Happy twenty twenty six. I forgot my noisemaker. I forgot my happy twenty twenty six sparkly hat.

I forgot any sort of regalia for my recording today because it’s still twenty twenty five.

Today, it is December eighteenth, and I am struggling to get all the stuff done that I need to get done before taking a two week break.

And boy, my friends, do I need it. I’m Malcolm. I’m the host of the CDO Matters podcast. I’m the chief data officer of Profisee. We make amazing MDM software.

And I’m the host of this here thing, and we’re gonna talk about my predictions. I’m gonna put on my prediction hat. I’ve been talking about hats. I should’ve got a hat.

I’m gonna put on my prediction hat. I’m gonna talk about where I see things heading in the world of data and analytics. And yes, I should say AI. Data, AI, and analytics. Data analytics and AI.

Whatever. I’m not a big fan of the alphabet soup. This c d a o c d a I o. Hi ho.

Hi ho. C d a I o. We go. I consider AI a data use case.

I consider analytics a data use case.

Can we just stick with CDO?

Maybe? Maybe?

If you’re, like, working for a fortune one hundred company, like a giant company, I could I I think you could make a compelling case that there could be some idea of specialization between data analytics and AI. You could you could separate the use cases of data from data itself, but the c thing is the problem. And what I mean by c is the c, the letter c.

Chief. Right? You should be as a part of the c suite. And yes, I know that that only about twenty to twenty five percent actually really are, but that still means something.

At least it should mean something. And if you’ve got, like, fifteen chiefs of, it doesn’t mean anything. It’s supersized. Right?

It’s like everything is supersized. Everything is big and better and and anyway, I’m all pining. Boy, where’d this come from?

I it’s because I had I wrote a post about this recently or a reaction to a post. It was it was one of Kyle Winterbottom’s posts. By the way, if you don’t follow Kyle Winterbottom on LinkedIn, you certainly should.

He runs a data and AI specific recruiting shop in the UK called Urbition, but I know that they have operations here in the US or they’re expanding in the US. And Kyle is a smart, smart guy. When it comes to what’s happening in the world of hiring for data experts, he’s the guy. He knows what’s going on.

He’s literally got his finger on the pulse, and and he had made a post about CDAIOs or CDAOs or anyway, whatever. And I I responded saying, I’m not a fan of the alphabet soup. Never have been. Not a fan.

Now if I’m a VP of data and analytics and somebody says, we need a chief analytics officer or a chief AI officer and, you know, I’m gonna I’m gonna I’m gonna throw my hat in the ring, third mention of hats so far this podcast. I’m gonna throw my hat in the ring and I’m not gonna complain because I’ve got a c in my title. I’ve got the chief in my title. Like I do.

Hey. I’m not gonna complain. So I certainly get it. It’s not like I’m gonna say, hey, gee, thanks for the offer of making me the chief analytics officer or the chief AI officer, but I really feel like this should be, you know, under the purview of the chief data officer.

So no thanks. I’m not gonna take your title that you’re offering me. Of course, that’s not going to happen. I have a stain on my shirt.

Ugh. Look at the little stains.

I don’t know why I just noticed that. I live in constant fear of staining my clothes, I don’t know why. Maybe I’m a Virgo because I’m a perfectionist and I hate stains drive me nuts.

They drive me nuts. Hey.

Let’s get into predictions. But my friends so anyway, hey. Happy twenty twenty six. Thanks for joining.

Thanks for downloading. Thanks for listening. Thanks for being a part of this growing community. I don’t say that enough.

I typically tend to say it towards the end of podcasts and our data shows that, you know, there there is drop off and not everybody gets to hear that.

So I should say that I should be a little bit more thankful because I really, really am.

Do you this community is great. The interactions I have on LinkedIn are great through my blog and through my now my Substack.

Amazing. Just really productive, really professional.

Like, I know that a lot of people who are active in social channels outside of LinkedIn, you know, whether it’s X, whether it’s Instagram, whether it’s the Tiki Toks, whatever, I don’t always have positive experiences. But I I gotta say, my friends, my experiences as somebody who spends a lot of time on LinkedIn are really positive. Like, really positive, really productive. Every now and then, sure, there’s there’s a, you know, there there’s a hater out there every now and then, and it happens.

But my my experiences here are really positive, and it’s it’s just it’s so I use the word community. I don’t use it lightly, but I use it purposefully because I really feel like that’s what we’re building. And it’s not just under the CDMAatters brand. It’s not just the fact that you’re listening to this and you’re downloading this, but it’s the broader community of data and analytics people, of data peeps.

It’s a great community with some great people. And I’m thankful that you’re here. I’m thankful you’re listening. Thankful you downloaded. I’m thankful I’m thankful that you care what I have to say.

