Qlik Connect 2026 | Matt Hayes, Qlik

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Matt Hayes

GM, Data Business Unit

Qlik

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play_circle_outline Agentic AI Raises the Stakes: Why Data Readiness and Quality Are Nonnegotiable

play_circle_outline Data products as consumable assets requiring quality, governance, and stewardship

play_circle_outline Automation with agents helpful, but human oversight and data stewardship remain essential

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Matt Hayes, Qlik

Matt Hayes

GM, Data Business Unit Qlik

In this interview from Qlik Connect 2026, Matthew Hayes, general manager of the Data Business Unit at Qlik, joins theCUBE Research's Rebecca Knight and Rob Strechay to discuss why data quality and governance have become the critical foundation for enterprise AI. Hayes describes a pattern emerging from his customer conversations: leadership is demanding AI velocity while practitioners are forced to pause and ask whether their data is genuinely AI-ready. He explains how the stakes have fundamentally shifted — data is no longer just operational infrastructure bu... Read more

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What are customers saying about their progress and challenges in adopting AI? add

What are organizations seeing as the foundation for trusted, AI‑ready data when investing in improved data platforms to bring their data together? add

How should organizations think about data products — their characteristics and lifecycle — and what roles do data quality, governance, stewardship, schema/semantic layers, and access controls play in making them usable for the business? add

What role will AI agents play in data stewardship and automation — will they act as helper tools with human oversight and guardrails, or replace humans with full automation? add

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Matt Hayes, Qlik

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Rebecca Knight

>> Good afternoon everyone, and welcome back to theCUBE's live coverage of Qlik Connect 2026 here in Orlando, Florida. I'm your host, Rebecca Knight. I'm alongside Rob Strechay, the one and only Rob Strechay.

Rob Strechay

>> Well, yes, that is very true. If you see another Rob Strechay walking around, it's an imposter.

Rebecca Knight

>> Exactly, exactly. Well, we're going to be talking...

Matt Hayes

>> Not as nice dressed as you.

Rob Strechay

>> That's for sure.

Rebecca Knight

>> Indeed.

Rob Strechay

>> I do try.

Rebecca Knight

>> I would like to welcome our next guest. He is Matthew Hayes, GM Data Business Unit at Qlik. Thanks so much for coming on the show. Thank you.

Matt Hayes

>> Thank you for having me.

Rebecca Knight

>> Yeah. So in your role, I'm sure you talk to a lot of senior leaders across lots of organizations who are struggling and facing a lot of challenges in terms of how they're deploying AI. Talk a little bit about what you're seeing in terms of commonalities with the successes they're having and where they're still stalling.

Matt Hayes

>> Yeah, no, I think a lot of it is exploration. I've met with customers all day today and they've shared a common theme, which is that their leadership is pushing AI. And they're trying to figure out how to move as fast as their leaders want them to move, but also make sure that they're applying it to the right use cases, that they're doing it in a way that actually helps the business as opposed to just moving fast. So I think there's a lot of companies are looking for some guidance. There's a lot of companies that feel that they're partially there from either a data integration standpoint or an analytics standpoint. And they just need to figure out how to make that next step.

Rob Strechay

>> I think one of the things, and it's been a theme that's been going on, is that AI can't survive poor quality data. What are you seeing from organizations as they want to invest in a better data platform and be able to bring it all together? What are you seeing as the foundation for trusted data?

Matt Hayes

>> Yeah. Well, so with trusted data, I think the data quality is absolutely key. It feels that every year we're talking about the same themes just at another level. So quality of data is always important, but now that you've introduced an agentic framework, you're introducing capabilities where the agents are making decisions based on the data. Nobody wants to be a news story. Nobody wants to have a situation where the agent does something absolutely crazy and it's because it's a data issue. And this is actually going to slow things down a little bit for some organizations because they need to take a step back and say, "Is our data really AI ready?" And when you ask that question, it's a complicated answer. Are we getting the data from all the sources? Is it arriving on time? What are the gaps in the data? What are the quality issues? That's a piece of it too. Just what is wrong with it? Is it just blank fields or is there problems with the data that need to be remediated before we can actually put it to work in an agentic scenario?

Rebecca Knight

>> So that's why it sounds as though it's becoming a real board level issue, a real level at the C-suite. And you said that it's causing a lot of organizations to slow down. Is that a good thing?

Matt Hayes

>> I think for most companies, it is a good thing to take a step back and say, "Do we have the right architecture in place? Do we have the right solutions in place?" This is what we're doing at Qlik. We're making sure that we're looking at it saying, "We've built for this moment. Everything that we've done over the past 10, 15 years has iterated us to this moment." And we need to build that trusted layer for the customers. I think we've heard that data's the new oil. The first time I heard that was like 10 years ago. Well, now it's not just about mining value out of that data, but the data's become a competitive advantage for the business. And this is where the boards are getting involved. They're looking at it saying, "Oh, wait a minute. The data's not just important as far as how we run our business. It's actually important into how we compete in the market because we know our competitors are doing this too." So that's why I think you get a lot of that velocity and push from the board because we know our competitors are doing it. We need to be doing it too. So what are we doing?

