>> Woman: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, This is a CUBE conversation. >> Hello and welcome to today's session of the 2021 AWS Global Public Sector awards for the award of best migration solution. I'm your host Natalie Erlich and now we're joined by very special guests. We have Omer Enaam, application modernization leader at Deloitte Consulting and Bart Mason, technology lead for the Office of Recovery Services at the Utah Department of Human Services. Welcome, gentlemen. Good to have you on the show. >> Thank you. >> Thank you for having us. >> Well, terrific. I'd love to hear more about your migration from mainframe to AWS. Bart. Let's start with you. >> The state of Utah has a mainframe system and we have our child support application that was first developed in 1996 on the mainframe written in COBOL. The application served us well through the 24 years that we had it running on the mainframe. The issue was that the mainframe, it was getting difficult to find people who knew how to program in COBOL. But the biggest problems were any type of modernization. We were pretty much stuck to using what are called green screens, and there was no real easy way to do any type of modernization. And a lot of our applications that were public-facing or employee-facing, a lot of those web applications had to be written in a separate system and set up to connect and talk to the mainframe system. So it was a system that served us well but it was time to try and figure out what are we going to do about this? Because the mainframe was expensive and it was old technology that didn't let us advance to where we wanted to go in the future. So roughly about 2016, we started to investigate what are the possible ways that we can migrate our child support application off the mainframe. And we went through discussion such as a complete rewrite where we would start from the very beginning and rewrite our child support application. The child support application is a case management and an accounting system. And if we would have done a total rewrite we were told it would be upwards of $200 million to do a complete rewrite. We started looking at other possibilities and came across one possibility, and that is to do a migration off of the mainframe into the cloud. It would be a pre-session where we could do a lift and shift and basically take the code, change it into Java, and put it into the cloud running in EC2 instances. So it was an, we called it an intermediate step to modernization because it would get us one step to where we need to do, or where we need to go. And for modernization, it helps us to, since the program that it was, or the language it was migrated to was Java it made it so that we could do modernization. And we decided that if we did a lift and shift from the mainframe to AWS, that we could modernize at our own pace, we could modernize screen by screen or function by function. So it gave us the ability to control roll-outs and getting our application to where we needed to be. >> Terrific. And Omer, I'd love it if you could weigh in as well. What were, what was the support that you provided towards this migration? >> Yeah, of course. So as Bart pointed out, the state was looking for a approach that had high chance of success, high probability of user adoption with minimal impact to the organization. At the same time, have the ability to for the state to maintain and modernize at their own pace. So we work with Bart and explain to him a few options. And one of the options was using a automated coding data conversion approach where we take legacy programming languages like COBOL and convert them into Java. Just like translating the code from one language to another. And in the process, we guarantee that your your new system will work exactly. It will be functionally equal of what you do currently. And at the same time, it minimizes the risk. And it also allows the state to have no issues with their business continuity and additional training for their staff. So in a nutshell, we brought in a solution demonstrated to Bart and team and they bought into that, the idea that this is exactly what they want to do as a first step. And as we speak, we are working with the state to help them take that system in the cloud to the next level. Now we have unlocked the potential of digital transformation. Bart can build mobile apps in front of that application. That the state can. There are new analytics capabilities for that their employees can be more productive in providing services to the citizen. They can implement native capabilities from AWS to implement a process automation, implement some artificial intelligence-based tools to optimize the processes and make life easy and better for the employees, at the same time more importantly, serve the citizens in a better way. >> Mhm. And Bart I'd love it If you could share some further details on some of the considerations that you had such as risk and whether it could be used later in the future. >> The biggest thing, the biggest risk to us was that if we, as we migrated off the mainframe, there's a risk that we have to recertify our system with the Office of Child Support Enforcement in Washington, DC. When we build a system, the child support system, we're required to have them come in and do a assessment of our application and certify that it is an application that can be used for child support. If we would have done a rebuild from scratch, the risk would be that first a rebuild, from what we've seen can take anywhere from five to 10 years. I've already touched on how expensive it is, but it takes up to five or what we've seen, up to 10 years to do a complete rewrite. And the risk for us was that if we did a complete rewrite, we would still be on the mainframe for quite a long time. And we would have to have our system recertified with OCSE. And that can take anywhere from five to 10 years for a recertification too, so the risk was that if we did anything with the complete rewrites it would be several, several years going through rewrites and recertifications to get our system up and running in AWS. And the other problem would be that taking that amount of time would also, it would bring us probably not up to date with the current technologies as we did our rewrite because we'd be focused on rewriting that application and not taking advantages of services and applications that come up and can help us with our rewrites. So one of the biggest risks was that we'd have to do recertification with OCSE, With the migration, coming off the migration because it is a one for one migration where it went from COBOL to Java, we did not have to do a recertification. This allowed us to move the application as is and it functioned the exact same way that recertification was not a problem for us. OCSE said that, told us that it was not a risk or an issue that we'd have to take on. So the biggest risk was recertification for us but with the migration and moving into the cloud we went through their security processes and we came out without any big issues coming out of that. >> Fantastic. Thank you. Omer. I'd love to go to you now. What are some of the unique benefits of working with AWS? >> Sure. I think the biggest benefit is there, the extensive services that are available and having the the proven platform where you cut down your operational costs drastically. So comparing the mainframe costs with the Amazon cloud costs. Clearly the state has benefited a lot from the from a savings standpoint, infrastructure savings standpoint, and at the same time now, as I said, the the system is in the cloud, running on open architecture in the Java programming language, The AWS cloud provides us several capabilities natively which allows the state to use, to digitally transform the experience for the citizens and employees by implementing modern DevOps practices for for managing the, operating the system providing new capabilities to workers and supervisors for analytics to business process automation, having better call center integration capabilities and so forth. So there are endless opportunities. And the state is in the process of executing on a prioritized list Just before the pandemic hit, we worked with the state to lay out the future for their system and for their organization in the form of a one day innovation lab, where major stakeholders from the state gathered with Deloitte and we worked through a prioritization process and determined how we can take this system to the next level and really digitally transform the system and in the process, provide new services and better services to state employees and the citizens. >> Yeah. Terrific insight there. Now Bart, I'd like to shift it to you, asking the same question. What are your thoughts on working with AWS? Why choose them for this? >> We always have been looking at moving a lot of our applications into the cloud. We've been looking at that for several years. The advantages of moving to AWS is, from my point of view, and state's point of view, is that AWS provides a lot of services and it provides the capability for us to do a lot more for our applications. So for example, when we were on the mainframe, one of the biggest problems that we had was disaster recovery. We had a disaster recovery site in the Southern end of our states with another mainframe that we would sync up with our application. The problem was that we have over a hundred data connections. We connect to banks, external entities, internal entities. We have different types of connections. We have to do printing. We have to print checks and several things. Disaster recovery on the mainframe was something that we were never really capable of doing. We could get our application up and running but it just sat on the mainframe. We had no data connections, all that was extremely difficult and extremely expensive to do for disaster recovery on a mainframe and on alternate sites. Moving to AWS, one of the biggest things for us was that disaster recovery requirement. Because now that we're in AWS, it makes it more easier for us to spin up servers once servers go down, restore servers when they go down. We have all of our data connections in one location, and as systems become unrecoverable or have issues, it's easy for us to spin up another one or several in their place, or even our data connection, because they're all located in one place and we're using them all of the time. So disaster recovery was one of the big key components for us. The other component was that, as we modernize our application, we're looking at what AWS services are out there to help us with modernization. We're looking at services such as AWS Batch to replace our batch system. We're looking at databases to replace the current database that we're using. We're looking at using containers to containerize our applications and our ORSIS application, and also microservices. So moving off the mainframe was the first step and putting it all into servers on an EC2 instance. But then we look and say, okay, how can we do this and make this more modern and run better and more efficient? And then we started looking at all the AWS services that are out there, that run outside of an EC2 instance, for example. And we see that there's an endless possibility, and endless capabilities that we have at our fingertips to say, okay, we're off the mainframe less modernize by moving to Batch or let's start looking at containers and things like that to help us with our applications. So disaster recovery and the available services that we can move to to help us with our applications, what we look at. >> Well, thank you both so much for your insights, Bart Mason, Utah Department of Human Services as well as Omer Enaam, Deloitte Consulting and LLP. I'm your host for theCUBE. Thanks so much for watching. (outro music)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> John: Hello and welcome back to theCUBE's coverage of IBM Think 2021 Virtual. I'm John Furrier, your host of theCUBE. We're here with two great guests, Andrew Coward's the GM, Software Defined Networking at IBM and Caroline Chappel. Research Director, Cloud and Platform Services at Analysys Mason. Folks, thanks for coming on. Caroline, good to see you. Andrew, thanks for coming on theCUBE. >> You're welcome, it's nice to be here. >> Thank you. >> So software defined networking, love it. Software-defined data center, software defined cloud, all that has been pointing to what is now a reality which is hybrid cloud and the Edge, and soon to be multicloud. This kind of makes networking, again, at the centerpiece. This has been this way for now, at least for five hardcore years, at the center of the value proposition discussion. And certainly networking is super relevant. Why is networking now more important than ever for IBM? >> Well, to your point, I think networking is weaved into pretty much everything we touch. From Red Hat Linux for its analytics, machine learning tools, security, cloud services, and so on. And the networking business is changing very radically at the moment. We're going through kind of massive shift. Not just to the cloud, but the desegregation of networking products that, you know, you think of being very tight and integrated are actually being separated into their constituent parts. Distribution of applications and data across multiple clouds, ensuring that the products really have industry-leading capabilities, so that networking is weaved into what they do. The other thing is the scary numbers, right? But now, there's like 15 billion network-capable devices out there with general computing capabilities. And so I don't mean like really dumb things but things that are now we call smart, like a smart car. A medical center that's got applications that even your fridge now, has general compute capabilities. And all of those are expected to connect into the public or private cloud. And so how they connect, where data moves across that really on critical concern to everything that we at IBM do. >> So I have to ask you, I love the word radical change. It gets my attention for certain. What specifically are you referring to in radical change? Because, I mean, I would, I mean, I'm pretty radical that COVID has hit everybody and I think everyone woke up and never thought 100% of the workforce would be working remotely. So, you know, there is radical kind of macro conditions. What specifically though about networking would you say is radical and how does that impact the enterprise? >> Well, right. I think it's about how compute is shifting and how network has to follow. You know, we've been speaking a lot of enterprise accounts and customers. And, you know, it's through COVID and over the last year, we've seen that the ongoing migration into, not just one cloud but many clouds. But we need to think the enterprise you can stop and say, two clouds is enough to be here and to be able to do that. That's not happening. There is no limit to the number of clouds that each enterprise is going into and it's not a coordinated decision, so the radicalism is that the network guys, the cloud architects are being left to pick up the pieces and their job now is to kind of join together applications and data that might be spread in three or four different locations. And that's really, really challenging. And nobody's thinking about things like latency or connectivity, data accountability when these decisions are made. And it is kind of like the business units are allowed to make their own decisions to get it, but corporate itself