Good morning. Good afternoon. Good evening. Good whatever time it is, wherever you are in this glorious, amazing, floating blue orb we call home.

I’m Malcolm Hawker. I’m the host of the CDO Matters podcast. Thank you for joining. Happy twenty twenty five.

Woo hoo. I would whatever the you know, it’s it’s it’s the third week of January by the time you’re watching this or maybe even later than that. Who knows? You could be watching this in the middle of April.

You could be watching this in the year twenty thirty. I don’t know.

I’m recording this in twenty twenty four.

So it’s not quite twenty twenty five yet for me, but it will be shortly.

And we’re gonna talk about twenty twenty five today. But, hey, let’s talk about the elephant in the room, shall we? If you are a longtime subscriber or listener or viewer on the YouTubes of the CDO Matters podcast, you’ll notice, hey, there’s something different about this.

Yes. Indeed, folks. Welcome to twenty twenty five, and welcome to the modern age of the CDL Matters podcast where I officially can have a virtual background on my recording platform because I’m trying a new recording platform.

I’m trying something called StreamYard, and, we’re migrating off your old platform called Goldcast that didn’t allow virtual backgrounds. So if you’re watching me on YouTube, I’ve got a story to tell. I’ve got a story to tell regardless of where you’re watching or listening. And if you’re just listening, well, my apologies because some of this will be your visual reference and appropriate only for the fine folks who are watching on YouTube. But if you’re a long term listener of the podcast, going back to, like, the first ten episodes, if you are, thank you. You you you have, you have strength and courage.

But if you are a longtime listener subscriber and you go back to some of the earlier episodes, you will have seen me trying to use a neon sign in my background. And for a couple episodes, it actually kind of worked. I I duct taped and bubble gummed the solution together. But to make a long story very short, when I convinced my amazing boss, Reed Gussmas, in one of his, I’m sure, bigger moments of weakness to, let us do this podcast, I convinced him to, let me buy a neon sign because I was convinced you needed a neon sign to be a legit podcaster.

This is in twenty twenty two.

That’s crazy.

We started this in twenty twenty two. I I can’t believe those words just came out of my mouth.

It’s twenty twenty five, and we started this podcast in twenty twenty two, where I bought this really goofy neon sign that you now see behind me.

As it turns out, to make a long story very short, it’s not a real neon sign. It’s an LED light, and LED emits light like an animation. Like, it’s basically a flash. It’s a flicker. It doesn’t emit light constantly. It’s on off, on off, on off. It has this neon this LED light has what is in essence called the frame rate.

So do cameras.

Digital cameras like this one right in front of my face right now it’s not actually capturing every moment of every second. It’s it’s on, off, on, off. It’s basically like an animation. Kinda crazy.

It has a frame rate.

And if the frame rate of the camera is not the same rate frame rate of the neon light, well, then it creates this weird flicker. And you may have maybe you’ve done this when you’ve had, LED lights in the background and you’re on Zoom or you’re on Webex or whatever, and you look at your camera, you’re like, there’s something something is not right here. It’s the LED light. Or maybe you’ve got an LED overhead light and and it just makes your your video go goofy.

It’s because of this issue of frame rates. And to make a long story very short, I spent well, truth in advertising, my company. Spent, some a decent chunk of change on this sign, and I couldn’t make it work.

I could not make it work.

The only thing I could do was to take a photograph of it and then put a photograph into a virtual background and make it look like there’s a sign hanging behind me, which you see now. If you’re on YouTube, if you’re just listening as you’re on the elliptical, or if you’re jogging, or if you’re driving, you’re like, what is this guy talking about? But if you saw me on YouTube right now, you’d see that there is this sign, and it’s a photograph.

And it honestly works out really, really well. A lot of people actually think it’s real sign.

So the virtual background works, except in my my recording platform for CDO Matters, I wasn’t unable to have a virtual background until today.

Tada.

I have my sign.

I’m tickled that I finally have my sign on my podcast.

Woo hoo. It’s gonna be a good episode, folks. It’s gonna be a good episode. Buckle your seat belts.

Gird your pipelines because today, we are gonna talk about twenty twenty five predictions.

In twenty twenty four, my predictions episode was one of the most watched and listened to, so I expect the same to be true this year.

I love doing these things, and it appears that you like watching and listening to them as well. So it’s a win win. Yay.

I a couple weeks ago, I did a twenty twenty four retrospective, so I would invite you to check that out. Just me looking back on twenty twenty four. This one is me looking forward on twenty twenty five. I’ll share a few of my high level insights. I’ll start with a review of what I think are some of the bigger macro forces that are at play, and, I’ll end with a couple of long shots. I like the long shots. My long shots last year, I mean, they were way off.

If you wanna see how I did or you wanna listen to how I did, I reviewed myself. I rate myself.

