Qlik Connect 2024 | Jamie Kiser | Qlik, Priscila de Jesus Papazissis Paolinelli | Vallourec & Angelika Klidas | Business Data Challengers

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Jamie Kiser

SVP Customer Success & Operations

Qlik

Priscila J. Papazissis Paolinelli

Head of Data Analytics

Vallourec

Angelika Klidas

Advisor & Educator

Business Data Challengers

Qlik focuses on improving AI literacy throughout its customer base

Companies may be trying to infuse artificial intelligence throughout their business, but first they have to ensure AI literacy for their employees.With its international base of customers, QlikTech International AB is aiming to improve knowledge of best AI and data practices across the industry, according to Jamie Kiser (pictured, second from right), senior vice president of customer success and operations at Qlik.Industry experts from Qlik, Vallourec and Business Data Challengers discuss the challenges of educating employees on best data practices.“The foundation becomes paramount because we’re infused with so much data now,” she said. “You just can’t get away from it. It’s pouring in

play_circle_outline Discussion on the importance of data literacy and AI literacy

play_circle_outline Challenges and excitement surrounding implementing AI and data literacy programs

play_circle_outline Overcoming barriers to adopting data culture and embracing new technologies

play_circle_outline The potential benefits and possibilities of AI in the future

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Jamie Kiser | Qlik, Priscila de Jesus Papazissis Paolinelli | Vallourec & Angelika Klidas | Business Data Challengers

Jamie Kiser

SVP Customer Success & Operations Qlik

Priscila J. Papazissis Paolinelli

Head of Data Analytics Vallourec

Angelika Klidas

Advisor & Educator Business Data Challengers

search

>> Good morning, data fans, and welcome back to beautiful, sunny Orlando, Florida. We're here ramping up the morning at Qlik Connect. My name's Savannah Peterson, you're watching theCUBE. And I am joined by a fantastic power packed panel here. We've got Angelika, Jamie, and Priscilla. Thank you so much for joining me this morning. How you doing? We're smiling. Good. We're excited. Well illuminated.

>> Happy to be here.

>> Yeah. Yes, that's important. Jamie, you work with customers all day long and you've brought two to our stage today. Tell me a little bit about them?

>> Yes, I'm happy to be here. And what better way than to be sitting here with fantastic customers? So Priscilla is from Vallourec and based in Brazil, and Angelika is from Business Data Challengers based in the Netherlands. So we're excited to talk to you today.

>> I appreciate that you brought such a global conversation to the stage. Actually, now I'm curious about it, how much of fixed business is international?

>> Oh gosh, a good percent. We're founded in Sweden. And then Talend, where I came from was French founded. So we've got this really global nature and I bet probably 30% of the business or more is international. It's a very global business.

>> Oh, that's impressive.

>> Serving many countries.

>> Yeah. That's exciting. You have an interesting role, Priscilla, and I want to open up with us because you're doing a talk about it later. It was in the keynote. It's a big part. AI literacy is on everyone's minds. Everyone's trying to ramp their teams up in terms of their AI literacy and capability. How do you do that as a leader in the space?

>> So I fell in love with the data literacy term in 2018, and since then I started to study. I started to understand that we deliver a lot of data products, but even the user look at that and didn't know how to do the things, didn't know how to extract value. So starting with data literacy, we understand that everything starts with data, data quality, data governance, data security. We move to AI literacy, that is the capacity that the user will have to understand the results of the models, to criticize the results, to know how to make questions to the data. So it's not just to have a platform, you have to understand how to deal with that. So the program is based on training paths for the users to understand better how to deal with AI.

>> Are the team members that you're working with, are they excited? Are they nervous? Are they overwhelmed? We're at such an interesting information moment, I feel like. And actually I'd be curious what all your answers are. So how's the team respond when you come in and tell them that you're going to get them AI literate.

>> Training programs, even we are living this hype, everybody's talking about AI, gen AI. Some of them are a little afraid of what will come. Some of them are very excited. We know that we have to balance the time because people has a lot of things to do, a lot of things to deliver in the work and to put training hours is a challenge situation. So for us, it's a cultural problem. We have to deal with all these feelings, some of them pushing, some of them I want to go. So it's a good challenge to work with.