then has to figure out how all this stuff works. And that's creating a lot of headaches. >> Caroline, If you could chime in on this, because this is kind of like what we're hearing. What's your thoughts? Because I mean, the platform shifting. I mean, five years ago. Oh, go move to the cloud, lift and shift. Now, the conversation is hyper-focused on cloud integration, at scale with kind of the features that enterprise really need. That's the confusion. What's your take on all this radical change? >> Well, I'd like to, to talk about another aspect of the radical change here, which I think is part of the story which is the radical change for the network itself. So the network itself is, as Andrew said, you know becoming desegregated into hardware and software and really becoming a software application if you think about it, that runs on the cloud itself. And that means you can distribute the network in a very different way, than you could in the past. And what that's really affecting is who can provide a network, how they can provide it, what services, what network services they can provide. And I think that is changing the decision points for operators, for enterprises. They're being faced with a very big choice about who do they, who will provide their connectivity services? Will it be an SD-WAN vendor? Who's not necessarily a traditional operator? Would it be a SaSS-y player that's basically just operating after the cloud. And if you look at the services themselves, there's the opportunity for enterprises to build really kind of rich, bespoke connectivity on demand and in a way that they've never had before. And I think that choice is obviously wonderful in one sense, but in another sense, it's pretty scary. And, and as Andrew said, it's not these decisions are not being taken particularly in a coordinated way. You know, you'll have your traditional network guys often very embedded with the lines of business and then you'll have the IT guys all going to the cloud. And these two parts of an enterprise don't necessarily even talk to each other in terms of how they're procuring their network services. So lot of choice, a lot of moving parts, a lot of change. And I think that's contributing to the situation we're finding ourselves in. >> So. First of all, great insight. I want to just double down on that one point around radical change, because what you just laid out is kind of the institutional lock-in or the way they've been operating things before You mentioned lines of business being embedded with the network guys. So you have radical change. So that's a disruption. So what's the disruption look like from your perspective because now you've got more choice, but it's hasn't been operationalized. What are the best practices? This is net new. Is it net new? How do I do security? This is all now new questions. So I got to ask you what's the disruption and what's it mean for the enterprise networks over the next couple of years going forward? >> Well, I think that there are a lot of disruptions but I think one of the ones that I haven't even mentioned. So I think, you know a lot of things are going to go, for example, I think that the idea of the network as being something fixed, persistent with fixed persistent connections is changing. So a lot of the enterprises I've talked to have said that their corporate networks, of course, they will need corporate networks with fixed VPNs between locations. Yeah, because they've got an awful lot of legacy they've got to support. But a lot of the new stuff that's coming along of the IOT driven stuff a lot of the changes around the edge and an operation, operational process automation and that kind of thing will actually be more on demand. We'll ask for on demand connectivity. A lot of it is will the applications themselves run on the cloud and not just on one cloud but as Andrew said on many, many distributed clouds. So you've got to think about zero trust security because you are basically spinning up these connections on demand. A lot of mobile will come in 5g. We know is going to be very important to operators in the future. So I think enterprises have got to deal with those data and security and all their best practices. We've got to shift to a much more dynamic, you know connectivity world, where they've got us to the playoffs. You know, what's the terministic on what's a network. That's just going to be on demand there when they need it and shut down when they don't. >> That's a great point. Andrew, I want you to weigh in on the IBM impact because what we just heard was application driven. That's dev ops. That's programmability. That's what we had hoped. Now you've got DevSecOps, all this is now the requirements. What's the bet on IBM side.? You got to make it happen. You got to bring the customers a solution and make it scale and be responsive to those you know, new, dynamically, flexible agile networks. >> Well, that's right. So the bet is that, you know that these applications that are being spent out there in containerize and they're being separated into these clouds and connecting those is what we as IBM have to have to do. And so kind of an example of that, kind of looking at the medical world, right? You think of an application that would today, monitor a patient. What's going on with that patient and all of the senses and so on. Well, the way we see it, the monitor itself, there might be monitoring temperature and heart rate etc. That what actually happens on that device might change moments depending on the patient's condition. That's one part of the application. Another part of that application may live in private data center. A third part of that application may live in the cloud. And depending on what's going on with that patient and what's going on with the ward and everything else. Those things may shift and move around. So, where does that data? Where's that data allowed to move to inform of what are the boundary points for that? How is the reliability, resiliency of our system guaranteed, but across many disparate parts of what's going on there. All of those things end up being a very vertically integrated solution. But fundamentally we've got a very different way, new ways of being able to react, dynamically. To both the network, the application and ultimately the unusual patient in this case and that's what kind of is the advantage of the outcome if you like for moving to this new world. >> So what are the implications then of the changes? These are massive changes for the better We're seeing that kind of innovation come from this transformational quick change. Hybrid cloud and edge is coming, you mentioned. Caroline talked about that too. What do you guys think about the implications and how enterprises specifically can prepare for these changes? >> Okay, well, I can pick that up. I think what enterprises are looking for at the moment is how do they get a holistic view of everything that's underneath them? I mean, I think the cloud providers individually are abstracting away as much of the network as they possibly can. They want it to appear to developers just as some kind of plumbing. And it's very easy now for enterprises to through API is you know, we've got a very API different world so it's very easy to say, okay I want this service and I'm just going to go through their API and connect to it. And that's why you get to the situation of multiple, multiple clouds. Now you've got this situation where you've got some companies are talking about needing 50 to 10,000 micro data centers, room closet data centers if you like ,to support some of the things that they want to do, like telemetry ,pick up telemetry from rental cars, for example. So what they really need is to look at all that connectivity, just as plumbing just as we don't worry about how electricity is being delivered to us. That's kind of how they want to do connectivity. So I think they want that view. They want that. Okay. I want to treat my network as one virtual thing. No matter how many different points of plumbing there are underneath. And it's getting to that point that I think they've really got to think about a plan for. You know, how do we get that to you? What's going to provide us with that holistic way that we can put a policy into our plumbing. And it proliferates across, you know all our applications and so on. I think that's a very difficult thing to achieve at the moment but it's certainly the way enterprises need to start thinking about things. >> Andrew, you know, when Caroline's talking, I can't help but kind of throw back to my days of the telephone closet. You know, back in the analog switches. But no, we're talking about a footprint. Radical footprint change too. You know, you need plumbing. Obviously that's a network. It's distributed. We just talked about that at the top of this interview. Now you have the plumbing, you got the footprint and data center could be in a closet, AKA, you know a couple of devices powering an edge. And the edge could be big, small, medium, extra large right? I mean, it's all now radically changed. This is reality now. what's your take on these implications and how do people prepare? >> Well, that's right. It's really the computer's generalized and it's everywhere and yes, it's in the closet. But as I say, it's also in your fridge, it's also in your medical censor and what loads and what runs on that is it's very intertwined with the network. And the lament, if you like, that network architects, the card architects have today is that they feel like they've lost control. They feel they've lost control of exactly what different business groups are doing, how these applications are playing out. And shout out to them, I guess for them is really that they need to be involved from a very early date on how these services are supposed to look. Just the latency of the patients, the data and where the data's supposed to live, where it's allowed to move to. All of those are deeply regulated and deeply controlled. And so making sure that that's aligned with how these applications will actually live and work. Even on a regular basis, sooner there has to be thought about now. An unplanned for so that we can get to the there and not trip up along the way. And then if it's bad enough now with all the different clouds, it's going to be much worse when everything can run a different workload on a minute by minute basis. Right. But that's cool. That's the world we have to find for. >> Okay. Andrew. Caroline. Thank you for your insight. Really appreciated coming on theCUBE. Thanks for coming. I really appreciate it. >> Thank you very much. >> Thank you >> Okay. This is the cube coverage of IBM Think 2021. I'm John Furrier, your host. Thanks for watching. (cheerful music playing)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> John: Hello and welcome back to theCUBE's coverage of IBM Think 2021 Virtual. I'm John Furrier, your host of theCUBE. We're here with two great guests, Andrew Coward's the GM, Software Defined Networking at IBM and Caroline Chappel. Research Director, Cloud and Platform Services at Analysys Mason. Folks, thanks for coming on. Caroline, good to see you. Andrew, thanks for coming on theCUBE. >> You're welcome, it's nice to be here. >> Thank you. >> So software defined networking, love it. Software-defined data center, software defined cloud, all that has been pointing to what is now a reality which is hybrid cloud and the Edge, and soon to be multicloud. This kind of makes networking, again, at the centerpiece. This has been this way for now, at least for five hardcore years, at the center of the value proposition discussion. And certainly networking is super relevant. Why is networking now more important than ever for IBM? >> Well, to your point, I think networking is weaved into pretty much everything we touch. From Red Hat Linux for its analytics, machine learning tools, security, cloud services, and so on. And the networking business is changing very radically at the moment. We're going through kind of massive shift. Not just to the cloud, but the desegregation of networking products that, you know, you think of being very tight and integrated are actually being separated into their constituent parts. Distribution of applications and data across multiple clouds, ensuring that the products really have industry-leading capabilities, so that networking is weaved into what they do. The other thing is the scary numbers, right? But now, there's like 15 billion network-capable devices out there with general computing capabilities. And so I don't mean like really dumb things but things that are now we call smart, like a smart car. A medical center that's got applications that even your fridge now, has general compute capabilities. And all of those are expected to connect into the public or private cloud. And so how they connect, where data moves across that really on critical concern to everything that we at IBM do. >> So I have to ask you, I love the word radical change. It gets my attention for certain. What specifically are you referring to in radical change? Because, I mean, I would, I mean, I'm pretty radical that COVID has hit everybody and I think everyone woke up and never thought 100% of the workforce would be working remotely. So, you know, there is radical kind of macro conditions. What specifically though about networking would you say is radical and how does that impact the enterprise? >> Well, right. I think it's about how compute is shifting and how network has to follow. You know, we've been speaking a lot of enterprise accounts and customers. And, you know, it's through COVID and over the last year, we've seen that the ongoing migration into, not just one cloud but many clouds. But we need to think the enterprise you can stop and say, two clouds is enough to be here and to be able to do that. That's not happening. There is no limit to the number of clouds that each enterprise is going into and it's not a coordinated decision, so the radicalism is that the network guys, the cloud architects are being left to pick up the pieces and their job now is to kind of join together applications and data that might be spread in three or four different locations. And that's really, really challenging. And nobody's thinking about things like latency or connectivity, data accountability when these decisions are made. And it is kind of like the business units are allowed to make their own decisions to get it, but corporate itself then has to figure out how all this stuff works. And that's creating a lot of headaches. >> Caroline, If you could chime in on this, because this is kind of like what we're hearing. What's your thoughts? Because I mean, the platform shifting. I mean, five years ago. Oh, go move to the cloud, lift and shift. Now, the conversation is hyper-focused on cloud integration, at scale with kind of the features that enterprise really need. That's the confusion. What's your take on all this radical change? >> Well, I'd like to, to talk about another aspect of the radical change here, which I think is part of the story which is the radical change for the network itself. So the network itself is, as Andrew said, you know becoming desegregated into hardware and software and really becoming a software application if you think about it, that runs on the cloud itself. And that means you can distribute the network in a very different way, than you could in the past. And what that's really affecting is who can provide a network, how they can provide it, what services, what network services they