(mellow music) >> Oh, welcome back to theCUBE. We continue our coverage here at AWS re:Invent 22. We're in the Venetian in Las Vegas, and this place is hopping. I'm tell you what. It is a nearly standing room only that exhibit floor is jam packed, and it's been great to be along for the ride here on Accenture's sponsorship at the Executive summit as well. We'll talk about Pfizer today, you know them quite well, one of the largest biopharmaceutical companies in the world but their tech footprint is impressive, to say the least. And to talk more about that is Wes Barnes, senior Director of Pfizer's Digital Hosting Solutions. Wes, good to see you, sir. >> Good to meet you, John. >> And Jon Harrison, the North American lead for Infrastructure and engineering at Accenture. Jon, good to see you as well. >> Good to see you as well. >> Thanks for joining us. >> Happy to be here. >> Alright, so let's jump in. Pfizer, we make drugs, right? >> Pharmaceuticals. >> Yes. >> Among the most preeminent, as I said biopharms in the world. But your tech capabilities and your tech focus as we were talking about earlier, has changed dramatically in the 18 years that you've been there. >> Yep. >> Now, talk about that evolution a little bit to where you were and what you have to be now. >> Yeah, yeah. It's interesting. When I started at Pfizer, IT was an enabling function. It was akin to HR or our facilities function. And over the past couple years, it's dramatically changed. Where Digital now is really at the center of everything we do across Pfizer. You know it really is a core strategic element of our business. >> Yeah. And those elements that you were talking about, just in terms of whether it's research, whether it's your patients, I don't want to go through the laundry lists the litany of things, but the touch points with data and what you need it to do for you in terms of you know, computations, what you, the list is long. It's pretty impressive. >> Yeah, yeah, for sure yeah. >> I mean, shed some light on that for us. >> We cannot release a medicine without the use of technology. And if you think about research now, a huge component of our research is computational chemistry. Manufacturing medicines now is a practice in using data and analytics and predictive machine learning and analytics capabilities to help us determine how to best you know, apply the capabilities to deliver the outcomes that we need. The way in which we connect with patients and payers now is wholly digital. So it's an entirely different way of operating than it was 10 years ago. >> And the past three years, pretty remarkable in many respects, to say the least, I would think, I mean, John, you've seen what Pfizer's been up to, talk about maybe just this, the recent past and all that has happened and what they've been able to do. >> Yeah, I mean, what is so exciting to me about working with a company like Pfizer and working in life sciences more broadly is the impact that they make on patients around the world world, right? I mean, think about those past three years and Pfizer stepped up and met the moment for all of us, right? And as we talk a little bit about the role that we played together with Pfizer with AWS in their journey to the cloud, it's so motivating for myself personally it's so motivating for every single person on the team that we ask to spend nights and weekends migrating things to the cloud, creating new capabilities, knowing that at the end of the day, the work that they're doing is making the world a healthier place. >> Yeah, we talk so much about modernization now, right? And it's, but it kind of means different things to different people depending on where you're coming into the game, right? If you've been smart and been planning all along then this is not a dramatic shift in some cases though, for others it is. Right? >> Yeah. >> Traumatic in some cases for some people. >> For sure. >> For Pfizer, I mean talk about how do you see modernization and what does it mean to your operations? >> Following our success of the COVID program of 2021, I mean it became evident to us that, you know we needed to maintain a new pace of innovation and in fact try to find ways to accelerate that pace of innovation. And as I said earlier everything we do at Pfizer is centered around digital. But despite that, and despite 10 years of consolidating infrastructure and moving towards modern technology, last year, only 10% of Pfizer's infrastructure was in the native public cloud. So we had a problem to solve. In fact, I remember, you know, we had to build up our clinical systems to support the volume of work that we were doing for COVID-19 vaccine. We were rolling things into our data center to build up the capacity to achieve what we needed to achieve. Moving to the public cloud became more imperative to try to achieve the scale and the modern capabilities that we need. >> And so where did you come into play here with this? Because obviously as a partner you're right alongside for the ride but you saw these inherent challenges that they had and how did Accenture answer the bell there? >> Well, so look, I mean we saw Pfizer react to the pandemic. We saw them seize the moment. We talked together about how IT needed to move quicker and quicker towards the cloud to unlock capabilities that would serve Pfizer's business well into the future. And together we laid out some pretty ambitious goals. I mean, really moving at a velocity in a pace that I think for both Accenture and AWS surpassed the velocity and pace that we've done anywhere else. >> Yeah, right, yeah. >> So we've set out on an ambitious plan together. You know, I was kind of reflecting about some of the successes, what went well what didn't in preparation for re:Invent. And you know, many of the folks that'll listen to this will remember the old days of moving data centers when you'd have a war room you'd have a conference bridge open the whole time. Someone would be running around the tile floor in the data center, do a task, call back up to the bridge and say, what do I do next, right? Then when I think about what we did together at Pfizer in moving towards the public cloud, I mean, we had weekends most weekends where we were running a wave with 10,000 plus discrete activities. >> Yeah. >> Wow. >> Right, so that old model doesn't scale. >> Right. >> And we really anchored, >> You have a very crowded data center with a lot of people running into each other. >> You'd have a whole lot of people running around. But we really anchored to an Accenture capability that we call myNav Migrate. I know you guys have talked about it here before so I won't go into that. But what we found is that we approached this problem of velocity not as a technical problem to solve for but as a loading and optimization problem of resources. Right, thought about it just a little bit different way and made sure that we could programmatically control command and control of the program in a way that people didn't have to wait around all Saturday afternoon to be notified that their next activity was ready, right? They could go out, they could live their day and they could get a notification from the platform that says, hey it's about your turn. Right, they could claim it they could do it, they could finish it, and that was really important to us. I mean, to be able to control the program in that type of way at scale. >> Yeah, by the way, the reason we went as fast it was a deliberate choice and you'll talk to plenty of folks who have a five year journey to the public cloud. And the reason we wanted to move as fast as we did and Jon talked about some of it, we wanted to get the capabilities to the business as quickly as we could. The pace of innovation was such that we had to offer native cloud capabilities we had to offer quickly. We also knew that by compressing the time it took to get to the cloud, we could focus the organization get it done as economically as possible but then lift all boats with the tide and move the organization forward in terms of the skills and the capabilities that we need to deliver modern outcomes. >> So, you know, we talk about impacts internally, obviously with your processes, but beyond that, not just scientists not just chemists, but to your, I mean, millions of customers, right? We're talking, you know, globally here. What kind of impacts can you see that directly relate to them, and benefits that they're receiving by this massive technical move you've made? >> Pfizer's mission is breakthroughs that change patient lives. I mean, the work that we do the work that everybody does within Pfizer is about delivering therapies that, you know provide health outcomes that make people live longer, live healthier lives. For us, modernizing our infrastructure means that we can enable the work of scientists to find novel therapies faster or find things that perhaps couldn't have been found any other way without some of the modern technologies that we're bringing to bear. Saving money within infrastructure and IT is treasure that we can pour back into the important areas of research or development or manufacturing. We're also able to, you know, offer an ecosystem and a capability in which we connect with patients differently through digital mechanisms. And modern cloud enables that, you know, using modern digital experiences and customer experience, and patient experience platforms means that we can use wearable devices and mobile technologies and connect to people in different ways and offer solutions that just didn't exist a couple years ago. >> And so, I mean, you're talking about IoT stuff too, right? >> 100%. >> It's way out on the edge and personal mobile, in a mobile environment. And so challenges in terms of you know, data governance and compliance and security, all these things, right? They come into play because it's personal health information. So how, as you've taken them, you know to this public cloud environment how much of a factor are those considerations? Because, you know, this is not just a product a service, it's a live human being. >> Yeah. I mean, you start with that, you think about it through the process and you think about it afterwards, right, I mean, that has to be a core factor in every stage of the program, and it was. >> So in, in terms of where you are now, then, okay, it's not over. >> It's never over. >> I mean, you know, as good as you are today and as fast as you are and as accurate and as efficient. >> Yeah. >> Got to get better, right? You got to stay competitive. >> Yeah. >> So where do you find that? Because, you know, with powers being what they are with speed and what it is how much more is there to squeeze out of this rock? >> There's a lot more to squeeze out of the rock. If you think about what we've done over the past year it's about creating sort of a new minimum viable product for infrastructure. So we've sort of raised the bar and created an environment upon which we can continue to innovate that innovation is going to continue sort of forever at this point. You know, the next focus for us is how to identify the business processes that deliver the greatest value ultimately to our patients. And use the modern platform that we've just built to improve those processes to deliver things faster, deliver new capabilities. Pfizer is making a huge investment in digital medicines therapies that are delivered through smart devices through wearables using, as I said technology that didn't exist before. That wouldn't be possible without the platform that we've built. So over the past year, we've come a long way but I think that we've effectively set the table for all of the things that are yet to come. >> So, Jon, how do you then, as you've learned a lot about life science or, and certainly Pfizer with what they're up to, how do you then apply, you know, what you know about their world to what you know about the tech world and make it actionable for growth to make it actionable for, for future expansion? >> Yeah, I mean, we start by doing it together, right? I think that's a really important part. Accenture brings a wealth of knowledge, both industry experience and expertise, technology experience and expertise. We work together with our clients like Pfizer with our partners like AWS to bring the best across that power of three to meet clients where they're at to understand where they want to go, and then create a bespoke approach that meets their business needs. And that's effectively what we're doing now, right? I mean, if you think about the phase that we've just went through, I mean, a couple of fast facts here no pun intended, right? 7,800 server instances across 11 operating system versions 7,500 databases across 20 database versions, right? 4,700 applications, 350,000 migration activities managed across an eight month period. >> In eight months. >> Yeah. But that's not the goal, right? The goal is now to take, to Wes' point that platform that's been developed and leverage that to the benefit of the business ultimately to the benefit of the patient. >> You know, why them, we have we've talked a lot about Pfizer, but why Accenture? What, what, what's, 'cause it's got to be a two way street, right? >> We've had a long partnership with Accenture. Accenture supports a huge component of our application environment at Pfizer and has for quite a long time. Look, we didn't make it easy on them. We put them up against a large number of world class SIs. But look, Accenture brought, you know, sort of what I think of as the trifecta here. They brought the technical capabilities and knowledge of the AWS environment. They brought the ability to really understand the business outcomes that we were trying to achieve and a program leadership capability that, you know I think is world class. And Jon talked about myNav, you know, we recognized that doing what we were trying to do in the time that we were doing it required new machinery, new analytics and data capabilities that just didn't exist. Automation didn't exist. Some people experience capabilities that would allow us to interface with application owners and users at a velocity and a pace and a scale that just hasn't been seen before at Pfizer. Accenture brought all three of those things together and I think they did a great job helping us get to where we need to be. >> When you hear Jon rattle through the stats like he just did, right? We talk about all, I mean, not that I'm going to ask you to pat yourself on the back but do you ever, >> He should. >> Does it blow your mind a little bit, honestly that you're talking about that magnitude of activity in that compressed period of time? That's extraordinary. >> It's 75% of our global IT footprint now in the public cloud, which is fantastic. I mean, look, I think the timing was right. I think Pfizer is in a little bit of a unique position coming off of COVID. We are incredibly motivated to keep the pace up, I mean across all lines of business. So, you know what we found is a really willing leadership team, executive leadership team, digital leadership team to endorse a change of this magnitude. >> Well, it's a great success story. It's beyond impressive. So congratulations to both you on that front and certainly you wish you continued success down the road as well. >> Thank you. >> Thank you gentlemen. >> Thank you. >> Good job. >> Pfizer, and boy, you talk about a job well done. Just spectacular. All right, you are watching our coverage here on theCUBE, we're at the AWS re:Invent 22 show. This is Executive Summit sponsored by Accenture and you're watching theCUBE the leader in high tech coverage.

(mellow music) >> Oh, welcome back to theCUBE. We continue our coverage here at AWS re:Invent 22. We're in the Venetian in Las Vegas, and this place is hopping. I'm tell you what. It is a nearly standing room only that exhibit floor is jam packed, and it's been great to be along for the ride here on Accenture's sponsorship at the Executive summit as well. We'll talk about Pfizer today, you know them quite well, one of the largest biopharmaceutical companies in the world but their tech footprint is impressive, to say the least. And to talk more about that is Wes Barnes, senior Director of Pfizer's Digital Hosting Solutions. Wes, good to see you, sir. >> Good to meet you, John. >> And Jon Harrison, the North American lead for Infrastructure and engineering at Accenture. Jon, good to see you as well. >> Good to see you as well. >> Thanks for joining us. >> Happy to be here. >> Alright, so let's jump in. Pfizer, we make drugs, right? >> Pharmaceuticals. >> Yes. >> Among the most preeminent, as I said biopharms in the world. But your tech capabilities and your tech focus as we were talking about earlier, has changed dramatically in the 18 years that you've been there. >> Yep. >> Now, talk about that evolution a little bit to where you were and what you have to be now. >> Yeah, yeah. It's interesting. When I started at Pfizer, IT was an enabling function. It was akin to HR or our facilities function. And over the past couple years, it's dramatically changed. Where Digital now is really at the center of everything we do across Pfizer. You know it really is a core strategic element of our business. >> Yeah. And those elements that you were talking about, just in terms of whether it's research, whether it's your patients, I don't want to go through the laundry lists the litany of things, but the touch points with data and what you need it to do for you in terms of you know, computations, what you, the list is long. It's pretty impressive. >> Yeah, yeah, for sure yeah. >> I mean, shed some light on that for us. >> We cannot release a medicine without the use of technology. And if you think about research now, a huge component of our research is computational chemistry. Manufacturing medicines now is a practice in using data and analytics and predictive machine learning and analytics capabilities to help us determine how to best you know, apply the capabilities to deliver the outcomes that we need. The way in which we connect with patients and payers now is wholly digital. So it's an entirely different way of operating than it was 10 years ago. >> And the past three years, pretty remarkable in many respects, to say the least, I would think, I mean, John, you've seen what Pfizer's been up to, talk about maybe just this, the recent past and all that has happened and what they've been able to do. >> Yeah, I mean, what is so exciting to me about working with a company like Pfizer and working in life sciences more broadly is the impact that they make on patients around the world world, right? I mean, think about those past three years and Pfizer stepped up and met the moment for all of us, right? And as we talk a little bit about the role that we played together with Pfizer with AWS in their journey to the cloud, it's so motivating for myself personally it's so motivating for every single person on the team that we ask to spend nights and weekends migrating things to the cloud, creating new capabilities, knowing that at the end of the day, the work that they're doing is making the world a healthier place. >> Yeah, we talk so much about modernization now, right? And it's, but it kind of means different things to different people depending on where you're coming into the game, right? If you've been smart and been planning all along then this is not a dramatic shift in some cases though, for others it is. Right? >> Yeah. >> Traumatic in some cases for some people. >> For sure. >> For Pfizer, I mean talk about how do you see modernization and what does it mean to your operations? >> Following our success of the COVID program of 2021, I mean it became evident to us that, you know we needed to maintain a new pace of innovation and in fact try to find ways to accelerate that pace of innovation. And as I said earlier everything we do at Pfizer is centered around digital. But despite that, and despite 10 years of consolidating infrastructure and moving towards modern technology, last year, only 10% of Pfizer's infrastructure was in the native public cloud. So we had a problem to solve. In fact, I remember, you know, we had to build up our clinical systems to support the volume of work that we were doing for COVID-19 vaccine. We were rolling things into our data center to build up the capacity to achieve what we needed to achieve. Moving to the public cloud became more imperative to try to achieve the scale and the modern capabilities that we need. >> And so where did you come into play here with this? Because obviously as a partner you're right alongside for the ride but you saw these inherent challenges that they had and how did Accenture answer the bell there? >> Well, so look, I mean we saw Pfizer react to the pandemic. We saw them seize the moment. We talked together about how IT needed to move quicker and quicker towards the cloud to unlock capabilities that would serve Pfizer's business well into the future. And together we laid out some pretty ambitious goals. I mean, really moving at a velocity in a pace that I think for both Accenture and AWS surpassed the velocity and pace that we've done anywhere else. >> Yeah, right, yeah. >> So we've set out on an ambitious plan together. You know, I was kind of reflecting about some of the successes, what went well what didn't in preparation for re:Invent. And you know, many of the folks that'll listen to this will remember the old days of moving data centers when you'd have a war room you'd have a conference bridge open the whole time. Someone would be running around the tile floor in the data center, do a task, call back up to the bridge and say, what do I do next, right? Then when I think about what we did together at Pfizer in moving towards the public cloud, I mean, we had weekends most weekends where we were running a wave with 10,000 plus discrete activities. >> Yeah. >> Wow. >> Right, so that old model doesn't scale. >> Right. >> And we really anchored, >> You have a very crowded data center with a lot of people running into each other. >> You'd have a whole lot of people running around. But we really anchored to an Accenture capability that we call myNav Migrate. I know you guys have talked about it here before so I won't go into that. But what we found is that we approached this problem of velocity not as a technical problem to solve for but as a loading and optimization problem of resources. Right, thought about it just a little bit different way and made sure that we could programmatically control command and control of the program in a way that people didn't have to wait around all Saturday afternoon to be notified that their next activity was ready, right? They could go out, they could live their day and they could get a notification from the platform that says, hey it's about your turn. Right, they could claim it they could do it, they could finish it, and that was really important to us. I mean, to be able to control the program in that type of way at scale. >> Yeah, by the way, the reason we went as fast it was a deliberate choice and you'll talk to plenty of folks who have a five year journey to the public cloud. And the reason we wanted to move as fast as we did and Jon talked about some of it, we wanted to get the capabilities to the business as quickly as we could. The pace of innovation was such that we had to offer native cloud capabilities we had to offer quickly. We also knew that by compressing the time it took to get to the cloud, we could focus the organization get it done as economically as possible but then lift all boats with the tide and move the organization forward in terms of the skills and the capabilities that we need to deliver modern outcomes. >> So, you know, we talk about impacts internally, obviously with your processes, but beyond that, not just scientists not just chemists, but to your, I mean, millions of customers, right? We're talking, you know, globally here. What kind of impacts can you see that directly relate to them, and benefits that they're receiving by this massive technical move you've made? >> Pfizer's mission is breakthroughs that change patient lives. I mean, the work that we do the work that everybody does within Pfizer is about delivering therapies that, you know provide health outcomes that make people live longer, live healthier lives. For us, modernizing our infrastructure means that we can enable the work of scientists to find novel therapies faster or find things that perhaps couldn't have been found any other way without some of the modern technologies that we're bringing to bear. Saving money within infrastructure and IT is treasure that we can pour back into the important areas of research or development or manufacturing. We're also able to, you know, offer an ecosystem and a capability in which we connect with patients differently through digital mechanisms. And modern cloud enables that, you know, using modern digital experiences and customer experience, and patient experience platforms means that we can use wearable devices and mobile technologies and connect to people in different ways and offer solutions that just didn't exist a couple years ago. >> And so, I mean, you're talking about IoT stuff too, right? >> 100%. >> It's way out on the edge and personal mobile, in a mobile environment. And so challenges in terms of you know, data governance and compliance and security, all these things, right? They come into play because it's personal health information. So how, as you've taken them, you know to this public cloud environment how much of a factor are those considerations? Because, you know, this is not just a product a service, it's a live human being. >> Yeah. I mean, you start with that, you think about it through the process and you think about it afterwards, right, I mean, that has to be a core factor in every stage of the program, and it was. >> So in, in terms of where you are now, then, okay, it's not over. >> It's never over. >> I mean, you know, as good as you are today and as fast as you are and as accurate and as efficient. >> Yeah. >> Got to get better, right? You got to stay competitive. >> Yeah. >> So where do you find that? Because, you know, with powers being what they are with speed and what it is how much more is there to squeeze out of this rock? >> There's a lot more to squeeze out of the rock. If you think about what we've done over the past year it's about creating sort of a new minimum viable product for infrastructure. So we've sort of raised the bar and created an environment upon which we can continue to innovate that innovation is going to continue sort of forever at this point. You know, the next focus for us is how to identify the business processes that deliver the greatest value ultimately to our patients. And use the modern platform that we've just built to improve those processes to deliver things faster, deliver new capabilities. Pfizer is making a huge investment in digital medicines therapies that are delivered through smart devices through wearables using, as I said technology that didn't exist before. That wouldn't be possible without the platform that we've built. So over the past year, we've come a long way but I think that we've effectively set the table for all of the things that are yet to come. >> So, Jon, how do you then, as you've learned a lot about life science or, and certainly Pfizer with what they're up to, how do you then apply, you know, what you know about their world to what you know about the tech world and make it actionable for growth to make it actionable for, for future expansion? >> Yeah, I mean, we start by doing it together, right? I think that's a really important part. Accenture brings a wealth of knowledge, both industry experience and expertise, technology experience and expertise. We work together with our clients like Pfizer with our partners like AWS to bring the best across that power of three to meet clients where they're at to understand where they want to go, and then create a bespoke approach that meets their business needs. And that's effectively what we're doing now, right? I mean, if you think about the phase that we've just went through, I mean, a couple of fast facts here no pun intended, right? 7,800 server instances across 11 operating system versions 7,500 databases across 20 database versions, right? 4,700 applications, 350,000 migration activities managed across an eight month period. >> In eight months. >> Yeah. But that's not the goal, right? The goal is now to take, to Wes' point that platform that's been developed and leverage that to the benefit of the business ultimately to the benefit of the patient. >> You know, why them, we have we've talked a lot about Pfizer, but why Accenture? What, what, what's, 'cause it's got to be a two way street, right? >> We've had a long partnership with Accenture. Accenture supports a huge component of our application environment at Pfizer and has for quite a long time. Look, we didn't make it easy on them. We put them up against a large number of world class SIs. But look, Accenture brought, you know, sort of what I think of as the trifecta here. They brought the technical capabilities and knowledge of the AWS environment. They brought the ability to really understand the business outcomes that we were trying to achieve and a program leadership capability that, you know I think is world class. And Jon talked about myNav, you know, we recognized that doing what we were trying to do in the time that we were doing it required new machinery, new analytics and data capabilities that just didn't exist. Automation didn't exist. Some people experience capabilities that would allow us to interface with application owners and users at a velocity and a pace and a scale that just hasn't been seen before at Pfizer. Accenture brought all three of those things together and I think they did a great job helping us get to where we need to be. >> When you hear Jon rattle through the stats like he just did, right? We talk about all, I mean, not that I'm going to ask you to pat yourself on the back but do you ever, >> He should. >> Does it blow your mind a little bit, honestly that you're talking about that magnitude of activity in that compressed period of time? That's extraordinary. >> It's 75% of our global IT footprint now in the public cloud, which is fantastic. I mean, look, I think the timing was right. I think Pfizer is in a little bit of a unique position coming off of COVID. We are incredibly motivated to keep the pace up, I mean across all lines of business. So, you know what we found is a really willing leadership team, executive leadership team, digital leadership team to endorse a change of this magnitude. >> Well, it's a great success story. It's beyond impressive. So congratulations to both you on that front and certainly you wish you continued success down the road as well. >> Thank you. >> Thank you gentlemen. >> Thank you. >> Good job. >> Pfizer, and boy, you talk about a job well done. Just spectacular. All right, you are watching our coverage here on theCUBE, we're at the AWS re:Invent 22 show. This is Executive Summit sponsored by Accenture and you're watching theCUBE the leader in high tech coverage.