>> Yeah, I can imagine it's really overwhelming. Angelika, what are you seeing? I know you're passionate about data literacy and AI literacy as well. People excited? Are they carving out the time? What are you seeing in the Netherlands?

>> First of all, I think still it's a hype. And my biggest concern is that when organization or people do not even have the descriptive and the diagnostic phase of data literacy in place and start thinking about amazing wild things around AI, I've seen it all and I have seen it fail. I think we should make trainings like Priscilla's is doing in small blocks to get people excited and inspired, but also how not to be concerned in using it, but also the ability to question it, to question the results that you're getting. Because don't assume that everything is right. Please don't.

>> I think that's an extraordinary point. Just-

>> I think this is the skillset that will be the most important skillset for everybody all around the world in the coming years.

>> Louder for everyone in the back. No, I think that's magical. And I do think we were talking about it on stage this morning, trust scores. You got to be able to trust your data. I mean, this has got to be at the epicenter of a lot of the conversations you're having, Jamie?

>> Oh, certainly. Right. And you heard Mike Capone say in the keynote this morning about garbage in, garbage out. And it's so cliche, we've heard it for years, but it's really true. It's where the foundation becomes paramount because we're infused with so much data now. You just can't get away from it. It's pouring in. And so the more you make sense of it and the more you build a strong data foundation, that leads you to sort of demystify this AI bit, right? Because you asked the question about what are you seeing? I think I'm seeing people embrace it, right? I think there has never been a more exciting time to be in tech, but if you're not enabled, if you can't learn, these are just buzzwords. So what's an LLM? What does it mean to have hallucinations in the data? And people are scared, but as they lean in, I think folks are really starting to embrace it.

>> So back to you, Jamie, actually, because I think that's a great point. You're a culture queen. How do you create a culture around data? How do you even approach that?

>> Yeah, I think it is first by embracing it, right? And it's about responsibility, right? Because we can buy any applications, you can use any systems, you can put models together, but what we have to do is be responsible. So it's like Priscilla said, you've got to educate. People are going to lean in if they feel like they understand it. But you talked about our education team talks about TikTok style of learning. If you put an 8, 12, 14 hour course in front of people and ask them to consume it, they're not going to do it. But if you can give people bite-sized chunks of learning, get them embracing and understanding the fundamentals, then you can create a data-driven culture. And I really think the start of it.

>> I do think that is great and it's got to be bite-sized, otherwise it's very overwhelming.

>> It's too much.

>> You do data escape rooms. Speaking of making learning digestible, tell us about those. Angelika, I'm so curious.

>> Yeah, me and my friends from the Smart Training Center, we truly believe in that playing games is the highest form of education. So education and the training should have fun elements in it. And that's the way how you can test people. So the data escape rooms is developed to let four or five people from a team or from a company work together and solve a murder or deliver some information to the CEO that needs to have a press conference. And what you see and what they fall in is the same exact pitfall, that they do not speak the same language and they do the same tasks. While you should divide your tasks and speak the same language. And that is amazing. We played it there last year in October, November in Lund with the partner ambassadors and they all and then they learned, leaned by having fun.

>> It's the best way to learn. And also so interesting that everyone makes the same mistake. I would imagine that's a bit of something that happens a lot across cultures as they're embracing data as everyone's stumbling over the same sort of issues. What are some of the tips or advice you might have, I'll turn to you Priscilla, if someone's watching this and trying to improve their data culture, what would you tell them to do?

>> One thing that I really believe is that we have to start step by step, like Jamie and Angelika said, it's impossible to... I work in a company with more than 15,000 employees. So we have to start with 30, with 100, with 400 with 1000 because we can create good advocates for a data leader culture. So for me, the tip is to start little, it's to become big. When you see that you have people that believes in data, is able to extract value from data so they will help us to spread this culture.

>> Yeah. How about you? Besides games, well actually that's probably the best gateway, but do you have any advice for, I mean they should probably just play your game, but for cultures about how to really get over that initial fear or to not trust the results. How do you balance that when you're trying to get people to trust new technology, but then also making it pretty clear that they might not be able to trust that data? How do you balance that?