It’s great. And let’s do some more of that in twenty twenty six. Let’s let’s keep the momentum going. We have much to learn.

We have much to share with each other.

We have much growing and development to do, and that’s really the kind of the whole goal here. I I started this podcast three years ago with the stated mission of extending the tenures of chief data officers and helping people who want to become chief data officers become chief data officers. So this is a big tent. This is a big pool.

Whether you are a CDO or you want to be a CDO, you’ve come to the right place. I believe in the transformative nature of data. I am a huge fan of data. I’m a believer in data.

It is an asset, and it can transform how our businesses operate. And I I remain I remain passionate about that. Even just saying that, they get tingling. I’m I’m really a dork.

But I I I believe it with every fabric of my being and I am beyond blessed that I can sit here and talk about it and that, you know, hundreds of you will listen.

So that’s awesome. Thank you. I don’t take your commitment of time lightly. I don’t commit I don’t take your download lightly. I don’t take your LinkedIn comment lightly. I don’t take your reading my blog or my Substack lightly.

I I view this as a very important responsibility. And I and I mean that. I mean that one hundred percent.

I do try to be provocative. I do try to shake the fruit from the tree. I I do want to be honest about the progress we’re making. I also want to celebrate our successes.

So sometimes I may say things that that sting a little bit, but it’s only because I know I know we can do better. I know we can and I believe in data and I believe that this is a career track you want to be on. This is a career track that is meaningful. This is a career track that can be incredibly rewarding professionally, monetarily, emotionally even.

Right? It’s not for everybody.

It’s not for everybody. We are a unique species, are we not?

We’re librarians, we’re technologists, we’re linguists, we are optimists, we are also pessimists, we’re skeptics, Maybe a little too skeptical at times. We’re cynics, for sure.

For sure, we are cynics.

But we’re a unique breed. We’re a very, very unique breed, and I’m honored.

I am literally honored to be a part of all of this. I love it. I love going to events. I love it when you approach me. Please continue to do so. If you see me at an event, come come talk to me. Let’s have a coffee.

Let’s nerd out. Let’s nerd out. I’ve built an amazing amazing group of peers and friends that I respect so greatly. I won’t I won’t name everybody, but it’s just it’s it’s it’s it’s mind blowing that three plus years after leaving Gartner right?

And and when I was at Gartner, man, you know, I’m I’m out there. I’m, like, I’m talking to CIOs and CDOs every day, but I’m basic I’m I’m access to me was tightly tightly controlled. Right? Gartner didn’t want me sticking stuff out on LinkedIn because that’s given away the that’s given away the milk.

Right? I’m the cow. I I’m the cow. You gotta manage access to the milk. Maybe it’s a bad metaphor.

I’m not a I’m not I wouldn’t make a good cow.

But anyway, when I was with Gartner, I’m I’m not I’m not sharing my stuff. I’m not sharing what I know. I’m not doing white papers and blogs because that’s what Gartner charging their clients thousands and thousands and thousands of dollars for. Right?

Gated access to yours truly. So when I left Gartner and I’m like, okay, I’m gonna stick it out there, man. I’m gonna throw it out there. I am gonna share what I know.

I had no idea.

I mean, I had I had a little bit of idea because I because Scott Taylor had gone before me. Scott is a friend of mine. He’s a he’s a dear and close friend. He’s a wonderful human being.

And I had watched him while I was a gardener. I kind of watched Scott go into the LinkedIn world and into the influencer world and into the thought leader world. And so I knew there was a path.

I knew that there was this thing. Right? I knew there was a data. Of course, I knew always knew there was a data community. I was active in this before I worked at Gartner. I was going to their I’ve been going to day diversity events for literally decades.

But I had no idea that there was this real community. And I had no idea that I would be able, like, just like, just sign me up And and the doors are open. I I had no idea I’d be able to walk into this community of really smart people.

Really smart people. You know, at the beginning, I’ll be honest, and I don’t know many people who are Gartner analysts who are Gartner analysts because they think they’re unintelligent.

Right? Most of us are I I mean, I I gotta say, I mean, there’s a little ego there. Right? We analysts tend to think they’re the smartest people in the room because they get told they’re the smartest people in their room because people wanna pay to hear what they have to say. So there’s evidence to suggest that there is a certain degree of smarts there. Don’t don’t don’t get me wrong.

But when I came out of Gartner, I I guess I guess I underestimated I underestimated how many smart people are out there.

But more importantly, I underestimated how many smart people would be willing to engage and share what they knew in a really productive, interactive, thoughtful, meaningful way.

I I had no idea.

And maybe it’s because I had come from this this this kind of, you know, secluded gated world of of Gardner.

