 Good morning everyone. It's theCUBE live at Clickroll 23 from beautiful Las Vegas, sunny and windy Las Vegas. We are so excited, Lisa Martin, Dave Vellante to be here covering this event. And fresh from the keynote stage today, we've got some awesome guests. We've got CEO Mike Capone on theCUBE for the first time, great to have you. Crawford Dell joins us as well, president of IDC. Guys, great to have you. Fresh from the keynote stage. Great, great to be here. So Mike, you talked about, I loved when we were leaving this morning, the keynote, standing room only, packed house. Great to see all these people back, wanting to see and hear what's on the horizon with Click, so much change in your five years. You talked this morning about the 30th anniversary of Click, so much innovation that's happened. I also saw, congrats are due to you that you were named a finalist for the EY entrepreneur of the year 2023, greater Philadelphia area. Talk to us a little bit about the state of the land at Click, what keeps you as optimistic as we saw you on the main stage. Yeah, well first, it's just great to be back. I mean, you look around and you see in-person events are back and live, and I think you feel the energy. You got to see the energy from the crowd. So actually what makes me optimistic and excited is this, like just all our customers and partners here giving us energy, giving us feedback, so it's been great. But look, no C level executives turning down a conversation about data analytics right now and how it can make their company stronger, better, compete more, and that's what we do. And we, over the past 30 years, you're right, we've been in business 30 years and we thrive and survive because we're resilient and we're innovative. And we started out as this little visual analytics company coming out of Sweden and over time we've added capabilities to the platform. We've done 10 acquisitions over the last five years to really extend out and add capabilities like data integration and data quality, artificial intelligence, machine learning. So it's been a really great journey. We're just really excited. And I think the next 30 years looks as good as the last 30. Over 38,000 customers across industries. You had some great folks on stage this morning. I always love when you had forward up, you talked about it's a 100 year old company. I always love seeing companies like that who've gone from the Industrial Revolution now to the digital age successfully because we all expect us in our consumer lives we're connected, regardless of where we are. Talk a little bit about some of the voice of the customer and how they're really articulating the value prop and the profit will bring you in. Yeah, so Ford is an excellent example. You're right. They were at the forefront of the Industrial Revolution. They invented the assembly line, the assembly line for cars. Model T Ford's came off the assembly line. That was a dramatic revolution in how manufacturing was done. And now Ford is in the midst of another revolution which is a data revolution. They have put connected technology in all of their cars. They're streaming data live from all of their cars to tell you things like hey, like you need an oil change, by the way, we're going to set you up. Everything's going to be there for you, anticipating customer needs. And that's really what we're seeing from our customers. How do we leverage data, not the old way, which is like locked up in an old database of data warehouse, but the new way, which is real time in the moment to improve experiences of our employees and our customers. That's what our customers are talking to us about. So Crawford, the last 120 days or so has just been amazing. I mean, prior to chat GPT coming up, everybody was talking about large language models and now that's all anybody talks about. You made a point that the time to create stuff is just being compressed. And so I'm curious, it's down to months and sometimes even days when you're looking at things like auto GPT. So how do you think about that? And you also talked about the era of multiplied innovation so those things come together. How do you see that in the context of disruption generally and where does click fit in that data? So it's a good point. So when you think about where we are in the cycle, before, when we were looking at the pandemic, we were seeing people leaning into digital transformation. We were seeing IT growth six, seven percent. We're seeing software still double digit growth. So now what we've seen is the market has started to rotate. And now we're seeing that companies are focused on a really turbulent, complicated economic environment, which I think is kind of creating a smoke screen. But the reality is companies are still investing. This is why we're seeing double digit software growth. This is why we're seeing exponential growth in data continuing going forward. So we believe that this era of multiplied innovation, when the four of us are at the old folks home, we'll be talking about this as this golden era of IT, this opportunity to build hundreds of millions of applications and get tremendous value from it. Now, enter what we're seeing with these large language models. We believe that companies data is going to be, will be more valuable than it's ever been before. Customers, the jobs customers want to get done around their data. Mike and I were talking about this last night. They don't really change. The jobs don't change. What changes is the technology that you can bring to make people better at their roles. And where click fits in is where from an end to end spectrum standpoint, whether that be getting your data set in order, being able to make better decisions on that data set, making decisions in the actual job flow of your constituents, whether that be by vertical or whether that by, whether that be by heterogeneous industries, that's what companies are going to need to do. People think about these large language models as something separate from applications. I think it's absolutely the wrong way to think about it. Sure, the large language models, I can make an argument they'll commoditize over time. The large language models are what's going to feed better decisions in the applications. And that's where click, having companies be able to present their data, better analytics, understand where they can make better decisions, that's where the power comes in. So Mike, follow up on that. So I like when you talk about the old way and the new way. The