can provide. And I think that is changing the decision points for operators, for enterprises. They're being faced with a very big choice about who do they, who will provide their connectivity services? Will it be an SD-WAN vendor? Who's not necessarily a traditional operator? Would it be a SaSS-y player that's basically just operating after the cloud. And if you look at the services themselves, there's the opportunity for enterprises to build really kind of rich, bespoke connectivity on demand and in a way that they've never had before. And I think that choice is obviously wonderful in one sense, but in another sense, it's pretty scary. And, and as Andrew said, it's not these decisions are not being taken particularly in a coordinated way. You know, you'll have your traditional network guys often very embedded with the lines of business and then you'll have the IT guys all going to the cloud. And these two parts of an enterprise don't necessarily even talk to each other in terms of how they're procuring their network services. So lot of choice, a lot of moving parts, a lot of change. And I think that's contributing to the situation we're finding ourselves in. >> So. First of all, great insight. I want to just double down on that one point around radical change, because what you just laid out is kind of the institutional lock-in or the way they've been operating things before You mentioned lines of business being embedded with the network guys. So you have radical change. So that's a disruption. So what's the disruption look like from your perspective because now you've got more choice, but it's hasn't been operationalized. What are the best practices? This is net new. Is it net new? How do I do security? This is all now new questions. So I got to ask you what's the disruption and what's it mean for the enterprise networks over the next couple of years going forward? >> Well, I think that there are a lot of disruptions but I think one of the ones that I haven't even mentioned. So I think, you know a lot of things are going to go, for example, I think that the idea of the network as being something fixed, persistent with fixed persistent connections is changing. So a lot of the enterprises I've talked to have said that their corporate networks, of course, they will need corporate networks with fixed VPNs between locations. Yeah, because they've got an awful lot of legacy they've got to support. But a lot of the new stuff that's coming along of the IOT driven stuff a lot of the changes around the edge and an operation, operational process automation and that kind of thing will actually be more on demand. We'll ask for on demand connectivity. A lot of it is will the applications themselves run on the cloud and not just on one cloud but as Andrew said on many, many distributed clouds. So you've got to think about zero trust security because you are basically spinning up these connections on demand. A lot of mobile will come in 5g. We know is going to be very important to operators in the future. So I think enterprises have got to deal with those data and security and all their best practices. We've got to shift to a much more dynamic, you know connectivity world, where they've got us to the playoffs. You know, what's the terministic on what's a network. That's just going to be on demand there when they need it and shut down when they don't. >> That's a great point. Andrew, I want you to weigh in on the IBM impact because what we just heard was application driven. That's dev ops. That's programmability. That's what we had hoped. Now you've got DevSecOps, all this is now the requirements. What's the bet on IBM side.? You got to make it happen. You got to bring the customers a solution and make it scale and be responsive to those you know, new, dynamically, flexible agile networks. >> Well, that's right. So the bet is that, you know that these applications that are being spent out there in containerize and they're being separated into these clouds and connecting those is what we as IBM have to have to do. And so kind of an example of that, kind of looking at the medical world, right? You think of an application that would today, monitor a patient. What's going on with that patient and all of the senses and so on. Well, the way we see it, the monitor itself, there might be monitoring temperature and heart rate etc. That what actually happens on that device might change moments depending on the patient's condition. That's one part of the application. Another part of that application may live in private data center. A third part of that application may live in the cloud. And depending on what's going on with that patient and what's going on with the ward and everything else. Those things may shift and move around. So, where does that data? Where's that data allowed to move to inform of what are the boundary points for that? How is the reliability, resiliency of our system guaranteed, but across many disparate parts of what's going on there. All of those things end up being a very vertically integrated solution. But fundamentally we've got a very different way, new ways of being able to react, dynamically. To both the network, the application and ultimately the unusual patient in this case and that's what kind of is the advantage of the outcome if you like for moving to this new world. >> So what are the implications then of the changes? These are massive changes for the better We're seeing that kind of innovation come from this transformational quick change. Hybrid cloud and edge is coming, you mentioned. Caroline talked about that too. What do you guys think about the implications and how enterprises specifically can prepare for these changes? >> Okay, well, I can pick that up. I think what enterprises are looking for at the moment is how do they get a holistic view of everything that's underneath them? I mean, I think the cloud providers individually are abstracting away as much of the network as they possibly can. They want it to appear to developers just as some kind of plumbing. And it's very easy now for enterprises to through API is you know, we've got a very API different world so it's very easy to say, okay I want this service and I'm just going to go through their API and connect to it. And that's why you get to the situation of multiple, multiple clouds. Now you've got this situation where you've got some companies are talking about needing 50 to 10,000 micro data centers, room closet data centers if you like ,to support some of the things that they want to do, like telemetry ,pick up telemetry from rental cars, for example. So what they really need is to look at all that connectivity, just as plumbing just as we don't worry about how electricity is being delivered to us. That's kind of how they want to do connectivity. So I think they want that view. They want that. Okay. I want to treat my network as one virtual thing. No matter how many different points of plumbing there are underneath. And it's getting to that point that I think they've really got to think about a plan for. You know, how do we get that to you? What's going to provide us with that holistic way that we can put a policy into our plumbing. And it proliferates across, you know all our applications and so on. I think that's a very difficult thing to achieve at the moment but it's certainly the way enterprises need to start thinking about things. >> Andrew, you know, when Caroline's talking, I can't help but kind of throw back to my days of the telephone closet. You know, back in the analog switches. But no, we're talking about a footprint. Radical footprint change too. You know, you need plumbing. Obviously that's a network. It's distributed. We just talked about that at the top of this interview. Now you have the plumbing, you got the footprint and data center could be in a closet, AKA, you know a couple of devices powering an edge. And the edge could be big, small, medium, extra large right? I mean, it's all now radically changed. This is reality now. what's your take on these implications and how do people prepare? >> Well, that's right. It's really the computer's generalized and it's everywhere and yes, it's in the closet. But as I say, it's also in your fridge, it's also in your medical censor and what loads and what runs on that is it's very intertwined with the network. And the lament, if you like, that network architects, the card architects have today is that they feel like they've lost control. They feel they've lost control of exactly what different business groups are doing, how these applications are playing out. And shout out to them, I guess for them is really that they need to be involved from a very early date on how these services are supposed to look. Just the latency of the patients, the data and where the data's supposed to live, where it's allowed to move to. All of those are deeply regulated and deeply controlled. And so making sure that that's aligned with how these applications will actually live and work. Even on a regular basis, sooner there has to be thought about now. An unplanned for so that we can get to the there and not trip up along the way. And then if it's bad enough now with all the different clouds, it's going to be much worse when everything can run a different workload on a minute by minute basis. Right. But that's cool. That's the world we have to find for. >> Okay. Andrew. Caroline. Thank you for your insight. Really appreciated coming on theCUBE. Thanks for coming. I really appreciate it. >> Thank you very much. >> Thank you >> Okay. This is the cube coverage of IBM Think 2021. I'm John Furrier, your host. Thanks for watching. (cheerful music playing)

>> From San Francisco, it's theCUBE. Covering Conga Connect West 2018. Brought to you by Conga. >> Welcome back everybody, Jeff Frick here at theCUBE. We're at the Conga Connect West event at the Thirsty Bear. It's Salesforce Dreamforce downtown San Francisco. Marc Benioff, he said it, 171,000 people. I don't know where they all fit. Please don't bring your car, but we're here, Thirsty Bear is a place to hang. There's no lines at the bar, no lines at the food, this a the place to be. So we're happy to be here. Have our next guest from Conga. We've got Sawyer Martin, he's the Director of Product Management from Conga. >> Sayer Martin. >> Sayer, I'm sorry. Sayer good to see you. Also Mason White, the Director of Product Strategy. Mason great to see you. >> Nice to meet you Jeff. >> Absolutely. So Sayer, you came in on an acquisition we're looking at almost exactly six months ago. >> That's right. >> Orchestrate. So what is Orchestrate, and how's it been so far? >> Yeah, it's been really good. So Orchestrate started as really a wealth management tool for process orchestration, so inside of Salesforce. So managing end to end processes for wealth management firms inside of Salesforce. That's the combination of human and automated work that are happening, tasks being generated-- >> So I was going to say, what type of tasks and stuff? What is it? >> So tasks to tell someone, so a tasks in Salesforce is essentially an instruction to have someone do something. >> Right, but I'm curious because you said very specifically it was for financial management. >> Yeah, so financial management, there's moving money, generating investment policies statements for clients, all kinds of different things that you might do, review meetings for clients. >> And how did you pick that vertical to get started? >> Well we came out up, so the company was actually spun out of a wealth management firm, and that wealth management firm was on Salesforce, couldn't find a way to automate their business basically. Wanted to take those processes that they were living everyday or that were in someone's head and put it down in a system that they could then use to train people as they grew. And so it was born out of that wealth management firm, and knowing that industry we thought, as a small company, let's establish a beachhead in that market and then move elsewhere. The tool's built generically so it applies to any industry really, but we knew that industry the best. So that we focused there. >> So did you spin out of, oh no you were, you spin out of the wealth management company or did those people who founded it left and figured if these guys need it there's probably few more that do as well. >> Yep, so it was the former. Spun out of the wealth management firm, and then took it as this independent entity, not doing wealth management at all, but doing technology exclusively. >> Right, and doing process flow and task management and those types of things. >> That's right. >> Alright, so Mason how does this fit in your portfolio strategy? >> That's a great question, and actually Sayer and I met at Dreamforce '17 last year. In terms of Orchestrate what we've done is really, certainly we are keeping the existing customer base, but we're bringing that type of work flow capability into other areas of Conga. So as you look at the Conga suite of products, that work flow and approval processes is really something that is vital for things like contract life cycle management. Who needs to be involved in reviewing and approving a contract depends greatly on the size of the contract, the level of complexity, the types of changes that are being asked for. So we're in the process of bringing Orchestrate capabilities into various of our product lines. First one we're showing to customers is how we've brought it into Conga contracts through Salesforce, and we'll be bringing it into other elements really through a suite type of play. We're calling it a platform internally, and as we mature that it will become available to other members of The Conga Product Suite. >> Right, so you guys have this interesting collection of products that I assume all started as silos, but they've all got this kind of interplay between the process flow, with the contracts, the document creation, the contract kind of management, they're all very very, you know, kind of different tranches of the same tree. >> Yeah very much so. In fact I'd throw in our recent acquisition of Counselytics with the artificial intelligence and machine learning capabilities related to contract analysis. There's a fairly consistent thesis in a lot of our recent, whether it's been product launches or product acquisitions around building out capabilities related to contract life cycle management. It's not the only place where those things come into play, but it's certainly the one that is exciting people as we go to market. >> Right, right, so Sayer you've been with him for six months now, how's been the absorption? >> It's been really good. We didn't fully understand when we were acquired that, sort of what the plan was, and we didn't get a lot of direction when we first came aboard, but we knew that contract life cycle management is a powerful piece of the business. It's a growing piece and it's one that's is increasingly important to customers. And so we looked at that from a process perspective, and we've really been focused on finding the gaps there and taking what was as you said, a silo, going from the contract management piece, generating the documents, doing the negotiations, and ultimately signing the documents, and tying it all together with the process engine we'd already built. >> Right, so is Orchestrate's go to market today still as a single product, or are you just getting completely embedded in the other ones? >> I think to Mason's point it becomes obvious to use more than one Conga product. When you buy