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Hello and welcome.
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I'm your host, Barbara Neshaw, and this is Inside Applied Data Governance.
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Whether you're just starting out in data governance or you've been working in the field for years, this podcast is for you.
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Will you learn real-life lessons and practical advice on the people behind the Applied Data Governance Practitioner Certification?
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To learn more about the ADGP Certification Program, visit training.dataversity.net.
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And today we're talking to John Ladley, one of the key contributors to the ADGP certification, about the importance of data governance for organizations.
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Let's jump right in.
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Hi, John, and welcome to the show.
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Hello, Barbara.
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How are you?
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Good to see you again and talk about data governance again.
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It is wonderful to talk about it.
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I'm so excited to hear all of your insights.
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But before we dive in, let's just talk a little bit about you and your background.
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So the first thing, just tell us about you and um your role in data governance.
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Yeah, the role in data governance, I have been connected with the data corner of technology since the late 1980s, actually.
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And uh just kind of evolved along the way.
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Uh along the way, I got to a point in the mid-2s that the literature out there was abysmal.
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Uh it was not useful.
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Uh, some of the literature we had was harmful.
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So I started to roll up my sleeves and wade deeper into it, less of a practitioner, more of a um educator, I guess, and a researcher.
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And I ended up writing uh two editions of uh a book.
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The long title is Data Governance, How to Deploy a blah, blah, blah, blah, blah, a data governance program.
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Um uh third edition is on hold right now due to AI and figuring out how to stuff all that AI stuff into the same number of pages we're allowed to have.
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So, but um, that's how I got into it.
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I, you know, I've been more than anything, I will tell people I am a practitioner.
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Uh, everything I talk about is something I've done.
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In fact, I will not talk about something, or I will not relay a concept I've heard unless it's actually been tried in the field.
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And that's how I got here.
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That's great.
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You have a wonderful background and so wonderful to have it as part of our certification.
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Thank you.
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Speaking of that, um, how did you get involved?
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Um, I was asked to be a reviewer of uh the initial uh conceptual versions of this uh of the ADGP.
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You're right, we're gonna have to just be careful saying that while we do this podcast, the ADGP.
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I enjoyed the review process and it became apparent that given the uh um frankly, the dominance of my book, that a lot of the material was coming from the book, even from other people.
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And it was probably a good thing that I uh stay on board with the thing.
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So uh Dataversity and I, we just kept rocking and rolling along with a handful of other brilliant people as well, uh, for this.
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Um and uh here we are.
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Yeah, well, we couldn't have done it without you.
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Definitely your insight and thank you core concepts from your book.
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Thank you very much.
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All right, well, let's jump right on in to the wisdom that you shared with us for the certification.
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Uh, we have a few questions here just about um, you know, what was included in the certification and within the body of knowledge.
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Sure.
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From your perspective, why is applied data governance important for data practitioners?
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You know, not just in theory, but also in practice.
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Yeah.
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And and that sounds like an obvious question, you know.
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It's the, you know, I have a podcast too, and I do a lot of interviews, and there's always that set it up question, right?
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You know, why are we here?
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Well, but this is a powerful question, okay?
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Um, the key word here is applied data governance.
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We the world of digital stuff, the digital sources, digital economies, digital organizations, data-driven organizations.
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You you immediately lay out that data is important to the operation and success of any sort of organization.
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And the minute you do that, you have to have some sort of oversight or controls or standardization or guardrails or a host of other synonyms that we can marry up to uh to to uh data governance.
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Um, in fact, I'm at the point where I de-emphasize the word data, it's just governance.
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And in practice, and again, the key here is that's why this is such a good question.
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In practice, no complicated organization, and our world is complicated now, does its complicated things, whether it's buying and selling, or moving, or serving, or shipping, or creating, or serving, or whatever, does that without some sort of controls, oversight, regulation, compliance, standards of behavior.
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You cannot be successful without it.
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Since Frederick Taylor was scratching his head about organizational theory, since the first steam engine started to drive the first pump in uh England in the 19th century, we have had to have oversight of complicated things, or else the wheels came off.
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With data, we've discovered in the last few years, and this is one of the reasons I wrote my books.
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It says it in the uh in my my preface before, you know, at the beginning of the book, that I wrote it out of blind rage.
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And and that and that's absolutely true.
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No one was saying anything that this was a business capability.
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All right.
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So, in practice, this is something you have to do.
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If you're going to do all this wonderful stuff that we have oversold, vendors and consultants and authors and all this, and and we we all yell and hoot and holler and and and run around at conferences going, oh, metadata just is so wonderful, and things like that.
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If we're gonna have any of that stuff, we gotta uh it's gotta be understood as mandatory.
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And that's why it's so important.
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You can't go without this.
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It's it's it's that simple.
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You have to do it.
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Yeah, that's a good way to sum it up.
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You know, and really you you answered some of it with the you know, right now, but why was it so important to have that in the ADG body of knowledge, especially when we look at business drivers, you know, whenever we're thinking about it?
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Yeah, because you know, carrying forward the first question, you have to have it.
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Why do you have to have it?
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Because if you don't have it, the organization's success or chances of success will be compromised.
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Various resources of the organization will be placed at risk across all the meanings of that word risk.
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So you need to connect where the organization wants to go with what you want to do with the data.
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Um, and candidly, and I still see it to this day.
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Organizations will say, Oh, um, we went to the conference and we're doing analytics or we're doing uh master data management or we're doing AI now.
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And the guru says we need data governance.
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So Henry here, go down the hall and start doing data governance.
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Um, just you know, as a sidebar, Barbara.
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Uh you we've chatted obviously many times, uh developing the ADGP.
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And uh I'm I'm getting to a point in my life where I can look back and be the real crabby old guy on the porch.
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So I tend to be candid about this stuff.
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If you go into this with that, well, look, I know that data governance is metadata and data quality and all this, and I know the systems are lacking in that, which is probably true.
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Um, we're just gonna go do things and the alignment can come later.
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I don't want to talk to those business people, or they're mad at me, or whatever.
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Well, well, well, you're you're you're you're you're throwing darts.
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You might get lucky and do it right, but you are wasting resources, you are wasting money, uh uh because your attention will not be where it's supposed to be.
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When I visit a program where someone has said, we've been trying this three times, it hasn't worked three times.
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I will go back and look at how they did it, and I will find one, no alignment at all the first time.
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The second time we had a meeting.
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Then we ran out of the meeting because the business people were looking at us with dirty looks.
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And and then, and that's it.
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And I will go, look, no, you have to itch up your pants and do this.
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Um, if you don't have it, you're missing all the valuable parts.
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You're doing, I'm trying not to be long-winded with this answer, so I'll sum it up this way.
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If you you're missing the parts that are important and that the organization will notice.
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If you just do metadata, data quality, look, they're important.
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I'm not gonna argue with that.
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But that's that's your baseline, that's your table stakes, that's your blocking and tackling.
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You hire someone to build a house, the assumption is they can saw a board and make a straight line.
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They can nail two pieces of wood together, they can run a wire and not burn the house down.
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That's table stakes.
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Data people hate to hear this.
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That's plumbing.
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That's your darn job.
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Don't come to me saying you're special with that.
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Come to me with if I'm a CEO, come to me with something special.
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And that means you've got to understand what special is to me.
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And that's the alignment conversation.
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Important to align with those business drivers for the CEOs and leaders.
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Yeah.
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Well, and if you're not for profit, organizations have goals, organizations have uh policies, organizations have guardrails.
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All of those are things that can shape shape how you do things, right?
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And and they have to all be considered.
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You know, and our next question just falls right into that.
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Is what do organizations or practitioners or even leaders often misunderstand or underestimate about data governance?
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I'm gonna divide that into two, into two responses here.
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It's the practitioner and the leader.
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Okay.
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And let me go to the practitioner first because that's the one that tends to surprise people.
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So I'm your I'm a data architect.
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Um I've been looking at study data governance for a while, and I own I know about data models and data architecture and uh semantics and semantic layers and all this stuff.
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And that that's my uh thing.
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And along comes uh data governance, and you go, many people do, especially for those of us that were there at the beginning, when I heard Gwen Thomas use the phrase governance, I won't say the year, it's it's hard to say it.
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Um, I went, that's the word we've been waiting for.
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Now, my background is important to note here.
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My background is not technical.
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I have no formal academic training in computer science or anything.
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I was one of those people in the 1980s that was grabbed because I could spell IBM.
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And I was taught to code, I was taught to be a technical person, and I did it because someone said, someone whispered in my ear, uh, no lie on a phone call, John.
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You really need to take this job.
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I said, I don't want to do computer stuff.
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They're not going to catch on.
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That's that's a quote.
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I actually said that.
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And and my friend laughed.
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And uh, and and then she said, Yeah, but I'm getting paid$18,000 a year to write code.
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Now you have to set the time when this when I heard this, this was in the early 1980s.
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And at that point in time, at$18,000, that was a fine middle class living.
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Times have changed.
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Yeah, and when I when and when I heard that, I said, oh, oh, really?
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18.
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I said, so so um, who do I call again?
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But my background was accounting.
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I mean, I've written the I've written the CPA exam, all right.
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I know about control, I know about these things, and immediately I I uh in my whole career I'm using this training of mine this way.
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And uh, but all the people that we deal with tend to go, a lot of people tend to go to computer science, they come in, it's a technical thing.
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They they don't understand that the basics of this are not what you sell.
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Uh is metadata required?
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Of course.
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Uh is a data model useful, of course.
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Do I need to grab the CEO and force them to accept the wonderfulness of the tools of my trade?
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No, they don't give a damn.
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All right, they don't give a damn.
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Um that is a huge misunderstanding.
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The misunderstanding is that what we do in the data profession has relevance in conversation to leadership.
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And it doesn't.
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What you need to understand is what we do with the tools of our trade.
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If I'm a carpenter and I'm moving into a new house, I don't care that my carpenter used a table saw or uh a radial arm saw or a circular saw.
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All I know is the house isn't gonna blow down in the wind.
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And that's all I care about.
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And and you know, what we do in our business is it's like I'm closing on the house, and before I close on a house, I have to give all the carpenters a pat on the head in a candy bar or a slice of pizza because of their wonderful carpentry they did.
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No, because that they're already on and look, that's an established profession, they're already on the next job, they could care less, right?
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That's where we think we think we're special.
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We're not.
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Right?
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We're not in terms of the tools of our trainer work.
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What's special is what the organization can do with that.
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And that's where we uh underestimate the work, and that's where we go wrong.
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Now, on the other side, let's talk about business.
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Over the years, we said, oh, the water cooler conversation, oh, the business just doesn't listen and they don't get it and all that.
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Well, we've never told them the right way.
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I put all of that 20 or 30 years of organizations where the business leaders say, I don't get that, I'll talk to you later, or we're too busy to do that, we'll deal with it later, or saying, Yeah, we support you, and then when you leave the room, their fingers are crossed behind their back, and I go, I ain't gonna pay any attention to this clown at all.
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All right, all of that is because what we told them was baloney.
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Okay, it was all that wonderful metadata, we got to do this.
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Yes, we do have to do that, but again, they don't care.
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The message for them was inadequate.
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Now, some of the message, the minute organizations around the mid-twos, the mid-nots into the teens of this century, businesses began to get serious about analytics.
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They began to get serious about using data more effectively.
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I'm not going to say data-driven, but data influenced.
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We'll use that, we'll use that phrase, okay?
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The minute they stepped across that line from get the report to let's uh let's answer a question we've never asked before.
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Analytics, right?
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The minute they stepped across that line, they became obligated to understand some of that process.
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There's not a CEO in the world who runs a manufacturing company that shows up at work and says, Well, I came out of finance.
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I don't give a darn about how we put widgets together.
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No, they go out in the factory and they learn a little bit about how widgets are made, even if they come from finance or marketing.
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All right.
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The ones that don't are the ones that are the CEOs that's that end up overseeing the dissolution of a large company.
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And there are hundreds of examples in modern business practice that when a CEO comes in who's a pure numbers person and doesn't get the culture or the model, the organization dies within 10, 20 years.
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All right.
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Um we need to have leadership understand.
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Now that they go say, I got to be data driven, now I want to use AI.
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Now they under have to understand some things like we need a data supply chain, and you need to have oversight of that data supply chain, and you need to back me up when I tell marketing they're messing up the data supply chain.
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And that's that's the part where leadership falls really short.
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They misunderstand, they underestimate that this governance of data.
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I don't even say data governance anymore, Barbara.
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I say governance of data.
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The governance of data is just as important as governance of your money or governance of your your uh employees or governance of the parking lot passes, okay?
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Or governance of expense reports, okay.
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It's just as important as those, and you cannot ignore it, and you need to know what you're talking about.
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And and once they embrace that, and we're seeing evidence now.
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Um, in my last podcast, uh our guest Stan Christians from Calibra talked about they have CEOs and leadership teams going to classes now, learning fundamentals of data management.
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So that so yeah, so now so that's where we and we have this big gap between the two sides.
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Please listen to me, please listen to me.
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Oh, I I don't have time for this.
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Oh, wait a minute.
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Maybe I have time for some of this now.
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Okay.
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And now, but then the other one, now that goes back into the the practitioner side, which is oh, great, you have time to listen to me.
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I can't communicate where it's a damn.
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So so we're working there.
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Bless all the people out there hardworking right now.
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We're getting there.
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But uh it's been a long road.
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Has been, but it seems like the tide's shifting a little bit with the reliance on AI, and now people really need to understand the governance of data that you're talking about.
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Another story to enhance this conversation, if I may.
00:20:02.319 --> 00:20:02.640
All right.
00:20:02.880 --> 00:20:03.359
Please.
00:20:03.599 --> 00:20:07.279
Uh so AI pops up uh summer of 2024, right?
00:20:07.440 --> 00:20:10.400
We all get our chat GPT subscription, remember?
00:20:10.720 --> 00:20:17.759
Yes, and we all say, I would I would like a picture of my brother, but I want him to look like a cocker spaniel.
00:20:17.920 --> 00:20:20.559
That's how we all start to use AI, right?
00:20:20.640 --> 00:20:23.599
And and of course, that's it's off to the races.
00:20:23.920 --> 00:20:28.559
Um, but it doesn't take long to realize this is really important stuff.
00:20:28.880 --> 00:20:36.720
And I was like, wait a second, I'm I'm uh uh 68 years old.
00:20:36.960 --> 00:20:39.359
I don't, I'm not gonna learn anything about this.
00:20:39.519 --> 00:20:42.319
I'm gonna just stay a data guy.
00:20:43.119 --> 00:20:51.119
I'm gonna put AI in a separate track because I want to fade into the sunset without having to learn anything new.
00:20:51.440 --> 00:20:59.839
Well, it didn't take long for us to realize that AI was AI is to our century as the steam engine was to the 19th century.
00:21:00.000 --> 00:21:05.680
It is transforming society, it's gonna transform human beings.