>> Balance is key. Balance in learning, the technical stuff and the soft skills stuff. And that needs to be balanced out. We cannot focus only on technical things. We have to focus on the soft skills as well, like communication, working together, speaking the same data, language, questioning, statistical thinking, it's all in there. And Priscilla and I will be presenting our vision, how to set that up for an organization today at 2:00.

>> I love it. Can you give us a little preview, a highlight from that, a little taste of your talk?

>> Well, I think we'll have the new framework that we have, how to get your organization up to speed with data and AI literacy. And secondly, we will use the quotes from Mr. Miyagi's karate vision, wax on, wax off, and we combine that with data and AI literacy elements.

>> I love it. You're fun, Angelika. I bet your team likes working with you. I can see why you were brought here. Let's talk about the... I want to stay on trust a little bit. Jamie, I'm going to turn to you because I can imagine you're working with so many different customers, so much data. How can folks level up their teams to be able to trust that data better? How do you get that data confidence?

>> So some of what I've seen have the most success with customers is make the data readily available. Self-serve data sets, governed data sets that have been put out by our data teams to ensure their trusted. We have this trust score. It's looking at the different elements. It's checking into where the sources are coming from. Giving an employee an ability to validate that what they're looking at as trustworthy is huge, right? Because I've had this in my career, I look at a report the first time, I don't trust something, you never go back to it. It fails you. And so then you're just jaded about it.

>> It's so true, and you're not even going to be looking at any of those insights whatsoever.

>> That's right. So it's a bit of a waste. So I think it's getting the data in front of them, giving them an ability to see that they can trust and validate it and having the tools to do that is huge.

>> Yeah. So what are the factors that go into the trust score? Just because that is a cool thing that was talked about this morning.

>> So we look at different attributes around the diversity. I think we talked about the diversity. There's availability, the sources of the data, and so they bring all those elements together and then run algorithms on it to really arrive at a score. I was just running one this morning and it came back at 58 and I'm like, okay, is 58 good? It's middle range. It means there's work I got to do on some of those elements. Is it the availability? Is it where it's coming from? Is it the source? And then it'll tell me where is it the lowest? Why am I getting that lower score? So I can focus in on the area that I need to improve.

>> Nice. Oh, I love that. No wonder everyone was so excited. There were some hoots and hollers.

>> Cheering.

>> There was a lot of cheering in the audience this morning whenever we were just talking about that, which I really loved. What are some of the barriers, Priscilla, I'm going to turn to you, when you're going out and helping level up the literacy, what are some of the barriers to getting buy-in?

>> For me, the comfort zone, it's the biggest one because people do the things in a way and they think that way is the best. And maybe they took hours to get the data to prepare, to transform, to deliver. And maybe my number is different from Jamie's number, different from Angelika's number. And if we don't have a buy-in from the executives to create this standard databases and to have this good data to work with and everybody believes on that, it's really difficult because we have people that doesn't want to change, doesn't want to use. So this is for me, the most important barrier we have today.

>> Is That the same for both of you? Angelika, I'll ask you first.

>> Yeah, I think it is. There is always a threshold that people have to go over. Or when you pass through a door, you always have... like I came here on Saturday and I took a sneak peek to find my way because it's something new and I have to get used, right? I'm the same, maybe a little bit artistic, but I like to find my way out. And that's what I love to do to help people get over the threshold, try to understand things, but start slow, start little, have the dot on the horizon and go. And then you will find the snowball effect that is rolling out. So my phrase that I also have written down in my book is think big, put a dot on the horizon, start small and then let it flow. Scale fast.

>> There you go. Jamie, is that barrier synonymous across all of your customer portfolio as well?

>> It's similar. There were two I was going to touch on. Change was one, change is hard. Most people don't embrace it naturally. And so having the change management methodologies to allow people to get over that hump, to get to that dot, I think is a big one. And then the other barrier I see is it's just the volume of data. And I think that it just causes people to say, there's too much. I don't want to deal with that. It's going too hard.

>> That can be really overwhelming.

>> So if you can make sense of it and again, build that strong data foundation, I think that helps to break that barrier.

>> I like that. Yeah, it's important. Okay. I'm going to ask you a question that's definitely not in our talking points because I want you to take off your... you can take off your professional hat, though you're all very smart. You can leave it on if you want. But I'm curious as a human being, not just a professional, what excites you most about our AI future? Priscilla, I'll start with you.

