Welcome back, everyone, to theCUBE's live coverage of Qlik Connect 2025 here in Orlando, Florida. I'm John Furrier. Bob Laliberte from theCUBE Research. Got a great lineup. Premier guests coming on theCUBE. Mike Capone, CEO of Qlik. Thanks for coming on. Good to see you again. Harveer Singh, Chief Data Officer, Truist, fast-growing financial institution, doing great. A lot of data. Thanks for coming on.
Good to see you. Both CUBE alumnis, welcome back.
Thank you. Good to be here.
Well, I want to get into the customer situation as you guys are on this massive expansion and journey. Mike, we'll start with you. Keynote, it was phenomenal. Great review. I love some of the narrative, trust, precision, decision engine, dashboards, all this great stuff. And then the open data lake with the managed service for Iceberg. Great call there. And then you got the cloud, you got answers kind of coming together product-wise. Strategy from last year is executing. One, how do you feel and what's your takeaway? What was the response? What was some of the feedback?
Yeah, well, I mean you're sitting here, right? So you hear it behind you. I feel great. I feel great. This is a journey we've been on for a number of years. A number of years ago we set out to build this end-to-end platform. We saw this AI thing coming, right? We saw this wave of AI, but we knew the key to it was going to be data. Data, data quality, data governance, trust. We talk about trust a lot and so 14 acquisitions later, lots of organic R&D, we built this out and all the innovation that you're talking about was always in our head is like, this is where we got to get to. And not only we got there, but I think we even got a little bit ahead. We talk about some of the open data lake work that we're doing, structuring unstructured data together. So to answer your question directly, I feel really great. I feel great.
Well, we feel great for you watching the story, but I think it's still undertold because a lot of people might not know, Qlik has got a lot of jewels in the treasure chest. You've been doing the in-memory thing, you had this gen AI before gen AI kind of hit the scene. You guys know data, you know dashboards, so you got a lot of value and I think it was demonstrated. So I think we'll do our best to get out. Harveer, I want to get your take because you know what I'm talking about here. They got the tools.
So every time they acquire something, I am like, okay, now it's their problem. One less thing for me to worry about. I'm a big believer of financial services like us. We are more assemblers than builders. We want to build solutions for our clients and customers. We want to focus on technology, but we're not Amazon, we're not Google. We want to bring in the best of these tools together. So once this starts happening, the number of moving parts reduces, which is what these guys are doing. It helps us saying one less thing to worry about, one less failure to worry about, one less sleepless night for me and my production support team. It helps us a lot. Putting everything end to end, looking at things end to end, it helps us a lot. So that keynote signifies the importance of less moving parts. Let's bring it into one place. Let's act on it when it's there and let's make sure it works end to end. So kudos to the team, kudos to you who make that happen. And again, like you said, the response is phenomenal. I go to other conferences as well. I'm pretty blown away by how many people were there at the keynote. Every single seat was taken. Very impressive, amazed, great to be a partner and great to use the products and services as well.
That's an authentic response. Mike, this speaks to the value of the word that's been kicked around a lot. I think it would encapsulate the show is, 'unlock,' is a word I'm hearing a lot. Unlocking value. It's all about assembling solutions, not being the R&D machine learning, $5 million salary DeepMind engineer. They're building, they have machine learning, good people. But I mean the point is these assembling, your customers want it to work.
That's right. The domain expertise and the business logic is in the enterprises.
That's right. So you guys are creating that unlocking. What's your take on that? Because this unlocking value is money or productivity or some task.
Yeah. Well, I mean first, a lot of our innovation is actually customer-led. We talked about this a lot today. And in fact, I remember I was down with Tom Mazzaffaro from Truist who actually mentioned, 'Hey, you guys should be thinking about this Iceberg stuff.' And we were, but it was great to get that validation. And let's talk about unlocking, but we work on outcomes. So what outcome are we trying to drive? In a lot of cases, 'Hey, we want to do AI, but it's getting really expensive. Can we afford to do AI at this rate? My bills are getting really big.' So it really is understanding the customer's perspective on what outcome they're trying to drive and then the capabilities of technology are just a means to an end, and then wrapping services and other things around that in a true partnership fashion is really how you unlock value for our customers.
And so along those lines, Harveer, you also had a long partnership with Qlik. Can you talk about how they've helped you execute faster, how they've helped you move to greater business benefits? You already talked about the personal benefits that you have that you actually get to go home at the end of the night. I'm wondering if you could share also maybe some of the other ones for your business as well.
