Qlik Connect 2024 | Mary Kern | Qlik & Nick Parrotta | HARMAN

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Mary Kern

Vice President, Analytics Product Marketing

Qlik

Nick Parrotta

President, Digital Transformation Solutions & CDIO

HARMAN International

Harman builds on Qlik for advanced AI-driven automation, data governance and insight generation

In today’s world where data rules decision-making processes, the future will be powered by analytics and predictive AI operations.Harman International’s partnership with QlikTech International AB exemplifies this, demonstrating the potent combination of advanced analytics and gen AI to revolutionize business processes.Harman’s Nick Parrotta and Qlik’s Mary Kern discuss advanced analytics and gen AI.“We’re trying to stay ahead very rapidly, and the partnership with Qlik has helped us do that,” said Nick Parrotta (pictured, right), president of digital transformation solutions and chief digital and information officer of Harman. “Fundamentally, we have built out some robust use cases

play_circle_outline Unveiling the Power of Analytics and AI: Introducing Qlik Answers for Generating Insights from Unstructured Data

play_circle_outline The Crucial Role of Domain Expertise and Human-Centric Solutions in Enhancing Productivity, Effectiveness, and Decision-Making through AI

play_circle_outline How embedding AI features revolutionizes culture and company dynamics: A comprehensive analysis

play_circle_outline Qlik's readiness and positioning for the AI revolution

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Mary Kern | Qlik & Nick Parrotta | HARMAN

Mary Kern

Vice President, Analytics Product Marketing Qlik

Nick Parrotta

President, Digital Transformation Solutions & CDIO HARMAN International

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>> Good afternoon Qlik community and welcome back to wonderful Orlando, Florida. We're here at Qlik Connect, power-packed coverage going to booth. We've been outside, we've been on the desk. I'm Savannah Peterson, joined by my fabulous co-host and CUBE co-founder John Furrier. John, you just had a power packed little thing.

>> We had the Qlik's AI Council. The brain power was off the charts. They're really thinking about the future from a pragmatic perspective, but also not throwing away the baby with the bath water as they say, and thinking about innovation. You got to have careful progress, at the same time, we want to go as fast as we can, right? So love scaling AI. That's a big conversation here is how to set the foundations. Just been a great day and our next segment was going to be all about that foundation and then scaling up AI for empowering companies.

>> I know. Speaking of scaling AI, let's welcome Mary and Nick to the stage. Hello. Thank you both for being here.

>> Thank you.

>> Great to be here.

>> You are just oozing smiles and energy right now, Nick, I got to tell you. How's your day going?

>> How can you not? I mean, what an amazing way to start. The keynote was fantastic, the demos, to see come from paper to product so quickly. And then I was just at the executive advisory board where I serve on the board and to hear people around the table around the amazing things that they're doing with analytics and AI, how could you not be energized?

>> Love to hear it. Mary, speaking of, keynote talked a lot about analytics. You have a lot to do with that. Can you give us a little recap?

>> Sure. So I mean, really a fantastic event for us and experience. So from an analytics perspective, we're really focused on enabling the 80% of business users in an organization to be able to make better decisions through analytics and AI. So it's really about ease of use, making all of our tools easier to use, easier to get the insights when you need them. So today, we had a lot of enhancements that we announced regarding augmenting our core analytics solutions with AI automation. We're doing a lot with natural language generation right now. We're doing that on the auto ML side as well in terms of continuing to automate and optimize model selections for predictive AI. And of course a really big announcement, which we're super excited about is Qlik Answers, which is actually using generative AI to get answers from enterprises' unstructured content, which is actually the majority of content that enterprises have. So we're super excited.

>> I've just been out talking to a lot of your customers and doing it a few segments away from the news desk. Everyone brought up Qlik Answers as something they were super excited about, so yeah.

>> Yeah. It's been really interesting. I've just been with Qlik for about a year, so this is my first time introducing a new product with Qlik, and so it's been really great, we've rapidly have put this together, really accelerated that with our acquisition of Kyndi in January and we've been... I guess that's a dirty little seeker, right? We've been briefing analysts and influencers on Qlik Answers for a while now, and the feedback has been really tremendous. The potential is definitely there, so we're really excited to bring that to market, which will be next month. So yeah.

>> It really highlights the changing of the guard, if you will, from the old AI machine learning to the new generative AI, because back in the day, it was ask good questions. Now, you have answers. So it really kind of flips the script. This is the era we're in. Can you guys share what else is getting flipped upside down? What else is happening? Obviously speed. You mentioned speed of solutions. What else is going on that's different now?

>> What's different? Well, I was going to say on the generative piece, I'd love to hear from Nick because you guys were actually ahead of the game right, when generative AI was coming out last year... And you guys have been a fantastic partner to us. We've worked together for a long time in analytics and AI. And when it came out, you really thought this, we've got so much data with Qlik, this is a great opportunity for us. So it's a little bit about what you do.