I don’t know. I I don’t I I just looked at LinkedIn, I thought it was people just trying to sell stuff.

And that’s a lot of LinkedIn. Of course, it is.

It’s a big part of it, and arguably maybe too much. Whatever.

You gotta pay the bills, man. I mean, and if and if something’s free, which is LinkedIn is free, you know, we’re the product here. So I I I take that I I willingly take that trade off of being chilled, BDRs hammering me like there’s no tomorrow, and trust me my friends, I’m getting hammered like there’s no tomorrow. And you know what? It’s okay. It’s fine because LinkedIn is free. And what I get in exchange is this this community that I didn’t I had no idea.

Like, Juan Cicada. I mean, smart, smart guy. Passionate. Knows his world. Has been talking about his world.

Has been talking about ontologies for literally decades. He’s a PhD. Like, one just so smart, but not just smart, but thoughtful.

And and engaging and willing to share what he knows and willing to interact.

Right? And willing to get into productive conversations about how to make things better.

Olsen Bagneaux. I mean, I was I was a little maybe aggressive as I don’t know. Maybe because I you know, I’m I’m provocative, and I and I wanna shake the fruit from the tree, and there’s a lot of things that I don’t agree with. And I really didn’t agree much with the data mesh. I still don’t. And I was I was fairly critical of some of old perspective.

And by the way, it’s not old, it’s old.

I was fairly critical of some of his perspectives, but man, another example, just somebody so smart and so engaging and who who wants to interact, who wants to learn, who wants to grow, and would be willing to interact with this hothead like me who is being fairly, maybe even combative at times. That’s not my nature. I don’t wanna be combative, but but certainly challenging. I will be challenging.

I could go on. There’s so many people. So many people who are just it’s and so anyway, if you want to be part of this group, if you want if you want to interact, if you want to chat, if you want like, it’s out there.

It’s it’s out there, and all you gotta do is plug into it. And I’ve been talking about this for twenty minutes. This is not the goal of my podcast, but I think it’s appropriate to take a moment to be grateful and and to express gratitude, and I am. And and I know there are many more wolves and wands out there, and I get to talk with them. I Samir Sharma, another one. I mean, I I it’s just I could keep going and going and going, and it’s it’s been incredible. It’s been amazing.

And I thank LinkedIn. I thank my employer, Prophecy, an amazing company with an amazing leader, Ray Ray Gustmus, who sees the value in thought leadership, who sees the value in what I do. I wanna thank you again for listening and for listening to me opine about this.

And maybe the message here, my friends, is is that there is value in building a network.

If you want to grow, you can. It’s out there. And these and these folks, they will engage with you. They’ll engage. Like, you’re talking about people who are like, PTEN Strenthold wrote the book, Data Management at Scale. Like, crazy smart.

Crazy. And and I don’t necessarily agree with everything that p ten says, but he’ll engage. And and he’ll tell you why he thinks the way that he thinks, and he wants to hear what you have to say.

And they’re just over and over and over. So so if you wanna engage, I’m I’m grateful you’re here. And and I hope you’re getting value from from these podcasts and from my blogs and white papers and LinkedIn and everything else.

Because I I feel like just so blessed and so lucky and I didn’t think I’ll be honest. Okay? This is the ex Gartner analyst with a pretty big head.

I didn’t think that there was much for me to learn.

Right? I just thought that I would go and be the teacher and I was wrong because I’m I’m learning. I’m learning every day. Now, I’m pretty set in some of my perspectives and I but I do when it comes to some the people that I mentioned or all the people that I mentioned, I welcome them to challenge me because what I know is that they have positive intentions and they are smart and they care and they want to advance our industry. I know this. I know this.

So if you want to be part of it, come along.

Comment on LinkedIn. Post on LinkedIn. You don’t have to be have a substack if you don’t want to, whatever. Just just come along.

Door’s open.

So ending of gratitude. Wow. That was a lot of gratitude.

Anyway, before I get into the predictions that now I only have about a half an hour to share because because, you know, schedules and and and the like, I do wanna go back. I do this every year, and I want to go back and assess my performance from last year. So let me pull that up. Let me take a look at my list here and and things that I got right. What did I do? I made about eight or nine predictions last year.

And I’m looking at a chatty PT summary, hopefully I got it right. Not not hallucinating.

Hopefully.

What did I get right? What did I get wrong? So one of my top predictions was I said that twenty twenty five would be a strong year for data and analytics investment.

Yes and no.

So I’ll give myself a six out of ten on this one. It has been a strong year, but that’s because of AI.

If you take AI out of it, I think we have been largely treading water.