new way presumably is the modern data stack. I was in last night talking to some of your cloud partners and we heard Ford is on GCP, you had Snowflake over there, Databricks, we know AWS, Azure, they've all got so-called modern data platforms. So when thinking about the new way and what Crawford just said, how do you see the new way evolving, not just the modern data stack, but even beyond that, in terms of data pipelines, data integration, and that intelligent automation, I think you've called it, in terms of the impact on the speed at which we'll be able to get to data quality and where that takes us through, let's call it the end of the decade. Yeah, that can certainly start off. Yeah, I think the point, in my keynote, I talked about the thing that matters is action, right? So, and CEOs like me, we don't really care about the plumbing so much, right? We want action, so the data pipeline, it has to be kind of prevalent everywhere, that ability to bring data, trust the data, high quality data, high velocity data, business ready into your analytics infrastructure, but then the important part is the point you alluded to, which is that's really take action. If information is sitting in a report or a dashboard, it's not helping your business. It's that last mile where you get an insight that the analytics gave you from this modern stack that you've built, but now through intelligent automation, you can automatically do something with it. No human involved, you don't need that anymore. What you need is the ability to trigger transaction, change pricing, update inventory, make a decision and then action it in the systems as Crawford talked about, that's kind of where their future is going. So, automating, historically, we've automated business processes. Now you're talking about automating decisions. Automating decisions based on insights that you get out of analytics that you maybe didn't know a minute ago, right? And that's where we talk about these large language models, we talk about AI, ML, AI, right? You're learning insights and you're learning all the time. It's continuous learning. It's not some predetermined algorithm that you've built. It really is kind of modern ML, AI, decision-making, looking forward, not backwards. Do you think that we'll get to the point? Right now, the data is embedded in the business logic. Do you think that is flipping? I mean, Ford talked about 650 data elements. Are we going to see a day where we embed the business logic in the data? And so we can actually make real-time decisions. You guys talk about stuff in IoT, and the edge, and factories, and that's a whole different ball game. Yeah, but we're talking about looking forward. So you're right. I mean, I think the whole world, everything was premised on historical patterns and then setting things up to say, right, when this happens is, you know, do this. But now it's, we don't know what's going to happen. This whole world today, right? So it's canal, the disruption, black swan events. So having the data real-time streaming, you may get a new insight and you may do something that you've never done before. That's kind of the modern data and analytics landscape, insights to action. We also caught active intelligence. That's the way the future's going. You talked about the massive uncertainty that we're living in. We have been for a while. And one of the themes of this event is delivering that certainty. You also shared a set of, for you to share to our audience, in terms of how click is enabling its customers to actually leverage AI and machine learning successfully. We talked with so many customers or hear so many stories of AI projects that aren't succeeding. How is click different? And I want to, Crawford, get your perspective on that as well. How are you helping customers really leverage AI and machine learning to drive those insights, take action? Yeah, well, I think the historical failing of AI and ML is that it's always been this thing over here, right? So you have the white coat data scientists were very specialized. And the problem was you had to take data out of wherever you had it, hand it to those people so they could write some R and Python code on it. You could put it into like a third-party tool to build some pattern recognition and get to an algorithm. Now what we've done is we brought AI ML into the analytics platform, right? So you did all this work to get your data ready for analytics and then you take it out. How dumb is that? That's crazy. Like bring the AI ML there. And what we're seeing now is two or three of our customers are in the cloud actually building models inside of their analytics platform. You don't have to take it out anymore. And when you do that and then you get the insight, you build that algorithm. Now it's there, it's already there and you can start using it right away. And that's what you were talking about Crawford. The AI ML is going to be embedded into the applications. You know, it's not like this separate. Yeah. The more you think about it as something separate, the more you're talking about what Mike's talking about which is sort of this unfortunate world that we were kind of in for a long time. This is really about making better decisions in the workflow which is what drives the workflow, the app, right? So the inside that app is where you can start making and you can do that across functions. It's the client success manager giving her a better recommendation to make that customer happier. It's the sales person. How do you have a follow on recommendation to be able to close the deal with an ML, with a piece of AI that basically says, look, this is 79 or 80% chance you're going to close this if you propose this given these conditions. That's I think where we're going to start evolving to and where we're seeing those early signs right now. So I want to run something by both of you. Mike, you talked about sort of we've been looking at the past and this is, we're in a new world now. I've been working on a model of data for the future. And I use Uber as the example where you have riders, drivers, roots, destinations and everything, those are data elements and they're all coherent. And so when I talk to customers, I'm inferring. They don't necessarily say this directly but they kind of do. We want a digital representation of our business. People places things in real time with all these coherent data elements. Is that futuristic to you? Is that here today? Is that sort of midterm, longterm? Is it even a viable