40:21

SUMMARY KEYWORDS

data, business, people, storytelling, problems, companies, enterprise, insight, quality, book, organization, privacy, management, analytics, important, talk, meaning, latest buzzword, terminology, hear

SPEAKERS

Debbie Reynolds, Scott Taylor

Debbie Reynolds  00:00

Personal views and opinions expressed by our podcast guests are their own and are not legal advice or official statements by their organizations. Hello, my name is Debbie Reynolds; they call me "The Data Diva". This is "The Data Diva" Talks Privacy podcast, where we discuss Data Privacy issues with industry leaders around the world with information that businesses need to know now. I have a special guest on the show. Scott Taylor. He is "The Data Whisperer" and principal Consulting at Meta Meta Consulting. Welcome.

Scott Taylor  00:40

Hi, Debbie. How are you? Yes, I'm Scott Taylor, "The Data Whisperer". Glad to be here today.

Debbie Reynolds  00:45

You're so much fun. So we've met on LinkedIn; I'm always interested in your point of view and the really smart and fun things that you say. But I would love for you to be able to introduce yourself and talk about your data journey. And then touch a bit on how Data Privacy impacts you and your business.

Scott Taylor  01:10

I am 30-something years in the data space; certainly pre 2000 is when I started, always on the data management side versus business intelligence and analytics. I like to say I focus on where data starts versus where data ends up. Where data starts tends to be data management, data governance, data stewardship, MDM, RDM, PAM, and DAM, all those foundational activities that enterprises must focus on to have the right kind of curated content throughout their workflows. My journey started at a company that was part of what is now Nielsen. So have a steeped in kind of enterprise data use cases, syndicated data. I've worked for a couple of iconic data brands out there and Nielsen being one Dun and Bradstreet Cantar, and have been fortunate enough on my journey to talk to every kind of enterprise at every level of maturity in every category all over the globe. So maybe that's a limited perspective. But I feel that it's pretty broad because I've found that people and companies tend to suffer from very similar challenges and look for very similar opportunities. When they try to leverage data and get value out of it, if they're not exactly the same, they're certainly more the same than they are different. I was always on and have still been on data telling stories about why data is important to an organization today, you would call that a form of data storytelling, although storytelling, see my air quotes, won't be a video is a hot thing. Now probably the highest technical thing going on in the data space. But it says old as human communication. So I find it a little ironic that storytelling is suddenly being discovered when we've been telling stories, certainly before there was data and before there was electricity. And helping some of these companies. So what I did at Nielsen was help them tell a better story about the data that I was representing and rebranded a particular set of offerings there. But that was storytelling. When I went to Dun and Bradstreet, I helped them change their story about the master data capabilities they have today, I help organizations tell a better story about why managing data is so critical to their enterprise. But that storyline about storytelling has been present throughout my entire career.

Debbie Reynolds  04:03

Yeah, I agree with that. So I'm a data person. That's why I call myself "The Data Diva", not "The Privacy Diva", because I'm interested in all the ways that data flows. But you mentioned an interesting point that I would love your thoughts on. So at the time that you were starting your work in data, there just weren't wasn't as much data as there is now, and it was harder to get. So now that we have this proliferation of data coming from everywhere, how has that changed your work, if at all?

Scott Taylor  04:36

It's made it even more relevant, which is exciting. Because you're correct that back in the day, there was lots of data some, at certain organizations, but it didn't have anywhere near the profile and has in our present digitally oriented times. And data was generally thought of as something that came out of it because Dan is on a computer. So we needed on it, you know, it was all focused on that's been a tragic flaw in the development of data value over the last decades that it was stuck in it. And today, organizations everywhere are struggling with trying to manage their data, trying to get value out of it, trying to communicate the importance of it. And at the same time, we're just flooded with all kinds of buzzwords and terminology of the day. And everybody's trying to come up with the latest, coolest little tagline or spin or a new way to call something that's been around forever. And this cacophony of terminology and vocabulary is holding us back. One of my core tenants of the work I do is one of the biggest things holding the data space back is the way we talk about it. Right? So helping people speak more clearly, more definitively, more declaratively in a business-oriented fashion, which is what I've done forever, is, as I mentioned, more relevant today than it's ever been so exciting for me in terms of my career.