>> Yeah, it's amazing. You could be ahead one day and then behind the next. So we're trying to stay ahead very rapidly and the partnership with Qlik has really helped us do that. I think fundamentally, we really have built out some robust use cases. We've been a Qlik partner for a long time, so getting the data right was always the first foundational element. So really having great governance and master data, having an organization that's trained and talented, both from the business side as well as the digital side is really important. So the combination of really focusing on data, training the users in the right way, and then democratizing and providing a self-serve model through our platform was great. So everyone in the company, all 30,000 employees who are building these personalized connected experiences for the enterprise and for our products, they have it at the touch of their fingertips. So they were already accustomed to the power of Qlik and the visualization and all of the analytics that helped us get through the pandemic, helped us get through the supply chain crisis. Every part of the enterprise, whether it's sales, finance, marketing, supply chain, has that DNA in them around how do I use this data to deliver better insights? Now, what we've done with our digital transformation solutions business, which is our third business, we have an automotive business and a consumer business, but we also have a global tech services business, we've built capabilities around algorithms and machine learning to embed into the analytics dashboards this prompt engineering capability where you can ask the AI to tell you what they see in addition to. So it's really an augmentation that we love and it drives even greater insights for those who really understand the data, really have the domain knowledge, having that power of not only the static dashboards, but now the interaction around augmented AI is fantastic for us.

>> How has that changed the culture and the company? Because one of the themes that's coming out of the generative AI use cases, it frees up more time to do other things, creativity. So how has that changed any kind of, well, I say entrepreneurial spirit, product experimentation, what net effect came out of embedding some of the AI features into the product?

>> Yeah, I think first and foremost, it put us in a position where we felt like we can manage AI governance and put the guardrails and guidelines around AI in terms of protecting our intellectual property or data privacy without compromising speed of innovation. So first and foremost, we wanted to make sure that the versions that we're using are in the enterprise, they're using our data, it's protected, it's controlled. We help manage the hallucinations and biases. So first and foremost is everyone understands the policies and the procedures, and we put guardrails and guidelines around the use of it, so people don't feel like we're using it as a barrier, but helping them set it up correctly. The second is certainly we have deep domain knowledge around AI and analytics, we have data scientists, data modelers, data engineers. Our chief product officer in DTS is an absolute digital thought leader. So having access to those people to help you set up the algorithms and the tuning and the explainability of the models is really powerful. So you don't feel like you're ever slowing down. You have the analytics, you have the experts, you have training. So we have very robust curriculums and training online, videos, sandboxes that you can play around with. So it helps manage that demand, that insatiable demand. Lastly, it's managing and prioritizing the use cases. So what we do is everything is intended with a very clear outcome, making sure that we have, for the most part, starting out with low level of effort, high impact use cases that are embedded in these analytics. How do we improve our marketing effectiveness on campaigns? How do we improve our win rates on the pipeline of things that we're quoting? How do we improve our supply chain forecasting? It's those things that we've already used analytics for that are really helping us empower and take that to the next level with GenAI.

>> I was going to say, just to quickly add onto that, because you had said something about the culture change, and I know that you guys are doing some predictive AI, and I mean it's really not just about making your employees and your organizations more productive, but more effective and increasing your performance, and it's such a game changer. I'm in marketing myself, so I mean talk about a data rich function and it's really hard to still get the answers you need to make the decisions that you need to make to go forward. And so being able to change that from hunting, getting an insight and guessing what you should do to actually getting a real prediction of what's likely going to happen, it just really not only improves your productivity, but your effectiveness, your ability to make better decisions. And I think from a performance perspective, it's really fulfilling as an individual as well as an organization to drive your performance.

>> Your confidence too, yeah.

>> So it really does change a lot in terms of how the company operates.

>> No question. I think anyone that thinks that you're going to take the human out of the loop is really not looking at it the right way. I mean, we are building human-centric solutions that transform and improves lives. You can't take the human out of that equation. You have to have the right domain expertise that help making the right decisions. And people that are living and working in the data all day long, they'll know when the data is wrong, they'll know when the algorithm is wrong. That validation authentication is really important. So we're building it with the human at the center of the loop and it's helping that person do their jobs better, not replacing them. And I think that is really the win-win equation.

>> I think that's the key point. I mean, Savannah and I were at another event this past year where it was about chatbots, the old, "Oh, chatbots," no. Now, agents are now coming in. Agent technology is essentially the automation of tasks for humans, not to replace humans. So we're moving to the world of, okay, I see the chatbot-like thing. Okay, now we got intelligence coming in. If your data is ready, you are in good position to leverage that. And that's your point, Mary. We've heard that at the AI council, "Car goes faster with brake."