one at least one other one will make sense for you, and Orchestrate included. >> Right, because Orchestrate is kind of like AI And I'm sure where and how you guys are going to apply AI in all these various applications. And I don't want to buy a bucket of AI, I want all of my applications to work better, work faster, auto-fill, auto-select, you know, take more and more of those manual steps out of the process. >> That's right, augment the human mind in many ways. Right? Come in at those points in the process where it can add value or give you insights that you wouldn't have otherwise had. >> Right, right. So Mason I'm just curious from a product strategy point of view, you've guys have made a lot of acquisitions, got some new money in the war chest, and you know, a really solid team of senior execs that have worked together a lot. The band is back together is a big theme that I've seen all day today. So when you are looking at kind of buy versus build decisions what are some of the things you're thinking about as you kind of continue to build out this suite of kind of cross-functional capability? >> We're always looking at things whether in buy, build, or license. So there are things that as we're looking at them right now, and I'm not going to mention them, the decision is between buy, build, or license in certain types of capabilities. Really depends on what's the maturity of the technology out there, is it something that we need that others have right now and they've got strong, could be a strong OEM business model, or could be something that is a rapidly growing area that we need to get in on. Own it and tune it for our needs specifically. >> Right, well great story and I'm sure you're going to see that Orchestrate stuff all over the place. >> That's what we hope! >> That's what we're working towards. >> Alright, so Sayer, Mason, thanks for taking a few minutes to tell your story, and inviting us here to Conga Connect West. >> Great, thanks Jeff. >> It's nice to talk to you Jeff, thanks. >> Oh my pleasure. Alright, you're watching theCUBE, like I said we're at Conga Connect West at Salesforce Dreamforce. Thanks for watching, see you next time. (upbeat music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Welcome back to the Fira in Barcelona. This is Dave Vellante with David Nicholson. Lisa Martin is also here. This is day two of our coverage of MWC 23 on theCUBE. We're super excited. We're in between hall four and five. Stop by if you're here. Dennis Hoffman is here. He's the senior vice president and general manager of the Telecom systems business at Dell Technologies, and he's joined by Yousef Khalidi, who's the corporate vice president of Azure for Operators from Microsoft. Gents, Welcome. >> Thanks, Dave. >> Thank you. >> So we saw Satya in the keynote. He wired in. We saw T.K. came in. No AWS. I don't know. They're maybe not part of the show, but maybe next year they'll figure it out. >> Indeed, indeed. >> Lots of stuff happened in the Telecom, but the Azure operator distributed service is the big news, you guys got here. What's that all about? >> Oh, first of all, we changed the name. >> Oh, you did? >> You did? >> Oh, yeah. We have a real name now. It's called the Azure Operator Nexus. >> Oh, I like Nexus better than that. >> David: That's much better, much better. >> Dave: The engineers named it first time around. >> I wish, long story, but thank you for our marketing team. But seriously, not only did we rename the platform, we expanded the platform. >> Dave: Yeah. >> So it now covers the whole spectrum from the far-edge to the public cloud as well, including the near-edge as well. So essentially, it's a hybrid platform that can also run network functions. So all these operators around you, they now have a platform which combines cloud technologies with the choice where they want to run, optimized for the network. >> Okay and so, you know, we've talked about the disaggregation of the network and how you're bringing kind of engineered systems to the table. We've seen this movie before, but Dennis, there are differences, right? I mean, you didn't really have engineered systems in the 90s. You didn't have those integration points. You really didn't have the public cloud, you didn't have AI. >> Right. >> So you have all those new powers that you can tap, so give us the update from your perspective, having now spent a day and a half here. What's the vibe, what's the buzz, and what's your take on everything? >> Yeah, I think to build on what Yousef said, there's a lot going on with people still trying to figure out exactly how to architect the Telecom network of the future. They know it's got to have a lot to do with cloud. It does have some pretty significant differences, one of those being, there's definitely got to be a hybrid component because there are pieces of the Telecom network that even when modernized will not end up centralized, right? They're going to be highly distributed. I would say though, you know, we took away two things, yesterday, from all the meetings. One, people are done, I think the network operators are done, questioning technology readiness. They're now beginning to wrestle with operationalization of it all, right? So it's like, okay, it's here. I can in fact build a modern network in a very cloud native way, but I've got to figure out how to do that all. And another big part of it is the ecosystem and certainly the partnership long standing between Dell and Microsoft which we're extending into this space is part of that, making it easier on people to actually acquire, deploy, and importantly, support these new technologies. >> So a lot of the traditional carriers, like you said, they're sort of beyond the technology readiness. Jose Maria Alvarez in the keynote said there are three pillars to the future Telecom network. He said low latency, programmable networks, and then cloud and edge, kind of threw that in. You agree with that, Yousef? (Dave and Yousef speaking altogether) >> I mean, we've been for years talking about the cloud and edge. >> Yeah. >> Satya for years had the same graphic. We still have it. Today, we have expanded the graphic a bit to include the network as one, because you can have a cloud without connectivity as well but this is very, very, very, very much true. >> And so the question then, Dennis, is okay, you've got disruptors, we had Dish on yesterday. >> Oh, did you? Good. >> Yeah, yeah, and they're talking about what they're doing with, you know, ORAN and all the applications, really taking account of it. What I see is a developer friendly, you know, environment. You got the carriers talking about how they're going to charge developers for APIs. I think they've published eight APIs which is nowhere near enough. So you've got that sort of, you know, inertia and yet, you have the disruptors that are going to potentially be a catalyst to, you know, cross the chasm, if you will. So, you know, put on your strategy hat. >> Yeah. >> Dave: How do you see that playing out? >> Well, they're trying to tap into three things, the disruptors. You know, I think the thesis is, "If I get to a truly cloud native, communications network first, I ought to have greater agility so that I can launch more services and create more revenue streams. I ought to be lower cost in terms of both acquisition cost and operating cost, right, and I ought to be able to create scale between my IT organization, everything I know how to do there and my Telecom network." You know, classic, right? Better, faster, cheaper if I embrace cloud early on. And people like Dish, you know, they have a clean sheet of paper with which to do that. So innovation and rate of innovation is huge for them. >> So what would you do? We put your Clay Christensen hat on, now. What if you were at a traditional Telco who's like, complaining about- >> You're going to get me in trouble. >> Dave: Come on, come on. >> Don't do it. >> Dave: Help him out. Help him out, help him out. So if, you know, they're complaining about CapEx, they're highly regulated, right, they want net neutrality but they want to be able to sort of dial up the cost of those using the network. So what would you do? Would you try to disrupt yourself? Would you create a skunkworks? Would you kind of spin off a disruptor? That's a real dilemma for those guys. >> Well for mobile network operators, the beauty of 5G is it's the first cloud native cellular standard. So I don't know if anybody's throwing these terms around, but 5G SA is standalone, right? >> Dave: Yeah, yeah. >> So a lot of 'em, it's not a skunkworks. They're just literally saying, "I've got to have a 5G network." And some of 'em are deciding, "I'm going to stand it up all by itself." Now, that's duplicative expense in a lot of ways, but it creates isolation from the two networks. Others are saying, "No, it's got to be NSA. I've got to be able to combine 4G and 5G." And then you're into the brownfield thing. >> That's the hybrid. >> Not hybrid as in cloud, but hybrid as in, you know. >> Yeah, yeah. >> It's a converge network. >> Dave: Yeah, yeah. >> So, you know, I would say for a lot of them, they're adopting, probably rightly so, a wait and see attitude. One thing we haven't talked about and you got to get on the table, their high order bit is resilience. >> Dave: Yeah, totally. >> David: Yeah. >> Right? Can't go down. It's national, secure infrastructure, first responder. >> Indeed. >> Anytime you ask them to embrace any new technology, the first thing that they have to work through in their minds is, you know, "Is the juice worth the squeeze? Like, can I handle the risk?" >> But you're saying they're not questioning the technology. Aren't they questioning ORAN in terms of the quality of service, or are they beyond that? >> Dennis: They're questioning the timing, not the inevitability. >> Okay, so they agree that ORAN is going to be open over time. >> At some point, RAN will be cloud native, whether it's ORAN the spec, open RAN the concept, (Yousef speaking indistinctly) >> Yeah. >> Virtual RAN. But yeah, I mean I think it seems pretty evident at this point that the mainframe will give way to open systems once again. >> Dave: Yeah, yeah, yeah. >> ERAN, ecosystem RAN. >> Any RAN. (Dave laughing) >> You don't have to start with the ORAN where they're inside the house. So as you probably know, our partner AT&T started with the core. >> Dennis: They almost all have. >> And they've been on the virtualization path since 2014 and 15. And what we are working with them on is the hybrid cloud model to expand all the way, if you will, as I mentioned to the far-edge or the public cloud. So there's a way to be in the brownfield environment, yet jump on the new bandwagon of technology without necessarily taking too much risk, because you're quite right. I mean, resiliency, security, service assurance, I mean, for example, AT&T runs the first responder network for the US on their network, on our platform, and I'm personally very familiar of how high the bar is. So it's doable, but you need to go in stages, of course. >> And they've got to do that integration. >> Yes. >> They do. >> And Yousef made a great point. Like, out of the top 30 largest Telcos by CapEx outside of China, three quarters of them have virtualized their core. So the cloudification, if you will, software definition run on industry standard hardware, embraced cloud native principles, containerized apps, that's happened in the core. It's well accepted. Now it's just a ripple-down through the network which will happen as and when things are faster, better, cheaper. >> Right. >> So as implemented, what does this look like? Is it essentially what we used to loosely refer to as Azure stacked software, running with Dell optimized Telecom infrastructure together, sometimes within a BBU, out in a hybrid cloud model communicating back to Azure locations in some cases? Is that what we're looking at? >> Approximately. So you start with the near-edge, okay? So the near-edge lives in the operator's data centers, edges, whatever the case may be, built out of off the shelf hardware. Dell is our great partner there but in principle, it could be different mix and match. So once you have that true near-edge, then you can think of, "Okay, how can I make sure this environment is as uniform, same APIs, same everything, regardless what the physical location is?" And this is key, key for the network function providers and the NEPs because they need to be able to port once, run everywhere, and it's key for the operator to reduce their costs. You want to teach your workforce, your operations folks, if you will, how to manage this system one time, to automation and so forth. So, and that is actually an expansion of the Azure capabilities that people are familiar with in a public cloud, projected into different locations. And we have technology called Arc which basically models everything. >> Yeah, yeah. >> So if you have trained your IT side, you are halfway there, how to manage your new network. Even though of course the network is carrier graded, there's different gear. So yes, what you said, a lot of it is true but the actual components, whatever they might be running, are carrier grade, highly optimized, the next images and our solution is not a DIY solution, okay? I know you cater to a wide spectrum here but for us, we don't believe in the TCO. The proper TCO can be achieved by just putting stuff by yourself. We just published a report with Analysys Mason that shows that our approach will save 36 percent of the cost compared to a DIY approach. >> Dave: What percent? >> 36 percent. >> Dave: Of the cost? >> Of, compared to DIY, which is already cheaper than classical models. >> And there's a long history of fairly failed DIY, right, >> Yeah. >> That preceded this. As in the early days of public cloud, the network operators wrestled with, "Do I have to become one to survive?" >> Dave: Yeah. Right. >> So they all ended up having cloud projects and by and large, they've all dematerialized in favor of this. >> Yeah, and it's hard for them to really invest at scale. Let me give you an example. So, your biggest tier one operator, without naming anybody, okay, how many developers do they have that can build and maintain an OS image, or can keep track of container technology, or build monitoring at scale? In our company, we have literally thousands of developers doing it already for the cloud and all we're doing for the operator segment is customizing it and focusing it at the carrier grade aspects of it. But so, I don't have half a dozen exterior experts. I literally have a building of developers who can do that and I'm being literal, here. So it's a scale thing. Once you have a product that you can give to multiple people, everybody benefits. >> Dave: Yeah, and the carriers are largely, they're equipment engineers in a large setting. >> Oh, they have a tough job. I always have total respect what they do. >> Oh totally, and a lot of the work happens, you know, kind of underground and here they are. >> They are network operators. >> They don't touch. >> It's their business. >> Right, absolutely, and they're good at it. They're really good at it. That's right. You know, you think about it, we love to, you know, poke fun at the big carriers, but think about what happened during the pandemic. When they had us shift everything to remote work, >> Dennis: Yes. >> Landline traffic went through the roof. You didn't even notice. >> Yep. That's very true. >> I mean, that's the example. >> That's very true. >> However, in the future where