(soft music) >> Welcome back, everyone. theCube's live coverage. Day two here at VMware Explore. Our 12th year covering VMware's annual conference formally called Vmworld, now it's VMware Explore. Exploring new frontiers multi-cloud and also bearing some of the fruit from all the investments in cloud native Tanzu and others. I'm John Furrier with Dave Vellante. We have the man who's in charge of a lot of that business and a lot of stuff coming out of the oven and hitting the market. Ajay Patel, senior vice president and general manager of the modern applications and management group at VMware, basically the modern apps. >> Absolutely. >> That's Tanzu. All the good stuff. >> And Aria now. >> And Aria, the management platform, which got social graph and all kinds of graph databases. Welcome back. >> Oh, thank you so much. Thanks for having me. >> Great to see you in person, been since 2019 when you were on. So, a lot's happened since 2019 in your area. Again, things get, the way VMware does it as we all know, they announce something and then you build it and then you ship it and then you announce it. >> I don't think that's true, but okay. (laughs) >> You guys had announced a lot of cool stuff. You bought Heptio, we saw that Kubernetes investment and all the cloud native goodness around it. Bearing fruit now, what's the status? Give us the update on the modern applications of the management, obviously the areas, the big announcement here on the management side, but in general holistically, what's the update? >> I think the first update is just the speed and momentum that containers and Kubernetes are getting in the marketplace. So if you take the market context, over 70% of organizations now have Kubernetes in production, not one or two clusters, but hundreds of clusters, sometimes tens of clusters. So, to me, that is a market opportunity that's coming to fruition. Sometimes people will come and say, Ajay, aren't you late to the market? I say, no, I'm just perfectly timing it. 'Cause where does our value come in? It's enterprise readiness. We're the company that people look to when you have complexity, you have scale, you need performance, you need security, you need the robustness. And so, Tanzu is really about making modern applications real, helping you design, develop, build and run these applications. And with Aria, we're fundamentally changing the game around multicloud management. So the one-two punch of Tanzu and Aria is I'm most excited about. >> Isn't it true that most of the Kubernetes, you know, today is people pulling down open source and banging away. And now, they're looking for, you know, like you say, more of a robust management capability. >> You know, last two years when I would go to many of the largest customers, like, you know, we're doing good. We've got a DIY platform, we're building this. And then you go to the customer a year later, he's got knocked 30, 40 teams and he has Log4j happen. And all of a sudden he is like, oh, I don't want to be in the business of patching this thing or updating it. And, you know, when's the next shoe going to fall? So, that maturity curve is what I was talking about. >> Yeah. Free like a puppy. >> Ajay, you know, mentioned readiness, enterprise readiness and the timing's perfect. You kind of included, not your exact words, but I'm paraphrasing. That's a lot to do with what's going on. I mean, I'll say Cloud Native, IWS, think of the hyper scale partner, big partner and Google and even Google said it today. You know, the market world's spinning in their direction. Especially with respect to VMware. You get the relationship with the hyperscalers. Cloud's been on everyone's agenda for a long time. So, it's always been ready. But enterprise, you are customer base at VMware, very cloud savvy in the sense they know it's there, there's some dabbling, there's some endeavors in the cloud, no problem. But from a business perspective and truly transforming the VMware value proposition, is already, they're ready and it's already time now for them, like, you can see the movement. And so, can you explain the timing of that? I mean, I get enterprise readiness, so we're ready to scale all that good stuff. But the timing of product market fit is important here. >> I think when Raghu talks about that cloud first to cloud chaos, to cloud smart, that's the transition we're seeing. And what I mean by that is, they're hitting that inflection point where it's not just about a single team. One of the guys, basically I talked to the CIO, he was like, look, let's assume hypothetically I have thousand developers. Hundred can talk about microservices, maybe 50 has built a microservice and three are really good at it. So how do I get my thousand developers productive? Right? And the other CIO says, this team comes to me and says, I should be able develop directly to the public cloud. And he goes, absolutely you can do that. You don't have to come through IT. But here's the book of security and compliance that you need to enforce to get that thing in production. >> Go for it. >> Go for it. >> Good luck with that. >> So that reality of how do I scale my dev developers is turning into a developer experience problem. We now have titles which says, head of developer experience. Imagine that two years ago. We didn't talk about it. People start, hey, containers Kubernetes. I'm good to go. I can go get all the open source technology you talked about. And now they're saying no. >> And also software supply chains, another board that you're think. This is a symptom of the growth. I mean, open source is the software industry. That is, I don't think debatable. >> Right. >> Okay. That's cool. But now integration becomes vetting, trust, trusting codes. It's very interesting software time right now. >> That's right. >> And how is that impacting the cloud native momentum in your mind? Accelerating it? What inning are we in? How would you peg the progress? >> You know, on that scale of 1 to 10, I think we're halfway marked now. And that moved pretty quickly. >> It really did. >> And if you sit back today, the kinds of applications we're involved in, I have a Chicago wealth management company. We're building the next generation wealth management application. It's a fundamental refactoring of the legacy application. If you go to a prescription company, they're building a brand new prescription platform. These are not just trivial. What they're learning is the lift and shift. Doesn't work for these major applications. They're having to refactor them which is the modernization. >> So how specifically, are they putting some kind of abstraction layer on that? Are they actually gutting it and rewriting it? >> There's always going to be brownfield. Remember the old days of SOA? >> Yeah, yeah. >> They are putting APIs in front of their main systems. They're not rewriting the core banking or the core platform, but the user experience, the business logic, the AIML capability to bring intelligence in the platform. It's surrounding the capability to make it much more intuitive, much more usable, much more declarative. That's where things are going. And so I'm seeing this mix of integration all over again. Showing my age now. But, you know, the new EAI so is now microservices and messaging and events with the same patterns. But again, being much more accelerated with cloud native services. >> And it is to the point, it's accelerated today. They're not having to freeze the code for six months or nine months and that which would kill the whole recipe for failure. So they're able to now to fast track their modernization. They have to prioritize 'cause they got limited resources. But how are you guys coming up to that? >> But the practice is changing as well, right? Well, the old days, it was 12, 18 months cycle or anything software. If you heard the CVS CIO, Rohan. >> Yeah. >> Three months where they started to engage with us in getting an app in production, right? If you look at the COVID, 10 days to get kind of a new application for getting small loans going with Pfizer, right? These are dramatically short term, but it's not rewriting the entire app. It's just putting these newer experiences, newer capability in front with newer modern developer practices. And they're saying, I need to do it not just once, but for 100, 200, 5,000 members. JPMC has 50,000 developers. Fifty thousand. They're not a bank anymore. >> We just have thousands of apps. >> Exactly. >> Ajay, I want to get your thoughts on something that we've been talking about on our super cloud event. I know we had an event a couple weeks ago, you guys were one of our sponsors, VMware was. It was called super cloud where we're defining that this next gen environment's a super cloud and every company will have a super cloud capability. And underneath that is cross cloud capabilities. So, super cloud is like a super set on top of a multi-cloud. And little word play or play on words is, ecosystem partners versus partners in the ecosystem. Because if you're coming down to the integration side of things, it's about knowing what goes what, it's almost like building an OS if you're a coder or an operating systems person. You got to put the pieces together right, not just go to the directory and say, okay, who's got the cheapest price in DR or air gaping or something or some solution. So ecosystem partners are truly partners. Partners in the ecosystem are a bunch of people out on a list. How do you see that? Because the trend we're seeing is, the development process includes partners at day one. >> That's right. Not bolt-on. >> Completely agree. >> Share your thoughts on that. >> So let's look at that. The first thing I'm hearing from my customers is, they're trying to use all the public clouds as a new IS. That's the first API or contract infrastructures code IS. From then on they're saying, I want more and more portable services. And if you see the success of some of the data vendors and the messaging vendors, you're starting to see best of breed becoming part of the platform. So you are to identify which of these are truly, you know, getting market momentum and are becoming kind of defacto leaders. So, Kafka goes hand in hand with streaming. RabbitMQ from my portfolio goes with messaging. Postgres for database. So these are the, in your definition, ecosystem partners, they're foundational. In the security space, you know, Snyk is a common player in terms of scanning or Aqua and Prisma even though we have Carbon Black. Those become partners from a container security perspective. So, what's happening is the industry stabilizing a handful of critical players that are becoming multi-cloud preference of choice in this. And our job is to bring it all together in a all coordinated, orchestrated manner to give them a platform. >> I mean, you guys always had ecosystem, but I think that priority more than ever. It wasn't really your job at VMware, even, Dave, 10 years ago to say, hey, this is the strategic role that you might play one partner. It was pretty much the partners all kind of fed off the momentum of VMware. Virtualization. And there's not a lot of nuance there. There's pretty much they plug in and you got. >> So what we're doing here is, since we're not the center of the universe, unfortunately, for the application world, things like Backstage is a developer portal from Spotify that became open source. That's becoming the place where everyone wants to provide a plugin. And so we took Backstage, we said, let's provide enterprise support for Backstage. If you take a technology like, you know, what we have with Spring. Every job where developer uses Spring, how do we make it modern with Spring cloud. We work with Microsoft to launch a service with Azure Spring Enterprise for Spring. So you're starting to see us taking communities where they have momentum and bringing the ecosystem around those technologies. Cluster API for Kubernetes, for have you managed stuff. >> Yeah. >> So it's about standard. >> Because the developers are voting with their clicks and their code repos. And so you're identifying the patterns that they like. >> That's right. >> And aligning with them and connecting with them rather than trying to sell against it. >> Exactly. It's the end story with everyone. I say stop competing. So people used to think Tanzu is Kubernetes. It's really Tanzu is the modern application platform that runs on any Kubernetes. So I've changed the narrative. When Heptio was here, we were trying to be a Kubernetes player. I'm like, Kubernetes is just another dial tone. You can use mine, you can use OpenShift. So this week we announced support for OpenShift by Tanzu application platform. The values moving up, it's around outcomes. So industry standards, taking lead and solving the problem. >> You know, we had a panel at super cloud. Dave, I know you got a question. I'll get to you in a second. But the panel was the innovator's dilemma. And then during the event, one of the panelists, Chris Hoff knows VMware very well, Beaker on Twitter, said it should be called the integrators dilemma. Because the innovations here, >> How do you put it all together? >> But the integration of the, putting the piece parts together, building the thing is the innovation. >> And we come back and say, it's a secure software supply chain. It starts with great content. Did you know, I published most of the open source content on every hyperscaler through my Bitnami acquisition. So I start with great content that's curated. Then I allow you to create your own golden images. Then I have a build service that secures and so on and so forth and we bring the part. So, that opinionated solution, but batteries included but you can change it is been one of our key differentiator. We recognize the roles is going to be modular, come back and solve for it. >> So I want to understand sort of relationship Tanzu and Aria, John was talking about, you know, super cloud before we had our event. We had an earlier session where we help people understand that Aria was not, you know, vRealize renamed. >> It's rebranded. >> And reason I bring that up is because we had said it around super cloud, that one of the defining characteristics was, sorry, super PaaS, which is a specific purpose built PaaS layer designed to support your objective for multi-cloud. And speaking to a lot of people this week, there's a federated architecture, there's graph relationships, there's real time ability to ingest and analyze. That's unique. And that's IP that is purpose built for what you're doing. >> Absolutely. When I think what came out of all that learning is after 20 years of Pivotal and BA and what we learned that you still need some abstraction layer. Kubernetes is too low level. So what are the developer problems? What are the delivery problems? What are the operations and management problems? Aria solves all the operations and management problem. Tanzu solves a super PaaS problems. >> Yes. Right. >> Of providing a consistent way to build great software and the secure software supply chain to run on any infrastructure. So the combination of Tanzu and Aria complete the value chain. >> And it's different. Again, we get a lot of heat for this, but we're saying, look, we're trying to describe, it's not just IAS, PaaS, and SaaS of last decade. There's something new that's happening. And we chose the name super cloud. >> And what's the difference? It's modular. It's pluggable. It fits into the way you operate. >> Whereas PaaS was very prescriptive. If you couldn't fit, you couldn't jump down to the next level. This is very much, you can stay at the abstraction level or go lower level. >> Oh, we got to add that to the attribute. >> We're recruiting him right now. (laughs) >> We'll give you credit. >> I mean, funny all the web service's background. Look at an app server. You well knew all about app servers. Basically the company is an app. So, if you believe that, say, Capital One is an application as a company and Amazon's providing all the CapEx, >> That's it. >> Okay. And they run all their quote, old IT spend millions, billions of dollars on operating expenses that's going to translate to the top line called the income statement. So, Dave always says, oh, it's on the balance sheet, but now they're going to go to the top line. So we're seeing dynamic. Ajay, I want to get your reaction to this where the business model shift if everything's tech enabled, the company is like an app server. >> Correct. >> So therefore, the revenue that's generated from the technology, making the app work has to get recognized in the income. Okay. But Amazon's doing all, or the cloud hyperscale is doing all the heavy lifting on the CapEx. So technically it's the cloud on top of a cloud. >> Yes and no. The way I look at it, >> I call that a super cloud. >> So I like the idea of super cloud, but I think we're mixing two different constructs. One is, the cloud is a new hardware, right? In terms of dynamic, elastic, always available, et cetera. And I believe when more and more customer I talk about, there's a service catalog of infrastructure services. That's emerging. This super cloud is the next set of PaaS super PaaS services. And the management service is to use the cloud. We spend so much time as VMware building clouds, the problem seems, how do you effectively use the cloud? What problems do we solve around digital where every company is a digital company and the product is this application, as you said. So everything starts with an application. And you look at from the lens of how you run the application, what it costs the application, what impact it's driving. And I think that's the change. So I agree with you in some way. That is a digital strategy. >> And that's the company. >> That's the company. The application is the company. >> That's the t-shirt. >> And API is the currency. >> So, Ajay, first of all, we love having you in theCube 'cause you're like a masterclass in multiple dimensions. So, I want to get your thoughts on the abstraction layer. 'Cause we were also talking earlier in theCube here as well as before. But abstraction layers happen when you have major movements in markets that are game changing or major inflection points because you've reached a complexity point where it's working so great, this new thing, that's too complex to reign it in. And we were quoting Andy Grove by saying, "let chaos reign then reign in the chaos". So, all major industry moments go back 30, 40 years happen with abstractions. So the question is is that, you can't be a vendor, we've observed you can't be a vendor and be the abstraction. Like, if Cisco's running routers, they can't be the abstraction layer. They have to be the benefit of the abstraction layer. And if you're on the other side of the abstraction layer, you can't be running that either. >> I like the way you're thinking about it. Yeah. Do you agree? >> I completely agree. And, you know, I'm an old middleware guy. And when I used to say this to my CEO, he's like, no, it's not middleware, it's just a new middleware. And what's middleware, right? It's a thing between app and infrastructure. You could define it whatever we want, right? And so this is the new distributed middleware. >> It's a metaphor and it's a good one because it does a purpose. >> It's a purpose. >> It creates a separation but then you have, it's like a DMZ zone or whatever you want to call it. It's an area that things happen. >> But the difference before last time was, you could always deploy it to a thing. The thing is now the cloud. The thing is a set of services. So now it's as much of a networking problem at the application layer is as much as security problem. It's how you build software, how we design. So APIs, become part of your development. You can't think of APIs after the fact, right? When you build an API, you got to publish API because the minute you publish it and if you change it, the API's out of. So you can't have it as a documentation process. So, the way you build software, you use software consume is all about it. So to me, digital product with an API as a currency is where we're headed towards. >> Yeah, that's a great observation. Want to make a mental note of that and make that a clip. I want to get your thoughts on software development. You mentioned that, obviously software development life cycles are changing. I'll say open sources now. I mean, it's unlimited codes, supply chain issue. What's in the code, I get that verified codes going to happen. Is software development coding as much or is coding changing the notion of writing code? Or is it more glue layer you're writing. >> I think you're onto something. I call software developments composition now. My son's at Facebook or Google. They have so many libraries. So you don't no longer start with the very similar primitive, you start with building blocks, components, services, libraries, open source technology. What are you really doing? You're composing these things from multiple artifacts. And how do you make sure those artifacts are good artifacts? So someone's not sticking in security in a vulnerability into it. So, the world is moving towards composition and there are few experts who build the core components. Most of the time we're just using those to build solutions. And so, the art here is, how do you provide that set of best practices? We call them patterns or building blocks or services that you can compose to build these next generation (indistinct) >> It's interesting. >> Cooking meals. >> I agree with you a hundred percent what you're thinking. I agree about that worldview. Here's a dilemma that I'm seeing. In the security world, you've got zero trust. You know, Which is, I don't know you, I don't trust you at all. And if you're going to go down this composed, we're going to have an orchestra of players with instruments, say to speak, Dave, metaphor. That's trust involved. >> Yes. >> So you have two spectrums of issues. >> Yes. >> If software's going trust and you're seeing Docker containers getting more verifications, software supply chain, and then you got hardware I call network guys, love zero trust. Where's the balance? How do you reconcile that? Is it just decoupled? Nuance? I mean, what's the point? >> No, no. I think it all comes together. And what I mean by that is, it starts with left shifting it all the way to hands of the developers, right? So, are you starting with good content? You have providence of the stuff you're using. Are you building it correctly? So you're not introducing bad things like solar winds along the process. Are you testing it along the way of the development process? And then once in production, do you know, half the time it's configurations of where you're running the stuff versus the software itself. So you can think of the two coming together. And the network security is protecting people from going laterally once they've got in there. So, a whole security solution requires all of the above, a secure software supply chain, the way to kind of monitor and look at configuration, we call posture management or workload management and the network security of SaaS-e for zero trust. That's a hard thing. And the boundary is the application. >> All right. >> So is it earned trust model sort of over time? >> No, it's designed in, it's been a thing. >> Okay. So it's not a, >> Because it developed. >> You can bolt in afterwards. >> Because the developers are driving it. They got to know what they're doing. >> And it's changing every week. If I'm putting a new code out every week. You can't, it can be changed to something else. >> Well, you guys got guardrails. The guardrails constant is a good example. >> It stops on the configuration side, but I also need the software. So, Tanzu is all about, the secure chain is about the development side of the house. Guardrails are on the operational side of the house. >> To make sure the developers don't stop. >> That's right. >> Things will always get out there. And I find out there's a CV that I use a library, I found after the fact. >> Okay. So again, while I got here again, this is great. I want to get test this thesis. So, we've been saying on theCube, talking about the new ops, the new kind of ops that emerging. DevOps, which we believe is cloud native. So DevOps moving infrastructure's code, that's happened, it's all good. Open source is growing. DevOps is done deal. It's done deal. Developers are doing that. That ops was IT. Then don't need the server, clouds my hardware. Check. That balances. The new ops is data and security which has to match up to the velocity of the developers. Do you believe that? >> Completely. That's why we call it DevSecOps. And the Sec is where all the action is. >> And data. And data too. >> And data is about making the data available where the app meets. So the problem was, you know, we had to move the logic to where the data is or you're going to move the data where the logic is. So data fabrics are going to become more and more interesting. I'll give you a simple example. I publish content today in a service catalog. My customer's saying, but my content catalog needs to be in 300 locations. How do I get the content to each of the repos that are running in 300 location? So I have a content distribution problem. So you call it a data problem. Yes, it's about getting the right data. Whether it's simple as even content, images available for use for deployment. >> So you think when I think about the application development stack and the analytics stack, the data stack, if I can call it that, they're separate, right? Are those worlds, I mean, people say, I want to inject data and AI intelligence into apps. Those worlds have deployment? I think about the insight from the historical being projected in the operational versus they all coming together. I have a Greenplum platform, it's a great analytics platform. I have a transactional platform. Do my customers buy the same? No, they're different buyers, they're different users. But the insight from that is being now plugged in so that at real time I can ask the question. So even this information is being made available on demand. So that's where I see it. And that's most coming together, but the insight is being incorporated in the operational use. So I can say, do I give the risk score? Do I give you credit? It's based on a whole bunch of historical analytics done. And at the real time, processing is happening, but the intelligence is behind it. >> It's a mind shift for sure because the old model was, I have a database, we're good. Now you have time series database, you got graphs. Each one has a role in the overall construct of the new thing. >> But it's about at the end. How do I make use of it? Someone built a smart AI model. I don't know how it was built, but I want to apply it for that particular purpose. >> Okay. So the final question for you, at least from my standpoint is, here at VMware Explore, you have a lot of the customers and so new people coming in that we've heard about, what's their core order of operations right now? Get on the bandwagon for modern apps. How do you see their world unfolding as they go back to the ranch, their places, and go back to their boss? Okay. We got the modern application. We're on the right track boss, full steam ahead. Or what change do they make? >> I think the biggest thing I saw was with some of the branding changes well and some of the new offerings. The same leader had two teams, the VMware team and the public cloud team. And they're saying, hey, maybe VMware's going to be the answer for both. And that's the world model. That's the biggest change I'm seeing. They were only thinking of us on the left column. Now they see us as a unifying player to play across cloud native and VMware, the uniquely set up to bring it all together. That's been really exciting this week. >> All right, Ajay, great to have you on. Great perspective. Worthy of great stuff. Congratulations on the success of all that investment coming to bear. >> Thank you. >> And on the new management platform. >> Yeah. Thank you. And thanks always for giving us all the support we need. It's always great. >> All right Cube coverage here. Getting all the data, getting inside the heads, getting all the specifics and all the new trends and actually connecting the dots here on theCube. I'm John Furrier with Dave Vellante. Stay tuned for more coverage from day two. Two sets, three days, Cube at VMware Explore. We'll be right back. (gentle music)

