>>Hello, and welcome to this cube conversation. I'm Sean for, with the cube here in Palo Alto, California, actually coming out of the pandemic this year. Hopefully we'll be back to real life soon. Uh it's uh, in March, shouldn't it be? April spring, 2021. Got a great guest Chris Lynch, who is executive chairman, CEO of scale, who took over at the helm of this company about two and a half years ago, or so, um, lots of going on Chris. Great to see you, uh, remotely, uh, in Boston, we're here in Palo Alto. Great to see you. >>Great to see you as well, but hope to see you in person, this sprint. >>Yeah. I got to say people really missing real life. And I started to see events coming back to vaccines out there, but a lot going on. I mean, Dave and I Volante, I was just talking about how, um, you know, when we first met you and big data world was kicking ass and taking names a lot's changed at Duke went the way it went. Um, you know, Vertica coming, you led, did extremely well sold. HP continue to be a crown jewel for HPE. Now the world has changed in the data and with COVID more than ever, you starting to see more and more people really doubling down. You can see who the winners and losers are. You starting to see kind of the mega trend, and now you've got the edge and other things. So I want to get your take at scale, took advantage of that pivot. You've been in charge. Give us the update. What's the current strategy of that scale? >>Sure. Well, when I took the company over about two and a half years ago, it was very focused on accelerating the dupe instances. And, uh, as you mentioned earlier, the dupe is sort of plateaued, but the ability to take that semantic layer and deliver it in the cloud is actually even more relevant with the advent of snowflake and Databricks and the emergence of, uh, Google big query, um, and Azure as the analytic platforms, in addition to Amazon, which obviously was, was the first mover in the space. So I would say that while people present big day in as sort of a passing concept, I think it's been refined and matured and companies are now digitizing their environment to take advantage of being able to deliver all of this big data in a way that, um, they could get actionable insights, which I don't think has been the case through the early stages of the development of big data concepts. >>Yeah, Chris, we've always followed your career. You've been a strong operator, but also see things a little bit early, get on the wave, uh, and help helps companies turn around also on public, a great career. You've had, I got to ask you in your opinion and you, and you can make sense for customers and make sure customers see the value proposition. So I got to ask you in this new world of the semantic layer, you mentioned snowflake, Amazon and cloud scales. Huge. Why is the semantic layer important? What is it and why is it important for customers? What are they really buying with this? >>Well, they're buying a few things, the buying freedom and choice because we're multicloud, um, they're, they're buying the ability to evolve their environments versus your evolution versus revolution. When they think about how they move forward in the next generation of their enterprise architecture. And the reason that you need the semantic layer, particularly in the cloud is that we separate the source from the actual presentation of the data. So we allow data to stay where it is, but we create one logical view that was important for legacy data workloads, but it's even more important in a world of hybrid compute models in multi-vendor cloud models. So having one source of truth, consistency, consistent access, secure access, and actual insights to wall, and we deliver this with no code and we allow you to turbocharge the stacks of Azure and Amazon Redshift and Google big query while being able to use the data that you've created your enterprise. So, so there's a demand for big data and big data means being able to access all your data into one logical form, not pockets of data that are in the cloud that are behind the firewall that are constrained by, um, vendor lock-in, but open access to all of the data to make the best decisions. >>So if I'm an enterprise and I'm used to on-premise data warehouses and data management, you know, from whether it's playing with a dupe clusters or whatever, I see snowflake, I see the cloud scale. How do I get my teams kind of modernized if you had to kind of go in and say, cause most companies actually have a hard time doing that. They're like they got to turn their existing it into cloud powerhouses. That's what they want to do. So how do you get them there? What's the secret in your opinion, to take a team and a company that's used to doing it on prem, on premises to the cloud? >>Sure. It's a great question. So as I mentioned before, the difference between evolution and revolution today, without outscale to do what you're suggesting is a revolution. And you know, it's very difficult to perform heart surgery on the patient while he's running the Boston marathon. And that's the analog I would give you for trying to digitize your environment without this semantic layer that allows you to first create a logical layer, right? This information in a logical mapping so that you can gradually move data to the appropriate place. Without us. You're asked to go from, you know, one spot to another and do that while you're running your business. And that's what discourages companies or creates tremendous risk with digitizing your environment or moving to cloud. They have to be able to do it in a way that's non-disruptive to their business and seamless with respect to their current workflows. >>No, Chris, I got to ask you without, I know you probably not expecting this question, but um, most people don't know that you are also an investor before you as CEO, um, angel investor as well. You did an angel investment deal with a chemical data robot. We've had a good outcome. And so you've seen the wave, you've seen a kind of how the progress, you mentioned snowflake earlier. Um, as you look at those kinds of deals, as they've evolved, you know, you're seeing this acceleration with data science, what's your take on this because you know, those companies that have become successful or been acquired that you've invested in now, you're operating at scale as a company, you got to direct the company into the right direction. Where is that? Where are you taking this thing? >>Sure. It's a great, great question. So with respect to AI and ML and the investment that I made almost 10 years ago and data robot, um, I believe then, and I believe now more than ever that AI is going to be the next step function in industrial productivity. And I think it's going to change, you know, the composition of our lives. And, um, I think I have enough to have been around when the web was commercialized in the internet, the impact that's having had on the world. I think that impact pales in comparison to what AI, the application of AI to all walks of life has gone going to do. Um, I think that, um, within the next 24 months companies that don't have an AI strategy will be shorted on wall street. I think every phone, every, every vertical function in the marketplace is