In this episode of FuseBytes, host Nate Rackiewicz talks with Scott Taylor, The Data Whisperer, about the critical aspects of data trustworthiness and integration. They explore the challenges of evaluating third-party data, the importance of data coverage, structure, and quality, and how to connect data initiatives to business outcomes. Scott shares insights on data governance, aligning data work with strategic goals, and the evolving roles in data leadership. This conversation is full of practical tips for executives aiming to get their organizations AI-ready through robust data management practices.
Nate Rackiewicz: Well, hello again, my friends, I'm Nate Rackiewicz and this is your podcast of the week, FuseBytes. It's a podcast that this year is focused on AI readiness in companies. You hear about AI everywhere you go. It's at every conference that you're at. It's in the news everywhere. It's been an explosion of interest since ChatGPT burst onto the scenes about a year and a half ago. And here we are talking about AI within companies and how difficult is it to implement.
The truth is, it's not simple, even though you hear about it everywhere. And we wanted to go through in this series of podcasts called FuseBytes some of the steps that are required to get AI ready within your company and among those is getting data ready because you can't have AI without high-quality data. It's the fuel that really drives the AI algorithms. And if you don't have high-quality data, you've got ‘garbage in garbage out’ – the old saying.
So how do we set up AI models and algorithms for success when thinking about data? And how is data for AI different than data we've worked with for the past 10, 15, 20 years, since the big data boom that happened and was being used for data analytics purposes on the predictive analytics and then into prescriptive analytics? Here we are now sitting around prescriptive analytics with AI. And how is that data different? Well, I'm excited to bring a guest to the table here today. A gentleman named Scott Taylor is joining me today! Hi, Scott!
Scott Taylor: Nate, how are you doing? Great to see you. Thanks for having me.
Nate Rackiewicz: It's great to see you again. Thanks for being here. I'm thrilled to have Scott on the show. Scott Taylor is known as the data whisperer. He's helped countless companies by enlightening business executives to the strategic value of proper data management.
As an avid business evangelist, he focuses on business alignment and the strategic why? Rather than the system implementation and the technical, how?
He shares his passion through all forms of thought leadership content, including public speaking, blogs, videos, podcasts, white papers, and believe it or not even cartoons and puppet shows. One of which I watched last night with their chief dog officer.
Accolades and recognition include DataIQ 100, he's listed by CDO Magazine as a leading data consultant, Onalytica ‘Who's Who in Data Management’, Dataversity ‘top 10 blogger’ and Thinkers360 ‘top 10 thought Leader’. He's written the book that I encourage you all to get called ‘Telling Your Data Story: Data Storytelling for Data Management’.
It's available now, Scott lives in Bridgeport, Connecticut, where he often kayaks in Black Rock Harbor, and for those that want to know he can also juggle pins and blow a square bubble.
So welcome again here to the show. Scott, how do you blow a square bubble?
Scott Taylor: I didn't blow a square bubble. That's what start from the bottom here. It's actually a bubble cube and you blow 6 bubbles that are attached to each other, and then you put a straw in the middle and when you blow there the magic happens, and a little cube appears.
Nate Rackiewicz: Imagine that.
Scott Taylor: Exactly. Yes, I saw somebody do it once on TV, and I went ‘I could do that’ when I was a kid. It was like, I can figure that out.
Nate Rackiewicz: Have you figured out the Rubik's Cube?
Scott Taylor: No, my son is a great Rubik's cuber, but I'm just a bubble cuber.
Nate Rackiewicz: Yeah, I was wondering about that as well. Well, listen we're really excited to have you on the show for this program where we're focused on the importance of data in the equation of AI readiness for companies. And nobody knows data better than you and the work that you've done in helping to educate senior management in particular about the ‘Why’ of data and why it's important and the importance of applications of that data as opposed to just getting stuck in the technical jargon that many people get stuck in.
And I think your data puppets example that you've got on Youtube that has 22,000 views is a good example of a bunch of puppets that are getting stuck in the jargon and missing the why of it all. So how did you become the data whisperer and get into this focus on consulting to senior management about the ‘Why’ of data.
