperer & Principal Consultant, MetaMeta
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