>> It's very interesting because I think we were just talking about how generative AI really did captivate our imaginations and from an enterprise perspective, how that can help us make better decisions, how that can help us be more productive and effective. But to implement those AI or analytics models, those are all trained with data and they all use data pretty voraciously to get those answers. And so it's also driven a resurgence into the fact like, do I have a good data foundation that I can use to build all these things on? So I think it's a really important inflection point that organizations are really looking at, do they have, and with AI, it's got to be trusted data. There's a lot of risk when it comes to analytics and AI, and so it's got to be trusted. You got to know where it comes from. You want to know that the quality controls are in place and you want to know that it's being governed, not just anybody is touching it, the few people that you trust to do it. So a really important inflection point on data.

>> It absolutely is. I liked your analogy a second ago, you said it's like a shot in the arm for artificial intelligence. Artificial intelligence, ML's been around for 30 years plus more than that, and here we are all of a sudden all talking about it. Was Qlik anticipating this moment?

>> Okay, I've only been here in the last year, but I can say I joined Qlik because I am so excited where the analytics market is, and I have been in analytics, data science, AI, and data for more than 15 years, so a little bit longer than I care to admit, but there's been so many challenges with it from the data perspective, getting access to the data, to the right data, it's got to be of high quality, it's got to be timely that you're running your analytics on it. And then if you do run your analytics on it, that data literacy, that is a beautiful chart. What is it telling me? What am I supposed to do? And so I joined Qlik because I looked at their portfolio, I'm like, they've got it all. They've got it all. They really are in a great position to solve all those challenges, and that's probably something you'd hear from a tech marketer to say, "I joined a company because I thought they had really good tech," but if you look over the course-

>> They have good loyal customers too....

>> they really were building for it with all the automation, the natural language, the building up on the data side. So I think, yeah, it's nicely positioned.

>> It is nicely positioned. John, you've just mentioned the customer retention rate. I learned in one of my interviews earlier today, 93% customer retention. That is insane. I'm sorry, that is more than kids in a candy bar. That is a very, very, very impressive.

>> I think part of it too is in having been a customer for a long time.

>> I was literally just going to ask you, what it's like in your shoes? Go for it.

>> I was just about to say, Mike has been in my chair. He was a CIO for a large company, knew the importance of data, and when you have a CEO of a company who really understands it from the customer's point of view, he's sat in that chair to look through those lenses, it makes a huge difference in terms of what the roadmap and where the company is going. And you can tell he's been there before. And so we say a lot in our organization, you can't do the brilliant without the basics, and getting your foundational models right, getting your data governance right, getting your integration aggregation, visualization all right, if you really want to take advantage of the power of applied and generative AI, you have to have all that fundamental foundational elements in place. The days where you'd generate a report, no one liked the report and then they said, "Well just do another report." They still didn't like the answers that they got. That's fundamentally the same way today, and I think you have to be really careful also in these public large language models as well to make sure that you have the right guardrails and guidelines. The tooting is really important. The observability is really important. The domain knowledge is important. The explainability and how it works is really important. So we worry of course about data poisoning as well. It's real. So understanding that data lineage. And this is why I like the Qlik product a lot too, the data lineage is incredibly important. It's going to continue to be really important. Whether you're using publicly large language models or small language models in the private enterprise, we're always thinking about that. And the last thing we want to do is build an algorithm like our Health GPT app, which we've launched for healthcare and life sciences. You want that patient provider interaction and diagnosis. You want accuracy, you want to make sure that it's right.

>> You need it.

>> Having domain knowledge-

>> ....

>> explainability is critical. So you don't want to screw those kinds of things up.

>> Yeah, exactly.

>> So again, you really have to take it seriously and you've got to have the right domain knowledge and the right technology and techniques in place.

>> Nick, you bring up a good point about... In fact, we called it out on our intro segment about the first conference we've been to, as theCUBE go to all the conferences, I've never heard on stage in the main keynote data supply chain as a concept on stage in mainstream. That means that's not just lineage, that's security. That's looking at the explainability of what's coming to be most important asset, data is now the new product. So absolutely, great point. Now, Mary, you brought up a good point about the things coming together for Qlik. That's kind of the way the world is right now. You have to get your act together on the data. Otherwise, you're going to go into the GenAI world unprepared for hallucinations, which is just gaps in data. So Nick, I got to ask you the question. In addition to the efficiencies that are going to come with automation and AI, lower cost, maybe help the human be more productive, there's a business benefit of top line revenue that comes with... Now, you got a double-edged sword. So talk about coming together. You got efficiencies and company gain, business benefit, revenue side, so you've got top end cost, revenue and cost being impacted. How do you guys talk about that internally? You mentioned the healthcare app. User experience has got to be perfect.