Scott Taylor: I've always been a storyteller. Data storytelling now is a super hot thing, but I've been in storytelling since it was 2 words and I go back in data Pre 2K, so I've got decades of exposure to what I saw as common challenges, similar issues, common goals at enterprises at the level that you and I deal with. They're certainly more the same than they are different when it comes to some of the issues that they face with enterprise data and enterprise data management.
The data whisperer moniker was just something that hit me one day, kind of like the dog whisperer the horse whisperer, speaking data management. We calm data down, we train data, make sure data is acceptable and can be used. So it was a little bit tongue-in-cheek. I put it on a badge once at a conference, and I got so much positive feedback I never took it off again. But spoiler alert, even though I'm the data whisperer, I don't do a lot of whispering. I am out there yelling, telling, and selling about the power and value of proper data management.
You can't yell and tell and sell that any louder or stronger these days given the deluge of AI and GenAI and the hype cycle we're in which is unlike anything I've ever seen, and I've seen a lot of it.
“Spoiler alert! Even though I'm the data whisperer, I don't do a lot of whispering. I am out there yelling, telling, and selling about the power and value of proper data management.”
Nate Rackiewicz: It really is a hype cycle like none other. I thought the big data hype cycle was extreme back when we went through that in the mid-2010’s and this really just blows that out of proportion. It has everybody talking about it everywhere. You can't turn on CNBC without coming across something related to AI or Generative AI. Nvidia is all over the place as the most valuable company out there in terms of market cap. And so it's it's something that I've never experienced anything as big as is what we're going through right now as well.
Nate Rackiewicz: I'm wondering, how do you think data is changing with the world of AI. We've got data, we've been using data. We've been using big data for years for analytics purposes, organizations navigate their analytics journey of descriptive analytics, to diagnostic analytics, to predictive analytics, to prescriptive analytics. And they've been using data to do that for years. In comes AI which allows for a lot of fast tracking of things.
How does that disrupt the world of data management? And how should we be thinking about refining our approach to data management in a world of AI.
Scott Taylor: It just reinforces the need for proper data management, formal data governance, data structure, data foundation, whatever you wanna call it. I like to call where data starts versus where it ends up, which are things like AI and analytics and business intelligence and whatever the flavor of the week is.
But you can boil my entire data philosophy down to 3 words. And as a storyteller it's important to be succinct and try and get things down to a crisp level, especially for business executives that don't have time to hear about data. But my 3 word data philosophy is ‘truth before meaning’. You have to determine the truth in your data before you derive meaning. It is not chicken or egg here, it is egg and omelet. If you don't have the truth in your data you're not going to get the meaning out of it that you expect, and that hasn't changed.
“My 3-word data philosophy is ‘truth before meaning’. You have to determine the truth in your data before you derive meaning.”
I haven't found a way that that's changed. It's more important now. The scale of AI and generative AI are, staggering compared to what we looked at when we were just dealing with things like implementing enterprise systems and spreadsheets and basic business intelligence. But the fundamentals are the same. And that's part of what I try to point out, even though the glossier and frothier and sexier and hotter and more spectacular, and kids know about it, and everybody's using the fundamental aspects of it are the same.
You mentioned GIGO in your intro there. We learned that on the 1st day of data right, garbage in garbage out, sure as gravity. What goes up what must come down, and what goes in must come out.
Nate Rackiewicz: Yep.
Scott Taylor: But it reinforces what I like to call the golden rule of data, which is, do unto your data as you would have it do unto you, what you're gonna put into is what you're gonna get out of it. And for some reason, 30 years in this space, I still have to pound that message as strongly as ever.
“It reinforces what I like to call the golden rule of data, which is, do unto your data as you would have it do unto you. What you're gonna put into is what you're gonna get out of it.”
Nate Rackiewicz: So it seems that you're a big proponent of really focusing on the ‘why’ as opposed to the ‘how’ and it seems that the ‘why’ is changing. Because the needs of that data are changing. So why we have that data and what we're doing with it is changing in the world of AI from where we were before and helping accelerate, you know, predictive and prescriptive analytics as well as everything else that it's doing.