Like, I’ve, like, of all the predictions I made last year, if you go back a couple of weeks, I think it’s two episodes now.

Let me double check.

I should double check.

That would have been published, on Boxing Day, the day after Christmas, or very maybe the week after. I’m I’m not entirely sure about our our vacation schedule. But, anyway, yes. It would have been two episodes ago.

It would have been episode sixty six, episode sixty six, where I rate my performance on my twenty twenty four predictions. What I got right, what I got wrong. My long shots, I got wrong. But that’s okay because they’re long shot.

They’re long shots. But, I think most, I was pretty right about. A couple I missed. For sure I missed.

But let’s get into it. What is twenty twenty five going to look like? What are some of the things you’re going to hear if you go to data and analytics conferences? What are you gonna see and hear on LinkedIn?

And on and on. Let’s start at the top.

Let’s start at, like, some of the macro forces.

This is an area that I actually kinda got wrong last year, because I thought that twenty twenty four was economically gonna be a pretty good year for most.

I thought that the wars couldn’t persist as long as they were persisting. I thought that, interest rates couldn’t persist as long as they were persisting.

I didn’t think that high prices would persist as long as they did, and I was quite convinced that twenty twenty four was gonna be a reasonably good year.

It wasn’t.

It it held steady for the first part of the year, and then towards the end of the year, things got a little a little rocky, I think, for a lot of folks, particularly over in Europe. I think twenty twenty five will be a very strong year for the data and analytics space.

Generally speaking, generally, we’ll speak we’ll talk about some of the exceptions to the strength of data and analytics. But I think from a vendor perspective, from a software perspective, for a demand for our services perspective, I think it is going to be a good year. I think that, there will be some general optimism going into twenty twenty five. I think there will be a renewed focus on spending in twenty twenty five. This is particularly the case in the United States. I think the United States is well positioned to have a great year in twenty twenty five.

I do believe that Canada will lag, and I do believe that Europe will lag. The rest of the world, I think will also lag a little bit, but most of our listeners and viewers are in North America and Canada and, USA and Canada and in Europe.

I do think that towards the second half of the year, in Canada, our friends to the north will will will begin to catch up from an economic optimism perspective, whether or not, you know, prime minister Trudeau actually pushes the election all the way in October. I doubt he will. I think the NDP are gonna force his hand before then.

And I think that that will actually probably be a good thing because I I I see I am optimistic that the iron fist of the Trudeau regime will end in twenty twenty five in Canada. Yes. I just expressed a political viewpoint.

But I think that there’s been some really, really, difficult stuff economically, over the last few years, particularly increasingly, you know, large debt levels in Canada. This is true for all developed countries, but it’s it’s been a real problem, I think, in Canada. And it been it been having an outsized effect on the, on the economy. And I think in twenty twenty five, the Canadian economy will improve. I think the US economy most certainly will improve.

There will be a renewed sense of optimism. There will be renewed spending. I think it’ll be a good time to be a vendor. I think it’ll be a good time to be a consultant.

First half of the year won’t be that way for Canada. I think things will continue to lag. But towards the end of the year, as we get clarity around when the election will be called, as we get clarity around things like the renewal of the Keystone XL pipeline, say whatever you want about, the environmental impacts there, which which I’m sure are are a viable concern.

I do think it’ll be a great time to be in Alberta in twenty twenty five because of Keystone, because of some of the political changes that we’re seeing in the US.

In Europe, however, I think things will continue to lag.

There will be the year starting with a lot of uncertainty. These wars are still going strong, wars in Europe and war, on Europe’s backdoor in the Middle East. And, I’m not optimistic that things there will change very quickly.

So I’m I’m concerned about, you know, lagging, performance in in Europe through through the year.

I’ll be there, in March, or in February. I’ll I’ll be an Amsterdam in February. I’ll be there, in London in March. I’ll get a sense for what’s what’s going on.

I’ll probably be or I’ll be there in May for, for Gartner in London again. So I’ll be in Europe at least three times in the first half of next year and interested to see kind of what’s going on and get a feel for for for how things are unfolding. But I’m quite optimistic from a macro perspective about what is happening in twenty twenty five. I do have a a list that I’m keeping, so forgive me if we’re on the YouTubes and you see my eyes wander over to my list.

I did actually spend some time to write some of my thoughts down, which is abnormal for me, believe it or not. Usually, I’m just riffing. Usually, I’m just going straight from, from the top of my old noggin. But I did wanna write some of these things down, make sure that I don’t miss anything.

So I think I think economically, at a high level, I think I I’m I’m optimistic about twenty, twenty five. Now, of course, your mileage will vary. Some industries will be better off than other industries.

If you are into crypto like I am, you’re gonna have a good year. It’s gonna be a good year for the crypto bros, like me.

