Good morning, afternoon, evening, everybody. This is Malcolm Hawker, your host of CDO Matters. The podcast focused on enabling chief data officers to become truly data driven.

I’m excited today to be joined by Laura Madsen, who is the CEO of Moxie Analytics, professional professional services company focused on helping companies enable thriving data cultures where everyone is equipped with the knowledge and tools to be better to ask better questions and get better, faster answers.

Laura is the author of three books, disrupting data governance, health care business intelligence, which just had its ten year anniversary. Congratulations.

Ten years. That’s awesome.

Data driven health care is her third book.

So obviously, incredibly intelligent in the space around BI analytics. Really excited to talk more today about data governance, which is an area that that Laura has written about extensively and helps her clients with extensively.

It it’s interesting. I I reached out to, to Laura a couple of days ago and said, hey. You know, do you have a bio that we wanna use for the podcast? Because we put, you know, bios up of all of our speakers because because you, the audience, wanna know who they are.

And I went and bought Laura’s book a couple of days ago, the disrupting data governance, book on, Amazon.

I’d write on Amazon, and I’m gonna read word for word. It says, Laura Manson isn’t shy about her dislike for formal bios.

To her, they come across as stuffy and disingenuous. Two things Laura is absolutely not. She’s a lifelong learner and champion for intentional inclusion and gender equity matters through organizations like SysTech, and she talks data. So anyway, we won’t just we won’t spend too much time on your, your bio and and and your experience.

Obviously, you know the space and you’re living and breathing in the space. But I but I think the little I included the little quip about, about your bio, just to give a little more insight in in in in who you are and what makes you tick. And one of the reasons why we’re talking talking today, also because you you are a self described rebel. It’s actually in your in your tagline of of of your LinkedIn description.

So with that, thank you, Laura. It’s a pleasure to have you on CDO Matters today.

Thank you. I’m happy to be here.

Wonderful. Well, let’s let’s let’s let’s dive into it.

So I I I read a lot of things in disrupting data governance that really resonated with me and I think will resonate with our listeners as well.

Some of the things that I enjoyed are you kind of laid out the state of the nation as it were. And it was published in twenty nineteen, but I suspect you largely believe that the state of the nation has not changed much. Would you agree in in three years?

Yes. I would you know, I mean, these are big efforts to turn around, and we’ve been practicing these, methods for decades.

So Yep.

Yeah. There definitely is, you know, slow shift. I would say though that since the book has been published some of the worst, offenders things like a pair you know, a page long definition of data governance has disappeared off the interwebs, which I deeply appreciate.

But, yes, for the most part, most organizations are still practicing a lot of what would be considered, you know, sort of traditional ways of data governance.

Okay. So I queued that up because what I like about the book is you laid out the state of the nation, which is largely the same. I started my career at Gartner in twenty nineteen. So over over the three years that I was there taking inquiries about data governance and helping organizations, you know, solve for data governance, I would totally agree.

I largely failed, sadly, at at at significantly moving the needle from a data governance perspective, so for for better or worse. But the best part about the book to me is that you do lay out a framework for how to solve things. Right? And then there’s so so I would certainly recommend to our readers to go to go or to our audience to go read the book.

The first line of the book says you’re laughing because you know what the line is. The first line of the book says, I really hate data governance.

And that was a fabulous hook for me to to to to keep going. But I’ve gotta say, Laura, after reading it, I think it’s a little bit of love hate.

Would you agree? It’s kind of like the you love to hate it, but you hate to love it.

I mean, so I get a lot of I get a lot of, shall we call it feedback about the first line of that book?

And certainly there’s an aspect, I suppose, of it that’s sort of love hate. There’s also an aspect of it that I I’m just a little masochistic. Right? When I see something that is wrong or lacking maybe logic. And I and I do think that a lot of the ways we think about data governance now lack logic, in a modern in a modern data environment.

I just tend to wanna blow those things up. And, so there there’s a fair part of me that I really still struggle with data governance as a result of that because there we we’re still doing a lot of the stuff.

What I where you probably hear a lot of the, sort of affection come through is you have been doing data for a really long time.

And I still think there’s a way more hope and promise for this work than there isn’t. And, when I work with clients, the thing that gets me excited is this idea that we can work on things and and improve them together, and and hopefully solve some of these bigger issues more than it is that I just really love or hate data governance.