(smooth music) >> Hi, welcome to this CUBE Conversation. I'm Lisa Martin. We've got James Wynia here with me, the Director of Product Management at Dell. We're going to be talking about modern data center networks. Jim, welcome to the program. >> Thank you, Lisa. Great to be here. >> So let's talk about this. We've had so many dynamics going on in the last 15, 16 months, I've lost count. A lot of dynamics in play that are contributing to IT complexity. There's new sources of data. We had this massive shift to work from home, work from anywhere, that's now kind of this hybrid environment. Talk to me about some of the core requirements of a modern network infrastructure that organizations need to deploy. >> Absolutely, and thanks for teeing that up. The modern networking requirements these days, so many people have moved home, and so as a result, then the infrastructure back on the farm, back in the data center have to be beefier. You have to have more capacity. You have to be able to handle more scaling operations. And so things like the ability to radically increase your backbone just by swapping in some different transceivers, possibly some different switches to support those faster transceivers, allow for us to multiply that bandwidth very quickly. So that's been a big result of what we have seen coming out of all this, the COVID madness. >> Yeah. (chuckles) Madness is a great description for it, and there's going to be that hybrid as we go forward. There's going to be that need to, for any industry, I imagine, to enable work from anywhere. But talk to me about where customers are from a speed perspective. 100 Gig, that's really mature at this point. Is that where most businesses are? And then what's the next step from there? >> Another great question. (chuckles) I mean, 100 Gig is pretty much the de facto standard at this point. It has really become very cost-competitive and very stable. I mean, we've really been shipping QSFP28 at the latest 100 Gig for five years, and it has become the de facto standard for many, many different scenarios. As we move forward, though of course, we just need to move more data is what it comes down to, and so the next logical jump from 100 is 400. And so 400 started rolling out about the time that COVID came on, a couple months before that. And so honestly, there was a slight kind of delay in the industry as COVID kind of made everybody take a step back and say, "Whoa, hold on." But now it's really come back in full force. >> So what does an organization, and we'll kind of just leave this as any industry, need to do to be able to prepare to go from 100 to 400, because as you mentioned, the data sources aren't diminishing. It's only going to continue to increase. >> Absolutely. And so one of the things is to make sure that the backbone infrastructure can handle 400 Gig. Ironically enough, the actual optical cable trunks, those are pretty much the same. And so if you were running single-mode fiber to go a long distance, you would use that same cable. So you don't have to rip out all your cable infrastructure. What you have to look at closely is when you plug that transceiver into a switch, what is it capable of running at? In olden days, that was probably 100 Gig. Now you have a 400 Gig, so you have to make sure that you have just the right hardware to go with that. And then as you go down the chain, down the stack, rather, from those, the switch from the cord, or the switch all the way to your server, on the servers we see a lot of interest in 100 Gig, even up to 200 Gig today. And so it's the same discussion. You're taking a close look at your NIC or your adapter. What is it rated at? Is it going to be able to handle a faster speed? >> So it's not a rip and replace. Can you give us an idea of the migration path that a customer would take, and how Dell would facilitate that? >> Absolutely. And so we have some great customers who have really stepped out in different ways. You have the Greenfield customers who, they're building out a whole additional data center, say. And so they would just, from the ground up, replace it with the latest and greatest equipment that's just already ready to go. Other customers that are just extending, maybe they're tapping a couple data centers together and replacing those 100 Gig links to aggregate them with 400 Gig links. And then they would maybe migrate, adding in an additional 400 Gig links down through the stack as it makes sense. So ethernet is ethernet, right? Whether you have 100 Gig on one link, 400 Gig on another link, it all plugs and plays nicely. And so you don't have to have this big step where you have to forklift everything out and then move all new equipment in. It's as it makes sense. >> As organizations have pivoted multiple times in the last 15, 16 months, as we've all seen, and there will continue to be that I mentioned, there's this sort of work from anywhere hybrid model, what are some of the benefits that a business could expect going from 100 Gig to 400 besides just quadrupling the speed? Talk to me about some of the business impact that can be made here. >> So business impact as is can be tremendous. Certainly, the capacity is the biggest one that jumps out at us here, as we can just combine, add on more services. Another area where we see this impact, and which, again, boils down to capacity, is IoT and edge. We have these new edge devices coming left and right. I mean, every time you turn around in the consumer world, there's some new thing that we never thought was possible, or we thought was 20 years down the road, and well, there it is. All of those cute little gadgets are just creating these streams of data, okay? So you just have so much more data that has to be processed. And so some of that gets processed at the edge, and that's kind of a cool new thing, but you still have more data that has to come back to the home base, either for storage, or for analytics, or for number-crunching, and so you have to be able to manage that. Bigger, fatter pipes going long distances, going short distances, going just in the same rank. >> Have you noticed, Jim, in the last year or so any industries in particular that are really prime candidates for this upgrade? As you mentioned, IoT, the explosion at the edge, sensors, sensors everywhere. Any industries that you saw that really are benefiting from doing this migration? >> Well, certainly the hyperscalers. The big companies that we all use social networking on. They're just moving around just piles of data, and everyone's working from home, and so they just have a little extra time to do the clicking and searching and stuff. And so that, and as well as entertainment. From home, people are just... They're just using up more bandwidth, and so the tier one, tier two providers certainly... We've seen just tremendous interest and growth as they have stepped out and adopted. >> Jim, can we do a double-click now on some deployment options and capabilities, maybe helping us understand it by industry segment? >> Yes, absolutely. And so some of the segments that we've been working closely with over the last 18 months here is like cloud service providers. Also large enterprise companies who have the large data centers. And then thirdly, federal is moving along very quickly. Federal's got all the security stuff that's been in the news of late. They have more calculation and just data transfer needs than ever, and so those are a couple of good ones. >> Got it, yeah. Ransomware is now, unfortunately, one of those common household words, as is pandemic and Pfizer, right? Talk to me about where automation comes into play as organizations look to migrate to become faster, to be able to manage more data coming in faster from more sources, where does automation factor in the mix? >> One of my favorite questions, actually, because in the networking industry, it has changed so much in the last five years. It used to be that when you were talking about large data centers, and just massive amounts of data, that the entire discussion revolved around these large modular chassis. And the reality is that nowadays, yeah, large modular chassis still exist, and they have a place, but they're not mandatory in all circumstances. And one of the big changes is that you can get building blocks that push out tremendous amounts of data within a single box. And you can use like a claw structure that allows you to do more data safer because you have higher availability than these really expensive modular chassis. And so when you come with kind of more switches, the reality is that now you have a bigger automation requirement. And so the tools to be able to automatically set that up, automatically maintain, and automatically monitor it, those are critical. And especially when we're talking about high capacity environments where you have millions of people watching the video being on the screen right now. It better be there no matter what blip happens on the backend. >> Yep. There's always that demanding consumer, (chuckles) no matter what you do. What about automating day one and day two operations? How does it play into managing this infrastructure, this modern data network infrastructure, with on-prem and in the cloud? >> Yes. So I work for Dell, and I forgot to mention upfront, I apologize, I'm a Dell employee, but I'm actually speaking from my opinion. I'm not representing Dell in terms of their viewpoint of all of these things we're talking about today. But one of the big things is that, as we have gone from those modular chassis to more individual units to get this cleaner deployment, the day one has to do with how do you design that. How do you, when you have more fiber cables connecting things up, how do you make sure that you don't oops, plug one into the wrong place? And so tools such as in Dell, we have tools like the Fabric Design Center that automatically generate all of those wiring diagrams for you, all of the testing. When you plug it in for the first time, it actually verifies that everything's clean. And then day two is monitoring what's happening. Are you getting issues, subtle issues that are maybe not noticed but are building up? And so things like the Smart Fabric Director can allow us to monitor those types of things and make recommendations for, "Hey, there's something happening we need to be aware of and watch it," or "Here's some corrective action." And so those kinds of tools really are the lifeblood to make sure that the team doesn't just get overwhelmed. And the reality is we all know as time goes on, we need, or we're given the opportunity to have fewer people working on maintaining stuff. And so you need more equipment that's more complex, but you have less number of eyes on what's going on, and so the tools just have to be locked in. >> So tools you mentioned. What about operating systems? Anything that you would recommend that customers looking into? >> That's another great question. So operating systems have changed. If you look back on the server world, you go back 20, 25 years ago, every server company, they made their own CPU, they made their own operating system, and then it evolved so that there was, now you buy a CPU from either maybe Intel, maybe AMD. But it's not like Dell goes out and makes its own CPU. We buy from other established leaders. When it comes to operating systems on the server side, the same thing happened. Well, the networking world has been catching up for quite a while, and so four years ago, we started talking about open networking, and the fact that there are options. You're not locked into just what is our primary operating system. And so there are opensource operating systems that you can run. There are things like SONiC, which has just really been taking the networking world by storm. And so we certainly support Dell Enterprise SONiC on our platforms. And that is another fantastic option. >> Excellent. Last question, Jim, for you. If you had a crystal ball, given the dynamics of the world today and how quickly things are changing, and how organizations need to be competitive, what are some of the things that you think we're going to see in the networking world in the next 12 to 18 months? >> Well, it doesn't take a whole lot of a crystal ball. We just follow the standards, bodies. We see that 400 Gig has really come on strong. And honestly, we played catch up in that industry, having all of the optics that we needed. We needed all the breakout optics to go from 400 to four by 100. Those took a good, well, six to eight months before those really came on board. And so now we're finally at the place where we're in a good place, but the next thing clearly is everything doubles. And so now we'll jump to 800 Gig over the same infrastructure, and so that's, again, everything doubles. And then there's a lot of talk about, "Well, what happens after that?" Well, then you go everything from 800 to 1.6T over that same infrastructure, and so it's just kind of mind-boggling capacity, but it's coming at us like a freight train. >> It is like a freight train. We'll say a good freight train. Jim, thank you so much for joining me on theCUBE today, talking to me about modern data center networks, what's going on there, the opportunities for businesses in any industry to take advantage of the latest and greatest. We appreciate your time. >> You bet. Thank you for inviting me. >> For Jim Wynia, I'm Lisa Martin. You're watching this CUBE Conversation. (smooth music)

(upbeat music) >> Announcer: From theCUBE studios in Palo Alto, in Boston connecting with all leaders all around the world, this is theCUBE conversation. >> Lisa Martin here with theCUBE conversation. One of our alumni is joining me Satyen Sangani the CEO and Co-Founder of Alation is back. Satyen, it's great to see you this morning. >> I know it's so great to see you especially so soon after we last talked. >> Yeah, we only spoke a couple of months ago when you guys launched the Alation Cloud Service and now big news raising 110 million in Series D led by Riverwood Capital from participation with some new investors, including Snowflake Ventures. Talk to us about this new funding raise. >> Yeah, it's so funny. I mean, we've seen market demand pick up ever since the sort of tail end of last year. And it's just been incredible. And quarter after quarter we keep on hitting and exceeding our numbers and we keep on hiring faster and faster and faster and it just doesn't seem like it's ever been fast enough. And so we've been aggressive since the beginning of the year. And even actually before that in spending and, taking the company from roughly 275 people at the end of the year to now, by the end of this year, 525 people. So with that kind of growth we definitely wanted to have the capital to, carry us to this year and then certainly beyond. And, so we went out and raised around and, obviously we're able to do that on great terms and to find a phenomenal partner in Riverwood. And so super excited about the outcome. >> Exactly saw a lot of demand as you and I talked about just a couple of months ago the acceleration of the business during the pandemic. Talk to me about, as you mentioned the demand has never been higher. Let's talk about the demand for the data intelligence platform how the funding is going to help. What are some of the things that you're specifically going to do? >> Yeah, so there's you know we're going to grow the business in a pretty balanced way. And so from our perspective, that means a couple of things right? So starting with sales and marketing, we've got just a need for more feet on the street. Everybody understands generally that they've got problems in data governance, data management, data search and discovery, enablement to people around data. These are things that people are now starting to understand but they don't always necessarily know how to solve the problem in the most efficient and best way. And many of the traditional approaches that sort of command and control top down, you know, let's go hire an army of consultants to figure this stuff out, tends to be the first thing that comes to mind. And so we're building our sales organization is one thing that we're going to do. The second thing that we're going to do is invest in our customer success and customer journey because everybody's looking for best practice and last but not least workforce investing in product and R&D. And so we're going to be growing the R&D organization by almost a factor of two, and that's going to be globally. And, just being the best in the market means you've got to still solve all these unsolved problems. And we're going to do that. >> Sounds like a tremendous amount of momentum kind of igniting this next era for Alation. When we talk about customers, I love that you're doubling down on the customer success. That's absolutely critical. That's why you're in business. But one of the things that we talk about with customers in every industry is being data-driven. And as we see data intelligence emerging as a very, very critical technology investment to enable an enterprise to become more data-driven or actually data-driven, what are some of the things that you're seeing that those customers are saying Alation help us with XYZ? >> Yeah, so I think everybody feels like they need to be on this. So let's first of all, talk about data intelligence. Like, what is this category? So historically there has been these sort of data management categories where the general approach has been let's curate or manage or clean the data in this manual way in order to be able to get good data in front of people so they can start to use it, right. And that data cleaning, that data work that data stewardship has lived often in IT sometimes with very technical people in the business. And it just doesn't scale. There's just too much data out there and there's too much demand for data. So the demand for data is increasing, the supply for data is increasing. So now there's this category of data intelligence. And basically what it's doing it's saying, look all these things that we're talking about machine learning, AI, all of that can be applied to actually the management of data. People can be way more intelligent about how they do this work. They can be more intelligent how they search. They can be more intelligent about how they curate the data. And so what we're seeing is that people are saying, look, I've got so much data. My entire business relies upon data, and now I need you Alation or somebody to help me do this better to do this faster, to do this more efficiently. And all of these really traditional approaches where you use, you know, predominantly workflows and all this stuff it's just not working. And so that's why people are coming after us. >> Well, that need for data in real time is something that we saw during the pandemic. It's for many industries and many different types of situations. It's no longer a nice to have. It's really going to be the defining element between those businesses that succeed in really kind of leveraged COVID as an accelerant versus those that don't succeed. But I'm curious where your conversations are going within the customer base. As we see the need for data across an organization, but the need to access data that they can trust quickly, data that tells the truth, data that can be shared. Are you seeing this elevate up to C-suite in terms of your customer conversations? >> Yeah, and it is and it is because of one really critical reason because a lot of these data projects both fail and under exceed expectations and they do it for reasons that the C-suite doesn't understand. And so now the C-suite is getting forced to say, well, why is this happening? Why are these not going like, wow, you know the boardroom is saying like, well, we need to do more AI. Well, why aren't we doing more AI? Well, it's 'cause your data isn't really clean 'cause you don't actually have the data that you think you have. Because people don't share your data because people are, you know, your data is locked in some on-premise instance in, some access database that nobody's ever heard of. And so all of these reasons are things that now because they're impeding the business or getting to more senior levels in the organization >> That's kind of what I was thinking. I want to talk now about the investment this particular Series D that we talked about. So you've got investment, as I mentioned from a couple of new partners, but talk to me about the Snowflake and the Salesforce Ventures and how that is helping to catalyze what Alation is doing. >> Yeah, so we've, you know had a long time relationship with Salesforce but we found in the last year in particular that our relationship with Snowflake has just taken off in a way that I have seen few partnerships taking off in in certainly in my career. And, you know, it started really with just scores of customers. I mean, literally scores of customers that are all global to 1000s and fortune 500s where we would often just say, hey, what's your data source. And, you know, let's start with Alation and they'd be like, yeah we are either about to invest in Snowflake or we're invested in Snowflake or, something like that. So we'd often see customers on the journey with Snowflake and Alation at the exact same time. And then the next order conversation became well, you know if we're expanding and rolling out with Snowflake, which customers, you know, everybody looks at Snowflakes 168 net percent net expansion rate where every customer is spending a dollar 68 more than they were last year on average. And, you know, says, wow, if I'm going to scale that much we need to govern all of that data. And so Snowflake customers came to Snowflake and to Alation at the same time, and we've been the natural solution of choice. And so that kind of marriage has been quite symbiotic and we're super excited to partner with them. You know, they think exclusively about data consumption. We think about, you know, finding, discovering understanding data. So it's a really natural marriage. And so we're really excited to partner with them and you're going to see a lot from the two companies moving forward. >> So it sounds like that really was driven from joint customers in terms of facilitating maybe an expansion of the partnership that Alation and Snowflake have. Talk to me a little bit more about what some of the things are that we can expect in the next year. >> Yeah, so I won't take away from the stories that we're about to release, but you are going to see really exciting innovations and product between Snowflake and Alation over the course of the next couple of months. And in particular, you're going to see, you know some fun announcements at the snowflake summit coming up next week. So stay tuned for that. Not surprisingly data governance is going to be a big topic for us. Data search and discovery is going to be a big topic for us. Data privacy and security is going to be a big topic us. And so those are all areas where you're going to see lots of fun products innovation. And then on the other side, you're going to see a lot of go to market innovation. So customers are moving data to the cloud, obviously and that's going to be a big place of discussion just enabling all of the field sales forces getting the stories and the customer stories to market. You're going to see a lot of that from us. >> In the last year, I'm curious if you saw any verticals in particular that really have pivoted with fuel from Alation. I think healthcare, life sciences, manufacturing anything that you, that really stood out to you in the last year >> You know, it's, I mean I think there's been the pandemic certainly hurt certain industries more than others transportation, travel and hospitality. And so we definitely saw a trend where there were dips in some of those industries but those were really temporary. And what we're finding is in a lot of those industries are now coming back bigger than ever. And the other industries in manufacturing and pharma in financial services, you know those are just as strong as they've ever been. And interestingly through the pandemic, what we found is that our user account within the company doubled. So even though the customer base itself didn't double the number of users on the platform across all of our customers, literally doubled on an active basis. And so, it's just been, interestingly enough it's just that across the board the growth has been consistent. And I think, really speaks to the fact that everybody's working from home and needs more data to do their job. >> Well, hopefully that's something that's going to be temporary. This, I was telling you, this is my first day back in the studio and not sitting in the home office. So in terms of the demand we talked about the demand we're customers, you're more than 250 customers now, big names, including one of the I think last year's most used terms household terms of Pfizer. Talk to me about the customer perspective on the funding and in terms of the things that you're going to be able to do to go to market. What are you hearing from your customer? >> Yeah I mean, literally the first thing I hear from 80 to 90% of my customers is go faster. You know, like there's this fun story, right? Where there's two people, they meet in the forest, they start walking together and then all of a sudden they both see a big bear. And the bear is, right about to come right after them. One person sits down and like puts on their running shoes. And they're like, well, you know, the other guy says, oh, there's no way you're going out run the bear. And they're like, well, I (indistinct)the bear. I've got to out run you. Right, and our customers are basically saying to us, look the bear of the data problem is gigantic. And yeah, you might be better than everything else out there, but I still have to as a customer contend with this massive data problem. And you know, if I have to do that, I need you to go faster because data's coming after me faster than ever. And I've got to contend with all of that work. And so they just want us to go faster and they want us to go faster in product. And they want us to go faster in developing the customer journey. And they want us to go faster in developing the ecosystem because many of our customers are you leveraging us as a platform. They want to see data on top of Alation. They want to see data privacy on top of Alation. They want to see data migration on top of Alation. So building out all these capabilities with our partners in our ecosystem and with partners like Snowflake and Salesforce, I mean, they just want us to move faster >> Moving faster, I think we all want that in certain senses but in any industry, consumers, users are getting more and more demanding as you're helping customers achieve their desire of going faster. How do you do that and help them foster a data culture that's, that supports that speed. >> Yeah, it's so interesting because cultural transformation, as you all know, like as we all know, that's like that's certainly slow work, right? Like you're not going to show up at an enterprise and say, hey, I installed Alation. You know what? You're going to have a totally different area culture. Everybody's going to start asking questions with data and the world's going to change, right. And so that, that, you know I'd love for that to be the eventual vision that we achieve. But it's certainly not where we are at today. I think, one of the things that I believe is that you can't go fast and big things you've got to break up big problems and turn them into small problems. And so one of the habits that we've seen within the organization, and one of the things that I talked to our team about every single day is look, you know make small promises and deliver on them. If you got to connect to data source, do that faster. If you're going to train a set of employees do that more quickly because customers have intent with data, but if they don't get the data in front of themselves quickly then they're just going to go to their gut decision. And so capturing that moment of intent and building a sort of velocity is where we see our best customer engagements go. And so that sort of incremental success approach, as opposed to the boil the ocean three month engagement, you know never see the finish line approach is really what I think makes us special and different. >> Tell me a little bit about speaking of culture, about Alations culture. What are some of the things that have changed in the last year? And it sounds like with the Series D round that you've just raised a lot of growth opportunities you mentioned that. Talk to me about the culture, how it's transformed in the last year and what you are excited for going forward. >> Yeah, it's so funny 'cause I always think about culture. You know, people think about culture and they say, companies (indistinct) culture and they think of that culture as being a fixed thing. And it's totally true that, yeah, there's got to be some shared vision, shared values shared ideals within a company in order for it to grow at the pace that we're growing, right. Adding 250 people in a 12 month period is not easy. But it's also the case that, you know, what we found is that there's a lot more specialization within the company. And so people now really, you know where you found the company on generalist you scale a company on specialists. And so getting those specialists inside of the company and respecting them and letting them do their jobs and really kind of building that expertise in the company is something that's been really fabulous and just wonderful to see the team work that way. I think the other thing that's been really interesting obviously is just the remote first work. I mean, we've seen zero loss in productivity and I've talked to CEOs who were like, yeah we need to get people back in the office. I don't really care where my team works. They're getting the job done and they're doing it fabulously for customers. And so if customers want them in front of them, totally great. Obviously love to see the team all the time but it is so wonderful to see how productive people can be when they don't have to spend two hours in a car every day. And so those have been two small things. I mean, at the core, there are other aspects of our culture that have been more permanent, but those two have been slightly different. >> That's great to hear that about the productivity. I was actually very excited to commute this morning for the first time. Although there was no traffic to navigate. As we look at the current market valuation, 1.2 billion the growth rate, the demand for the technologies. What are some, you mentioned some of the events that you're going to be at you mentioned Snowflakes event. Where can folks go to hear more information about this? >> Yeah, absolutely. You can come to our website, of course, at alation.com. There's a ton of information there. Anybody who's watching this interview obviously is a experienced and thoughtful enterprise IT buyers. So certainly, you know, this is a fairly expert audience but we do have tons of field resources that are available. The Alation Cloud instance allows you to get up and running super quickly. And you're going to see that speed increase further over the coming 12 months, but, you know start with alation.com and go from there. And then there's a whole bunch of people who are sitting behind that front door waiting to help you. >> Excellent, alation.com. Well, Satyen congratulations on the funding announcement. Thank you for joining me today helping us unpack what at means the impact, the demand from the customers and how we're going see Alation go even faster. I'm excited to see what happens next in the next couple of months. I'm sure I'll see you again. >> I know. Me too. Thank you Lisa, it's always great to talk. >> Likewise, for Satyen Sangani, I'm Lisa Martin. You're watching this CUBE conversation. (upbeat music)