If or if you’re in oil and gas, I think you’re gonna have a good year. I I think there’s a lot of things to be optimistic about, especially in the US. I’ll end it with that.

Alright. What else is going on? Oh, macro. Let’s start to talk about kind of the data and analytics world writ large.

I think that twenty twenty five will continue to be a difficult year for CDOs. And and I say that with an extremely heavy heart an extremely heavy heart because, folks, my mission, we exist. This podcast exists. I’m sharing this.

I’m doing all this with the goal of helping CDOs extend their tenure. This is my goal. This is my North Star.

So to say that I I see CDOs continuing to struggle in twenty twenty five, is not said lightly and is not something I I say, because I really want to. I don’t want to say that, but I think it to be true.

I’m seeing a lot of CDOs continue to struggle. I’m seeing things like, AI functions being stripped away from CDOs.

I see I see a lot of, FUD, fear, uncertainty, and doubt. I see a lot of apprehension. A lot of I see a lot of CDOs focusing on all the wrong things. I see a lot of CDOs continue to, avoid building any sort of quantifiable metrics around the value they deliver to their organizations.

I see a lot of organizations getting frustrated with a lack of traction around AI, around being able to operationalize AI at scale. And I see a lot of businesses doing it on their own without the support of CDOs.

That’s a bad sign for CDOs. If you are one of the seventy five to eighty percent of companies that are using Gen AI solutions day in and day out, you’ve gone ahead. You didn’t wanna wait. You didn’t you didn’t wanna wait for clarity on governance, guidelines. You didn’t wanna wait for customized Gen AI solutions of which only five percent of you are doing.

And so companies are using Gen AI solutions. They’re using them extensively in marketing. They’re using them in HR.

They’re using in in in development, engineering.

You name it. Procurement. They’re using GenAI solutions. So, I I mean, these solutions are being adopted, and they’re being adopted with or without the support of CDO organizations. And I think if there was one thing that is holding CDOs back, it is also the one thing that has the greatest potential to propel them. And that is the operationalization operationalizing, integrating, implementing, pick your word, Gen AI into the workflows of their organizations.

Most are not supporting that. Most organizations are just going and DIY ing it. I can hear many of you saying this is recipe for disaster. It might be.

Right? It might be. There may be. And I’m not predicting this for twenty twenty five, but there may be companies out there that run into problems because of, you know, lax governance over the usage of Gen AI based solutions in their organizations.

I’m sure it’s gonna happen. Right? I’m sure there are companies out there that are exposing intellectual property. I’m sure that there are companies that that are doing and people that are doing things that are not advisable in terms of using off the shelf commercially available GenAI solutions, but they’re doing that because they’re driving productivity increases.

Productivity is enhancing. I don’t know a software engineer that isn’t using a Copilot. I don’t know anybody in marketing that isn’t using a Copilot. And on and on, these solutions are being adopted because they drive productivity.

The CDO opportunity has been for the last two years to find a way to integrate themselves into being the service provider for Gen AI based solutions, for doing things like implementing more complex rag patterns where you can start to actually leverage some of your internal data to turbocharge the behavior of some of these solutions and make them more accurate and consistent and trustworthy. But so many CDOs have failed to answer that call. It’s in the data. We see it in the data. And it’s unfortunate.

It’s unfortunate that we still have many CDOs out there who are saying things like, well, I gotta focus on foundations.

Well, that’s great. But if you focus on the foundations that have historically excluded unstructured data from the scope of your governance program, from your data management programs, you’re probably missing the mark because that’s what Gen AI based solutions are basically running on and optimized by is unstructured data text.

So if you’re one of the CDOs who is talking about focusing on foundations but not focusing on unstructured data, you’re putting yourself in your precar in a precarious spot. If you’re one of the CDOs who’s saying you need more time to figure it all out, you’re putting yourself in a precarious spot. If you’re one of the CDOs that is focused in taking a framework driven approach to understanding Gen AI without figuring out how to operationalize it, you’re putting yourself in a precarious precarious spot. Many of these many CDOs are choosing the latter of all of these examples that I’m giving.

Yes. There are concerns about Gen AI. Yes. It hallucinates.

Yes. We need to get our hands around this. Yes. We also need to focus on our foundations.

But too much of a focus on foundations and not enough on a focus on implementation and execution and customer support when it comes to implementing Gen AI is what will cause the number of CDOs to continue to recede in twenty twenty five. We saw that in twenty twenty four.

The overall count of CDO remains high because so many government agencies, particularly in the United States, are are implementing CDOs. So overall, the numbers look good and overall, the numbers are relatively high. But I would argue that in the private sector, CDOs are losing ground. And they’re and they’re losing ground for all the reasons that I just shared. I don’t say this lightly and I I say this with a very, very heavy heart.

I’m here to help.