>> Yeah, it really does. I think part of it for us is starting with the basics. When we look at our commerce data, we've got credible products online, our gbl.com platform, the revenue has grown considerably and a lot of it is because of our intelligence around the data, understanding the customer data, understanding what the buying patterns are, what the top-selling SKUs are, what the channel inventory, the average days on hand are. Customers want available product to be able to be delivered to them tomorrow. So being able to have that, personalizing the product, being able to use AI to design the product on the fly and personalize it, all of that's table stakes. Having a VPA that will provide recommended buying. Now, we're at the point where you can take a picture of your ear and it'll give a recommendation in terms of what earbud you want to use because it'll fit your ear, that image analysis and generative AI, again, another example of personal-

>> That's a fun use case.

>> That is.

>> Fun use case. I mean, we're thinking of constantly these use cases around just how do you make the personalized buying experience more, I think, unique, more immersive. Again, when you drive in our vehicles, with our software defined vehicle, all of the advanced software and generative AI and personalization in the vehicle is pretty remarkable. When you look at our smart audio technology and again, personalized experiences around what you like to listen to and how that may affect your mood or how the mood affects what you pick in terms of your playlist, et cetera, it's so many really cool things that are coming out and happening across the entire company and how that connected experience... And again, we've got 30,000 people connected experiences in the car, in the home, on the go and in the workplace, providing that ubiquitous, generative and personalized experience across all of that ecosystem is going to be really exciting.

>> A lot of unstructured data coming in for you guys too.

>> Absolutely.

>> So share with the audience watching, people have to lean into this wave. Otherwise, it's pretty clear there's going to be two sides of history here. You're either in the wrong side of history or the right side of history, generative, it's a no-brainer. Everyone pretty much gets it. The playbook. How do you get the prerequisites? Can you share your success story, what you've done to gather these benefits, someone watching now, they know they're in health, they're taking baby steps, they're putting their toe on the water, get their little floaty on, they're in the low end of the pool.

>> I think it's early days. First and foremost, let me just say that we think about four fundamental aspects of success versus the governance model. You've got to get the governance model right, and that is how you're going to manage the data privacy and intellectual property. Get that governance right, manage the demand coming in. How do you control the costs? We remember the days of cloud where you see significant costs, lots of instances getting spun up and a lot of vulnerabilities. In the products, we don't want to make that same mistake. So putting the guardrails and guidelines around cost optimization, demand optimization, and then prioritization of use cases. What's the small low level effort, high impact stuff that you can start doing proof of concepts for it and kind of testing the water? So how do you make sure that those are the use cases that'll bring you the best benefit at the lowest cost and start getting you acclimated to it. The third is picking the right partnerships. You've got to pick technology partners like Qlik who understand the fundamental points of view from the customer, what other customers are gleaning value from those use cases and the product usage, and then being able to apply that. I certainly learn a lot from everybody around me. The only reason why I've been in my role is because I've just been smart enough to surround myself with great people. So the partnerships matter. So the partnerships in the tech stack, understanding that the tech stack will continue to evolve. Every part of the tech stack's going to evolve. And so being able to build out small proof of concepts, try out different products, making a determination on whether you want to build or buy. It's still early days to figure that out.

>> Hit that winning culture, get the wins, the quick wins, the little wins.

>> Absolutely.

>> Get on base, little small ball as we say in baseball, get the little singles and then hit the home runs.

>> And you expect failure at the end of the day. Right now, you're going to learn what success looks like and what failure is. It's very early days. Personally, we're doing things that are big and small alike, but at the end of the day, it's very early days. And having that entrepreneurial spirit with the expectations that some things aren't going to always go right, but being able to contain and control and manage through that is really important.

>> All about that curiosity. Yeah.

>> Oh, I was just going to say, and what I loved about that, because I agree, it's not whether you're going to adopt AI, it's whether or not you can do it effectively. And so we do see a lot of companies that are being cautious around this. They want to make sure that they are really thinking through the governance, the security implications or thinking through the data implications. But coming from analytics and AI, near and dear to my heart, what I loved what you said is really focused on the business. I think a lot of people are like, "How can we adopt this new technology? How can we have an AI strategy?" And it's really about how can you have a business strategy that's enabled by AI, and flipping it. And everything you talk to from a use case perspective is very much business driven in terms of how it's going to advance your business, your customer success, and using that as a point of innovation of how can we take those experiences even further to delight our customers, I think is really excellent.

>> Great. And I think one of the things that are unique about my role as a CDIO, I get to try it and see what works and what doesn't work as a reference point with Harman and Samsung, but then based on that, we're able to go do that for other customers with Qlik as well. So we've served industrial healthcare communication software companies on their journey around analytics and AI based on our knowledge and experience that we've applied in-house. And so I have my cake and can eat it too. I'm the CDIO, but also the president of the P&L. And so it's a lot of fun. People can learn from our success and our failures, and that makes my life a lot more interesting and fun.