>> Narrator: From around the globe, it's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021. This is the cubes ongoing coverage, where we go out to the events, we extract the signal from the noise, of course virtually in this case, now we're going to talk about ecosystems, partnerships and the flywheel they deliver in the technology business. And with me are Jason Kelly, he's the general manager global strategic partnerships, IBM global business services and Mani Dasgupta, who's the vice president of marketing for IBM global business services. Folks it's great to see you again. I wish we were face-to-face, but this'll have to do. >> Good to see you Dave and same, I wish we were face to face, but we'll, we'll go with this. >> Soon. We're being patient. Jason, let's start with you. You, you have a partner strategy. I wonder if you could sort of summarize that and tell us more about it. >> So it's interesting that we start with the strategy because you said, we have a partner strategy Dave and I'd say that the market has dictated back to us, a partner strategy. Something that we it's not new, we didn't start it yesterday. It's something that we continue to evolve in and build even stronger. This thought of a, a partner strategy is it... Nothing's better than the thought of a partnership and people say, "Oh, well, you know you got to work together as one team and as a partner." And it sounds almost as a one to one type relationship. Our strategy is much different than that Dave and our execution is even better. And that, that execution is focused on now the requirement that the market, our clients are showing to us and our strategic partners, that one... One player, can't deliver all their needs. They can't design solution and deliver that from one place. It does take an ecosystem to the word that you called out, this thought of an ecosystem. And our strategy and execution is focused on that. And the reason why I say it evolves is because the market will continue to evolve and this thought of being able to look at a client's, let's call it a workflow, let's call it a value chain from one end to the other, wherever they start their process to wherever it ultimately hits that end user, it's going to take many players to cover that. And then we as IBM want to make sure that we are the general contractor of that capability with the ability to convene the right strategic partners, bring out the best value for that outcome, not just technology for technology's sake, but the outcome that the end client is looking for so that we bring value to our strategic partners and that end client. >> I think about when you talk about the, the value chain, you know, I'm imagining, you know the business books years ago where you see the conceptual value chain, you could certainly understand that and you could put processes together to connect them and now, you've got technology. I think of APIs. It's, it's, it really supports that everything gets accelerated and, and Mani, I wonder if you could address sort of the the go to market, how this notion of ecosystem which is so important is impacting the way in which you go to market. >> Absolutely. So modern business, you know demands a new approach to working. The ecosystem thought that Jason was just alluding to, it's a mutual benefit of all these companies working together in the market. It's a mutual halo of the brands. So as responsible, you know, for the championship of, of the IBM and the Global Business Services brand, I am very, very interested in this mutual working together. It should be a win, win, win as we say in the market. It should be a win for, our clients first and foremost, it should be a win for our partners and it should be a win for IBM, and we are working together right now on an approach to bring this go-to-market market strategy to life. >> So I wonder if we can maybe talk about, how this actually works and, and pulling some examples. You must have some favorites that we can touch on. Is that, is that fair? Can we, can we name some names? >> Sure. Names always work in debut writing. It's always in context of reality that we can talk about, as I said, this execution and not just a strategy and I'll, I'll start with probably what's right in the front of many people's minds. As we're doing this virtually because of what, because of an unfortunate pandemic. Just disastrous loss of life and things that have taken us down a path we go, whoa! (clears throat) How do we, how do we address that? Well, anytime there's a tough task IBM raises its hand first. You know, whether it was putting a person on the moon and bringing them home safely, or standing up a system behind the current social security administration, you know during the depression, you pick it. Well here we are now and why not start with that as an example because I think it calls out just what we mentioned here. First, Dave, this thought of, of an ecosystem because the first challenge, how do we create and address the biggest data puzzle of our lives which is, how do we get this vaccine created in record time? Which it was. The fastest before that was four years. This was a matter of months. So Pfizer created the first one out and then had to get it out to distribution. Behind that is a wonderful partner of ours, SAP trying to work with that. So us working with SAP, along with Pfizer in order to figure out, how to get that value chain and some would say supply chain, but I'll, I'll address that in a second, but there's many players there. And, and so we were in the middle of that with Pfizer committed to saying, how do we do that with SAP? So now you see players working together as one ecosystem. But then think about the ecosystem that that's happening where you have a federal government agency. You have Ms. State, Alocal, you have healthcare life science industry, you have consumer industry. Oh, wait a second Dave, this is getting very complicated, right? Well, this is the thought of convening in the ecosystem. And this is what I'm telling you is, is our execution and it, it has worked well and so it's, it's it's happening now and we see it still developing and being, being, you know very productive in real time. But then, I said there was a another example and that's with me, you, Mani, whomever. You pick the consumer. Ultimately we are that outcome of, of the value chain. That's why I said I don't want to just call it a supply chain because at the end is, is, is someone consuming and in this case we need a shot. And so we partnered with Salesforce, IBM and Salesforce saying, wait a minute that's not a small task. It's not just get, get the content there and put it in someone's arm. Instead there's scheduling that must be done. There's follow up, and entire case management like system. Salesforce is a master at this. So work.com team with IBM we said now, let's get that part done for the right type of UI UX capability, that user experience, user interaction interface and then also, in bringing another player in the ecosystem. One of ours, Watson health, along with our blockchain team, we brought together something called a digital health pass. So, I've just talked about two ecosystems where multiple ecosystems working together. So you think of an ecosystem of ecosystems. I call it out blockchain technology and obviously supply chain, but there's also AI, IOT. So you start to see where, look, this is truly an orchestration effort that has to happen with very well designed capability and so of course we master in design and tying that, that entire ecosystem together and convening it so that we get to the right outcome. You, me, Mani are all getting the shot, being healthy. That's a real-time example of us working with an ecosystem and teaming with key strategic partners. >> You know Mani, I, I, I mean, Jason you're right. I mean this pandemic's been horrible. I have to say, I'm really thankful it didn't happen 20 years ago because it would have been like, okay here's some big PCs and a modem and go ahead and figure it out. So, at least, the tech industry has saved the business. I mean, with, and earlier we mentioned AI, automation, data, you know, even things basic things like, security at the end point. I mean so many things and you're right. I mean, IBM in particular, other large companies, you mentioned, SAP who have taken the lead and it's really, I, I don't, I Mani I don't think the tech industry gets enough credit but I wonder if there's some of your favorite partnerships that you can talk about. >> Yeah. So I'm going to, I'm going to build on what you just said, Dave. IBM is in this unique position amongst this ecosystem. Not only the fact that we have the world's leading most innovative technologies to bring to bear, but we also have the consulting capabilities that go with it. Now to make any of these technologies work towards the solution that Jason was referring to in this digital health pass, it could be any other solution, you would need to connect these disparate systems sometimes make them work towards a common outcome to provide value to the clients. So I think our role as IBM within this ecosystem is pretty unique in that we are able to bring both of these capabilities to bear. In terms of, you know, you asked about favorites. There are, this is really a co-opetition market where everybody has products, everybody has services. The most important thing is how are we, how are we bringing them all together to serve the need or the need of the hour in this case? I would say one important thing in this, as you observe how these stories are panning out. In an ecosystem, in a partnership, it is about the value that we provide to our clients together. So it's almost like a "sell with" model from, from a go-to-market perspective. There is also a question of our products and services being delivered through our partners, right? So think about this, the span and scope or what we do here and so that's the sell through, and then of course we have our products running within our partner companies and our partner products for example, Salesforce, running within IBM. So this is a very interesting and a new way of doing business. I would say it's almost like the, the modern way of doing business with modern IT. >> Well, and you mentioned co-opetition. I mean, I look at it, you're, you're, you're part of IBM that will work with anybody 'cause you're your customer first. Whether it's AWS, Microsoft, I mean, Oracle is a, is a, is a really tough competitor but your customers are using Oracle and they're using IBM. So I mean, as a, those are some, you know good examples I think of your point about co-opetition. >> Absolutely. If you pick on any other client, I'll mention in this case, Delta. Delta was working with us on moving, being more agile and now this pandemic has impacted the airline sector particularly hard, right? With travel stopping and anything. So they are trying to get to a model which will help them scale up, scale down be more agile, be more secure be closer to their customers to try and understand how they can provide value to their customers and customers better. So we are working with Delta on moving them to cloud, on the journey to cloud. Now that public cloud could be anything. The, the beauty of this model in a hybrid cloud approach is that you're able to put them on red hat openshift, you're able to do and package the, the services into microservices kind of a model. You want to make sure all the applications are running on a... On a portable almost a platform agnostic kind of a model. This is the beauty of this ecosystem that we are discussing as the ability, to do what's right for the end customer at the end of the day. >> How about some of the like SaaS players? Like some of the more prominent ones. And we, we, we watched the ascendancy of ServiceNow and Workday, you mentioned Salesforce. How do you work with those guys? Obviously there's an AI opportunity but maybe you could add some color there. >> So I like the fact Dave that you call out the different hyperscalers, for example whether it's AWS, whether it's Microsoft, knowing that they have their own cloud instances, for example. And when you, when you mentioned, hey, had this happened a long time ago, you know you started talking about the, the heft of the technology. I started thinking of all the, the the truck loads of servers or whatever they, you know they'd have to pull up, we don't need that now because it can happen in the cloud. And you don't have to pick one cloud or the other. And so when people say hybrid cloud, that's what comes out. You start to think of what I call, I call, you know, a hybrid of hybrids because I told you before, you know these roles are changing. People aren't just buyers or suppliers. They're both. And then you start to say, what are, what are different people supplying? Well, in that ecosystem, we know there's not going to be one player. There's going to be multiple. So we partner by doing just what Mani called out as this thought of integrating in hybrid environments on hybrid platforms with hybrid clouds, multi-clouds. Maybe I want something on my premises, something somewhere else. So in giving that capability, that flexibility, we empower and this is what it's doing is that co-opetition. We empower our partners, our strategic partners. We want them to be better with us and this is just the thought of, you know, being able to actually bring more together and move faster. Which is almost counter-intuitive. You're like, wait a minute, you're adding more players but you're moving faster. Exactly. Because we have the capability to integrate those, those technologies and get that outcome that Mani mentioned. >> I would add to one Jason, you mentioned something very, very interesting. I think if you want to go just fast, you go alone. But if you want to go further, you go together. And that is the core of our point of view, in this case is that we want to go further and we want to create value that is long lasting. >> What about like, so I get the technology players and there's maybe things that you do, that others don't or vice versa so the gap fillers, et cetera. But what about, how, maybe customers do they get involved? Perhaps government agencies, maybe they be, they they be customer or an NGO as another example. Are they part of this value chain part of this ecosystem? >> Absolutely. I'll give you... I'll stick with the same example when I mentioned a digital health pass. That digital health pass, is something that we have as IBM and it's a credential. Think of it as a health credential, not a vaccine passport cause it could be used for a test for, a negative test on COVID, it could be used for antibodies. So if you have this credential it's something that we as IBM created years back and we were using it for learning. When you think of, you know getting people certifications versus a four-year diploma. How do we get people into the workforce? That was what was original. That was a Jenny Rometty thought. Let's focus on new collar workers. So we had this asset that we'd already created and then said wait, here's a place for it to work with, with health, with validation verification on someone's option, it's optional. They choose it. Hey, I want to do it this way. Well, the state of New York said that they want it to do it that way and they said, listen we are going to have a digital health pass for all of our, all of our New York citizens and we want to make sure that it's equitable. It could be printed or on a screen and we want it to be designed in this way and we want it to work on this platform and we want to be able to, to work with these strategic partners, like Salesforce and SAP, Alocal. I mean, I can just keep going. And we said, "Okay, let's do this." And this is this thought of collaboration and doing it by design. So we haven't lost that Dave. This only brings it to the forefront just as you said. Yes, that is what we want. We want to make sure that in this ecosystem, we have a way to ensure that we are bringing together, convening not just point products or different service providers but taking them together and getting the best outcomes so that that end user can have it configured in the way that they, they want it. >> Guys, we've got to leave it there but it's clear you're helping your customers and your partners on this, this digital transformation journey that we already, we all talk about. You get this massive portfolio of capabilities, deep, deep expertise. I love the hybrid cloud and AI focus. Jason and Mani, really appreciate you coming back in the cubes. Great to see you both. >> Thank you so much, Dave. Fantastic. >> Thank you Dave. Great to be with you. >> All right, and thank you for watching everybody. Dave Vellante, for the cube and in continuous coverage of IBM Think 2021, the virtual edition. Keep it right there. (poignant music) (bright uplifting music)

>>From around the globe. It's the Cube with digital coverage of IBM think 2021 brought to you by >>IBM. Welcome back to IBM Think 2021. This is the cubes ongoing coverage where we go out to the events, we extract the signal from the noise of course, virtually in this case now we're going to talk about ecosystems, partnerships in the flywheel, they deliver in the technology business and with me or Jason kelly, general manager, global strategic partnerships, IBM global business services and Mani Das Gupta, who is the vice president of marketing for IBM Global Business services folks. It's great to see you again in which we're face to face. But this will have to do >>good to see you Dave and uh same, I wish we were face to face but uh we'll we'll go with this >>soon. We're being patient, Jason. Let's start with you. You have a partner strategy. I wonder if you could sort of summarize that and tell us more about it. >>So it's interesting that we start with the strategy because you said we have a partner strategy dave and I'd say that the market has dictated back to us a partner strategy something that we it's not new and we didn't start it yesterday. It's something that we continue to evolve and build even stronger. This thought of a partner strategy is it nothing is better than the thought of a partner ship. And people say oh well you know you got to work together as one team and as a partner And it sounds almost as a 1-1 type relationship. Our strategies is much different than that. David our execution is even better and that that execution is focused on now. The requirement that the market our clients are showing to us and our strategic partners that one player can't deliver all their needs, they can't Design solution and deliver that from one place. It does take an ecosystem to the word that you called out. This thought of an ecosystem and our strategy and execution is focused on that. And the reason why I say it evolves is because the market will continue to evolve and this thought of being able to look at a client's let's call it a a workflow, let's call it a value chain from one end to the other, wherever they start their process to wherever it ultimately hits that end user. It's going to take many players to cover that. And then we, as IBM want to make sure that we are the general contractor of that capability with the ability to convene the right strategic partners, bring out the best value for that outcome, not just technology for technology's sake, but the outcome that the incline is looking for so that we bring value to our strategic partners and that in client. >>I think about when you talk about the value chain, you know, I'm imagining, you know, the business books years ago you see the conceptual value chain, you can certainly understand that you can put processes together to connect them and now you've got technology, I think of a P. I. S. It's it's really supports that everything gets accelerated and and uh money. I wonder if you could address some of the the go to market how this notion of of ecosystem which is so important, is impacting the way in which you go to market. >>Absolutely. So modern business, you know, demands a new approach to working the ecosystem. Thought that Jason was just alluding to, it's a mutual benefit of all these companies working together in the market, it's a mutual halo of the brands, so as responsible for the championship of the IBM and the global business services brand. I am very, very interested in this mutual working together. It should be a win win win, as we say in the market, it should be a win for our clients, first and foremost, it should be a win for our partners and it should be a win for IBM and we are working together right now on an approach to bring this, go to market strategy to life. >>So I wonder if we could maybe talk about how this actually works and and pull in some examples, uh you must have some favorites that that we can touch on. Uh is that, is that fair? Can we, can we name some names, >>sure names, always working debut, right. And it's always in context of reality that we can talk about, as I said, this execution and not just a strategy. And I'll start with probably what's right in the front of many people's minds as we're doing this virtually because of what because of an unfortunate pandemic, um, this disastrous loss of life and things that have taken us down a path. We go well, how do we, how do we address that? Well, any time there's a tough task, IBM raises its hand first. You know, whether it was putting a person on the moon and bringing them home safely or standing up a system behind the current Social Security Administration, you know, during the Depression, you pick it well here we are now. And why not start with that as an example? Because I think it calls out just what we mentioned here first day, this thought of a, of an ecosystem because the first challenge, how do we create uh and address the biggest data puzzle of our lives, which is how do we get this vaccine created in record time, which it was the fastest before that was four years. This was a matter of months. Visor created the first one out and then had to get it out to distribution. Behind. That is a wonderful partner of R. S. A. P. Trying to work with that. So us working with S. A. P. Along with Pfizer in order to figure out how to get that value chain. And some would say supply chain, but I'll address that in a second. But there's many players there. And so we were in the middle of that with fires are committed to saying, how do we do that with S. A. P. So now you see players working together as one ecosystem. But then think about the ecosystem that that's happening where you have a federal government agency, a state, a local, you have healthcare, life science industry, you have consumer industry. Oh wait a second day. This is getting very complicated, Right? Well, this is the thought of convening an ecosystem and this is what I'm telling you is our execution and it has worked well. And so it's it's it's happening now. We still it's we see it's still developing and being, being, you know, very productive in real time. But then I said there was another example and that's with me, you mani whomever you pick the consumer. Ultimately we are that outcome of of the value chain. That's why I said, I don't want to just call it a supply chain because at the end is a someone consuming and in this case we need a shot. And so we partnered with Salesforce, IBM and Salesforce saying, wait a minute, that's not a small task. It's not just get the content there and put it in someone's arm instead they're scheduling that must be done. There's follow up an entire case management like system sells force is a master at this, so work dot com team with IBM, we sit now let's get that part done for the right type of UI UX capability that the user experience, user interaction interface and then also in bringing another player in the ecosystem, one of ours Watson health along with our block changing, we brought together something called a Digital Health pass. So I've just talked about two ecosystems work multiple ecosystems working together. So you think of an ecosystem of ecosystems. I called out Blockchain technology and obviously supply chain but there's also a I I O T. So you start to see where look this is truly an orchestration effort. It has to happen with very well designed capability and so of course we master and design and tying that that entire ecosystem together and convening it so that we get to the right outcome you me money all getting into shot being healthy. That's a real time example of us working with an ecosystem and teeming with key strategic partners, >>you know, money, I mean Jason you're right. I mean pandemics been horrible, I have to say. I'm really thankful it didn't happen 20 years ago because it would have been like okay here's some big pcs and a modem and go ahead and figure it out. So I mean the tech industry has saved business. I mean with not only we mentioned ai automation data, uh even things basic things like security at the end point. I mean so many things and you're right, I mean IBM in particular, other large companies you mentioned ASAP you have taken the lead and it's really I don't money, I don't think the tech industry gets enough credit, but I wonder if there's some of your favorite, you know, partnerships that you can talk about. >>Yeah, so I'm gonna I'm gonna build on what you just said. Dave IBM is in this unique position amongst this ecosystem. Not only the fact that we have the world leading most innovative technologies to bring to bear, but we also have the consulting capabilities that go with it now to make any of these technologies work towards the solution that Jason was referring to in this digital health pass, it could be any other solution you would need to connect these disparate systems, sometimes make them work towards a common outcome to provide value to the client. So I think our role as IBM within this ecosystem is pretty unique in that we are able to bring both of these capabilities to bear. In terms of you know, you asked about favorite there are this is really a coop petition market where everybody has products, everybody has service is the most important thing is how how are we bringing them all together to serve the need or the need of the hour in this case, I would say one important thing in this. As you observe how these stories are panning out in an ecosystem in in part in a partnership, it is about the value that we provide to our clients together. So it's almost like a cell with model from from a go to market perspective, there is also a question of our products and services being delivered through our partners. Right? So think about the span and scope of what we do here. And so that's the sell through. And then of course we have our products running within our partner companies and our partner products, for example. Salesforce running within IBM. So this is a very interesting and a new way of doing business. I would say it's almost like the modern way of doing business with modernity. >>Well. And you mentioned cooperation. I mean you're you're part of IBM that will work with anybody because your customer first, whether it's a W. S. Microsoft oracle is a is a is a really tough competitor. But your customers are using oracle and they're using IBM. So I mean as a those are some good examples. I think of your point about cooper Titian. >>Absolutely. If you pick on any other client, I'll mention in this case. Delta, Delta was working with us on moving, being more agile. Now this pandemic has impacted the airline sector particularly hard, right With travel stopping and anything. So they are trying to get to a model which will help them scale up, scale down, be more agile will be more secure, be closer to their customers, try and understand how they can provide value to their customers and customers better. So we are working with Delta on moving them to cloud on the journey to cloud. Now that public cloud could be anything. The beauty of this model and a hybrid cloud approach is that you are able to put them on red hat open shift, you're able to do and package the services into a microservices kind of a model. You want to make sure all the applications are running on a portable, almost platform. Agnostic kind of a model. This is the beauty of this ecosystem that we are discussing is the ability to do what's right for the end customer at the end of the day, >>how about some of the like sass players, like some of the more prominent ones and we watched the ascendancy of service now and and, and work day, you mentioned Salesforce. How do you work with those guys? Obviously there's an Ai opportunity, but maybe you could add some, you know, color there. >>So I like the fact that you call out the different hyper scholars for example, uh whether it's a W. S, whether it's Microsoft, knowing that they have their own cloud instances, for example. And when you, when you mentioned, he had this happened a long time ago, you know, you start talking about the heft of the technology, I started thinking of all the truckloads of servers or whatever they have to pull up. We don't need that now because it can happen in the cloud and you don't have to pick one cloud or the other. And so when people say hybrid cloud, that's what comes out, you start to think of what I I call, you know, a hybrid of hybrids because I told you before, you know, these roles are changing. People aren't just buyers or suppliers, they're both. And then you start to say what we're different people supplying well in that ecosystem, we know there's not gonna be one player, there's gonna be multiple. So we partner by doing just what monty called out is this thought of integrating in hybrid environments on hybrid platforms with hybrid clouds, Multi clouds, maybe I want something on my premises, something somewhere else. So in giving that capability that flexibility we empower and this is what's doing that cooperation, we empower our partners are strategic partners, we want them to be better with us. And this is this thought of being able to actually bring more together and move faster which is almost counterintuitive. You're like wait a minute you're adding more players but you're moving faster. Exactly because we have the capability to integrate those those technologies and get that outcome that monty mentioned, >>I would add to this one. Jason you mentioned something very very interesting. I think if you want to go just fast you go alone but if you want to go further, you go together. And that is the core of our point of view in this case is that we want to go further and we want to create value that is long lasting. >>What about like so I get the technology players and there may be things that you do that others don't or vice versa. So the gap fillers etcetera. But what about how to maybe customers that they get involved? Perhaps government agencies, may they be they be customer or an N. G. O. As another example, Are they part of this value chain? Part of this ecosystem? >>Absolutely. I'll give you I'll stick with the same example when I mentioned a digital health past that Digital Health Pass is something that we have as IBM and it's a credential Think of it as a health credential not a vaccine passport because it could be used for a test for a negative test on Covid, it could be used for antibiotics. So if you have this credential, it's something that we, as IBM created years back and we were using it for learning. When you think of getting people uh certifications versus a four year diploma, how do we get people into the workforce? That was what was original. That was a jenny Rometty thought, let's focus on new collar workers. So we had this asset that we'd already created and then it's wait, there's a place for it to work with, with health, with validation verification on someone's option, it's optional. They choose it. Hey, I want to do it this way. Well, the state of new york said that they wanted to do it that way and they said, listen, we are going to have a digital health pass for all of our, all of our new york citizens and we want to make sure that it's equitable, it could be printed or on a screen and we want it to be designed in this way and we wanted to work on this platform and we want to be able to, to work with the strategic Partners, a Salesforce and ASAP and work. I mean, I can just keep and we said okay let's do this. And this is the start of collaboration and doing it by design. So we haven't lost that day but this only brings it to the forefront just as you said, yes, that is what we want. We want to make sure that in this ecosystem we have a way to ensure that we are bringing together convening not just point products or different service providers but taking them together and getting the best outcome so that that end user can have it configured in the way that they want it >>guys, we got to leave it there but it's clear you're helping your customers and your partners on this this digital transformation journey that we already we all talk about. You get this massive portfolio of capabilities, deep, deep expertise, I love the hybrid cloud and AI Focus, Jason and money really appreciate you coming back in the cubes. Great to see you both. >>Thank you so much. Dave Fantastic. All >>Right. And thank you for watching everybody's day Vigilante for the Cuban. Our continuous coverage of IBM, think 2021, the virtual edition. Keep it right there. Yeah. Mhm. Mhm. >>Mhm.