So interestingly, I have sat on both sides of the equation. I've sat on the consulting side. I used to work for Deloitte and Accenture's of the world where I actually started a lot of my journey with Talend, to be honest, looking at Talend and building that. During the first phase of Hadoops and stuff where people really didn't know how to use that, Talend came in and said, we'll tell you how to use Hadoop the right way. And really taking a different lens on so-called ETL. Don't make it rocket science. It's data, transformation of data, it should be as simple. If you can see it, you can do it, right? That was the motto. And I think they've kept on to that as well. And Qlik and Talend coming together was even more interesting because honestly, the race for AI, the winner is the one that will have access to the data the fastest. It's not about having the data, it's having access to the data. How soon can you move the data close to the model? How soon can you execute it? A lot of people will have data, but how soon can you act on it is going to be that winner. I often say this from our side, banking institutions, everyone's running a customer churn model. Everyone's running a customer acquisition model. It's not about that. It is how soon can you take that data and act upon it is going to be the ultimate winner. So for a lesson learned for a lot of companies is bring the data closer to that. How soon can you move it? Because when data moves, money moves.
And you guys are in growth mode right now of the company.
Take us through your thinking around how you're looking at the data. Obviously the one-stop shop that Qlik has a lot of, again, tools in the platform.
So for us, there are two sides of this equation. One is obviously modernizing our tech stack. Truist as a bank came together about three to five years ago, two smaller regional banks merged to become, they call it the super regional. We are very proud of our heritage. With any new mergers, there are issues that multiple technologies exist. So we took a hard stance in the last few years where we said if either we fix the issues that we currently have, and there were a lot, or we go modernize where we have to spend less time working on the issues, more time servicing our clients and customers. So for us it was a speed at which we could use tools like Talend and Qlik to modernize the stack, then worry about investing in modern-legacy technology. Hadoop is still active and alive, but I'm almost calling it modern-legacy. But do we want to stay with modern-legacy or do we want to be modern-modern? Because again, by the time we are done, something else will come out. So it's the speed at which we want to execute. It was the key. If we tried to build this ourselves, it'll take three years by the time something else will come in. This is where I think Qlik and Talend came in together and say, 'You guys do this fast. We are going to support you with whatever you guys need.' So it's been a great 9 to 12 months and we've already made things live in production. It's running in Snowflake today. Six months ago it wasn't there. So having a major turnaround like this in a financial institution with all the risk and the controls, it's almost unheard of. I'm super proud of that as well and super proud of the partnership that these guys brought to the table.
That's a huge accomplishment, by the way. I just want to amplify that. The resilience bar that you have to hurdle over is massive. When just putting it in perspective, what's your reaction to that? It's a great testimonial. Talk about how that comes about. Is it the platform? What is the main driver on the Qlik side that's enabling him to move faster and clear those hurdles and get stuff into production?
Yeah, look, I think it is not having to be a construction worker and not having to actually cobble together five or six different tools to build an AI data pipeline. I mean, they really have doubled down on Qlik and Snowflake. We work great with Snowflake to build these data pipelines, land it in Snowflake and run their business. And again, I wore two hats too, right? So I was a CIO, I was a CTO for many, many years of ADP and I was the one buying all these tools and it was super frustrating to have to be a systems integrator. And so having trusted partners who I knew were interested in my outcome, not just in selling me software at the end of the quarter, but actually showing what's possible. And I know what Tom says is, 'Man, you guys are all so flexible in how you contract with us. Our success is your success, all those things.' But it makes me really proud because this is what we've gone for. We've gone for success. Did your project succeed the way you wanted it to? That's our measure of success, not how many dollars we get.
One of the trends I want to get your both reaction to is the platformization, I call this, at RSA mainly in security, but security is pretty much a good proxy for the AI market. There's a lot of data involved in security. Platforms can be the best of breed point solutions that were once standalone companies now are featured. You're starting to see, I'm overgeneralizing, but the point is a platform has to be highly robust and work. Do you see platforms becoming key here? And is that what you're thinking is?
Platform is a key, and again, the more you have means more moving parts. My goal is to eliminate as many moving parts as possible, keep a simplified architecture because simplified architecture is easy to maintain, more cost-effective, total cost of ownership. And if the data doesn't move that much, it means that you're relatively able to access it and democratize it much faster. Now, a term that is used a lot recently or mostly is I want to be real-time. The question is why? I want to be batched. The question is why? Why can't you just be just in time for you to act upon certain things? Take out that notion of real-time and batch, just in time is the key to go. And that is the platform. Platformization is behind the scene as long as the data is available just in time, the business users, the application owners, it should not matter how it got there at the end of the day.