Right? If you want to talk about investments in analytics and investments in data management software, investments in services, investment in other software. If you factored in AI and you looked across all the investments in the space, I think net net, it’s a positive year and there have been and it has been a strong year, but that’s all thanks to AI. Really is all thanks to AI. My number two prediction was that many CDOs would continue to struggle and many would lose ground. I think that’s true.

I think it’s true. We can have an argument about that. Right? Post if you disagree.

But I think that many CDOs continue to struggle. This is one of my top trends that I identified last week. If you are if you or two weeks ago, I should have said in my last episode before the Christmas break, I said that, you know, I called it the the idea in the AI headlights. I think a lot of a lot of us are in paralysis.

A lot of us are not doing enough to operationalize GenAI at scale. We’re just not. Right? We we’re not figuring out complex write patterns.

We’re not figuring out vector databases. We’re not figuring out all of these how to build more customized bespoke instantiations of Gen AI based solutions. We’re just not. Because a lot of this stuff is hard and a lot of the sheen has kind of come off the Gen AI genie as it were.

So I would argue I was largely correct about CDOs losing some ground. Number three, AI adoption will accelerate with or without data leadership. Okay. Yeah.

It has accelerated. Now again, this is interesting here. There is there is I would say that it has not accelerated from the perspective customized solutions. This is the ninety five percent of AI POC’s failing.

Right?

Allowing somebody in engineering to use a copilot is not part of that MIT study that said ninety five percent of POC’s were failing. Right? It’s not. Right?

So adoption is is absolutely scaling up. It’s hockey sticking up. Right? Pick a pick a consultancy.

Doesn’t matter. They all say between eighty and ninety percent. Right around there. Adoption. And this is at the desktop.

People using it during their marketing workflows or their finance workflows or their HR workflows. Right? We’re all using it. I’m using it.

I’m literally looking at a chat UPD screen to that did my summary of what I wrote last year. So we’re all using it. So that prediction, I think, arguably was true. However, also untrue from the perspective of customized solutions.

I said that AI governance would dominate conversations but remain largely unsolved. I think that’s true, although I don’t think it’s dominating conversations nearly as much. It remains unsolved, most certainly. Most people cannot tell me what AI governance actually means, and we’ve got work to do there.

It extends beyond just ethics and bias. It extends well beyond that. We’re talking about new governance models, new governance frameworks that have yet to be figured out, And that’s okay. It’s okay.

But we’ve got our work cut out because we need to because we need to. The old deterministic rules based model is not flexible enough, is not adaptive enough, is not contextual enough.

We need to rethink data governance across the board, and not enough work has been done there. My number five prediction was data fabric will continue up the hype cycle. I think that’s true.

Microsoft is continuing to invest in it. Although Microsoft is pushing the the four here, but you could argue it’s not there from a market acceptance and market adoption perspective. And I think that’s true as well.

But, you know, Microsoft is committed to it and I think we will continue to see fabric rise in the coming year. And and what I mean by fabric is not Microsoft fabric, I mean just fabric as an architecture. Fabric as an architecture. Number six, this one, boy, I got wrong.

I said data products will fade and go away, basically. I couldn’t have been more wrong. Totally wrong. Got this one wrong.

The exact opposite. I said sustainability will divert sharply by region. This is absolutely true. You’re hearing more and more and more talk about sustainability and data management, sustainability and AI, sustainability and all things.

You’re hearing this in Europe. Most certainly, you’re hearing this expressed through EU based regulations, whether this is data product or data product passport, any other regulations. The deforestation regulation, for heaven’s sake, over in Europe is is affecting how we manage product related data and ingredient material and item masters. So and that’s all based on sustainability.

Europe, still talking about it. US, nope.

You know, we I want a sustainable future. I wanna give a planet to my kids, but is it like a top three or a top five? No. It’s not even a top ten in the US.

So no judgment. It is what it is, but I got this one right. I got this one right, and we’ll just move on. In the US, we’re not talking about sustainability.

Oh, here’s the good one. This was Malcolm Damas. Okay? I nailed this. This this one, I nailed.

And and what I said, a top prediction for twenty twenty five would be consolidation in data management software market.

I said particularly in the data catalog market.

Nailed it. And I even went so far as to say that Salesforce would acquire Informatica. A ping.

Got it. Now, arguably, this was their set it was not arguably, it’s a fact. It was their second attempt to acquire Informatica. So this didn’t take I mean, was not like like completely out of the blue, but this consolidation’s happening.

Right? Data dot world gets acquired by ServiceNow. Socota gets acquired by Atlassian. And this will continue next year.

This will continue next year, and it will bleed into other data management platforms like MDM and DataQuality. You’re gonna continue to see it. But this one, the consolidation, yeah, that one, I got I nailed. So I’ll give myself, woo, out of eight or nine, what did I get?

