Driving AI-Powered Analytics with Mike Potter of Qlik – Tech Talks

We are here at Click Connect 2025. And one of the best things about Click Connect is connecting with the people within the company, learning what they're building, how they're building it, and most importantly, why they're building it, how it serves the needs of the customers. Now, I am here talking to Mike Potter, CTO of Qlik to learn a little bit more about what the customers are looking for and how Qlik is meeting those needs and helping them to achieve their business goals.

Thank you for joining us. Um, Great to be here. So you are A-C-T-O-I imagine that means that your job is sort of in the intersection of customers and technology that you are intended, right?

To try to figure out how customers, what, what customers need. I, I think that's a great definition. I also would describe it as, uh, my job is to make sure everything works.

It scales, it's of high quality, and, uh, it solves problems. Yes. And, and problem solving.

I mean, if we want to kind of dive into it, you know, Qlik, the thing that impresses me about Qlik is that it's, it's focused on, on data quality. It's focused on the needs of the enterprise. It's not a US Silicon Valley centric company.

It's a globally centric company. You know, you've got customers of all sizes. What are the challenges that customers are facing in terms of data and analytics, and how are you meeting those?

Well, uh, lot to, that's a big question, right? Yes. Lot to unpack there.

Uh, so I mean, we've been, uh, uh, you know, our vision behind Qlik is really to manage the entire lifecycle of data, right? Uh, whether it be from source, uh, uh, getting it into targets, taking it from targets, and turning it into true analytic value experiences, AI experiences, and then translating that subsequently into action. And so this problem has been around for a very long time, uh, and it has taken a lot of forms.

And really, I think what we've been able to do, uh, at Qlik is, is take that and put a focused effort on making that an integrated solution. More importantly, I think what it is, is allowing people to get to their data regardless of where it is, um, and be able to make sure that they're able to interact with it on their terms, um, on the, in terms of their strategy, whether it be an on-premise strategy or a cloud strategy. And then finally being able to do that in a governed way at scale.

And there are a lot of tactical things that you need to do in order to make that happen. I know that you have had to worry about, for example, uh, you know, companies have had to worry about data formats. You know, you've got the whole, uh, structured versus semi-structured versus unstructured.

That was a big, uh, aspect of, uh, previous acquisitions, uh, that we talked about last year. Uh, you have to worry about, uh, now data lakes and how do you integrate, uh, data lake with analytics solutions. Uh, we hear a lot about, uh, data lakehouse, uh, this year.

Uh, we've also, as you mentioned, you said ai, uh, we've heard a lot about ai. And of course, AI could be multiple things. Uh, in, in my mind, you have sort of AI in the backend that's, uh, agentic that's actually doing work on your behalf, whether that's data structuring or q queries or, or it could be on the front end as a user interface for the customer, which, uh, kind of solves another customer need, which is how do we actually use this stuff.

Now, again, I've given you a lot to talk about there. Um, let's kind of zoom in on a few of those things. So first off, uh, this whole question of, of structuring and organizing data, how, what's click's approach to that?

Well, I think what we're doing here, uh, particularly as it relates to AI is, uh, being able to integrate it in all phases of, uh, the data process, right? Being able to integrate it into transformations, be able to integrate it into how we access data, how we'd be able to, um, direct what a, a pipeline would look like, and, uh, how we, uh, capture information around metadata in our catalog. How we're able to create, you know, this idea of business glossaries on the fly.

And so there's a lot of feature centric work that can be automated through, uh, the introduction and adding of AI into those processes. And all that's really designed to do is, um, take those route tasks and allow the data engineer or the data steward or whoever's in that process, the data product manager to be able to do their jobs more effectively and really focus on the content rather than the mechanics. Right?

Um, in addition to that, I think what, uh, it it allows us to do is make sure that we are creating a consistency of approach, um, so that as regardless of what data source we're going after and what process we're following, in terms of getting that data to where it needs to go, um, the, the rules that we create with the help of ai, um, uh, allow us to do that in a, in a, in a governed way. And, and actually I think that's one of the overlooked aspects of ai. People are talking about agentic ai.

