CDO MATTERS WITH MALCOLM HAWKER

CDO Matters Ep. 23 | Key Takeaways from the Gartner Data & Analytics Summit [Live Show – From March 2023]

May 4, 2023

Episode Overview:

Who better to discuss an annual Gartner data summit than a former Gartner analyst himself?

Host and former Garter analyst, Malcolm Hawker, sits down with Profisee’s Director of Digital Marketing, Ben Bourgeois, to talk about his experiences and key takeaways after attending this year’s Gartner Data & Analytics Summit.

Other topics of discussion include:

  • Demystifying the data fabric
  • Malcolm’s journey in releasing 20+ episodes of the CDO Matters podcast
  • Defining data by its business value

…and more!

Episode Links & Resources:

Good morning or afternoon, depending on where you are. Hello, everybody. It’s Malcolm Hawker, your host of CDO Matters, and we are here with another episode of CDO Matters LIVE, where I will be taking your live questions about data governance, data strategy, MDM, anything data related, or anything else that we’re gonna be talking about in the first fifteen, twenty minutes or so.

Super excited to have everybody here. Happy Friday. It’s Friday as we record this, but knowing also that we will put this out on LinkedIn and put it on YouTube. And you may be consuming this at a later date, so happy whatever day you’re consuming this on.

As usual, I am joined by my trusty sidekick, my my Andy to my Conan, my my my my Ed McMahon to my Johnny Carson. That’s just such an old reference. I guess I shouldn’t even be doing that anymore.

Paul Shafferty Letterman, maybe?

No. Not even that. What what’s a buddy duo out there that actually is, like, relevant today?

There’s Higgins and, Jimmy Fallon at tonight show.

Oh, okay. Alright. Well, there you go. Or, who’s with the the security guard, on, it’s not Jimmy.

It’s, anyway hey. Anyway girl. Yeah. It I’m joined, by my fellow professor, mister Ben Bourgeois.

Ben, how are you doing?

Doing good. Thanks for having me.

Groovy. What you got on the docket today, Ben?

Yeah. Yeah. I figured we’d start, start with kind of recency here. Right? So I’m sure you had a busy week kinda getting back in the swing of things, this week and late last week because you spent several days down in Orlando at the the Gartner Data and Analytics Summit.

What what was that like? What was kind of a big takeaway from that or highlight for you?

Yeah. So that was last week, Last Monday, Tuesday, Wednesday, and it feels like I still have a little bit of a Gartner hangover, from that. It’s not not just that it happens at Disney. It’s just when when you’re a software vendor like we are and we were a sponsor, it’s it’s you you are up at, you know, and you’re up and out and pressing palms and having breakfast at seven o’clock and talking with people and talking with clients and talking with prospects and talking with fellow analysts and all those others, you know, starting at seven AM and you’re for us, again, because we’re there to meet people and and and interface and interact.

I mean, we were going until nine thirty, ten o’clock every day. So Always on. Yeah. It’s, yeah, it’s a grind, especially and and but it’s not a bad grind.

It’s just a long day, particularly for you some of you may find this, you know, amazing to to believe, and and and this may define reality for many of you, but but I’m what they call the Myers Briggs introvert. So if you’ve ever done the Myers Briggs test, right, the kind of the personality test, Believe it or not, I’m an introvert. What what that means is not that I’m introverted because, obviously, I’m not. Right.

But but what what that means is is that I I gain energy through solitude. I I don’t gain energy when I’m around people. I I I I energy is drained from me when I’m around people. I love being around people.

I love having conversations. I love the Gartner event. It’s amazing.

It’s just at the end of the day, it it feels like my soul has been vacuumed out of my body. And not for a bad reason. It’s all great. I mean, at the end of these days, it’s just like, hey, man.

That was awesome. I got to see person a. I got to see person b. I met with this client.

I had a conversation about governance or a like, all this stuff is just is just great. So, it’s it’s just it’s just a long a long three days.

But That’s granted out of you.

Yeah. Yeah. But getting to the conference itself in terms of the the kind of the some of the key takeaways, I did make a blog post, about this, a long form kind of blog post that’s available on prophecy dot com. I did make a a post on LinkedIn as well about kind of some of my key takeaways. And I will resummarize that here, obviously, with a little more color because because, we can do that because of this, this venue.