(soft music) >> Hey, welcome to this "CUBE Conversation". I'm Lisa Martin today talking to a CUBE alumni who's been on many times talking about data, all things data. Please welcome Satyen Sangani the Co-Founder and CEO of Alation. Satyen, it's great to have you back on theCUBE. >> Hi Lisa, it's great to see you too. It's been a while. >> It has been a while. And of course in the last year we've been living in this virtual world. So, I know you've gotten to be on theCUBE during this virtual world. Hopefully someday soon, we'll get to actually sit down together again. There's some exciting news that's coming out of Alation. Talk to us about what's going on. What are you announcing? >> So we're announcing that we are releasing our Alation Cloud Service which actually comes out today, and is available to all of our customers. And as a consequence are going to be the fastest, easiest deploy and easiest to use data catalog on the Marketplace, and using this release to really double down on that core differentiation. >> So the value prop for Alation has always been about speed to deployment, time to value. Those have really been, what you've talked about as the fundamental strengths of the platform. How does the cloud service double down on that value prop? >> Well, if you think about data, our basic premise and the reason that we exist is that, people could use data with so many of their different decisions. People could use data to inform their thinking. People can use data in order to figure out what decision is the best decision at any given point in time. But often they don't. Often gut instinct, or whatever's most fast or easy to access is the basis off of which people decide to do what they do. And so if you want to get people to use data more often you've got to make sure that the data is available that the data is correct, and that the data is easy to find and leverage. And so everything that we can do at Alation to make data more accessible, to allow people to be more curious, is what we get excited about. Because unlike, paying your payables or unlike, figuring out whether or not you want to be able to have greater or lesser inventory, those are all things that a business absolutely has to do but people don't have to use data. And to get people to use data, the best thing you can do is to make it easy and to make it fast. >> And speaking of fast, that's one of the things I think the last year has taught us is that, real-time access to data is no longer a nice to have. It's really a competitive differentiator. Talk to me about how you enable customers to get access to the right data fast enough, to be able to do what so many companies say, and that is actually make data-driven decisions. >> Yeah, that's absolutely right. So, it really is a entire continuum. The first and most obvious thing that we do is we start with the user. So, if you're a user of data, you might have to hunt through a myriad of reports, thousands of tables in a database, hundreds of thousands of files in a data lake, and you might not know where to find your answer and you might have the best of intentions but if you don't have the time to go through all of those sources, the first thing you might do is, go ask your buddy down the hall. Now, if your buddy down the hall or your colleague over Zoom can't give you the time of day or can't answer your question quickly enough then you're not going to be able to use that data. So the first thing, and the most obvious thing that we do is we have the industry's best search experience and the industry's best browse experience. And if you think about that search experience, that's really fueled by our understanding of all of the data patterns in your data environment. We basically look at every search. We look at every log within a company's data environment to understand what it is that people are actually doing with the data. And that knowledge just like Google has page rank to help it inform which are the best results for a given webpage. We do the exact same thing with data. And so great search is the basis of what we do. Now, above and beyond that, there's a couple of other things that we do, but they all get to the point of getting to that end search experience and making that perfect so that people can then curate the data and leverage the data as easily as possible. >> Sounds like that's really kind of personalized based on the business, in terms of the search, looking at what's going on. Talk to me a little bit more about that, and how does that context help fuel innovation? >> Yeah. So, to build that context, you can't just do, historically and traditionally what's been done in the data management space. Lots of companies come to the data management world and they say, "Well, what we're going to do is we're going to hire... "We've got this great software. "But setting the software up is a journey. "It takes two to three to four years to set it up "and we're going to get an army of consultants "and everybody's going to go and assert quality of data assets "and measure what the data assets do "and figure out how the data assets are used. "And once we do all of that work, "then in four years we're going to get you to a response." The real key is not to have that context to be built, sort of through an army of consultants and an army of labor that frankly nine times out of 10 never gets to the end of the road. But to actually generate that context day one, by understanding what's going on inside of those systems and learning that by just observing what's happening inside of the company. And we can do that. >> Excellent. And as we've seen the acceleration in the last year of digital transformation, how much of that accelerant was an accelerator revelation putting this service forward and what are customers saying so far? >> Yeah, it's been incredible. I mean, what we've seen in our existing accounts is that, our expansions have gone up by over 100% year over year with the kind of crisis in place. Obviously, you would hypothesize that these catalogs, these, sort of accessibility and search tools and data in general, would be leveraged more when all of us are virtual and all of us can't talk to each other. But, it's been amazing to see that we've found that that's actually what's happening. People are actually using data more. People are actually searching for data more. And that experience and bringing that to our customers has been a huge focus of what we're trying to do. So we've seen the pandemic, in many cases obviously been bad for many people but for us it's been a huge accelerant of customers using our product. >> Talk to me about Alation with AWS. What does that enable your customers to achieve that they maybe couldn't necessarily do On-Prem? >> Yeah, so, customers obviously don't really care anymore, or as much as they used to, about managing the software internally. They just want to be able to, get whatever they need to get done and move forward with their business. And so by leveraging our partnership with AWS, one, we've got elastic compute capability. I think that's obviously, something that they bring to the table, better than perhaps any other in the market. But much more fundamentally, the ability to stand up Alation and get it going, now means that all you have to do is go to the AWS Marketplace or call up an Alation rep. And you can, within a matter of minutes, get an Alation instance that's up and running and fit for purpose for what you need. And that capability is really quite powerful because, now that we have that elasticity and the speed of deployment, customers can realize the value, so much more quickly than they otherwise might've. >> And that speed is absolutely critical as we saw a lot last year that was the difference between the winners and those that were not going to make it. Talk to me a little bit about creating a data culture. We talk about that a lot. It's one thing to talk about it, it's a whole other thing to put it in place, especially for legacy institutions that have been around for a while. How do you help facilitate the actual birth of a data culture? >> Yeah, I mean, I think we view ourselves as a technology, as a catalyst, to our best customers and our best customer champions. And when we talk to chief data officers and when we talk to data leaders within various organizations that we service, organizations like Pfizer, organizations like Salesforce, organizations like Cisco, what they often tell me is, "Look, we've got to build products faster. "We've got to move at the speed and the scale "of all of the startups that are nipping at our heels. "And how do we do that? "Well, we've got to empower our people "and the way that we empower our people "is by giving them context. "And we need to give them the data "to make the right decisions, "so that they can build those products "and move faster than they ever might've." Now those are amazing intentions but those same leaders also come and say, "I've just been mired in risk "and I've been mired in compliance, "and I've been mired in "doing all of these data janitorial projects. "And it's really hard for me to get "on the offense with data. "It's really hard for me to get proactive with data." And so the biggest thing that we do, is we just help companies be more proactive, much more easily, because what they're able to do, is they're able to leave a lot of that janitorial work, lead a lot of that discovery work, lead a lot of that curation work to the software. And so what they get to focus on is, how is it that I can then drive change and drive behavioral change within my organizations so that people have the right data at their disposal. And that's really the magic of the technology. >> So I was reading the "Alation State of Data Culture Report" that was just published a few weeks ago. This is this quarterly assessment that Alation does, looking at the progress that enterprises have made in creating this data culture. And the number that really struck out at me was 87% of respondents say, data quality issues are a barrier to successful implementation of AI in their organizations. How can Alation help them solve that problem? >> Yeah, I think the first is, whenever you've got a problem, the first thing you've got to do is acknowledge that you've got a problem. And a lot of the time people, leaders will often jump to AI and say, "well, hey, everybody's talking about AI. "The board level conversation is AI. "McKinsey is talking about AI, let's go do some AI." And that sounds great in theory. And of course we all want to do that more, but the reality is that many of these projects are stymied by the basic plumbing. You don't necessarily know where the data's coming from. You don't know if people have entered it properly in the source systems or in the systems that are online. Those data often get corrupted in the transformation processes or the processes themselves don't run appropriately. And so you don't have transparency. You don't have any awareness of what people are doing, what people are using, how the data is actually being manipulated from step to step, what that data lineage is. And so that's really where we certainly help many of our customers by giving them transparency and an understanding of their data landscape. Ironically, what we find is that, data leaders are super excited to get data to the business but they often don't themselves have the data to understand how to manage the data itself. >> Wow, that's a conundrum. Let's talk about customers because I was looking on the website and there's some pretty big metrics-based business outcomes that Alation is helping customers drive. I wanted to kind of pick through some examples from your perspective. First one is 364% ROI. Second one is 70% less time for analysts to complete projects. Workforce productivity is huge. Talk to me about how Alation is helping customers achieve business outcomes like that. >> Yeah, so if you think about a typical analytical project you would think that most of the time is spent inside of the analytical tool, inside of your Excel, inside of your Tableau, that where you're thinking about the data and you're analyzing it, you're thinking deep thoughts. And you're trying to hypothesize you're trying to understand. But the reality is going back to the data quality issue that most of the time is spent with figuring out which are the right datasets. Because at one of our customers, for example, there were 4,000 different types of customer transaction datasets, that spoke to the exact same data. Which data set do I actually use out of a particular database? And then once I figured out which ones to use, how do I construct the appropriate query and assumptions in order to be able to get the data into a format that makes sense to me. Those are the kinds of things that most analysts and data scientists struggle with. And what we do is we help them by not having them reinvent the wheel. We allow them to figure out what the right dataset is fast, how to manipulate it fast, so that they can focus most of their time on doing that end analytical work. And that's where all the ROI or a lot of the ROI is coming from because they don't know how to reinvent the wheel. They can do the work and they can move on with the much faster business decision which means that that business moves significantly faster. And so what we find is that for these very highly priced resources, some data scientists who make 200, 300, $400,000 fully load it for a company, those people can do their job 74% faster which means they can get not only the answer faster but they can get many more tasks done, for over a given period of time. >> Well, that just opens up a potential suite of benefits that the organization will achieve, not just getting the analyst productivity cranked up in a big way, but also allowing your organization to be more agile which many organizations are striving to be. to be able to identify new products, new services, what's happening, especially, in a changing chaotic environment like we've been living in the last year. >> Yeah, absolutely. And they also can learn... Not only can they help themselves figure out what new products to launch, but they can also help themselves figure out where their risks happen to be, and where they need to comply, because it could be the case that analysts are using datasets that they ought not to be using or the businesses using the data incorrectly. And so you can find both the patterns but also the anti-patterns, which means that you're not only moving faster, but you're moving forward with less risk. And so we've seen so many failures with data governance, regimes, where people have tried to assert the quality of data and figure out the key data elements and develop a business glossary. And there's that great quote, "I wanted data governance but all I got is a data glossary." That all happens because, they just don't have enough time in the day to do the value added work. They only have enough time in the day to start doing the data cleaning and all of the janitorial work that we, as a company, really strive to allow them to completely eliminate. >> So wrapping things up here, Alation Cloud Service. Tell me about when it's available, how can customers get it? >> So it's available today, which is super exciting. Customers can get it either through the AWS Marketplace or by calling your Alation representative. You can do that coming to our website. And that's super easy to do and getting a demo and moving forward. But we try to make it as easy as possible. And we really want to get out of the way, of allowing people to have a seamless frictionless experience and are super excited to have this cloud service that allows them to do that, even faster than they were able to do before. >> And we all know how important that speed is. Well, Satyen, congratulations on the announcement of Alation Cloud Service. We appreciate you coming on here and sharing with us the news and really what's in it for the customers. >> Thank you, Lisa. It's been phenomenal catch up and great seeing you. >> Likewise. For Satyen Sangani, I'm Lisa Martin. You're watching this "CUBE Conversation." (soft music)

>>Hello everybody. I am Toshihiko Nishimura from Stanford. University is there to TTT out here, super aging, global OMIM global transportation group about infections, uh, or major point of concerns. In addition, this year, we have the COVID-19 pandemic. As you can see here, while the why the new COVID-19 patients are still increasing, meanwhile, case count per day in the United state, uh, beginning to decrease this pandemic has changed our daily life to digital transformation. Even today, the micro segmentation is being conducted online and doctor and the nurse care, uh, now increase to telemedicine. Likewise, the drug development process is in need of major change paradigm shift, especially in vaccine in drug development for COVID-19 is, should be safe, effective, and faster >>In the >>Anastasia department, which is the biggest department in school of medicine. We have Stanford, a love for drug device development, regulatory science. So cold. Say the DDT RDS chairman is Ron Paul and this love leaderships are long mysel and stable shaper. In the drug development. We have three major pains, one exceedingly long duration that just 20 years huge budget, very low success rate general overview in the drug development. There are Discoverly but clinical clinical stage, as you see here, Tang. Yes. In clinical stage where we sit, say, what are the programs in D D D R S in each stages or mix program? Single cell programs, big data machine learning, deep learning, AI mathematics, statistics programs, humanized animal, the program SNS program engineering program. And we have annual symposium. Today's the, my talk, I do like to explain limitation of my science significance of humanized. My science out of separate out a program. I focused on humanized program. I believe this program is potent game changer for drug development mouse. When we think of animal experiment, many people think of immediately mouse. We have more than 30 kinds of inbred while the type such as chief 57, black KK yarrow, barber C white and so on using QA QC defined. Why did the type mice 18 of them gave him only one intervention using mouse, genomics analyzed, computational genetics. And then we succeeded to pick up fish one single gene in a week. >>We have another category of gene manipulated, mice transgenic, no clout, no Kamal's group. So far registered 40,000 kind as over today. Pretty critical requirement. Wrong FDA PMDA negative three sites are based on arteries. Two kinds of animal models, showing safety efficacy, combination of two animals and motel our mouse and the swine mouse and non-human primate. And so on mouse. Oh, Barry popular. Why? Because mouse are small enough, easy to handle big database we had and cost effective. However, it calls that low success rate. Why >>It, this issue speculation, low success rate came from a gap between preclinical the POC and the POC couldn't stay. Father divided into phase one. Phase two has the city FDA unsolved to our question. Speculation in nature biology using 7,372 new submissions, they found a 68 significant cradle out crazy too, to study approved by the process. And in total 90 per cent Radia in the clinical stages. What we can surmise from this study, FDA confirmed is that the big discrepancy between POC and clinical POC in another ward, any amount of data well, Ms. Representative for human, this nature bio report impacted our work significantly. >>What is a solution for this discrepancy? FDA standards require the people data from two species. One species is usually mice, but if the reported 90% in a preclinical data, then huge discrepancy between pretty critical POC in clinical POC. Our interpretation is data from mice, sometime representative, actually mice, and the humor of different especially immune system and the diva mice liver enzyme are missing, which human Liba has. This is one huge issue to be taught to overcome this problem. We started humanized mice program. What kind of human animals? We created one humanized, immune mice. The other is human eyes, DBA, mice. What is the definition of a humanized mice? They should have human gene or human cells or human tissues or human organs. Well, let me share one preclinical stages. Example of a humanized mouse that is polio receptor mice. This problem led by who was my mentor? Polio virus. Well, polio virus vaccine usually required no human primate to test in 13 years, collaboration with the FDA w H O polio eradication program. Finally FDA well as w H O R Purdue due to the place no human primate test to transgenic PVL. This is three. Our principle led by loss around the botch >>To move before this humanized mouse program, we need two other bonds donut outside your science, as well as the CPN mouse science >>human hormone, like GM CSF, Whoah, GCSF producing or human cytokine. those producing emoji mice are required in the long run. Two maintain human cells in their body under generation here, South the generation here, Dr. already created more than 100 kinds based on Z. The 100 kinds of Noe mice, we succeeded to create the human immune mice led the blood. The cell quite about the cell platelets are beautifully constituted in an mice, human and rebar MAs also succeeded to create using deparent human base. We have AGN diva, humanized mouse, American African human nine-thirty by mice co-case kitchen, humanized mice. These are Hennessy humanized, the immune and rebar model. On the other hand, we created disease rebar human either must to one example, congenital Liba disease, our guidance Schindel on patient model. >>The other model, we have infectious DDS and Waddell council Modell and GVH Modell. And so on creature stage or phase can a human itemize apply. Our objective is any stage. Any phase would be to, to propose. We propose experiment, pose a compound, which showed a huge discrepancy between. If Y you show the huge discrepancy, if Y is lucrative analog and the potent anti hepatitis B candidate in that predict clinical stage, it didn't show any toxicity in mice got dark and no human primate. On the other hand, weighing into clinical stage and crazy to October 15, salvage, five of people died and other 10 the show to very severe condition. >>Is that the reason why Nicole traditional the mice model is that throughout this, another mice Modell did not predict this severe side outcome. Why Zack humanized mouse, the Debar Modell demonstrate itself? Yes. Within few days that chemistry data and the puzzle physiology data phase two and phase the city requires huge number of a human subject. For example, COVID-19 vaccine development by Pfizer, AstraZeneca Moderna today, they are sample size are Southeast thousand vaccine development for COVID-19. She Novak UConn in China books for the us Erica Jones on the Johnson in unite United Kingdom. Well, there are now no box us Osaka Osaka, university hundred Japan. They are already in phase two industry discovery and predict clinical and regulatory stage foster in-app. However, clinical stage is a studious role because that phases required hugely number or the human subject 9,000 to 30,000. Even my conclusion, a humanized mouse model shortens the duration of drug development humanize, and most Isabel, uh, can be increase the success rate of drug development. Thank you for Ron Paul and to Steven YALI pelt at Stanford and and his team and or other colleagues. Thank you for listening.