It’s interesting you use the word masochist, because, in many ways, I would I would describe myself the exact same way. When people have asked me, why do you do what you do? Right, and what why are you talking about best practices, and why are you a Thought leader. I know you don’t like the word I don’t I know you don’t like the phrase thought leader, but but why why do you choose to be a thought leader? My answer to that question is is that I like solving really, really hard things.

Yeah.

Right? I and it sounds like that that that you’re you’re there as well.

I I I’m I’m intrigued by the notion of you needing to blow it up.

The the the exact words that you use in your own book is data governance is broken. There’s no way to make incremental changes to fix it, which is in line with this notion of, of blowing it all up. Now I would I would argue that you that you do actually have a bit of a road map and an incremental approach to governance where where in terms of what you what you lay out. Mhmm. But I do tend to agree with you. I think a lot of things are broken. So so you use the word illogical approaches to data governance.

You also use the I’m paraphrasing you now. I think you just said looking for older school or legacy approaches, nonmodern maybe a a way to say it. Yeah. Can you give us a a couple of examples of what some of these illogical approaches would be?

Sure. Sure. Yeah. So the big thing is when I started writing the book in twenty eighteen, and and by the way, I never I never intended to write a book about data governance.

It was not on my radar at all.

But I left my corporate job in twenty eighteen and started sort of doing a lay of the land in the data industry. I’ve been in it for two decades at that point. And, you know, when you’re running programs like this, you go very heads down, and you just kinda do the work. Right? And you’re you know, I was building a modern data platform, and I was, you know, working with my staff, and I just I didn’t really care about the landscape.

So fast forward, I I leave that job. I decide I’m gonna, you know, go out and and work as a consultant again. And, I did a little bit of research, and I and I found the thing that kept coming back for me was data governance was the Achilles heel of most programs and our ability to deliver results.

And the the simplest thing I did is I I googled data governance, and I wanted to know how people were defining it. And it it came back with a page long definition, and I’m not kidding. It was two paragraphs and it was a full page.

And I remember distinctly, I shut my browser, I stepped away from my laptop, and I was like, I gotta write that book. You know?

There’s gotta be a better way.

I just, like, like, there’s how do you succeed with anything that’s a page long in terms of how you describe the work? And knowing where I had come from and how difficult it is in terms of operationalizing the effort, I knew that there was a better way to break it down. And I also knew at that point that the way we had created data governance so So think about it like this, right? The first sort of concept around data stewardship I believe and I’m sure somebody will correct me was, referenced right about the time that the corporate information factory, the late 90s was a popular sort of primary book on the space written, of course, by Bill Inman and Claudia Imhoff.

And and so when I talked to Claudia about this concept of data stewards, she was literally, like, well, we had data warehouses, which were tiny compared to the stuff we have now. And we literally needed the business to steward this stuff for us because we were just pulling data in and landing it. We didn’t have contextual information about the data.

And they liked the term data steward because they were stewarding the information.

And, hence, this whole thing was born in a space where in the late nineties, most of our data warehouses, and I was, you know, out in the space by then, were, you know, maybe a handful of tables, twenty, thirty tables, couple hundred thousand rows, maybe maybe millions of rows in your entire data warehouse.

And nothing in terms of the construct of data governance changed from the late nineties to when I googled that definition in twenty nineteen.

Two decades. And I promise you, there’s very few things I’m doing that I did in the late nineties. Thank god I’m you know, that ship has sailed.

But why are we still doing the same things with data governance in the data space? It’s it that’s why it’s illogical to me. It’s very illogical to me. And so that’s hence, the book was born.

Seems like some definition of insanity.

Stuff.

Now I think some would argue that some of those approaches, at least from maybe a compliance or legal or audit framework may be still appropriate, maybe. You you you you go to great lengths to kind of challenge the notion of of centralized paradigms of of governance, command and control paradigms in in in data governance. I mean, that that’s something you really, really focus on.

Mhmm.

I think there there there could be a case where you could argue for command and control for some processes in some use cases and in some industries, but certainly not all. One of the things that you focus on extensively is the notion of context.

Right? And and and you just mentioned that that that now and how important context is is because one person’s context is different from another, is different from another and on and on. So so so I think this notion of context centricity well aligns to a lot of the things that I’m at least I’m hearing about in the market today. True.

But I would argue are a massive are a massive leap for a lot of companies. I just came back from the you agree. Okay.

Absolutely.

I I came back from the the Gartner data and analytics summit in Orlando where where where some of my very close friends, were were presenting these ideas largely wrapped around the notion of what they would call adaptive forms of of governance. That’s the word that that that they like to use.