I don’t want there to be fewer CDOs in twenty twenty five, but I do think that this is gonna be a very, very common theme throughout the year.

Well, what else?

I do think, oddly, as transformative as AI is, as widely as it is being adopted, particularly Gen AI, as it becomes deeply embedded into the operating processes of our businesses, I do think that in twenty twenty five, through the lens of data and AI, through the lens of the CDO organization, we’ll hear people talk less about AI. This is, yes, a little bit of a paradox. I think we’re gonna be hearing less of people talk about how to do things like reg. Right? I think we’re gonna hear people talk less about how to operationalize AI. I think we’re barely gonna hear a peep around the idea of being AI ready. That was a common theme over the last two years, particularly two years ago, immediately following the launch of ChatGPT.

Everybody was talking about being AI ready, but I think in twenty twenty five, you’re not gonna hear many people talking about being AI ready at all at all.

And I think, frankly, and unfortunately, that’s a result of so many data organizations really not being able to figure out how to find the balance between the focus on foundations and getting stuff done and getting stuff done. And for many, it’s just gonna cause a paralysis. For many, it’s just gonna cause this okay. Well, you know what?

My organization is deploying GenAI. They’re doing it. They’re doing it in marketing. They’re doing it in HR.

They’re doing it in procurement. They’re doing it in engineering. I’ll just let them keep doing what they’re doing. Maybe things break and maybe I’ll be asked to help clean it up.

But in terms of me pushing, me being on the offensive related to Genii, some will, but I suspect most will not. Unfortunately unfortunately, again, I don’t say this lightly and I don’t want to say this, but this is something that I’m gonna see for twenty twenty five. There’ll be a little bit of a pullback.

There’ll be a little bit of a pullback from kind of beating the AI drum from within data and analytics organizations, at least insofar as it comes to enabling AI for the rest of the organization.

That’s something I think we’re gonna see.

What else? I’m looking at my handy dandy list.

Meanwhile, when it comes to AI, I do think that we’ll see more and more and more capabilities that are included into data governance and data management solutions.

We’re gonna talk about that a little bit more, in some in some more some of the more specific trends that I go into. So there is a little bit slightly of a paradox here.

We’ll be talking less about AI from the perspectives of the products or solutions that we as data practitioners offer to our customers. We’ll be talking less about that. But I think internally, when we pull back, we will be talking more about AI to improve data management, data governance.

So there’s a little bit of a paradox there. But I think it instead of how do I help externally, how do I be externally focused and customer focused around AI, I think the focus will be how do I use it for me?

How do I improve stuff internally? That I do think there will be a common trend there in twenty twenty five.

Alright. Let’s dive into some very specific trends, after I have a sip of lovely water.

By the way, if you’re on YouTube and, you’re on a desktop and you have the ability to comment, let me know how this video is and the audio is. Hopefully, it’s it’s better. It’s supposed to be better. I don’t know. Of course, there’s the amazing sign. Right?

Specific trends for twenty twenty five. This is the Malcolm Hocker hype cycle.

That’s a trademarked phrase from my old employer. I guess I can’t say that. It’s the, the what would be appropriate? Not the hell it pipe cycle, the popularity cycle maybe. I don’t know. Trends. Top trends twenty twenty five by blah blah.

In the first half of the year, I see the popularity of data catalogs waning slightly.

I think that over the last two years of all of the software solutions in the space, I would argue data catalogs have had kind of the their moment in the sun.

Data catalogs, I would argue, for the last two years have been kind of the the main focus from a software perspective within the broader data and analytics space. I can’t think of any other software solution that’s had more attention.

I’ve of course, other than just AI, a high level AI.

But has it been a focus on m d MDM? No. Data quality? Practically nonexistent.

As important as it is, haven’t had anybody really talking about like a Deque software innovations in Deque software.

Even though there’s been there was a new magic water last year.

It’s been data catalogs. They have been kinda sucking up all the oxygen in the software world related to data management.

And I see that losing a little bit of steam in the first half of the year.

I think that’s just kind of a natural thing because I honestly think that a lot of companies will have a bit of a hangover.

What do I mean by that? What I mean is that a lot of companies over the last two years have invested in data catalogs, And a lot of those companies did it.

Many of them did it tied to some idea of a data mesh, which is, like, gone, by the way. Like, RIP data mesh. Sorry.

But a lot of companies implemented a data catalog tied to a data failed data mesh initiative or extremely stripped down, descoped data mesh initiative.

We’re the only things that survived were data catalogs and maybe some idea of a data product. And a lot of companies spent a lot of money on this over the last couple of years.

And I think there’ll be a hangover the first half of the year because these companies will be looking for value, expecting value, and instead what they’ll get is a list of data assets in a catalog that end users find overly cumbersome, difficult to use, and not really helping very much. You know, I I think a lot of companies will be struggling with the classic data catalog conundrum, which is if I build it, why don’t they come?