Five, five and a half? Not bad. Maybe even closer to six. I could date a product that I flubbed.

Flubbed that one bad.

But, yeah, those were those were my top predictions for last year. And let’s transition into what I see coming for twenty twenty six.

Well, macro, good grief. High level, I don’t know.

Will the economy get better?

Will the economy get worse? I think we’re gonna keep treading water. I I I think I just think we’re gonna just keep treading water. It’s not the wheels aren’t gonna completely fall off. And at this point, hey, I I don’t think just giving the aging demographic of the planet, the aging demographic of boomers, the aging demographic of people dependent on stability in financial markets, Right? If you are a if you are a baby boomer and you’ve now retired and you’ve got money sitting in RRSPs or four zero one k’s or in any other retirement account and you are now kind of the the dominant political force, which you are. You were and you will continue to be.

In terms of the most active voting bases, I I have a hard time seeing a world where government western governments, Europe, North America, Australia, others, where western governments let any major wheels fall off economies. I just I just don’t see it. So as much as many want to have dire predictions about the impacts of tariffs, have dire impacts about the dire predictions about the impacts of rising energy costs and electric bills, thanks to data center build outs, talk about that a little bit in a bit.

As dire as many want to be, I just don’t see it happening. I just I just don’t see it happening because western western countries, western economies will just continue to pump money into those economies to stop the wheels from falling off.

This is part of a broader trend, and I I don’t wanna go there because that’s not my thing. I’m a data guy.

I think twenty twenty six will be an okay year.

I don’t think it’ll be a blockbuster year, and I but I don’t think the wheels are gonna fall off. I think it’s gonna be an okay year. I think we will continue to see more mergers and more acquisitions in the data and analytics space. Right? I think we’ll see more startup activity. I’ll talk about that in a little bit.

But is it gonna be, you know, a a blockbuster year? Is this gonna be a twenty eighteen, twenty nineteen?

You know, like things were leading up before the pandemic. Are we gonna see that? I doubt it. Just I just just doubt it.

But are we gonna see wheels fall off like we did during the pandemic? Nope. I don’t think we will. So I think it’ll be okay here.

I think we’ll be okay. I think I think bank accounts will be okay. I think unemployment is gonna be okay.

I think economies will be largely okay. I think if you’re a vendor or a consultant in the data and analytics space, I think you’re gonna be okay in twenty twenty six. There’s there’s money to be spent to figure this stuff out, particularly AI.

What else? Number two. Well, number one. That was just kind of the broader macro view.

I I think that twenty twenty six will be the the year of renewed focus on trust.

And and I don’t mean that through a kind of misinformation, disinformation control of access to data and censorship perspective. I just think that there will be a renewed focus on trust in twenty twenty six. You know, it’s always kind of been there, but I think I think one of the and this isn’t I I guess you could say it’s a data quality thing, it drives me nuts when people say, well, AI isn’t being adopted because low quality data. Well, no. I don’t think that’s the case.

I think that there is there is a trust barrier, though. And I think the trust barrier is is real, and I think it’s something that we need to focus on in twenty twenty six. And I think it it will increasingly be the gear of trust and the delivery of trust. Right?

And, you know, Gartner talked a little bit about this. So I’m not I’m not trying to rip off Gartner because but it was one of the key themes in their in their keynotes, their data analytics summits this year in in Orlando and London where they were talking about, you know, trust matrices and and managing for trust and okay. I mean, I’m okay. And and building trust models, that’s not really what I’m talking about.

It’s not not.

It’s not not. I mean, every every time I’ve tinkered with this, like, building out a trust model, like, it’s like it becomes this three-dimensional vector space that is just when it comes to building trust models, it it can be really hard. Right? Because you’re talking about the user, you’re talking about the data, you’re talking about the use case, those are kind of the the the three dimensions that you that you need to manage trust across.

And things just kinda get complex, and I and I and I think that twenty twenty six will be a year when we just start talking more about trust through the lens of, can I use this? Is it operational? Will will I let the model free? Will I productionalize this? And I think a lot of the underlying stories will be related to trust. And how do we enable trust?

Right? And this isn’t this isn’t it’s not about matrices and it’s not about trust models per se.

And I know I’m being really, really wishy washy here, but but I but I think it’s a it’s about understanding how our businesses operate. And I think it’s it’s a it’s a it’s about a better aligning ourselves to our business and being more adaptive and understanding that trust in one case is not is not the same as trust in another use case. And that the what is needed for finance to trust something is not the same as what is needed for marketing to trust something. And I don’t think we need to necessarily formalize complex three-dimensional court, you know, causal AI models around it, which would help.