model of the data future? It's 100% viable. So great customer, public reference, our mark. They run all sports concessions for sports stadiums, for example. Real time, they're streaming information about what's going on at that game. Obviously all the concessions, all the food, everything, what's selling, what's not selling should be changing pricing. But they're also streaming in the weather. They're streaming in the score of the game. Are people going to start leaving? Is it really cold? And they're representing that real time. And then they're making decisions about should we unfreeze more hot dogs? Should we make more hot chocolate? Should we lower pricing? The hot dogs are going to taste very good tomorrow if they're all still here. And then they push pricing changes back dynamically to their point of sale systems to optimize what's going on in the game. So what you're talking about exists today and they have big command center and they see it all in real time. And that's one of the things I think we learned in the pandemic was access to real time data and insights is no longer a nice to have for organizations in any industry. It absolutely is demanded. It's required to be successful going forward. Right? I mean, as consumers, we expect to be connected 24 seven as we talked about and get whatever I want from whoever, wherever I am. And that expectation isn't going anywhere. And I think the key is embedding that into workflows that exist today and then extending that into new workflows in the future. All right, different subject, M&A. You guys have been on a TAM expansion tear. Yeah. So I was telling the audience up front that maybe click used to come on in the early days of big data, you know, kind of cool viz, totally transformed the company. I think I counted nine acquisitions. I'm sure I'm probably maybe missing some. How are you thinking about go forward with regard to organic investment versus inorganic investment? Yeah, so your accounting is good. MathisGrab, we've done nine and Talon, which is in flight right now when it closes will be the 10th in my tenure. So since in the last five years. Look, it's all been customer informed. So it's all it's all been informed by customers saying, look, the analytics, your analytics are great. They're best in class and my problem is bigger. My problem is much bigger. It's getting raw data out of all these crazy places out of all these sources into an analytics already format. And then as Crawford pointed out, it's about then actually getting into the app, getting those insights into where it's useful for the customers, they can get the most value out of it, right? And so that's informed our M&A strategy. And the good news is it's been some really terrific companies that we've been able to partner with and acquire that have extended our platform. Well, at the same time, we spent last kind of five years or since 2016, we spent half a billion dollars organically. So it's a really great balance of internal R&D spend as well as M&A and you should expect that to continue. We've got a very strong balance sheet. You should expect us to continue doing more M&A as appropriate. Well, Attunity was a home run. Again, those are guys early on in the big data world that they would come on theCUBE and we try to figure out, okay, where do they fit? And that's been a game changing for you guys. Talk about the rationale for the talent acquisition, where there's overlap, where there's differentiate. I mean, I looked at the data that I had and there was a lot of click inside of talent accounts. I mean, pretty heavy overlap, maybe not so much the reverse. So that was, I think, really a real positive. But what's the rationale and where are the gaps and where's the sort of overlaps? Yeah, it's highly complimentary. So I was saying on stage today that from the first day I got to click, talent was on my shopping list of companies that I thought would make an excellent partner for us. What we bring, initially what we brought was a strong analytics, MLAI. You mentioned the Attunity acquisition. Yeah, it was a home run. Like I'm really proud of that one. The time was a little nervous. Spent half a billion dollars, but it turned out to be an amazing acquisition because we caught the wave of this kind of modernization into modern cloud data lakes. So we had the data integration and then we've got the analytics. What talent brings is data transformation, ability to actually transform data in flight to help make a business ready. They bring data quality, they bring data preparation. So these are highly complimentary capabilities to our existing platform. So we honestly think it's a match made in heaven and there's a very, very little overlap between the two organizations, which is the exact kind of acquisition that you want to do to be strategic. Graf, I got to ask you. So you said with some people out there think this is going to require a new platform. That's like, that's me. But you said, no, that's not good. So let's double click on that a little bit. What did you mean by, you're saying there's a lot we could do with the existing platforms, obviously, but how far do you think existing platform, and what do we mean by platform? Are we talking database? Are we talking hardware? Double click on that, please. So I think that when you think about the way companies are structuring and thinking about their data today, this is a completely different relationship with data. It's a completely different set of tools that you can apply to your data to make better decisions. And I think that when you think about those centralized data analytics models, all the investments companies have made, there's now a way to access external tools, bring those into the organization, and then think about taking those ML engines and applying those to workflows that you have, applying those to applications, and by the way, it's incumbent on application companies to bring those tools in as well. That's going to feed a whole different set of skills, and it's going to mean that people are going to be interacting with their data in an entirely different way. And I would argue that from a platform standpoint, it's going to drive a whole different set of development and application development on a new set of data tools. And that is a new platform. And I would argue, and I'm going to come full circle on you here, this whole new world we're entering is going, and I didn't really talk about it today because we didn't have time, but if you look