>> Let's start with me. I believe that we will have many possibilities to do things faster and to have more knowledge in our hands. And that's why I believe that we have to be in an AI awareness program, as human being, not just inside the companies. Because I believe in this future, I think it will be good for us. So we know we have to understand about ethics, bias, hallucinations, and everything that came in this last month. But I think we'll do things more easy, faster and it will be good for us.

>> I like it. All right, Jamie, what about you?

>> All right, so work hat off, on a personal level?

>> Yes.

>> I think the pace of innovation and the convergence of technologies and what it's doing for our lives is incredible. And I just read this book recently and I was geeking out because it went from everything... it was from self-driving cars to what advancements are happening in healthcare and talking about we wear these rings that can tell us things going on in our bodies. And it was going through this notion of what if we live to 120 or that becomes more the norm. I'm raising two young children right now, and so for me it's like the things they're going to see and the things that are going to happen so much more rapidly than the pace at which we've seen innovation so far, I think is super cool.

>> It is super cool. It actually gave me goosebumps as you said that because it really is magical to think about what's going to happen. Well, hopefully happen.

>> I'm like, will my kids need driver's licenses or are they going to... Just these things that were so common to us historically, I feel like are fundamentally going to change over the coming 10, 20, 30 years.

>> Yeah, it's really exciting. Angelika, what about you?

>> I think it will change the way we work. We can do more in a better way. I did already some amazing projects, whole new way of engaging with customers. We will show some demos at the session at 2:00. My colleagues at the Business Data Challenges, they are working on solution to improve MRI images with de-noising technology based on algorithms. It's fantastic what they are doing. Another project that we are doing and that's almost going live is that we help runners to run better. So you can film a runner and we put the algorithms in it and then you can improve your posture and so you will run more efficiently and prevent injuries. So amazing things are about to happen, I think, and I think it will change our lives completely, but I do hope we will be precautious, careful in the things that we do with it.

>> Those are some really great words to close on. I'm going to have one closing question for you, Jamie, just so we can tease next year's Qlik Connect. What do you hope to be able to say next year that we can't yet say this year?

>> Oh wow. We talk a lot about AI being the buzzword, the hot topic. I hope that next year we are up there just filled with customer stories about what we've done and what customers have accomplished. We have a ton of it, but I want it to just be so infused in everything we do that it's less about the demystification of talking to every customer. They all have an OKR or a KPI that they've got to do something around AI. I want them to have nailed it, right? Nailed that KPI and they're rocking and rolling. That's what I want us to be doing next year.

>> I love that. Well, we can't wait to have it and more lovely customers. Priscilla, Angelika, Jamie, thank you so much for being on the show. This is such a wonderful start to the day. And thank all of you for tuning into our fantastic all day long live coverage here in Orlando, Florida at Qlik Connect. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.

Jamie Kiser | Qlik, Priscila de Jesus Papazissis Paolinelli | Vallourec & Angelika Klidas | Business Data Challengers

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>> Good morning, data fans, and welcome back to beautiful, sunny Orlando, Florida. We're here ramping up the morning at Qlik Connect. My name's Savannah Peterson, you're watching theCUBE. And I am joined by a fantastic power packed panel here. We've got Angelika, Jamie, and Priscilla. Thank you so much for joining me this morning. How you doing? We're smiling. Good. We're excited. Well illuminated.

>> Happy to be here.

>> Yeah. Yes, that's important. Jamie, you work with customers all day long and you've brought two to our stage today. Tell me a little bit about them?

>> Yes, I'm happy to be here. And what better way than to be sitting here with fantastic customers? So Priscilla is from Vallourec and based in Brazil, and Angelika is from Business Data Challengers based in the Netherlands. So we're excited to talk to you today.

>> I appreciate that you brought such a global conversation to the stage. Actually, now I'm curious about it, how much of fixed business is international?

>> Oh gosh, a good percent. We're founded in Sweden. And then Talend, where I came from was French founded. So we've got this really global nature and I bet probably 30% of the business or more is international. It's a very global business.

>> Oh, that's impressive.

>> Serving many countries.

