Hey, hurdles. I’m Chris Detteland. I’m Michael Burke. How are you doing, Chris?

Pretty good. How about you?

Good. Good. I had an amazing, little mini vacation the other week, so I feel totally recharged, focused, and excited to be on the podcast today. We have a special guest, Malcolm Hocker, the CEO of Prophecy. Welcome, Malcolm.

Thank you for having me. Glad to be here.

I think everybody knows who you are, Malcolm. So that’s it’s really exciting that you came on, so we’re excited.

Is that true? Data people do.

I can’t tell. I had to double check, and I was like, I’m pretty sure we’ve met in person. And then I realized we hadn’t. It’s just been you’re so active on LinkedIn. I see your clips. I see your voice.

It’s funny. You and Scott, I think, are the two guys. You’re always fun to see. That’s a fact. It’s amazing. Right?

He’s a tough act to follow. Dudes. He’s a tough act.

He is a tough act to follow, but I’m honored to do it.

I learned everything that I know about LinkedIn, about public speaking more importantly. I actually learned from him. Scott and I worked together for years at at Dun and Bradstreet. We used to do these road tours where we would go on the road extolling the virtue of third party data, extolling the virtue of Dun and Bradstreet, and why should we be doing business with Dun and Bradstreet.

And he was like the headliner. He was the opening act. And I was the guy that had to go on stage after the opening act, after he softened up the crowd, and everybody’s excited about about, in this case, data. And, he was the why and I was the how, but it was an awesome experience that I love Scott to death.

He’s a wonderful human being. There there are other Malcoms in the space, though. I just got off a conference call with Malcolm Chisholm, who’s another Malcolm. So there’s oddly a couple of those, and sometimes we get confused.

No Malcolm’s on my feed, but you, really.

Yeah.

Every day. I I awesome. Cool. I love the loyalty.

I I was looking at your LinkedIn the other day. I think it was yesterday or day before and a chief data officer promotion. Congratulations.

Thank you very much. Thank you very much. Yep. We’re excited about being in the MDM space. I’m excited about doing what I do. I have great conversations with c level executives all the time. I had them every day for three years stay three years straight while I was a Gartner analyst, and I continue to do it with prophecy.

And and my role is an externally facing CDO. It’s all about sharing what I know with the market. It’s all about having one to one interactions with prospects, with clients, with anybody, frankly, who has questions about data management, data governance, data strategy. I’m here to share what I know. So that’s really my role. It’s a bit of a unique twist on CDO, but I I do appreciate I do appreciate you saying that.

Yeah. Of course. Go ahead, Michael.

I I was just gonna say, it’s been such an interesting period of my career engaging with you and some of these other thought leaders in the space. Could you walk us through how you got to where you are today as a chief data officer?

Oh, boy. Interesting. So the first fifteen years of my career, I was on a product management track. I was trying and ultimately became a chief product officer.

And that that taught me just so much about the world of software, about this the world of software engineering, building software, because I was a CPO for a software based company. And I loved that track. I loved managing a product team. I love the problem solving inherent to product management.

I love the interaction with customers inherent to that. And I was given an opportunity. I stumbled into IT, and I stumbled into more data centric goals. Although, I would argue when I was leading a product team, data was critical.

Right? We we were we needed data to run our business.

Who’s buying things? Why are they buying things under what situations? What are they clicking on, not clicking on? I could continue. But I worked my way into an IT role and and worked my way again up the ladder where I got into a senior leadership role running an IT function, implementing data governance, implementing MDM. And from there, I just fell down this data rabbit hole.

I I really had a bit of a formulative moment when I got hired as a consultant to answer the question at the time that I thought was just the most simple question ever asked, which was, hey, Malcolm. I need to hire you, and I’m gonna pay your consultant wage. And I need to hire you to answer this question of how many customers do we have.

And I’m like I know where you’re going.

I got I got this.

We’ll run we’ll set up some dashboards.

Right? We’ll, we’ll run some reports. We’ll maybe even set up some datamarts or this is back in the day. Datamarts or we’ll do this.

This is a slam dunk. Right? This is the the easiest SOW I will ever sign. No problem.

And it turns out that simple question was like my gateway drug to data and analytics and into MDM, into data governance, into some of the more complicated things that we live in and and breathe with every day as data professionals.

And and from there, I just fell down this data rabbit hole, and and and I really haven’t come out nor do I want to come out.