Rob Strechay

>> So building on that, how should CIOs and CDOs and such really embrace and go down the path and operationalize governance then?

Matt Hayes

>> Yeah. So I think a big piece of this is taking ownership of your own data. Mike talked about this this morning in the keynote. There's a lot of vendors that are creating vendor lock-in around your data. There's a lot of decisions that are being made that are not customer-centric as far as getting the most value from your data. We've taken the completely opposite approach to that. We want to be customer-centric. Your data is your data and we will help you get it no matter what you have to do from where it is to where it needs to be. So the freedom of data is really important here because you just can't have contractual obligations or vendor lock-in issues restricting what you're going to get out of the value of that data.

Rebecca Knight

>> Talk a little bit about that. You said that all these things are the same things, just a little bit of a different angle or a different level. But talk a little bit about what a company or organization would be grappling with if they did this vendor lock-in and then their data is not their data.

Matt Hayes

>> First of all, I want to just push back and say that your data is your data just because it's an knee-jerk reaction.

Rebecca Knight

>> Because someone says it's not.

Matt Hayes

>> Because it's how every conversation starts with a customer. But you look at a lot of these enterprise vendors out there are recognizing that they can... Having your data in their environment, having your data in their applications is valuable. It can help them shift and shape what you're doing with that data and what technologies you choose. So now you've got these companies that are building stacks around data, AI, and analytics, and they want you to go that route. You don't want your data to be a hostage. You don't want to be making decisions in your architecture that aren't good for the business. If you choose a data platform or an AI solution, you don't want to be making that decision because it's the easiest because we already have this vendor, or it's the easiest because they're difficult to deal with and they already have our data. You need to be making these decisions autonomously based on what you want to do with the data.

Rob Strechay

>> So one of the things that keeps coming up, we were just in Europe for a show a couple of weeks ago, and regulation is starting to creep in everywhere. And it's not just about sovereignty and AI regulations, but it's industry specific stuff and even greater. And more companies are saying, "I want to get ready for that." One of the things that you've got to look at and scrutinize, and it seems like AI really, it's become more important, and love your take on this, is really lineage is one of those things that when you start to look at where's the data coming from, who's processed it, who can touch it, and all of these different things, and to the whole medallion strategy and things of that nature, how does lineage and the medallion strategy, how does that all play in to helping people get ahead of the regulation? I feel like there's this tidal wave of regulation that we're going to have over the next couple of years.

Matt Hayes

>> Yeah. I think sovereignty drives a lot of it. So the data sovereignty laws that are going into place across the world, we have to pay attention to. We have to honor that and respect that. And we understand that it's something our customers value. But when it comes to what you're doing with that data, not all data is equal. Some of it is mission-critical enterprise data. Some of it might be IoT or log stream data that needs to be ingested quickly. So I think what we're going to anticipate is that workloads are going to vary, controls and governance around data is going to vary, and this is where Qlik's capabilities can handle that. So we introduced streaming for Open Lakehouse. I've had two customers this morning thank us for that because they're ahead of the curve in that regard. They're bringing highly valuable, high velocity IoT data into their Open Lakehouse. Both of them are currently loading it into an enterprise data warehouse that's a little bit more expensive than they'd like it to be. So the Lakehouse strategy is really important. So we're going to see that governance is going to vary data to data, customer to customer, and I do think the sovereignty issue is going to be prevalent. I'm even hearing about customers overseas that are training LLM models based on their own culture, based on how their business operates. They don't want to use an anthropic LLM. They want to train an LLM based on how they work and how they interact with customers and vendors.

Rebecca Knight

>> So say more about that. When you say trained on their culture, is this their ethos, their mission, their values in addition to their workflows and how different divisions work together?

Matt Hayes

>> Yeah. Yeah. I think a lot of it is their workflows. Some of it is just aspects of their culture, whether it be documented through HR initiatives, what are our core values, things like that. It even goes down into communications. How do we communicate with our customers? What language do we use? How do we work with our vendors? Do we treat our vendors as vendors or do we treat them as partners? So I think those are some really interesting and nuanced aspects of how this AI world is evolving.

Rebecca Knight

>> How are these regulatory expectations shaping the way companies approach innovation?

Matt Hayes

>> I think it slows them down. We talked about that earlier. I think it causes everybody a little bit of pause to say, "Okay, we know what we want to do. We know we want to move fast, but are we doing it right? Are we compliant with laws? Are we compliant with legal?" I had breakfast with a customer this morning that said some of their challenges are legal, slows them down, because legal hasn't vetted this out completely to figure out what they can and can't do, especially when it comes to customer data. When it comes to coding, we're a software company and we code, but our customers code and they use things like Claude Code and Copilot to generate code and that raises questions about the legalities of what's their IP and what can they trademark and copyright and things like that. So it's interesting, but I do think the regulations are going to probably, they're going to need to get more advanced, but they can't get in the way of progress and innovation, at least in an ethical way.