>> Yeah. That's exciting. You have an interesting role, Priscilla, and I want to open up with us because you're doing a talk about it later. It was in the keynote. It's a big part. AI literacy is on everyone's minds. Everyone's trying to ramp their teams up in terms of their AI literacy and capability. How do you do that as a leader in the space?

>> So I fell in love with the data literacy term in 2018, and since then I started to study. I started to understand that we deliver a lot of data products, but even the user look at that and didn't know how to do the things, didn't know how to extract value. So starting with data literacy, we understand that everything starts with data, data quality, data governance, data security. We move to AI literacy, that is the capacity that the user will have to understand the results of the models, to criticize the results, to know how to make questions to the data. So it's not just to have a platform, you have to understand how to deal with that. So the program is based on training paths for the users to understand better how to deal with AI.

>> Are the team members that you're working with, are they excited? Are they nervous? Are they overwhelmed? We're at such an interesting information moment, I feel like. And actually I'd be curious what all your answers are. So how's the team respond when you come in and tell them that you're going to get them AI literate.

>> Training programs, even we are living this hype, everybody's talking about AI, gen AI. Some of them are a little afraid of what will come. Some of them are very excited. We know that we have to balance the time because people has a lot of things to do, a lot of things to deliver in the work and to put training hours is a challenge situation. So for us, it's a cultural problem. We have to deal with all these feelings, some of them pushing, some of them I want to go. So it's a good challenge to work with.

>> Yeah, I can imagine it's really overwhelming. Angelika, what are you seeing? I know you're passionate about data literacy and AI literacy as well. People excited? Are they carving out the time? What are you seeing in the Netherlands?

>> First of all, I think still it's a hype. And my biggest concern is that when organization or people do not even have the descriptive and the diagnostic phase of data literacy in place and start thinking about amazing wild things around AI, I've seen it all and I have seen it fail. I think we should make trainings like Priscilla's is doing in small blocks to get people excited and inspired, but also how not to be concerned in using it, but also the ability to question it, to question the results that you're getting. Because don't assume that everything is right. Please don't.

>> I think that's an extraordinary point. Just-

>> I think this is the skillset that will be the most important skillset for everybody all around the world in the coming years.

>> Louder for everyone in the back. No, I think that's magical. And I do think we were talking about it on stage this morning, trust scores. You got to be able to trust your data. I mean, this has got to be at the epicenter of a lot of the conversations you're having, Jamie?

>> Oh, certainly. Right. And you heard Mike Capone say in the keynote this morning about garbage in, garbage out. And it's so cliche, we've heard it for years, but it's really true. It's where the foundation becomes paramount because we're infused with so much data now. You just can't get away from it. It's pouring in. And so the more you make sense of it and the more you build a strong data foundation, that leads you to sort of demystify this AI bit, right? Because you asked the question about what are you seeing? I think I'm seeing people embrace it, right? I think there has never been a more exciting time to be in tech, but if you're not enabled, if you can't learn, these are just buzzwords. So what's an LLM? What does it mean to have hallucinations in the data? And people are scared, but as they lean in, I think folks are really starting to embrace it.

>> So back to you, Jamie, actually, because I think that's a great point. You're a culture queen. How do you create a culture around data? How do you even approach that?

>> Yeah, I think it is first by embracing it, right? And it's about responsibility, right? Because we can buy any applications, you can use any systems, you can put models together, but what we have to do is be responsible. So it's like Priscilla said, you've got to educate. People are going to lean in if they feel like they understand it. But you talked about our education team talks about TikTok style of learning. If you put an 8, 12, 14 hour course in front of people and ask them to consume it, they're not going to do it. But if you can give people bite-sized chunks of learning, get them embracing and understanding the fundamentals, then you can create a data-driven culture. And I really think the start of it.

>> I do think that is great and it's got to be bite-sized, otherwise it's very overwhelming.

>> It's too much.

>> You do data escape rooms. Speaking of making learning digestible, tell us about those. Angelika, I'm so curious.

>> Yeah, me and my friends from the Smart Training Center, we truly believe in that playing games is the highest form of education. So education and the training should have fun elements in it. And that's the way how you can test people. So the data escape rooms is developed to let four or five people from a team or from a company work together and solve a murder or deliver some information to the CEO that needs to have a press conference. And what you see and what they fall in is the same exact pitfall, that they do not speak the same language and they do the same tasks. While you should divide your tasks and speak the same language. And that is amazing. We played it there last year in October, November in Lund with the partner ambassadors and they all and then they learned, leaned by having fun.

