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Three insights you might have missed from theCUBE’s coverage of Qlik Connect

Comprehensive data solutions are becoming increasingly important as generative AI adoption grows. There is a broader platform shift ongoing, making accessibility of quality data crucially important.How to respond to this seismic transformation in data integration and analytics innovations was a central focus of Qlik Connect. The event made it clear that QlikTech International AB has a strong culture, but also that there’s no such thing as an “easy button,” according to Shelly Kramer, managing director and principal analyst at theCUBE Research, during the event.Shelly Kramer of theCUBE discusses what’s next for Qlik during Qlik Connect 2024.“Data integration is key

Harnessing the power of gen AI: Qlik’s vision for the future of data analytics

The data and artificial intelligence industry is undergoing a transformative phase, driven by the integration of generative AI and advancements in data analytics.This transformation is characterized by the ability to measure and analyze vast amounts of data in real time, creating opportunities for businesses to optimize processes and drive innovation. The focus is now on leveraging high-quality, diverse data to inform decisions and generate actionable insights. As these technologies evolve, organizations must adapt by ensuring data quality, trust and accessibility to fully harness the power of AI.TheCUBE on set at Qlik Connect

play_circle_outline Gen AI and its practical applications

play_circle_outline Importance of data quality and variety

play_circle_outline Hopes for real-world use cases and value generation in the future.

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James Fisher | Qlik

>> Hello, welcome back to theCUBE's Coverage here, live in Orlando, Florida. I'm John Furrier, host of theCUBE with Shelly Kramer. We are here at Qlik Connect. James Fisher, chief strategy officer. Qlik is here on theCUBE. James, welcome to the show. Appreciate you coming on.

>> Appreciate you. Thank you for having me.

>> So a lot of strategy, a lot of data involved. The keynote today really highlights the practical nature of how gen AI is translating into reality. Live demos, new products will be available within 30 days. Shipping real product. You guys have been in this data business is paying off now the gen AI wave is here. It's like you guys were prepared as a company dealing with the data. It's almost a dream scenario for Qlik and your customers. I mean, you got to be pretty excited about what's happening right now.

>> Yeah, absolutely. Look, you've only got to look around the event here just to see the excitement and the passion, the dialogue that's happening around data analytics and AI. And we like to talk about the fact that this is a moment that we've really been waiting for for 30 years. We made investments right at the heart of Qlik and our analytics engine, to bring the power of the human with the machine. We took that big data wave, we've taken that wave to cloud and now everything that's happening around AI and generative AI is just such a huge opportunity, but we've got to do it right. And I think we heard this morning about how we've got to optimize our processes, optimize our data, get that foundation, and then we can achieve great things.

>> It's interesting, you said 30 years, it's like the time is now. We saw a glimpse into the future of data and generative AI on keynote as it's just happening, and so the big trend is everything could be measured now, and so the role of data used to be 10 years ago during the Hadoop big data craze, ask good questions and maybe you'll get answers. Now the answers product actually highlights that everything now is built in. Generative AI will have those prompts built in. The data will have intelligence, new ways to manage data is coming out of it, a whole other set of things. You guys talk about experiences. Tell us in your view, what is the key underpinning that's driving the gen AI wave? Because these questions or prompts are going to start to be under the covers, are going to be in the data itself.

>> Exactly. If we set the technology aside for a moment and you reference that big data wave. Right? You'll remember the digital transformation wave as well. Right?

Shelly Kramer

>> Right.

>> Well, at its heart transformation is about change and it's about doing something different. And unless you can get good insights that you trust that your people trust that you can then not just inform, but compel the action, then that's really where that point of value creation comes. And I think we've realized as an industry that we've gone through that big data wave. We've moved volumes of data, but the goal posts have been shifted now. Data quality, the variety of data that's needed, structured and unstructured, it enriches the conversation and it's now about how do we capture that, the technology, we can align that. We can think about pipelines of data, we can talk about GPT and chat interfaces and conversational analytics, but fundamentally we've got to move away from a science project that isn't tied to a business outcome and really think about what use case, what problem are we solving and what tools and technologies and data we need to solve that problem.