00:21:06.480 --> 00:21:10.319
As a species, we're going to be different in 20 years because of AI.
00:21:10.559 --> 00:21:13.359
And when that light bulb came on, I went rats.
00:21:14.079 --> 00:21:15.440
I have to learn something.
00:21:15.599 --> 00:21:17.680
I have to learn something about this.
00:21:17.839 --> 00:21:21.440
I had to, you talk about misunderstanding and underestimating.
00:21:21.599 --> 00:21:28.240
I underestimated the impact and the role of governance of data when it comes to AI.
00:21:28.720 --> 00:21:31.599
Um, and now it's super important.
00:21:31.680 --> 00:21:35.119
And I've had to sit down and study here recently.
00:21:35.920 --> 00:21:37.680
Well, and it's constantly changing.
00:21:37.839 --> 00:21:41.039
So Yeah, yeah, it's a moving target.
00:21:41.440 --> 00:21:49.599
Well, that leads us to the next question, which is what does doing data governance well actually look like in an organization?
00:21:50.079 --> 00:21:54.480
Let's think about it from mid-stage, no early mid stage maturity.
00:21:54.799 --> 00:21:55.039
Okay.
00:21:55.200 --> 00:21:55.759
Yeah, yeah.
00:21:56.240 --> 00:22:06.960
Let's that we're not we're gonna again, again, um the uh The the uh person being interviewed writes on early, mid and ladder, okay?
00:22:07.200 --> 00:22:12.559
Because I think it's important to understand the ladder to understand what the mid and the early should be.
00:22:12.799 --> 00:22:13.200
All right.
00:22:13.440 --> 00:22:14.160
Sounds good.
00:22:14.400 --> 00:22:21.680
So early on, and you'll see when I get to the ladder, why I'm saying what I'm saying for the early.
00:22:22.079 --> 00:22:26.480
What you have is engagement from leadership, not buy-in.
00:22:26.799 --> 00:22:27.279
Buy-in.
00:22:28.160 --> 00:22:37.599
If you're listening to this podcast and the boss says let you sit in your cubicle and listening to a podcast, you have buy-in because they've allocated time for you to do that.
00:22:37.920 --> 00:22:40.400
But that's not engagement, okay?
00:22:40.720 --> 00:22:44.160
You need leadership as part of the process.
00:22:44.319 --> 00:22:49.200
So you've got engagement early on, and everyone's finding their way together.
00:22:49.440 --> 00:22:52.960
You have a grasp of your culture early on.
00:22:53.200 --> 00:23:00.079
You're not changing your culture, but you are trying to figure out how to get something done within your culture.
00:23:00.720 --> 00:23:08.559
You are flexible, all right, because what worked for someone else isn't going to work for you.
00:23:08.960 --> 00:23:29.440
I had a conversation this morning with a company that has to uh work very, very closely with uh master data management, with data quality problems, um, and um big serious ERP type things.
00:23:30.079 --> 00:23:41.839
Um and after some examination and cultural introspection and a few questions, we came up with the uh um direction that we're not even going to use the words data governance.
00:23:43.519 --> 00:23:51.039
We we found a tone and a pace that honored the culture, all right.
00:23:51.440 --> 00:24:06.480
And um so what I see early on is flexibility, creativity, um, engagement of leadership, and then looking for managing something and governing something.
00:24:08.559 --> 00:24:21.759
Don't care what it is, but you do not take a year to do a strategy, you don't take 18 months to do a catalog, you find something that shows what it looks like right away.
00:24:22.400 --> 00:24:24.720
And that's an early stage organization.
00:24:25.200 --> 00:24:29.359
Mid-stage, we now start to get something formalized.
00:24:29.440 --> 00:24:34.400
Someone acknowledges that yes, we did a little something over here and it looked good.
00:24:34.559 --> 00:24:38.880
So now can we formalize uh the uh the informal?
00:24:38.960 --> 00:24:42.480
Uh I use uh Bob Seiner's phrase for that one, okay?
00:24:42.799 --> 00:25:02.880
And can we also um uh appoint someone with some, and this is where you you start to mature, is there is some responsibility and some accountability for some components of your data architecture or your data supply chain.
00:25:03.039 --> 00:25:03.599
All right.
00:25:04.000 --> 00:25:09.119
And that puts you at mid-stage, and then you start to measure things, all right.
00:25:09.279 --> 00:25:19.519
So you you and and you you you you start to leave mid-stage when you are actually invited to the table.
00:25:20.319 --> 00:25:33.119
You enter mid-stage by inviting yourself to, say, a strategic meeting or a planning meeting, and going to your sponsor and saying, Hey, you need to put me in that meeting and you need to spend some political capital to do it.
00:25:33.359 --> 00:25:36.720
And they plunk you in the corner of the room and you just sit there and listen.
00:25:36.880 --> 00:25:38.160
That's entering mid-stage.
00:25:38.319 --> 00:25:50.640
Leaving mid-stage is you are asked to the meetings, and app dev teams and leadership teams start to think about data proactively.
00:25:51.599 --> 00:25:55.359
So the first half of the cycle is reactive, the last half is proactive.
00:25:55.440 --> 00:25:55.920
Right.
00:25:56.240 --> 00:26:00.160
Well, the middle stages are proactive, then the last stage we'll get to in a second.
00:26:00.319 --> 00:26:06.960
But now you're you're proactive and you're in the room and you you establish relevance as a capability.
00:26:07.200 --> 00:26:07.680
All right.
00:26:07.839 --> 00:26:13.599
Now that now the latter stage, you disappear.
00:26:15.519 --> 00:26:24.640
There is no invite the data governance person to the room because everyone has been working with it enough that everybody is pretty much self-governed.
00:26:25.200 --> 00:26:25.759
All right.
00:26:26.000 --> 00:26:29.759
Um, some will say, well, what about the data quality project?
00:26:29.920 --> 00:26:32.240
Well, no, we do data quality every day.
00:26:32.480 --> 00:26:34.000
Well, what about the stewards?
00:26:34.160 --> 00:26:36.079
No, everybody's a data steward.
00:26:36.799 --> 00:26:46.400
There might be some people with accountability of a domain or something as part of their job, not as part of a separate program, but as part of their job.
00:26:46.559 --> 00:26:51.359
In other words, governance disappears into the atmosphere of the organization.
00:26:51.519 --> 00:26:53.759
It becomes part of everyday conversation.
00:26:53.839 --> 00:26:57.759
It became, it becomes just like any other governing, all right?
00:26:57.920 --> 00:27:00.240
What about governing of money?
00:27:00.640 --> 00:27:11.759
I mean, if I'm at a marketing and it's September, and I get a note and it says this from the boss, and the boss says, Hey, October 1st, we start budget cycles for next year.
00:27:12.000 --> 00:27:17.759
I need to see your budget, and here's the guidelines, and here's the percentages, and here's the things, and all that kind of stuff.
00:27:17.920 --> 00:27:22.160
And I'm gonna say, is it I'm VP of marketing, I'm gonna do my own budget.
00:27:22.240 --> 00:27:24.559
We're gonna have our own general ledger.
00:27:24.960 --> 00:27:29.759
No, you would never ever dispute that.
00:27:30.000 --> 00:27:30.559
Why?
00:27:30.880 --> 00:27:33.519
Because it's just the way it is, right?
00:27:34.240 --> 00:27:36.480
And that's your mature stage.
00:27:36.799 --> 00:27:40.880
But to get there, your emphasis is on cultural acceptance.
00:27:41.200 --> 00:27:44.240
Forget the tools, forget policies.
00:27:44.480 --> 00:27:59.359
Your emphasis is on cultural acceptance and embedding thinking and making governance of data no different than governance of money or governance of HR or anything like that.
00:27:59.599 --> 00:28:00.000
All right.
00:28:00.160 --> 00:28:11.279
I mean, uh it it that's the example I like to give, and uh I was inspired to this by a conversation with uh Gwen Thomas, one of our other contributors to this, right?
00:28:12.400 --> 00:28:13.920
Um, a few months ago.
00:28:14.079 --> 00:28:17.359
And she said, John, do you ever with my aviation is my hobby?
00:28:17.599 --> 00:28:18.720
The listener might not know that.
00:28:18.880 --> 00:28:23.039
I know everyone else that knows me, like you, Barbara, know because I'm I'm unbearable with it.
00:28:23.119 --> 00:28:24.559
I just I'm a pain in the ass.
00:28:24.720 --> 00:28:28.079
Anyways about my aviation.
00:28:28.319 --> 00:28:34.880
But I I said, yes, I took something away from aviation and imputed it into my professional life.
00:28:35.039 --> 00:28:39.759
And I always thought that that my computer world would would change my aviation world.
00:28:40.000 --> 00:28:41.359
And it's been the opposite.
00:28:41.680 --> 00:28:50.160
What I took was if you take a look at a commercial jetliner, you know, there's two people up front and there's 180 people behind them going somewhere, right?
00:28:50.720 --> 00:28:54.640
At 35,000 feet at 500 miles an hour.
00:28:54.960 --> 00:28:55.519
Right.
00:28:55.759 --> 00:29:25.119
Now, we take it for granted, but you have no idea the physics involved with that, the systems behind that, the complexity of pulling that off to hold 182 butts in the air at 35,000 feet and move them at 500 miles an hour and gently deposit them at the other end so they can all go out and sit on the beach for a little while.
00:29:25.680 --> 00:29:29.039
It is astonishingly intricate and complicated.
00:29:29.680 --> 00:29:30.720
How does that happen?
00:29:30.880 --> 00:29:37.440
Well, up front, those two people driving that bus uh deploy governance.
00:29:38.640 --> 00:29:59.839
That's their whole job, is to adhere to predetermined standards of uh communication, standard protocols of behavior, standardized checklists, data standards, numerical standards, standard metrics, standard units of measure, all there.
00:29:59.920 --> 00:30:07.279
And if any of that breaks down, those 182 people are at risk for making it to the beach.
00:30:07.519 --> 00:30:08.079
All right.
00:30:08.480 --> 00:30:11.359
That's what you want latter stage with data governance.
00:30:11.440 --> 00:30:17.839
You want the cockpit of an airplane where the two people are flying the airplane and doing our job, that's the execution side.
00:30:18.079 --> 00:30:23.920
But the oversight, there's not a dude in the backseat going, okay, now by the way, I'm the standards guy.
00:30:24.240 --> 00:30:28.799
And when you when you talk on a radio, you need to say over or Roger or Wilco.
00:30:29.200 --> 00:30:29.680
All right.
00:30:29.759 --> 00:30:33.839
We don't want anyone saying breaker breaker here on the on uh you know up in the airway.
00:30:34.319 --> 00:30:35.599
There's no one doing that.
00:30:35.759 --> 00:30:36.160
Why?
00:30:36.319 --> 00:30:37.440
Why is that?
00:30:37.759 --> 00:30:41.680
Because they've acculturated the standardization.
00:30:41.839 --> 00:30:44.960
That's governance, that's what we want with data.
00:30:45.200 --> 00:30:47.440
We want it in a cockpit, nothing special.
00:30:47.599 --> 00:30:48.319
It disappears.
00:30:48.480 --> 00:30:49.519
Another long answer.
00:30:49.599 --> 00:30:50.240
I'm sorry.
00:30:50.480 --> 00:30:51.279
No, not at all.
00:30:51.359 --> 00:30:59.359
That is a great analogy, and I think that really helps us understand the impact and what happens when it becomes business as usual.
00:30:59.519 --> 00:31:01.839
It's just part of doing the job.
00:31:02.240 --> 00:31:07.440
You know, if you've got uh um, I don't I don't have any kids at home anymore.
00:31:07.599 --> 00:31:08.559
I don't know if you do or not.
00:31:08.720 --> 00:31:10.720
None of my gone.
00:31:10.799 --> 00:31:11.279
There you go.
00:31:11.440 --> 00:31:15.680
So, but if I had someone saying, you know, I'm getting ready to graduate college, dad.
00:31:15.759 --> 00:31:21.039
I think I'm gonna go for a career in data governance, I'd go, hell no, sell insurance.
00:31:21.359 --> 00:31:24.720
Because we're the field's maturing.
00:31:24.960 --> 00:31:31.759
What I used to do, the exciting Wild West where that's hopefully, hopefully going to go away shortly, right?
00:31:32.480 --> 00:31:32.799
Right.
00:31:32.960 --> 00:31:35.680
Um, and besides that, AI is going to change everything anyway.
00:31:35.839 --> 00:31:38.880
So, yeah, I mean, I it's it's boring.
00:31:39.759 --> 00:31:41.119
It's boring.
00:31:42.000 --> 00:31:43.119
No excitement at all.
00:31:43.200 --> 00:31:44.720
You might excitement at all.
00:31:44.880 --> 00:31:53.119
I I was I I was mentoring somebody a few weeks ago, and I called and said, John, I I'm a data architect and I'm really thinking about some a career in data governance.
00:31:53.200 --> 00:31:55.599
I'm thinking of leaving this architecture and being in governance.
00:31:55.680 --> 00:31:58.319
And I said, Why in the hell would you want to do that?
00:31:58.559 --> 00:31:59.680
was my first question.
00:31:59.920 --> 00:32:04.720
And they went, Well, because of the the adventure, the excitement, the drama.
00:32:04.880 --> 00:32:11.440
I'm like, no, no, that's that means then you don't know really where this is supposed to end up, do you?
00:32:11.680 --> 00:32:12.000
Right.
00:32:12.240 --> 00:32:13.359
So anyway.
00:32:15.519 --> 00:32:18.319
Entry-level analyst to executive data leaders.
00:32:18.480 --> 00:32:25.200
Dataversity delivers the most comprehensive training in the industry, led by experts who are actually doing the work.
00:32:25.440 --> 00:32:36.079
Upskill with on-demand courses, earn exclusive certifications, and join a global community of data pros committed to driving real change at dataversity.net.
00:32:38.960 --> 00:32:48.960
Well, you know, that's a perfect segue for the next question because it's what piece of advice would you give to someone responsible for the governance of data?
00:32:49.119 --> 00:32:50.880
I'm going to take your terminology.
00:32:51.119 --> 00:32:51.279
Yeah.
00:32:51.599 --> 00:32:52.240
Because I like it.
00:32:52.640 --> 00:33:06.000
I would be, I mean, the piece of advice is you are tasked with embedding a new capability.
00:33:06.319 --> 00:33:09.119
I don't even call it a program anymore.
00:33:10.160 --> 00:33:10.960
And here's why.
00:33:11.200 --> 00:33:19.200
Program was a great word for about 10 years for us because it differentiated us from um project, right?
00:33:20.240 --> 00:33:29.279
So, so I know in your career before Dataversity, you were in a big corporation and you had to work on a data governance program, right?
00:33:29.599 --> 00:33:29.839
Yes.
00:33:30.160 --> 00:33:33.279
And every and you had to explain to people this is a program, not a project.