>> It's the best way to learn. And also so interesting that everyone makes the same mistake. I would imagine that's a bit of something that happens a lot across cultures as they're embracing data as everyone's stumbling over the same sort of issues. What are some of the tips or advice you might have, I'll turn to you Priscilla, if someone's watching this and trying to improve their data culture, what would you tell them to do?

>> One thing that I really believe is that we have to start step by step, like Jamie and Angelika said, it's impossible to... I work in a company with more than 15,000 employees. So we have to start with 30, with 100, with 400 with 1000 because we can create good advocates for a data leader culture. So for me, the tip is to start little, it's to become big. When you see that you have people that believes in data, is able to extract value from data so they will help us to spread this culture.

>> Yeah. How about you? Besides games, well actually that's probably the best gateway, but do you have any advice for, I mean they should probably just play your game, but for cultures about how to really get over that initial fear or to not trust the results. How do you balance that when you're trying to get people to trust new technology, but then also making it pretty clear that they might not be able to trust that data? How do you balance that?

>> Balance is key. Balance in learning, the technical stuff and the soft skills stuff. And that needs to be balanced out. We cannot focus only on technical things. We have to focus on the soft skills as well, like communication, working together, speaking the same data, language, questioning, statistical thinking, it's all in there. And Priscilla and I will be presenting our vision, how to set that up for an organization today at 2:00.

>> I love it. Can you give us a little preview, a highlight from that, a little taste of your talk?

>> Well, I think we'll have the new framework that we have, how to get your organization up to speed with data and AI literacy. And secondly, we will use the quotes from Mr. Miyagi's karate vision, wax on, wax off, and we combine that with data and AI literacy elements.

>> I love it. You're fun, Angelika. I bet your team likes working with you. I can see why you were brought here. Let's talk about the... I want to stay on trust a little bit. Jamie, I'm going to turn to you because I can imagine you're working with so many different customers, so much data. How can folks level up their teams to be able to trust that data better? How do you get that data confidence?

>> So some of what I've seen have the most success with customers is make the data readily available. Self-serve data sets, governed data sets that have been put out by our data teams to ensure their trusted. We have this trust score. It's looking at the different elements. It's checking into where the sources are coming from. Giving an employee an ability to validate that what they're looking at as trustworthy is huge, right? Because I've had this in my career, I look at a report the first time, I don't trust something, you never go back to it. It fails you. And so then you're just jaded about it.

>> It's so true, and you're not even going to be looking at any of those insights whatsoever.

>> That's right. So it's a bit of a waste. So I think it's getting the data in front of them, giving them an ability to see that they can trust and validate it and having the tools to do that is huge.

>> Yeah. So what are the factors that go into the trust score? Just because that is a cool thing that was talked about this morning.

>> So we look at different attributes around the diversity. I think we talked about the diversity. There's availability, the sources of the data, and so they bring all those elements together and then run algorithms on it to really arrive at a score. I was just running one this morning and it came back at 58 and I'm like, okay, is 58 good? It's middle range. It means there's work I got to do on some of those elements. Is it the availability? Is it where it's coming from? Is it the source? And then it'll tell me where is it the lowest? Why am I getting that lower score? So I can focus in on the area that I need to improve.

>> Nice. Oh, I love that. No wonder everyone was so excited. There were some hoots and hollers.

>> Cheering.

>> There was a lot of cheering in the audience this morning whenever we were just talking about that, which I really loved. What are some of the barriers, Priscilla, I'm going to turn to you, when you're going out and helping level up the literacy, what are some of the barriers to getting buy-in?

>> For me, the comfort zone, it's the biggest one because people do the things in a way and they think that way is the best. And maybe they took hours to get the data to prepare, to transform, to deliver. And maybe my number is different from Jamie's number, different from Angelika's number. And if we don't have a buy-in from the executives to create this standard databases and to have this good data to work with and everybody believes on that, it's really difficult because we have people that doesn't want to change, doesn't want to use. So this is for me, the most important barrier we have today.