I I I I I found my way from a consulting engagement to to to to leading an IT function to to becoming what Dunnet Bradstreet called the distinguished architect to becoming a Gartner analyst to now being CEO at Prophecy. It’s all been a bit of a natural progression, but at every step in the way, I’ve been passionate about problem solving. I I think that’s the the the the the through line of everything that I’ve done in my career. I’ve been truly and honestly passionate about problem solving, and I’ve been truly and honestly passionate about making things easy for my customers.

And and when you take that mindset, you can apply that into product management. You can apply that into the sanitation arts.

Apply that into anything, and it and it’s just that perspective and that mindset and attacking things with a hundred and ten percent of everything I’ve got. It’s just really served me well. And I I looking back, I can’t imagine how else or what else I would have done because it’s all been fun and it’s all been a great ride.

Oh, that’s excellent. No. That’s go ahead, Chris.

No. And and problem the problem solving opportunity here is the when you just start looking at all this stuff around AI. Right? And so when you look at and and more specifically, when you look at, like, AI governance as it’s taking center stage.

Right? As a CDO, how do you look at when you look at the rapid, like, proliferation of AI, especially, like, generative AI impacted their approach to data? How do you look? How does that impact the approach to data governance in organizations?

How do you is it different than what you would think of normal data governance, or is it just more intense now?

It is. Yeah. It is and it isn’t.

Bit of a consulting answer. It isn’t. It isn’t. And I think we need to be and I think CDOs need to be extremely careful at separating reality from platitudes.

There’s a lot of platitudes out there, and that’s mostly what I’m hearing. I’m hearing these useless sound bites like we need to focus on found foundations, and we need to get our hands around AI ethics, and we need responsible AI, and we need AI governance.

What does any of that actually even really mean? And and I’m not trying to to be rhetorical.

I honestly mean that because I don’t see very many people answering that question. I hear people talking about governance frameworks for AI, and that’s fine. I hear people talking about regulatory impacts that are soon to be coming, if not already here. I hear people talking about ethics, and these are important things, and I want our systems to be ethical. But what does that actually mean to a CDO? What do I need to do in order to enable more ethical AI? What’s the difference between ethical management of data versus ethical behaviors of models?

Right? We need to go we we really desperately need to go from this world of platitudes and eighty thousand pithy feet, pithy soundbites into, okay, how do I put a shovel in the ground? How do I alter my operating my data and analytics operating model to integrate AI into the governance that I’m doing all day, every day? What does it actually mean to be ethical?

How do I quantify that? How do I measure that? These are all very difficult questions. I don’t think we even have any solid grasp at answering.

That said, there’s a few things that I think that CDOs must be focusing on if they are not already focused on them.

Generative AI based solutions are both built by and optimized by unstructured data.

Text, these things are cons they consume the internet. Okay? Which is not necessarily known as the bastion of data quality, but it’s text. It’s text based.

So if you’ve got a significant chunk of your data state that is, in unstructured data, chances are pretty good it’s entirely completely unstructured. You need to get your hands around that quickly quickly. Right? You need to start to understand things like rag patterns, vector databases, other things that you can use right now to try to influence the behavior of these models that you’re probably buying off the shelf and are not building from the ground up.

Most people aren’t building models because who who has twelve thousand GPUs just picking around that they can use for the next three to four months, right, running seventy billion parameter models? Very few. And I would argue you don’t even need to do that because we they’ve become commoditized at this point. So separate the sound bites, sep separate the platitudes, start figuring out, like, how do I get my hands around unstructured data?

How do I even understand what a rag pattern is or vector databases? How do I start what do I need to do in order to start using my data, my internal data that I have right now that is sitting in structured databases? How do I use that to start influencing generative AI models? What can I do to start deploying small scale POCs, internally focused POCs so that I can check a box to my board that says I’m showing progress here?

Because if you’re talking about platitudes, you’re not making progress.

I completely agree. And I think that even as somebody who’s worked in the AI space for a long time, it is mind boggling how many platforms there are that people are talking about, and it’s what are you using them for. Right? What is your real problem that you’re solving?

Yeah.

Even if you have unstructured data and you need to make sense of it, why?

These challenges and walking through the sequence of what needs to be done today versus what do you need to be worrying about five to ten years from now. And although AI ethics is incredibly important and you should discuss it and the foundation of it early on, You know, where we’re getting in this space is becoming more and more complex. We’re building off of more and more layers of other people’s work. And until we get to that point where we say, okay.

But why are we doing that? What is the problem we’re trying to solve? And how is unstructured data or rag or fine tuning going to play into helping solve that problem? We’re just shouting from a soapbox.

And you raise a really good point.

And and another advice I would give to CDOs out there is is that there’s really two swim lanes here. There’s companies with a data science function, and there’s companies without.