there's innovation and it's going to be driven by developers, right, that's where the open ecosystem comes in. >> Yousef: Indeed. >> And that's the hard transition for a lot of these folks because the developers are going to win that with new workloads, new applications that we can't even think of. >> Dennis: Right. And a lot of it is because if you look at it, there's the fundamental back strategy hat back on, fundamental dynamics of the industry, forced investment, flat revenues. >> Dave: Yeah. Right. >> Very true. >> Right? Every few years, a new G comes out. "Man, I got to retool this massive thing and where I can't do towers, I'm dropping fiber or vice a versa." And meanwhile, most diversification efforts into media have failed. They've had to unwind them and resell them. There's a lot of debt in the industry. >> Yousef: Yeah. >> Dennis: And so, they're looking for that next big, adjacent revenue stream and increasingly deciding, "If I don't modernize my network, I can't get it." >> Can't do it. >> Right, and again, what I heard from some of the carriers in the keynote was, "We're going to charge for API access 'cause we have data in the network." Okay, but I feel like there's a lot more innovation beyond that that's going to come from the disruptors. >> Dennis: Oh yeah. >> Yousef: Yes. >> You know, that's going to blow that away, right? And then that may not be the right model. We'll see, you know? I mean, what would Microsoft do? They would say, "Here, here's a platform. Go develop." >> No, I'll tell you. We are actually working with CAMARA and GSMA on the whole API layer. We actually announced a service as well as (indistinct). >> Dave: Yeah, yeah, right. >> And the key there, frankly, in my opinion, are not the disruptors as in operators. It's the ISV community. You want to get developers that can write to a global set of APIs, not per Telco APIs, such that they can do the innovation. I mean, this is what we've seen in other industries, >> Absolutely. >> That I critically can think of. >> This is the way they get a slice of that pie, right? The recent history of this industry is one where 4G LTE begot the smartphone and app store era, a bevy of consumer services, and almost every single profit stream went somewhere other than the operator, right? >> Yousef: Someone else. So they're looking at this saying, "Okay, 5G is the enterprise G and there's going to be a bevy of applications that are business service related, based on 5G capability and I can't let the OTT, over the top, thing happen again." >> Right. >> They'll say that. "We cannot let this happen." >> "We can't let this happen again." >> Okay, but how do they, >> Yeah, how do they make that not happen? >> Not let it happen again? >> Eight APIs, Dave. The answer is eight APIs. No, I mean, it's this approach. They need to make it easy to work with people like Yousef and more importantly, the developer community that people like Yousef and his company have found a way to harness. And by the way, they need to be part of that developer community themselves. >> And they're not, today. They're not speaking that developer language. >> Right. >> It's hard. You know, hey. >> Dennis: Hey, what's the fastest way to sell an enterprise, a business service? Resell Azure, Teams, something, right? But that's a resale. >> Yeah, that's a resale thing. >> See, >> That's not their service. >> They also need to free their resources from all the plumbing they do and leave it to us. We are plumbers, okay? >> Dennis: We are proud plumbers. >> We are proud plumbers. I'm a plumber. I keep telling people this thing. We had the same discussion with banks and enterprises 10 years ago, by the way. Don't do the plumbing. Go add value on the top. Retool your workforce to do applications and work with ISVs to the verticals, as opposed to either reselling, which many do, or do the plumbing. You'd be surprised. Traditionally, many operators do around, "I want to plumb this thing to get this small interrupt per second." Like, who cares? >> Well, 'cause they made money on connectivity. >> Yes. >> And we've seen this before. >> And in a world without telephone poles and your cables- >> Hey, if what you have is a hammer, everything's a nail, right? And we sell connectivity services and that's what we know how to do, and that both build and sell. And if that's no longer driving a revenue stream sufficient to cover this forced investment march, not to mention Huawei rip and government initiatives to pull infrastructure out and accelerate investment, they got to find new ways. >> I mean, the regulations have been tough, right? They don't go forward and ask for permission. They really can't, right? They have to be much more careful. >> Dennis: It is tough. >> So, we don't mean to sound like it's easy for these guys. >> Dennis: No, it's not. >> But it does require a new mindset, new skillsets, and I think some of 'em are going to figure it out and then pff, the wave, and you guys are going to be riding that wave. >> We're going to try. >> Definitely. Definitely. >> As a veteran of working with both Dell and Microsoft, specifically Azure on things, I am struck by how you're very well positioned in this with Microsoft in particular. Because of Azure's history, coming out of the on-premises world that Microsoft knows so well, there's a natural affinity to the hybrid nature of Telecom. We talk about edge, we talk about hybrid, this is it, absolutely the center of it. So it seems like a- >> Yousef: Indeed. Actually, if you look at the history of Azure, from day one, and I was there from day one, we always spoke of the hybrid model. >> Yeah. >> The third point, we came from the on-premises world. >> David: Right. >> And don't get me wrong, I want people to use the public cloud, but I also know due to physics, regulation, geopolitical boundaries, there's something called on-prem, something called an edge here. I want to add something else. Remember our deal on how we are partner-centric? We're applying the same playbook, here. So, you know, for every dollar we make, so many of it's been done by the ecosystem. Same applies here. So we have announced partnerships with Ericson, Nokia, (indistinct), all the names, and of course with Dell and many others. The ecosystem has to come together and customers must retain their optionality to drum up whatever they are on. So it's the same playbook, with this. >> And enterprise technology companies are, actually, really good at, you know, decoding the customer, figuring out specific requirements, making some mistakes the first time through and then eventually getting it right. And as these trends unfold, you know, you're in a good position, I think, as are others and it's an exciting time for enterprise tech in this industry, you know? >> It really is. >> Indeed. >> Dave: Guys, thanks so much for coming on. >> Thank you. >> Dave: It's great to see you. Have a great rest of the show. >> Thank you. >> Thanks, Dave. Thank you, Dave. >> All right, keep it right there. John Furrier is live in our studio. He's breaking down all the news. Go to siliconangle.com to go to theCUBE.net. Dave Vellante, David Nicholson and Lisa Martin, we'll be right back from the theater in Barcelona, MWC 23 right after this short break. (relaxing music)

(corporate electronic xylophone jingle intro) >> Good afternoon and welcome back to our very last segment of Tuesday's live broadcast here on theCUBE from AWS re:Invent in fabulous Las Vegas, Nevada. My name is Savannah Peterson and I am joined here by the brilliant Paul Gillin. Paul, end of our first day. You holding up, are you still feeling overwhelmed with fire hose... >> Savannah, yet my feet are killing me. (savannah laughs) >> Yeah, we've done so much walking in these chairs. >> 14,000 steps already today. It's not even dinner time. >> Hey, well, at least you've earned your dinner, Paul. I love that. I love that. I'm very excited about our next guests. We have Krishna and Sowmya joining us from Tata Consultancy Services. Now, I was impressed when I was doing my background research on you all. The Tata Group has locations in 150 different spots, 46 different countries. You have over 600,000 employees on the team. We are talking about absolutely massive scale here but, today we're going to be focused specifically on the Tata Consultancy Services. Sowmya, can you tell me what you all do? What is that team specifically in charge of? >> Yeah, TCS, first of all, thank you very much for inviting us. >> Savannah: Our pleasure. >> Maybe the last session but, we'll make it very lively. >> Savannah: It's going to be the best session. That's the best part of the day. >> Yes, that's the attitude. From a company standpoint, we are a 50 plus year old company. Part of the Tata group. We focus on IT services. We are categorized as industry verticals and we have horizontal services where AWS is one of the horizontal services that we have. And, when I talk about TCS, we focus a lot more on growth and transformation of our customers. That is one of the key objectives of the current company's growth, I would say. So, that is TCS in a nutshell. >> Extraordinarily important topic to be focused on right now. Growth, transformation, pretty much the core topics of the show. I know you're on the hospitality and transportation side of the business, which is very exciting. And, we're going to dig into that a little bit more. Krishna, you're overseeing the world. Tell us a little bit more about your role within the whole ecosystem. >> Yeah, thank you for the opportunity. Great meeting all of you. It's been awesome experience here. re:Invent is coming back, catching up, right? 50,000 people compared to 25,000 last year. So, great to see and meet all of you. Coming to my role, I am responsible for AWS Business Unit within TCS. That means I am responsible for anything that happens on cloud, on AWS. It's a Full Stack unit. I have the global responsibility. That's whether it's a applications, data, infrastructure, transformation that happens, as well as OT at the edge. So, that's my responsibility. >> Savannah: Well, I love talking about the edge. One of my favorite. >> Transformation is a theme of what you do. We heard that the pandemic accelerated digital transformation initiatives at many companies. How did you see the pandemic affecting your business, affecting the customers you were working with? >> Pandemic definitely kind of accelerated a lot of cloud adoption, right? A lot of companies initially focused on resiliency, coming back to handling the pandemic, the situation. But, it also drove a lot of innovation in the business models. They had to think on their feet, re-look at their business models, change the channels and that continued. Pandemic is thankfully gone by but, the transformation actually continued. The way that we actually see on cloud, especially transformation, it has evolved. What we call as Cloud 2.0. Now, cloud is actually more focused on future-proofing the businesses. And, the initial days it was more about future-proofing the technology and technology architecture. But, it has evolved to future-proofing businesses. That means implementing new business models, bringing in agility, measuring the business value. And, that's where we see a significant traction. >> So, it's not about technology then. It's not about infrastructure. >> It is about technology but, really delivering business value. It's about, how can I improve the customer experience? >> Well, can you give us a couple of examples of companies you work with that embody this idea? >> I can imagine in the travel and hospitality zone. Probably few communities more sensitive than when someone's having a disruption or frustration within that process. And, perhaps few time periods less chaotic than the last few years. Tell us about your experience and what you've seen. >> Absolutely. To answer your question, first of all, coming out of pandemic, right? Many customers in the travel and hospitality industry where legacy, did not modernize for the last decade or so because, there have been many ups and downs in the industry. So, during pandemic, post-pandemic, one of the the way they wanted to rebound was, can we do the transformation? First of all, cloud as a technology adoption, but, beyond that, how do customers derive value, business value? That is one of the key aspects of the old transformation. And, if you take, I can give a couple of examples. Avis Car Rental, they had monolith mainframe applications and, that was there for almost couple of decades, right? But, over a period of time, they were not able to have the availability of those applications. There were many outages. As a result, businesses could not do the bookings. Like OTAs, customers could not do the bookings, the application was not available most of the time. And, it's all legacy, right? So, that is where we all came in, TCS. How do we first of all, simplify the complexity of the landscape? That is one. Then, second is, modernize the legacy application. That's the second thing. Third is, how do you scale it? Because, everyone wants to go faster, right? How do you scale it? That is where we partnered with AWS as well, to bring in some specific solutions. One example for Avis', their Rent Shop. Because, of the lack of availability, because, it's monolith application and legacy application. It was not available. So, as a result, we partnered and we brought in our contextual knowledge of the car rental industry to kind of transform, move it to cloud. And, today, as a result of it, Avis was able to save millions of dollars from a MIB standpoint. Second, in terms of availability, that was 99.9% availability. As a result, they had a pick in their business revenue as well. So, this is one of the ways that its helped. The second example I want to quote is, United Airlines. Here again, we've been present for a long time. We have a deep industry knowledge of the airline industry. So, we brought in our airline contextual knowledge and the United landscape to bring in a TCS's solution that we developed. It's called the Aviana. It's an intelligent operations solution for the airline industry, which we have developed. It's on AWS as well, that is being implemented in United. As a result, the ground staff, they have to take decisions on the moment when there is a irregular operation. That could be flight delays, as a result, customers connections will be lost. >> Savannah: Baggage. >> Baggage, right? Baggage delays. >> So many variables. The complexity... >> exactly >> in this matrix is wild. >> So, leveraging the Aviana solution, the ground staff were able to take decisions based on exceptions. They were able to take decisions quickly so that, they improved the customer experience. I think that was one of the key successes for United in the recent times. So, those two are the examples that I would call where customers have the right business value. So, cloud was not just for technology. They all are deriving a lot of business value as well. I would say. >> How important do you think it is for companies facing these unique challenges and scaling to work with partners like TCS? And, I'm sure you would say very important, but, tell me a little bit more why it's so important and those core benefits that they're going to get. Krishna, let's start off with you. Yeah, let me take again the AWS cloud transformation, right? TCS has formed AWS Business Unit two years back. So, we are a covid baby in a way. We have been working with the AWS for more than a decade but, we formed a dedicated Full-Stack Unit