Right? And they don’t come because when they log in and search on customer, what they see is four thousand lit a list of four thousand things related to customer that aren’t relevant to the task at hand, that aren’t relevant to the the thing that that that that end user is trying to do. Maybe build a customer three sixty report. I search a customer in the catalog. I see four hundred things. I don’t know what to do.

Like, that’s kind to me, that’s the the classic data catalog conundrum that I used to hear all the time when I was an analyst. And I think that a lot of the companies who had these big grandiose ideas about data products, the data mesh, data catalogs, deployed a data catalog without a reasonable focus on governance, without a reasonable focus, most certainly, on product management and building amazing great products that solve customer problems.

They rush to get catalogs out. They have, in essence, what could be called a cheesecake factory style menu of hundreds of pages of data assets that are difficult to decide to to decipher, difficult to understand.

And that’s the hangover, which is, hey. You told me this data product thing is gonna help, you know, save the planet and make my life easier, and it’s gonna do all of these things and angels will sing. And I’m looking at this list of, you know, five hundred customer master records, right, or different versions of customer or different tables of customer, different whatever. But I’m an end user.

I don’t I don’t even know what a table is.

Right?

So, I think the first half of the year for data catalogs will be a bit of a hangover.

However, to the rescue to the rescue.

The second half of the year, I believe this is I don’t think this is a long shot. He’s been he’s been saying this. He’s been telegraphing it on LinkedIn. I think my friend, Ole Olson Bagnell will publish his book. I think it’s his third, which is amazing, by the way.

He’ll publish his book called The MetaGrid, and, I think it will renew some of the fervor, around data catalogs. And the phoenix will rise from the ashes.

And once again, towards the second half of the year, I suspect maybe around the q four time frame. Yeah. Maybe maybe fall fall q four. Who knows? Around there, the data catalog movement will refresh itself because of this idea of the meta grid, that I don’t have full, full grasp around just yet.

I look forward to reading Ole’s book.

Where he’s talking about these repositories of metadata just kind of naturally being already around the organization, and we just need to go find them. And we and we need to we need to make this grid. I think that’s what he means by the grid. There’s there’s there’s this metadata all over the place.

It’s in transactional systems. It’s in other systems designed to carry metadata like maybe a CMDB, configuration management database or a project management or workflow system. The other systems all outside the data and analytics world that is managing that are managing metadata, which is very true by the way. And I think his meta grid concept finds a way to unify all these things and to make sense of them from the perspective of managing the data and maybe even what Gartner would call activating the metadata, perhaps.

I don’t know what is the entire premise of the meta grid. I just described some of it.

But I do know that this world is very prone what I mean by world, that this world, our our world, our beloved world of data and analytics is prone to hype.

And I think the meta grid will be, our our hype for twenty twenty five.

It will be the new data mesh for twenty twenty five, and everybody will jump on the bandwagon.

And once again, data catalogs will be the fit. Now don’t get me wrong.

I’m a believer.

Not necessarily MetaGrid. I I reserve the right to to to to read the book and to to see what Oli has to say about his grid. But data catalogs are foundational. We we can’t avoid data catalogs. Well, I’m I’m a huge fan of data catalogs.

And this does bring me to kind of trend number three. Trend number one was data catalogs losing steam on the verge of falling off the the hype cycle, but second half of the year, they resurrect themselves. This is trend number two. Data catalogs rise again, and they all become very popular because of this new book.

And number three trend will help to buttress the idea of the the data catalog being important. And this idea is something I’ve been talking about recently, but it is the resurgence. Is that the right word? I don’t know. It it is the rise of awareness.

It is the increased awareness of the importance of semantics, ontologies, taxonomies.

What I would just loosely call but this is not this is a well established space, knowledge management.

So, thankfully, there is increasing awareness that knowledge management is what we will need in a Gen AI driven future state.

We and I’ve posted about this many times on LinkedIn. I will continue to do this. We as data practitioners need to make a slow pivot. And this isn’t away from.

This is also towards. So it’s not a choice. It’s not a binary. I’m not I’m I’m it’s not this is not a I’m giving this up to go over here.

Today, we manage data.

Tomorrow, we we we need to manage data and knowledge.

Right? So a useful tool here, folks, is this something called the d I k w framework. Data, information, knowledge, wisdom.

Data plus context is equal to information. Information when applied in a real world to solve a specific problem is knowledge.

K? The ability to apply information to solve a problem is knowledge. We apply knowledge every day. We use knowledge every day. You and I use use knowledge every day, and we use it in organizations as well.

Knowledge management is basically the management of let’s just call them narratives, stories. Maybe this and and there there are those in the knowledge management world that may be listening to this in chicken and fish is like, no. No.