If you have a data science function, if you’re paying the expensive wages to have data scientists who are actually building and managing these models, Some of those other conversations are gonna be nearer and closer around ethics, responsible use of data, and and those things. So I’m not trying to say those things aren’t important, but I will say that the majority of companies out there will be operationally operationalizing AI through commercially available models that are mostly not exclusively pre trained.

Stop talking about training models. If you’re in that swim lane, if you don’t have a data science team, chances are you’re not training. You may be fine tuning. There’s a difference between those two things.

Again, fine tuning. You’re using text to do it. You’re not using rows and columns to do it. So these are two very distinct worlds.

Now if you do have a data science function, one of the things you need to be focused on is finding a way to bring them with what I would call traditional data management. Because one of the elephants in the room when it comes to AI is that data scientists are not using the output of traditional data management approaches. They’re not using the output of MDM systems, data quality systems. They’re not using data integration software from Boomi or from Tibco or from whoever.

They’re writing custom scripts to do everything. They’re going straight to the source. They’re getting data straight from the source. Right?

And they’re using custom Python, custom scripts, custom everything to do what they need to do, which means if you are doubling down on foundations, if you’re ex if you were investing in your legacy approach to data governance, if you’re investing in your legacy approach to data management, data quality, data integration, it’s not even moving the needle with your data science function because they’re doing what they’ve always done. Yep. So how do you bring these worlds together? I would argue the way that you build them through you bring them together as you start focusing on unstructured data.

You start talking about the use of of of graph as in some sort of rank pattern to start providing context on structured data that can be fed into some of these models. But you need to find a way to bring these worlds together because, honestly, I think that there’s going to be economies of scale there that companies that do have data science functions that are paying for AI custom development need to start to to find ways to leverage.

Absolutely. And I also wonder I’d love your perspective on this.

Data science, when it started off, was a couple of guys in the company in a room together building out Python docs. In many companies, that’s still the same way that people operate. Right? They have scripts. They’ve moved to AWS.

But this idea of a curated dataset for AI and for machine learning Yeah.

Who owns that? That’s a good question. That within the organization.

That’s that’s a good question. There’s a lot of people out there talking about data products and that that you expose these curated managed certified.

Master data actually happens to align nicely to that. Right? So if you have a gold master customer record, you could say it’s a data product and you say, hey. Data science.

Data scientist, go use this. Like, he could expose it in a catalog. Okay. Great. But if I’m a data scientist and I’m looking at this, and I was like, and you don’t know the business rules that were used to create it.

You don’t know its source. You don’t know its lineage. You don’t know any of the rules that were used to create it. Are you gonna use it confidently?

No. Yep. Because you need to be you need to be able to explain how your model is behaving and why it’s behaving. And if you’ve got data that is basically come out of the black box that you can’t explain, you’re not gonna use it in your model.

So there are things I think we need to do to provide confidence to say to to data scientists to say, hey. You know what?

Yes. You are inherently a different discipline because you are experimental.

You are iterative. We don’t lead you to be a hundred percent all the time. Right? We we honor the fact that you know what? The fact that eighty percent of models don’t go to production is a feature, not a bug.

Right? Data scientists hear that stuff. It’s, of course, only twenty percent because I’m an experimenter.

I have to keep throwing pieces of spaghetti at the fridge to see which one sticks. Yet on the other side of of of our house, I’m hearing that’s a bad thing. Right? Because we have these different standards.

The the standards we have for data management are are very different than the standards we have for data science. One of the things that is that I think we need to start to understand here is we’re gonna bring these two worlds together. We’d have to honor the the the artistic endeavor in many ways. It’s a scientific endeavor, but in many ways, it’s an artistic endeavor, but find ways to bring these things together.

And if we’re talking about data products that data scientists can use, make them your customer. Start having requirements gathering sessions with them, understand what they need in order to do their jobs because, trust me, they’re doing the best that they can do. It’s not, oh, you need to improve your hit rate with those models that are going to production.

I’m not entirely sure we do. But there’s a lot of finger waving around that right now that I I just don’t understand it.

Oh, that’s really interesting. Completely agree. I think that the depending on where you are in your AI and ML journey, right, you have a lot of people that are used to doing experimentation at the tip of the spear.

Yeah.

And then you’ve got everybody else underneath it who’s productionizing and molding and conforming to standards.

Right? But those guys at the front, by the time they finish that experiment, they’re not the ones that are going to necessarily run and own that throughout the entire ecosystem.