So key takeaway number one was, the robots are coming.

And I and I don’t mean that in a in a bad way.

And this isn’t just chat GPT because really that wasn’t the kind of the core focus. Generative AI and the kind of the undertone of chat GPT was not the main focus of of of kind of this theme.

And and what I mean by the robots are coming folks is is automation of data management.

Right?

Starting with augmentation. This is this is this is a spectrum.

Right? On one end of the spectrum, you have kind of fully manual data management. Right? And the other end of the spectrum is you have fully automated data management. We’re on a path, from from manual to automated.

Somewhere in the middle is is this idea of what’s called augmented, where it’s machines helping people.

And and we’re in the midst of that right now. We’re we’re knee deep in augmentation, and we are progressing in augmentation. So when I say the robots are coming, real really what I mean is is that we’re on this path, and there’s no turning back. We’re on a path towards increasing levels of augmentation of data management.

And and what do I mean by data management? Classic stuff in our world. Right? Modeling of data, defining of data, glossarying data, lineaging of data, all the data cataloging, data profiling, data quality, MDM, all the data integration even.

Right? So so the the the kind of the core foundational building blocks of the discipline of data management where we are on a path towards more and more and more and more automation, but that path will involve, you know, heavy doses of augmentation where people are still involved and people are still driving.

But but machines are heavily informing and increasingly and increasingly forming. And that that we the the horse is out of the barn. Pick whatever metaphor you wanna use here, but but that that’s the road that we are on. And some of the messaging from from Gartner was kind of consistent and relatively conservative.

And, hey, let’s not go crazy here.

It’s years away.

Yeah. Yeah. And and it is.

But but but, you know, what what I saw over and over again is the is is that this is kind of inevitable. This is where we’re going even if you wanna go slowly or if you wanna go quickly.

Arguably, even a vendor like IBM, right, was was all in on this even ten years ago when they when they announced Watson and it had the investments in in in in Watson. So, yeah, this die has been cast for a while, but I think finally, Gartner’s kind of catching up a little bit. And some of the messaging there was really, really cool and really innovative and really thought provoking. So as much as there was a lot of content around foundational issues, as much as Gartner was saying, hey. You know? Be be be careful. Move slowly.

Let’s not do make another kind of big data. Let’s not, you know, repeat the the past of Hadoops where we run-in and spend zillions and zillions of dollars on things that have no value prop.

That’s kind of one of the messages that was said during your keynote on Monday morning.

Don’t fall for the shiny object necessarily.

Yeah. Exactly right. Don’t do that. Smart.

But at the same time, other other messages they were given was like, hey. This is coming, and you gotta start preparing now. I I had a hard time, honestly, with the, you know, hey. Take it slow. Take it easy.

Don’t make this another Hadoop error.

Because I said this on my blog post. I said it on LinkedIn as well. It’s like, well, okay. Comparing Hadoop and generative AI or AI of any form or automated even data management for that matter. I mean, comparing these two things. I mean, it’s like you’ve you’ve you’ve literally got teenagers using chat g p t to do their homework.

Right.

Right?

There’s there was no equivalent to that in Hadoop. Right? I mean, we were just standing up HDFS clusters. We we were we were hiring data scientists. We were we were shoveling literally shoveling money in into into big data, hoping we’d figure out a way to use it. Right? And and the the pithy quote that I always give around this and and and and trust me, folks, I was I was involved in spending a lot of this money, so I’m I’m I’m just as guilty as all the rest of them.

The pithy quote that I that I will use is that, like, Hadoop was, you know, answering a whole bunch of very, very interesting questions that nobody in the business was asking.

Right?

Like Yeah.

Like, building all of these interesting correlations and building all of these all of these interesting insights, and it’s like, oh, wow. Did you know a a is heavily correlated with b or that I could build some predictions to to to to model, you know, like, when people will show up for work based on their eye color?

Like, oh, okay. Corollary. Right. That’s interesting. But is anybody asking for that? Right? Have have we actually figured out how we’ll we’ll we’ll operational these insights, let alone you know, does anybody even really care?

But Yeah.

That’s much different than, you know, chat g b t getting a passing grade on, like, an actual Wharton MBA, which I read or email.

Exactly. So I’m I’m sitting in the Monday morning keynote, and I’m I’m like, I’m fired up. Right? Like, Carter does this stuff so well.