Please make a comment about how I’m wrong. But knowledge management to me is the management of context. It’s the management of narratives. It’s the management of stories. The management of information plus context plus how to apply it, what to do in what situations.

Right? That’s knowledge. If it rains, open the umbrella.

Right?

Knowledge.

These things, knowledge management, this stuff, knowledge management is needed in a world of Gen AI. Gen AI is built on and optimized by text, stories, narratives.

Prompts are narratives, and we need to evolve our data management capabilities in our data and analytics functions to incorporate the idea of knowledge.

I do believe that the data catalog will be ground zero for doing all of this. Now maybe it’s not the data catalog that we know today. I talked about this on a catalogs and cocktails podcast with my beloved friends, Tim and Juan.

Tim Casper wants Ikeda. Alright.

If you haven’t checked that episode out, you you probably should.

It was awesome talking about next gen data catalogs where I’m talking about how knowledge management gets incorporated into data catalogs, where we build a bridge between data management and knowledge management. Right? That bridge that bridge is in essence semantics, I I think. And it’s the ability to profile and classify text into structured formats.

So so our classic data and analytics systems can consume and govern and manage and apply data quality to things in highly structured formats that were previously in unstructured formats. So the bridge of these two worlds, I think, is going to happen in a data catalog.

So data catalogs, don’t get me wrong, and I’d love to see if all is talking about this, but data catalogs will be the bridge, I believe, Or at the very least, they’ll be the interface for the output of this bridge.

And maybe that bridge is the data fabric. I I I I don’t know.

And the bridge that I’m talking about here is going from unstructured data, video, text, files, semi even semi structured data, HTML, others, XML.

Going from that world to more structure. Once we get the data in a structured format that we can apply some of the consistent legacy approaches to governing and managing the data and even dropping it into analytics platforms.

So I think the data catalog is relevant, and this increasing awareness is trend number three. This increasing awareness that we need to expand the scope of our governance efforts and our quality efforts and all of our data management efforts into this whole other world that largely has been independent that largely has been independent of the data analytics function. So I’m excited about that. If I had a bag of money, and if I was an investor, if I was a VC, that that thing, whatever I just described, call it a data catalog if you will.

I’m not entirely sure that’s correct.

But what I just described, the built the the bridge between these worlds, that’s where I’d be investing because there is there is so much gold there.

It’s just interesting. I’m reading a book from the godfather of the data warehouse, mister Bill Inman.

His book is called Turning Text Into Gold.

It’s it’s a short read, and, it’s all about how to, you know, classify data, put data into ontological structures.

Something that graph kind of does pretty well, puts things into triples that would could arguably be be used to help classify as well. But figuring that out at scale at scale, maybe AI can do it. Maybe it can’t. I don’t know.

I think it can. Quite certain it can.

But there’s a whole world here we need to figure out. That’s trend number three.

Trend number four is the increasing use of AI to manage data. So I know I said earlier that AI will kind of lose some of its luster, and I do believe this to be true, at least insofar as us pushing it within our organization. But I do think that we will increasingly see not just the value, but the need the need to better leverage AI to manage data. I’ll I think we’ll see more people embrace the idea of using AI for things like data modeling or even using AI to, yes, classify data. My friend Bill Linman would say that’s not possible or trustworthy at the very least. But I think it’s the only way that we’re gonna get our hands around all the data we have and to do that in a way that is repeatable and governable and manageable.

We’re gonna have to use AI to help with that because most of the data out there is is just not governed. It’s just not. Eighty percent of our data is just not. It’s it’s it’s sitting, you know, basically collecting digital dust in in in, you know, in servers within the cloud, within all these massive data centers.

So we need AI to help us.

We need AI to be more adaptable in our governance processes. I just said, like, I just Canadian came up. Processes.

And I think that we will see across the board, whether that’s MDM, data quality, data management, data integration, AI will continue to be integrated, but I’m most excited about AI and its integration into this whole world to help us build a bridge between unstructured data and structured data or vice versa.

So that’s trend number four, increasing use of AI within data management, data governance solutions. Yeah. It’s a bit of a layup, but, I think it’s inevitable and extremely, extremely necessary.

Number five, data products. Oh, here’s another one with a very heavy heart. I am a huge believer but I still think that twenty twenty five will be the last year we hear anybody talking a lot about data products. I say that I say that with with a lot of kind of sorrow and a lot of grief because I am a believer of data product management. I’m a believer product management, but we will hear less and less people talking about data products in twenty twenty five because they didn’t focus enough on product management, because they didn’t focus on building amazing products, because they didn’t focus on building business cases for all their initiatives, because they can’t understand how much it costs to build a product or the value that that product is delivering. They’re not focused enough on user centric design.