And they probably shouldn’t be just based off of the value that they provide by creating the next thing. But I think it’s a really interesting shift and definitely something that I’ve seen from an opposite angle in the data science space of needing to take on more responsibility and saying, hey. You can’t just jump to the next thing. You have to build out a data structure and productionize your models, and you have to manage them. And that’s where this ML ops and these other kind of subsets of large organizations are really starting to emerge.

Yeah. Don’t get me wrong. Everything you just said is absolutely true. And just because I can assert confidently that data science is more an iterative experimental endeavor than traditional data management, that doesn’t mean we need to be chasing unicorns.

Right? It doesn’t mean we need to be trying to build models to explain things that the business doesn’t care about. We we learned that the hard way in the big data days when people were deploying Hadoop clusters and and and coming up with answers to questions that nobody was asking. So can we do a better job of pulling data scientists closer to the business?

Can we do a better job of making sure that they move faster so that by the time something does get to production, it’s still relevant?

Yes. Yes. Yes. This is the reason why I’m really optimistic about what I would, not data products, but data product management product management integrated into data and analytics.

I love it.

App applying more product management disciplines and product management rigor to everything we do in the data and analytics world, whether that’s building data science models or whether that is building traditional data products like a gold master record or a pipeline. So more product management get us closer to the business. Those things are all very good because doing just useless science experiments isn’t helping anybody.

Totally agree. I agree a hundred percent.

So let’s another question I think that that Chris and I have both been pondering a little bit offline, we talked about this a bit, is where does the data mesh architecture fit into all of this? Right?

And how are chief data officers Before you get into that, I do know Malcolm is a big data fabric guy, and so near this would have some Gosh.

So let’s get into that. Oh, okay. You oh, okay.

So I’m a data mesh contrarian.

And just saying that will probably cause some of the people listening to this to to clutch virtual pearls. And let me tell you why.

I let me tell you why I’m a data mesh contrarian.

The underlying assumption the underlying driving assumption sitting underneath the data mesh. And what and everything I’m about to say is through the lens of what I believe the holy book of the data mesh movement to be, which is data mesh by Zemmak Nagani.

If you have other perspectives, and there are many other perspectives out there because they’ve adapted as meshes have continued to fail and not get deployed and not get deployed. People have changed the the definition and moved the goalposts of the data mesh.

We have the separate discussions about that, but I I I look at the data mesh through data mesh, that I’ve read three times Yep.

Because I had because I had to read it three times. It’s that dense. I am not a believer in the mesh because the underlying business assumption and the underlying assumption that is basically fuels it is that centralized data management architectures are inherently bad, do not meet customer needs, are too slow to adapt, is too slow to move, and are ultimately unproductive for the business. That’s the underlying premise. Like, the centralized data management architectures are no bueno.

So what the data mesh does is it it swings everything to the other extreme without fully abandoning centralization of some things. And and this is where things get a little bit murky in the book is is that some acknowledges that there’s a place for shared data management and and for the management of I think she calls them polyseems, the the the containers of data that would be moving between business functions and between domains, what I would call master data. So there seems to be a recognition that there is some role for centralized data management to play, but we’ve gone all in on decentralized data management.

And I have yet to see a compelling business case, like a legit business case that says going to a complete spider web architecture is more cost efficient for the business. It may be better for individual autonomy within domains. Don’t argue that can argue that it’s impossible to argue that, But overall, from the business perspective as a whole, is there a net positive ROI of implementing a data mesh insofar as Zomak would define a data mesh? I have yet to see it. What I what I see is data product owners, domain authority, domain centricity without data sharing, without any sort of centralized data management patterns, without any sort of approach to data governance where that’s problematic.

Because the fact go ahead.

I was just gonna say the funny thing is, like, we centralized data before as things grew within our ecosystems because we couldn’t maintain governance. Right? And definitions fell apart. So your pendulum. Right? That’s what it is.

It’s the pendulum in my opinion.

Yes. And it doesn’t recognize the the reality that everybody has always known, but seems unable to fundamentally acknowledge, which is that requirements, governance. I could use a lot of different words here. Let’s for for now, let’s use governance in essence.

Exist at three layers within organizations.

A a enterprise wide level, a cross functional level, and a functional level. And you can give autonomy over the control of data, the governance data at the functional level, but that how does that support a cross functional enterprise wide use case? If your CEO asks you how many customers do we have, there is only one answer. Hard stop.

And you don’t wanna be the guy or gal that’s saying, oh, it depends. Through what lens? Who’s asking? I’ve tried that.

It doesn’t work. It it, it it it doesn’t work.