>> I love that attitude and I love that transparency, and I love that I can actually say, I think with confidence, you're the first person on the stage who said they can have their cake and eat it too.

>> Absolutely.

>> So congratulations on that.

>> Why not?

>> That is absolutely lovely. Nick. Mary, this has been a thrilling conversation. Thank you both.

>> Thank you so much.

>> Thank you so much.

>> Thank you so much for having us.

>> We just settled it and started learning here. I felt John and I both just kind of taken all you had to say, and that was exciting. And scaling AI on the top of everybody's minds right now. Thank all of you for tuning in for this absolutely fascinating segment here on Scaling AI. John and I are at Qlik Connect. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.

Mary Kern | Qlik & Nick Parrotta | HARMAN

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>> Good afternoon Qlik community and welcome back to wonderful Orlando, Florida. We're here at Qlik Connect, power-packed coverage going to booth. We've been outside, we've been on the desk. I'm Savannah Peterson, joined by my fabulous co-host and CUBE co-founder John Furrier. John, you just had a power packed little thing.

>> We had the Qlik's AI Council. The brain power was off the charts. They're really thinking about the future from a pragmatic perspective, but also not throwing away the baby with the bath water as they say, and thinking about innovation. You got to have careful progress, at the same time, we want to go as fast as we can, right? So love scaling AI. That's a big conversation here is how to set the foundations. Just been a great day and our next segment was going to be all about that foundation and then scaling up AI for empowering companies.

>> I know. Speaking of scaling AI, let's welcome Mary and Nick to the stage. Hello. Thank you both for being here.

>> Thank you.

>> Great to be here.

>> You are just oozing smiles and energy right now, Nick, I got to tell you. How's your day going?

>> How can you not? I mean, what an amazing way to start. The keynote was fantastic, the demos, to see come from paper to product so quickly. And then I was just at the executive advisory board where I serve on the board and to hear people around the table around the amazing things that they're doing with analytics and AI, how could you not be energized?

>> Love to hear it. Mary, speaking of, keynote talked a lot about analytics. You have a lot to do with that. Can you give us a little recap?

>> Sure. So I mean, really a fantastic event for us and experience. So from an analytics perspective, we're really focused on enabling the 80% of business users in an organization to be able to make better decisions through analytics and AI. So it's really about ease of use, making all of our tools easier to use, easier to get the insights when you need them. So today, we had a lot of enhancements that we announced regarding augmenting our core analytics solutions with AI automation. We're doing a lot with natural language generation right now. We're doing that on the auto ML side as well in terms of continuing to automate and optimize model selections for predictive AI. And of course a really big announcement, which we're super excited about is Qlik Answers, which is actually using generative AI to get answers from enterprises' unstructured content, which is actually the majority of content that enterprises have. So we're super excited.

>> I've just been out talking to a lot of your customers and doing it a few segments away from the news desk. Everyone brought up Qlik Answers as something they were super excited about, so yeah.

>> Yeah. It's been really interesting. I've just been with Qlik for about a year, so this is my first time introducing a new product with Qlik, and so it's been really great, we've rapidly have put this together, really accelerated that with our acquisition of Kyndi in January and we've been... I guess that's a dirty little seeker, right? We've been briefing analysts and influencers on Qlik Answers for a while now, and the feedback has been really tremendous. The potential is definitely there, so we're really excited to bring that to market, which will be next month. So yeah.

>> It really highlights the changing of the guard, if you will, from the old AI machine learning to the new generative AI, because back in the day, it was ask good questions. Now, you have answers. So it really kind of flips the script. This is the era we're in. Can you guys share what else is getting flipped upside down? What else is happening? Obviously speed. You mentioned speed of solutions. What else is going on that's different now?

>> What's different? Well, I was going to say on the generative piece, I'd love to hear from Nick because you guys were actually ahead of the game right, when generative AI was coming out last year... And you guys have been a fantastic partner to us. We've worked together for a long time in analytics and AI. And when it came out, you really thought this, we've got so much data with Qlik, this is a great opportunity for us. So it's a little bit about what you do.