Rob Strechay

>> So one of the biggest failures, it's like you said, I feel like we keep going around on this wheel a lot, is one of the biggest failures is really quality of data. And one of the things is being, to your point about training your own models, having SLMs, small language models or specific language models used as part of your agentic workflow, it helps to overcome that because you're controlling the data from source to output. Are you seeing that people are really looking to govern the data across the entire life cycle and that's one of the places that Qlik is really double clicking on?

Matt Hayes

>> Yeah. I think for us, a lot of it manifests in the data product. So the data product, which you saw us launch today, we turned that on earlier this year. We have over 3000 data products that customers have enabled. But when you back up from a data product, when you look at it from the consumer standpoint, the data product is, if you think about it, it's a perishable good. It's something that you would buy at a grocery store. That it's only good for so long, you need it, you need it right now, and it needs to do what it's supposed to do. But behind that, there's the data quality, there's the governance, there's the data stewardship, there's the schema or semantic...

Rob Strechay

>> Semantic layer, yeah....

Matt Hayes

>> information. There's just so much behind the data product, and I do think governance is a big part of that. Who can use it, who can access it, who can see it, when they can access it. So when you look at the scope of a data product, there's a lot of these components that back up into this that ultimately serve the business to make sure that, yes, that data product that you just bought off the shelf, that you're using in this use case is good and ready to go, and you're allowed to use it, and this is the proper use case for it.

Rebecca Knight

>> So what best practices are emerging in terms of when you are looking out across the spectrum of AI projects that deliver ROI, what do they get right consistently about the data that the others aren't?

Matt Hayes

>> I do think we're in a period of experimentation. That's big. I think, do you remember when the iPhone came out?

Rebecca Knight

>> Sure.

Matt Hayes

>> And the apps where Heineken had that app where you could pour a beer with the iPhone. Or you could do the lighter. So when you go to a concert, you could just hold up the lighter. That's where I think we are with these AI and agentic use cases around business. I think everybody wants to do something, everybody wants to move quickly, but you really have to boil down the use cases and say, "Where are we right now? What are the use cases that are the best fit?" One of the things that comes up all the time is, "Are these software companies going to get eaten up by AI?" And I would argue that we're part of the hungry part of that. We have a little teeth in us because some of our customers have come to us and said, "We have 20, 30 vendors banging down our door with AI scenarios that are point niche solutions that fix a specific problem." And I challenge those customers to say, "You know what? Of those 30 or 40 vendors, pick two of them that are really interesting to you and let's see if we can do it with Qlik because I bet we can." So our customers light up when they see that because they're thinking, "Oh yeah, that's right. You guys have already moved the data. You guys have already helped us transform and clean that data and I can use it." So as long as I can build my use case out with a Qlik app or with some of the agents that you saw this morning, I might be able to produce that capability on my own with my Qlik license. I don't have to buy another AI niche tool to solve a problem.

Rob Strechay

>> To me, that would make total sense is, I think you're right. Again, it's focusing on a platform that can help you up level across multiple different use cases. And one of the things, again, that I've seen is really around the people who are managing it and really just the might that they have to go through and the data. And I know there was the discussion of the different personas that we're going to be able to take advantage of this. Do you see that organizations really are leaning into more automation around this, around the data quality, around the governance, because it's just so unknowable, a lot of the different pieces that you have to touch, that they're looking for partners like Qlik to come in and help them at all of these different points?

Matt Hayes

>> Yeah. I think this is an area where I think agents will help, but it'll be more like agents as a helper, as opposed to automation and for full action. Take data stewardship, for example. Data stewardship, somebody has to look at a lot of these scenarios and say, "What do we do with this? This isn't right. We need to make sure that this data is formulaic and follows this formula." So some things like that need to be done, but then you can build that into the agent. You can build that into the agent and have something like data quality remediation automated from that point forward. We talk about human in the loop. It's almost like AI with human oversight. So I think Mike talked about this. I have so many things that I wrote down which were great soundbites this morning. And I don't have my notebook out right now, but he said something about... I can't remember it right now, but it was basically how the human drives the process. And if you understand the process, you understand what you need to do. You give it the rules, you give it the guardrails, I think that's one of the things, and then you have built confidence around that, then you can have some automation that's then continually in motion. Doesn't mean you don't have to tweak it and change it now and then. And in a true AI world, it would change itself and modify itself, but some of those things really help.

Rebecca Knight

>> It's just a tool. AI is just a tool.

Matt Hayes

>> Yeah.

Rebecca Knight

>> There you go.

Matt Hayes

>> Workload. Yeah.

Rebecca Knight

>> Right. Matt Hayes, thank you so much for coming on the show. Really fun conversation.

Matt Hayes

>> Yeah. Thanks for having me.

Rebecca Knight

>> I'm Rebecca Knight for Rob Strechay. Stay tuned for more of theCUBE's live coverage of Qlik Connect. You're watching theCUBE, the leader in enterprise tech news and analysis.