00:33:33.359 --> 00:33:34.720
And we need a program, not a project.
00:33:34.799 --> 00:33:35.519
And we did that again.
00:33:35.599 --> 00:33:36.400
And you know what?
00:33:36.559 --> 00:33:41.599
There's nothing wrong that we did that, but to me, that was an intermediate stage now.
00:33:42.079 --> 00:33:42.400
Okay.
00:33:43.440 --> 00:33:53.200
Now what you're done is you're responsible for taking that program and making it disappear.
00:33:54.240 --> 00:34:01.359
All right, getting the word program out of it and making it a permanent capability.
00:34:02.720 --> 00:34:03.279
Right?
00:34:03.599 --> 00:34:07.359
So and organizations do this all the time.
00:34:07.599 --> 00:34:09.360
Um, people are surprised.
00:34:09.440 --> 00:34:33.440
Uh, if you were doing business in the United States and you were a corporate executive um in the 1950s, you were confronted with a radical change in organization charts because some experts decided to tell you that you needed a department to manage human resources.
00:34:35.360 --> 00:34:45.519
People don't realize that before the mid-1950s, middle managers took care of their own people, just like middle managers take care of their departmental databases now.
00:34:45.760 --> 00:34:46.079
Okay.
00:34:46.559 --> 00:34:47.599
And interesting.
00:34:47.840 --> 00:34:52.400
When you read, you know, Wall Street Journal, all FM, it was like, oh, we've no, we don't need that.
00:34:52.480 --> 00:34:54.000
Now that's just that's overkill.
00:34:54.079 --> 00:34:55.440
We don't need the overhead and all that.
00:34:55.599 --> 00:34:58.320
And then compliance drove it.
00:34:58.800 --> 00:35:00.559
We've heard that before, right?
00:35:01.119 --> 00:35:04.000
So um this stuff happens all the time.
00:35:04.239 --> 00:35:16.400
So if you're going to be responsible for this, you need to take it upon yourself to do something like putting HR into place or maybe putting accounting, you know, accounting standards have existed for a very, very long time.
00:35:16.719 --> 00:35:21.519
Double entry accounting has existed for, I don't know, since the pyramids or something like that.
00:35:21.920 --> 00:35:26.880
You know, you you you you know, uh, you you need to make all this stuff not special.
00:35:27.519 --> 00:35:33.199
You need to just see that it's executed, it always serves itself, it always serves its its its thing.
00:35:33.360 --> 00:35:34.400
It's not special.
00:35:34.480 --> 00:35:40.880
You're not setting aside, you're not setting up fireworks every time someone loads a data element into a catalog or anything like that.
00:35:41.039 --> 00:35:49.119
You just need to just make it normally part and get rid of the program label and have it as an embedded capability.
00:35:49.280 --> 00:35:50.239
That's your job.
00:35:50.400 --> 00:35:51.840
That's your job.
00:35:52.480 --> 00:35:53.920
Very good advice.
00:35:54.159 --> 00:35:56.639
Again, leading to the next question perfectly.
00:35:56.880 --> 00:36:01.599
What typically triggers organizations to finally take governance seriously?
00:36:02.000 --> 00:36:11.440
Well, you know, boy, we had a long talk of that at a conference I was in uh Ireland last week at the uh uh the Data Leader Summit um in Wexford.
00:36:11.679 --> 00:36:13.280
Uh lovely event, by the way.
00:36:13.440 --> 00:36:16.079
I encourage everyone to look into it next year.
00:36:16.400 --> 00:36:21.360
Um if you like going to Ireland, that's you do have to jump in an airplane across the ocean.
00:36:21.440 --> 00:36:21.679
Okay.
00:36:21.840 --> 00:36:26.480
Anyway, um uh uh but we were talking about what triggers people.
00:36:26.719 --> 00:36:35.280
And of course, the joke was I don't get, you know, I doctors have been telling me to lose weight for 20 years, then I had a heart attack.
00:36:35.440 --> 00:36:36.000
Now look at me.
00:36:36.079 --> 00:36:37.599
I'm Schwarzenegger, all right.
00:36:37.679 --> 00:36:39.039
So I had a heart attack.
00:36:39.280 --> 00:36:49.360
Um uh organizations are careless with their inventory control or financial controls, and then someone embezzles a million dollars, and all of a sudden, there are the controls, right?
00:36:49.840 --> 00:37:01.760
There is this tendency for human beings to not embrace something until um, you know, we're not going to embrace the fire extinguisher until we smell smoke, and it's our trousers that are on fire.
00:37:02.079 --> 00:37:10.400
Human beings tend to be uh motivated by what we would call last-minute reactive triggers.
00:37:10.639 --> 00:37:12.800
In other words, I never had to learn how to swim.
00:37:12.960 --> 00:37:15.840
Oh, Crikey, the boat sinking, right?
00:37:16.480 --> 00:37:31.360
True now, but good organizations start to look ahead a little bit and say things like um, I always go, I use the example of the automobile industry a lot in my talks.
00:37:31.840 --> 00:37:40.159
Because after World War II, the United States went back to making cars the way they made cars, and this guy named it Edwards, J.
00:37:40.320 --> 00:37:50.159
Edwards Deming, I believe it's Jay, um, said, American cars are terrible, but I have this really great way to build a better car.
00:37:50.320 --> 00:37:52.719
And it's called quality management.
00:37:52.800 --> 00:37:57.199
And you look at the whole process and you break the process down and blah, blah, blah, blah, blah.
00:37:57.760 --> 00:38:01.519
And American Automobile, they laughed at him and said, We're number one in the world.
00:38:01.599 --> 00:38:03.599
We don't need this, you know.
00:38:04.960 --> 00:38:06.480
They went to Japan.
00:38:06.880 --> 00:38:12.000
And Japan, of course, they had no industry, they were starting from scratch.
00:38:12.719 --> 00:38:14.880
So they said, Why not?
00:38:15.599 --> 00:38:16.880
This guy's laid it all out.
00:38:16.960 --> 00:38:19.679
At least we don't have to figure out how to copy how the Americans do cars.
00:38:19.760 --> 00:38:20.880
This guy's laid it out for us.
00:38:21.039 --> 00:38:39.920
And over the next 20 years, all of a sudden, one Toyota, two Toyota, three Toyota, all of a sudden, in between all the Ford Galaxies and GTOs and the high school parking lot is this little crappy yellow Honda thing going, where did they get that color?
00:38:40.079 --> 00:38:44.880
But it's there, and all of a sudden, it's like, and then all of a sudden, boom.
00:38:45.360 --> 00:38:46.159
And then the U.S.
00:38:46.239 --> 00:38:48.800
automobiles said, Oh, it's a fad.
00:38:49.119 --> 00:38:55.039
Well, we all know in the in the late, in the mid-80s or so, the American automobile industry collapsed.
00:38:55.760 --> 00:39:01.760
And they didn't, they didn't embrace better manufacturing process until they had to get bailed out by the government.
00:39:01.840 --> 00:39:02.719
So it was the same thing.
00:39:02.880 --> 00:39:06.079
Their pants were on fire, they had to learn what a fire extinguisher was.
00:39:06.400 --> 00:39:07.920
Japan didn't do that.
00:39:08.079 --> 00:39:12.960
They took back and they said, we can either do it the way they're doing it or we can try something different.
00:39:13.199 --> 00:39:17.199
And they had an open-minded creative, and then that's why I talk early on.
00:39:17.360 --> 00:39:19.760
I look for open-mindedness and creativity in this.
00:39:19.920 --> 00:39:22.000
Tony Mazzarella, uh, now Dr.
00:39:22.159 --> 00:39:27.920
Tony Mazzarella, is a uh uh um an executive at a large insurance company.
00:39:28.079 --> 00:39:34.480
He wasn't a contributor to our work, but he's just done tremendous research as part of his PhD.
00:39:34.639 --> 00:39:41.599
And he talks about organizations that are successful with this are really light on their feet and very creative.
00:39:42.320 --> 00:39:52.639
So what triggers an organization to take it seriously is a the normal way, which is you smell smoke.
00:39:52.800 --> 00:39:54.239
That's that's part A.
00:39:54.719 --> 00:40:02.559
Number two is could how could we do this better, differently, that fits our culture, fits our direction, fits our alignment?
00:40:02.880 --> 00:40:04.159
See where that comes from, folks?
00:40:04.320 --> 00:40:05.599
That's why you have to have it.
00:40:05.840 --> 00:40:11.440
And and maybe it's not what everyone else is doing, or maybe it's not exactly what the talking head is saying.
00:40:11.599 --> 00:40:13.519
And but that's really it.
00:40:13.679 --> 00:40:15.440
You know, there has to be a driver.
00:40:15.599 --> 00:40:22.480
Now, if that driver is your pants on fire or trying to match your culture and your alignment together, that's fine.
00:40:22.639 --> 00:40:28.639
But there has to be an external driver normally to get data governance taken seriously.
00:40:29.920 --> 00:40:31.760
Yeah, I love your analogies, Sean.
00:40:31.840 --> 00:40:32.960
They really hit home.
00:40:33.199 --> 00:40:39.119
You know, and it'd be so nice if companies chose B, option B instead of option A.
00:40:39.440 --> 00:40:41.519
But um unfortunately.
00:40:50.639 --> 00:40:52.000
And he says, no, both.
00:40:52.079 --> 00:40:55.039
I'm like, whoa, you're asking way too much.
00:40:55.519 --> 00:40:57.199
They're like, please, no, not both of them.
00:40:57.440 --> 00:41:02.559
I'll go for a walk, but when I when I'm done with my walk, I've earned myself a Klondike bar.
00:41:02.639 --> 00:41:03.360
I'm sorry, doctor.
00:41:03.440 --> 00:41:04.559
That's all there is, too.
00:41:04.800 --> 00:41:06.239
Well, Klondike bars are good.
00:41:06.320 --> 00:41:07.440
So well, yeah.
00:41:07.519 --> 00:41:09.360
We're talking Klondike bars, really.
00:41:09.440 --> 00:41:12.880
I mean, well, I think we're having fun.
00:41:13.119 --> 00:41:14.480
Yeah, we're having a really good time.
00:41:15.360 --> 00:41:17.039
I hope our listeners are too.
00:41:17.360 --> 00:41:18.960
Sure, they are, because they're learning a lot.
00:41:19.119 --> 00:41:24.079
You know, I think this one's really important because, you know, in my previous world, I did this a lot as well.
00:41:24.239 --> 00:41:25.599
You know, it's very challenging.
00:41:26.159 --> 00:41:30.079
How do you explain data governance to executives without losing them?
00:41:30.400 --> 00:41:32.719
You don't explain data governance.
00:41:33.920 --> 00:41:39.840
That's the carpenter saying, you know, the radio arm saw was a lot better than a circular saw.
00:41:41.840 --> 00:41:43.039
I don't care.
00:41:45.360 --> 00:41:49.679
You know, your house is going to be rock solid because I'm a good carpenter.
00:41:50.320 --> 00:41:57.440
And it will resist these storms and these uh cataclysms and keep you warm and safe in the winter.
00:41:57.840 --> 00:41:59.760
And you go, thank you very much.
00:41:59.920 --> 00:42:01.840
That's how you talk to leadership.
00:42:02.000 --> 00:42:04.480
All right, they want results, all right?
00:42:04.719 --> 00:42:05.920
They want value.
00:42:06.079 --> 00:42:28.800
Um, my podcast, our last interview for this season, season four of the rock bottom data feed, available on all podcast channels, by the way, is a real honest to gosh chief executive officer who's a good friend of mine, and by design and by humor, because we we take the Mickey out of each other, can't spell IBM.
00:42:29.280 --> 00:42:34.800
Okay, that's we you know, but he I will tell you, I'll give you a hint.
00:42:35.119 --> 00:42:56.480
When you talk to leadership or an executive, and and they're a good executive, um, just as a sidebar, right now AI is flushing out the bad executives, flushing them out better than any uh Draeno flushing the hair out of the sink.
00:42:56.639 --> 00:43:02.480
I mean, we are talking because they're going to AI and they're going, oh, AI, everyone go do AI.
00:43:02.559 --> 00:43:04.480
How much headcount can you give me?
00:43:05.679 --> 00:43:09.119
All this headcount stuff is disastrous.
00:43:09.760 --> 00:43:18.000
And I was talking to my CEO friend who will be on the rock bottom data feed available on all podcast channels here in a in a month or so.
00:43:18.320 --> 00:43:22.400
And that is a good CEO doesn't look at the headcount.
00:43:22.960 --> 00:43:25.199
Um, a good CEO looks at value.
00:43:25.360 --> 00:43:27.440
How are you going to create value?
00:43:27.679 --> 00:43:36.239
Now, if lowering headcount to be more efficient will create value by a lower price to the client or a higher margin for the stockholders, great.
00:43:36.400 --> 00:43:39.840
But just chopping heads to give you a quarterly bump?
00:43:40.719 --> 00:43:41.280
No way.
00:43:41.599 --> 00:43:42.079
All right.
00:43:42.320 --> 00:43:46.559
So a good CEO wants value, and that's how you talk to executives.
00:43:46.800 --> 00:43:47.119
Okay.
00:43:48.000 --> 00:43:50.880
So you're in it's the elevator speech.
00:43:51.280 --> 00:43:53.519
I'll ask people to do and I'll do a talk.
00:43:53.760 --> 00:43:55.920
Do what's your elevator speech after my class?
00:43:56.000 --> 00:43:59.760
And they go, Oh, well, um uh, and I say I'll be the CEO.
00:44:00.400 --> 00:44:02.159
And I say, so there, Sally.
00:44:02.639 --> 00:44:06.000
Um uh I hear you're on this data governance thing.
00:44:06.079 --> 00:44:07.280
Could you tell me about that?
00:44:07.440 --> 00:44:08.159
Oh, wow.
00:44:08.320 --> 00:44:14.719
Yeah, we're gonna do a data catalog and then we'll be able to get agreement on all things that people get their reports really, really quick.
00:44:14.960 --> 00:44:20.960
And and then, and at that point, the CEO is gonna put up the hand and say, keep doing your job, Sally.
00:44:21.599 --> 00:44:24.880
But Sally hasn't sold anything to that CEO.
00:44:25.039 --> 00:44:28.960
Sally hasn't increased any engagement in any way, shape, or form.
00:44:29.280 --> 00:44:42.960
But if Sally says, well, um, governing data is going to make it a lot easier for this organization to achieve its value targets for shareholders and lower our risk in doing that.
00:44:44.000 --> 00:44:47.840
That CEO is going to hit the stop button on the elevator and want to hear more.
00:44:48.880 --> 00:44:56.880
That do not say metadata, do not say star schema, do not say data catalog.
00:44:57.119 --> 00:45:03.840
Um I uh just don't know that that's that's that you know story I've already told three times.
00:45:03.920 --> 00:45:06.239
That's like the carpenter saying, Aren't I special?