I just think we need to be closer to our businesses and find ways to deliver trust across all data, structured and unstructured. So I know that’s a little squishy and a little wishy washy, but I think we we need to we need to revisit what trust actually really means. And it’s not absolute, and it’s not deterministic, and it’s very user centric, and we need to march towards that.

Because I do believe in some ways, we may have lost that as a North Star in some ways.

Number two, will this year be the year of agents?

Many were talking about last year being the year of agents, and I don’t think that came to pass. Right? I don’t I don’t think it happened.

Will this year be the year of agents?

Maybe. There’s a few things to be concerned about.

You know, I I think that when you talk about trust, when you talk about ongoing struggles to find the right use cases for Gen AI, the fit between Gen AI use cases, Gen AI capabilities, gen AI users, there’s still a lot to be concerned about. But I have a sneaking suspicion that that this year, I think, Agentic will start to actually deliver some value.

Start to deliver some value on things like maybe quote to cash or other sales related processes where today it’s not happening. But I think that that’ll start to happen this year. And I think I think towards the end of twenty twenty six, I I think, you know, one of the new sexy roles will be agentic engineer or some form of an agentic engineer. The people will be marketing themselves as agentic engineers, whatever that means.

Because I I do think that agentic will kind of come out of the trough of disillusionment. Speaking of, that’s definitely definitely my prediction number three, is Gen AI comes out of the trough of disillusionment. It’s most certainly there right now. It is it is deep in the trough. It’s neck deep in the trough. It’s troughing in the trough.

It’s so troughy, I don’t know. Pick your metaphor. But it starts to come out. And I I think we will stop hearing these constant deluge of stories about how Gen AI isn’t delivering value, and we will start to have conversations about how it actually really is delivering value. Right? And how companies are starting to reengineer some of the core business processes around the capabilities of Gen AI. So I think I think we will start to see better news about ROI from Gen AI.

Number four, twenty twenty six will most certainly be the year of the ontology, but that’s selling it short. I’m not just talking about techno ontologies. I’m talking about twenty twenty six being the year that data and analytics professionals, particularly CDOs, finally start to wake up to the need to expand the scope of their organizations beyond data management and into knowledge management.

This is something I started talking about two years ago. I talked about it a little bit last year.

And hold your hats because I’m gonna be another reference to hats. I’m going to be talking about this incessantly next year. In twenty twenty six, I’m going to be talking about the need for data management and knowledge management to come together. And this is on you, CDOs. This is on you.

The knowledge management team, the knowledge management group that is is highly scattered, highly fractured, often doesn’t even have a single person leading it. They’re just people smash smash across the organization. People working in marketing, doing helping SEO and doing search. There are people or people in customer service and customer support that are doing knowledge management within knowledge management systems, customer FAQs, that type of thing. There are other people all over the organization that are doing various forms of knowledge management like content management. Maybe you’re in marketing and you’re managing a content management server with like videos and images.

Right? It’s all that. We need to get our hands around all of that.

And I’m gonna be talking about this a lot in twenty twenty six and I think that it becomes the year not just of the ontology, not just of an ontology, but also all of the underlying scaffolding of knowledge management. My friend Jessica Talisman puts this very very well. She calls it an ontology pipeline where she describes the kind of the core enabling capabilities in knowledge management, which are controlled vocabularies. Yes.

These these are definitions. Right? Thesauri, how things relate to each other. Right? How we how how how some concept a relates to concept b.

Taxonomies, which is the formalized structure of how things relate to each other at more of a physical level. Like, take for example, a genus of animals or a product family. Right? Trucks relate to light, trucks relate to two door trucks relate that’s taxonomy.

Sitting on top of that is ontology. Ontology is how things conceptually relate to each other. I relate to my house.

I have a relationship to my house. I have a relationship conceptually to beer because I like beer a lot.

All these things are some of the components in enabling scaffolding of knowledge management. There are technologies, of course.

Content management systems, knowledge management systems, learning management systems.

It’s where all the unstructured data lives, people.

It’s where all the unstructured data lives and it’s where it’s being managed. It’s where it’s being governed.

We need to pull the man the management and governance of that stuff closer to the to the governance and management of the structured stuff.

Ontologies are literally the connective tissue between these two worlds, which necessarily must come together in twenty twenty six. We’ll start to see more data practitioners and data leaders and CDOs figure this out.

Speaking of, number five, context becomes the new king.

It’s always been important. We’ve just never really fully recognized how important it was. We’ve always known that the way that marketing looks at the world is different than the way that finance looks at the world.

That’s context.

But in many ways, we’ve just conveniently ignored context when we shouldn’t have been. But now it’s important. Why is it important?