>> Yeah, it's amazing. You could be ahead one day and then behind the next. So we're trying to stay ahead very rapidly and the partnership with Qlik has really helped us do that. I think fundamentally, we really have built out some robust use cases. We've been a Qlik partner for a long time, so getting the data right was always the first foundational element. So really having great governance and master data, having an organization that's trained and talented, both from the business side as well as the digital side is really important. So the combination of really focusing on data, training the users in the right way, and then democratizing and providing a self-serve model through our platform was great. So everyone in the company, all 30,000 employees who are building these personalized connected experiences for the enterprise and for our products, they have it at the touch of their fingertips. So they were already accustomed to the power of Qlik and the visualization and all of the analytics that helped us get through the pandemic, helped us get through the supply chain crisis. Every part of the enterprise, whether it's sales, finance, marketing, supply chain, has that DNA in them around how do I use this data to deliver better insights? Now, what we've done with our digital transformation solutions business, which is our third business, we have an automotive business and a consumer business, but we also have a global tech services business, we've built capabilities around algorithms and machine learning to embed into the analytics dashboards this prompt engineering capability where you can ask the AI to tell you what they see in addition to. So it's really an augmentation that we love and it drives even greater insights for those who really understand the data, really have the domain knowledge, having that power of not only the static dashboards, but now the interaction around augmented AI is fantastic for us.

>> How has that changed the culture and the company? Because one of the themes that's coming out of the generative AI use cases, it frees up more time to do other things, creativity. So how has that changed any kind of, well, I say entrepreneurial spirit, product experimentation, what net effect came out of embedding some of the AI features into the product?

>> Yeah, I think first and foremost, it put us in a position where we felt like we can manage AI governance and put the guardrails and guidelines around AI in terms of protecting our intellectual property or data privacy without compromising speed of innovation. So first and foremost, we wanted to make sure that the versions that we're using are in the enterprise, they're using our data, it's protected, it's controlled. We help manage the hallucinations and biases. So first and foremost is everyone understands the policies and the procedures, and we put guardrails and guidelines around the use of it, so people don't feel like we're using it as a barrier, but helping them set it up correctly. The second is certainly we have deep domain knowledge around AI and analytics, we have data scientists, data modelers, data engineers. Our chief product officer in DTS is an absolute digital thought leader. So having access to those people to help you set up the algorithms and the tuning and the explainability of the models is really powerful. So you don't feel like you're ever slowing down. You have the analytics, you have the experts, you have training. So we have very robust curriculums and training online, videos, sandboxes that you can play around with. So it helps manage that demand, that insatiable demand. Lastly, it's managing and prioritizing the use cases. So what we do is everything is intended with a very clear outcome, making sure that we have, for the most part, starting out with low level of effort, high impact use cases that are embedded in these analytics. How do we improve our marketing effectiveness on campaigns? How do we improve our win rates on the pipeline of things that we're quoting? How do we improve our supply chain forecasting? It's those things that we've already used analytics for that are really helping us empower and take that to the next level with GenAI.

>> I was going to say, just to quickly add onto that, because you had said something about the culture change, and I know that you guys are doing some predictive AI, and I mean it's really not just about making your employees and your organizations more productive, but more effective and increasing your performance, and it's such a game changer. I'm in marketing myself, so I mean talk about a data rich function and it's really hard to still get the answers you need to make the decisions that you need to make to go forward. And so being able to change that from hunting, getting an insight and guessing what you should do to actually getting a real prediction of what's likely going to happen, it just really not only improves your productivity, but your effectiveness, your ability to make better decisions. And I think from a performance perspective, it's really fulfilling as an individual as well as an organization to drive your performance.

>> Your confidence too, yeah.

>> So it really does change a lot in terms of how the company operates.

>> No question. I think anyone that thinks that you're going to take the human out of the loop is really not looking at it the right way. I mean, we are building human-centric solutions that transform and improves lives. You can't take the human out of that equation. You have to have the right domain expertise that help making the right decisions. And people that are living and working in the data all day long, they'll know when the data is wrong, they'll know when the algorithm is wrong. That validation authentication is really important. So we're building it with the human at the center of the loop and it's helping that person do their jobs better, not replacing them. And I think that is really the win-win equation.

>> I think that's the key point. I mean, Savannah and I were at another event this past year where it was about chatbots, the old, "Oh, chatbots," no. Now, agents are now coming in. Agent technology is essentially the automation of tasks for humans, not to replace humans. So we're moving to the world of, okay, I see the chatbot-like thing. Okay, now we got intelligence coming in. If your data is ready, you are in good position to leverage that. And that's your point, Mary. We've heard that at the AI council, "Car goes faster with brake."

>> It's very interesting because I think we were just talking about how generative AI really did captivate our imaginations and from an enterprise perspective, how that can help us make better decisions, how that can help us be more productive and effective. But to implement those AI or analytics models, those are all trained with data and they all use data pretty voraciously to get those answers. And so it's also driven a resurgence into the fact like, do I have a good data foundation that I can use to build all these things on? So I think it's a really important inflection point that organizations are really looking at, do they have, and with AI, it's got to be trusted data. There's a lot of risk when it comes to analytics and AI, and so it's got to be trusted. You got to know where it comes from. You want to know that the quality controls are in place and you want to know that it's being governed, not just anybody is touching it, the few people that you trust to do it. So a really important inflection point on data.