00:45:06.480 --> 00:45:08.960
No, did you nail the boards together straight?
00:45:09.039 --> 00:45:10.239
That's all I need to know.
00:45:11.039 --> 00:45:11.360
Okay.
00:45:11.920 --> 00:45:15.920
Um, so you want to load if you don't want to lose them, you have to talk their language.
00:45:16.159 --> 00:45:20.400
The water cooler conversations were they just need to take time to understand us.
00:45:20.639 --> 00:45:26.079
No, here and I I used to be one of those persons around the water cooler.
00:45:26.639 --> 00:45:28.400
You probably have been too, right?
00:45:28.559 --> 00:45:29.039
Oh, yes.
00:45:30.000 --> 00:45:30.320
Idiots.
00:45:30.480 --> 00:45:31.519
I went, what the heck?
00:45:31.599 --> 00:45:32.559
I mean, you know.
00:45:32.880 --> 00:45:39.599
Um uh uh uh and we're all standing around a water cooler like you know, Homer Simpson going, all right.
00:45:39.760 --> 00:45:45.519
So we're no, here's the reality of an organization that wants to get things done.
00:45:45.679 --> 00:45:47.920
Everyone has their job, right?
00:45:48.079 --> 00:45:56.320
And the job of that leader, that executive, is to herd all the cats in one direction, and that's increase value.
00:45:57.920 --> 00:46:00.400
Razor sharp focus, nothing else but that.
00:46:00.559 --> 00:46:02.159
And if you can talk to that, fine.
00:46:02.239 --> 00:46:08.960
If you can't, you're part of the delegatees um and the delegators, and I don't have time for you.
00:46:09.280 --> 00:46:10.719
Because just go do your job.
00:46:10.800 --> 00:46:12.239
I'm not saying it's bad.
00:46:12.480 --> 00:46:13.920
I just don't have time for you.
00:46:14.159 --> 00:46:14.639
All right.
00:46:14.880 --> 00:46:16.159
You know, it's all static.
00:46:16.239 --> 00:46:18.400
I don't, I don't, I don't need it.
00:46:19.280 --> 00:46:20.800
Truly, they don't need it.
00:46:20.880 --> 00:46:22.559
And and that is the reality of the world.
00:46:22.639 --> 00:46:27.199
And data people, you need to understand they're not gonna listen to your data speak.
00:46:27.360 --> 00:46:31.840
You need to go learn to communicate with leadership.
00:46:32.079 --> 00:46:32.480
Period.
00:46:32.639 --> 00:46:36.320
And that means don't give them a 20-page deck when you have 10 minutes for them.
00:46:36.480 --> 00:46:39.599
Give them a one-page deck and say everything you want to say on one page.
00:46:39.679 --> 00:46:44.880
And if you say to me, I can't do that, I tell you, well, I'm gonna go find someone else who can.
00:46:45.760 --> 00:46:47.519
But you're off the team.
00:46:47.840 --> 00:46:50.320
Or go figure out a way to do it in one page.
00:46:50.480 --> 00:46:52.320
That's where we are now.
00:46:53.280 --> 00:46:53.679
Yep.
00:46:53.920 --> 00:46:58.159
Have to speak in their language or their pain points or don't talk.
00:46:58.719 --> 00:46:58.960
Yep.
00:46:59.199 --> 00:47:00.000
That's wonderful.
00:47:00.639 --> 00:47:06.159
What happens when governance is treated as a technology initiative instead of a business one?
00:47:07.280 --> 00:47:09.760
Short answer, you fail.
00:47:11.840 --> 00:47:12.719
Next question.
00:47:12.960 --> 00:47:13.199
No.
00:47:13.440 --> 00:47:17.920
Uh okay, so what are the mechanics of that failure?
00:47:18.079 --> 00:47:26.480
Well, one, you're not, let's go back to um all these questions about communicating with leadership and and what do we say?
00:47:26.559 --> 00:47:30.400
We've we've been touching on that uh again and again, right?
00:47:31.039 --> 00:48:02.639
Um you are not speaking the right language, you are not orienting yourself to speaking the right language, you are subverting a major business capability that is supporting society-changing initiatives like AI and analytics, and you're subverting that to overhead things like taking out the trash, all right, um, and reloading the coffee machine.
00:48:03.519 --> 00:48:10.400
And and you immediately put yourself in the place where you don't want to be, which is no one's gonna listen to you.
00:48:11.679 --> 00:48:18.960
Here's why technology here, first of all, they don't remember, I've already said this, they don't need to know about a catalog.
00:48:19.920 --> 00:48:25.119
I I tell people, don't even put catalog as a separate line item in your budget request, just put it in there.
00:48:25.280 --> 00:48:26.800
It's it's a tool of your trade.
00:48:26.880 --> 00:48:31.599
All right, just you know no one needs to know about that.
00:48:34.239 --> 00:48:37.199
But that can't be your sole purpose for existence, all right.
00:48:37.360 --> 00:48:40.320
Now you're gonna say, well, but they won't listen to me.
00:48:40.880 --> 00:48:42.239
All right, I had that last week.
00:48:42.400 --> 00:48:44.800
Some said, I've tried everything in the book, they won't listen to me.
00:48:44.880 --> 00:48:48.719
I said, then pretend they are listening to you.
00:48:49.199 --> 00:48:52.639
And do it as a business initiative anyway.
00:48:53.119 --> 00:48:56.880
And do your business alignment without any sponsor.
00:48:57.039 --> 00:49:00.079
Do your business alignment with an outside consultant.
00:49:00.400 --> 00:49:01.840
Rent me for a day.
00:49:02.079 --> 00:49:03.199
I'm reasonable.
00:49:03.440 --> 00:49:05.360
I'm not cheap, but I'm reasonable.
00:49:05.519 --> 00:49:05.840
Okay.
00:49:06.239 --> 00:49:13.679
Rent me for a day, and we and and you build your language, but get this out of techno speak.
00:49:14.480 --> 00:49:24.000
Because you just you you basically pull the covers over yourself, and you're doing what you've been doing for 20 or 30 years, just happy as a clam doing your techie stuff.
00:49:24.079 --> 00:49:30.400
Now, remember, in conclusion here, remember what leadership has been seeing for the last 40 years or so.
00:49:30.880 --> 00:49:42.480
Um uh and there's a great video on YouTube of Admiral Grace Hopper talking to a bunch of security people at the Pentagon in the 1980s, early 80s.
00:49:42.719 --> 00:49:43.280
And Dr.
00:49:43.360 --> 00:49:47.920
Hopper, by the way, Admiral Hopper, invented COBOL.
00:49:48.159 --> 00:49:49.039
Oh, okay.
00:49:49.199 --> 00:49:49.920
Okay, all right.
00:49:50.079 --> 00:49:53.760
Um, and she talks about all the problems we're going to have.
00:49:54.000 --> 00:50:03.199
And this is 1982, and here we are 44 years later, and everything she said has come true, and we haven't fixed any of it.
00:50:04.719 --> 00:50:05.039
Okay.
00:50:05.920 --> 00:50:08.880
She said, because you can't, this is not just technology.
00:50:09.039 --> 00:50:11.280
We've got to get people pulled in.
00:50:11.440 --> 00:50:15.599
The other thing is, in the ensuing 40-some years, we've tried to do lots of techie stuff.
00:50:15.679 --> 00:50:19.119
You know that you worked in a big company, a couple of big companies doing this stuff.
00:50:19.280 --> 00:50:23.360
How successful have are we with our uh corporate IT projects?
00:50:23.679 --> 00:50:24.880
What's the success rate?
00:50:25.519 --> 00:50:26.159
Not very.
00:50:26.400 --> 00:50:29.039
And we just keep doing them because you got to have IT.
00:50:29.119 --> 00:50:35.840
And what we have is this acculturation now for those of us in technology, that we can just do what we want.
00:50:35.920 --> 00:50:38.960
And if it's a little bit useful, we can just keep going.
00:50:39.280 --> 00:50:48.159
And what we don't know, and you don't remember, is all these people have been watching us for 40 years, and they're not impressed.
00:50:48.719 --> 00:51:04.079
And and and they remember, and so they're very being polite to you because you kick off your new program and your whatever, and you're gonna do this, and you're speaking all the speak you've spoken for 40 years and all that, and they're nodding and are being polite because they're executives, they get paid to do that.
00:51:04.320 --> 00:51:09.039
And when you leave the room, they go, shut the front door, here we go again.
00:51:09.360 --> 00:51:12.320
Okay, I mean, that's why you can't treat as tech.
00:51:13.039 --> 00:51:18.239
If you keep treated as tech, it's gonna stay tech, and nobody cares about tech.
00:51:19.119 --> 00:51:23.119
They want tech to fix everything so that then we can do it.
00:51:23.280 --> 00:51:24.639
Right, just fix it and then we'll do it.
00:51:25.280 --> 00:51:29.840
It took us 50 years to get in this mess, but you go buy some tech to fix it all in a year.
00:51:30.079 --> 00:51:30.719
Exactly.
00:51:30.960 --> 00:51:32.800
Right, yeah, yeah, not gonna happen.
00:51:33.119 --> 00:51:34.480
Thank you so much, John.
00:51:34.639 --> 00:51:35.519
This has been great.
00:51:35.679 --> 00:51:37.119
I have thoroughly enjoyed it.
00:51:37.360 --> 00:51:39.280
I'm sure our listeners have too.
00:51:40.239 --> 00:51:41.679
Barbara, it's been a pleasure.
00:51:41.840 --> 00:52:01.119
I really am glad to add to the body of knowledge for the ADGP, and I'm hoping that the listeners uh enjoy this content and then click on whatever they need to click on and explore certification through Dataversity for data governance.
00:52:06.480 --> 00:52:16.079
For our listeners, if you'd like to learn more about the ADGP certification program and the applied data governance body of knowledge, visit training.dataversity.
00:52:17.360 --> 00:52:23.119
Until next time, I'm Barbara Nishaw, and this has been Inside Applied Data Governance.
00:00:05.440 --> 00:00:06.480
Hello and welcome.
00:00:06.639 --> 00:00:11.679
I'm your host, Barbara Neshaw, and this is Inside Applied Data Governance.
00:00:11.839 --> 00:00:18.239
Whether you're just starting out in data governance or you've been working in the field for years, this podcast is for you.
00:00:18.559 --> 00:00:26.320
Will you learn real-life lessons and practical advice on the people behind the Applied Data Governance Practitioner Certification?
00:00:26.719 --> 00:00:34.159
To learn more about the ADGP Certification Program, visit training.dataversity.net.
00:00:38.960 --> 00:00:48.240
And today we're talking to John Ladley, one of the key contributors to the ADGP certification, about the importance of data governance for organizations.
00:00:49.280 --> 00:00:51.039
Let's jump right in.
00:00:53.119 --> 00:00:55.119
Hi, John, and welcome to the show.
00:00:55.439 --> 00:00:56.799
Hello, Barbara.
00:00:56.880 --> 00:00:57.439
How are you?
00:00:57.600 --> 00:01:01.520
Good to see you again and talk about data governance again.
00:01:01.920 --> 00:01:03.679
It is wonderful to talk about it.
00:01:03.840 --> 00:01:06.640
I'm so excited to hear all of your insights.
00:01:06.799 --> 00:01:11.519
But before we dive in, let's just talk a little bit about you and your background.
00:01:11.760 --> 00:01:16.400
So the first thing, just tell us about you and um your role in data governance.
00:01:16.719 --> 00:01:28.560
Yeah, the role in data governance, I have been connected with the data corner of technology since the late 1980s, actually.
00:01:29.120 --> 00:01:33.280
And uh just kind of evolved along the way.
00:01:33.439 --> 00:01:44.719
Uh along the way, I got to a point in the mid-2s that the literature out there was abysmal.
00:01:45.840 --> 00:01:48.400
Uh it was not useful.
00:01:49.599 --> 00:01:52.239
Uh, some of the literature we had was harmful.
00:01:52.400 --> 00:02:04.799
So I started to roll up my sleeves and wade deeper into it, less of a practitioner, more of a um educator, I guess, and a researcher.
00:02:05.120 --> 00:02:10.000
And I ended up writing uh two editions of uh a book.
00:02:10.159 --> 00:02:15.680
The long title is Data Governance, How to Deploy a blah, blah, blah, blah, blah, a data governance program.
00:02:16.240 --> 00:02:29.840
Um uh third edition is on hold right now due to AI and figuring out how to stuff all that AI stuff into the same number of pages we're allowed to have.
00:02:30.000 --> 00:02:32.479
So, but um, that's how I got into it.
00:02:32.560 --> 00:02:38.639
I, you know, I've been more than anything, I will tell people I am a practitioner.
00:02:39.360 --> 00:02:42.879
Uh, everything I talk about is something I've done.
00:02:43.039 --> 00:02:52.479
In fact, I will not talk about something, or I will not relay a concept I've heard unless it's actually been tried in the field.
00:02:53.360 --> 00:02:54.800
And that's how I got here.
00:02:55.120 --> 00:02:55.919
That's great.
00:02:56.159 --> 00:03:00.560
You have a wonderful background and so wonderful to have it as part of our certification.
00:03:00.800 --> 00:03:01.039
Thank you.
00:03:01.280 --> 00:03:04.479
Speaking of that, um, how did you get involved?
00:03:06.000 --> 00:03:19.120
Um, I was asked to be a reviewer of uh the initial uh conceptual versions of this uh of the ADGP.
00:03:20.159 --> 00:03:25.520
You're right, we're gonna have to just be careful saying that while we do this podcast, the ADGP.
00:03:25.680 --> 00:03:41.680
I enjoyed the review process and it became apparent that given the uh um frankly, the dominance of my book, that a lot of the material was coming from the book, even from other people.
00:03:42.879 --> 00:03:46.719
And it was probably a good thing that I uh stay on board with the thing.
00:03:46.879 --> 00:03:55.039
So uh Dataversity and I, we just kept rocking and rolling along with a handful of other brilliant people as well, uh, for this.
00:03:55.439 --> 00:03:57.840
Um and uh here we are.
00:03:58.879 --> 00:04:00.879
Yeah, well, we couldn't have done it without you.
00:04:01.039 --> 00:04:06.159
Definitely your insight and thank you core concepts from your book.
00:04:06.479 --> 00:04:07.439
Thank you very much.
00:04:07.759 --> 00:04:13.680
All right, well, let's jump right on in to the wisdom that you shared with us for the certification.
00:04:13.919 --> 00:04:21.279
Uh, we have a few questions here just about um, you know, what was included in the certification and within the body of knowledge.
00:04:21.680 --> 00:04:22.240
Sure.
00:04:23.279 --> 00:04:28.319
From your perspective, why is applied data governance important for data practitioners?
00:04:28.480 --> 00:04:31.360
You know, not just in theory, but also in practice.
00:04:31.759 --> 00:04:32.079
Yeah.