Debbie Reynolds  06:24

Yeah, I think you make a great point, a great point there around people trying to talk about data in different ways. You know, I feel like, sometimes we have a Plymouth Rock moment, or someone asked, like, oh, I discovered this new thing. It's like, well, this new thing you discover has been around forever.

Scott Taylor  06:44

What's that attach? I call it the fetch school of marketing. I'll tell you more of what I mean by that. So if you remember the movie Mean Girls. The Mean Girls School of marketing, yes, I should know the characters better. But that one of the supporting mean girls keeps saying, oh, that's so fetch that. So fetch. And the head mean girl says stop trying to make fetch happen. It's not gonna happen. And I feel that way. Today, when I hear about the latest analytics, graph hub, fabric mesh, Lake House observability, I don't mean to ding the practical value of some of these things. But they are wrapped in this notion that people are trying to come up with the latest buzzword. And if I can just coin this new term, that's going to unlock a whole set of new business for me, and everybody's going to get it. And you'll see a disproportionate amount of time and effort, marketing data terminology and buzzwords for the sake of it. And I think it's it's generally not always, but generally a waste of time and effort.

Debbie Reynolds  07:59

I agree with that. I want your thoughts on data insight. So now that we have so much data, companies have the idea that as long as I collect more data, that means it's good, but a lot of companies, because they're overwhelmed with data, don't know how to use it, and they don't know how to get insight out of it. So I've heard people say data is the new gold, and I say data is a lump of coal if you don't know how to use it. So tell me a little bit about the insight part of data, not just the data hoarding part of it.

Scott Taylor  08:35

You can boil my entire data philosophy down to three words, truth before meaning, you have to determine the truth in your data. That's data management, data governance, master data, reference data, all those foundational things I talked about before you derive meaning out of it through insight, business, intelligence, analytics, data science, data visualization, and all the sexy stuff. And that's being talked about, that Plymouth Rock moment that keeps being rediscovered. You can't go through LinkedIn without somebody going, you know, data quality is really important for machine learning algorithms. It's like yeah, right before meaning right. Data science data scientists should spend more time on the quality of their data than they should on their models. Again, big data there. This is not an earth-shaking moment. That falls back on what I keep trying to remind people to determine the truth first before you derive meaning. It's not chicken or egg here. It is an egg and omelet. If you do not have that true first, you're not going to get the meaning you want out of it. And it is, you know, people say all the time, well, garbage in, garbage out. That's an accurate statement. The challenge we have is that statement doesn't drive any action. So I try and put it in a more positive-oriented way. I've got up approach I called the golden rule of data do unto your data as you would have it do unto you.

Debbie Reynolds  10:18

Right? That's a good one.

Scott Taylor  10:20

But it's still the same, and that's not going to change. So we live in a world, especially with digital disruption, transformation, everybody's always saying, Well, if you don't think things are going to completely change, then you're some sort of, you know, backward, refusing to disrupt them, blah, blah, blah, blah, you know, we, it's as sure as gravity, okay, these are the laws of physics, what you put into something content-wise, is going to have a direct relationship to what you get out of it. Right? Yeah. And we just have to keep pounding that message out. Yeah. So for me, insight, back to your first comment, I don't believe the more data you have, the better; I don't believe that people should just grab as much as they can and then see what they can figure out. Most data that people have don't, doesn't have any sort of broad business and enterprise relevance. A lot of tactical things people do and specific use cases are really focused on a teeny tiny part of the business. Which is why I have a lot more fun and think there's a lot more important work to be done around making sure people understand you got to build that foundation correctly. What kind of data do you use all across your business? Right master data, reference data, metadata? Well, what is all that stuff? And people get confused by it? How do I explain that? Well, if you have relationships, you have relations, you have data about your relationships. If you call those relationships customers, then you have customer data, and that customer data better be as strong as possible because it touches every part of your organization. And if you don't think your customer data is important, then you don't understand your job. I hate to be that bold, but every business as customers, and if you don't think your customers are important to your business, you don't have customers, you don't have a business. I'm that guy, you know, spoiler alert, they call me"The Data Whisperer", but I don't do a whole lot of whispering. Because we have to sell and tell and yell about the value of proper data management across an enterprise, support those efforts, and make sure we get the funding and engagement we need to build that foundation.