Shelly Kramer

>> To your point about the goal posts moving, I think one of the important things, and we've been, of course, talking about digital transformation for the last decade plus. Well, I don't know that this is really all that different, this AI revolution that's happening, because the thing about a digital transformation journey is that you're never done. Okay? You're always iterating, you're always measuring, you're always tweaking. And I think that same is true. Mike Capone said this in his keynote, it's really constantly iterating, paying attention to what's happening with those goal posts, anticipating that, adapting your strategies. And so, I think that's part of what makes this such an exciting space.

>> Absolutely, and if you think about some of the challenges that have held organizations back with big data with BI, it's actually some of the same problems. It's about the availability of data. It's about trusting data, it's about good data quality. It's about education of your end users. We've talked a lot about data literacy on theCUBE in previous previous events, in previous years. AI literacy is just another facet of that data literacy challenge. So in many respects we've got the same challenges, but a bigger opportunity. And now really is the time to get our act together and solve this problem once and for all.

>> Absolutely. You've got a great tailwind with generative AI. Everyone's talking about it and that's a big part of the growth strategy. Share with us the strategy of Qlik going forward. We saw AWS keynoting in on stage from remote, Chris Cruz from Marketplace Partner Network. Obviously a big partner is the cloud players. You got on-premise data, you got cloud operations. What's the strategy? Take us through the vision of what Qlik's strategy is going forward.

>> Yeah, absolutely. Well, at its heart, it is all about helping customers work with the data. Wherever that data resides to solve real-world problems or even the world's greatest challenges with data, that's easy to say, right? The question is what do we need to do in order to enable that as a company and an organization? We think we've caught all of those waves over that last 30 years. We've invested where we want our customers to be, where we know our customers are going to be, and that's exactly what brings us to this point. We've built a platform that delivers best-in-class capabilities that can be consumed independently of one another, but of course holistically together. We overcome some of those challenges, that technical debt that goes with the modern data stack by doing that. We're focused on engaging more users in the data and analytics pipeline so they can drive more consumption and more value from the data they have. But we are focused as well on doing that in a truly open and agnostic way. We have the unique advantage of being able to support that data supply chain wherever the data resides across any cloud platform, and that creates a huge ecosystem advantage for Qlik. The landscape is going to change, the landscape is going to change around the cloud platforms that customers are using. I think Meredith from IDC mentioned it this morning, your AI strategy today, your LLM today is not going to be the same as in the future. You've got to future-proof that.

>> How has Qlik's product changed? Because data was an ingredient of the product in the past scenario. Now, data is the product. We see a lot on stage data products, data marketplaces, data is code, data is supply chain. This is now a complete game changer as data becomes the product for Qlik. How would you describe the difference between how Qlik looks at their products if data is not just an ingredient, it's the product itself.

>> Yeah, it is a great question. I've lived in this world of data for 30 years, so it is sometimes hard to see what's new and what's been around for a while. I think that fundamentally we are now asking ourselves the question, what problem a customers trying to solve? And therefore, with a data product, you think about the delivery of a laptop, for example, as a consumer of a laptop, what problem are you trying to solve? What functionality do you need in the world of data? We need to think what data is required to solve that problem? Who's going to use that data, right? So it's, I'm a product management person at heart, so it's about applying the principles of product management to the way we think about data, the way we think about the technology, and that infuses across everything we're doing in the roadmap. The two are inseparable now.

Shelly Kramer

>> Yeah.

>> One of the things you mentioned, the data is the product. These days we hear without data, you really can't have AI. That's a clear message. Everyone kind of gets that now. Are there verticals that are more set up than others? Healthcare, some regulated industries actually have great governance. Turns out they did all the heavy lifting already. So are they in a good position? Which industries do you see as prime? And they're all going to be impacted by generative AI. We see the application of generative AI impacting all industries, but are there ones that you see strategically that are more important now, that might be low-hanging fruit, healthcare for instance, oil and gas, which ones?

>> I think quite honestly, if you'd have asked me that question five years ago, I would've probably called out a bunch. Right? And you mentioned some of them. Pharma, healthcare, tech-

Shelly Kramer

>> Finance....

>> Financial services have all been kind of pioneers around the use of data and applications around it. They've made big investments in their compliance, so the compliance framework, the regulatory framework wasn't driven by data. It was driven by all of the different rules around drug development or things that came out of the result of the financial events that we've seen over the last few years. I think today the opportunity for any industry to get value from data is ubiquitous and the applications of it, I think, are use case driven. I talked to folks a couple of weeks ago from a large airline, equally the CEO of a large pharma company, and they're all very, very focused on what problems that they're trying to solve and how do they use data to address it.