Right? Like, they just do this so well. It’s it’s, you know, it’s it’s it’s like a rave for for old data people like me. Right?

Like like like, there we are, and I’m all I’m all pumped up. And, you know, I was like, welcome to the stage. I’m like, yes. Yes.

Yes. And then then it’s then it’s like, hey. Slow down, folks.

Yeah.

So you’ve all heard about to a degree of the screaming on stage.

Yeah. Yeah.

Not quite. I love this company.

We but but kind of. Yeah. And then it was like, but, hey. Let’s take it easy on AI.

And I’m just kinda like, okay. Wait a minute. Hold on a second. I I’m not so sure that this is a great metaphor or Hadoop is a great metaphor.

But, I mean, but that being said, I mean, great presenters as usual. Great great content as usual. Yeah. And the content that I was seeing around what Gartner calls active metadata, we can talk about that in more detail.

The content that I was seeing around what Mark Bayer calls the active metadata double helix, It just bad bad marketing there. I’ll I’ll maybe I’ll work with him and try to figuring that out. It’s just sounds a little complex. But other things related to augmentation and and and automation in the data management space were just really thought provoking and and well worth the time.

So that was one of the that was one of the the the very kinda high level kinda key takeaways for me that that, that I I’m still thinking about, you know, two weeks later. That’s kinda what you want from a conference like that.

Yeah. But it sounds like it was a healthy mix of kind of, hey. Hey. Here are some things that are perhaps on the bleeding edge, some new technologies, things to look out for, but then you also have that that you know, we had Brian Zank, a Prophesy customer, for Messick. You’re gonna speak on but you’re saying very core data management principles, build a business case, you know, start from, you know, the the right foundation when trying to build these things. So it’s it’s kind of a healthy lens. Right?

Yeah. Yeah. And this is this is the world of MDM, and I would argue the world of data management writ large, which which is there is a shiny thing out there. Right? Augmented data management, automated it’s there, and it’s becoming more and more real.

Meanwhile, you still have a lot of people who are struggling. Many of you of you, I’m sure it’s the reason why you’re you’re you’re here today, are kind of struggling with some of the kind of the basic tenants, right, with some of the blocking and tackling of data governance and MDM and data quality. And we can’t forget that that part, and we can’t forget the basics. We can’t forget the foundational components of what we all do for a living and and and focus one on the other.

It’s it’s really about a balance. And that’s that’s what really kind of made, you know, the the presentation for for Brian Zink awesome tangible. He he went from, hey. We have a problem.

We’re Purina company. We’re Nestle Purina, a huge brand, and we’ve got this problem in that, you know, we’re we’re we’re selling pet food, and we really don’t have much of an understanding of of of our total universe of users, like our our a Pet three sixty or a Vet three sixty of vets are a huge part of their of of their value chain. And and and just solving that problem, like a a a Vet three sixty and how would you use MDM and better data quality and better data management to do it. And he actually gave some tangible advice, about how to do that and and some fairly innovative approaches from an organization perspective and and, where the data and analytics function actually should live.

In the case of Purina, they they put the data and analytics function into the business unit. Right? Like, what would otherwise be considered Right. What would otherwise be considered a kind of a a centralized data and analytics function that would typically be in a CIO organization.

They they actually put it into a business unit.

So some some pretty cool stuff there. So so point taken. Right? Like, as much as we wanna kinda rush towards the the shiny future, you’re not gonna be able to do that if if you don’t have a core foundation.

Right? If you don’t have a base level of of maturity when it comes to MDM or data quality or data integration or all those things still still critically important. And at a Gartner conference, you’re gonna get some of that, but this was my kind of, like, my core concern number two that I that I made in my my blog post, which was, you know, Gartner is really good, really good. I would argue the best in many ways when it comes to high level strategic insights for CDOs and CIOs.

Right? If you’re a chief data officer, if there’s if there’s one or two conferences that I would go to, it would be it would be Gartner, and it would be the CDOIQ conference this later this summer in July. I mean, there’s that’s it.

There’s those are the two that I recommend going to. Because you’re gonna have thought provoking exchanges.

You’re gonna meet with birds of a feather.