00:04:32.319 --> 00:04:36.079
And and that sounds like an obvious question, you know.
00:04:36.480 --> 00:04:43.680
It's the, you know, I have a podcast too, and I do a lot of interviews, and there's always that set it up question, right?
00:04:43.839 --> 00:04:45.519
You know, why are we here?
00:04:45.759 --> 00:04:49.360
Well, but this is a powerful question, okay?
00:04:49.600 --> 00:04:53.199
Um, the key word here is applied data governance.
00:04:54.959 --> 00:05:02.879
We the world of digital stuff, the digital sources, digital economies, digital organizations, data-driven organizations.
00:05:03.040 --> 00:05:12.720
You you immediately lay out that data is important to the operation and success of any sort of organization.
00:05:13.279 --> 00:05:29.279
And the minute you do that, you have to have some sort of oversight or controls or standardization or guardrails or a host of other synonyms that we can marry up to uh to to uh data governance.
00:05:29.439 --> 00:05:35.839
Um, in fact, I'm at the point where I de-emphasize the word data, it's just governance.
00:05:36.000 --> 00:05:41.439
And in practice, and again, the key here is that's why this is such a good question.
00:05:41.839 --> 00:06:11.680
In practice, no complicated organization, and our world is complicated now, does its complicated things, whether it's buying and selling, or moving, or serving, or shipping, or creating, or serving, or whatever, does that without some sort of controls, oversight, regulation, compliance, standards of behavior.
00:06:12.560 --> 00:06:15.040
You cannot be successful without it.
00:06:15.199 --> 00:06:37.040
Since Frederick Taylor was scratching his head about organizational theory, since the first steam engine started to drive the first pump in uh England in the 19th century, we have had to have oversight of complicated things, or else the wheels came off.
00:06:38.240 --> 00:06:42.959
With data, we've discovered in the last few years, and this is one of the reasons I wrote my books.
00:06:43.120 --> 00:06:50.720
It says it in the uh in my my preface before, you know, at the beginning of the book, that I wrote it out of blind rage.
00:06:51.279 --> 00:06:53.199
And and that and that's absolutely true.
00:06:53.759 --> 00:06:57.519
No one was saying anything that this was a business capability.
00:06:57.680 --> 00:06:58.160
All right.
00:06:58.399 --> 00:07:01.600
So, in practice, this is something you have to do.
00:07:01.839 --> 00:07:19.920
If you're going to do all this wonderful stuff that we have oversold, vendors and consultants and authors and all this, and and we we all yell and hoot and holler and and and run around at conferences going, oh, metadata just is so wonderful, and things like that.
00:07:20.079 --> 00:07:24.079
If we're gonna have any of that stuff, we gotta uh it's gotta be understood as mandatory.
00:07:24.240 --> 00:07:27.040
And that's why it's so important.
00:07:27.920 --> 00:07:30.720
You can't go without this.
00:07:31.519 --> 00:07:33.519
It's it's it's that simple.
00:07:34.079 --> 00:07:34.959
You have to do it.
00:07:35.360 --> 00:07:37.120
Yeah, that's a good way to sum it up.
00:07:37.279 --> 00:07:50.480
You know, and really you you answered some of it with the you know, right now, but why was it so important to have that in the ADG body of knowledge, especially when we look at business drivers, you know, whenever we're thinking about it?
00:07:50.800 --> 00:07:54.800
Yeah, because you know, carrying forward the first question, you have to have it.
00:07:54.959 --> 00:07:56.480
Why do you have to have it?
00:07:56.720 --> 00:08:03.920
Because if you don't have it, the organization's success or chances of success will be compromised.
00:08:04.720 --> 00:08:13.519
Various resources of the organization will be placed at risk across all the meanings of that word risk.
00:08:13.759 --> 00:08:21.120
So you need to connect where the organization wants to go with what you want to do with the data.
00:08:21.279 --> 00:08:25.600
Um, and candidly, and I still see it to this day.
00:08:26.639 --> 00:08:40.720
Organizations will say, Oh, um, we went to the conference and we're doing analytics or we're doing uh master data management or we're doing AI now.
00:08:41.120 --> 00:08:43.759
And the guru says we need data governance.
00:08:44.000 --> 00:08:48.960
So Henry here, go down the hall and start doing data governance.
00:08:49.360 --> 00:08:52.639
Um, just you know, as a sidebar, Barbara.
00:08:53.039 --> 00:08:58.879
Uh you we've chatted obviously many times, uh developing the ADGP.
00:08:59.360 --> 00:09:09.919
And uh I'm I'm getting to a point in my life where I can look back and be the real crabby old guy on the porch.
00:09:10.559 --> 00:09:13.600
So I tend to be candid about this stuff.
00:09:13.919 --> 00:09:24.799
If you go into this with that, well, look, I know that data governance is metadata and data quality and all this, and I know the systems are lacking in that, which is probably true.
00:09:25.039 --> 00:09:28.480
Um, we're just gonna go do things and the alignment can come later.
00:09:28.879 --> 00:09:32.480
I don't want to talk to those business people, or they're mad at me, or whatever.
00:09:32.720 --> 00:09:36.879
Well, well, well, you're you're you're you're you're throwing darts.
00:09:37.200 --> 00:09:48.639
You might get lucky and do it right, but you are wasting resources, you are wasting money, uh uh because your attention will not be where it's supposed to be.
00:09:48.879 --> 00:09:54.879
When I visit a program where someone has said, we've been trying this three times, it hasn't worked three times.
00:09:55.120 --> 00:10:01.279
I will go back and look at how they did it, and I will find one, no alignment at all the first time.
00:10:01.519 --> 00:10:03.919
The second time we had a meeting.
00:10:04.159 --> 00:10:08.480
Then we ran out of the meeting because the business people were looking at us with dirty looks.
00:10:08.720 --> 00:10:10.080
And and then, and that's it.
00:10:10.159 --> 00:10:14.559
And I will go, look, no, you have to itch up your pants and do this.
00:10:14.720 --> 00:10:19.200
Um, if you don't have it, you're missing all the valuable parts.
00:10:19.440 --> 00:10:24.720
You're doing, I'm trying not to be long-winded with this answer, so I'll sum it up this way.
00:10:25.200 --> 00:10:31.279
If you you're missing the parts that are important and that the organization will notice.
00:10:31.759 --> 00:10:36.480
If you just do metadata, data quality, look, they're important.
00:10:37.120 --> 00:10:38.480
I'm not gonna argue with that.
00:10:38.720 --> 00:10:46.240
But that's that's your baseline, that's your table stakes, that's your blocking and tackling.
00:10:46.799 --> 00:10:52.720
You hire someone to build a house, the assumption is they can saw a board and make a straight line.
00:10:52.879 --> 00:10:57.919
They can nail two pieces of wood together, they can run a wire and not burn the house down.
00:10:58.159 --> 00:11:00.399
That's table stakes.
00:11:00.720 --> 00:11:02.240
Data people hate to hear this.
00:11:02.320 --> 00:11:03.200
That's plumbing.
00:11:03.519 --> 00:11:05.600
That's your darn job.
00:11:06.480 --> 00:11:09.759
Don't come to me saying you're special with that.
00:11:11.200 --> 00:11:14.240
Come to me with if I'm a CEO, come to me with something special.
00:11:14.320 --> 00:11:17.919
And that means you've got to understand what special is to me.
00:11:18.159 --> 00:11:20.240
And that's the alignment conversation.
00:11:21.360 --> 00:11:24.879
Important to align with those business drivers for the CEOs and leaders.
00:11:25.200 --> 00:11:25.360
Yeah.
00:11:25.519 --> 00:11:34.879
Well, and if you're not for profit, organizations have goals, organizations have uh policies, organizations have guardrails.
00:11:35.120 --> 00:11:39.679
All of those are things that can shape shape how you do things, right?
00:11:40.000 --> 00:11:41.919
And and they have to all be considered.
00:11:42.399 --> 00:11:45.679
You know, and our next question just falls right into that.
00:11:45.840 --> 00:11:53.919
Is what do organizations or practitioners or even leaders often misunderstand or underestimate about data governance?
00:11:54.720 --> 00:11:58.639
I'm gonna divide that into two, into two responses here.
00:11:58.720 --> 00:12:01.919
It's the practitioner and the leader.
00:12:02.159 --> 00:12:02.480
Okay.
00:12:02.960 --> 00:12:07.440
And let me go to the practitioner first because that's the one that tends to surprise people.
00:12:08.240 --> 00:12:11.440
So I'm your I'm a data architect.
00:12:11.679 --> 00:12:23.200
Um I've been looking at study data governance for a while, and I own I know about data models and data architecture and uh semantics and semantic layers and all this stuff.
00:12:23.279 --> 00:12:26.000
And that that's my uh thing.
00:12:26.080 --> 00:12:42.799
And along comes uh data governance, and you go, many people do, especially for those of us that were there at the beginning, when I heard Gwen Thomas use the phrase governance, I won't say the year, it's it's hard to say it.
00:12:43.200 --> 00:12:48.080
Um, I went, that's the word we've been waiting for.
00:12:48.559 --> 00:12:51.519
Now, my background is important to note here.
00:12:51.919 --> 00:12:53.679
My background is not technical.
00:12:53.840 --> 00:12:58.240
I have no formal academic training in computer science or anything.
00:12:58.480 --> 00:13:04.559
I was one of those people in the 1980s that was grabbed because I could spell IBM.
00:13:04.879 --> 00:13:14.320
And I was taught to code, I was taught to be a technical person, and I did it because someone said, someone whispered in my ear, uh, no lie on a phone call, John.
00:13:14.480 --> 00:13:15.600
You really need to take this job.
00:13:15.759 --> 00:13:17.519
I said, I don't want to do computer stuff.
00:13:17.679 --> 00:13:19.519
They're not going to catch on.
00:13:19.919 --> 00:13:21.360
That's that's a quote.
00:13:21.919 --> 00:13:23.519
I actually said that.
00:13:24.080 --> 00:13:26.320
And and my friend laughed.
00:13:26.960 --> 00:13:35.360
And uh, and and then she said, Yeah, but I'm getting paid$18,000 a year to write code.
00:13:36.080 --> 00:13:42.240
Now you have to set the time when this when I heard this, this was in the early 1980s.
00:13:42.559 --> 00:13:48.000
And at that point in time, at$18,000, that was a fine middle class living.
00:13:49.519 --> 00:13:50.559
Times have changed.
00:13:50.799 --> 00:13:55.039
Yeah, and when I when and when I heard that, I said, oh, oh, really?
00:13:55.440 --> 00:13:55.919
18.
00:13:56.000 --> 00:14:00.000
I said, so so um, who do I call again?
00:14:01.919 --> 00:14:03.919
But my background was accounting.
00:14:04.080 --> 00:14:07.600
I mean, I've written the I've written the CPA exam, all right.
00:14:08.159 --> 00:14:19.200
I know about control, I know about these things, and immediately I I uh in my whole career I'm using this training of mine this way.
00:14:19.360 --> 00:14:27.120
And uh, but all the people that we deal with tend to go, a lot of people tend to go to computer science, they come in, it's a technical thing.
00:14:27.360 --> 00:14:35.360
They they don't understand that the basics of this are not what you sell.
00:14:36.559 --> 00:14:38.639
Uh is metadata required?
00:14:38.799 --> 00:14:39.600
Of course.
00:14:39.759 --> 00:14:42.639
Uh is a data model useful, of course.
00:14:42.960 --> 00:14:48.399
Do I need to grab the CEO and force them to accept the wonderfulness of the tools of my trade?
00:14:48.639 --> 00:14:50.559
No, they don't give a damn.
00:14:51.440 --> 00:14:53.519
All right, they don't give a damn.
00:14:53.759 --> 00:14:56.080
Um that is a huge misunderstanding.
00:14:56.240 --> 00:15:04.159
The misunderstanding is that what we do in the data profession has relevance in conversation to leadership.
00:15:05.200 --> 00:15:06.720
And it doesn't.
00:15:07.200 --> 00:15:10.639
What you need to understand is what we do with the tools of our trade.
00:15:10.720 --> 00:15:21.120
If I'm a carpenter and I'm moving into a new house, I don't care that my carpenter used a table saw or uh a radial arm saw or a circular saw.
00:15:21.279 --> 00:15:24.720
All I know is the house isn't gonna blow down in the wind.
00:15:24.960 --> 00:15:26.720
And that's all I care about.
00:15:27.279 --> 00:15:39.440
And and you know, what we do in our business is it's like I'm closing on the house, and before I close on a house, I have to give all the carpenters a pat on the head in a candy bar or a slice of pizza because of their wonderful carpentry they did.
00:15:39.679 --> 00:15:45.120
No, because that they're already on and look, that's an established profession, they're already on the next job, they could care less, right?
00:15:45.360 --> 00:15:47.679
That's where we think we think we're special.
00:15:47.840 --> 00:15:48.639
We're not.
00:15:48.879 --> 00:15:49.519
Right?
00:15:49.759 --> 00:15:52.080
We're not in terms of the tools of our trainer work.
00:15:52.240 --> 00:15:55.519
What's special is what the organization can do with that.
00:15:55.840 --> 00:16:03.279
And that's where we uh underestimate the work, and that's where we go wrong.
00:16:03.440 --> 00:16:06.000
Now, on the other side, let's talk about business.
00:16:06.159 --> 00:16:11.279
Over the years, we said, oh, the water cooler conversation, oh, the business just doesn't listen and they don't get it and all that.
00:16:11.440 --> 00:16:13.600
Well, we've never told them the right way.
00:16:13.919 --> 00:16:33.840
I put all of that 20 or 30 years of organizations where the business leaders say, I don't get that, I'll talk to you later, or we're too busy to do that, we'll deal with it later, or saying, Yeah, we support you, and then when you leave the room, their fingers are crossed behind their back, and I go, I ain't gonna pay any attention to this clown at all.
00:16:34.000 --> 00:16:38.960
All right, all of that is because what we told them was baloney.
00:16:39.440 --> 00:16:42.559
Okay, it was all that wonderful metadata, we got to do this.
00:16:43.120 --> 00:16:45.360
Yes, we do have to do that, but again, they don't care.
00:16:45.519 --> 00:16:47.279
The message for them was inadequate.
00:16:47.440 --> 00:17:02.720
Now, some of the message, the minute organizations around the mid-twos, the mid-nots into the teens of this century, businesses began to get serious about analytics.
00:17:02.960 --> 00:17:07.519
They began to get serious about using data more effectively.
00:17:07.599 --> 00:17:10.480
I'm not going to say data-driven, but data influenced.
00:17:10.640 --> 00:17:13.039
We'll use that, we'll use that phrase, okay?
00:17:13.440 --> 00:17:23.839
The minute they stepped across that line from get the report to let's uh let's answer a question we've never asked before.
00:17:24.559 --> 00:17:25.680
Analytics, right?