to drive cloud transformation on AWS. In these last two years, we've grown three X and customers we have added 400 new customers we have added. >> Nicely done. Just want to see you there. That's huge. Especially during these times. Congratulations. >> So, it's basically about the scale that we bring in. What we have done as a differentiation is, if you look at the entire cloud journey, right from taking a decision which cloud is, right, all the way to the cloud migration modernization and running operations. So, we have built complete platform. AML based platforms, where we have taken our delivery wisdom and codified it onto these platforms. So, we support around thousand plus customers on AWS in varying capacity. All of that knowledge is codified and, that is what we bring to the table, to the customers. And, so, customers obviously appreciate that value that best practices that are coming. And, coupled with that, the industry knowledge that we have on banking, life sciences, healthcare, automotive. So, it's partly the IT, it is the industry transformation as well. Because, we are working on connected cars, for example, in automotive. We are working on accelerated drug development platforms. We're working on complete banks as a platform that we have. TCS has built on AWS. So, 400 customers are there. It's the complete banking and insurance platform. So, this is the combination of the technical expertize that is digitized using platforms, as well as the industry knowledge, is the reason why customers work with us on the cloud transformation. >> So, we're seeing you talk about the vertical industry knowledge. AWS also has its own vertical industry plays. How do you, I guess, coordinate with them or, do you compete with them or, do you stay out of each other's way? >> No, we actually collaborate aggressively. >> Savannah: I like that (laughs) >> Right, so, it's not.. >> Savannah: With vigor. >> With vigor. TCS supports approximately 14 verticals. With AWS, we went with the focused industry play. We said we look at financial services, travel, transportation, hospitality, healthcare, life sciences and automotive, to start with. And, we have Go Big plans with AWS. very focused. The collaboration is actually at the industry solutions because, AWS is a great platform, ever evolving, keeps you on on your toes to really adapt it. But, that is always going on, the collaboration. But, the industry, I'm actually glad AWS last year took a pivot on focusing on industries. Now, we talk the same language when we go in front of a board or a CEO or COO. Present it. We are talking about the future of the industry not just the future of the technology. So, it's a win-win. >> You are also developing products on top of AWS that are not industry verticals, that build on the platform. What kinds of products are those? >> For cloud transformation, for example, consulting. We have a product called Cloud Counsell. We have a decision engine on the data side. We have something called Cloud Foundation, Mason. CloudMason. It's just the foundation, right? And, entire migration and modernization factory. And, the last one on cloud operations is actually Cloud Exponence. So, these are time tested. You have Fortune 500 customers using this regularly actively leveraging that. And, these are all AWS in a well architecture framework certified. So, they work well and they're designed to work on cloud, not only in the native environment, but, also legacy environment. Because, enterprises is not just only native, cloud-native. There is a lot of legacy. Sowmya spoke about the mainframe model... >> So much legacy, we were talking about it. >> So, you have to have a combination of solutions. So, the platforms that we're building, the products we're building, work in both the environments. >> Yeah, and that agility and ability to help customers navigate that prioritization. I mean, there's so many options. We talk about how many new companies there are every year. New solutions. Our adoption of technology is accelerating. As, McKinsey said, we went through 10 years of technological evolution and workplace evolution over the first six months of the pandemic. So, really everything's moving at unprecedented velocity unlike ever before. We have a new game here on theCUBE specifically for this show. And, we are challenging our guests, prompting our guests, to give us a 30 second sizzly sound bite with your hot take on the most important themes of this year's show. Think of it as a thought leadership moment. Opportunity to plug if you really want it. Krishna, you've just given me the nod. I'm going to start with you first and then we'll then we'll pass it along, yeah >> Sure. I think on thought leadership, the way that on cloud, business value is the focus, not the technology. Technology is important, but business value is the focus. And, the way that I see it evolving is with quantum computing coming out more and more, becoming relevant, and Edge is actually becoming quite active as well. All this while on cloud, we focused on business value at the centralized place at the corporate. But, I think the real value of cloud is when you deliver the results, business results, where the customers consume it, that is at the edge. I think that's basically the combination of centralized and the edge is where the real value of cloud is, right. And, I also loud, I know you said 30 seconds but, give me 30 more seconds. >> I like your answer right now. So, I'm going to give you a little more time. Yeah, thank you. >> You've earned more time. (laughs) >> So, I like the way Adam said in the keynote, if you look at it broadly, I categorizes two things. There are a lot of offerings that are becoming comprehensive, like AWS Connect, bringing in workforce management into it, making it a complete end to end product. Similarly, Security Lake, all bringing in the entire security and compliance under one, similarly data. So, there are lot of things that he announced where it is an end to end comprehensiveness of the thing. But, what I love about is, what Amazon is known for, supply chain. So, they rolled out AWS Supply Chain offering. Walk Out technology. So, the Amazon proposition is actually being brought to AWS as a core proposition. I think that's very futuristic and I think we can see more and more customers, enterprise customers, adopting AWS more to drive transformation >> Badly needed right now. Supply chain resiliency. >> Supply chain really having its moment the last two years. File under two words. No one knew, many of us did who worked in it before this. And, here we are, soon as we lost our toilet paper, everyone's freaked out. I love that you talked about business value and also that the end customer is on the edge and, everyone kind of forgets we are essentially the edge device. This is the edge device, it's all around us. And, all the technology that we're all using that you're even talking about is built right inside here from my airlines app to my car rentals to all of it. All right Sowmya, give us your 30 second hot take, roughly. >> Taking the cue from Krishna, right? Today, things are available on AWS Marketplace. So, tomorrow, somebody wants to start an airline, they just have to come and plug and play the apps that are available in the marketplace. Especially your supply chain. The Amazon is known for that. And, a small and medium business they want to start something, right, a .com. It's very easy. So, that's something that we are all looking for. The future is going to be very, very bright and great for the businesses, is what I would say because, most of it could be plug and play with all the solutions. >> Paul: It's already been built. >> On the cloud, so, we are looking forward to it. The second thing I would talk about is, we have to take it to scale. How more and more people can leverage AWS, right? The talent is very important and, that is where partners like us focus on re-scaling our talent. We have 600,000 people, right? We are not just... >> 600,000 people! That's basically as many people live in the San Francisco Bay area for contexts for our listeners. It's how many people work for Walmart? >> It's 1.2 million in Walmart? >> Is it really? >> It is, yes, yes. That's work for Walmart, sidebar. >> So from that standpoint, as the company, we are focusing on re-skilling, up-skilling our talent in order to work AWS cloud and so on, so, that they can go and support our customers. That is something that is very important and that's going to be the future as well. Bring it to scale, go faster. >> I love that you just touched on the fact that you essentially have to practice what you preach because, you've got to think about those 600,000 people in a 100 locations across 40 plus different countries. I love it. Sowmya, I'm going to close on that note. The future is bright, just like your fabulous blazer. >> Thank you so much. Krishna, Sowmya, thank you so much for being here with us. We can't wait to see what happens next, who you help next, and how Tata continues to transform. Thank all of you for tuning in today. A full jam packed day of coverage live here from Las Vegas, Nevada. We are at AWS re:Invent with Paul Gillin. I'm Savannah Peterson. We're theCUBE, the leader in High-Tech Coverage. (corporate electronic xylophone jingle outro)

>>My name is Dave Volonte, and with me are two world class technologists, visionaries and entrepreneurs. And Wa Dodgeville is the he co founded Snowflake, and he's now the president of the product division. And Florian Duetto is the co founder and CEO of Data Aiko. Gentlemen, welcome to the Cube to first timers. Love it. >>Great to be here >>now, Florian you and Ben Wa You have a number of customers in common. And I have said many times on the Cube that you know, the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud is really the scale platform. So is that premise your relevant to you? Do you buy that? And and why do you think snowflake and data ICU make a good match for customers? >>I think that because it's our values that are aligned when it's all about actually today allowing complexity for customers. So you close the gap or the democratizing access to data access to technology. It's not only about data data is important, but it's also about the impact of data. Who can you make the best out of data as fast as possible as easily as possible within an organization. And another value is about just the openness of the platform building the future together? Uh, I think a platform that is not just about the platform but also full ecosystem of partners around it, bringing the level off accessibility and flexibility you need for the 10 years away. >>Yeah, so that's key. But it's not just data. It's turning data into insights. Have been why you came out of the world of very powerful but highly complex databases. And we know we all know that you and the snowflake team you get very high marks for really radically simplifying customers lives. But can you talk specifically about the types of challenges that your customers air using snowflake to solve? >>Yeah, so So the really the challenge, you know, be four. Snowflake. I would say waas really? To put all the data, you know, in one place and run all the computers, all the workloads that you wanted to run, You know, against that data and off course, you know, existing legacy platforms. We're not able to support. You know that level of concurrency, Many workload. You know, we we talk about machine learning that a science that are engendering, you know, that our house big data were closed or running in one place didn't make sense at all. And therefore, you know what customers did is to create silos, silos of data everywhere, you know, with different system having a subset of the data. And of course, now you cannot analyze this data in one place. So, snowflake, we really solve that problem by creating a single, you know, architectural where you can put all the data in the cloud. So it's a really cloud native we really thought about You know how to solve that problem, how to create, you know, leverage, Cloud and the lessee cc off cloud to really put all the die in one place, but at the same time not run all workload at the same place. So each workload that runs in Snowflake that is dedicated, You know, computer resource is to run, and that makes it very Ajai, right? You know, Floyd and talk about, you know, data scientists having to run analysis, so they need you know a lot of compute resources, but only for, you know, a few hours on. Do you know, with snowflake they can run these new work lord at this workload to the system, get the compute resources that they need to run this workload. And when it's over, they can shut down. You know that their system, it will be automatically shut down. Therefore, they would not pay for the resources that they don't use. So it's a very Ajai system where you can do this, analyzes when you need, and you have all the power to run all this workload at the same time. >>Well, it's profound what you guys built to me. I mean, of course, everybody's trying to copy it now. It was like, remember that bringing the notion of bringing compute to the data and the Hadoop days, and I think that that Asai say everybody is sort of following your suit now are trying to Florian I gotta say the first data scientist I ever interviewed on the Cube was amazing. Hilary Mason, right after she started a bit Lee. And, you know, she made data science that sounds so compelling. But data science is hard. So same same question for you. What do you see is the biggest challenges for customers that they're facing with data science. >>The biggest challenge, from my perspective, is that owns you solve the issue of the data. Seidel with snowflake, you don't want to bring another Seidel, which would be a side off skills. Essentially, there is to the talent gap between the talented label of the market, or are it is to actually find recruits trained data scientist on what needs to be done. And so you need actually to simplify the access to technologies such as every organization can make it, whatever the talent, by bridging that gap and to get there, there is a need of actually breaking up the silos. And in a collaborative approach where technologists and business work together and actually put some their hands into those data projects together, >>it makes sense for flooring. Let's stay with you for a minute. If I can your observation spaces, you know it's pretty, pretty global, and and so you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends may be differences between regions or maybe within different industries. What are you seeing? >>Yes. Yeah, definitely. I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to start data in the last few years and no age, a level of maturity where they can invest more and get to the next steps. And it's really rely on the ability of certain medial certain organization actually to have built this long term strategy a few years ago and no start raping up the benefits. >>You know, a decade ago, Florian Hal Varian, we, you know, famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of change that to data scientists and then everybody. All the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What what skills >>do >>you see as critical for the next generation of data science? >>Yeah, it's a good question because I think the first generation of the patient is became the licenses because they could done some pipe and quickly on be flexible. And I think that the skills or the next generation of data sentences