going to be impacted by AI. >>And, um, we're just seeing the infancy of mass adoption application when it comes to at scale. I think we're going to be right in the middle of that. We're about the democratization of those AI and machine learning models. One of the interesting things we developed it, this ML ops product, where we're able to allow you with your current BI tool, we're able to take machine learning models and just all the legacy BI data into those models, providing better models, more accurate, and precise models, and then re publish that data back out to the BI tool of your choice, whether it be Tableau, Microsoft power, BI Excel, we don't care. >>So I got to ask you, okay, the enterprises are easy targets, large enterprises, you know, virtualization of the, of this world that we're living with. COVID virtualization being more, you know, virtual events, virtual meetings, virtual remote, not, not true virtualization, as we know it, it virtualization, but like life of virtualization of life companies, small companies like the, even our size, the cube, we're getting more data. So you start to see people becoming more data full, not used to dealing with data city mission. They see opportunities to pivot, leverage the data and take advantage of the cloud scale. McKinsey, just put out a report that we covered. There's a trillion dollars of new Tam in innovation, new use cases around data. So a small company, the size of the cube Silicon angle could be out there and innovate and build a use case. This is a new dynamic. This is something that was seen, this mid-market opportunity where people are starting to realize they can have a competitive advantage and disrupt the big guys and the incumbents. How do you see this mid market opportunity and how does at-scale fit into that? >>So you're as usual you're spot on John. And I think the living breathing example of snowflake, they brought analytics to the masses and to small and medium enterprises that didn't necessarily have the technical resources to implement. And we're taking a page out of their book. We're beginning to deliver the end of this quarter, integrated solutions, that map SME data with public markets, data and models, all integrated in their favorite SAS applications to make it simple and easy for them to get EnLink insight and drive it into their business decisions. And we think we're very excited about it. And, you know, if, if we can be a fraction, um, if we can, if we get a fraction of the adoption that snowflake has will be very soon, we'll be very successful and very happy with the results this year. >>Great to see you, Chris, I want to ask you one final question. Um, as you look at companies coming out of the pandemic, um, growth strategies is going to be in play some projects going to be canceled. There's pretty obvious, uh, you know, evidence that, that has been exposed by working at remote and everyone working at home, you can start to see what worked, what wasn't working. So that's going to be clear. You're gonna start to see pattern of people doubling down on certain projects. Um, at scales, a company has a new trajectory for folks that kind of new the old company, or might not have the update. What is at scale all about what are what's the bumper sticker? What's the value proposition what's working that you're doubling down on. >>We want to deliver advanced multi-dimensional analytics to customers in the cloud. And we want to do that by delivering, not compromising on the complexity of analytics, um, and to do that, you have to deliver it, um, in a seamless and easy to use way. And we figure out a way to do that by delivering it through the applications that they know and love today, whether it be their Salesforce or QuickBooks or you name, the SAS picked that application, we're going to turbocharge them with big data and machine learning in a way that's going to enhance their operations without, uh, increase the complexity. So it's about delivering analytics in a way that customers can absorb big customers and small customers alike. >>While I got you here, one final final question, because you're such an expert at turnarounds, as well as growing companies that have a growth opportunity. There's three classes of companies that we see emerging from this new cloud scale model where data's involved or whatever new things out there, but mainly data and cloud scale. One is use companies that are either rejuvenating their business model or pivoting. Okay. So they're looking at cost optimization, things of that nature, uh, class number two innovation strategy, where they're using technology and data to build new use cases or changed existing use cases for kind of new capabilities and finally pioneers, pioneering new net, new paradigms or categories. So each one has its own kind of profile. All, all are winning with data as a former investor and now angel investor and someone who's seen turnarounds and growing companies that are on the innovation wave. What's your takeaway from this because it's pretty miraculous. If you think about what could happen in each one of those cases, there's an opportunity for all three categories with cloud and data. What's your personal take on that? >>So I think if you look at, um, ways we've seen in the past, you know, particularly the, you know, the internet, it created a level of disruption that croup that delivered basically a renewed, um, playing field so that the winners and losers really could be reset and be based on their ability to absorb and leverage the new technology. I think the same as an AI and ML. So I think it creates an opportunity for businesses that were laggerts to catch, operate, or even supersede the competitors. Um, I think it has that kind of an impact. So from my, my view, you're going to see as big data and analytics and artificial intelligence, you know, mature and coalesce, um, vertical integration. So you're going to see companies that are full stack businesses that are delivered through AI and cloud, um, that are completely new and created or read juvenile based on leveraging these new fundamentals. >>So I think you're going to see a set of new businesses and business models that are created by this ubiquitous access to analytics and data. And you're going to see some laggerts catch up that you're going to see some of the people that say, Hey, if it isn't broke, don't fix it. And they're going to go by the wayside and it's going to happen very, very quickly. When we started this business, John, the cycle of innovation was five it's now, you know, under a year, maybe, maybe even five months. So it's like the difference between college for some professional sports, same football game, the speed of the game is completely different. And the speed of the game is accelerating. >>That's why the startup actions hot, and that's why startups are going from zero to 60, if you will, uh, very quickly, um, highly accelerated great stuff. Chris Lynch veteran the industry executive chairman CEO of scale here on the cube conversation with John furrier, the host. Thank you for watching Chris. Great to see you. Thanks for coming on. >>Great to see you, John, take care. Hope to see you soon. >>Okay. Let's keep conversation. Thanks for watching.