Because Gen AI needs it.

Throw a table into Gen AI and see how it does. It struggles. There’s no context there. The intersection of a row and column has practically zero context. About the only context you could you could infer is from the name of the table or maybe some joins, how that table may relate to other tables.

There’s no context there. Not meaningful. Right? What Gen AI needs is a narrative. It needs a story.

It doesn’t need the intersection of a row and a column. It needs narrative. That needs context. Context is king.

In the twenty twenty six, more and more will come to that awareness.

Number six or seven. What am talking about? Oh oh, okay. I already talked about this. Ongoing mergers and acquisitions. This is not gonna slow down.

Not going to slow down.

There are half dozen or so, give or take, best of breed MDM providers.

Maybe an equal number in the data quality space, and I think by the end of the year, at least two in each will will come by or be gobbled up by some provider of a converged data management platform, whatever that means.

But we’ll see more consolidation here.

It doesn’t necessarily mean that best of breed solutions are bad. Don’t don’t that’s not what I’m saying at all.

One could argue that I actually believe there is a, and I fervently believe, that there’s a strong place for best of breed solutions, at least in so far as they integrate to these platforms.

Right? So you can you can create a best of breed MDM solution, best of breed integration platform, a best of breed data quality solution, But increasingly, you you need to have that deeply integrated across multiple hyperscalers, multiple analytics platforms, aka Databricks and Snowflake.

And if you’re a best of breed provider, you can do that. You most certainly can do that. And you have a compelling story to tell to others where, hey, all we do is MDM, and we do it incredibly well. And we’re not sidetracked with a whole bunch of other stuff and other priorities and other capabilities. And we know MDM. We’re experts in that world. This is the world of prophecies, the world that I live in.

But those providers do need to have a story to tell when it comes to integrating those platforms. But still, those providers, because of their expertise, because of their capabilities, because of their innovation, will look very attractive to the big and slower moving providers as a means to drive innovation into those specific capabilities. MDM, integration, data quality. So we’ll see more consolidation there in twenty twenty six. Most certainly, we will.

I do think that that’s at one end a kind of the of the corporate spectrum. The other end, we’re gonna see more and more and more smaller providers kind of popping up.

They may be micro providers, maybe tiny little software companies that are using AI to get into the space of helping us better manage our data, better govern our data using really, really small one off bespoke tools that are coming from these micro providers, that are coming from these shops with, like, ten people, maybe, max. Right?

And frankly, we need that. We need it because we need disruption in our space. We need people talking about how to reengineer and rearchitect how we do things, right, across the board, because we desperately need innovation in our space. And I think that we’re gonna see more and more and more of these extremely small providers come up who are solving very specific, very difficult problems, and more and more and more of them will be making noise in our space. And that’s great. And I think it’s a really, really good thing.

What else? I’m looking at my list.

Well, we are in a in an election year in twenty twenty six here in the US of a.

And I think that both AI and data centers will become a hot tamale from a political perspective. They already are.

You have many local jurisdictions where people are rising up and saying, no. We don’t want data centers for a lot of different reasons.

Primary ones are being this perception that they are correlated to local energy cost increases.

I mean, I I don’t know. I struggle with that one because energy, particularly electric, is it’s it’s a it’s a commodity.

But there is very much local forces here from a cost perspective. So, you know, I think that whole issue, data centers and AI, will become a bit of a political hot tamale.

And I think you’re gonna see increasing galvanization of the views of of how people think about AI.

Right? And there’s those of us who are in the space who are doing the things.

Right, who are, you know, in the AI world, because we are, who will generally, I think, have, for the most part, a fairly generally positive perspective and who would typically think of, okay, well, the cost may be going up, but there’s certainly gonna be benefit there. And we start talking about, you know, solving cancer, and we start talking about solving, you know, boundless energy, we start talking about abundance, and we start talking about all the things that potentially could be there with AI. I think a lot of people like me were like, okay.

Yeah. If you think about it, of course, there’s concerns about socializing the cost of AI. Are there concerns about not socializing access to it? Right? If we are gonna socialize the cost, that certainly suggests that the benefits should be equally socialized, and will that be the case? I I don’t know.

Generally not how things have worked historically.

But I think that’s gonna become a bit of an issue because politicians will make it an issue. And I think that there will be an increasing kind of anti tech, anti AI sentiment growing. It’s there today. It’s there. I see it.

But it’s gonna increase.

And it’ll get pumped up by the media and it’ll get pumped up across the board where increasingly you will have to choose between being pro AI and anti AI. I know which side I will stand on. I’ll stand on pro AI.

But also being sympathetic to some of the concerns. Right? I I think some of the big brother concerns are are not what they were two years ago.