>> Is That the same for both of you? Angelika, I'll ask you first.

>> Yeah, I think it is. There is always a threshold that people have to go over. Or when you pass through a door, you always have... like I came here on Saturday and I took a sneak peek to find my way because it's something new and I have to get used, right? I'm the same, maybe a little bit artistic, but I like to find my way out. And that's what I love to do to help people get over the threshold, try to understand things, but start slow, start little, have the dot on the horizon and go. And then you will find the snowball effect that is rolling out. So my phrase that I also have written down in my book is think big, put a dot on the horizon, start small and then let it flow. Scale fast.

>> There you go. Jamie, is that barrier synonymous across all of your customer portfolio as well?

>> It's similar. There were two I was going to touch on. Change was one, change is hard. Most people don't embrace it naturally. And so having the change management methodologies to allow people to get over that hump, to get to that dot, I think is a big one. And then the other barrier I see is it's just the volume of data. And I think that it just causes people to say, there's too much. I don't want to deal with that. It's going too hard.

>> That can be really overwhelming.

>> So if you can make sense of it and again, build that strong data foundation, I think that helps to break that barrier.

>> I like that. Yeah, it's important. Okay. I'm going to ask you a question that's definitely not in our talking points because I want you to take off your... you can take off your professional hat, though you're all very smart. You can leave it on if you want. But I'm curious as a human being, not just a professional, what excites you most about our AI future? Priscilla, I'll start with you.

>> Let's start with me. I believe that we will have many possibilities to do things faster and to have more knowledge in our hands. And that's why I believe that we have to be in an AI awareness program, as human being, not just inside the companies. Because I believe in this future, I think it will be good for us. So we know we have to understand about ethics, bias, hallucinations, and everything that came in this last month. But I think we'll do things more easy, faster and it will be good for us.

>> I like it. All right, Jamie, what about you?

>> All right, so work hat off, on a personal level?

>> Yes.

>> I think the pace of innovation and the convergence of technologies and what it's doing for our lives is incredible. And I just read this book recently and I was geeking out because it went from everything... it was from self-driving cars to what advancements are happening in healthcare and talking about we wear these rings that can tell us things going on in our bodies. And it was going through this notion of what if we live to 120 or that becomes more the norm. I'm raising two young children right now, and so for me it's like the things they're going to see and the things that are going to happen so much more rapidly than the pace at which we've seen innovation so far, I think is super cool.

>> It is super cool. It actually gave me goosebumps as you said that because it really is magical to think about what's going to happen. Well, hopefully happen.

>> I'm like, will my kids need driver's licenses or are they going to... Just these things that were so common to us historically, I feel like are fundamentally going to change over the coming 10, 20, 30 years.

>> Yeah, it's really exciting. Angelika, what about you?

>> I think it will change the way we work. We can do more in a better way. I did already some amazing projects, whole new way of engaging with customers. We will show some demos at the session at 2:00. My colleagues at the Business Data Challenges, they are working on solution to improve MRI images with de-noising technology based on algorithms. It's fantastic what they are doing. Another project that we are doing and that's almost going live is that we help runners to run better. So you can film a runner and we put the algorithms in it and then you can improve your posture and so you will run more efficiently and prevent injuries. So amazing things are about to happen, I think, and I think it will change our lives completely, but I do hope we will be precautious, careful in the things that we do with it.

>> Those are some really great words to close on. I'm going to have one closing question for you, Jamie, just so we can tease next year's Qlik Connect. What do you hope to be able to say next year that we can't yet say this year?

>> Oh wow. We talk a lot about AI being the buzzword, the hot topic. I hope that next year we are up there just filled with customer stories about what we've done and what customers have accomplished. We have a ton of it, but I want it to just be so infused in everything we do that it's less about the demystification of talking to every customer. They all have an OKR or a KPI that they've got to do something around AI. I want them to have nailed it, right? Nailed that KPI and they're rocking and rolling. That's what I want us to be doing next year.

>> I love that. Well, we can't wait to have it and more lovely customers. Priscilla, Angelika, Jamie, thank you so much for being on the show. This is such a wonderful start to the day. And thank all of you for tuning into our fantastic all day long live coverage here in Orlando, Florida at Qlik Connect. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.