So so you need to be able to support all three. You absolutely, positively need to be able to support all three, and there is a role for cross functional data management and data governance. There is absolutely a role for that because you need to find a way to be able to support all three. Another way to look at this is the idea or the concept of truth. Right? Truth is contextually bound, and there’s three context in any business.

So the idea of there being a single version of truth to rule them all is simply untrue. There are multiple versions of the truth. Now one may matter more than another. The CEO’s view may matter more than the director of marketing, but there are multiple versions of the director we need to be able to support. And that’s something that that the data mesh just seems to have a hard time with.

And, really, nobody has much of an answer when I start asking questions around, okay. That’s fine. Give domains complete and total control over their analytics.

But what does that mean from a cross functional perspective, and how do we and how do we support those cross functional use cases? There’s not a lot of guidance there with with within the data mesh. And I think that that is highly problematic.

Yeah. And I think the other thing that we we always consolidate when we talk about this at conferences and even in reality is, you know, this completely varies depending on what industry you’re in. What are your priorities? Are you prioritizing speed, or are you prior prioritizing structure?

Right? Yeah. Are you prioritizing governance? I’m in a startup now. Right? We have one database and half the people have root access.

And I’m like, hey. Let’s do a little bit better of a job with governance here. People are like, what is governance? And so scope just changes depending on where you are.

And at a five thousand person company, things would move so slow with getting access to datasets we couldn’t innovate. So I think that the same thing with a data mesh or data fabric, or even in my case at a startup, one database or a table. The operation needs to be defined based off the outcome.

Yes.

And and in a complicated ecosystem, it becomes harder and harder to understand what is your top priority. Right? Yeah. How are you gonna mold a strategy around that?

Yeah. The the complexities this is one area where I love talking about AI for governance because I honestly think that AI will save us.

Because we have a hard enough time coming up for with business rules, like, one set of business rules to rule them all. Right? The top level of that pyramid that I was talking about. We have a hard enough time, like, coming up with one set of business rules that can be deployed.

Now the the right model, the model that I’m talking about is n sets, and the the three is a conceptual model. There’s probably more level layers than that, but you get my point. There’s no and you could even have business rules at a domain level. I don’t argue that point of the mesh, by the way.

I I that. That’s not an either or. It’s an and. Right? So you’re talking about n sets of business rules.

Right? N sets of quality standards, n sets of definitions, n numbers of models, n numbers of hierarchies.

And for the average governance maturity of most organizations out there, let’s forget about it. Right? It’s a nonstarter.

This is where the fabric comes in, which is, I believe, is a great conceptual model to help us start talking about how to automate the management and governance of data because we desperately need it. And I will argue until I’m out of breath that that AI is not a viable that that AI somebody who says that AI isn’t a viable option here because I think it absolutely positively is. AI seems to be really good at languages, and that’s exactly what data is. It’s an abstraction of a language. It is an abstraction of a language, the language of business, quote to cash, procure to pay.

We stick things into rows and columns to to because that’s how we’ve always done it. But the irony to me is that what we’re struggling with now from a Genii perspective, just take things out of rows and columns where there’s no context at all and ways to overlay context, which is narrative, which is a story that the AI can actually consume and make meaningful. So I asked the question to a friend of mine, last week, Juan Cicada.

I said, hey. We seem to be moving heaven and earth here to try to bring these two worlds together, unstructured data, structured data, adding context to rows and columns. I said, wouldn’t it be wouldn’t it be cool if our business applications just tracked data in this, like, ongoing narrative?

Right? Instead of dropping things in as rows and columns and joins, and and and maybe we just start storing things like an ongoing narrative, like Malcolm clicked button.

Right? Michael bought product x at date at y time. Right? No. And just start storing things in stories instead of rows and columns. I I think it’s a bit of a a mindblower, but I don’t know. I’m optimistic that AI enabled data fabrics will get us to the point in the future of automating the classification management of data because we desperately need it.

I totally agree. I I think that there are so many use cases where that holds true.

I still get hesitant about some areas like health data in places where you’re gonna have like, the cost outweighs the reward quite a bit, and that manual painstaking review has merit. But in ninety percent of the use cases, what are you really doing with the data? You’re putting it into some form of report or spreadsheet, and you’re making a business decision based off of it. And you don’t need it to be a hundred percent accurate. You don’t even need it to be ninety nine point nine percent accurate. You need it to be above eighty percent accuracy or ninety percent accuracy.

And that’s where I think classifications with AI can get us there. I’m already seeing that it’s doing better than some data store. And in a lot of organizations, it’s doing better. It’s amazing.

I tell I I hear still a lot of yelling at clouds out there around AI. Like, it’s never gonna automate that. And don’t get me wrong, folks. When I’m talking about automation, I’m talking about a future state.