I had some of the big brother concerns, but I think we’re a long way from AGI. You know, When somebody like a Sam Altman says, hey, I need seven trillion with a t. I need seven trillion to build the energy infrastructure and the data centers in order to reach this magical idea of AGI, I think you should listen to him.

But he’s not gonna get seven trillion.

We’re already seeing companies stress to the point where excessive upfront investments are slowing are slowing investments in AI because companies are saying, yeah, I’ll invest but I’m not gonna I’m not gonna disproportionately bear all of the cost for this infrastructure build out when the returns aren’t there yet. Some are willing to, but this is exactly why you’re starting to see these kind of these circular investments.

This this shell game of moving money around.

That’s why you’re seeing it. It’s because there is pain in these investments with a long tail of return.

Particularly in situations where you’re talking about building out data centers where the chips have a useful life of three to five years, and there are many who believe that the investment returns aren’t gonna be there for five years plus.

Like, asking somebody to pay that as an investor, Yeah. That’s tough. It’s tough.

So will Sam Altman get his seven trillion? Of course not. Right?

So will we get to AGI? I’m I I just I I maybe, but I don’t see it happening anytime in the short term because there’s just not enough energy there. Just and there’s not enough chips.

Just not enough chips.

So still, we may not necessarily need to be concerned about Big Brother, but and, you know, the robot overlords, I do think there’s a there there are valid concerns related to energy costs. Right? And those those costs are real. There are costs related there there are concerns related to governance, behavior of these systems.

All these things are real. So, yeah, I think it’s gonna be it’s it’s gonna be a politicized issue in twenty twenty six, and and I think increasingly, you will see the media force you into an either pro or anti AI stance.

And that that could make things a little spicy for those of us in the industry. I think it could be spicy.

So I don’t think it’s bad. I don’t think it’s bad per se, because I can make a compelling business case for this stuff.

It’s gonna be spicy.

Gonna be spicy. Alright. My long shot. Before I wrap up, my long shot.

And my long shot last year was Salesforce buying Informatica. My long shot for this year is Microsoft breaks up with Databricks.

There is a split. It’s not you, it’s me. And in this case, the you is Databricks and the me is Microsoft.

I think that in twenty twenty six, Microsoft figures out that their Sun Tzu like approach of keeping friends close and enemies closer has played out.

It’s played out. And that Microsoft will figure out that in order to win this battle, they’re going to need to force their customers to pick a side.

Why?

Why, you may ask?

Because the value is moving out of the commodity layer.

The value in cloud and the value of these hyperscalers is moving out of persistence. Microsoft is doing it itself.

The fabric, I argue, is a commoditization of persistence.

Who cares if it’s sitting over here? Who cares if it’s sitting in Cassandra? Who cares if it’s sitting in whatever an Excel spreadsheet? Who cares if it’s sitting in Hadoop? Who cares if it’s it doesn’t matter. We will virtualize it all.

It doesn’t matter where it lives. It could live in Redshift. It could live in GCP. Nobody cares.

Ultra commoditized. Nobody cares. The value is moving up the chain. It’s moving out of persistence, and it’s moving out of compute, and it’s moving into management.

It’s moving into the apps. It’s moving into purview. It’s moving into fabric. It’s moving into what will be left of unity after all of this happens.

That’s where the value is.

And that will list that will exist for two to three years, and then after that, the value move up again into the business applications, which is what Microsoft owns.

So Microsoft, I believe, in twenty twenty six will figure out that taking a I don’t care, like, I I go I don’t care if my customers go in Azure Databricks.

Okay.

But Databricks is creating data management solutions. Databricks is trying to move into that space.

Databricks is trying to get beyond storage and compute because when you get in the commodity game, it’s a low margin business.

It’s a low margin business and they don’t wanna be pigeonholed there. And Microsoft will see that and like, wait a minute. Wait a minute.

We need to have a hard conversation about the state of our relationship. That’s what I think.

I could be wrong.

I could be wrong. I’ve been wrong many times before.

Alright, my friends. Thanks for putting up with my soliloquy in the first half of the podcast. If you’re still here, go ahead and click like. Go ahead and subscribe. Go ahead and do the socials, all the things.

I’d be thrilled if you continued this ride with us in twenty twenty six. I’d be thrilled if this is the first time you’ve ever listened to the CDMatters podcast and you listen again.

Because I’m here to help. I’m here to share what I know.

Sometimes prognosticate, sometimes ruminate, but always hopefully always hopefully enlightened and always hopefully maybe a little bit entertained. Both can be true at the same time. My friends, have a great twenty twenty six. Happy New Year. We’ll see you on another episode of CDO Matters sometime very, very soon. Bye for now.