will definitely be different. It will be first about being able to speak the language of the business, meaning, oh, you translate data inside predictive modeling all of this into actionable insight or business impact. And it would be about you collaborate with the rest of the business. It's not just a farce. You can build something off fast. You can do a notebook in python or your credit models off themselves. It's about, oh, you actually build this bridge with the business. And obviously those things are important. But we also has become the center of the fact that technology will evolve in the future. There will be new tools and technologies, and they will still need to keep this level of flexibility and get to understand quickly, quickly. What are the next tools they need to use the new languages or whatever to get there. >>As you look back on 2020 what are you thinking? What are you telling people as we head into next year? >>Yeah, I I think it's Zaveri interesting, right? We did this crisis, as has told us that the world really can change from one day to the next. And this has, you know, dramatic, you know, and perform the, you know, aspect. For example, companies all the sudden, you know, So their revenue line, you know, dropping. And they had to do less meat data. Some of the companies was the reverse, right? All the sudden, you know, they were online, like in stock out, for example, and their business, you know, completely, you know, change, you know, from one day to the other. So this GT off, You know, I, you know, adjusting the resource is that you have tow the task a need that can change, you know, using solution like snowflakes, you know, really has that. And we saw, you know, both in in our customers some customers from one day to the to do the next where, you know, growing like big time because they benefited, you know, from from from from co vid and their business benefited, but also, as you know, had to drop. And what is nice with with with cloud, it allows to, you know, I just compute resources toe, you know, to your business needs, you know, and really adjusted, you know, in our, uh, the the other aspect is is understanding what is happening, right? You need to analyze the we saw all these all our customers basically wanted to understand. What is that going to be the impact on my business? How can I adapt? How can I adjust? And and for that, they needed to analyze data. And, of course, a lot of data which are not necessarily data about, you know, their business, but also data from the outside. You know, for example, coffee data, You know, where is the States? You know, what is the impact? You know, geographic impact from covitz, You know, all the time and access to this data is critical. So this is, you know, the promise off the data crowd, right? You know, having one single place where you can put all the data off the world. So our customers, all the Children you know, started to consume the cov data from our that our marketplace and and we had the literally thousands of customers looking at this data analyzing this data, uh, to make good decisions So this agility and and and this, you know, adapt adapting, you know, from from one hour to the next is really critical. And that goes, you know, with data with crowding adjusting, resource is on and that's, you know, doesn't exist on premise. So So So indeed, I think the lesson learned is is we are living in a world which machines changing all the time and we have for understanding We have to adjust and and And that's why cloud, you know, somewhere it's great. >>Excellent. Thank you. You know the kid we like to talk about disruption, of course. Who doesn't on And also, I mean, you look at a I and and the impact that is beginning to have and kind of pre co vid. You look at some of the industries that were getting disrupted by, you know, we talked about digital transformation and you had on the one end of the spectrum industries like publishing which are highly disrupted or taxis. And you could say Okay, well, that's, you know, bits versus Adam, the old Negroponte thing. But then the flip side of that look at financial services that hadn't been dramatically disrupted. Certainly healthcare, which is ripe for disruption Defense. So the number number of industries that really hadn't leaned into digital transformation If it ain't broke, don't fix it. Not on my watch. There was this complacency and then, >>of >>course, co vid broke everything. So, florian, I wonder if you could comment? You know what industry or industries do you think you're gonna be most impacted by data science and what I call machine intelligence or a I in the coming years and decades? >>Honestly, I think it's all of them artist, most of them because for some industries, the impact is very visible because we're talking about brand new products, drones like cars or whatever that are very visible for us. But for others, we are talking about sport from changes in the way you operate as an organization, even if financial industry itself doesn't seems to be so impacted when you look it from the consumer side or the outside. In fact, internally, it's probably impacted just because the way you use data on developer for flexibility, you need the kind off cost gay you can get by leveraging the latest technologies is just enormous, and so it will actually transform the industry that also and overall, I think that 2020 is only a where, from the perspective of a I and analytics, we understood this idea of maturity and resilience, maturity, meaning that when you've got a crisis, you actually need data and ai more than before. You need to actually call the people from data in the room to take better decisions and look for a while and not background. And I think that's a very important learning from 2020 that will tell things about 2021 and the resilience it's like, Yeah, Data Analytics today is a function consuming every industries and is so important that it's something that needs to work. So the infrastructure is to work in frustration in super resilient. So probably not on prime on a fully and prime at some point and the kind of residence where you need to be able to plan for literally anything like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we're just getting to the next step for the analytics. >>I wonder, Benoit, if you have anything to add to that. I mean, I often wonder, you know, winter machine's gonna be able to make better diagnoses than doctors. Some people say already, you know? Well, the financial services traditional banks lose control of payment systems. Uh, you know what's gonna happen to big retail stores? I mean, maybe bring us home with maybe some of your final thoughts. >>Yeah, I would say, you know, I I don't see that as a negative, right? The human being will always be involved very closely, but the machine and the data can really have, you know, see, Coalition, you know, in the data that that would be impossible for for for human being alone, you know, you know, to to discover so So I think it's going to be a compliment, not a replacement on. Do you know everything that has made us you know faster, you know, doesn't mean that that we have less work to do. It means that we can doom or and and we have so much, you know, to do, uh, that that I would not be worried about, You know, the effect off being more efficient and and and better at at our you know, work. And indeed, you know, I fundamentally think that that data, you know, processing off images and doing, you know, I ai on on on these images and discovering, you know, patterns and and potentially flagging, you know, disease, where all year that then it was possible is going toe have a huge impact in in health care, Onda and And as as as Ryan was saying, every you know, every industry is going to be impacted by by that technology. So So, yeah, I'm very optimistic. >>Great guys. I wish we had more time. I gotta leave it there. But so thanks so much for coming on. The Cube was really a pleasure having you.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.

>>Fly from San Francisco. It's the cube covering RSA conference 2020 San Francisco brought to you by Silicon angled medias >>live in. Welcome to the cube coverage here in San Francisco at Moscone hall for RSA 2020 I'm John furrier, host of the cube. We're here breaking down all the actions in cyber security. I'll say three days of wall-to-wall cube coverage. You got two great guests here, experts in the cybersecurity enterprise security space. Over 25 years. We've got two gurus and experts. We've got Bob Mindell, executive vice president of North America cyber practice for cap Gemini and Joe McMahon, head of North America cyber strategy, even a practitioner in the intelligence community. Langley, you've been in the business for 25 years. You've seen the waves guys, welcome to the cube. Thank you John. Thanks for having us. So first let's just take a step back. A cyber certainly on the number one agenda kind of already kind of broken out of it in terms of status, board level conversation, every CSO, risk management and a lot of moving parts. >>Now, cyber is not just a segment in the industry. It is the industry. Bob, this is a big part of business challenge today. What's your view? What was going on? So John has a great point. It's actually a business challenge and that's one of the reasons why it's now the top challenge. It's been a tech challenge for a long time. It wasn't always a business challenge for you as was still considered an it challenge and once it started impacting business and got into a board level discussion, it's now top of mind as a business challenge and how it can really impact the business continuity. Joe is talking before we came on camera about you know CEOs can have good days here and there and bad days then but sees us all have bad days all the time because there's so much, it's so hard. You're on the operations side. >>You see a day to day in the trenches as well as the strategy. This is really an operations operationalizing model. As new technology comes out, the challenge is operationalizing them for not only a business benefit but business risk management. It's like changing an airplane engine out at 35,000 feet. It's really hard. What are you seeing as the core challenge? This is not easy. It's a really complex industry. I mean, you take the word cybersecurity, right? Ready? Cybersecurity conference. I see technology, I see a multitude of different challenges that are trying to be solved. It means something different to everybody, and that's part of the problem is it's a really broad ecosystem that we're in. If you meet one person that says, I know all of cyber, they're lying, right? It's just like saying, I know active directory and GRC and I know DNS and I know how to, how to code, right? >>Those people don't exist and cyber is a little bit the same way. So for me, it's just recognizing the intricacies. It's figuring out the complexities, how people processing technology really fit together and it's an operation. It is an ongoing, and during operation, this isn't a program that you can run. You run it for a year, you install and you're done. There's ebbs and flows. You talked about the CISOs and the bad days. There's wins and there's losses. Yeah. And I think part of that is just having the conversation with businesses. Just like in it, you have bad days and good days wins and losses. It's the same thing in cybersecurity and we've got to set that expectation. Yeah, you didn't bring up a good point. I've been saying this on the cube and we've been having conversations around this. It used to be security as part of it, right? >>But now that it's part of the business, the things that you're mentioning around people, process, technology, the class, that kind of transformational formula, it is business issues, organizational behavior. Not everyone's an expert specialism versus generalists. So this is like not just a secure thing, it's the business model of a company is changing. So that's clear. There's no doubt. And then you've got the completion of the cloud coming, public cloud, hybrid multi-cloud. Bob, this is a number one architectural challenge. So outside of the blocking and tackling basics, right, there's now the future business is at risk. What does cap Gemini do? And because you guys are well known, great brand, helping companies be successful, how do you guys go to customers and say, Hey, here's what you do. What's the, what's the cap Gemini story? >>So the cat termini stories is really about increasing your cybersecurity maturity, right? As Joe said, starting out at the basics. If you look at a lot of the breaches that have occurred today have occurred because we got away from the basics and the fundamentals, right? Shiny new ball syndrome. Really. Exactly exasperates that getting away from the basics. So the technology is an enabler, but it's not the be all and end all right, go into the cloud is absolutely a major issue. That's increasing the perimeter, right? We've gone through multiple ways as we talked about, right? So now cloud is is another way, cloud, mobile, social. How do you deal with those from on prem, off prem. But ultimately it's about increasing your cyber cyber security maturity and using the cloud as just increasing the perimeter, right? So you need to, you really need to understand, you have your first line defense and then your maturity is in place. Whether the data resides in your organization, in the cloud, on a mobile device, in a social media, you're responsible for it all. And if you don't have the basics, then you're, you're really, and you guys bring a playbook, is that what you guys come in and do? Correct. Correct. Right. So our goal is to coordinate people, process technology and leverage playbooks, leverage the run books that we had been using for many years. >>I want to get down to you on this one because of what happens when you take that to the, into the practitioner mode or at implementation. Customers want the best technology possible. They go for the shiny new choice. Bob just laid out. There's also risks too because it may or may not be big. So you've got to balance out. I got to get an edge technically because the perimeters becoming huge surface area now or some say has gone. Now you've got edge, just all one big exposed environment, surface area for vulnerabilities is massive. So I need better tech. How do you balance and obtain the best tech and making sure it works and it's in production and secure. So there's a couple of things, right, and this is not, it's not just our, and you'll hear it from other people that have been around a long time, but a lot of organizations that we see have built themselves so that their cybersecurity organization is supporting all these tools that we see. >>That's the wrong way to do it. The tools should support the mission of the organization, right? If my mission is to defend my enterprise, there are certain things that I need to do, right? There's questions I need to be able to ask and get answers to. There's data I need visibility into. There's protections and controls I need to be able to implement. If I can lay those out in some coordinated strategic fashion and say, here's all the things I'm trying to accomplish, here's who's going to do it. Here's my really good team, here's my skilled resources, here's my workflows, my processes, all that type of stuff. Then I can go find the right technology to put into that. And I can actually measure if that technology is effective in supporting my mission. But too