Mhmm. K.

And I’m like, man, that sounds awesome and really hard because if it was if it was hard to do one set of if thens Mhmm.

Right, That we’re we’re we’re forced upon everybody for better or worse.

Right.

The the idea of having hundreds or maybe even thousands of sets of if thens seems like a big jump. What what do you think?

Yeah. Yeah. Absolutely. You know, it’s one of the areas I think from a data quality perspective that we struggle with. It’s that kind of fit for purpose, right, or context, driven data quality.

And and and so what happens from a scale perspective is that you have an enormous amount of variability. And and that’s one of the things we try to reduce, of course, in data is that, you know, as you’re as you’re trying to exercise the word control over your data, right, you try to reduce the variability because otherwise, how do you do it? How do you execute the work?

So, you know, context is king and it always will be because from a data perspective, it’s very difficult to gain information out of data that doesn’t have context.

The truth is that data is not binary. Not really. Right? Not and not in the way that we use it in the industry. It’s not it’s not binary. So I think where we struggle with with the context piece is, in a lot of spaces that we could actually avoid even needing to worry about it.

So here’s a great here’s a great example.

For a very long time and and still often, companies think that they have to have one definition of something.

I’m an MDM guy, so you’re you’re you’re you’re talking my language right now.

Yeah. Don’t don’t have one definition of something.

Right? And, again, it’s I I just one of the things I really want people to take away from the book is to think about something. You know? If you don’t wanna blow the whole thing up, that’s fine. Don’t blow the whole thing up. But I want you to look critically at what you’re executing, what’s not working, and try to figure that piece out. So let’s just talk about definitions of data for a hot minute.

In what reality do we want everybody to be operating off of one definition of something? For one thing, it takes you forever to get there.

For another thing, what about my reality is the same as your reality? The one I use in the book is I am very comfortable with the idea that a nurse manager has a different definition of patient than a finance manager does, and I think it’s completely okay. And as a data governance leader, don’t chase that down because you’re chasing your tail. You know? Let them have different definitions when it matters.

So that’s the context. They each have a different context. When it matters is when you wanna make sure that you have a better sense of, management around it. And that’s when you’re comparing.

You’re comparing units. You’re comparing departments. You’re comparing campuses. Then everybody should be using the same thing because that’s just good math.

Right?

But but the rest of the time, we chase our tail way too often trying to get everybody on the same quote, unquote page.

And it’s one of the reasons why I love to, you know, I love to, poke on the single source of truth thing, because there is no such thing. There’s no single source of truth. We need to stop trying.

So anyway.

So so I agree as a a single version of the truth is is a panacea that will never exist. And couldn’t agree more. Yep. And I couldn’t agree more that the context should fuel data definitions at an operational level.

Right? That that to paraphrase you, it’s okay for a nurse to define a patient different than somebody in the billing department would define a patient. Totally and completely acceptable, and this is something that our governance program should be able to support and our technology should be able to support. But to your point, if the CEO asks how many customers do we have, there can only really be one answer.

You better be able to answer one more answer. Mhmm.

Right. So I this may be a little nuance y, but the way that the the the phrasing that I used for my clients is is that there’s really no such thing as a single version of the truth. But a single source, yes, you could technically have a single source, whether that source is a governance program, whether it’s an MDM hub, whether it’s something else. I think maybe a little hair split y, but single version, for for the companies that I’ve spoken to, a a handful of companies could still operate that way, and they tend to be making, like, you know, very process centric manufacturing companies that are making, like, medical devices maybe. Right? Or or or or maybe jet engines or other things that that where people’s lives are literally on the line, but totally totally agree with you. So to data quality, I was constantly given these blast of refreshment in your book because you you kept you kept hammering home this notion that quality is not absolute.

Amen. And you kept hammering home the notion that you’ll never get to a hundred percent. Right. Here here. Yeah. Could could couldn’t agree more to to to to quote you.

Here’s the quote. There’s really no such thing as clean data. Not when you’re talking about the accuracy of petabytes of data. It’s just not feasible to guarantee it’s climbing this.

Yet yet, as you know rightfully in your book, so many companies still chase it and still spend a lot of money with data quality as a dependency of digital transformation, supply chain optimization of True. Of in insert strategic project name.

Right. I still talk with companies that are like, well, we have to clean our data. What do you say to those companies?

Well, I certainly don’t wanna discourage clean data.

I mean, we should all aspire to to be our very best selves, and the data should aspire to be its very best self.