They're thinking about autonomous agents out there, like ordering lunch for you or whatever. Honestly, one of the most valuable aspects of AI right now, at least from my perspective, is taking unstructured data and, and, and deriving data points from that in a structured fashion. And I think that that's one way that Qlik is using AI now on the is, is to bring some structure and, and to, to, to uncover what this data is, right?

Well, I think so. So there's, you know, unstructured and structured data coinciding have always been a problem, right? And I, I remember lots of iterations of attempts to try to solve it over the years.

And I think really what we wanna do is recognize the fact that they both bring value in their own independent ways. And how do you then bring them together in a, in a, you know, a a combined fashion, right? And so, um, a lot of our AI strategy, particularly on the analytic side, is to introduce the notion of unstructured data into the analytic process.

Through click answers, you can ask questions that allow you to translate that into a meaningful structured query, a meaningful unstructured query, and then being able to see how they relate to each other, right? Um, and so, you know, for me, the, the biggest opportunity is really to uncover a new way for end users to look at their data other than having to go through traditional user interfaces. Well, that absolutely is another area I think that is un underappreciated with ai.

The fact that a non-technical, or maybe just a non-data centric user can ask a natural language query and have, uh, AI translate that into the, the query that the machine needs in order to return an answer. Is that what you mean by user interface? Well, so frankly, um, one of the things that, uh, genai has done with, particularly with chat GPT and, and all the things that have followed since, is that it's been able to broaden the horizon of users with less technical skills to be able to get value from the data, right?

And so that, that broadening of, of is, is equally relevant inside of an organization. Um, heritage, bi heritage analytics solutions have often been, you know, the tool of the few that then would have to turn around and be translated for the many. Mm-hmm.

Right? And now we have an ability to actually allow the many to interact with the data at the same level on an equal footing Yeah. And, and interact with it in a way that's meaningful to them, which I think is something that we saw in the demonstrations, the tech field bay demonstrations that we did here at Click Connect, as well as the main stage and the breakout demonstrations.

There was a lot of examples of customers asking a business question and, and having the system respond in a business way and be backed by data. That's right. And so, you know, one of the things that, that, um, that the trap of course with, with, you know, uh, uh, systems with generative AI approaches is that by nature of the technology, you may get answers that are either hallucinations, they get answers that are, um, where there may be more than one right answer to the same question.

What you wanna be able to do is provide that information in context with structured data mm-hmm. So that the informa information provides an additional point of view that informs the decision. Right.

And so what it's doing is it's converging a, uh, a technology that fundamentally is non-deterministic with tech, with approach algorithmic approaches that are very deterministic so that you get an overall additive experience. That sounds great. And I think that that's one thing that's really gonna resonate with this audience.

How real is that? How, how ready is that for production? So, Um, we've made tremendous strides with respect to our approach to, um, uh, AI up to this point.

Uh, we just talked about the agenda framework. Uh, you know, this week here at the, at the conference, uh, we feel really confident in what we built, and we're really looking forward to, um, getting it out there. I think for us right now, we have built some very key agents that work very nicely with our analytic engine.

And, and the key point that we made this week is that even with this, um, agentic technology and our addition of AI to the product, our differentiation is still our associative analytic engine. Mm-hmm. And the fact that these can now work together, um, I think, uh, are really what the win is about.

And more importantly, I think what it does is, um, it allows us set up for the next step where we're just gonna continue to expand the role of agents within the architecture. Yeah. Well, another thing that we saw a lot of here is, uh, work.

And I think that this segues kind of from that agent perspective is work with partners and other industry players and open standards to help broaden the availability of these technologies. Uh, certainly we've seen, uh, AWS quite a lot here at the show, but we've also seen some companies that are traditionally, um, maybe not, uh, friendly, you know, I mean, I don't wanna say competitors, but, you know, uh, uh, companies that are often mentioned as competitors for Qlik, uh, presenting alongside Qlik. And, um, a lot of this, I think, bodes well for the industry because it's all about open standards.