Between now and the future state, of course, there’s going to be human oversight. Right? And nobody’s gonna turn over making signing their name to some sort of regulatory sign off and turn it over to robots. It’s just not it’s just not gonna happen in the short term, but I think we’re gonna ultimately, we’ll we’ll get there.

So we get a long way to go between where we are and where we’re gonna be. Don’t get me wrong. I’m not talking about, like, this automation happening tomorrow. But what I see in a future state data fabric is this world where we’ve what finally and once and forever bring together the world of analytics and and operations.

And if we paid attention to metadata, if we paid attention to transactional data, and we did it in a way that understands when transactions are successful and understands when transactions fail and under what conditions are they successful, under what conditions do they fail, what data led to successful transactions, or fast transactions. There’s data everywhere that tells us when data quality is sufficient and when data quality is insufficient based on the speed of transactions as they execute within our systems, based on this I I could go on, but we we have systems that can tell us when things are working or when they’re not working.

A little tiny microcosm of this today is something called data observability, where where where somehow we’ve been able during in one little use case, in one little swim lane to say, okay. Here are the conditions that mean things are gonna work, and here are the conditions that suggest things are gonna break. This is exactly what I’m talking about. But instead of looking at a pipeline, look at a business process.

Quote to cash, procure to pay. What are the conditions that lead to successful transactions or valuable transactions or repeat customers?

Right? How do we bring all these worlds together so that we get to a point where, oh, okay. I don’t have to have squabbles around how do I define data quality as a part of my data governance committee. I can actually look at the data, and it’ll tell me when data is fit for purpose and when it’s not fit for purpose.

I love that.

I know it sounds fairly conceptual, but that’s what I view a future state data fabric to be. Future, future, future state. Today, it’s just basically this hyper virtualization layer that’s cool, that allows you to run SQL queries against graph databases and allows you to virtualize and and basically remove the differences in essence between a lake and a warehouse, and that’s cool.

But I see things going far farther future down the road in the future that that that becomes something completely different.

Yeah. Totally.

Speaking of future real quick, but before we do the future stuff, I knew that once we started talking about data mesh, you’d probably start talking about data fiber.

So I knew I asked you to get a new research.

I I used to work at Forrester, you know, for many years.

And the one thing that they really started writing about was the data mesh piece years ago. And you guys start or Gartner started writing more about, you know, the data fabric piece. And one went big digging one area and the other one the other and then, yeah, I’ve I’ve listened to some of your stuff. So no.

I’m I’m not opposed to domain autonomy. Right? I just I just know that incentive structures are such in a corporation that sharing across domains is not gonna organically spring from the earth. Yep.

It’s just not.

So anyway Completely agree.

And so I had a bunch of other questions, but we’re gonna skip a lot of those and and get to future state as we think about CDOs and, you know, what are some of the trends over the next few years that CDOs should be aware of in your mind, in your thinking?

Boy, we’ve we’ve we could talk a lot about one of them, and it’s not going anywhere.

So check on the AI box. That’s certainly one. I’m optimistic that the seed of product management will slowly take root because I wanted to, and I think we could desperately benefit from having more product management integrated to to the, you know, data and analytics function.

There is I I think there is more that we could be doing and more that we could be talking about from the perspective of sustainability.

I was a guest on, NPR in in San Francisco Bay area last week in a radio show talking about this very thing. I published an article in Forbes, oh, two years ago now, about our problem with data hoarding, and it’s a huge problem, and the environmental impacts that has, because this data needs to live somewhere. That’s another area where I I think we could be certainly doing a better job. We absolutely, positively need to figure out once and forever and for all how to quantify the value that we are delivering to our business partners.

We need to get over this hump that quantifying the value of data and analytics is like some sort of combination of world peace and cold fusion and, like, this impossible Gordian knot that cannot be unraveled because it’s simply untrue.

The idea we can’t quantify the value we deliver to organizations is a pure myth. And I would argue if we’re out there saying that it can’t be done, we are the data and analytics people. Right? We’re the people that the the pithy joke I say is that we’re in the modeling business.

I’m not talking about Blue Steel. I’m talking about building models that actually help you to understand correlations between things and potentially even causal relationships between things that are not directly related. And so my message here is that data leaders absolutely need to be focused on quantifying the value that they deliver. Right?

We need to be closer to our business partners. We need to put the customer, and I use that word in, you know, purposefully.

We need to put the customer at the center of everything we do. We need to stop moving away from the use excessive use of platitudes.