Debbie Reynolds  12:44

Right? Yeah, I agree. So I want your thoughts a bit on data quality. So in trust, so I feel like people who don't trust organizations don't give them accurate data. So then the data that they get, and the insights that they derive is off, is not accurate. And so I think trust is very foundational with your customers because then they feel like the value that they're getting isn't a benefit, they're not going to give you good data; what are your thoughts?

Scott Taylor  13:25

Truth before meaning? Good truth before meaning trust is the equation for trust is truth over time. If you don't have that hard truth about those core entities of your business, relationships, and brands are the two big domains that I talk about a lot, then the rest of it is just gonna get worse and worse. So you've got to build that good; you got to build that foundation. And in terms of term data quality, I will pick on that just as a term. So since what I do is talk about how people talk about data. Meta meta consulting is my company name, meta, meta, we're about what it's about, right? We're not changing. We're not changing our name to Facebook. By the way, I get that question a lot. So no, we're not. It was called Meta Meta before they were called Meta. But data quality, and this is in my book. I don't think the word quality sells. It's super important. It's critical. You have to measure it. When data leaders go in and try and get funding for data quality effort, and they start time, we need better data quality, and our data quality is low, and it's they just sound like I didn't sound like they're whining. Just that’s, and your CEO is going to go well why? So you've got to answer that. Why, when you come into that discussion, and just simply the word data quality is really subjective, it's emotional content, a word that everybody has a definition their own definition of. So the value of that terminology is really low, even though the importance of what that terminology represents is high. If you want to pitch this stuff, which is what I help people focus on, how do you sell the value of data quality? You use the Fight Club word; the way to sell in a fight club approach, the way to talk about data quality is not to talk about data quality because people don't care. It's if the product, if the Head of Product walked into the board and said, you know, our product quality is really bad, difficult, why aren't you taking care of it, you should know how to take care of this, or you need investments for what? So as data leaders, we have to change the narrative, we have to change the story, we have to change the approach we have to get support because it's not working. If it was working, then why are we still talking about it? Right? Why are you still seeing it? Why are you still seeing people talk about 20, 30 or 50 years later, writing about here's the importance of data quality? Well, that message has not gotten through.

Debbie Reynolds  16:18

Right? Oh, my goodness, well, let's chat a bit about your book, "Telling Your Data Story". I know you're giving us some good tidbits out of there. But I want your thoughts about information governance. So I've seen traditionally through the years that information governance folks don't get the type of support and funding that they need. Because a lot of times, companies are really interested in those kinds of those later insights, those bells and whistles, those dashboards that they get at the end and not really the beginning. So tell me a bit about the challenge in that data, information governance, and the truth space that you talked about.

Scott Taylor  16:58

That was exactly why I wrote the book. So the book is called "Telling Your Data Story", data storytelling for data management. Right on the book cover, it says 99%, buzzword free. I didn't want to over-promise. So we just kept it at 99% Everybody's gonna slip one or two in there. And it was focused on the purpose of the book was to help data governance, information management, data stewardship, data quality. However, you want to bucket all those kinds of folks together, help them get the funding and support, and stakeholder engagement and executive leadership attention for these important data management initiatives. Because people are attracted and distracted by the shiny, glimmering newest, coolest thing that certainly sounds better, looks better, it is sexier; I'm not saying it's that you can see these things; you can see the results of analytics more clearly than you can see the results of the tangible results of data management, good data management if you've got great data management, it's basically invisible. Your point earlier about a consumer interaction with a company and the trust you're trying to build, people will go out of their way to go, Wow, this was great. You got all my information, right? I'm so happy I can move on to the next page in this form. They just take it as a given. Okay, if it's not right, you're going to hear about it. If it is right, you're not going to hear about it. So just as an anecdotal example of why data management doesn't get the attention it deserves, because you don't really see bad data management, you feel it. So you feel good data, you feel bad data. But it's hard to see it in a tangible way. Like you can see, the output of some of these other sexier, more attention-grabbing areas. But the book was there also because I was listening to the marketplace as I do. That's part of what I do as a content creator, and try to guide people and help them on whatever part of the data journey they're at. And listen to all this talk about data storytelling. And I'm listening. There's 30 books out there about data storytelling. And all of them talked about analytics. I wish it was called analytic storytelling because that's really what it is. That horses left the barn, we're not going to change that terminology. It became apparent we need there's a voice out there I decided it was mine to put some content and some structure around the idea of data storytelling, but for data management, and so on, instead of telling stories with data, which classic data storytelling does, how do I put a metric or analytics or an insight into a business context that drive action? Super important, you got to do that. My approach was, let's tell stories about the data about why managing it is important to you need both stories. It's not Sophie's Choice here, you don't have to choose between both enterprises need to tell both kinds of data stories, the one with data, which is analytics, and the one about data, which is data management,