Right? These are things that a good product manager would do. So it’s not too late. Go hire one if you want. Go hire one. You should.

These are things a good product manager would help and would help you to make great products.

And had you done that, then I don’t think data products would necessarily die in the vine.

But so many of us so many of us are focused on data products as more of a raw ingredient.

And if you wanna understand what I mean by a raw ingredient, check out, episode number sixty two, data products demystified. I will tell you. I go into great detail around the world of data products, and so many of us. There was a great opportunity there. There remains a great opportunity, by the way. There remains a great opportunity, but so many of us did not focus on the product management aspects here or sufficiently on the product management aspects. So in twenty twenty five, we will hear less and less about data products, sadly.

Number six, data fabric. Yes. Data fabric will continue to slowly, chugga chugga chugga chugga chugga, gain speed.

In part, this will be due to continued investments by Microsoft who is head and shoulders above, everybody else in terms of their data fabric. I think you will see other major infrastructure providers also talk about fabrics and maybe even implement their own. Maybe like a Snowflake or an Amazon or Databricks, maybe.

But Data Fabric will continue to build steam. I think what we will see is companies like Microsoft understand the role that unstructured data will play in our future and will be doing increasingly interesting things through their fabric to enable data and analytics professionals, data pipeline people, data managers, data scientists to access that unstructured data in ways that are actionable.

Right? I think you will see more and more focus of unstructured data as a source into data fabrics, right, where I could run a Spark job on unstructured data if I wanted to. I could do a data profile on a structured data, maybe even do that through, like, a purview, a data catalog from Microsoft, AB.

That’s an interesting Microsoft has got an interesting and any data fabric provider, you know, are arguably all of the hyperscalers have the ability to start doing some of this stuff. And it’s really, really, really interesting where we’re pulling all these worlds together through a single operational pane. That single operational pain is the data fabric.

Those of you out there who are acolytes of decentralization and assume that all centralization is bad, well, it’s not. It’s not. Hubs and spokes exist for a reason.

So if you wanna say data fabric is bad because it’s centralized, there are economies of scale that are enabled through centralizations that are simply untenable or undeliverable.

You can’t do in peer to peer networks like a data mesh.

Anyhoo, data fabric will continue to to slow be on a slow boil. It’s gonna continue along and it’s gonna continue along and more people will be implementing and more people will be implementing. They’ll see the value of the single operational plane. They will see the value of being able to run SQL queries against anything, anywhere. They’ll see the value of being able to access unstructured data from within SharePoint sites, from within their Dynamics infrastructures, being able to pull operational data, analytical data, unstructured data, and and pull it into a single place and apply common methodologies, common pipelines, common pipe common processes, common rules from within one place.

I just see the fabric becoming more and more ubiquitous for all the reasons I just stated. Meanwhile, MDM, as as awesome as the fabric is, it will continue to be focused primarily, I would argue, exclusively on analytical workloads.

And MDM will remain the only thing that lives in a fabric that is actually talking about pushing data back into operational systems like CRMs and ERPs.

Right?

So MDM will remain as relevant as it’s ever been, arguably even more so in twenty twenty five.

Number seven, AI governance.

AI governance will be continued to be the thing that most people are talking or not most, but many people are talking about at data and analytics conferences in twenty twenty five.

So it’ll be the thing that everybody’s talking about, but it’ll also be the one thing that people are generally unable to completely and totally articulate, at least from an from an operations perspective.

Right? There’s a lot of companies out there. There’s a lot of thought leaders. There’s a lot of people, that have built AI governance frameworks. Hey. That’s great.

Frameworks are a great place to start.

How do I operationalize AI ethics?

As a data and analytics practitioner who’s responsible for managing data, how do I make sure that the data that I’m exposing to my data scientists is ethical?

What rules do I apply against that data?

How do I define those rules? How do I execute those rules at scale?

Do you hear the crickets?

Yes. You do. Because I have yet to hear anybody come up with a compelling answer to any of those questions.

I’m not trying to be overly negative. I’m just saying this is an incredibly hard problem to solve.

Incredibly hard.

So, yes, AI governance will be a top focus of twenty twenty five. Everybody’s gonna be talking about it. But you as the CDO, you as the data practitioner, you as the VP of data and analytics, you need to start asking some hard questions around, how do I actually implement this?

How do I implement this?

I don’t have the answers here. I don’t have all the answers here. But as an industry, we need to be asking more of these questions. We can’t just be waving around frameworks and claiming mission accomplished because we all know that’s not how things work.

When it comes to governance, this is about defining rules and executing and implementing them. And until I can implement a rule that says this is how an ethical data collection process runs, This is how an ethical data governance process runs. This is how an ethical data pipeline runs. This is how I can ethically source the data. Here are the behaviors and attributes of ethical data. Here are the attributes of unethical data. Until I can quantify that, there are.