Uh, you've got, uh, a lot of talk about Apache iceberg, for example, with the, the data lake, uh, announcements. Yep. Um, we are, we're talking a lot about the cloud, so let's talk about how Qlik works in a broader perspective with, uh, other partners and in the cloud.

Well, so, uh, one of the core, uh, tenets of what we built, uh, and our architecture is the ability to support integration. Yep. Um, not only integration in terms of, uh, who we use as endpoints for data, but also integration programmatically, um, through our open APIs and, um, our, our basically our platform.

And so we have a lot of customers that have built complete solutions on top of us, um, uh, a variety of points during that, uh, data lifecycle chain, um, to add value. Right. And we have a number of those partners here with us this week.

Uh, they were on screen at the conference, and really what they do is they're celebrating the value we bring to them by introducing and solving a problem that is unique. And, and maybe it's a vertical, maybe it's a particular technology, uh, focus, but it's really about extending, creating an extendable environment. And that goes to our strategic relationships as well.

Right. We're able to do, uh, we have very deep relationships with AWS but we have a, uh, relationships with, uh, other major, uh, uh, players in the space. And the whole goal of that is to make sure that whether it's a, a data storage technology, whether it's a AI technology, whether it's a cloud technology, uh, we're able to work in the ecosystem that the customer wants us to work in.

Yeah. And of course, Qlik has its own talent cloud, um, as well, but that's, uh, you know, that's our, our customers getting to the point now where they're just completely accepting of cloud versus on-prem. Uh, is this still an argument?

I think the, the argument is not whether they should be in cloud. The argument is how do we get there fast enough? Mm.

Right. And so a lot of customers have made tremendous investments in their current on-premise technology, and those investments are, uh, they work. Mm-hmm.

And they're, they're very happy with those investments. And so what, um, what they wanna be able to do is take those investments and extend them into the cloud, um, and come up with plans of how to move forward. Okay.

And a lot of that is, uh, you know, we have, uh, we've been working with customers in terms of their cloud journey, but there's really no question that cloud is the destination that a lot of people wanna be. Yeah. And, and it seems to that you're also investing, uh, I guess if we can wrap on this, uh, in tools that would allow people to make that migration more smoothly.

Yeah. So we just recently acquired a tool from one of our partners, a great example of a, a technical integration, um, uh, through our extensible platform that allows to, uh, improve the, uh, accuracy and the volume in which they can then, uh, migrate, uh, content from their current environments into Click platform. Yeah.

And, and you know, it's really powerful stuff because essentially, uh, the, the combination of Qlik and other trusted enterprise providers, whether they're, uh, cloud service providers, whether there's ISVs, is really what customers are looking for. You know, they don't want lock-in, they want open standards, and they want a flexible and responsive system. And I think that that's the message that we're getting from the main stage at at click Connect.

Most definitely. We wanna partner. Absolutely.

Well, it's, it's great to talk with you. Um, before we go, uh, where can people connect with you? Where can they learn more about click, uh, give us a little call to action?

Well, I think, you know, obviously, uh, people have access to the content of the, of the conference. com. Uh, I would say that is your portal to getting, uh, uh, information.

You can obviously reach out to us. We'd be more than happy to have a conversation with you. Um, and that conversation ranges from what your, your strategy is from data analytics, what the combined strategy is and, and, and how you get there from here.

Right. A lot of customers are on different parts of this journey, have different requirements. And, uh, we would love to insert ourselves into where you are and help you get from where you are to where you wanna be.

Excellent. And another thing I'll suggest, uh, we did record, uh, about four, four and a half hours of Tech Field Day presentations where we dive deep into a lot of the products that were just announced this week here at Click Connect. Uh, we've got demos, we've got, uh, you know, in-depth slide driven q and a, that sort of thing.

com or the Tech Field Day YouTube channel to watch those. Or if you're interested in a little bit more perspective like this, go to the tech field, a plus YouTube channel, where we've got some great interviews from other folks from Qlik, as well as some of the AI council and end users, uh, that they've brought here to click connect. Thanks for watching and catch the next video.