Where it becomes dangerous is, you know, when you’re willing to throw everything sort of against that without, again, really critically thinking what that means.

I would I would venture to say that even organizations that focus a lot on it don’t ever get close to what looks like, you know, quote, unquote, clean data.

Because, you know, I can’t imagine the systems operating in a way that would allow you to prevent human error, which is primarily, you know, particularly in the health care space where I’ve spent most of my career.

You know, it is one of those places where almost all the data is hand entered or has as a byproduct of a individual or a person entering that data. Mhmm. And is probably, in terms of data quality industry perspective, one of the most critical places we get that right. And and so those two things are just enormously at conflict.

So I really encourage organizations to think a lot about data quality and construct what would be, you know, probably more pragmatic ways of thinking about data quality.

But one thing I wanted to make sure was clear is that there and this is always fascinating to me when I talk about this in person because people kinda, like, look at me like I’ve lost my mind.

Heresy. Right?

Yes. It which is there is no data governance without data quality, and there’s no data quality without data governance.

And you think I’ve lost my mind.

A paradox. Yeah. Okay.

Yeah. And and and the reality is that you you can’t it’s very difficult to prove that you’re doing data governance well without having something like data quality as a metric.

Dashboard.

And it’s very difficult to do data quality well if you don’t have somebody guiding you to understand contextually what that data should should be. Because quality is really defined by the end users, and the end users are thinking about it from their point of view. And that’s the the purpose thing that makes this a very complicated thing to execute against.

Yes. And and I suspect the notion of telling some of your clients, let it go. You’ll you’ll never get to a hundred. Maybe twenty is okay. Right? Twenty percent error, maybe ten percent error. It depends on your industry.

Right? But that notion for a lot of people is like, what do you mean? It’s okay to have that data. Because I what I’ve seen over and over and over again is that is if you put data quality in front of a group of technologists, it’s like this Rubik’s cube that they cannot pick up.

And and and it just happens over and over again. So I I’m glad both of us are on the, on the same page there in terms of data quality. You’ll never get to a hundred percent of data quality and and and don’t try.

So I I was I was interested by some of what you shared in your book related to technology.

You know, I I I kinda come from the MDM space. And while I agree that, there is really kinda no such thing as a single version of the truth anymore.

I do from do you think from a technology perspective that there is a lot of promise? You actually call that out in the book, and you and you say that there is promise, and there’s way too much data now to to ignore technology as a as a tool to properly govern it, to properly manage it.

What do you see out there that that’s that’s you you’re an optimist. You said that.

That’s wonderful. What are some of the things out there that give you some optimism at least from a technology perspective?

Yeah. So I will say that, I am a pessimist, but I have some optimism about some aspects of data.

Sounds complicated.

Well, yes.

So so where I have some optimism so we again, when we sort of look back from a data governance perspective for a really long time, we had no tools in this space at all. I could couldn’t count on both hands how many times I had personally created an Excel sheet where we did most of our data governance tracking.

Yeah.

You know, where we sort of back ended our way into what, you know, was considered more of a a modern data catalog. So data catalogs are changing the game, and there is no question in my mind that technology is that lever that we need in terms of our you know, that scale. Right? That ability to do the things that we need to do with petabytes of data.

So, you know, I I think that there’s a lot of excite exciting things that are happening in that space.

The ability to have lots of people look at the data and weight it so we know what is being used more often than others and flag it if there’s questions and concerns.

And one of the things that I think from a quality perspective, and this is all sort of related to data governance, of course, is more eyeballs on the data, better data.

And so from a data governance perspective, if you’re not focusing on usage of the data, then what are you doing? And and and, really, you could say this about almost any aspect of of data as a as a function in your organization. But if if you’re not focusing on getting people using the data, then what are you doing?

And and, again, tools can help us with that. They cannot solve all of our problems. So, really, a lot of the time I get called is when they’ve been trying to do this for a really long time and can’t get traction.

They bought a tool, implemented it, and it didn’t magically fix all their problems.

Or my personal favorite, they’ve spent the last three and a half years collecting all of their metadata, and now they wanna know what they should do.

So, you know, these are are all problems that can be solved with some some thinking about process and and people and the culture of an organization and and a way less focus on the technology.

Well, so you are getting into now we’re transitioning into the answer, into the solution, which you recommended the at the end of of your book, which, is in essence at a very high level what you call the radical democratization of data. So you you just said that in the more eyes on Mhmm.