We need to stop talking about the thing that gives us jobs, the thing that gives us jobs as garbage.

It’s not. It’s unrefined gold.

Can you imagine if the COO of Frito Lay kept referring to their corn as garbage or if the COO of Anheuser Busch kept referring to their water as garbage, but we do it every day.

We need to we need to stop with that stuff. We need to recognize data quality as an opportunity and not a burden.

We need to reckon data quality gives us jobs. And by the way, we need to start seeing the positive intention of everybody in our organizations because data quality problems exist not because people are trying to make our lives hard, but because our business functions operate differently, and that’s by design.

So there’s a whole bunch of things we need to do as data leaders to look at our data, our positions, our roles, our jobs, our value differently than what we do today because the data suggests that we’ve got a little bit of an uphill battle.

The last CDO survey from Gartner said that only forty four percent of CDOs believe they’re delivering tangible value to their organizations.

And that’s a self and that’s a self assessment.

That’s a self assessment. Right? So how do we do better? I just gave some perspectives that I think are gonna be important to help us improve our mindset, to help us improve our data culture.

Oh, by the way, data culture is not a dependency for CDO success. It’s an outcome of CDO success. Mhmm. Yep.

So if you’re a CDO out there saying I can’t do anything because our data culture sucks, that’s that is you’re disempowering yourself for one.

And and for two, you’re you’re you’re putting things in the wrong order. Find a way to deliver value. Build a customer three sixty. Build do some dashboard.

Find a way to solve a customer problem. Find a way to deploy some Gen AI solution for an internal use case that’s relatively low risk that can still allow you to show progress when it comes to AI. Deliver value to the organization and do that and do it again and do it again and do it again. And over time, you will be seen as a trusted business partner that is delivering value.

And when you do that, people will say, wow. How did we ever operate without that CDO? Then all of a sudden, miraculously, you’ll have a data culture.

Yeah. But data cultures don’t happen overnight, and they don’t happen as a deliverable. They happen as a result of you providing value.

It’s so interesting you brought that up too because I I saw that time and time again when I was at all of these different conferences, with Relteo and others working in the data space.

This self victimization of your inability to push forward. And at the end of the day, when you look at the people that you dive into, what really are their problems?

They’re smart people.

Yeah.

Right? They’re motivated people. It’s they can’t communicate what’s at stake effectively to their leadership, and they have trouble sequencing their work. And I think that, like, to what you’re saying, what is the smallest increment of value that you can deliver in your organization to move you forward in a direction that’s meaningful?

And I think that a lot of people who work in this space a long time, they see that. Right? They see that. But there’s just especially people that kind of had this linear progression of being a follower, doing a task, cleaning and munging data, and then they get to this position of leadership.

And all of a sudden, they have to sell themselves. Like, they have to sell their department to create value. And I think that it’s one of these things where a lot of the customers I even see that are solving and and investing in solutions to help with that, to help get more organized. Some of them are really big and they need it, but others just need some hand holding.

They need to say, how do I do this? How do I sequence the work to get to a place where I can enable the rest of the business?

Yeah. And when they get and when they go to get that hand holding, they approach analysts.

They approach consultants.

They approach some thought leaders, and or they approach thought leaders in the space. And what do they hear? They hear, hey. You’re gonna need to embark on a six to nine month maturity assessment followed by the current state assessment, followed by the gap analysis, followed by the implementation of our twelve step data governance framework.

I used to hear this over and over and over again. So first of all, forget the current state assessment. Forget the maturity assessment. It’s not moving. They’re not moving the needle. By the way, if you wanna know what your data and analyst governance maturity is or any data management maturity, it’s two point two to two point seven on a five point scale. I can already tell you what it is because every time you do it, the answer is always the same.

And it’s not solving any problems for your customers in the short term. It’s not driving any value. It’s putting money in the consultant’s pocket. Yep.

There’s nothing to stop you from sitting down and having lunch with your chief revenue officer or your chief procurement officer talking about the three things that could be different in order to make their lives easier, and you don’t need Deloitte to tell you how to do that and to have that and to have that conversation. Yeah. You’re exactly right, Michael. The the people are looking for help around how do I get started.

I wanna put a shovel on the ground. I wanna deliver value. I wanna get going. But when they go to the the thought leaders, the the the people that are supposed to advise, the people that are are are supposed to guide, whether it’s any of those groups that I mentioned, what they typically hear is the same old that will lead to a lot of money being spent, very little value being delivered, and a lot of frustration along the way, which a big part of why CDO tenure is only eighteen months by compared to double and triple that for CTOs.

That was great.

Oh, that’s true.

