 Hello and welcome to this CUBE Conversation. I'm John Furrier with theCUBE here in Palo Alto, California. So obviously coming out of the pandemic this year, hopefully we'll be back to real life soon. It's March, soon to be April, spring 2021. Got a great guest, Chris Lynch, who's the executive chairman and CEO of AtScale, who took over at the helm of this company about two and a half years ago or so. Lots going on, Chris, great to see you remotely 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 spring. Yeah, I got to say, people are really missing real life and I started to see events coming back, the vaccines out there, but a lot going on. I mean, Dave and I, Palo Alto, I was just talking about how, you know, when we first met you and big data world was kicking ass and taking names, a lot's changed. Hadoop went the way it went, you know, Vertico company you led did extremely well sold to HP, continue to be a crown jewel for HPE. Now the world has changed in the data and with COVID, more than ever, you're starting to see more and more people really doubling down, you can see who the winners and losers are. You're starting to see kind of the mega trend and now you got the edge and other things. 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 at scale? Sure, well, when I took the company over about two and a half years ago, it was very focused on accelerating Hadoop instances. As you mentioned earlier, Hadoop 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 Google BigQuery and Azure as analytic platforms in addition to Amazon, which obviously was the first mover in the space. So I would say that while people present big data sort of a passe 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 they can get actionable insights, which I don't think has been the case through the early stages of the development of big data concepts. You know, 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 and help companies turn around. Also, on public, a great career you've had. I got to ask you, in your opinion, and you can make sense for customers and make sure customers keep the value proposition. So I got to ask you, in this new world of the semantic layer, you mentioned Snowflake, Amazon, obviously cloud scales, huge, why is this 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. They're buying freedom of choice because we're multi-cloud. 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 and multi-vendor cloud models. So having one source of truth, consistency, consistent access, secure access and actionable insights to all. And we deliver this with no code and we allow you to turbocharge the stacks of Azure and Amazon Redshift and Google BigQuery while being able to use the data that you've created in your enterprise. 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 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, from whether it's playing with Hadoop 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, because most companies actually have a hard time doing that, 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 in a company that's used to doing it 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 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 the semantic layer that allows you to first create a logical layer, right? All this information and logical mapping so that you can gradually move data to the appropriate place. Without us, you're asked to go from 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. Chris, I got to ask you without, I know you probably not expecting this question, but most people don't know that you're also an investor before you as CEO, angel investor as well. You did an angel investment deal with a company called Data Robot, which had a good outcome. And so you've seen the wave, you've seen it kind of how the progress, you mentioned Snowflake earlier. As you look at those kinds of deals as they've evolved, you're seeing this acceleration with data science. What's your take on this? Because those companies that have become successful or have 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 are you taking this thing? Sure, it's a great question. So with respect to AI and ML and the investment that I made almost 10 years ago in Data Robot, 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 the composition of our lives. And I think enough to have been around when the web was commercialized and the internet, the impact that's had on the world. I think that impact pales in comparison to what AI, the application of AI to all walks of life is going to do. I think that within the next 24 months, companies that don't have an AI strategy will be shorted on Wall Street. I think every vertical function in the marketplace is going to be impacted by AI. And we're just seeing the infancy of mass adoption and 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 is this ML Ops product where we're able to allow you with your current BI tool, we're able to take machine learning models ingest all the legacy BI data into those models, providing better models, more accurate and precise models, and then republish that data back out to the BI tool of your choice, whether it be Tablo, Microsoft Power BI, Excel, we don't care. So I got to ask you, okay, the enterprises are easy targets, the large enterprises, virtualization of this world we're living with COVID virtualization being more virtual events, virtual meetings, virtual remote, not true virtualization as we know it, IT virtualization. But like life, a virtualization of life. Companies, small companies, like 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. So you mentioned they see opportunities to pivot, leverage the data and take advantage of the cloud scale. And when Kinsey 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 we're seeing 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 spot on John and I think the living breathing example of Snowflake, they brought analytics to the masses into 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 SaaS applications to make it simple and easy for them to get insight and drive it into their business decisions. And we think we're very excited about it. And if we can be a fraction of the adoption that Snowflake has, 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. As you look at companies coming out of the pandemic, growth strategy is going to be in play. Some projects going to be canceled. There's pretty obvious evidence 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 going to start to see pattern of people doubling down on certain projects. At scale as a company has a new trajectory. For folks that kind of knew 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. And to do that, you have to deliver it 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 sales force or QuickBooks or you name this aspect application, we're going to turbo charge them with big data and machine learning in a way that's going to enhance their operations without increase the complexity. So it's about delivering analytics in a way that customers can absorb big customers and small customers alike. Well, 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 is involved or whatever new things out there, but mainly data and cloud scale. One is companies that are either rejuvenating their business model or pivoting, okay? So they're looking at cost optimization, things of that nature. Class number two, innovation strategies where they're using technology and data to build new use cases or change existing use cases for kind of new capabilities. And then finally, pioneers, pioneering new, net new paradigms or categories. So each one has its own kind of profile. 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 ways we've seen in the past, particularly the internet, it created a level of disruption that delivered basically a renewed 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 laggards to catch up or even supersede the competitors. I think it has that kind of an impact. So from my view, you're going to see as big data and analytics and artificial intelligence, mature and coalesce, vertical integration. So you're going to see companies that are full stack businesses that are delivered through AI and cloud that are completely new and created or rejuvenized 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 laggards catch up and 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 years. It's now under a year, maybe even five months. So it's like the difference between college sports and 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, very quickly, highly accelerated. Great stuff. Chris Lynch, veteran of the industry, executive chairman, CEO of AdScale here on the CUBE Conversation with John Furrier, me the CUBE 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. Bye-bye. Okay, it's CUBE Conversation. Thanks for watching.