00:17:25.920 --> 00:17:31.599
The minute they stepped across that line, they became obligated to understand some of that process.
00:17:31.759 --> 00:17:38.079
There's not a CEO in the world who runs a manufacturing company that shows up at work and says, Well, I came out of finance.
00:17:38.160 --> 00:17:40.400
I don't give a darn about how we put widgets together.
00:17:40.559 --> 00:17:46.400
No, they go out in the factory and they learn a little bit about how widgets are made, even if they come from finance or marketing.
00:17:46.559 --> 00:17:47.039
All right.
00:17:47.359 --> 00:17:55.839
The ones that don't are the ones that are the CEOs that's that end up overseeing the dissolution of a large company.
00:17:56.000 --> 00:18:08.559
And there are hundreds of examples in modern business practice that when a CEO comes in who's a pure numbers person and doesn't get the culture or the model, the organization dies within 10, 20 years.
00:18:08.720 --> 00:18:09.279
All right.
00:18:09.519 --> 00:18:12.319
Um we need to have leadership understand.
00:18:12.400 --> 00:18:15.599
Now that they go say, I got to be data driven, now I want to use AI.
00:18:15.759 --> 00:18:26.720
Now they under have to understand some things like we need a data supply chain, and you need to have oversight of that data supply chain, and you need to back me up when I tell marketing they're messing up the data supply chain.
00:18:26.799 --> 00:18:30.000
And that's that's the part where leadership falls really short.
00:18:30.079 --> 00:18:34.400
They misunderstand, they underestimate that this governance of data.
00:18:34.559 --> 00:18:36.960
I don't even say data governance anymore, Barbara.
00:18:37.119 --> 00:18:39.119
I say governance of data.
00:18:39.599 --> 00:18:50.559
The governance of data is just as important as governance of your money or governance of your your uh employees or governance of the parking lot passes, okay?
00:18:51.119 --> 00:18:54.240
Or governance of expense reports, okay.
00:18:54.480 --> 00:19:00.720
It's just as important as those, and you cannot ignore it, and you need to know what you're talking about.
00:19:00.960 --> 00:19:05.599
And and once they embrace that, and we're seeing evidence now.
00:19:05.839 --> 00:19:17.279
Um, in my last podcast, uh our guest Stan Christians from Calibra talked about they have CEOs and leadership teams going to classes now, learning fundamentals of data management.
00:19:17.839 --> 00:19:23.519
So that so yeah, so now so that's where we and we have this big gap between the two sides.
00:19:23.759 --> 00:19:25.519
Please listen to me, please listen to me.
00:19:25.680 --> 00:19:26.960
Oh, I I don't have time for this.
00:19:27.119 --> 00:19:27.839
Oh, wait a minute.
00:19:27.920 --> 00:19:29.680
Maybe I have time for some of this now.
00:19:29.839 --> 00:19:30.160
Okay.
00:19:30.640 --> 00:19:36.480
And now, but then the other one, now that goes back into the the practitioner side, which is oh, great, you have time to listen to me.
00:19:36.559 --> 00:19:38.559
I can't communicate where it's a damn.
00:19:40.079 --> 00:19:42.079
So so we're working there.
00:19:42.799 --> 00:19:45.119
Bless all the people out there hardworking right now.
00:19:45.200 --> 00:19:46.160
We're getting there.
00:19:46.480 --> 00:19:48.720
But uh it's been a long road.
00:19:49.440 --> 00:19:59.039
Has been, but it seems like the tide's shifting a little bit with the reliance on AI, and now people really need to understand the governance of data that you're talking about.
00:19:59.440 --> 00:20:02.079
Another story to enhance this conversation, if I may.
00:20:02.319 --> 00:20:02.640
All right.
00:20:02.880 --> 00:20:03.359
Please.
00:20:03.599 --> 00:20:07.279
Uh so AI pops up uh summer of 2024, right?
00:20:07.440 --> 00:20:10.400
We all get our chat GPT subscription, remember?
00:20:10.720 --> 00:20:17.759
Yes, and we all say, I would I would like a picture of my brother, but I want him to look like a cocker spaniel.
00:20:17.920 --> 00:20:20.559
That's how we all start to use AI, right?
00:20:20.640 --> 00:20:23.599
And and of course, that's it's off to the races.
00:20:23.920 --> 00:20:28.559
Um, but it doesn't take long to realize this is really important stuff.
00:20:28.880 --> 00:20:36.720
And I was like, wait a second, I'm I'm uh uh 68 years old.
00:20:36.960 --> 00:20:39.359
I don't, I'm not gonna learn anything about this.
00:20:39.519 --> 00:20:42.319
I'm gonna just stay a data guy.
00:20:43.119 --> 00:20:51.119
I'm gonna put AI in a separate track because I want to fade into the sunset without having to learn anything new.
00:20:51.440 --> 00:20:59.839
Well, it didn't take long for us to realize that AI was AI is to our century as the steam engine was to the 19th century.
00:21:00.000 --> 00:21:05.680
It is transforming society, it's gonna transform human beings.
00:21:06.480 --> 00:21:10.319
As a species, we're going to be different in 20 years because of AI.
00:21:10.559 --> 00:21:13.359
And when that light bulb came on, I went rats.
00:21:14.079 --> 00:21:15.440
I have to learn something.
00:21:15.599 --> 00:21:17.680
I have to learn something about this.
00:21:17.839 --> 00:21:21.440
I had to, you talk about misunderstanding and underestimating.
00:21:21.599 --> 00:21:28.240
I underestimated the impact and the role of governance of data when it comes to AI.
00:21:28.720 --> 00:21:31.599
Um, and now it's super important.
00:21:31.680 --> 00:21:35.119
And I've had to sit down and study here recently.
00:21:35.920 --> 00:21:37.680
Well, and it's constantly changing.
00:21:37.839 --> 00:21:41.039
So Yeah, yeah, it's a moving target.
00:21:41.440 --> 00:21:49.599
Well, that leads us to the next question, which is what does doing data governance well actually look like in an organization?
00:21:50.079 --> 00:21:54.480
Let's think about it from mid-stage, no early mid stage maturity.
00:21:54.799 --> 00:21:55.039
Okay.
00:21:55.200 --> 00:21:55.759
Yeah, yeah.
00:21:56.240 --> 00:22:06.960
Let's that we're not we're gonna again, again, um the uh The the uh person being interviewed writes on early, mid and ladder, okay?
00:22:07.200 --> 00:22:12.559
Because I think it's important to understand the ladder to understand what the mid and the early should be.
00:22:12.799 --> 00:22:13.200
All right.
00:22:13.440 --> 00:22:14.160
Sounds good.
00:22:14.400 --> 00:22:21.680
So early on, and you'll see when I get to the ladder, why I'm saying what I'm saying for the early.
00:22:22.079 --> 00:22:26.480
What you have is engagement from leadership, not buy-in.
00:22:26.799 --> 00:22:27.279
Buy-in.
00:22:28.160 --> 00:22:37.599
If you're listening to this podcast and the boss says let you sit in your cubicle and listening to a podcast, you have buy-in because they've allocated time for you to do that.
00:22:37.920 --> 00:22:40.400
But that's not engagement, okay?
00:22:40.720 --> 00:22:44.160
You need leadership as part of the process.
00:22:44.319 --> 00:22:49.200
So you've got engagement early on, and everyone's finding their way together.
00:22:49.440 --> 00:22:52.960
You have a grasp of your culture early on.
00:22:53.200 --> 00:23:00.079
You're not changing your culture, but you are trying to figure out how to get something done within your culture.
00:23:00.720 --> 00:23:08.559
You are flexible, all right, because what worked for someone else isn't going to work for you.
00:23:08.960 --> 00:23:29.440
I had a conversation this morning with a company that has to uh work very, very closely with uh master data management, with data quality problems, um, and um big serious ERP type things.
00:23:30.079 --> 00:23:41.839
Um and after some examination and cultural introspection and a few questions, we came up with the uh um direction that we're not even going to use the words data governance.
00:23:43.519 --> 00:23:51.039
We we found a tone and a pace that honored the culture, all right.
00:23:51.440 --> 00:24:06.480
And um so what I see early on is flexibility, creativity, um, engagement of leadership, and then looking for managing something and governing something.
00:24:08.559 --> 00:24:21.759
Don't care what it is, but you do not take a year to do a strategy, you don't take 18 months to do a catalog, you find something that shows what it looks like right away.
00:24:22.400 --> 00:24:24.720
And that's an early stage organization.
00:24:25.200 --> 00:24:29.359
Mid-stage, we now start to get something formalized.
00:24:29.440 --> 00:24:34.400
Someone acknowledges that yes, we did a little something over here and it looked good.
00:24:34.559 --> 00:24:38.880
So now can we formalize uh the uh the informal?
00:24:38.960 --> 00:24:42.480
Uh I use uh Bob Seiner's phrase for that one, okay?
00:24:42.799 --> 00:25:02.880
And can we also um uh appoint someone with some, and this is where you you start to mature, is there is some responsibility and some accountability for some components of your data architecture or your data supply chain.
00:25:03.039 --> 00:25:03.599
All right.
00:25:04.000 --> 00:25:09.119
And that puts you at mid-stage, and then you start to measure things, all right.
00:25:09.279 --> 00:25:19.519
So you you and and you you you you start to leave mid-stage when you are actually invited to the table.
00:25:20.319 --> 00:25:33.119
You enter mid-stage by inviting yourself to, say, a strategic meeting or a planning meeting, and going to your sponsor and saying, Hey, you need to put me in that meeting and you need to spend some political capital to do it.
00:25:33.359 --> 00:25:36.720
And they plunk you in the corner of the room and you just sit there and listen.
00:25:36.880 --> 00:25:38.160
That's entering mid-stage.
00:25:38.319 --> 00:25:50.640
Leaving mid-stage is you are asked to the meetings, and app dev teams and leadership teams start to think about data proactively.
00:25:51.599 --> 00:25:55.359
So the first half of the cycle is reactive, the last half is proactive.
00:25:55.440 --> 00:25:55.920
Right.
00:25:56.240 --> 00:26:00.160
Well, the middle stages are proactive, then the last stage we'll get to in a second.
00:26:00.319 --> 00:26:06.960
But now you're you're proactive and you're in the room and you you establish relevance as a capability.
00:26:07.200 --> 00:26:07.680
All right.
00:26:07.839 --> 00:26:13.599
Now that now the latter stage, you disappear.
00:26:15.519 --> 00:26:24.640
There is no invite the data governance person to the room because everyone has been working with it enough that everybody is pretty much self-governed.
00:26:25.200 --> 00:26:25.759
All right.
00:26:26.000 --> 00:26:29.759
Um, some will say, well, what about the data quality project?
00:26:29.920 --> 00:26:32.240
Well, no, we do data quality every day.
00:26:32.480 --> 00:26:34.000
Well, what about the stewards?
00:26:34.160 --> 00:26:36.079
No, everybody's a data steward.
00:26:36.799 --> 00:26:46.400
There might be some people with accountability of a domain or something as part of their job, not as part of a separate program, but as part of their job.
00:26:46.559 --> 00:26:51.359
In other words, governance disappears into the atmosphere of the organization.
00:26:51.519 --> 00:26:53.759
It becomes part of everyday conversation.
00:26:53.839 --> 00:26:57.759
It became, it becomes just like any other governing, all right?
00:26:57.920 --> 00:27:00.240
What about governing of money?
00:27:00.640 --> 00:27:11.759
I mean, if I'm at a marketing and it's September, and I get a note and it says this from the boss, and the boss says, Hey, October 1st, we start budget cycles for next year.
00:27:12.000 --> 00:27:17.759
I need to see your budget, and here's the guidelines, and here's the percentages, and here's the things, and all that kind of stuff.
00:27:17.920 --> 00:27:22.160
And I'm gonna say, is it I'm VP of marketing, I'm gonna do my own budget.
00:27:22.240 --> 00:27:24.559
We're gonna have our own general ledger.
00:27:24.960 --> 00:27:29.759
No, you would never ever dispute that.
00:27:30.000 --> 00:27:30.559
Why?
00:27:30.880 --> 00:27:33.519
Because it's just the way it is, right?
00:27:34.240 --> 00:27:36.480
And that's your mature stage.
00:27:36.799 --> 00:27:40.880
But to get there, your emphasis is on cultural acceptance.
00:27:41.200 --> 00:27:44.240
Forget the tools, forget policies.
00:27:44.480 --> 00:27:59.359
Your emphasis is on cultural acceptance and embedding thinking and making governance of data no different than governance of money or governance of HR or anything like that.
00:27:59.599 --> 00:28:00.000
All right.
00:28:00.160 --> 00:28:11.279
I mean, uh it it that's the example I like to give, and uh I was inspired to this by a conversation with uh Gwen Thomas, one of our other contributors to this, right?
00:28:12.400 --> 00:28:13.920
Um, a few months ago.
00:28:14.079 --> 00:28:17.359
And she said, John, do you ever with my aviation is my hobby?
00:28:17.599 --> 00:28:18.720
The listener might not know that.
00:28:18.880 --> 00:28:23.039
I know everyone else that knows me, like you, Barbara, know because I'm I'm unbearable with it.
00:28:23.119 --> 00:28:24.559
I just I'm a pain in the ass.
00:28:24.720 --> 00:28:28.079
Anyways about my aviation.
00:28:28.319 --> 00:28:34.880
But I I said, yes, I took something away from aviation and imputed it into my professional life.
00:28:35.039 --> 00:28:39.759
And I always thought that that my computer world would would change my aviation world.
00:28:40.000 --> 00:28:41.359
And it's been the opposite.
00:28:41.680 --> 00:28:50.160
What I took was if you take a look at a commercial jetliner, you know, there's two people up front and there's 180 people behind them going somewhere, right?
00:28:50.720 --> 00:28:54.640
At 35,000 feet at 500 miles an hour.
00:28:54.960 --> 00:28:55.519
Right.
00:28:55.759 --> 00:29:25.119
Now, we take it for granted, but you have no idea the physics involved with that, the systems behind that, the complexity of pulling that off to hold 182 butts in the air at 35,000 feet and move them at 500 miles an hour and gently deposit them at the other end so they can all go out and sit on the beach for a little while.
00:29:25.680 --> 00:29:29.039
It is astonishingly intricate and complicated.
00:29:29.680 --> 00:29:30.720
How does that happen?
00:29:30.880 --> 00:29:37.440
Well, up front, those two people driving that bus uh deploy governance.
00:29:38.640 --> 00:29:59.839
That's their whole job, is to adhere to predetermined standards of uh communication, standard protocols of behavior, standardized checklists, data standards, numerical standards, standard metrics, standard units of measure, all there.
00:29:59.920 --> 00:30:07.279
And if any of that breaks down, those 182 people are at risk for making it to the beach.
00:30:07.519 --> 00:30:08.079
All right.