>> From theCUBE's studios in Palo Alto, in Boston, connecting with all leaders all around the world. This is a CUBE Conversation. >> Hello, everyone, this is Dave Vellante and welcome. We're going to do a little preview of ThoughtSpot Beyond, and we're going to look at the intersection of cloud, data, search and analytics. For a decade, we've been collecting all this information and tapping data sources for many, many different places. Now we're at the point where we can very cost-effectively and quickly put data into the hands of many orders of magnitude, more users so the data can inform opinions and ultimately actions. With me is Sudheesh Nair, who's the CEO of ThoughtSpot. Sudheesh, it's always a pleasure to have you on theCUBE. Thanks for coming on. >> Absolutely, my pleasure, Dave. Thanks for having me. >> You know it's ironic that we start this decade with so much disruption to our lives. It's forced us to become digital businesses really overnight. I wonder if you could talk about the role of data as it relates to our digital lives? >> I think the idea that data somehow directly impacts our lives sometimes can be farfetched. That is because we don't really talk about it in the right way. Data can be this archaic mountain of things that people don't really connect with. What we should really be talking about is what data does, the byproduct, the end product of data, which is the signal that we get out of the mountain of data, the insight that we derive from it and the action, the bespoke actions that makes our lives possible in this new world that we are all living in. If you really do a good job of talking about what data does for you or the by-product of what the data does for you, I think people will understand that we are incredibly connected, incredibly dependent on the signals that we derive from the data that we are giving out to the world that we are operating in today. >> We had a fire ready and aim because the speed at which we've had to adapt as we've never seen this before. I'm wondering if you could share with us what you're seeing. What kind of challenges this creates for organizations, specifically in terms of being able to leverage their data assets? >> See, I think if you think of the last eight, nine months, sometimes in our industry, it is easy to sort of look at this as an opportunity, more of an opportunistic way of looking at how can I sell more data driven things when the world is sort of falling apart. You walk on a downtown, you see all these restaurants closed, parking lots empty. My sort of less than in the last eight, nine months is to be more outside-in as opposed to inside-out. That is, why are we doing this, is now more important than what we are doing. In that context, my biggest lesson that I've learned is that the thing that stand in the way of delivering value for customers almost always is not technology, not product and not even quality of data. A lot of data people will say it is the data quality that is holding me back from doing. It is lack of courage, lack of vision, lack of ability to sort of empathize with your customers and truly see what can we do to make their lives better, where data driven insights might be a part of it. I really believe that organizations that are differentiating by providing better services where they use data to do that are clearly coming out ahead as we are looking at the end of this global pandemic. >> It's interesting what you're saying about data quality, because I agree with you. I actually think it's access to data because as a business user, I can look at data, ask a couple of questions and say, I can get pretty close to the truth. If you think about organizations generally, but specifically business users, they've been clamoring for more fast style access to data and really the time is now for them to realize this vision. I wonder if you could share with us what's happening in ThoughtSpot business in the past month, 'cause that's what you're all about, is that easy, fast access to data. >> I always talk about the decision making pipeline. I know one end, you have the data that customers are happy to give. However, it's a two way street. They are saying, look, I'll give you my data, in return I want you to do two things. Number one, make sure it is safe and protected. Number two, you are using that data to deliver a bespoke experiences for me, bespoke services for me. That is I'm giving you the data so you will get to know me and treat me as an individual, as a person with the likes and dislikes that are different from someone else's. If you don't do that, you're breaking that contract. When I think of this continuum of data to insight to knowledge to action, action is where the users benefit. I sort of sometimes worry that the chasm that exists between the people who can speak the data, the SQL, the data, warehouse people who have usually the answers and not necessarily have the questions because questions are usually coming from the business users. Our sort of purpose in life as a company in the world has been simple. That is let us break that barrier. Let's move that silos and then unify so that people with questions can get answers. People who know the business can get the answer from the data without any tax on their curiosity. It is easier said than done, but it is a journey. I strongly believe that pushing the ability to inquire and get insights from the data all the way to the front line, where business users interact with their customers, the businesses customers, the consumers, the clients, if you don't do that properly, there is no way to keep up with the velocity of change that the world is throwing at your business. >> So speaking of the data sources, one of the data sources I sometimes look at it, you look at the stock market, it is funny. The last month Pfizer announces they got a very highly successful trial and the stock market goes up 800 points. You sort of look at that and say, that's a data point. I recently released a number of pieces on cloud and its impact. After that you saw up on a cloud stocks, everybody panicked, sell tech. Even though written cloud's not immune to COVID, it's clear from our data that cloud migration has been very much accelerated since the pandemic hit and I don't really see that changing. I wonder if you could talk about the ways in which you see cloud changing, how organizations operate and really what's missing when it comes to getting the most out of their cloud investments, specifically around analytics. >> It is like any other function. Data analytics is not different in what the cloud does for the customers. I used to always talk about the world of computing, the world of technology as a race against commoditization. Imagine that it's a ocean that is warming and there's an iceberg that is floating on it. As the ocean warms the iceberg is melting and if you want to survive, you've got to keep going up the mountain, the iceberg mountain. In this example, the commoditization of technology is the ocean. Anything that you think is unique, anything that you think is proprietary, it's going to get commoditized. The reason why that's happening is because people want to go up the value chain. That's the iceberg, that's the mountain. If you use that metaphor, what you will see here is that people want to go up the value that the data analytics deliver as opposed to how cool or how differentiated the process of delivering value is. Let me explain that. Imagine that you are producing a lot of content, I am pretty sure that you have ways to sort of collect the data on how it is making an impact. That is how many people watched it, how many of them were young versus old versus Salesforce engineering versus marketing versus... You can slice and dice the data. That is where today's data analytics stops. Now, imagine if you can take it to the next level, that is what impact is it having on my consumers? Are they able to get better jobs, for example, because of a technology that you talked about or theCUBE's ability to sort of democratize access, the way sometimes you take complex technology and simplify it. Is that making easier for some execs to catch up with the speed with which technology is changing? In turn, which makes their business model agile. Our thesis is that when we stop data analytics at the noise level, the data level, the insight level, we are only doing half the job. We need to go all the way through that value chain, climb all the way up in that iceberg and think for the customer. What am I doing for the customer? There are recent examples of our banks, largest of large banks, where they had inherent bias when it comes to how they were giving loans to minorities and people of color, or the people who have an accent on the phone, they're actually calling on customer support. These sort of things are not an AI problem or a BI problem, these are human problems. By breaking the barrier between business users and their consumers, where data become an inherent part of deficient making, you can make tangible difference in the world. I think that is what we are trying to do. I know it sounds somewhat naive and utopian, but I do think this is possible if you really approach it outside-in. >> And outside-in thinking is critical. I want to pick up on something you said about kind of moving up the value chain. We've watched over the last decade, sort of the SASification of many industries. You guys recently announced ThoughtSpot Cloud, which was your first SAS offering. Tell us, how's it going? What's the uptake like, the adoption? What are customers telling you about what it's doing for their business? >> Again, this is the same outside-in story. It is relatively new, it's only been a month. The interest is pretty high and we have closed a handful of customers. I don't want to claim victory yet, but the signs have been very positive and it does not surprise me because it aligns with that story that I talked about growing up the value chain. Traditionally, when we deployed ThoughtSpot, we deployed in the customer's VPC, their own cloud or in the data center. The problem is when you are doing that, they are responsible for integrating the data, connecting the data, prepping the data, managing it. There's a lot of work that goes with it. But ThoughtSpot I would ask you, is it possible for us to do as much for the customer with TS Cloud, ThoughtSpot Cloud? That is you just go to ThoughtSpot Cloud and connect to your SAS data warehouse services that you may have, but there's Snowflake or Redshift or in a DBQ, Google BigQuery, or a Microsoft synapse and then get going immediately. To give you an idea, a typical ThoughtSpot deployment used to take around four to five months, now it is taking around 35 minutes. That's what ThoughtSpot Cloud does for our customers. If it happens in 35 minutes, their business of delivering value to their clients is happening that much faster. >> Everything shifts to actually getting insights as opposed to setting stuff up. One of the other things to do that I've been reporting on. I've said in the last decade, we kind of moved from really a product centric world to one that's more platform centric, particularly with cloud and SAS. The latest research that we've been doing shows that ecosystems, we think are going to power the next wave of innovation. I wonder what your view is of that premise and how you're thinking about ecosystems as a lever of growth. >> This word platform is one of the most abused word in our industry because people like to say, don't say product, say solution, and then say, don't say solution use platform. In reality, a platform is useless if people are not standing on. If you're standing on a railway platform, nobody's there, watch the point? The same thing applies to business, our business as to when it comes to platform. A platform is only a real platform if there are other players making money of what you have built. If you build a platform, all it does is a bunch of API. Nobody's consuming, it's not useful. In that context, we have long ways to go, we have really long ways to go. I do think one of sort of... I wouldn't say mistake, but one of the oversights that our sport had was not delivering on the vision of platform. That it is easy to make for others to come together and do commerce on ThoughtSpot. Most importantly, make sure that it is not just easy but when customers come to them, that one plus one is like 10 or 11, as opposed to one plus one equal two. That is something that we have to remedy. At the Beyond Conference, next month on December 9th, you will see us make some interesting announcements around this thing. It is one of my favorite sort of projects because once we do that very well, you will see that it becomes a platform. Think of Stripe, think of Square. These are platforms because it made their customers' lives easier, but at the same time, multiple companies could come together to deliver joint solutions where the sum is much bigger than equals of the parts. That is a vision that ThoughtSpot needs to really deliver on and Beyond will be a stock. >> I mean, the power of many versus the resources of one and this is well understood over time and now we're seeing it really applied to our industry. Sudheesh, a lot of the analytics that we produce today are the result of humans clicking and typing and interacting with systems. That's obviously going to continue to grow, but you think about things like IOT, the build-out of 5G, it brings this whole new dimension of machine to machine communications and tons of new data. Much of the data out there is analog, today, it's being increasingly become digital. How are you thinking about these trends in terms of the impact on your company and your customers? >> I think if anyone asks me, what does ThoughtSpot do for the data analytics world? My answer is very simple. We have introduced a new interface to access structure data that can be used by anybody, search that is driven by AI, that's an AI driven search. That core idea is about scale, but more importantly, rate of change. That's where the new inventions around 5G where the bottlenecks are being removed at IoT and mobile. I mean, we want to put mobile as well. So you have mobile devices, IOT devices, very big pipe, and then cloud on the backend where processing and storing is cheap. Now if you think of that, it is a 12 lane super highway, all the way to the end user, all the way to the end device, to the mothership. When you have that much speed and when you remove everything, you have to think about the asset, the artifacts that you build out of that kind of a data stream. That's where the old way of looking at dashboards will die. It's not a question of if or when it is dying. What we need is now to make sure that at that speed, when the data is changing much faster than ever before, you have new way to deliver insight to the people who can act on it, which is business users. If you think of it, there used to be cases where companies used to make supply chain decisions for the year. Now, supply chain decisions are made monthly because you don't know what next month will look like with COVID. When you have annual decisions become monthly decisions, monthly decisions become weekly decisions, weekly decisions became minute by minute decisions sometimes like placing social media sentiment changes, things like that, there is no way that you can depend on a Monday morning report or a Monday morning meeting, and then send out, here is what you need to do, action items to the front end. Everyone should have the pulse on where the business is, which is where the data is going to help them. However, human experience is so critical. You don't want to remove human experience. That's why as we deliver more and more on 5G and IoT, making the data as it is changing and then delivering those signals that insights directly to business users in the frontline is going to be like the de facto way businesses will operate. I think we are just beginning that journey in terms of what is possible. >> Well, it reminds me of when we were kids, the coaches would tell us, go to where you think the ball is going to be, find opportunities for that open space, not to where it is today. That's the notion of whether it's soccer or basketball, or of course, hockey skate to the puck is obviously a famous term. So how do you stay ahead of that disruption curve in a space like analytics? What are the innovation opportunities that organizations should be tapping today and beyond? >> I was thinking about this a lot myself, which is the important thing is to be ready to unlearn. I know it is a simple thing but it was one of the most difficult things because as you grow up in the organizations, as you become an exec, as you gain more experience, we actually calcify our knowledge. That's a problem, because things are changing. There are new way to do things, new opportunities. Being open to unlearning is going to be more critical than learning new things sometimes. That will require humility. I won't say it's a go learn AI, or go learn a new language or Python or coding. Those things might be necessary, but having that mentality of willing to unlearn and then having the courage to make some difficult decisions. If you do those two things, I think this is an exciting role. And if you're not, you're going to go the wayside of a lot of industries have been going. >> That's great advice. I mean, we saw that a lot coming into the pandemic. There was a lot of complacency around digital and of course there isn't anymore. Sudheesh, thanks so much for joining me in this CUBE Conversation. It's always great to talk to you. >> Thank you for taking the time, I appreciate it. >> My pleasure. Thank you for watching, everybody. This is Dave Vellante for theCUBE, will see you next time. (bright upbeat music)

>> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello, and welcome to theCUBE studios, in Palo Alto, California for another CUBE conversation where we go in depth with though leaders driving innovation across tech industry. I'm your host, Peter Burris. The whole concept of self service analytics has been with us decades in the tech industry. Sometimes its been successful, most times it hasn't been. But we're making great progress and have over the last few years as the technologies matures, as the software becomes more potent, but very importantly as the users of analytics become that much more familiar with what's possible and that much more wanting of what they could be doing. But this notion of self service analytics requires some new invention, some new innovation. What are they? How's that going to play out? Well, we're going to have a great conversation today with Stephanie McReynolds, she's Senior Vice President of Marketing, at Alation. Stephanie, thanks again for being on theCUBE. >> Thanks for inviting me, it's great to be back. >> So, tell us a little, give us an update on Alation. >> So as you know, Alation was one of the first companies to bring a data catalog to the market. And that market category has now been cemented and defined depending on the industry analyst you talk to. There could be 40 or 50 vendors now who are providing data catalogs to the market. So this has become one of the hot technologies to include in a modern analytics stacks. Particularly, we're seeing a lot of demand as companies move from on premise deployments into the cloud. Not only are they thinking about how do we migrate our systems, our infrastructure into the cloud but with data cataloging more importantly, how do we migrate our users to the cloud? How do we get self-service users to understand where to go to find data, how to understand it, how to trust it, what re-use can we do of it's existing assets so we're not just exploding the amount of processing we're doing in the cloud. So that's been very exciting, it's helped us grow our business. We've now seen four straight years of triple digit revenue growth which is amazing for a high growth company like us. >> Sure. >> We also have over 150 different organizations in production with a data catalog as part of their modern analytics stack. And many of those organizations are moving into the thousands of users. So eBay was probably our first customer to move into the, you know, over a thousand weekly logins they're now up to about 4,000 weekly logins through Alation. But now we have customers like Boeing and General Electric and Pfizer and we just closed a deal with US Air Force. So we're starting to see all sorts of different industries and all sorts of different users from the analytics specialist in your organization, like a data scientist or a data engineer, all the way out to maybe a product manager or someone who doesn't really think of them as an analytics expert using Alation either directly or sometimes through one of our partnerships with folks like Tableau or Microstrategy or Power BI. >> So, if we think about this notion of self- service analytics, Stephanie, and again it's Alation has been a leader in defining this overall category, we think in terms of an individual who has some need for data but is, most importantly, has questions they think data can answer and now they're out looking for data. Take us through that process. They need to know where the data is, they need to know what it is, they need to know how to use it, and they need to know what to do if they make a mistake. How is that, how are the data catalogs, like Alation, serving that, and what's new? >> Yeah, so as consumers, this world of data cataloging is very similar if you go back to the introduction of the internet. >> Sure. >> How did you find a webpage in the 90's? Pretty difficult, you had to know the exact URL to go to in most cases, to find a webpage. And then a Yahoo was introduced, and Yahoo did a whole bunch of manual curation of those pages so that you could search for a page and find it. >> So Yahoo was like a big catalog. >> It was like a big catalog, an inventory of what was out there. So the original data catalogs, you could argue, were what we would call from an technical perspective, a metadata repository. No business user wants to use a metadata repository but it created an inventory of what are all the data assets that we have in the organizations and what's the description of those data assets. The meta- data. So metadata repositories were kind of the original catalogs. The big breakthrough for data catalogs was: How do we become the Google of finding data in the organization? So rather than manually curating everything that's out there and providing an in- user inferant with an answer, how could we use machine learning and AI to look at patterns of usage- what people are clicking on, in terms of data assets- surface those as data recommendations to any end user whether they're an analytics specialist or they're just a self- service analytics user. And so that has been the real break through of this new category called data cataloging. And so most folks are accessing a data catalog through a search interface or maybe they're writing a SQL query and there's SQL recommendations that are being provided by the catalog-- >> Or using a tool that utilizes SQL >> Or using a tool that utilizes SQL, and for most people in a- most employees in a large enterprise when you get those thousands of users, they're using some other tool like Tableau or Microstrategy or, you know, a variety of different data visualization providers or data science tools to actually access that data. So a big part of our strategy at Alation has been, how do we surface this data recommendation engine in those third party products. And then if you think about it, once you're surfacing that information and providing some value to those end users, the next thing you want to do is make sure that they're using that data accurately. And that's a non- trivial problem to solve, because analytics and data is complicated. >> Right >> And metadata is extremely complicated-- >> And metadata is-- because often it's written in a language that's arcane and done to be precise from a data standpoint, that's not easily consumable or easily accessible by your average human being. >> Right, so a label, for example, on a table in a data base might be cust_seg_257, what does that mean? >> It means we can process it really quickly in the system. >> Yeah, but as-- >> But it's useless to a human being-- >> As a marketing manager, right? I'm like, hey, I want to do some customer segmentation analysis and I want to find out if people who live in California might behave differently if I provide them an offer than people that live in Massachusetts, it's not intuitive to say, oh yeah, that's in customer_seg_ so what data catalogs are doing is they're thinking about that marketing manager, they're thinking about that peer business user and helping make that translation between business terminology, "Hey I want to run some customer segmentation analysis for the West" with the technical, physical model, that underlies the data in that data base which is customer_seg_257 is the table you need to access to get the answer to that question. So as organizations start to adapt more self- service analytics, it's important that we're managing not just the data itself and this translation from technical metadata to business metadata, but there's another layer that's becoming even more important as organizations embrace self- service analytics. And that's how is this data actually being processed? What is the logic that is being used to traverse different data sets that end users now have access to. So if I take gender information in one table and I have information on income on another table, and I have some private information that identifies those two customers as the same in those two tables, in some use tables I can join that data, if I'm doing marketing campaigns, I likely can join that data. >> Sure. >> If I'm running a loan approval process here in the United States, I cannot join that data. >> That's a legal limitation, that's not a technical issue-- >> That's a legal, federal, government issue. Right? And so here's where there's a discussion, in folks that are knowledgeable about data and data management, there's a discussion of how do we govern this data? But I think by saying how we govern this data, we're kind of covering up what's actually going on, because you don't have govern that data so much as you have to govern the analysis. How is this joined, how are we combining these two data sets? If I just govern the data for accuracy, I might not know the usage scenario which is someone wants to combine these two things which makes it's illegal. Separately, it's fine, combined, it's illegal. So now we need to think about, how do we govern the analytics themselves, the logic that is being used. And that gets kind of complicated, right? For a marketing manager to understand the difference between those things on the surface is doesn't really make sense. It only makes sense when the context of that government regulation is shared and explained and in the course of your workflow and dragging and dropping in a Tableau report, you might not remember that, right? >> That's right, and the derivative output that you create that other people might then be able to use because it's back in the data catalog, doesn't explicitly note, often, that this data was generated as a combination of a join that might not be in compliance with any number of different rules. >> Right, so about a year and a half ago, we introduced a new feature in our data catalog called Trust Check. >> Yeah, I really like this. This is a really interesting thing. >> And that was meant to be a way where we could alert end users to these issues- hey, you're trying to run the same analytic and that's not allowed. We're going to give you a warning, we're not going to let you run that query, we're going to stop you in your place. So that was a way in the workflow of someone while they're typing a SQL statement or while they're dragging and dropping in Tableau to surface that up. Now, some of the vendors we work with, like Tableau, have doubled down on this concept of how do they integrate with an enterprise data catalog to make this even easier. So at Tableau conference last week, they introduced a new metadata API, they introduced a Tableau catalog, and the opportunity for these type of alerts to be pushed into the Tableau catalog as well as directly into reports and worksheets and dashboards that end users are using. >> Let me make sure I got this. So it means that you can put a lot of the compliance rules inside Alation and have a metadata API so that Alation effectively is governing the utilization of data inside the Tableau catalog. >> That's right. So think about the integration with Tableau is this communication mechanism to surface up these policies that are stored centrally in your data catalog. And so this is important, this notion of a central place of reference. We used to talk about data catalogs just as a central place of reference for where all your data assets lie in the organizations, and we have some automated ways to crawl those sources and create a centralized inventory. What we've added in our new release, which is coming out here shortly, is the ability to centralize all your policies in that catalog as well as the pointers to your data in that catalog. So you have a single source of reference for how this data needs to be governed, as well as a single source of reference for how this data is used in the organization. >> So does that mean, ultimately, that someone could try to do something, trust check and say, no you can't, but this new capability will say, and here's why or here's what you do. >> Exactly. >> A descriptive step that says let me explain why you can't do it. >> That's right. Let me not just stop your query and tell you no, let me give you the details as to why this query isn't a good query and what you might be able to do to modify that query should you still want to run it. And so all of that context is available for any end user to be able to become more aware of what is the system doing, and why is recommending. And on the flip side, in the world before we had something like Trust Check, the only opportunity for an IT Team to stop those queries was just to stop them without explanation or to try to publish manuals and ask people to run tests, like the DMV, so that they memorized all those rules of governance. >> Yeah, self- service, but if there's a problem you have to call us. >> That's right. That's right. So what we're trying to do is trying to make the work of those governance teams, those IT Teams, much easier by scaling them. Because we all know the volume of data that's being created, the volume of analysis that's being created is far greater than any individual can come up with, so we're trying to scale those precious data expert resources-- >> Digitize them-- >> Yeah, exactly. >> It's a digital transformation of how we acquire data necessary-- >> And then-- >> for data transformation. >> make it super transparent for the end user as to why they're being told yes or no so that we remove this friction that's existed between business and IT when trying to perform analytics. >> But I want to build a little bit on one of the things I thought I heard you say, and that is that the idea that this new feature, this new capability will actually prescribe an alternative, logical way for you to get your information that might be in compliance. Have I got that right? >> Yeah, that's right. Because what we also have in the catalog is a workflow that allows individuals called Stewards, analytics Stewards to be able to make recommendations and certifications. So if there's a policy that says though shall not use the data in this way, the Stewards can then say, but here's an alternative mechanism, here's an alternative method, and by the way, not only are we making this as a recommendation but this is certified for success. We know that our best analysts have already tried this out, or we know that this complies with government regulation. And so this is a more active way, then, for the two parties to collaborate together in a distributed way, that's asynchronous, and so it's easy for everyone no matter what hour of the day they're working or where they're globally located. And it helps progress analytics throughout the organization. >> Oh and more importantly, it increases the likelihood that someone who is told you now have self- service capability doesn't find themselves abandoning it the first time that somebody says no, because we've seen that over and over with a lot of these query tools, right? That somebody says, oh wow, look at this new capability until the screen, you know, metaphorically, goes dark. >> Right, until it becomes too complicated-- >> That's right-- >> and then you're like, oh I guess I wasn't really trained on this. >> And then they walk away. And it doesn't get adopted. >> Right. >> And this is a way, it's very human centered way to bring that self- service analyst into the system and be a full participant in how you generate value out of it. >> And help them along. So you know, the ultimate goal that we have as an organization, is help organizations become our customers, become data literate populations. And you can only become data literate if you get comfortable working with the date and it's not a black box to you. So the more transparency that we can create through our policy center, through documenting the data for end users, and making it more easy for them to access, the better. And so, in the next version of the Alation product, not only have we implemented features for analytic Stewards to use, to certify these different assets, to log their policies, to ensure that they can document those policies fully with examples and use cases, but we're also bringing to market a professional services offering from our own team that says look, given that we've now worked with about 20% of our installed base, and observed how they roll out Stewardship initiatives and how they assign Stewards and how they manage this process, and how they manage incentives, we've done a lot of thinking about what are some of the best practices for having a strong analytics Stewardship practice if you're a self- service analytics oriented organization. And so our professional services team is now available to help organizations roll out this type of initiative, make it successful, and have that be supported with product. So the psychological incentives of how you get one of these programs really healthy is important. >> Look, you guys have always been very focused on ensuring that your customers were able to adopt valued proposition, not just buy the valued proposition. >> Right. >> Stephanie McReynolds, Senior Vice President of Marketing Relation, once again, thanks for being on theCUBE. >> Thanks for having me. >> And thank you for joining us for another CUBE conversation. I'm Peter Burris. See you next time.