You’re you’re you’re going to be challenged around your strategy and your road map and making sure that you’re thinking about that stuff down down the road. But what there’s a real shortage of at at Gartner is for that core audience, and this is something I didn’t make that clear in my in my blog post or on LinkedIn, but for that core audience of senior vice presidents of data and analytics, CDOs, even senior directors of data and analytics at extremely large companies. For that audience, there’s not a lot of content about the kind of the a, b, c’s and one, two, threes of how you go from where you are Right. To where you wanna get to.

Right? And and I you could say, oh, well, that’s really not for that conference. That’s for some lower for conference that’s more focused on the doers instead of the the strategizers.

Mhmm. But even the strategizers, I would argue, and I’ve I’ve said this many times in many posts. I’ve I’ve I’ve written a couple of white papers about this in terms of the importance of having a high level road map be integrated into your data strategy. So strategy is not road map.

But at the same time, if you’re working on a strategy, you should have some idea what that road map looks like at a high level. Right? We’re not talking details, at least a high level understanding of what that road map looks like, which would include some high level milestones of, okay, if I wanna get to this future state, what are the some of the the the big four or five steps I need to take? What are the things I need to be focused on?

What should I do now versus what should I do later? Right? How important is governance in all this, and should I drop everything and just be focused on data governance? Like, the how all the pieces fit together, right, in a data management ecosystem?

If I’m a CDO or VP of data and analytics or even a senior data senior director of data and analytics, there was none of that at Gartner. They they don’t Right. They don’t put those pieces together to say, hey. Okay.

At a strategy level, that’s just where you wanna go. K. Got that check. But here here are the four or five things you should be thinking about along the way, and here’s some order of operations.

Right?

They just don’t touch on it. And and I think that’s that’s really a high level function of a lot of folks in Gartner who are And this isn’t a bad thing. I’m not being negative because these are some incredibly smart people, like, way smarter way smarter than me, but they’re career analysts, and and who haven’t kind of been in the trenches with, you know, putting shovels in the ground and actually and actually doing this stuff.

So Yeah.

It’s almost a gap. Right? Where you have the the high level strategic thinking, what you’re trying to do, what your active strategy is, and you have, of course, the vendors who can get you solutions and things you need to kind of put those things together. But like you said, those building blocks actually actually put a roadmap together.

Yeah. I mean, order of operations. Right? Like, when I was a gardener, I would hear that all the time.

Right? Like, okay. I’m there’s I’ve got all these pieces. I know I need to focus on data quality.

I know I need to focus on data governance. I know I need a data strategy.

I I know there’s all these other things that I should be focused on. I need to build out the data and analytics team. There’s I need to be focused on, you know, my my culture. Right?

Yeah. And and becoming more data driven, and and there’s this my consultants over here is telling me I need to focus on data literacy. And if I don’t focus on data literacy, nothing’s gonna work. And there’s so there’s all these moving pieces.

And and and where do I start?

Right? And, man, Gartner would go from being a really good conference to an amazing conference if it was able to figure some of that stuff out. Yeah. Because there’s a gap.

Because there’s plenty of conferences where you can go. We’re sponsoring one later this summer. It’s the, DGO, DG IQ data governance information quality conference in San Diego, July first week of July. We’re sponsoring that.

I’ll be there. I’ll be speaking.

Where it’s very focused on kind of the doers and and and and management level and senior management level about how to do governance and do data quality and do MDM. But there’s a big gap between that and the, hey. Here’s your strategy. Mhmm.

Absolutely.

Yeah. And speaking of Gartner, you’re saying not a lot of concern about any economic challenges, not not a lot of talk of the reception before I don’t know.

It’s maybe I live in it. Like, so I live in the East Coast of Florida, and often, when I’m trying to save my company money, which is often, I’ll fly out of Orlando. I do have a little local airport here. It’s called Melbourne, Florida, but but there’s not a ton of flights, and they tend to be expensive.

And maybe I’m a little bit I don’t know what the word is.

Misguided because I I spend a lot of time in Orlando, but it’s busy. Right? Disney, busy crazy busy. Go to Gartner, which is on it’s in literally in Disney. It’s on the ground. It’s on the park grounds. Busy.

People want it look busy busy. Go to the Gartner event. Busy.

People I I hundreds of people we talked to. Our our booth was really busy. That’s awesome. It’s exciting.