Debbie Reynolds  20:41

Right. Yeah, yeah.

Scott Taylor  20:43

The book has been out there for two years. It's still a best seller for my publisher, which is great. It's one of their top-selling books, which is lovely and gratifying. People still like it, it's still selling, and you can get on Amazon and get on Technics Publications; maybe we could put some links in the show notes. Yeah. And it really filled a niche out there for a different kind of data, and storytelling and data managers seem to appreciate it as well appreciate the focus.

Debbie Reynolds  21:18

So tell me a little bit about how Data Privacy has crept into your world and your thought process around data, or maybe your client's data.

Scott Taylor  21:31

I'm not a Data Privacy expert by any means. But I know that if you want to keep your data, part of what's inherited Data Privacy is good data management. If you do not manage the data correctly. And securely, then you will abuse the privacy and trust of your users and your relationships. And the results of that are all over the headlines. Those are the kinds of bad headlines that brands get. A really good way to help destroy your brand is to not guard your data correctly. And a lot of that, while some of it is highly technical about system access, some of it is also based on making sure that data is truly managed and governed in the right way. Do we have unique identifiers? Do we have? Have we reduced duplicates? Do we know who can look at what when all of that is executed? Through having a really strong data structure that's supported by whatever operational systems holding that data. And I've worked in you know, and I tend to work my background in terms of where I worked in data companies was always with data that was about things that people wanted to know. So company data. Folks talk a lot about PII, personally identifiable information, I never delved into that people data part; I probably worked in something I would call CIA, commercially identifiable information. So they're the data that was part of the companies that are represented. That was data about things that people that those things wanted to communicate about. For instance, at Nielsen, I worked on a database that was about supermarkets. Every supermarket wants everybody to know about all their locations. So that was nothing hidden there. But you still need to guard against making sure the right data goes to the right place, and the wrong people don't see the, you know, data that they shouldn't. And all of that data management has a seat at that table. And if it doesn't, you're probably having problems.

Debbie Reynolds  23:56

Yeah. I agree with that. I feel like companies who aren't getting the message right now, they think, okay, all I need is the best products, and then I don't have to answer to people about what's happening with their data. So I think part of that trust, even though you may have the best product in the world, you know, I think we're at the show your work phase of society right now in terms of how brands manage data. So, in addition to being able to provide a service to someone, they want to know what are you doing to protect my privacy or protect the data that that I've given you or that you have about me?

Scott Taylor  24:44

That happens a lot. I think a lot of consumers feel that way. If you look on both sides of the line. I come from a b2b background. We're business professionals. Data is a good thing in business and tends to be a good thing; if you had to be binary about it, and you went internally to business people and talk to enterprises about data, they would say it's a good thing. If you talk to most consumers, and most people who aren't related to any kind of technology enterprise, and in their personal lives, I think you would probably get more votes for data is a bad and scary thing. So that perception is interesting, I don't have much to talk about in terms of dealing with that perception. But I just know it exists. Because anytime I hear data, I think it's positive. But if I talk to my family who is not in the data business, they don't hear good stories about data; they don't see data driving their own personal enterprise, they see data as something that companies are breaching or giving away or it's at risk, or you know too much about me or all these big companies are tracking all this personal activity, I've gotten them, they just want to sell me something. None of that is inherently positive. I don't know how we break through there. And again, I only deal with the business-to-business activity. But that just struck me as an observation that most people think data is negative. Most companies think data is positive.