Nate Rackiewicz: How do you advise, senior management in companies that you go into or talk with at conferences? About how that ‘Why’ is changing around that and what is the reception that you tend to get from those executives that you speak with?
Scott Taylor: I don't know if the ‘why’ is really changing as much as maybe it appears the stakes are different. The speed is different. The scale is different. The expense is different, the players are a lot more, the effects can be much more widespread, but the essence of the ‘why’ is based on my belief that every company is trying to provide value to their relationships through their brands at scale.
That's what they want to do. Generative AI makes that scale a lot more exciting and a lot more expandable, but it's still the same thing. Again at its essence boil it down, so the why that I reinforce is why does what you want to do with data fill in the blanks as to what the current hype cycle is telling you. Why does that enable the strategic intentions of your enterprise? Where is your company going? And why does data help you get there?
And taking that apart at an organization I look at 2 basic buckets. If you go back to that statement providing value to your relationships with your brands at scale, relationships every company has relationships. You don't have relationships. You don't have a business.
Nate Rackiewicz: Right.
Scott Taylor: The data about those relationships is as critical as any data you've got. And then every company has brands, products, services, offerings, whatever they might be. But that's how they provide that value to those relationships, and that keeps me really steady in an exercise I do, which is anything new that comes up, I find, where that is. Where's the truth before meaning hook? Where's the foundational need? Where's the reinforcement of providing value to relationships with their brands at scale. So I'm not a trends guy, so I don't look up at what's new, but I wait till what's new shows up, and then I go– Okay, how do I fit this into what I see are inextricable, undeniable laws of data in an enterprise.
Nate Rackiewicz: So that's really interesting. And I love when you talk about storytelling as well. And your book really focuses on data storytelling and data management. I wonder over the past year and a half what are some of the new stories that have emerged that you find yourself talking about at these conferences, in your blogs, in your consulting opportunities with these executives. What are some of those key stories that are emerging?
I saw the data puppets was one that was focusing on the chief dog officer instead of the chief data officer arguing with the chief information officer and all of them, forgetting the fact that the monthly reports weren't even working so they were caught up in all the jargon and things like that. How would you? And and I think that that was created about 2 years ago. If I if memory serves me correctly.
Scott Taylor: Yeah, yeah.
Nate Rackiewicz: If you were to create data puppets for today and with an AI slant with all of the stories that you've seen and are emerging. You know. What might that look like today?
Scott Taylor: It again. It doesn't look so different. I do have another, an updated data pup that I'm working on an epic multi part series with the data puppets. If anybody's interested in finding them just simply Google, the term data put that's it.
Nate Rackiewicz: They're great because they simplify. You know, some of the main things into a story that people can understand.
Scott Taylor: The most common feedback I get is that's just like my company. I was just in that conversation, and you're talking about a monkey and a bee and a dog and a cat, and they're arguing, and they are literal hand puppets and it seems to strike a chord with folks because the chief CDO, the chief dog officer, he's struggling to convince and manage data in his organization. He's just being bombarded with every type of of problem that people show up within the latest one.
It's a preview of this multi-part series called Journey to the center of the single version of the truth, and he meets up with a cat Sultan from Meow Kinsey, who only wants to buy more software. He meets up with a couple of software vendors, sales, fork, and micro spoon, and so there's culinary utensil theme in there. There's a puppy intern that only wants snacks, so he doesn't care about what work he's doing. Then he runs off to go at Arf B&B. So it's definitely full of infused with dad jokes in the data space, but definitely resonates with folks. And you just mentioned, this is my book. So everybody knows what it is just mentioned telling your data story data storytelling for data management, 99% buzzword free.
I didn't want to overpromise. And it takes people through this, I still think a very common journey of how to explain why telling a story about your data's importance is as critical as ever.
I joked with my publisher to just ‘update’. It was written during Covid so it came out the end of 2020. It's like, okay, we could update this. I'll just throw GenAI in there every 3rd paragraph, and it's still the same thing. I think that would decrease the buzzword free count, however. The new stories that people are telling they're just more enhanced. They're a little more urgent. They're a little more concerned.