So hundreds of people that I talked to. And that the issue of, you know, kind of global recession, economic uncertainty, all those things, it really didn’t come up, and I didn’t sense that vibe at all in any of the presentations, in any of the conversations that I had, whether that was informally at the booth or through dinner conversations that I was having every morning or or every night or or lunch and breakfast conversations.

It doesn’t mean it’s not gonna happen. It doesn’t mean it’s not concerned. I just I just didn’t hear that much of it, as opposed to, you know, you know, one year ago, even just going back to the Gartner conference that happened actually in August because, yeah, because they had delayed it because of COVID, that that was that was a topic that people were talking about that. But now, I I I they’re not. So I don’t I don’t know. I I I don’t know. There there just wasn’t a lot of talk of it, and I and, like, that did kinda surprise me for sure.

Yeah. Absolutely. So I wanted to lead with Gartner and takeaways just because you’re really just kinda coming right out of your those are the most timely thing. But, you know, we did have a featured topic today in today’s episode.

Data fabric demystified.

You know, you had a post on LinkedIn recently about the hype around data fabrics, you know, whether the hype kinda matches reality, what’s the actual time frame of people actually commercializing such a thing. Could could you just touch on that and kind of what you think the the future is for data fabrics?

Yeah. So I really expected going into the Gartner conference that that would be, like, the number one drumbeat, and and it wasn’t.

Yep. Doesn’t doesn’t mean doesn’t mean the Gartner is still not promoting it and hyping it. They most certainly are.

They believe that it is progressing around what they call their their hype cycle, which is, you you know, going from the the shiny thing to something that can actually be operationalized and is driving value for organizations.

Disillusion, man.

I I I am absolutely a believer.

So everything I’m I’m about to say, is is within the context of I’m a believer in the data fabric, but let’s step back. Mhmm. First of all, what is the data fabric, versus maybe a data mesh?

But a a data fabric to me and this this is my kind of slightly pedestrian kind of slimmed down version of of of of how to define a data fabric. And I suspect some of my peers at Gartner may ex peers at Gartner may may take issue with this because it’s it’s pretty simple, but I think it’s actually digestible.

And and the way I define a data fabric is is a data management architecture. So that’s what it is, first and foremost, a data management architecture Mhmm. Wherein data itself starts to inform its own classification and use.

So just it’s a data management architecture where data informs its classification and use, meaning governance, meaning quality rules, meaning integration patterns, meaning how do I use the data? What are the rules for using the data? How do I classify the data? How do I label the data? How do I manage the data? Data itself is going to start informing all of those things.

Mhmm.

So I have a podcast coming up where I spend an entire hour going through all of this. So I would welcome you to, check out future editions of the CDO Matters podcast where I have where I go into a lot of detail around this. But for for today, the way I try to explain this is that, historically, let’s go back to that spectrum I was talking about. Right? Full automation.

Where did I start? Left. Right? I don’t know. Full manual data management, automated data management. Go back to a world of manual data management, right, where human beings define the data models.

Human beings define the data quality rules. Human beings define the integration patterns. Right?

Based roughly off of some governance patterns, governance policies, and procedures that were all defined by humans as well based on specific outcomes that you were trying to drive, rules that you were trying to maintain, compliance, fraud, what whatever the desired outcomes where you would kinda work backwards from that and say, okay. Well, here’s how I define a customer. Here’s all the rules and if you can if if if you can kinda conceptualize a chat GPT model here and and and work with me. I’m not I’m not trying to turn everything into chat GPT, but, hopefully, this will sync sync in. If it’s possible for computers and AI, largely, AI, to use all data from the Internet prior to twenty twenty one as a means to make informed decisions to basically any question.

Right? If if if if we now live in a world where I can ask a question and get a relatively solid, high confidence, predictable, maybe not as predictable. This is this is a v one technology here, guys. So so acknowledge that GP is not this is a v one technology. It’s still pretty amazing.

But if if we can at least get our heads around the idea that we can use that all of that data is a training set, and that training set can allow us to have some pretty insightful answers around just about any question.

Mhmm.

Could we do the same thing in the world of data? Could we use all of our corporate data, all log data, all transactional data, all all data everywhere within our organization. And we’ve got a lot of it, folks. Right? It’s all over the place. Every application is developed is is spitting out logs. Your your local network is spitting out log data and on and on and on.