Is better. You just said the more adopted a data solution, for lack of a better phrase, a data solution, a data product, a report, a little, whatever. Mhmm. The more adopted it is, the better it will be, because there’s, you know, in theory that it’d be valued there.

So help our listeners kind of understand. Let’s go into a little more depth around what does radical democratization sound like, or what does that actually what form does that actually take? What are the three or four things that a CDO would know if should know if he or she was was interested at at following your model?

Yeah.

So the first thing and, you know, we love we love data, of course. So we’d love to have data about our programs. But one of the biggest things that I see as the hallmark, sort of a leading indicator, is you just start getting more questions.

You just start getting more questions. And the questions aren’t how do I get this you know, how do I get access to this tool? It is, hey. I saw the data and and I’m curious about x, y, or z. You just start getting more questions.

And one of the things I always want, you know, CDOs to think about is the way they respond to those questions lays the groundwork for everything that they do in the future.

If if they if they respond to that question with, we’ll get back to you, and they never get back to them, then you lost that person as a as a possible champion for your, you know, for your, work.

If you get excited, even if somebody comes to you and says the data’s wrong, which, of course, every data person cringes. How many times I’ve heard that? There’s a million reasons why they’re seeing something and probably has nothing to do with the data.

But we are we have historically been very bad about that response. Right? We’re just like, oh, the data is not wrong. Let’s blah blah blah.

You know? We just have to get really good about saying, that’s awesome. Thank you. Let’s talk about that.

Because when you start to get more questions and that means people are looking. And when that person has this conversation with you and you get an opportunity to explain what happened, and then that person goes to the next person and says, you know, I actually talked to her about that, and, this is what happened and, you know, I think they’re gonna, you know, fix it or it’s doesn’t need to be fixed or what have you. Right? It’s laying that groundwork.

Another way to think about it, which the analogy that my son likes the most is to think about it like a car engine.

Right? If you don’t have fuel and the fuel is the people using the data, then your car is never gonna go anywhere. And for a really long time, what we’ve done in data governance in particular is sort of said, I am the keeper of all this. I know what is correct. I will give it to you when I, you know, think it’s appropriate to give it to you. And then we wonder why they don’t interact with us and why all of the things that we work so hard to deliver aren’t adopted because we haven’t created any relationship that would allow us to have a commune you know, a conversation about what that thing could or should be. And so the first thing I think, you know, I help organizations understand is if you start getting questions, even if you hate the questions, that is the first sign that you’re actually democratizing your data.

And and then the other things are just straight up like usage. You start to see more people using the products. You start to see these things pop up in in meetings that you didn’t anticipate.

Those are all areas where if you can start to get more eyeballs on the data and keep that line of communication open, you might actually have a shot at, you know, making all of the making all of your data dreams come true.

Another thing that I would throw out and and you tell me if I’m correct here is is that, another thing that CDO should be thinking about is, and I’m paraphrasing now, is in a governance by design. Right? The notion of maybe DevOps or Data GovOps where as a part of a change management process, which could be a new policy, it could be building new software, it could be whatever. There’s a lot of change management in today’s modern organizations.

But as a part of a change management process, that there’s governance built into that in this kind of this collaborative back and forth with the role that you are calling the data ambassador. I I assume that is the the person that is taking these questions that is taking the feedback about the data that’s working and not working. But but do did you agree with my paraphrasing? Do do do you see, you know, a a high functioning governance organization as one that is kind of wired into the change management process?

Absolutely. I mean, data governance is change management, and I talk a fair amount about how to manage change in the book. And I think I even sort of, talk a little bit about data ops or data gov ops and what that could look like. Now any kind of hashtag ops thing, right, is more about, environment and and methods than it is about, so the hinting at the process aspect of it, than it really is about the people and and, you know, what we traditionally like to think about, like, policies and data governance, which is a whole other conversation.

And it and it’s not necessarily about the technology either. It’s it’s really about how do you constantly introduce, change in your data governance space.

Love it.

So let’s transition.

Let’s in our last five minutes or or so, I I’d love to touch on on one last subject, which should be worthy of another conversation. So maybe we can use this as a as a placeholder for another conversation, But kind of getting away from your wonderful book that I would most certainly recommend, and talking about the gender gap that exists within in technology. I mean, it’s you can’t be in this space and not see it on a day to day basis. I wanna I wanna quote one more thing from from your book because it just it it it to me, it’s summarized, obviously, you know, it can be my personal experience, but it summarized a lot of the experiences that I’ve heard others that I’ve worked with over the years.