00:30:08.480 --> 00:30:11.359
That's what you want latter stage with data governance.
00:30:11.440 --> 00:30:17.839
You want the cockpit of an airplane where the two people are flying the airplane and doing our job, that's the execution side.
00:30:18.079 --> 00:30:23.920
But the oversight, there's not a dude in the backseat going, okay, now by the way, I'm the standards guy.
00:30:24.240 --> 00:30:28.799
And when you when you talk on a radio, you need to say over or Roger or Wilco.
00:30:29.200 --> 00:30:29.680
All right.
00:30:29.759 --> 00:30:33.839
We don't want anyone saying breaker breaker here on the on uh you know up in the airway.
00:30:34.319 --> 00:30:35.599
There's no one doing that.
00:30:35.759 --> 00:30:36.160
Why?
00:30:36.319 --> 00:30:37.440
Why is that?
00:30:37.759 --> 00:30:41.680
Because they've acculturated the standardization.
00:30:41.839 --> 00:30:44.960
That's governance, that's what we want with data.
00:30:45.200 --> 00:30:47.440
We want it in a cockpit, nothing special.
00:30:47.599 --> 00:30:48.319
It disappears.
00:30:48.480 --> 00:30:49.519
Another long answer.
00:30:49.599 --> 00:30:50.240
I'm sorry.
00:30:50.480 --> 00:30:51.279
No, not at all.
00:30:51.359 --> 00:30:59.359
That is a great analogy, and I think that really helps us understand the impact and what happens when it becomes business as usual.
00:30:59.519 --> 00:31:01.839
It's just part of doing the job.
00:31:02.240 --> 00:31:07.440
You know, if you've got uh um, I don't I don't have any kids at home anymore.
00:31:07.599 --> 00:31:08.559
I don't know if you do or not.
00:31:08.720 --> 00:31:10.720
None of my gone.
00:31:10.799 --> 00:31:11.279
There you go.
00:31:11.440 --> 00:31:15.680
So, but if I had someone saying, you know, I'm getting ready to graduate college, dad.
00:31:15.759 --> 00:31:21.039
I think I'm gonna go for a career in data governance, I'd go, hell no, sell insurance.
00:31:21.359 --> 00:31:24.720
Because we're the field's maturing.
00:31:24.960 --> 00:31:31.759
What I used to do, the exciting Wild West where that's hopefully, hopefully going to go away shortly, right?
00:31:32.480 --> 00:31:32.799
Right.
00:31:32.960 --> 00:31:35.680
Um, and besides that, AI is going to change everything anyway.
00:31:35.839 --> 00:31:38.880
So, yeah, I mean, I it's it's boring.
00:31:39.759 --> 00:31:41.119
It's boring.
00:31:42.000 --> 00:31:43.119
No excitement at all.
00:31:43.200 --> 00:31:44.720
You might excitement at all.
00:31:44.880 --> 00:31:53.119
I I was I I was mentoring somebody a few weeks ago, and I called and said, John, I I'm a data architect and I'm really thinking about some a career in data governance.
00:31:53.200 --> 00:31:55.599
I'm thinking of leaving this architecture and being in governance.
00:31:55.680 --> 00:31:58.319
And I said, Why in the hell would you want to do that?
00:31:58.559 --> 00:31:59.680
was my first question.
00:31:59.920 --> 00:32:04.720
And they went, Well, because of the the adventure, the excitement, the drama.
00:32:04.880 --> 00:32:11.440
I'm like, no, no, that's that means then you don't know really where this is supposed to end up, do you?
00:32:11.680 --> 00:32:12.000
Right.
00:32:12.240 --> 00:32:13.359
So anyway.
00:32:15.519 --> 00:32:18.319
Entry-level analyst to executive data leaders.
00:32:18.480 --> 00:32:25.200
Dataversity delivers the most comprehensive training in the industry, led by experts who are actually doing the work.
00:32:25.440 --> 00:32:36.079
Upskill with on-demand courses, earn exclusive certifications, and join a global community of data pros committed to driving real change at dataversity.net.
00:32:38.960 --> 00:32:48.960
Well, you know, that's a perfect segue for the next question because it's what piece of advice would you give to someone responsible for the governance of data?
00:32:49.119 --> 00:32:50.880
I'm going to take your terminology.
00:32:51.119 --> 00:32:51.279
Yeah.
00:32:51.599 --> 00:32:52.240
Because I like it.
00:32:52.640 --> 00:33:06.000
I would be, I mean, the piece of advice is you are tasked with embedding a new capability.
00:33:06.319 --> 00:33:09.119
I don't even call it a program anymore.
00:33:10.160 --> 00:33:10.960
And here's why.
00:33:11.200 --> 00:33:19.200
Program was a great word for about 10 years for us because it differentiated us from um project, right?
00:33:20.240 --> 00:33:29.279
So, so I know in your career before Dataversity, you were in a big corporation and you had to work on a data governance program, right?
00:33:29.599 --> 00:33:29.839
Yes.
00:33:30.160 --> 00:33:33.279
And every and you had to explain to people this is a program, not a project.
00:33:33.359 --> 00:33:34.720
And we need a program, not a project.
00:33:34.799 --> 00:33:35.519
And we did that again.
00:33:35.599 --> 00:33:36.400
And you know what?
00:33:36.559 --> 00:33:41.599
There's nothing wrong that we did that, but to me, that was an intermediate stage now.
00:33:42.079 --> 00:33:42.400
Okay.
00:33:43.440 --> 00:33:53.200
Now what you're done is you're responsible for taking that program and making it disappear.
00:33:54.240 --> 00:34:01.359
All right, getting the word program out of it and making it a permanent capability.
00:34:02.720 --> 00:34:03.279
Right?
00:34:03.599 --> 00:34:07.359
So and organizations do this all the time.
00:34:07.599 --> 00:34:09.360
Um, people are surprised.
00:34:09.440 --> 00:34:33.440
Uh, if you were doing business in the United States and you were a corporate executive um in the 1950s, you were confronted with a radical change in organization charts because some experts decided to tell you that you needed a department to manage human resources.
00:34:35.360 --> 00:34:45.519
People don't realize that before the mid-1950s, middle managers took care of their own people, just like middle managers take care of their departmental databases now.
00:34:45.760 --> 00:34:46.079
Okay.
00:34:46.559 --> 00:34:47.599
And interesting.
00:34:47.840 --> 00:34:52.400
When you read, you know, Wall Street Journal, all FM, it was like, oh, we've no, we don't need that.
00:34:52.480 --> 00:34:54.000
Now that's just that's overkill.
00:34:54.079 --> 00:34:55.440
We don't need the overhead and all that.
00:34:55.599 --> 00:34:58.320
And then compliance drove it.
00:34:58.800 --> 00:35:00.559
We've heard that before, right?
00:35:01.119 --> 00:35:04.000
So um this stuff happens all the time.
00:35:04.239 --> 00:35:16.400
So if you're going to be responsible for this, you need to take it upon yourself to do something like putting HR into place or maybe putting accounting, you know, accounting standards have existed for a very, very long time.
00:35:16.719 --> 00:35:21.519
Double entry accounting has existed for, I don't know, since the pyramids or something like that.
00:35:21.920 --> 00:35:26.880
You know, you you you you know, uh, you you need to make all this stuff not special.
00:35:27.519 --> 00:35:33.199
You need to just see that it's executed, it always serves itself, it always serves its its its thing.
00:35:33.360 --> 00:35:34.400
It's not special.
00:35:34.480 --> 00:35:40.880
You're not setting aside, you're not setting up fireworks every time someone loads a data element into a catalog or anything like that.
00:35:41.039 --> 00:35:49.119
You just need to just make it normally part and get rid of the program label and have it as an embedded capability.
00:35:49.280 --> 00:35:50.239
That's your job.
00:35:50.400 --> 00:35:51.840
That's your job.
00:35:52.480 --> 00:35:53.920
Very good advice.
00:35:54.159 --> 00:35:56.639
Again, leading to the next question perfectly.
00:35:56.880 --> 00:36:01.599
What typically triggers organizations to finally take governance seriously?
00:36:02.000 --> 00:36:11.440
Well, you know, boy, we had a long talk of that at a conference I was in uh Ireland last week at the uh uh the Data Leader Summit um in Wexford.
00:36:11.679 --> 00:36:13.280
Uh lovely event, by the way.
00:36:13.440 --> 00:36:16.079
I encourage everyone to look into it next year.
00:36:16.400 --> 00:36:21.360
Um if you like going to Ireland, that's you do have to jump in an airplane across the ocean.
00:36:21.440 --> 00:36:21.679
Okay.
00:36:21.840 --> 00:36:26.480
Anyway, um uh uh but we were talking about what triggers people.
00:36:26.719 --> 00:36:35.280
And of course, the joke was I don't get, you know, I doctors have been telling me to lose weight for 20 years, then I had a heart attack.
00:36:35.440 --> 00:36:36.000
Now look at me.
00:36:36.079 --> 00:36:37.599
I'm Schwarzenegger, all right.
00:36:37.679 --> 00:36:39.039
So I had a heart attack.
00:36:39.280 --> 00:36:49.360
Um uh organizations are careless with their inventory control or financial controls, and then someone embezzles a million dollars, and all of a sudden, there are the controls, right?
00:36:49.840 --> 00:37:01.760
There is this tendency for human beings to not embrace something until um, you know, we're not going to embrace the fire extinguisher until we smell smoke, and it's our trousers that are on fire.
00:37:02.079 --> 00:37:10.400
Human beings tend to be uh motivated by what we would call last-minute reactive triggers.
00:37:10.639 --> 00:37:12.800
In other words, I never had to learn how to swim.
00:37:12.960 --> 00:37:15.840
Oh, Crikey, the boat sinking, right?
00:37:16.480 --> 00:37:31.360
True now, but good organizations start to look ahead a little bit and say things like um, I always go, I use the example of the automobile industry a lot in my talks.
00:37:31.840 --> 00:37:40.159
Because after World War II, the United States went back to making cars the way they made cars, and this guy named it Edwards, J.
00:37:40.320 --> 00:37:50.159
Edwards Deming, I believe it's Jay, um, said, American cars are terrible, but I have this really great way to build a better car.
00:37:50.320 --> 00:37:52.719
And it's called quality management.
00:37:52.800 --> 00:37:57.199
And you look at the whole process and you break the process down and blah, blah, blah, blah, blah.
00:37:57.760 --> 00:38:01.519
And American Automobile, they laughed at him and said, We're number one in the world.
00:38:01.599 --> 00:38:03.599
We don't need this, you know.
00:38:04.960 --> 00:38:06.480
They went to Japan.
00:38:06.880 --> 00:38:12.000
And Japan, of course, they had no industry, they were starting from scratch.
00:38:12.719 --> 00:38:14.880
So they said, Why not?
00:38:15.599 --> 00:38:16.880
This guy's laid it all out.
00:38:16.960 --> 00:38:19.679
At least we don't have to figure out how to copy how the Americans do cars.
00:38:19.760 --> 00:38:20.880
This guy's laid it out for us.
00:38:21.039 --> 00:38:39.920
And over the next 20 years, all of a sudden, one Toyota, two Toyota, three Toyota, all of a sudden, in between all the Ford Galaxies and GTOs and the high school parking lot is this little crappy yellow Honda thing going, where did they get that color?
00:38:40.079 --> 00:38:44.880
But it's there, and all of a sudden, it's like, and then all of a sudden, boom.
00:38:45.360 --> 00:38:46.159
And then the U.S.
00:38:46.239 --> 00:38:48.800
automobiles said, Oh, it's a fad.
00:38:49.119 --> 00:38:55.039
Well, we all know in the in the late, in the mid-80s or so, the American automobile industry collapsed.
00:38:55.760 --> 00:39:01.760
And they didn't, they didn't embrace better manufacturing process until they had to get bailed out by the government.
00:39:01.840 --> 00:39:02.719
So it was the same thing.
00:39:02.880 --> 00:39:06.079
Their pants were on fire, they had to learn what a fire extinguisher was.
00:39:06.400 --> 00:39:07.920
Japan didn't do that.
00:39:08.079 --> 00:39:12.960
They took back and they said, we can either do it the way they're doing it or we can try something different.
00:39:13.199 --> 00:39:17.199
And they had an open-minded creative, and then that's why I talk early on.
00:39:17.360 --> 00:39:19.760
I look for open-mindedness and creativity in this.
00:39:19.920 --> 00:39:22.000
Tony Mazzarella, uh, now Dr.
00:39:22.159 --> 00:39:27.920
Tony Mazzarella, is a uh uh um an executive at a large insurance company.
00:39:28.079 --> 00:39:34.480
He wasn't a contributor to our work, but he's just done tremendous research as part of his PhD.
00:39:34.639 --> 00:39:41.599
And he talks about organizations that are successful with this are really light on their feet and very creative.
00:39:42.320 --> 00:39:52.639
So what triggers an organization to take it seriously is a the normal way, which is you smell smoke.
00:39:52.800 --> 00:39:54.239
That's that's part A.
00:39:54.719 --> 00:40:02.559
Number two is could how could we do this better, differently, that fits our culture, fits our direction, fits our alignment?
00:40:02.880 --> 00:40:04.159
See where that comes from, folks?
00:40:04.320 --> 00:40:05.599
That's why you have to have it.
00:40:05.840 --> 00:40:11.440
And and maybe it's not what everyone else is doing, or maybe it's not exactly what the talking head is saying.
00:40:11.599 --> 00:40:13.519
And but that's really it.
00:40:13.679 --> 00:40:15.440
You know, there has to be a driver.
00:40:15.599 --> 00:40:22.480
Now, if that driver is your pants on fire or trying to match your culture and your alignment together, that's fine.
00:40:22.639 --> 00:40:28.639
But there has to be an external driver normally to get data governance taken seriously.
00:40:29.920 --> 00:40:31.760
Yeah, I love your analogies, Sean.
00:40:31.840 --> 00:40:32.960
They really hit home.
00:40:33.199 --> 00:40:39.119
You know, and it'd be so nice if companies chose B, option B instead of option A.
00:40:39.440 --> 00:40:41.519
But um unfortunately.
00:40:50.639 --> 00:40:52.000
And he says, no, both.
00:40:52.079 --> 00:40:55.039
I'm like, whoa, you're asking way too much.
00:40:55.519 --> 00:40:57.199
They're like, please, no, not both of them.
00:40:57.440 --> 00:41:02.559
I'll go for a walk, but when I when I'm done with my walk, I've earned myself a Klondike bar.
00:41:02.639 --> 00:41:03.360
I'm sorry, doctor.
00:41:03.440 --> 00:41:04.559
That's all there is, too.
00:41:04.800 --> 00:41:06.239
Well, Klondike bars are good.
00:41:06.320 --> 00:41:07.440
So well, yeah.
00:41:07.519 --> 00:41:09.360
We're talking Klondike bars, really.