>> It absolutely is. I liked your analogy a second ago, you said it's like a shot in the arm for artificial intelligence. Artificial intelligence, ML's been around for 30 years plus more than that, and here we are all of a sudden all talking about it. Was Qlik anticipating this moment?

>> Okay, I've only been here in the last year, but I can say I joined Qlik because I am so excited where the analytics market is, and I have been in analytics, data science, AI, and data for more than 15 years, so a little bit longer than I care to admit, but there's been so many challenges with it from the data perspective, getting access to the data, to the right data, it's got to be of high quality, it's got to be timely that you're running your analytics on it. And then if you do run your analytics on it, that data literacy, that is a beautiful chart. What is it telling me? What am I supposed to do? And so I joined Qlik because I looked at their portfolio, I'm like, they've got it all. They've got it all. They really are in a great position to solve all those challenges, and that's probably something you'd hear from a tech marketer to say, "I joined a company because I thought they had really good tech," but if you look over the course-

>> They have good loyal customers too....

>> they really were building for it with all the automation, the natural language, the building up on the data side. So I think, yeah, it's nicely positioned.

>> It is nicely positioned. John, you've just mentioned the customer retention rate. I learned in one of my interviews earlier today, 93% customer retention. That is insane. I'm sorry, that is more than kids in a candy bar. That is a very, very, very impressive.

>> I think part of it too is in having been a customer for a long time.

>> I was literally just going to ask you, what it's like in your shoes? Go for it.

>> I was just about to say, Mike has been in my chair. He was a CIO for a large company, knew the importance of data, and when you have a CEO of a company who really understands it from the customer's point of view, he's sat in that chair to look through those lenses, it makes a huge difference in terms of what the roadmap and where the company is going. And you can tell he's been there before. And so we say a lot in our organization, you can't do the brilliant without the basics, and getting your foundational models right, getting your data governance right, getting your integration aggregation, visualization all right, if you really want to take advantage of the power of applied and generative AI, you have to have all that fundamental foundational elements in place. The days where you'd generate a report, no one liked the report and then they said, "Well just do another report." They still didn't like the answers that they got. That's fundamentally the same way today, and I think you have to be really careful also in these public large language models as well to make sure that you have the right guardrails and guidelines. The tooting is really important. The observability is really important. The domain knowledge is important. The explainability and how it works is really important. So we worry of course about data poisoning as well. It's real. So understanding that data lineage. And this is why I like the Qlik product a lot too, the data lineage is incredibly important. It's going to continue to be really important. Whether you're using publicly large language models or small language models in the private enterprise, we're always thinking about that. And the last thing we want to do is build an algorithm like our Health GPT app, which we've launched for healthcare and life sciences. You want that patient provider interaction and diagnosis. You want accuracy, you want to make sure that it's right.

>> You need it.

>> Having domain knowledge-

>> ....

>> explainability is critical. So you don't want to screw those kinds of things up.

>> Yeah, exactly.

>> So again, you really have to take it seriously and you've got to have the right domain knowledge and the right technology and techniques in place.

>> Nick, you bring up a good point about... In fact, we called it out on our intro segment about the first conference we've been to, as theCUBE go to all the conferences, I've never heard on stage in the main keynote data supply chain as a concept on stage in mainstream. That means that's not just lineage, that's security. That's looking at the explainability of what's coming to be most important asset, data is now the new product. So absolutely, great point. Now, Mary, you brought up a good point about the things coming together for Qlik. That's kind of the way the world is right now. You have to get your act together on the data. Otherwise, you're going to go into the GenAI world unprepared for hallucinations, which is just gaps in data. So Nick, I got to ask you the question. In addition to the efficiencies that are going to come with automation and AI, lower cost, maybe help the human be more productive, there's a business benefit of top line revenue that comes with... Now, you got a double-edged sword. So talk about coming together. You got efficiencies and company gain, business benefit, revenue side, so you've got top end cost, revenue and cost being impacted. How do you guys talk about that internally? You mentioned the healthcare app. User experience has got to be perfect.

>> Yeah, it really does. I think part of it for us is starting with the basics. When we look at our commerce data, we've got credible products online, our gbl.com platform, the revenue has grown considerably and a lot of it is because of our intelligence around the data, understanding the customer data, understanding what the buying patterns are, what the top-selling SKUs are, what the channel inventory, the average days on hand are. Customers want available product to be able to be delivered to them tomorrow. So being able to have that, personalizing the product, being able to use AI to design the product on the fly and personalize it, all of that's table stakes. Having a VPA that will provide recommended buying. Now, we're at the point where you can take a picture of your ear and it'll give a recommendation in terms of what earbud you want to use because it'll fit your ear, that image analysis and generative AI, again, another example of personal-

>> That's a fun use case.

>> That is.