And maybe you figured this out. If you have, please put something in the comments. DM me on LinkedIn. If you throw a virtual shoe at your TV screen right now and tell me how you’ve solved for that Because I don’t know. I don’t think anybody has any answers to these questions. So just beware. You’re sitting at a conference and you’re hearing people talk about AI governance like it’s a solved problem.

Nope.

It’s not.

It’s not.

As I said before, a lot of the data a lot of the data that we’re gonna need to turbocharge Gen AI based efforts is completely and totally uncovered. And we gotta get our hands around it. We got to get our hands around it.

And just saying I’ve got a framework, let’s not cut it.

Number eight. Well, again, I say this with a heavy heart, sustainability.

Sustainability from a data perspective, will largely fall off the map in the United States in twenty twenty five, sadly.

On the bright side, our friends in Europe will continue to focus it on it.

Things like the digital product passport will continue to gain speed in Europe because of EU regulations related to sustainability.

I suspect more and more CDOs in Europe will be given sustainability targets.

That’s a challenge, and it’s an opportunity. So Europe, thank you from a sustainability perspective. The planet thanks you. You got a lot to figure out in twenty twenty five. If you if your CDO is given a CDO a a a sustainability target, if you are trying to figure out how to be more any energy efficient, if you are trying to figure out how to reduce your data center cost, your data center footprints from all this dark data that is just sitting and collecting dust not being used. Massive opportunity, but also a massive challenge. How to integrate sustainability into your data governance programs.

How to integrate audits and on and on. Huge opportunities in Europe.

Don’t think it’s that’s just gonna have much focus in twenty twenty five in the United States.

It just I just don’t see it.

Canada will be probably somewhere in between as we usually are.

Alright. Time for my long shots.

Long shots.

I’ve I’ve got a couple. I I couldn’t I couldn’t think of any really compelling ones, and I thought about it reasonably long time.

Last year’s long shots were related to, quantum computing and how quantum computing was gonna, like, break, encryption, which I still think it will, by the way. FYI, I think it will.

But what are some of the long shots? I I see I see a couple, and these aren’t really maybe even not even that long. And by the way, I have no insider information here. I’m just a guy making a podcast before. So no insider information. This is not investment advice.

Go talk to an investment adviser.

Don’t take it my word for it because I don’t know what I’m talking about.

Because most of both of these actually could have financial implications, I suspect.

One, I think Informatica will be sold.

I think Salesforce will come back once the kind of the regulatory environment becomes a little more favorable to merger and acquisition activity, in the beginning of the year, and as Informatica continues to struggle, which they are, I think they will be sold, and I think it’ll be to Salesforce. I I think Salesforce will come back, and I think that deal will be consummated. I’m guessing. It’s my long shot to guess.

But, if I were a betting man, and I am, that would be a long shot that I would most certainly be comfortable betting on and probably be able to get some pretty decent odds on it as well. But I think informatically will be, will be sold.

Long shot number two. And this one isn’t that hard to see because this is very common. I do think that there will be some consolidation in the data catalog space. I think that Alation, Calibra, and even Atlan, which is kind of on the on the periphery of data catalogs, they’re they’re a bit of a different play.

But I think one of those three or one of their competitors, that there will be some consolidation there. One or more of them will combine or be bought by somebody else because of all the focus in the data catalog space. A lot or maybe even some of the extremely smaller providers, StarDog, others, some of the startups, the data catalog startups that exploded after the data mesh went haywire, I think those will certainly be gobbled up. But I think some of the bigger players, and I’m thinking Calibra, that there will be some activity from an m and a perspective next year from a bigger provider that is kind of on the outside looking in, who sees the value of data catalog, who sees all the things that I was talking about earlier as the catalog potentially being the bridge of all these worlds potentially becoming what Gartner would call a data and analytics governance platform, that sees the tight interconnection between catalogs and fabrics and sees the growth of both.

I think that’s something that we can probably bet on in twenty twenty five. So long shots. Good long shots. With that, I will leave you, my dear data and analytics friends.

Happy twenty twenty five. It’s gonna be a good year. I’m telling you, it’s gonna be a good year. I look forward to seeing you on the road in twenty twenty five.

I I’m as usual, I’m always traveling, and I really hope I get a chance to run into you in person. If we do, let’s have a chat. Let’s have a beverage. Let’s talk about data.

Let’s build a community. Let’s fortify our community. That’s what I’m here to do and I’m here to help. I hope you have a wonderful year.

I hope you tune in to another episode of the CDM Matters podcast or go back to some of our old episodes. That would be awesome. And if you haven’t subscribed, hey. Doesn’t cost anything.

Go ahead and subscribe in your podcast provider of choice.

I will see you again in another episode of this podcast sometime very soon. Thanks, everybody. Happy New Year all.