00:41:09.440 --> 00:41:12.880
I mean, well, I think we're having fun.
00:41:13.119 --> 00:41:14.480
Yeah, we're having a really good time.
00:41:15.360 --> 00:41:17.039
I hope our listeners are too.
00:41:17.360 --> 00:41:18.960
Sure, they are, because they're learning a lot.
00:41:19.119 --> 00:41:24.079
You know, I think this one's really important because, you know, in my previous world, I did this a lot as well.
00:41:24.239 --> 00:41:25.599
You know, it's very challenging.
00:41:26.159 --> 00:41:30.079
How do you explain data governance to executives without losing them?
00:41:30.400 --> 00:41:32.719
You don't explain data governance.
00:41:33.920 --> 00:41:39.840
That's the carpenter saying, you know, the radio arm saw was a lot better than a circular saw.
00:41:41.840 --> 00:41:43.039
I don't care.
00:41:45.360 --> 00:41:49.679
You know, your house is going to be rock solid because I'm a good carpenter.
00:41:50.320 --> 00:41:57.440
And it will resist these storms and these uh cataclysms and keep you warm and safe in the winter.
00:41:57.840 --> 00:41:59.760
And you go, thank you very much.
00:41:59.920 --> 00:42:01.840
That's how you talk to leadership.
00:42:02.000 --> 00:42:04.480
All right, they want results, all right?
00:42:04.719 --> 00:42:05.920
They want value.
00:42:06.079 --> 00:42:28.800
Um, my podcast, our last interview for this season, season four of the rock bottom data feed, available on all podcast channels, by the way, is a real honest to gosh chief executive officer who's a good friend of mine, and by design and by humor, because we we take the Mickey out of each other, can't spell IBM.
00:42:29.280 --> 00:42:34.800
Okay, that's we you know, but he I will tell you, I'll give you a hint.
00:42:35.119 --> 00:42:56.480
When you talk to leadership or an executive, and and they're a good executive, um, just as a sidebar, right now AI is flushing out the bad executives, flushing them out better than any uh Draeno flushing the hair out of the sink.
00:42:56.639 --> 00:43:02.480
I mean, we are talking because they're going to AI and they're going, oh, AI, everyone go do AI.
00:43:02.559 --> 00:43:04.480
How much headcount can you give me?
00:43:05.679 --> 00:43:09.119
All this headcount stuff is disastrous.
00:43:09.760 --> 00:43:18.000
And I was talking to my CEO friend who will be on the rock bottom data feed available on all podcast channels here in a in a month or so.
00:43:18.320 --> 00:43:22.400
And that is a good CEO doesn't look at the headcount.
00:43:22.960 --> 00:43:25.199
Um, a good CEO looks at value.
00:43:25.360 --> 00:43:27.440
How are you going to create value?
00:43:27.679 --> 00:43:36.239
Now, if lowering headcount to be more efficient will create value by a lower price to the client or a higher margin for the stockholders, great.
00:43:36.400 --> 00:43:39.840
But just chopping heads to give you a quarterly bump?
00:43:40.719 --> 00:43:41.280
No way.
00:43:41.599 --> 00:43:42.079
All right.
00:43:42.320 --> 00:43:46.559
So a good CEO wants value, and that's how you talk to executives.
00:43:46.800 --> 00:43:47.119
Okay.
00:43:48.000 --> 00:43:50.880
So you're in it's the elevator speech.
00:43:51.280 --> 00:43:53.519
I'll ask people to do and I'll do a talk.
00:43:53.760 --> 00:43:55.920
Do what's your elevator speech after my class?
00:43:56.000 --> 00:43:59.760
And they go, Oh, well, um uh, and I say I'll be the CEO.
00:44:00.400 --> 00:44:02.159
And I say, so there, Sally.
00:44:02.639 --> 00:44:06.000
Um uh I hear you're on this data governance thing.
00:44:06.079 --> 00:44:07.280
Could you tell me about that?
00:44:07.440 --> 00:44:08.159
Oh, wow.
00:44:08.320 --> 00:44:14.719
Yeah, we're gonna do a data catalog and then we'll be able to get agreement on all things that people get their reports really, really quick.
00:44:14.960 --> 00:44:20.960
And and then, and at that point, the CEO is gonna put up the hand and say, keep doing your job, Sally.
00:44:21.599 --> 00:44:24.880
But Sally hasn't sold anything to that CEO.
00:44:25.039 --> 00:44:28.960
Sally hasn't increased any engagement in any way, shape, or form.
00:44:29.280 --> 00:44:42.960
But if Sally says, well, um, governing data is going to make it a lot easier for this organization to achieve its value targets for shareholders and lower our risk in doing that.
00:44:44.000 --> 00:44:47.840
That CEO is going to hit the stop button on the elevator and want to hear more.
00:44:48.880 --> 00:44:56.880
That do not say metadata, do not say star schema, do not say data catalog.
00:44:57.119 --> 00:45:03.840
Um I uh just don't know that that's that's that you know story I've already told three times.
00:45:03.920 --> 00:45:06.239
That's like the carpenter saying, Aren't I special?
00:45:06.480 --> 00:45:08.960
No, did you nail the boards together straight?
00:45:09.039 --> 00:45:10.239
That's all I need to know.
00:45:11.039 --> 00:45:11.360
Okay.
00:45:11.920 --> 00:45:15.920
Um, so you want to load if you don't want to lose them, you have to talk their language.
00:45:16.159 --> 00:45:20.400
The water cooler conversations were they just need to take time to understand us.
00:45:20.639 --> 00:45:26.079
No, here and I I used to be one of those persons around the water cooler.
00:45:26.639 --> 00:45:28.400
You probably have been too, right?
00:45:28.559 --> 00:45:29.039
Oh, yes.
00:45:30.000 --> 00:45:30.320
Idiots.
00:45:30.480 --> 00:45:31.519
I went, what the heck?
00:45:31.599 --> 00:45:32.559
I mean, you know.
00:45:32.880 --> 00:45:39.599
Um uh uh uh and we're all standing around a water cooler like you know, Homer Simpson going, all right.
00:45:39.760 --> 00:45:45.519
So we're no, here's the reality of an organization that wants to get things done.
00:45:45.679 --> 00:45:47.920
Everyone has their job, right?
00:45:48.079 --> 00:45:56.320
And the job of that leader, that executive, is to herd all the cats in one direction, and that's increase value.
00:45:57.920 --> 00:46:00.400
Razor sharp focus, nothing else but that.
00:46:00.559 --> 00:46:02.159
And if you can talk to that, fine.
00:46:02.239 --> 00:46:08.960
If you can't, you're part of the delegatees um and the delegators, and I don't have time for you.
00:46:09.280 --> 00:46:10.719
Because just go do your job.
00:46:10.800 --> 00:46:12.239
I'm not saying it's bad.
00:46:12.480 --> 00:46:13.920
I just don't have time for you.
00:46:14.159 --> 00:46:14.639
All right.
00:46:14.880 --> 00:46:16.159
You know, it's all static.
00:46:16.239 --> 00:46:18.400
I don't, I don't, I don't need it.
00:46:19.280 --> 00:46:20.800
Truly, they don't need it.
00:46:20.880 --> 00:46:22.559
And and that is the reality of the world.
00:46:22.639 --> 00:46:27.199
And data people, you need to understand they're not gonna listen to your data speak.
00:46:27.360 --> 00:46:31.840
You need to go learn to communicate with leadership.
00:46:32.079 --> 00:46:32.480
Period.
00:46:32.639 --> 00:46:36.320
And that means don't give them a 20-page deck when you have 10 minutes for them.
00:46:36.480 --> 00:46:39.599
Give them a one-page deck and say everything you want to say on one page.
00:46:39.679 --> 00:46:44.880
And if you say to me, I can't do that, I tell you, well, I'm gonna go find someone else who can.
00:46:45.760 --> 00:46:47.519
But you're off the team.
00:46:47.840 --> 00:46:50.320
Or go figure out a way to do it in one page.
00:46:50.480 --> 00:46:52.320
That's where we are now.
00:46:53.280 --> 00:46:53.679
Yep.
00:46:53.920 --> 00:46:58.159
Have to speak in their language or their pain points or don't talk.
00:46:58.719 --> 00:46:58.960
Yep.
00:46:59.199 --> 00:47:00.000
That's wonderful.
00:47:00.639 --> 00:47:06.159
What happens when governance is treated as a technology initiative instead of a business one?
00:47:07.280 --> 00:47:09.760
Short answer, you fail.
00:47:11.840 --> 00:47:12.719
Next question.
00:47:12.960 --> 00:47:13.199
No.
00:47:13.440 --> 00:47:17.920
Uh okay, so what are the mechanics of that failure?
00:47:18.079 --> 00:47:26.480
Well, one, you're not, let's go back to um all these questions about communicating with leadership and and what do we say?
00:47:26.559 --> 00:47:30.400
We've we've been touching on that uh again and again, right?
00:47:31.039 --> 00:48:02.639
Um you are not speaking the right language, you are not orienting yourself to speaking the right language, you are subverting a major business capability that is supporting society-changing initiatives like AI and analytics, and you're subverting that to overhead things like taking out the trash, all right, um, and reloading the coffee machine.
00:48:03.519 --> 00:48:10.400
And and you immediately put yourself in the place where you don't want to be, which is no one's gonna listen to you.
00:48:11.679 --> 00:48:18.960
Here's why technology here, first of all, they don't remember, I've already said this, they don't need to know about a catalog.
00:48:19.920 --> 00:48:25.119
I I tell people, don't even put catalog as a separate line item in your budget request, just put it in there.
00:48:25.280 --> 00:48:26.800
It's it's a tool of your trade.
00:48:26.880 --> 00:48:31.599
All right, just you know no one needs to know about that.
00:48:34.239 --> 00:48:37.199
But that can't be your sole purpose for existence, all right.
00:48:37.360 --> 00:48:40.320
Now you're gonna say, well, but they won't listen to me.
00:48:40.880 --> 00:48:42.239
All right, I had that last week.
00:48:42.400 --> 00:48:44.800
Some said, I've tried everything in the book, they won't listen to me.
00:48:44.880 --> 00:48:48.719
I said, then pretend they are listening to you.
00:48:49.199 --> 00:48:52.639
And do it as a business initiative anyway.
00:48:53.119 --> 00:48:56.880
And do your business alignment without any sponsor.
00:48:57.039 --> 00:49:00.079
Do your business alignment with an outside consultant.
00:49:00.400 --> 00:49:01.840
Rent me for a day.
00:49:02.079 --> 00:49:03.199
I'm reasonable.
00:49:03.440 --> 00:49:05.360
I'm not cheap, but I'm reasonable.
00:49:05.519 --> 00:49:05.840
Okay.
00:49:06.239 --> 00:49:13.679
Rent me for a day, and we and and you build your language, but get this out of techno speak.
00:49:14.480 --> 00:49:24.000
Because you just you you basically pull the covers over yourself, and you're doing what you've been doing for 20 or 30 years, just happy as a clam doing your techie stuff.
00:49:24.079 --> 00:49:30.400
Now, remember, in conclusion here, remember what leadership has been seeing for the last 40 years or so.
00:49:30.880 --> 00:49:42.480
Um uh and there's a great video on YouTube of Admiral Grace Hopper talking to a bunch of security people at the Pentagon in the 1980s, early 80s.
00:49:42.719 --> 00:49:43.280
And Dr.
00:49:43.360 --> 00:49:47.920
Hopper, by the way, Admiral Hopper, invented COBOL.
00:49:48.159 --> 00:49:49.039
Oh, okay.
00:49:49.199 --> 00:49:49.920
Okay, all right.
00:49:50.079 --> 00:49:53.760
Um, and she talks about all the problems we're going to have.
00:49:54.000 --> 00:50:03.199
And this is 1982, and here we are 44 years later, and everything she said has come true, and we haven't fixed any of it.
00:50:04.719 --> 00:50:05.039
Okay.
00:50:05.920 --> 00:50:08.880
She said, because you can't, this is not just technology.
00:50:09.039 --> 00:50:11.280
We've got to get people pulled in.
00:50:11.440 --> 00:50:15.599
The other thing is, in the ensuing 40-some years, we've tried to do lots of techie stuff.
00:50:15.679 --> 00:50:19.119
You know that you worked in a big company, a couple of big companies doing this stuff.
00:50:19.280 --> 00:50:23.360
How successful have are we with our uh corporate IT projects?
00:50:23.679 --> 00:50:24.880
What's the success rate?
00:50:25.519 --> 00:50:26.159
Not very.
00:50:26.400 --> 00:50:29.039
And we just keep doing them because you got to have IT.
00:50:29.119 --> 00:50:35.840
And what we have is this acculturation now for those of us in technology, that we can just do what we want.
00:50:35.920 --> 00:50:38.960
And if it's a little bit useful, we can just keep going.
00:50:39.280 --> 00:50:48.159
And what we don't know, and you don't remember, is all these people have been watching us for 40 years, and they're not impressed.
00:50:48.719 --> 00:51:04.079
And and and they remember, and so they're very being polite to you because you kick off your new program and your whatever, and you're gonna do this, and you're speaking all the speak you've spoken for 40 years and all that, and they're nodding and are being polite because they're executives, they get paid to do that.
00:51:04.320 --> 00:51:09.039
And when you leave the room, they go, shut the front door, here we go again.
00:51:09.360 --> 00:51:12.320
Okay, I mean, that's why you can't treat as tech.
00:51:13.039 --> 00:51:18.239
If you keep treated as tech, it's gonna stay tech, and nobody cares about tech.
00:51:19.119 --> 00:51:23.119
They want tech to fix everything so that then we can do it.
00:51:23.280 --> 00:51:24.639
Right, just fix it and then we'll do it.
00:51:25.280 --> 00:51:29.840
It took us 50 years to get in this mess, but you go buy some tech to fix it all in a year.
00:51:30.079 --> 00:51:30.719
Exactly.
00:51:30.960 --> 00:51:32.800
Right, yeah, yeah, not gonna happen.
00:51:33.119 --> 00:51:34.480
Thank you so much, John.
00:51:34.639 --> 00:51:35.519
This has been great.
00:51:35.679 --> 00:51:37.119
I have thoroughly enjoyed it.
00:51:37.360 --> 00:51:39.280
I'm sure our listeners have too.
00:51:40.239 --> 00:51:41.679
Barbara, it's been a pleasure.
00:51:41.840 --> 00:52:01.119
I really am glad to add to the body of knowledge for the ADGP, and I'm hoping that the listeners uh enjoy this content and then click on whatever they need to click on and explore certification through Dataversity for data governance.
00:52:06.480 --> 00:52:16.079
For our listeners, if you'd like to learn more about the ADGP certification program and the applied data governance body of knowledge, visit training.dataversity.
00:52:17.360 --> 00:52:23.119
Until next time, I'm Barbara Nishaw, and this has been Inside Applied Data Governance.