They see this double edged sword of with GenAI Data can literally run away from you in ways that it hasn't before, where you couldn't contain it, and a lot of folks are still struggling with that.
“GenAI came out and everybody was, Oh, wow! This is fantastic, and all of a sudden not too much longer after that they started talking about the need for a proper training corpus. We need AI governance which just doesn't cover the models, but it covers the inputs.”
This is a common story. You go from GenAI all the way back to General Ledger. When people were said we need a chart of accounts, and you're gonna find these common themes in there.
Nate Rackiewicz: One of the things that we think about with big data and LLMs is concern about bias in the data, concern about the sensitivity of data that you're using to train those models also getting out. How do you think about that? In the concept of the storytelling that you do with senior management about the ‘why’ of the data as it relates to concerns that they hear about the board hears about. Oh, I should be concerned about bias in my data. So I don't want to adopt AI.
Scott Taylor: If you find the truth in that data, and when I say truth, I don't mean anything philosophical or political, it can really structure data around let's say, your customer file, your customer hierarchies, your product categories, your product distribution, the fundamental piece parts of your business, when you have that really squared away, and in a shape that people are confident that they can believe in it.That should do a lot to influence the lack of bias in whatever result, you're getting.
The security part. I'm not a security expert, so you need system folks to put that in there, but getting too far off of the truth of what you're trying to find meaning in starts to happen when you have things as simple as conflicting hierarchies or lots of duplicates in your relationship data or hierarchies that don't make sense or geographies where people have different definitions.
An exercise I used to take people through is ‘How do you define Chicago?’ and you've got media markets and scanner markets and sales markets and measurement markets, lots of different configurations of something that's called the same thing. And that along my theme of problems that have always been there, but that's exacerbated. If you start to put these LLMs on top of it, you may pick the wrong one without knowing. So I think a lot of it still comes back to some form of data governance, some form of data management, some form of stewardship, some form of agreement around the organization about standards.
Everybody knows what a standard is. Standards work all across organizations and industries and markets. And so that concept for me helps explain why it's important to invest in this.
“And the ‘why’ that I focus on is, why should the business care? Why should the business care about the work you're trying to do in data management? Why should they care so much that they can give you money that they could spend on things that frankly appear and end up being more tangible usually than data management, data stewardship and so on.”
So it gets back to that, starting off the right way.
Nate Rackiewicz: So you've mentioned both data governance and AI governance. Do you see the 2 of them working together? Or do you think that they're distinct entities within a company in terms of how they should be handled? Should they be one in the same in your opinion or do you think that they do need to operate independently.
Scott Taylor: I think that they have to be as close together as possible. I think the scope of what AI governance is still in discussion and being defined. But AI governance certainly includes the inputs as well as the models, and probably the distribution and a few other things. But they've got to be as close together as possible. I always struggle when data science was really hot and people kept talking about how data scientists spend 80% of their time munging and wrangling data. And then the joke was, they spend the other 20% complaining about munging and wrangling data.
But it struck me and I asked around and it seemed to be true. Why isn't that team talking to the data governance team, who knows the data management team, who knows where a lot of that data is, who knows which data is good and which isn't? Who can probably help you avoid some munging and wrangling.
So there's this bifurcation in the data space, if you will, between the folks who manage the data and the folks who use the data, between data management and business intelligence analytics. There's always been this separation that for me is extremely frustrating. And I hope that AI governance doesn't get too far away and becomes its own department versus data governance. Because these things are so inextricably linked.
Nate Rackiewicz: Yeah. And I rode that curve there with the data scientists through the 80% problem of data wrangling and the 20% of complaining. And I see the same thing emerging to a degree with AI. Data science has really evolved to a degree into AI and it's the data scientists that were kind of at the leading edge of tackling the new AI algorithms and have become the AI engineers in many cases that we see today.