>> One of the things I saw, you guys had a great research study that came out, Shelly and I were talking about on our intro segment. Investment levels in gen AI aren't yet there. What are you seeing in the marketplace? I know Shelly, you have some stats there. Obviously that we see the low hanging fruit in certain applications, but still the investment in gen AI is not yet there, levels we're seeing. Are you seeing the same thing? What are some of the things you guys have found in the marketplace?

>> Yeah, I think the big stat we talked about this morning was around the volume of unstructured data and the opportunity that we think organizations have to create more value from that. It takes up a large volume of the data that they have, but we're not yet getting the true value from it. I think that what's interesting is the investment in data and analytics technologies, whether you look at IDC data, Gartner data, or any of the other commentators that are out there, investments in data analytics have been a priority for CIOs and CDOs for a number of time. The pandemic, the financial crisis is really just an opportunity to calibrate around data. You make the investments, then you're able to accelerate into a growth phase much more quickly as economic or environmental conditions change. So I think the investment levels have been there. I think we're now moving beyond science projects to discrete use cases, and that is, frankly, creating a rising tide for all investments across data and analytics as you see here today.

Shelly Kramer

>> I think that though, there's so much to get your arms around it. I'm not telling you anything you don't know, but when we talk about data and the data that's needed for Gen AI models, you've got customer data, you've got your internal proprietary data, you've got open source public data, you've got licensed data from third-party providers. And Meredith Whalen from IDC, part of what she touched on was your ability to navigate through this is based on your road mapping capabilities and how you devote your time and energy to that. And also, one of the things that I say often as it relates to digital transformation is that there was a time when strategists used to be able to develop a year-long strategy or a five-year strategy, or whatever. We're not doing that now. I think we're developing strategies today that we have to re-evaluate in six months or in 18 months or whatever. Is that kind of what you are seeing as well on the strategy front?

>> Well, I'm responsible for Qlik Strategy and developing the plan that we have in place. So I absolutely recognize the challenges that are there. Look, it's all about getting the right inputs, as you say. There's a whole thesis around this idea of diversity and we think about the value that diversity brings in our personal lives, in our family, in our education systems. If you apply diversity, the notion of data diversity is a good thing. You get new points of view, new experiences, new learning opportunities. That's the value of diversity in our lives, and that's the value of diversity in data. So the more you can consume those wide variety of sources, bring them together, ask questions across that broad base, then I think the better quality insight you're going to get and the more confidence you can have in not just that near-term view, but that longer-term set of assumptions as well.

>> James, you bring up a point that we were just talking on the last segment before you came on. We were talking about the power of analytics in sports. We had two Martins on, Martin from Qlik and Martin who's managing the hockey team, one of your customers. And the thing that jumped out was situational awareness, executing the individual. You bring up performance strategy that you're involved in. As Qlik looks beyond the data scientists and the CDO and the CISO, it's basically every application's going to have generative AI in it. So at the end of the day, as you guys see your TAM expanding beyond just data analytics, you are in the data business now, and so I'm sure the TAM is going to be expanded. So two questions. What's that TAM look like for you guys? And two, how are you guys using data for your own performance? Because productivity, you guys mentioned on stage will be massive. How are you guys doing and how are your customers starting to instrument their businesses to get that kind of performance edge, whether it's personnel makeup or strategy instrumentation? Take us through the TAM and then how you guys are using the data, and how you see that evolving.

>> Yeah, absolutely. So if you look at all of the different segments in which we operate from data integration, data management, catalog governance, data science, analytics, embedded analytics and automation, that's roughly a $67 billion TAM that's growing at about 16% CAGA over the next five years. What's really interesting is you see all these reports around investments in discrete gen AI technologies, that's great, but the wave of investment in JAI has lifted the investments in data and analytics. So that tam, that growth rate has increased by 24% actually over the last 18 months. So there's a huge opportunity for us to deliver solutions that create value for customers in terms of that market opportunity that is out there.

>> So you see that growth rate changing as a precursor to the app side growing because you got to get your data plumbing and foundation set up pretty much before you can get into the ... Is that the reasoning?