>> Fun use case. I mean, we're thinking of constantly these use cases around just how do you make the personalized buying experience more, I think, unique, more immersive. Again, when you drive in our vehicles, with our software defined vehicle, all of the advanced software and generative AI and personalization in the vehicle is pretty remarkable. When you look at our smart audio technology and again, personalized experiences around what you like to listen to and how that may affect your mood or how the mood affects what you pick in terms of your playlist, et cetera, it's so many really cool things that are coming out and happening across the entire company and how that connected experience... And again, we've got 30,000 people connected experiences in the car, in the home, on the go and in the workplace, providing that ubiquitous, generative and personalized experience across all of that ecosystem is going to be really exciting.

>> A lot of unstructured data coming in for you guys too.

>> Absolutely.

>> So share with the audience watching, people have to lean into this wave. Otherwise, it's pretty clear there's going to be two sides of history here. You're either in the wrong side of history or the right side of history, generative, it's a no-brainer. Everyone pretty much gets it. The playbook. How do you get the prerequisites? Can you share your success story, what you've done to gather these benefits, someone watching now, they know they're in health, they're taking baby steps, they're putting their toe on the water, get their little floaty on, they're in the low end of the pool.

>> I think it's early days. First and foremost, let me just say that we think about four fundamental aspects of success versus the governance model. You've got to get the governance model right, and that is how you're going to manage the data privacy and intellectual property. Get that governance right, manage the demand coming in. How do you control the costs? We remember the days of cloud where you see significant costs, lots of instances getting spun up and a lot of vulnerabilities. In the products, we don't want to make that same mistake. So putting the guardrails and guidelines around cost optimization, demand optimization, and then prioritization of use cases. What's the small low level effort, high impact stuff that you can start doing proof of concepts for it and kind of testing the water? So how do you make sure that those are the use cases that'll bring you the best benefit at the lowest cost and start getting you acclimated to it. The third is picking the right partnerships. You've got to pick technology partners like Qlik who understand the fundamental points of view from the customer, what other customers are gleaning value from those use cases and the product usage, and then being able to apply that. I certainly learn a lot from everybody around me. The only reason why I've been in my role is because I've just been smart enough to surround myself with great people. So the partnerships matter. So the partnerships in the tech stack, understanding that the tech stack will continue to evolve. Every part of the tech stack's going to evolve. And so being able to build out small proof of concepts, try out different products, making a determination on whether you want to build or buy. It's still early days to figure that out.

>> Hit that winning culture, get the wins, the quick wins, the little wins.

>> Absolutely.

>> Get on base, little small ball as we say in baseball, get the little singles and then hit the home runs.

>> And you expect failure at the end of the day. Right now, you're going to learn what success looks like and what failure is. It's very early days. Personally, we're doing things that are big and small alike, but at the end of the day, it's very early days. And having that entrepreneurial spirit with the expectations that some things aren't going to always go right, but being able to contain and control and manage through that is really important.

>> All about that curiosity. Yeah.

>> Oh, I was just going to say, and what I loved about that, because I agree, it's not whether you're going to adopt AI, it's whether or not you can do it effectively. And so we do see a lot of companies that are being cautious around this. They want to make sure that they are really thinking through the governance, the security implications or thinking through the data implications. But coming from analytics and AI, near and dear to my heart, what I loved what you said is really focused on the business. I think a lot of people are like, "How can we adopt this new technology? How can we have an AI strategy?" And it's really about how can you have a business strategy that's enabled by AI, and flipping it. And everything you talk to from a use case perspective is very much business driven in terms of how it's going to advance your business, your customer success, and using that as a point of innovation of how can we take those experiences even further to delight our customers, I think is really excellent.

>> Great. And I think one of the things that are unique about my role as a CDIO, I get to try it and see what works and what doesn't work as a reference point with Harman and Samsung, but then based on that, we're able to go do that for other customers with Qlik as well. So we've served industrial healthcare communication software companies on their journey around analytics and AI based on our knowledge and experience that we've applied in-house. And so I have my cake and can eat it too. I'm the CDIO, but also the president of the P&L. And so it's a lot of fun. People can learn from our success and our failures, and that makes my life a lot more interesting and fun.

>> I love that attitude and I love that transparency, and I love that I can actually say, I think with confidence, you're the first person on the stage who said they can have their cake and eat it too.

>> Absolutely.

>> So congratulations on that.

>> Why not?

>> That is absolutely lovely. Nick. Mary, this has been a thrilling conversation. Thank you both.

>> Thank you so much.

>> Thank you so much.

>> Thank you so much for having us.

>> We just settled it and started learning here. I felt John and I both just kind of taken all you had to say, and that was exciting. And scaling AI on the top of everybody's minds right now. Thank all of you for tuning in for this absolutely fascinating segment here on Scaling AI. John and I are at Qlik Connect. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.