Imagine a world where we use all of that data as a training set for governance policies where you could actually ask when is customer data accurate.

Mhmm.

I would argue I would argue based off of all sorts of data you already have. Log data that says when transactions succeed or when they don’t succeed. Right? Log data that shows when data or or when a process is completing successfully or is not completing, or maybe it’s slowing down, or maybe there’s some some situation where people are actually involved in updating data that that an AI engine could look at and say, oh, okay.

Well, there’s a problem there. What was the problem? Uh-huh. It was associated with this character type or this character width.

Right? You could very easily visualize. I think you could at least next to GPT, a world where really, really smart AI is actually able to tell you when data is correct, right, when it’s incorrect. Could tell you what the optimal integration patterns were between system a and system b, where they could tell you what is master data, what’s not master data.

Know knowing that master data is is data that is widely shared across organization that needs a common set of business rules and and governance policies procedures assigned to it. I think you could very easily see that world thanks to chat g b t. That’s the data fabric.

The the data fabric is a is a world where it where advanced technologies, primarily knowledge graphs, not exclusively, primarily knowledge graphs with a heaping dose of AI layered over top of it, where the data itself, thanks to machines and AI, is is actually telling us the best way to manage it.

Right? Where where the where the entire architecture of data management is a reflection of what the data is telling us is best and optimal for specific use cases. So this is not just about by the way, folks, this is not just about data management, like classic back office data management. This is even business operations optimization.

You you in theory, the same thing could be true about, well, what should I be charging my customers? What’s the optimal to my customers? Who’s the best supplier to fulfill this material need based on historicals?

Right? So it’s not just about how we manage data. This is this is gets into business operations as well. Yeah.

So it’ll be interesting to see how how Gartner kind of kinda splits that because the same the very, very same technology here, could could be enabled to to to fuel data management decisions or just even business management decisions for that matter. So that’s the high level around the data fabric. It’s still being talked about by by Gartner. It is still very, very real, but we are talking probably, like, seven to ten years out.

We got a long way to go, but, I mean, things are accelerating. Like, a year ago, I could never even imagine a chat g b t. Obviously, people were working on it. We’re working on it.

But, like, a year ago, like, I would have been like, nah. Right? Now I ask I ask Chad GPT, like, the the top five things that I was asked when I was a Gartner analyst. Right?

Like, what’s the best way to start a a data governance program or, you know, like, how do I define data quality? I mean, all of these things that I was asked as an analyst. The answers he gives, they’re pretty good. Not great.

They’re they’re not great, but they’re pretty good.

So but but you don’t get this, folks. Yeah.

That’s right. Yeah. You guys said. We’d pay for that. Alright.

Got some chance EBT. I mean, this this is worth the price of admission right here. Right? You know, the entertainment value of your, of your your data bot here. So Absolutely.

Absolutely.

So we also hit a a big milestone recently. So you hit twenty episodes of the CDO Matters podcast last week, and you went back to where it first started. You had your buddy, Scott Taylor, data whisperer back on. What was it like getting to episode twenty?

Well, so first of all, one, thank you to my team, including you, sir, and and and everybody else who helps do this because this does most certainly take a village.

I mean, I didn’t think that this old dog had a lot of new tricks left in him, but apparently, I do, which is which is pretty cool.

I I, you know, go back a year and a half. I never thought that I would be, like, a content creator. Right. I’m still waiting for LinkedIn to reach out and give me whatever that is that their their content creator starter kit that I see sometimes my friends on LinkedIn all have.

I I have some envy there. But, twenty episodes of the podcast has been it’s been a lot of fun. It’s been it’s been a lot of work. We’re all kind of learning as we go.

I’ve it’s I’m still trying to figure out, and I think we’re we’re still trying to figure out what, you know, what the audience really wants and what is it responds to.

You know, we’re we’re kind of figuring this out as as we go.

There’s been episodes where I won’t use any specifics at all, where where I’ve assumed that, you know, hey. This is gonna be a blockbuster. Right? We’re we’re gonna get an we’re gonna get an Academy Award for this, the, you know, the podcast academy awards, whatever those are. And it’s been it’s been, you know, it’s been reasonably well, received, but not incredibly well received. And then there’s been others where I thought, okay. Well, this is this is good.