Debbie Reynolds  26:17

Yeah. Wow. I think that's true. I hadn't thought about it that way. So, as an executive that has to go in and say get money, or get funding or get some type of support or sponsorship for maybe internal data projects, what these people do wrong, that you think that they need to correct.

Scott Taylor  26:38

They tend to focus on how it's going to get done. I emphasize you want to focus on why it's important. You need, again you need both. But I've never met a CEO or senior leader that cares about how you're going to do it until they understand why it's important to their company. And data leaders tend to lead with here's my latest API configuration. And we're going to use reverse ETL with the automatic AI and run the data backward in circles. Who cares? I know your tech team loves it. Right? That is it. That's got nothing to do with why it's important. Why is what you're going to do with data enable the strategic intentions of your enterprise? Where does your company need to go? And why does data help it get there? Those are the things that if you don't do, you're not going to get the funding that you want. Save your data models and your data schemas in your modern data stack schematics for later after they say yes, right, because most business-oriented executives just glaze over. And at this point, they're absolutely cynical; if you are a seasoned executive in this space, in the business space, you have heard over and over and over again, it people and data, people come into your office with the latest, greatest coolest thing that they just heard about at some conference that's called whatever latest buzzword it is, that's going to fix all the problems that you were in charge of fixing the last time you came in with the latest, greatest thing. And they think that that's, I don't know how we get so blinded by this consistent churn and cycle of solutions, again, big air quotes here that are gonna solve all the problems. And if you listen to the problems, if you peel it back, what I love to do, like I watch a lot of webinars, only to hear the first like five or 10 minutes, I want to hear that they set it up, I don't care how it's gonna get done, I want to hear how they set up the characteristics of the problem in whatever that situation is. And more times than not, it is the same stuff. Inequality is a challenge. Data is everywhere. We have so much data now. We don't know what to do with it. We have all these different silos. These are not new issues. I don't have all the answers. But I'm thinking if you keep coming up with the same five problems every year and you have a different solution, is there more of a root cause analysis we can go to for this, please? And that's what we get into that so that you've got, you know, to sum it up, when you're walking in and trying to get money, it will be really specific here, you need money for your data programs. They're not going to happen on their own. And you're going to fight for funding across a whole bunch of different other departments in your organization who are, frankly, most of them are better storytellers than you. If you're an IT or data person, and you're fighting up against marketing. Marketing tells stories for a living, they're going to pitch their stuff better. If you're up against sales, they're going to pitch their stuff. Better sales, by definition, are the best storytellers in our organization. And I mean that as a compliment. You better get your storytelling chops honed before you walk into that shark den, or, you know, wherever and fight for that budget and what you have. So don't start with how you're going to do it. Don't start by whining about data quality. Don't start with the same three cliches you always start with, there's more data now than there has ever been before. Who cares? You know, there's more data now than there was before we started this podcast, Debbie. And now there's even more data now than there was before I started the sentence; there's more data now than ever before. These are, you know, our data is the new oil or gold or bacon or avocado or whatever ridiculous cliche; you've got to d, on the beach to everywhere, right? Everywhere. So turn that around, and it's all through the book; focus on what your business problems are. And there's all sorts of exercises we can talk about next, if you want, how to figure out those business opportunities, but that's all people care about. And as data professionals, we seem to rediscover this too. You look at, oh, you know, what we should do with data, it should bring value to the business? Yes, we know that you never hear the people from in marketing go, you know, what we should do in marketing, we should help bring value to the business? Or does your finance department ever say, you know, we should do in finance, we should really be focused on the business? That's not a question in these other departments, right? We have this, I don't know what the psychological problem is in the data space in terms of us, you know, as an entire group being unable to truly articulate what we need. But there seems to be a problem there.

Debbie Reynolds  32:38

And, one thing that kind of annoys me a lot is that people think technology is going to solve all their data problems. What are your thoughts about that?