>>Live from San Diego, California at the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of CubeCon cloud date of con 2019 in San Diego, 12,000 in attendance. I'm just two minute and my cohost is John trier. And welcome back to the program. A multi-time cube alumni. You're on Aviv, who is the CTO and cofounder of a Gwoza. We've had quite a lot of, you know, founders, CTOs, you know, their big brains at this show, your own. So you know, let, let, let's start, you know, there's, there's really a gathering, uh, there's a lot of effort building out, you know, a very complicated ecosystem. Give us first, kind of your overall impressions of the show in this ecosystem. Yeah, so we're very early on on Desecco system. We were one of the first in the first batch of CNCF members when there were a few dozens of those. Not like a thousand of those. Uh, so I've been, I've been to all those shows. >>Uh, we're part of the CNCF committees for different things. And any initiating, I think this has become much more mainstream. I told you before, it's sort of the new van world. You know, I lot a lot more, uh, all day infrastructure vendors along with middleware and application vendor are coming here. All right, so, so one of the things we like having you on the program you're on is you don't pull any punches. So we've seen certain waves of technology come with big promise and fall short, you know, big data was going to allow us to leverage everything and you know, large percentage of, uh, solutions, you know, had to stop or be pulled back. Um, give us, what's the cautionary tale that we should learn and make sure that we don't repeat, you know, so I've been a CTO for many years in different companies and, and what everyone used to say about it, I'm always right. >>I'm only one year off usually. I'm usually a little more optimistic. So, you know, we've been talking about Cloudera and Hadoop world sort of going down and Kubernetes and cloud services, essentially replacing them. We were talking about it four years ago and what do you see that's actually happening? You know, with the collapse of my par and whore, then we're going to Cloudera things are going down, customer now Denon guys, we need equivalent solution for Kubernetes. We're not going to maintain two clusters. So I think in general we've been, uh, picking on many of those friends. We've, we've invented serverless before it was even called serverless with, with nuclear and now we're expanding it further and now we see the new emerging trends really around machine learning and AI. That's sort of the big thing. I'm surprised, you know, that's our space where essentially you're doing a data science platform as a service fully automated around serverless constructs so people can, can develop things really, really quickly. >>And what I see that, you know, third of the people I talk to are, have some relations to machine learning and AI. Yeah. Maybe explain that for our audience a little bit. Because when, you know, Kubernetes first started very much an infrastructure discussion, but the last year or two, uh, very much application specific, we hear many people talking about those data use cases, AI and ML early days. But you know how, how does that fit into the overall? It's simple. You know there, if you're moving to the cloud are two workloads. There is lift and shift workloads and there are new workloads. Okay, lift and ship. Why? Why bother moving them to Kubernetes? Okay, so you end up with new workloads. Everyone is trying to be cloud native server, elastic services and all that. Everyone has to feed data and machine learning into those new applications. This is why you see those trends that talk about old data integration, various frameworks and all that in that space. >>So I don't think it's by coincidence. I think it's, that's because new applications incorporate the intelligence. That's why you hear a lot of the talk about those things. What I loved about the architecture, what you just said is like people don't want to run into another cluster. I don't want to run two versions of Kubernetes, you know, if I'm moving there you, because you, but you're still built on that, that kind of infrastructure framework and, and knowledge of, of how to do serverless and how to make more nodes and fewer nodes and persistent storage and all that sort of good stuff and uh, and, and run TensorFlow and run, you know, all these, all these big data apps. But you can, um, you can talk about that just as a, as a, the advantage to your customer cause you could, it seems like you could, you could run it on top of GKE. >>You could run it on prem. I could run my own Coobernetti's you could, you could just give me a, uh, so >> we, we say Kubernetes is not interesting. I didn't know. I don't want anyone to get offended. Okay. But Kubernetes is not the big deal. The big deal is organizations want to be competitive in this sort of digital world. They need to build new applications. Old ones are sort of in sort of a maintenance mode. And the big point is about delivering new application with elastic scaling because your, your customers may, may be a million people behind some sort of, uh, you know, uh, app. Okay. Um, so that's the key thing and Kubernetes is a way to deliver those microservices. But what we figured out, it's still very complicated for people. Okay. Especially in, in the data science work. Uh, he takes him a few weeks to deliver a model on a Jupiter notebook, whatever. >>And then productizing it is about the year. That's something we've seen between six months to a year to productize things that are relatively simple. Okay. And that's because people think about the container, the TensorFlow, the Kuda driver, whatever, how to scale it, how to make it perform, et cetera. So let's, we came up with is traditionally there's a notion of serverless, which is abstraction with very slow performance, very limited set of use cases. We sell services about elastic scaling paper, use, full automation around dev ops and all that. Okay. Why cannot apply to other use cases are really high concurrency, high-speed batch, no distributed training, distributed workload. Because we're coming, if you know my background, you know, been beeping in Mellanox and other high-performance companies. So where I have a, we have a high performance DNA so we don't know how to build things are extremely slow. >>It sort of irritates me. So the point is that how can we apply this notion of abstraction and scaling and all that to variety of workloads and this is essentially what it was. It is a combination of high speed data technology for like, you know, moving data around on between those function and extremely high speed set though functions that work on the different domains of data collection and ingestion, data analytics, you know, machine learning, training and CIN learning model serving. So a customer can come on on our platform and we have testimonials around that, that you know, things that they thought about building on Amazon or even on prem for months and months. They'd built in our platform in few weeks with fewer people because the focus is on building the application. The focus is not about joining your Kubernetes. Now we go to customers, some of them are large banks, et cetera. >>They say, Alrighty, likes Kubernetes, we have our own Kubernetes. So you know what, we don't butter. Initially we, we used to bring our own Kubernetes, but then you know, I don't mind, you know, we do struggle sometimes because our level of expertise in Coobernetti's is way more sophisticated than what they have to say. Okay, we've installed Kubernetes and we come with our software stack. No you didn't, you know, you didn't configure the security, they didn't configure ingress, et cetera. So sometimes it's easier for us to bring, but we don't want him to get into this sort of tension with it. Our focus is to accelerate development on the new application that are intelligent, you know, move applications from, if you think of the traditional data analytics and data science, it's about reporting and what people want to do. And some applications we've announced this week and application around real time cyber collection, it's being used in some different governments is that you can collect a lot of information, SMS, telephony, video, et cetera. >>And in real time you could detect terrorists. Okay. So those application requires high concurrency always on rolling upgrades, things that weren't there in the traditional BI, Oracle, you know, kind of reporting. So you have this wave of putting intelligence into more highly concurrent online application. It requires all the dev ops sort of aspects, but all the data analytics and machine learning aspects to to come to come along. Alright. So speaking of those workloads for, for machine learning, uh, cube flow is a project, uh, moving the, moving in that space along it. Give us the update there. Yeah. So, so there is sort of a rising star in the Kubernetes community around how to automate machine learning workflows. That's cube flow. Uh, I'm personally, I one of the committers and killed flow and what we've done, because it's very complicated cause Google developed the cube cube flow as one of the services on, on a GKE. >>Okay. And the tweaked everything. It works great in GK, even that it's relatively new technology and people want to move around it in a more generic. So one of the things in our platform is a managed cube flow that works natively with all the rest of the solutions. And other thing that we've done is we make it, we made it fully. So instead of queue flow approach is very con, you know, Kubernetes oriented containers, the ammos, all that. Uh, in our flavor of Coupa we can just create function and you just like chain functions and you click and it runs. Just, you've mentioned a couple of times, uh, how does serverless, as you defined it, fit in with, uh, Coobernetti's? Is that working together just functions on top or I'm just trying to make here, >> you'll, you'll hear different things. I think when most people say serverless, they mean sort of front end application things that are served low concurrency, a Terra, you know, uh, when we mean serverless, it's, we have eight different engines that each one is very good in, in different, uh, domain like distributed deep learning, you know, distributed machine learning, et cetera. >>And we know how to fit the thing into any workloads. So for me, uh, we deliver the elastic scaling, the paper use and the ease of use of sort of no dev ops across all the eight workloads that we're addressing. For most people it's like a single Dreek phony. And I think really that the future is, is moving to that. And if you think about serverless, there's another aspect here which is very important for machine learning and Israel's ability. I'm not going to develop any algorithm in the world. Okay. There are a bunch of companies or users or developers that can develop an algorithm and I can just consume it. So the future in data science but not just data science is essentially to have like marketplaces of algorithms premade or analytic tools or maybe even vendors licensing their technology through sort of prepackaged solution. >>So we're a great believer of forget about the infrastructure, focus on the business components and Daisy chain them in to a pipeline like UFO pipeline and run them. And that will allow you most reusability that, you know, lowest amount of cost, best performance, et cetera. That's great. I just want to double click on the serverless idea one more time, but, so you're, you're developing, it's an architectural pattern, uh, and you're developing these concepts yourself. You're not actually, sometimes the concept gets confused with the implementations of other people's serverless frameworks or things like that. Is that, is that correct? I think there are confusion. I'm getting asked a lot of times. How do you compare your technology compared to let's say a? You've heard the term gay native is just a technology or open FAS or, yeah. Hold on. Pfizer's a CGIs or Alito. An open community is very nice for hobbies, but if you're an enterprise and it's security, Eldep integration, authentication for anything, you need DUIs, you need CLI, you need all of those things. >>So Amazon provides that with Lambda. Can you compare Lambda to K native? No. Okay. Native is, I need to go from get and build and all that. Serverless is about taking a function and clicking and deploying. It's not about building. And the problem is that this conference is about people, it people in crowd for people who like to build. So they, they don't like to get something that work. They want to get the build the Lego building blocks so they can play. So in our view, serverless is not open FAS or K native. Okay. It's something that you click and it works and have all the enterprise set of features. We've extended it to different levels of magnitude of performance. I'll give you an anecdote. I did a comparison for our customer asking me the same question, not about Canadian, but this time Lambda. How do you guys compare with London? >>Know Nokia is extremely high performance. You know we are doing up to 400,000 events on a single process and the customer said, you know what, I have a use case. I need like 5,000 events per second. How do you guys compare a total across all my functions? How do you compare against Lambda? We went into, you know the price calculator, 5,000 events per second on Lambda. That's $50,000 okay. $50,000 we do about, let's say even in simple function, 60,000 per process, $500 VM on Amazon, $500 VM on Amazon with our technology stick, 2000 transactions per second, 5,000 events per second on Lambda. That's 50,000. Okay. 100 times more expensive. So it depends on the design point. We designed our solution to be extremely efficient, high concurrency. If you just need something to do a web hook, use Lambda, you know, if you are trying to build a high concurrency application efficient, you know, an enterprise application on it, on a serverless architecture construct come to us. >>Yeah. So, so just a, I'll pause at this for you because a, it reminds me what you were talking about about the builders here in the early days of VMware to get it to work the way I wanted to. People need to participate and build it and there's the Ikea effect. If I actually helped build it a little bit, I like it more to get to the vast majority, uh, to uh, adopt those things. It needs to become simplified and I can't have, you know, all the applications move over to this environment if I have to constantly tweak that. Everything. So that's the trend we've been really seeing this year is some of that simplification needs to get there. There's focus on, you know, the operators, the day two operations, the applications so that anybody can get there without having to build themselves. So we know there's still work to be done. >>Um, but if we've crossed the chasm and we want the majority to now adopt this, it can't be that I have to customize it. It needs to be more turnkey. Yeah. And I think it's a friendly and attitude between what you'll see in Amazon reinvent in couple of weeks. And then what you see here, because there is those, the focus of we're building application a what kind of tools and the Jess is gonna just launch today on the, on the floor. Okay. So we can just consume it and build our new application. They're not thinking, how did Andy just, he built his tools. Okay. And I think that's the opposite here is like how can you know Ali's is still working inside underneath dude who cares about his team. You know, you care about having connectivity between two points and and all that. How do you implement it that, you know, let someone else take care of it and then you can apply your few people that you have on solving your business problem, not on infrastructure. >>You know, I just met a guy, came to our booth, we've seen our demo. Pretty impressive how we rise people function and need scales and does everything automatically said we want to build something like you're doing, you know, not really like only 10% of what you just showed me. And we have about six people and for three months where it just like scratching our head. I said, okay, you can use our platform, pay us some software license and now you'll get, you know, 10 times more functionality and your six people can do something more useful. Says right, let's do a POC. So, so that's our intention and I think people are starting to get it because Kubernetes is not easy. Again, people tell me we installed Kubernete is now installed your stack and then they haven't installed like 20% of all the things that you need to stop so well your own have Eve always pleasure to catch up with you. Thanks for the all the updates and I know we'll catch up with you again soon. Sure. All right. For John Troyer, I'm Stu Miniman. We'll be back with more coverage here from CubeCon cloud date of con in San Diego. Thanks for watching the cube.