You know, this is, you know, some one zero one type stuff that could be valuable.

But, you know, like, it’s it’s not it’s not going to be, you know, you know, the academy award winner that has been incredibly well received.

Yes.

So so a lot of the preconceived notions that I had about what the what our audience, you know, really wants and doesn’t want have have have been difficult to kind of pin down. So I look forward to to to working with the community and interfacing more and more and more with the community, getting input on on what we should be focusing on and what we shouldn’t be focusing on.

Mhmm.

It’s it’s interesting, though. When I was at Gartner, you know, I had I had people walking up to our booth and and saying, hey. I I like your podcast. And I was like, are you talking to me?

Like, is there somebody behind me? Like It recognizes me. Yeah. There’s somebody behind me with a podcast?

Like, oh, you mean me?

Which is kind of fun. I never I never expected that in in in a long shot. So it’s it’s been a lot of fun. It’s been fun doing the content creation. It’s been fun learning all of this.

Would love to provide more value and more insight to the community. Right? And figuring out what what that means, whether it’s talking about MDM or data strategy or building an organization or technology.

We’ll figure it out as we go. But to the degree that anybody has feedback, whether that’s through LinkedIn, this medium, any other medium, love to hear it because this is ultimately about the audience, and I don’t wanna make that sound, you know, kind of kinda too pithy. But the core reason why we’re doing this, guys, is because what I saw and what I still see, on LinkedIn and on kinda traditional channels and media is a lot of the same old, same old. Right? Like, a lot of the same things that we’ve been hearing in the data management space and the NBM space for a long time. You know? Like, do this and do this and do this.

And, frankly, a lot of it isn’t working because we’re not doing it. Right? We’re we’re we’re not aligning our efforts to business outcomes. We’re not quantifying business outcomes. A lot of the things that we’re doing from a kind of a, you know, data management perspective aren’t necessarily optimized from the perspective of best practices when we know the best practices work. Right? Yeah.

That’s why we started doing all this was we wanted a different voice. We wanted a different perspective, and I had a theory that, well, maybe, you know, there are different channels. Right? May maybe data leaders are going to different places that they didn’t go to in the past.

Right? And maybe we can touch more people by having a podcast. Maybe we can touch more people by doing stuff like this on LinkedIn. Maybe we can touch more people by me being on LinkedIn every day, to get a new message and to get new insights and new perspectives on new media.

Not the podcast or new media, but there’s not a ton of data related podcast out there, frankly.

So that’s what it’s been like.

Twenty episodes been a lot of fun. Good to reconnect with my buddy, the the data whisperer, mister mister Scott Taylor. We’re we literally sat in the kitchen for an hour and talked about data, and I drank beer. He drank beer too, but I wouldn’t call Bud Light. Wow.

I guess he technically is beer, but says the guy with a gluten free beer.

Right? This is like they make gluten free beer. Yeah. They do. Yeah.

I had a a Florida tap water, the Bud Light. We call it that.

Florida tap water. Yeah. Bud Light and Miller Light. Yeah. I think you may perhaps.

But still, it was it was it was great to connect with Scott. He’s there I’ll he was my first guest. He was guest number one. Yeah.

And, yeah, I I I wanted him as guest number one because he has, like, sixty thousand followers or something on the weekend. And and and every single one of them is well earned.

But, really, the reason why he was guest number one was not because he has a lot of followers. He does.

But because he to me, Scott Taylor epitomizes doing things a little differently. Right? And breaking away from the same old, same old, kind of finger wavy, you really need to do these things. Right.

He’s a different he the way that he does things is different. He uses children’s books and finger puppets and and pithy little, like, you know, voiceovers and and songs and all this just all this other stuff that that is clearly working, and the guy knows his stuff. When it comes to data storytelling, I there is nobody better than him. He is has an uncanny, unbelievable ability to take complex technical stuff where data people like us will will will focus on the how, we’ll focus on the technology, we’ll we’ll we’ll talk about all of these kind of very technical things.

He has ability to take that and actually transform it into words that c level people will go, oh, oh, nobody’s explained it to I I’ve watched this, guys. I’ve seen this happen in real time. Mhmm. Where where where data leader will say, hey.