But the AI engineers are often left to themselves, separate from the data engineers, let's say. And so that's a common pattern that I see in companies that those two are really distinct. And the AI engineers end up doing their own data annotation, they do end up doing their own data, wrangling, their own ETL work in some cases, because they're not connecting those dots that you talked about. And I think that for a company to be AI ready, it really is important that we fuse those things together so that they can be more seamless and work together. But what does that mean in terms of governance? I think it is up in the air, in my opinion, around AI governance like you said so I agree with you on that, too.
Scott Taylor: I know you're into fusing. So that's good. How's that?
Nate Rackiewicz: Yes, fusing as a team, fusing with machines over here at FuseBytes.
Scott Taylor: Yes.
Nate Rackiewicz: So you speak about some of the fundamentals that people know. What are some of the fundamentals of data governance that you think need to be in place for any company that is looking to tackle the data problem and the ‘why’ of data governance head on and recognizing that it is supportive of both traditional analytics as well as AI? What would you advise companies to put in place with data governance so that they don't end up with the chief dog officer problem, where they're just talking about buzzwords and not actually getting anything done?
Scott Taylor: Not speaking as an organizational expert, but just as somebody who has lived in the space for as long as I have, data governance needs to be able to tell the story of why it's an important part of the organization and provides horizontal value across everything an organization does. And when I define data governance. I'm gonna split the hairs between data governance, data management, data stewardship, data quality, these are all parts of that high level the phylum level, if you will, of data management versus business intelligence, and both of those tend to roll up into just a thing called data or data and analytics and data management. It also includes pipelines, data observability, things that don't have to do with quality. But for me to simplify it, I just say, okay, 2 big buckets here, getting the data ready where data starts and using the data where data ends up.
And there's a little bit of a gray area in between. But big things happen on both sides, and so, making sure you align those efforts and get the stakeholder engagement, get the C-level attention, getting in the room somewhere when a lot of these big ideas are happening that are technology based, that's the clue. If they're gonna use technology in your organization, there's gonna be hardware, there's gonna be software, and there's gonna be data and if there's data, there's gonna be data management.
So trying to make sure you get into those rooms where it happens is critical. And it's still a challenge because data governance data management that whole bucket tends to be relatively boring. It's never really sexy. It's clerical. It's back office, it's you know, things like MDM and reference data management sound like that. They've been around for a long time. Are those really thrilling? You know, we want to do GenAI.
“But when people say that it's like– Oh, I want to do GenAI, but I don't care about data management. That's like saying, I want to have a great meal, but I don't care about the ingredients.”
Now you can get somebody to cook the ingredients, cook the meal for you but you hope that they care about the ingredients. Food is one of my favorite analogy sets to use when you get into the data management space because everybody's got to eat. And it's relatable to everyone.
Nate Rackiewicz: And I'm looking forward to the fork and the spoon analogies coming out with your next puppets.
Scott Taylor: Yeah. So for me it's really just important to keep pounding that message and the reason I wrote my book and the reason I do a lot of the work I do is because I had met so many data leaders focused on the data management side, who had this dual emotional state where they were really passionate and devoted around what they believed data could do for their organization, but they're really frustrated because people didn't listen to them.
Nate Rackiewicz: Yeah. Well, and why do you think that is that people didn't listen to them? How were they not successful in getting through?
Scott Taylor: They tell a terrible story they explain it with, and this is the same for all. I'll throw this cast across most data people, if you will. They love to start with how they did it.
Okay, first we did this, then we tried this, then that didn't work. So then we tried this, and you know we've got the latest that. And then these guys came in and showed us that this will be even better, and you've lost them already. They don't care. Tell a story, but don't tell your life story. I don't need to start with, I started off as a child, and then I grew up. You gotta get to the point. And the story you tell about data when I present data storytelling the type of story it is, is a pitch, it really is, and it's a sales pitch. I got no problem with selling. I'm 4th generation sales and proud of it.
Selling is the way to convince somebody of the benefits of what you have to offer. It makes things happen. So all the good parts of selling I think people should really focus on. But you need to be able to get somebody to say, yes, that's what you want, right? You wanted to go from “I have no idea what you're talking about” to “How do we live without this?”