Scott Taylor  32:51

You buy a new frying pan, but that doesn't solve all your eating problems if you don't have good ingredients and you don't know how to cook, right? It's simple, you know, you can watch most data software pitches, I think are like watching a ninja foodie, commercial infomercial, where they just keep showing this machine in the middle of the screen, and you put all this stuff in it, and you set it and forget it, you come back, and you have this delicious meal. It's harder than that. And what they don't talk about in that ninja foodie commercial is if you have a low-quality chicken, it's going to taste terrible no matter what you do. So we're back to these basic premises again, truth before meaning, what goes in, will come out, and focus on the business problems. And there's no magic here. It's hard work. And the tools themselves aren't going to do it. The tools are fed off of data, right. And I haven't seen a tool that can magically take really bad data and somehow transform it into this miraculous business insight. Without a lot of governance and structure and input and guidance.

Debbie Reynolds  34:20

Yeah, yeah. Well, one thing that I find is that a lot of people think that technology is magic and that it will tell them the question they need to ask. So I think people just don't know the question that they want answered. And so it's hard for them to get the right insight,

Scott Taylor  34:38

I guess, I guess, but I get it. There's this debate about it. People will often say oh, it's not the technology. It's the people. It's the culture. It's ok, then why are you talking to me about technology, right? You don't have to remind me that we always to be reminding ourselves of the obvious a lot and then not really doing something about it. I'm not cynical about that. I think there's hope. Right? But the consistency of that is a little bit troublesome.

Debbie Reynolds  35:14

Yes, I agree with that. I agree. So if it were the world according to you, Scott, "The Data Whisperer", what would be your wish for data or privacy or just anything in the future? What would your magic wand do? If you could change anything?

Scott Taylor  35:32

It isn't the world according to me yet, I thought it was. I would focus on the same stuff I've talked about, determine the truth first before you derive meaning, and concentrate on why it's important versus how you're going to do it. Work, understand, and learn your business. These are things that I don't think are magic tricks, but they will have a magical result in your day of work. The advice I gave my children who are off on their own, you know, got their wings and grow. Now, as you cannot learn too much about your business, it's impossible to learn too much about the business that you're in. And the more you listen, and the more you hear, and are exposed to the business problems, these data opportunities are gonna come. I'm, I love being in the database. And I think data offers have spoken a lot, you know, some people might characterize as negatively throughout this podcast because I'm kind of ranting on some of these things. Because I'm so early because I care so much about us fixing them, right? But data can work across an entire organization. That's right, show me another department that offers that brings this kind of value to the entire organization. You know, sales, sell stuff, I'm marketing, maybe that can change the nature of an organization. Finance doesn't change the nature of the business legals; they're just purely to keep you out of, you know, out of trouble. So they're not; their objective isn't to change the nature of the business. But we've shown you can show example after example, after example, about how leveraging the right kind of data and insights in tandem can truly transform the very nature of a business. Show me another department that has got that opportunity. There isn't one. So my magic wand, I guess, is what would sprinkle pixie dust over many of our data leaders and have them focus on those things, the why, not the how, the truth before meaning and continuing to enable the strategic intentions of their enterprise.

Debbie Reynolds  37:58

Wow, I agree with that. I'd love people to check out the book. Oh my goodness. Well, it's been such a pleasure to have you on the show; you're so funny, oh my God.

Scott Taylor  39:27

It is "Telling Your Data Story". Data Storytelling For Data Management by Scott Taylor, "The Data Whisperer", it's on technicspubs.com. If you want to save 20%, you can use the data whisperer promo code or you can get it on Amazon as well. Where for some reason. I am the number one data book on Amazon in the Performing Arts category. Right up there with Suzuki violin and tips on dramatic improv, for some reason, and I love it. My book was characterized; they put your book in several categories in Amazon. So I'm an information intelligence information, data management, and then also performing arts. So even though I rank anywhere between the highest I've ever been as literally number 80 something, and then I've been down to, like, you know, 2057. But I'm the only data book and performing arts, which makes me the number one data book and performing arts every day. And that's proof you can make analytics. Do whatever you want. That's right. God,  Debbie got me all revved up here.

Debbie Reynolds  39:32

Well, yeah, we have to find ways we can collaborate. The future is so much fun.

Scott Taylor  39:36

Oh, certainly. That'd be a blast. Yeah. Well, thank you so much for being on the show. This is wonderful. And we'll talk soon. Thank you, Debbie. This is a blast. Thank you so much.