>> Yeah, I think it's a good assumption. I think as well that it's really difficult now I think to do a data analytics project without AI. But I also think we now come into the realization as we move into real world use cases that you can't really do an analytic AI or gen AI project without data and analytics. So there's a rising tide for all opportunities.

Shelly Kramer

>> We see that gen AI has stolen budget across the tech stack from many, many places where it is not stealing budget is from the data and analytics side, because that's foundational. So yeah, good for you.

>> Exactly. And I think one of the pieces of advice we would give to customers is don't look at AI and gen AI as an island. Think about your data infrastructure. How does your data infrastructure support your good, old-fashioned, mode one, pixel-perfect printed report. How does that same data infrastructure support guided analytics, self-service BI, predictive analytics? And that's, I think, part of the vision that we built is a singular platform that can support all of those use cases. Now extending into Gen AI as well.

>> It's clear that gen AI is the application, the data and the infrastructure capabilities drive that piece. Back to the sports question, I want to follow up on that. How are you guys instrumenting within Qlik or have you guys gotten there yet? Or is we still early days of how companies are re-instrumenting their business? Because we can get that level of productivity down to the athlete level, businesses will probably start thinking about, okay, maybe the work at home is working or not working or maybe certain kind of personnel makeup. What do we automate? What do we double down on the human loop side? So are you guys starting to see forward progress on instrumenting the business for situational awareness on execution, business execution?

>> Yeah, absolutely. In fact, I'm looking-forward this afternoon to a great discussion that's going to be led by one of our customers around the idea of the data customer 360 or the data Consumer 360, and it's really all about understanding how users in that organization are using data, the type of decision they're making, the value that they're creating from it. The other side of it is just how we run our platform, how we run our cloud. We obviously don't have access to our customer's data. That's their data. We don't touch that. But we do understand how they use our product, where the data resides, where the data comes from, how it's flowing, who's using it, which capabilities they're using in our product. So that wealth of telemetry that we have is really, really key to how we optimize our processes, how we optimize our platform, but also how we guide the type of innovation that you saw come out today based upon that real-world usage.

>> James, that's awesome. Giving them a chance to be successful with the data-driven approach, get data culture. Final question to wrap up next year, Qlik Connect. What do you think is going to happen? What if you do your job and the strategy clicks, okay, what's going to happen next year? What are we going to be talking about next year?

>> Yeah, so next year I hope to see on stage some real-world use cases. We heard some great customers today that have built great data pipelines, that have got great analytics technologies and they're now starting that integration into AI platforms. Next year, what I want to see is the value that came from that end-to-end pipeline. The value that came from Qlik talent cloud, the value that came from Qlik answers. And of course, as you heard this morning, that roadmap is not standing still. We've got an incredible vision for where we want to take that. So I'm anticipating at the back of my mind, I can't tell you now, some of the product announcements that I think we'll be able to make next year, and that's just going to be equally as exciting as this morning.

>> You like the trajectory you're on right now? You like the trajectory of the business right now?

>> Absolutely. I think I feel really confident in what's happening in the market. I feel really confident with the great engagement we get from our customers here, but we can't rest on our laurels. Just as we've responded to every one of those industry changes over the last 30 years. We've got to anticipate what's going to happen, not just in the next 30 years, but platforms are changing, technology's changing. AI is evolving so rapidly, we've got to stay on our toes.

>> You guys are hitting all the right notes. I got to say, I'm impressed. End-to-end workloads, data, new intellectual property. Making that automated with generative AI is going to be a nice dream scenario for you guys. Thanks for coming on theCUBE.

>> Thank you.

>> All right. James Fisher, he's the chief strategy officer at Qlik. I'm John Furrier, host of theCUBE, with Shelly Kramer. We'll be right back with more live coverage here at Qlik Connect 24 after the short break.

James Fisher | Qlik

>> Appreciate you. Thank you for having me.

>> Exactly. If we set the technology aside for a moment and you reference that big data wave. Right? You'll remember the digital transformation wave as well. Right?

Shelly Kramer

>> Right.

Shelly Kramer

>> Yeah.

>> I think quite honestly, if you'd have asked me that question five years ago, I would've probably called out a bunch. Right? And you mentioned some of them. Pharma, healthcare, tech-

Shelly Kramer

>> Finance....

Shelly Kramer

>> You like the trajectory you're on right now? You like the trajectory of the business right now?

>> Thank you.