I’m quoting you now. It says, I’m not a technologist. I’m actually an analyst by training. Moreover, I’m a woman in a male dominated industry, which shouldn’t matter, but it does.

I censor myself when it comes to technology to avoid the inevitable know it all bullying. I’m talking about the person who will rate this book as a failure because I didn’t go deep into some arbitrary technology or explain in nauseating detail the danger of Cartesian joints.

I read that and I’m like, mic drop. Right? Like, if if I could launch some some fireworks, I would do it right now. I would be dropping mics. I’d be like because I’ve been in that conference room where I’ve seen that happen.

Right? Like, you may think you know something, but let’s talk about a b c esoteric, really kind of a deep level of technology.

You know, I’ll see your technology knowledge and I’ll raise you factor ten technology knowledge to shut you down. I’ve seen that I’ve been in that room when it’s happened. Sure.

So I found that really refreshing, but in your role as a leader, in your role as a consultant, in your role working with large organizations, how how do how do we kinda close the gender gap? Right? How do we how do we have less of those meetings? How do we get more equity in in in compensation and roles and opportunity within the technology space? And I know that that’s that in five minutes, that’s we’ll never solve for that.

But Yeah.

But in maybe in Twitter like style here, you can go more than a hundred forty characters. But but, you know, as as maybe a segue into an additional conversation, what what are some of the things that you would want the rest of the, of the the world of CDOs to know?

Yeah. Well so first and foremost, thank you for asking the question.

I I think that just like with almost anything in the data industry, if we’re not willing to face these things and have the discussions about them, then we’re never going to improve them. You know? You can’t fix what you won’t face.

So, so first and foremost, the just the opportunity to kinda talk about it is is really, important.

You know, I think we’re I I see a shift. I definitely see a shift.

And so, you know, when you think about somebody that is my age in terms of I think we’re a gen x or something like that, the generation that doesn’t exist, and how long I’ve been I know. Right? Here I am.

And how long I’ve been doing this work. You know, there have been way more situations that I’ve had and stories I couldn’t I couldn’t share on a podcast because of the rating. And and these are common references to body parts kinds of stuff, you know? And and it’s like you never would have thought to even ask the question back then about, like, what did that make you uncomfortable, Laura?

So so we have made improvements, and I and I sometimes feel like we don’t acknowledge at least that. So we’ve thank you for asking the question, and we have made improvements. But we still have a long way to go, and I think probably the biggest thing that I have learned as a white person is it’s intentional. It’s intentionality. It’s making yourself uncomfortable, realizing that you have some culpability there, owning that and moving forward and being really intentional about that.

And I think that’s true for for you know, from a gender side, which is the only part that I can really speak to.

So, yeah, intentionality. And and when you see situations where, you know, some woman is being gaslighted, you know, to the degree that you’re comfortable, you know, facing that conversation and and helping come to her aid, if you’re comfortable in that situation. Depends on, you know, lots of variables. So what’s that for a non answer?

No. It’s it’s it’s a real it’s a real answer because I encountered this last week, and it was with it was in a a partner setting. It wasn’t an internal setting. So it wasn’t it it was is a partner during a conversation, but I was like, wow. That was not cool.

And and I found myself it’s like, okay. Wait a minute. Do do I challenge now? Do I challenge offline?

What what what do I do? And, I mean, I I I I wanna do better, and I wanna be intentional to to your word. That’s that’s one of the reasons why I wanted to ask the question today. So clearly, there’s no easy answers here, but I I know just from personal experience that that these things will come up.

Mhmm. And, and, you know, yes. Awareness is is key. So that’s good.

But then where where do we go? So so not expecting any magic answers today, but I I couldn’t thank you more for your time. I I really enjoyed your conversation. I really enjoyed your your book.

I I really enjoyed some of the kind of the pragmatic aspects to it. I also enjoyed the, hey. We’ve been doing this for two decades and it’s not working.

It’s it’s it’s a lot of what I share when in in my conversations with my clients as well. It’s it’s time to look at things differently. I do like the notion of of modernness, and I think we we should be applying that, more broadly across everything we do. But, again, couldn’t thank you more for your time.

Laura Madsen, thank you so much. Again, I would urge folks to to to go and and and get Laura’s book around disrupting data governance. It’s it’s a fantastic read, and I would look forward to additional conversations in the future. So thank you.

Awesome. Thank you.

Alright. With that, we will sign off. Thank you so much for listening and and watching this episode of CDO matters, and we will see you on a feature episode sometime very soon.