I’m kinda struggling with a or b, or I’m having a hard time getting my my the people, you know, in the C suite to kinda get a line behind what I’m trying to do here. I’m having a hard time doing all these things. And then I’ve heard Scott say, well, you know, think have you thought about, you know, explaining it this way or telling it this way or using this as a metaphor or using this this tool or or explaining it this way. And and I’ve heard senior management people say, oh, wow.

Okay. Bingo. That’s what I need to do. Thank you. So to me, Scott epitomized kind of the breaking away from the same old, same old of data management best practices in this Right.

Without doubts.

And I couldn’t have been more thrilled that he was number twenty, and hopefully, he’s number forty and sixty and eighty one hundred. Exactly. Yeah.

Yeah. Yeah. I mean, even in the title. Right? It wasn’t, you know, how do I get a data program off the ground?

How do I sell it to leadership? How do I implement data x y z? It’s, you know, the art and science of data storytelling. How do you get back to the the core and the basics of actually getting people to care about what you’re trying to solve and getting people to buy into it?

Yep. Exactly.

Absolutely.

Absolutely. And and who’s your guest that you’re speaking to in a few weeks on the the data fabric demystified?

It’s me. Oh. I yes. I have a very compelling and engaging guest. It’s me. I’m the alter ego.

Exclusive. On on that one, I I thought I I actually thought that I’d probably be best to help explain that versus having a guest.

I was at kinda I I I could say ground zero. I don’t think that that would be fair. I was involved in the creation of the data fabric narrative at Gartner. Right?

I was doing peer reviews. I was I was providing feedback on how to explain a and how to explain b. So so I I have some rather unique insights here into something that was, I would argue, and Gartner would largely agree with me, Gartner created. Right?

And and and I was there. Right? I wasn’t the person doing the creation, but I was in the factory helping.

So I thought that having kinda me help, you know, focus on the data fabric and just me instead of kind of getting another guest involved that that, I thought it was best for me to do it. So it’s just me. Little old.

Makes makes total sense. Total sense. Yeah.

Any, one thing you’re saying What was that?

Go ahead.

Oh, so one thing you had on LinkedIn recently on, just defining data and its business value and, like, just just the perspective of people. You know, if you look at it purely semantically. Right? Datas can be just an output or an exhaust. Right? You’re saying it’s Oh, yeah. The the report you’re getting from your your your network stats or something.

What is it that people have that’s kind of a misconception about data and what it can actually do for you?

Great question. So I I had some really, I’m continually amazed by the the quality of interactions and the quality of insight and the quality of provoking thought. Is that is that a good sentence? I I don’t know. The thought provoking nature of posts on LinkedIn. Right? Yeah.

You know, I wasn’t real when I was at Gartner, I wasn’t engaged on LinkedIn.

And and and most Gartner analysts analysts really, really aren’t for a lot of different reasons.

But but I really wasn’t engaged. And leaving Gartner, I and and coming to prophecy, I really got engaged and I’m just getting blown away by the quality of the thought, the quality of the the content that people are providing, and the responses that I’m getting.

And and I had a I I I made a post recently really kind of talking about the data fabric, right, and and how data could be used to to start automating decisions about how to manage data.

Right? And some of the responses I got back, which I at the time, I I I I I thought were a bit curious and and and arguably incorrect, but they still made me think. And some of the responses were, well, why would you do that data? It’s just exhaust.

Yeah. Right? Like like, data is this byproduct. It’s it’s like the excrement of the business.

Right? And and at first, I kind of like, you know, no. Wrong. Wrong. Mhmm. But as LinkedIn often makes me do and as I find myself doing more as I age, thankfully, I step back and I I I thought about it for a bit.

And I’m like, okay. Wait a minute. I get that. I I can see that. And and the metaphor that I kinda came up with in my head, whether it’s a good one or a bad one, I don’t know.

I’ve never been much of a boat guy or into boats, but, the metaphor that I came up with is if you’re always looking backwards.

Right? Data is kind of looking backwards, arguably, some of it. Obviously, you can use data to create models and predictions and do forecast and all that stuff. But but if data is something that happened before right?

The metaphor I came up with in my head is, like, it’s like it’s being on a boat and then looking backwards, and and you can drive a boat in a completely straight line off a wake. Me meaning, if you were looking backwards, if you’re not even looking forward and you’re driving a boat, you can keep it going in a completely straight line if you’re just looking backwards. But if you wanna turn, if