>> Hello everyone. Welcome to this Cube conversation here in Palo Alto. On John Furrier, co host of the Cube. I'm here. Aaron Kalb is the co founder and VP of design and Alation. Great to see them on some fresh funding news. Aaron, Thanks for coming. And spend the time. Good to see you again. >> Good to see you, John. Thanks for having me >> So big news. You guys got a very big round of financing because you go to the next level. A startup. Certainly coming out that start up phase and growth phase super exciting news. You guys doing some very innovative things around, date around community around people and really kind of cracking the code on this humanization democratization of data, but actually helping businesses. I want to talk about it with you. First. Give us the update on the financing, the amount what it means to the company. A lot of cash. >> Yeah. So we're very excited to have raised a fifty million dollar round. Sapphire led the round, and we also had, you know, re ups from all of our existing investors. And, you know, as as a co founder, he always had big dreams for growth. And it's just validating tohave. Ah, a community of investors who can see the future, too, as well as our great community of over one hundred customers now who want to build this data democratized future with us. >> We've been following you guys since the founding obviously watching you guys great use of capital. Fifty million's a lot of capital, so obviously validation check. Good, good job. But now you go to a whole other level growth. What's the capital gonna be deployed for? What's going on with company where you guys I and in terms of innovation, what's the key focus? >> It's a great question. So you know, obviously we have revenue from our customers. But getting this extra infusion from VC lets us just supercharge our development. It's growth. It's going to more customers, both domestically and abroad, goingto a broader user base. And we're Enterprise-wide Adoption within those customers, as well as innovation in the core product, new technology, great design and futures. that are really going to change the organization's access and use data to make better decisions? >> What was the key Learnings As you guys went into this round of funding outside the validation to get through due diligence, all that good stuff. But you guys have made some successful milestones. What was the key? Notable accomplishments that Alation hit to kind of hit this trigger point here for the fifty million? >> Yeah, I'm glad you asked about that. I think that the key thing that's changed it's enabled this. This next phase is that the data catalog market has really come into its own right. In the beginning, in the early days, we were knocking on doors, trying to say, You know, we don't even know it was going to be called data catalog in our first few months. And even though we had the technology, we said, Hey, we got this thing and we know it's useful. Please buy it. Please want it. And the question was, you know, what's the data catalog by what I ever even look at that? And it's just turned a corner. Now, you know, Thanks. In part of things like Gartner telling companies you know, in the next year by twenty twenty, if you have a data catalog, you're goingto see twice the ROI from your existing data investments than if you don't your stories like that are making companies say? Of course, you want to data catalog. It just turned out a dime. Now they're asking, Which data catalog should we get? Why is yours the best in this change of the market maturing? I think it's the biggest change we've seen >> with one thing that we've observed. I want to get your reaction to This is that I'll stay with cloud computing economics, a phenomenally C scale data data science working the cloud. We see great success there. Now there's multiple clouds, multi clouds, a big trend, but also the validation that it's not just all cloud anymore. The on premises activity steel is relevant, although it might have a cloud. Operations really kind of changes the role of data. You mentioned the data catalogue kind of being kind of having a common mainstream visibility from the analysts like Gardner and others on Wiki Bond as well. It makes data the center of the innovation. Now you have data challenges around. Okay, where's the data deployed? Where my using the data? Because data scientists want ease of data, they want quality data. They want to make sure their their algorithm, whether it's machine learning component or software actually running a good data. So data effectiveness is now part of the operations of most businesses. What's your reaction to that? Which your thoughts. Is that how you see it? Is there something different there? What's going on with the whole date at the center? >> Absolutely hit on two key themes for us. One of that idea of the center and the other is your point about data quality and data trust. So, so centrality, we think, is really essential. You know, we're seeing cataloging technology crop up more and more. A lot of people were coming out with catalogs or catalog kind of add ons to their products. But what our customers really tell us is they want the data catalog to be the hub, that one stop shop where they go to to access any data, wherever it lives, whether it's in the cloud or on Prem, whether it's in a relational database or a file system, so is one of Alations key. Differentiators early on was being that central index, much like Google is out of the front page to the Internet, even though it's linking to ad pages all over the place. And the other thing in terms of that data quality and data trustworthiness has been a differentiator, and this was something that was part of our technology when we launched that we didn't put the label out till later. Is this idea of Behavior IO, that's kind of looking at previous human behavior to influence future human behavior to be better. And there's another place we really took some inspiration from Google and Terry Winograd at Stanford before that, you know, he observed. You know, if you remember back before Google search sucked, frankly, right, the results on top are not the most development were not the most trustworthy. And the reason was those algorithms were based on saying, how often does your key word appear in that website? Built, in other words, and so you'd get results on top. That might just not be very good. Or even that were created by spammers who put in a lot of words to get SEO and and, you know, that isn't the best result for you on what Google did was turned that around with page rank and say, Let's use the signals that other people are getting behind about the pages they find valuable to get the best result on top. And Alation is the exact same thing our patented proprietary behavior technology lets us say Who's using this data? How were they using it? Is it reputable? And that enables us to get the right data and transfer the data in front of decision makers. >> And you call that Behavioral IO >> Behavior IO, that's right. >> I mean, certainly remember Google algorithmic search was pooh poohed. It first had to be a portal. Everyone kind of my age. You can't remember those those days and the results were key word stuff by spammer's. But algorithmic search accelerated the quality. So I got to ask you the behavioral Io to kind of impact a little bit. Go a little deeper. What does that mean for customers? Because now I'll see as people start thinking, OK, I need to catalogue my data because now I need to have replication, all kinds of least technical things that are going on around integrity of the data. But why Behavioral Aya? What's the angle on that? What's the impact of the customer? Why is this important? Absolutely so. >> Might have to work through an example, you know we joke about. You might be looking around in your SharePoint drive and find an Excel file called Q three Numbers final. Underscore final. Okay, that seems that'S inject the final numbers, and then you see next to it when it says underscore final underscore, final underscore finalist. Okay, well, is that one final? And it turns out what Data says about itself is less reliable than what other people say about the data. Same thing with Google that if everyone's linking with Wikipedia Page, that's a more reliable page than one that just has, you know, paid for a higher placement, Right? So what a means an organization is with Alation will tell you. You know, this is the data table that was refreshed yesterday and that the CFO and everybody in this department is using every day. That's a really strong signal. That's trustworthy data, as opposed to something that was only used once a year ago. >> So relevance is key there. >> Absolutely. It's relevant. And trustworthiness. We find both all right, indicated more strongly by who's using it and how than by the data itself. >> Are you seeing adoption with data scientist and people who were wrangling date or data analysts that if the date is not high quality, they abandoned. The usage is they're getting kind of stats around that are because that we're hearing a lot of Hey, you know, that I'm not going to really work on the data. But I'm not going to do all the heavy lifting on the front end the data qualities, not there. >> Absolutely. We see a really cool upward spiral. So in Alation, we have a mix of manual, human curated metadata, you know, data stewards and that a curator saying, this is endorsed data. It's a certified data. This is applicable for this context. But we also do this automatic behavior. Io. We parse the query logs. These logs were, you know, put there for audit on debugging purposes. But we were mining that for behavioral insight, and we'll show them side by side on what we see is overtime on day one. There's no manual curation. But as that curation gets added in, we see a strong correlation between the best highest quality data and the most used data. And we also see an upward spiral where, if on day one. People are using data that isn't trustworthy that stale or miscalculated as soon as Ah, an Alation steward slaps a deprecation or a warning on the data asset because of technology like trust check talking about last time I was here, that technology, that's the O part of behavior IO We then stop the future behavior from being on bad data, and we see an upward spiral where suddenly the bad sata is no longer being used and everyone's guided put the pound. >> One thing I'm really impressed with you guys on is you have a great management team and overall team with mixed disciplines. Okay, I think last night about your role, Stanford and the human side of the world. But you have to search analogy, which is interesting because you have search folks. You got hardcore data data geeks all working together. And if you think about Discovery and navigation, which is the Google parent, I need to find a Web page and go, Go, go to it. You guys were in that same business of helping people discover data and act on it or take action. Same kind of paradigm, so explain some customer impact anecdotes. People who bought Alation, what your service and offering and what happened after and what was it like before? We talk about some of that? And because I think you're onto something pretty big here with this discovery. Actionable data perspective. >> Yeah, well, one of our values, it Alation, is that we measure our success through customer impact, you know, not do financing or other other milestones that we are excited about them. So I I would love to talk about our customers. One example of a business impact is an example that our champion at Safeway Albertsons describes where, after safe, it was acquired by Albertson's. They've been sort of pioneers of sort of digital, ah, loyalty and engagement. And there was a move to kind of stop that in its tracks and switch should just mailing people big books of coupons that of customizing, you know, deals for you based on your buying behavior. And they talked about getting a thirty x  ROI on the dollars they've spent on Alation by basically proving the value of their program and kind of maximizing their relationship with their customers. But the stories they're even more exciting to me, then just business impacts in dollars and cents when we can leave a positive impact on people's lives with data. There's a few examples of that Munich reinsurance, the biggest being sure and also a primary ensure in Europe, had some coverage and Forbes about the way that they use Alation, other data tools to be able to help people get back on their feet more quickly after, ah, earthquakes and other natural disasters. And similarly, there's a piece in The Wall Street Journal about how Pfizer is able to create diagnostics and treatments for rare diseases where it wouldn't have been a good ROI even invest in those if they didn't get that increased efficient CNN analytics from Alation on the other data. >> So it's not just one little vertical. It's kind of mean data is horizontally. Scaleable is not like one. Industry is going to leverage Alation, >> Absolutely so you know, I mentioned just now. Insurance and health care and retail were also in tech were in basically every vertical you can imagine and even multiple sectors. You know, I've been focusing on industry, but there's another case that you can read about at the city of San Diego were there. They're doing an open data initiative, enabling people to figure out everything from where parking is easiest, the hardest to anything else. >> The behavioral Io. And it's all about context and behavior, role of data and all this. It's kind of fundamental to businesses. >> That's right. It's all about taking everything about how people using data today and driving people to be even more data driven, more accurate, better able to satisfy their curiosity and be more rational in >> the future. So if I'm a from a potential customer and I heard a rAlation, get the buzz out there, why would I need you? What air? Some signals that would indicate that I should call Alation. What's some of that Corvette? What's the pitch? >> Yeah, it's a great question. No, I sometimes joke with the team that you know every five minutes another enterprise reaches that point where they can't do it the old way anymore. And the needle ations. And the reason for that is that data is growing exponentially and people can only grow at most, you know, linearly. So I compare it a bit again to the days of of Yahoo When the Internet was small, you make a table of contents for it. But as there came to be trillions of red pages, you needed an automatic index with pay drink to make sense of it. So I would say, once you find that your analytics team has spread out and they're spending, you know eighty percent of their time calling up other people to find where development data is, you're asked to Your point is this data high quality show even spend my time on it? You know that's probably not money is well spent with these highly paid people spending other times scrounging If you switch from scrounging to finding understanding and trusting their data for quick and accurate analysis, give us >> a call. So basically the pitches, if you want to be like Yahoo, do it the old way. We know what happened. Yeah, you want to be like Google, two algorithmic and have data >> God rAlation, and you'll be around for a while very well. After that, maybe the one see that that's my words. >> And and that's part of turning that corner. I think in the beginning we were trying to tell people this could be a nice toe have. And now customers are coming to us realizing it's a must have to stay a relevant, you know, And if you've made all these investments in data infrastructure and data people, but you can't connect the dots is you said, between the human side and the tech side that money's all wasted and you're going to not be able to compete against your competitors and impact of customers what you want. >> Well, Eric, congratulations. Certainly is the co founder. It's great success. And how hard is that you start ups? You guys worked hard and again. Why following you guys? Been interesting to see that growth and this innovation involved in creative, A lot of energy. You guys do a good job. So final question, talk about the secret sauce of Alation. What's the key innovation formula? And now that you got the funding where you're going to double down on, where's the innovation going to come next? So the innovation formula and where the innovation, the future, >> absolutely innovation has been critical for us to get here on our customers didn't just buy the exciting features with behavioral and trust. Check that we had but also are buying into the idea that we're going to continue to be the leaders and to innovate. Andi, we're going to do that. So I think the secret sauce which we've had in the past, we're going to continue to innovate in this vein, is to be really conscious of water computers great at and what humans uniquely good at what you humans like doing and trying to have the human and computers work together to really help the human achieve their goals. Right? So, Doctor, the Google example. You know, there's a bunch of systems for collaboratively ranking things, but it takes work to, you know, write a review on the upper Amazon. Google had the insight that we could leverage people are already doing and make it about it. Out of that, we're going to continue to do that. >> The other kind of innovation you'll see is bringing Alation to a wider and wider audience, with less and less technical skill needed. So I came from Syria Apple, and the idea is you have to learn a programming language to Queria database. You could just speak in English. That helps you ask answer questions like What's the weather today? Imagine taking that same kind of experience of seamless integration to the more important questions enterprises are asking. >> We'll have to tap your expertise is we want to have an app called the Cube Syria, which is a cube. What's the innovation in Silicon Valley and have it just spit out a video on the kidding? Final question just to double down on that piece, because I think the human interactions a big part of what you're saying I've always loved that about with your vision is. But this points to a major problems. Seeing whether it's, you know, media, the news cycle These days, people are challenging the efficacy of finding the research and the real deep research on the media. So I was seeing scale on data scale is a huge challenge. You mentioned the growth of data. Computers can scale things, but the knowledge and the curation kind of dynamic of packaging it, finding it, acting on it. It's kind of where you guys are hitting. Talk about that tie name, my getting that right and set is that important? Because, you know, certainly scale is table stakes these days. >> That is super insightful John, because I think human cognition and human thought excuse me, is the bottleneck four being data driven right we have on the Internet trillions of Web pages, you know, more than the Library of Alexandria a hundred times over, and we have in databases millions of columns and trillions of rose. But for that to actually impact the business and impact the world in a positive way, it's got to go through a person who could understand it. And so, in the same way that Google became the mechanism by which the Internet becomes accessible, we think that Alation for organizations is becoming the way that data can become actionable. And the other thing I would say is, you know, in this age of alternative facts and mistrust of data, you know, we've sort of realizing the just having more information out there doesn't actually make people wiser and better able to reason. It can actually be a lot of noise that muddies the signal and confuses people. So we think Alation by also using human computer interaction to help separate the signal from the noise and the quality from the garbage can help stop the garbage in garbage out and make people more rational and more curious and have more trust than what there. Hearing understanding >> build that Paige rang kind of metaphor is interesting because the human gestures, whether it's work or engaging on the data, is a signal tube, not just algorithmic meta data extraction. >> Absolutely anything you do with data and any tool, even outside of Alation. Alation will capture that and use it to guide future behavior for you and your appears to be better and smarter. >> Fifty million dollars. Where's this all going to lead to wins the next innovation. What do you guys see? The future for rAlation? >> Well, you know, I, uh I was just thinking before the show I used to be an apple kind of in the golden Age when Apple was really innovative. And there was the joke where they released something new and say, Redman, start your photocopier. So in this interview, I'm going to be a little close to the chest about the specifics, but we're releasing. But I will tell you we have a room that we're really excited about to go to a broader and broader audience that impactor customers more fully >> well you feel free to say one more thing? >> Yeah. I think the secret to the future is Aaron. Thanks for coming on. >> Really preachy. Congratulations on the funding. He has got a very innovative formula. Good luck. And we'll be following you guys. Thanks, but come on, keep commerce. Thanks so much. Eric Kalb, co founder and VP of designing Alation. Interesting formula. Great. Successful. Former great innovation. Alation. Check him out. I'm Jennifer here in Palo Alto for cube conversation. Thanks for watching.

(bright classical music) >> Hi, I'm Peter Burris and welcome to another CUBE Conversation from our studios here in Palo Alto, California. We've got another great conversation today, specifically we're going to talk about some of the trends and changes in data catalogs, which were emerging as a crucial technology to advance data-driven business on a global scale. And to do that, we've got Alation here, specifically Stephanie McReynolds who's the Vice-President of Marketing at Alation. Stephanie, welcome back to theCUBE. >> Thank you, it's great to be here again. >> So Stephanie, before we get into this very important topic of the increasing, obviously role or connection between knowing what your data is, knowing where it is, and business outcomes in a data-driven business world, let's talk about Alation. What's the update? >> Yeah, so we just celebrated, yesterday in fact, the sixth anniversary of incorporation of the company. And upon, reflecting on some of the milestones that we've seen over those six years, one of the exciting developments is we went from initially about seven production implementations a couple years after we were founded, to now over a hundred organizations that are using Alation. And in those organizations over the last couple of years, we've seen many organizations move from hundreds of users, to now thousands of users. An organization like Scout24 has 70 percent of the company as self-servicing analytics users and a significant portion of those users now using Alation. So we're seeing companies in Europe like Scout24 who's in Germany. Companies like Pfizer in the United States. Munich Reinsurance in the financial services industry. Also hit about 2000 users of Alation, and so it's exciting to look at our origins with eBay as our very first customer, who's now up to about 3000 users. And then these more recent companies adopt Alation all of them now getting to a point where they really have a large population that's using a data catalog to drive self-service analytics and business outcomes out of those self-serving analytics. >> So a hundred first-rate brands as users, it's international expansion. Sounds like Alation's really going places. What I want to do though, is I want to talk a little bit about some of the outcomes that these companies are starting to achieve. Now we have been on the record here at circling the angle with theCUBE wiki bomb for quite some time, trying to draw a relationship between business, digital business, and the role that data plays. Digital business transformation, in many respects, is about how you evolve the role the data plays in your business to become more data-driven. It's hard to do without knowing what your data is, where it is, and having some notion of how it's being used in a verified trusted way. How are you seeing your company's start to tie the use of catalogs to some of these outcomes? What kind of outcomes are folks trying to achieve first off? >> Yeah, you're right. Just basic table stakes for turning an organization into an organization that relies on data-driven decision-making rather than intuitive-decision making requires an inventory. And so that's table stakes for any catalog, and you see a number of vendors out there providing data inventories. But what I think is exciting with the customers that we work with, is they are really undertaking transformative change, not just in the tooling and technology their company uses, but also in the organizational structure, and data literacy programs, and driving towards real business impact, and real business outcomes. An example of an Alation customer, who's been talking recently about outcomes, is Pfizer. Pfizer was covered in a Wall Street Journal article, recently. Also was speaking at TABLO Conference, about how they're using a combination of the Alation data catalog with TABLO on the front end, and a data science platform called Data IQ, in an integrated analytics workbench that is helping them with new drug discovery. And so, for populations of ill individuals, who may have a rare form of heart disease, they're now able to use machine learning and algorithms that are informed by the data catalog to catch one percent, two percent of heart disease patients who have a slight deviation from the norm, and can deliver drugs appropriately to that population. Another example of the business outcome would be with an insurance company; very different industry, right? But, Munich Reinsurance is a huge global reinsurance company, so you think about hurricanes or the fires we had here in the United States, they actually support first line insurers by reinsuring them. They're also founding new business units for new types of risks in the market. An example would be a factory that is fully controlled by robots. Think about the risks of having that factory be taken over by hackers in the middle of the night, where there's not a lot of employees on the floor. Munich Reinsurance is leveraging the data catalog as a collaboration platform between actuaries and individuals that are knowledgeable in the business to define what are the data products that could support an entirely new business units, like for cyber crimes. And investing in those business units based on the innovation they're doing using the data catalog as a collaboration platform. So these are two great examples of organizations that, a couple years ago started with a data catalog, but have driven so many more initiatives than just analyst productivity off of that implementation. >> Oh, those are great outcomes. One of them talking about robots in the factory, automated factory, one thing, if they went haywire, would make for some interesting viral video. (gently laughs) >> That's right. That's right. >> But coming back, but the reason I say that is because in many respects, these practices, these relations with the outcomes, the outcomes are the real complex thing. You talked about becoming more familiar with data, using data differently, becoming more data driven. That requires some pretty significant organizational change. And it seems to me, and I'm querying you on this, that the bringing together these users to share their stories about how to achieve these data driven outcomes, made more productive by catalogs and related technologies. Communities must start to be forming. Are you seeing communities form around achieving these outcomes and utilizing these types of technologies to accelerate the business change? >> So what's really interesting at an organization like Munich Reinsurance or at Pfizer, is there's an internal community that is using the data catalog as a collaboration platform and as kind of a social networking platform for the data nerds. So if I am a brand new user of self-service analytics, I may be a product manager who doesn't know how to write a sequel query yet. Who doesn't know how to go and wrangle my own data. >> Yeah, may never want to. (playfully laughs) >> May never want to. May never want to. Who may not know how to go and validate data for quality or consistency. I can now go to the data catalog to find trusted resources of data assets, be that a dashboard to report that's already been written or be that raw data that someone else has certified, or just has used in the past. So we're seeing this social influence happen within companies that are using data catalogs, where they can see for the data catalog pages, who's used, who's validated this data set so that I now trust the data. And then, what we've seen happen, just within the last year and-a-half or so, is these organizations, the sponsors of the data of these organizations, are starting to share best practices naturally with one another, and saying, hey >> Across organizations. >> Across organizations. And so there has been a demand for Alation to get out into the market and help catalyze the creation of communities across different organizations. We kicked off, within the last two months, a series of meetings that we've called RevAlation. >> R-E-V-A-L >> That's right >> A-T-I-O-N >> R-E-V-A-L-A-T-I-O-N And the thing behind the name is, if you can start to share best practices in terms of how you create a data-driven culture across organizations, you can begin to really get breakthrough speed, right? In making this transformation to a data-driven organization. And so, I think what's interesting at the RevAlation events, is folks are not talking just about how they're using the tool, how they're using technology. They're actually talking about how do we improve the data literacy of our organizations and what are the programs in place that leverage, maybe the data catalog, to do that. And so they're starting to really think about, how does, not just the technical architecture and the tooling change in their organizations, but how do we close this gap between having access to data and trusting the data and getting folks who maybe aren't, too familiar with the technical aspects of the data supply chain. How do we make them comfortable in moving away from intuitive decisions to data-driven decisions? >> Yeah, so the outcome really is not just the application of the tool, it's the new behaviors in the business that are associated with data-driven. But to do that, you still have to gain insight and understand what kinds of practices are best used with the tool itself. >> That's right. >> So it's got to be a combination. But, you know, Alation has been, if I can say this. Alation's been on this path for a while. Not too long ago, you came on theCUBE and you talked about trust check. >> Right. >> Which was an effort to establish conventions and standards for how data could be verified and validated so that it would be easy to use, so that someone could use the data and be certain that it is what it is, without necessarily having to understand the data. Something that could be very good for, for example, for folks who are very focused on the outcome, and not focused on the science of the data associated with it. >> That's right. >> So, is this part of, it's RevAlation, it's trust check. Is this part of the journey you're on to try to get people to see this relation between data-driven business and knowing more about your data? >> It absolutely is. It's a journey to get organizations to understand what is the power that they have internally, within this data. And close the gap on, which is in part organizational, but in part for individuals user's psychological and how do you get to a trusted decision. And so, you'll continue to see us invest in features like trust check that highlight how technology can make recommendations; can help validate and verify what the experts in the organization know and propagate that more widely. And then you'll also see us share more best practices about how do you start to create the right organizational change, and how do you start to impact the psychology of fear that we've had in many organizations around data. And I think that's where Alation is uniquely placed, because we have the highest number of data catalog customers of any other vendor I'm familiar with in this space. And we also have a unique design approach. When we go into organizations and talk about adopting a data catalog, it's as much about, how do our products support psychological comfort with data as well as, how do they support the actual workflow of getting that query completed, or getting that data certified. And so I think we've taken a bit of a unique approach to the market from the beginning where we're really designing holistically. We're not just, how do you execute a software program that supports workflow? But how do you start to think about how the data consumer actually adopts that best practices and starts to think differently about how they use data in a more confident way? >> Well I think the first time that you and I talked in theCUBE was probably 2016, and I was struck by the degree to which Alation as a tool, and the language that you used in describing it was clearly designed for human beings to use it. >> Right. >> As opposed to for data. And I think that, that is a unique proposition, because at the end of the day, the goal here, is to have people use data to achieve outcomes and not just to do a better job of managing data. >> And that doesn't mean that, I mean we have a ton of machine learning, >> Sure. >> And AI in the products. That doesn't take away from the power of those algorithms to speed up human work and human behavior. But we really believe that the algorithms need to compliment human input and that there should be a human in the loop with decision-making. And then the algorithms propagate the knowledge that we have of experts in the organization. And that's where you get the real breakthrough business outcomes, when you can take input from a lot of different human perspectives and optimize an outcome by using technology as a support structure to help that. >> In a way that's familiar and natural and easy for others in your organization. >> That's right. That seems, you know, if you go back to. >> It makes sense. >> When we were all introduced to Google it was a little bit of an odd thing to go ask Google questions and get results back from the internet. We see data evolving in the same way. Alation is the Google for your data in your organization. At some point it'll be very natural to say, 'Hey Alation, what happened with revenue last month?' And Alation will come back with an answer. So I think that, that future is in sight, where it's very easy to use data. You know you're getting trusted responses. You know that they're accurate because there's either a certification program in place that the technology supports, or there's a social network that's bubbling this information up to the top, that is a trusted source. And so, that evolution in data needs to happen for our organizations to broadly see analytic driven outcomes. Just as in our consumer or personal life, Google had to show us a new way to evolving, you know, to a kind of answering machine on the internet. >> Excellent. Stephanie McReynolds, Vice-President of Marketing Alation, talked to us about building communities, to become more of a, to achieve data-driven outcomes, utilizing data catalog technology. Stephanie, thanks very much for being here. >> Thanks for inviting me. >> And once again, I'm Peter Burris, and this has been another CUBE Conversation until next time. (bright classical music)