at the upper right-hand side of my slide, I've reintroduced the idea of the metaverse. Let me tell you something, metaverse is an entirely different experience when you can apply AI to it, when you can apply the ability to basically enter the metaverse, summon up new decisions, summon up new data, and interact with that data in a new way. And I believe this will, in a few years, we're going to start to have a whole different conversation about the metaverse because of the investments we're making in AI today. And so, Mike, you're down on crypto, but you're not down, are you down on the metaverse too? Yeah, I'm not down on crypto. I just think that we serve our customers better by investing our earnings back into analytics technology. As opposed to putting Bitcoin in your balance sheet. I didn't mention crypto. I love crypto, come on, I want to record it. Crawford, talk a little bit about, if we look at the recent advances in analytics in data and AI, when customers are coming to IDC, hey guys, help us, we've got a ton of data, the data explosion is going nowhere, it's only getting bigger, what are some of the things that you talked to them about in terms of where it click is, where those trends are going? Yeah, so what we really talk about is you've got to bring it back to your internal customers, you've got to bring it back to the customer job that that customer is trying to get done. And if you can't do that, if you're just looking for, quote, insights in your data, you're completely lost, right? I mean, you don't really have a strong opportunity. And I think that customers right now are terrified. They're terrified because there's a lot of products out there that are sort of instantiating data and they're getting scraped and they're not very active, what I would say. So for example, I wouldn't want to be WebMD right now. I mean, those kinds of sites are in a really, really tough spot because they can basically, you can get better answers out of them than sort of the static data that's put out there right now. So what we're trying to do is coach customers to be able to really think about zeroing in on the core jobs that their customers want to get done and how you can use this data to better assist the roles. Now, you have to then work backwards to the data structure that you have, the data you're collecting, what data can you augment? And we talk a lot with customers about this, which is how do I take my data and augment it with external data that I can get to make better decisions? Yes. Are you, do you see yourselves, I know the answer, but I'm going to ask anyway, do you see yourselves as an ISV or a platform? Click? No, we see ourselves as a platform, but we enable ISVs and we enable our partners, but 100% we're a platform. Of course, and I knew that was going to be the answer because you know, the cloud guys think there's three platforms, right? It's so, you know, but we know better. So given that, I think you said you're going to be number one, sorry Crawford, Gartner Magic Partners analytics, I think I got this, analytics data integration tools and data quality solutions. Is that, are those the three that you talked about? Is it that, I'd like to see Gartner take all their MQs and put them together as a platform view? I mean, is it that the right way that the customers should be thinking about this as opposed to what Crawford was saying, bespoke tools? So I don't work at Gartner, I don't know what goes on there. But I'm not trying to criticize Gartner. I'm talking about how you look at the market. But this is how we look at it, it was interesting because we got, like, we, I get the question, like, oh, you bought talent, does that mean you're de-emphasizing analytics? It's like, no, it's the exact opposite. We're making the analytics better. Like, the data needs to be better, the data quality needs to be good, so the analytics could be good. So what we're doing is we're actually doubling down on our analytics by enhancing data integration, data quality, to provide better outcomes. And I think that's the thing that everybody misses, but, and I'm sure Crawford agrees with me, like, over time, everybody's going to get it. Like, this is one continuum, this isn't a different thing. And I didn't mean to, Gartner, I mean, they do a great job, but in terms of customers making decisions, I see it in security, it's just this mess. It's bespoke tools, and it's similar things in data. So they, my point to you, Crawford, is shouldn't customers be thinking about the whole house of the analytics and data integration and data quality platform? 100%. And I'll go back to why security is a mess. Security is a mess because the threat landscape changes daily. And therefore you have all these different tools that you can't argue, because consolidation and security won't even fix the problem, because the problem the customer is facing is changing dramatically month by month by month. In this world, this is my whole argument. The customer problem isn't necessarily changing, but the tools are. And that's where the customer really needs to think about optimizing for a different set of tools. And to your point, I believe this is a platform, and I believe this is a platform that can have rich application development around it. Guys, last question. And first of all, you brought the optimism, you both brought it. It's definitely contagious. This is day one of a two day show. The show floor is already packed. Mike, what can we expect at this show? Yeah, well, we're just getting started. So today you got kind of the strategic overview and Crawford and I thought did a nice job together of kind of painting what the future could look like. But now, next comes the product overview and the demos and the touching and feeling. So more action, less talk, right? More show, less tell. And we're going to blow people away because we're going to show the end-to-end platform all the way from raw data, all the way through analytics, show some of the talent capabilities, analytics platform, auto ML, LAI, and then some actions. It is going to be mind blowing. Show and tell, touchy feely. We love it. Mike Crawford, thank you so much for joining us on theCUBE, kicking off day one. Great commentary, great insights. Looking forward, as you say. We appreciate it. Thank you. For our guests and for Dave Vellante, this is Lisa Martin coming to you live from Las Vegas, baby. This is day one of our coverage of Clickworld 23. Stick around. Our next guest joins us in just a minute.