I I remember when years ago, the the CIO role was eighteen to twenty four months. Right? And so that’s getting better. So I think over time as the CDO kinda continues to drive the value, drives those outcomes that the business needs, just like CIOs do today.

And as important as it becomes, people start seeing the more important the importance of and people just get smarter as they go. Some of these CDOs are just like, okay. We’ve never done this before, so here’s how we’re gonna go do it. And they’re making shit up and Yeah.

Saying so Well, Yeah.

But, Michael, to your point, we do suck at marketing.

So so Yep. Mhmm. You know, this is why another reason why I’m optimistic about the benefits of of product management because all product managers will tell you need marketing as a part of a go to market effort. They’ll tell you that they you need exceptional product design.

And I could go on and on, but imagine a world where you were tasked to build a product. Right? Like, maybe it was a a phone, or maybe it was something. It doesn’t matter.

And you launch the product, and nobody used it. Or the people are using it or complaining about it, or they even went so far as to go buy it from somebody else and not you, the internal provider of that product. If you were the product manager of that product, what would you think? What would you assume?

You’d probably say, man, what did we miss? We didn’t solve the customer’s problem. Right? We didn’t we didn’t deliver value to the customer.

What do we need to do better differently?

Instead, a lot of it, a lot of what we do, frankly, is blame the customer. This is this is data literacy.

Right? And don’t get me wrong. Some training will probably always be required, and I’d prefer the word training over data literacy, because it’s an it speaks to enablement. It speaks to education.

But the idea that a lack of customer skill is the reason why you’re failing to deliver problem deliver value, again, is disempowers you.

Right? Because it puts the hands in their it puts the power in their hands. It says, okay. My success is a function of their knowledge level, which probably isn’t even true, by the way.

Right? Because if you build a very customer centric, design centric, easy to use, easy to understand product, I would argue people will use it. If it’s solving a problem, they’re going to use it. And if people are complaining about it, it’s not because they lack literacy.

It’s because you probably built a crappy product.

Yep.

True. It’s true. And we don’t we as data professionals do not dive into problem spaces the same way that product people do. And to your point earlier, that emergence of either data leaders going and getting that training or experience to understand how to operate and build a product, or bringing in product people who can help with that. And saying that the CDO isn’t just a technical leader. They are a product leader. And the same way that the chief product officer and even the chief technology officer sell their services, data has to be in the same sphere.

Love it.

Awesome. Last question I will ask. How do you see the role of the CDO evolving from these trends? Sounds kinda changed. No?

Yeah. I’m bullish because I’m an optimist by nature, and I tend to see things positively.

I think we’re gonna continue to have a few bumps along the road, particularly for the next few years, but I am bullish in the CDO role in the long term because I cannot imagine a future where data isn’t where data isn’t the primary fuel of the business. I I I just cannot see a future where that is not the case. So we need people who are experts. We need people who understand data. We need people who understand data governance and data management. We need to understand, we need people who understand how AI systems are built and optimized and governed.

So I’m really optimistic for the role in in the longer term. In the shorter term, I think we’re gonna continue to have some bumps, but I’ll tell you, I spent all last week in Boston, and there there are rays of hope, I think, on the horizon. I think we have some younger generations of people that look at problems slightly differently, a newer generation of CDOs that see the the same old that we’ve been doing over and over again, there’s probably a better way. I see a younger generation of of CDOs who are willing to question the status quo, who are willing to take some risks, who are willing to try some new things, and who are willing to say that the DM box isn’t the answer to everything.

And, and don’t get me wrong. I I own one. It’s sitting over there, and it’s got some great answers in it, but it’s not the answer to everything. So I there’s a new generation, I think, of people that are asking all the right questions, that are willing to and and able to take the reins.

I I think we just need to equipment with equip them. And that’s a big part of what I see my role to be here is to be provocative and to get people thinking a different way because I’m honestly I’m truly dedicated to changing that forty four percent number to something closer to seventy to eighty percent because I know I know data can transform organizations. I know it can because I’ve seen it happen, and I could list the number of companies here that are doing it right now. We don’t talk enough about successes, but I’m bullish about the future.

I’m bullish about the role. I’m bullish about data. It’s why I choose to do what I do.

So passionate, man. I love it. That’s awesome.

We do more of that too.

Yeah. I mean, people could be as passionate as you are about data, man. Wow. Love it.

No. Malcolm, thank you so much for your time. This was incredibly helpful and informative. I’m sure viewers are gonna love it. Don’t forget to rate and review us if you’re listening, and have a great day. Thank you.

Thanks, everyone.

Enjoy the chat.