often we start with the technology and then we hammer against it and we run into CISOs and they say, I bought all this stuff and it's not working and come hell yeah. >>And that's backing into it the wrong. So I've heard from CSOs, I'd like they buying all these tools. It's like a tool shed. Don't be the fool with the wrong tool as they I say. But that brings up the question of, okay, as you guys go to customers, what are some of the main pain points or issues that they're trying to overcome that that are opportunities that you guys are helping with? Uh, on the business side and on the technical side, what are some of the things? So on the business side, you know, one is depending on their level of maturity and the maturity of the organization and the board of directors and their belief in, in how they need to help fund this. We can start there. We can start by helping draw out the threat landscape within that organization where they are maturity-wise and where they need to go and help them craft that message to the board of directors and get executive sponsorship from the board down in order to take them from baby, a very immature organization or you know, a reactive organization to an adaptive organization, right. >>And really become defenders. So from a business perspective, we can help them there. From the technology perspective, Joe, uh, you know, or an implementation perspective. I think, you know, it's been a really interesting road like being in this a long time, you know, late two thousands when nation States were first really starting to become a thing. All the industries we were talking to, every customer is like, I want to be the best in my industry. I want to be the shining example. And boards in leadership were throwing money at it and everybody was on this really aggressive path to get there. The conversation is shifted a little bit with a lot of the leadership we talked to. It's, I just want to be good enough, maybe a little bit better than good enough, but my, my objective anymore is it to leave the industry. Cause that's really expensive and there's only one of those. >>My objective is to complete my mission maybe a little bit above and beyond, but I need the right size and right. So we spent a lot of time helping organizations, I would say optimize, right? It's what is the right level of people, what is the right amount of resources, what's the right spend, what's the right investment, the right allocation of technology and mix of everything, right? And sometimes it's finding the right partner. Sometimes it's doing certain things in house. It's, there's no one way to solve this problem, but you've got to go look at the business challenges. Look at the operational realities of the customer, their budgets, all those, their geographies mattered, right? Some places it's easy to hire talent. Some places it's not so easy to hire talent. And that's a good point, right? Some organizations, >>they just need to understand what does good look like and we can, we have so many years of experience. We have so many customers use skates is we've been there and we've done that. We can bring the band and show them this is what good looks like and this is sustainable >>of what good looks like. I want to get your reactions to, I was talking to Keith Alexander, general Keith Alexander, a former cyber command had last night and we were talking about officers, his defense and that kind of reaction. How the Sony hack was was just was just, they just went after him as an example. Everyone knows about that hack, but he really was getting at the idea of human efficiency, the human equation, which is if you have someone working on something that here, but their counterpart might be working on it maybe from a different company or in the same company, they're redundant. So there's a lot of burnout, a lot of people putting out fires. So reactive is clearly, I see as a big trend that the conversation's shifting towards let's be proactive, let's get more efficient in the collaboration as well as the technology. What you, how do you guys react to that? What's your view on that statement? So >>people is the number one issue, in my opinion. In this space, there's a shortage of people. The people that are in it are working very long hours. They're burnt out. So we constantly need to be training and bringing more people into the industry. Then there's the scenario around information sharing, right? Threat information sharing, and then what levels are you comfortable with as an organization to share that information? How can you share best practices? So that's where the ice sacks come into play. That's also where us as a practitioner and we have communities, we have customers, we bring them together to really information, share, share, best practice. It's in all of our best interests. We all have the same goal and the goal is to protect our assets, especially in the United States. We have to protect our assets. So we need, the good thing is that it's a pretty open community in that regards and sharing the information, training people, getting people more mature in their people, process technology, how they can go execute it. >>Yeah. What's your take on the whole human equation piece? Right? So sharing day, you probably heard a word and the word goes back to where I came from, from my heritage as well, but I'm sure general Alexander used the word mission at some point, right? So to me, that's the single biggest rallying point for all of the people in this. If you're in this for the right reasons, it's because you care about the mission. The mission is to defend us. Stop the bad guys from doing days, right? Whether you're defending the government, whether you're defending a commercial enterprise, whether you're defending the general public, right? Whatever the case is, if you're concerned, you know, if you believe in the mission, if you're committed to the mission, that's where the energy comes from. You know, there's a lot of, there's a lot of talk about the skill gap and the talent gap and all of those types of things. >>To me, it's more of a mindset issue than anything. Right? The skill sets can be taught. They can be picked up over time. I was a philosophy major. All right? Somehow I ended up here. I have no idea how, um, but it's because I cared about the mission and everybody has a part to play. If you build that peer network, uh, both at an individual level and at an organizational and a company level, that's really important in this. Nobody's, nobody's an expert at everything. Like we said, you brought a philosophy. I think one of the things I have observed in interviewing and talking to people is that the world's changed so much that you almost need those fresh perspectives because the problems are new problems, statements, technology is just a part of the problem set back to the culture. The customer problem, Bob, is that they got to get all this work done. >>And so what are some of the use cases that you guys are working on that that is a low hanging fruit in the industry or our customer base? How do you guys engage with customers? So our target market is fortune 500 global 1000 so the biggest of the big enterprises in the world, right? And because of that, we've seen a lot of a complex environments, multinational companies as our customers. Right? We don't go at it from a pure vertical base scenario or a vertical base solution. We believe that horizontal cybersecurity can it be applied to most verticals. Right. And there's some tweaking along the way. Like in financial services, there's regulars and FFIC that you need to be sure you adapt to. But for the most part the fundamentals are applicable. All right. With that said, you know, large multinational manufacturing organization, right? They have a major challenge in that they have manufacturing sites all over the world. >>They building something that is, you know, unique. It has significant IP to it, but it's not secure. Historically they would have said, well, nobody's really gonna just deal steal what we do because it's really not differentiated in the world, but it is differentiated and it's a large corporation making a lot of money. Unfortunately ransomware, that'd be a photographer. Ransomware immediately, right? Like exact down their operations and their network, right? So their network goes down. They can have, they can, they can not have zero downtown and their manufacturing plants around the world. So for us, we're implementing solutions and it's an SLA for them is less than six seconds downtime by two that help secure these global manufacturing environment. That's classic naive when they are it. Oh wow. We've got to think about security on a much broader level. I guess the question I have for you guys, Joe, you talk about when do you guys get called in? >>I mean what's your main value proposition that you guys, cause you guys got a broad view of the industry, that expertise. Why do, why are customers calling you guys and what do you guys deliver? They need something that actually works, right? It's, it's you mentioned earlier, I think when we were talking how important experiences, right? And it's, Bob said it too, having been there, done that I think is really important. The fact that we're not chasing hype, we're not selling widgets. That we have an idea of what good looks like and we can help an organization kind of, you know, navigate that path to get there is really important. So, uh, you know, one of our other customers, large logistics company, been operating for a very long time. You know, very, very mature in terms of their, it operations, those types of things. But they've also grown through merger and acquisition. >>That's a challenge, uh, cause you're taking on somebody else's problem set and they just realize, simply put that their existing security operations wasn't meeting their needs. So we didn't come in and do anything fancy necessarily. It's put a strategic plan in place, figure out where they are today, what are the gaps, what do they need to do to overcome those gaps? Let's go look at their daily operations, their concept of operations, their mission, their vision, all of that stuff down to the individual analysts. Like we talked about the mindset and skillset. But then frankly it's putting in the hard work, right? And nobody wants to put in the heart. I don't want to say nobody wants to put in the hard work. That's fun. There's a lot of words that's gets done I guess by the questions that you guys getting called in on from CSOs chief and Mason security officers. >>Guess who calls you? So usually we're in talking to the Cisco, right? We're having the strategic level conversation with the Cisco because the Cisco either has come in new or has been there. They may have had a breach. Then whatever that compelling event may be, they've come to the realization that they're not where they need to be from a maturity perspective and their cyber defense needs revamping. So that's our opportunity for us to help them really increase the maturity and help them become defenders. Guys, great for the insight. Thanks for coming on the cube. Really appreciate you sharing the insights. Guys. Give a quick plug for what you guys are doing. Cap Gemini, you guys are growing. What do you guys look to do? What are some of the things that's going on? Give the company plug. Thanks Sean show. It's been a very interesting journey. >>You know this business started out from Lockheed Martin to Leidos cyber. We were acquired by cap Gemini a year ago last week. It's a very exciting time. We're growing the business significantly. We have huge growth targets for 2020 and beyond, right? We're now over 800 practitioners in North America, over 2,500 practitioners globally, and we believe that we have some very unique differentiated skill sets that can help large enterprises increase their maturity and capabilities plug there. Yeah, I mean, look, nothing makes us happier than getting wins when we're working with an organization and we get to watch a mid level analyst brief the so that they just found this particular attack and Oh by the way, because we're mature and we're effective, that we were able to stop it and prevent any impact to the company. That's what makes me proud. That's what makes it so it makes it fun. >>Final question. We got a lot of CSOs in our community. They're watching. What's the pitch to the CSO? Why, why you guys, we'd love to come in to understand what are their goals, how can we help them, but ultimately where do they believe they think they are and where do they need to go and we can help them walk that journey. Whether it's six months, a year, three years, five years. We can take them along that journey and increase the cyber defense maturity. Joe, speak to the CSO. What are they getting? They're getting confidence. They're getting execution. They're getting commitment to delivery. They're getting basically a, a partner in this whole engagement. We're not a vendor. We're not a service provider. We are a partner. A trusted partner. Yeah, partnerships is key. Building out in real time. A lot new threats. Got to be on offense and defense going on. A lot of new tech to deal with. I mean, it's a board level for a long time. Guys, thanks for coming on. Cap Gemini here inside the cube, bringing their practices, cybersecurity, years of experience with big growth targets. Check them out. I'm John with the cube. Thanks for watching.

>>from San Francisco. It's the queue every day to thank you. Brought to you by day to like you. Hey, >>welcome back already, Jeffrey. Here with the Cube were a day to IQ's headquarters in downtown San Francisco. They used to be metal sphere, which is what you might know them as. And they've rebranded earlier this year. And they're really talking about helping Enterprises in their journey to cloud native. And we're really excited to have really one of the product guys he's been here and seeing this journey and how through with the customers and helping the company transforming his Chandler hosing tonight. He's the s VP of engineering and product. Chandler, great to see you. Thanks. So, first off, give everyone kind of a background on on the day to like you. I think a lot of people knew mesosphere. You guys around making noise? What kind of changed in the marketplace to to do a rebranding? >>Sure. Yeah, we've been obviously, Mason's here in the past and may so so I think a lot of people watching the cube knows No, no one knows about Mace ose as as we were going along our journey as a company. We noticed that a lot of people are also asking for carbonates. Eso We've actually been working with kubernetes since I don't know 16 4017 something that for a while now and as Maur Maur as communities ecosystem starting involving mature more. We also want to jump in and take advantage of that. And we started building some products that were specific to kubernetes and eso. We thought, Look, you know, it's a little bit confusing for people May, SOS and Kubernetes and at times those two technologies were seen almost as competitive, even though we didn't always see it that way. The market saw it that way, so we said, Look, this is going too confusing for customers being called Mesa Sphere. Let's let's rebrand around Maur what we really do. And we felt like what we do is not just focus around one specific technology. We felt like we helped customers with more than that more than just may so support more than just community support, Andi said. Look, let's let's get us a name that shows what we actually do for customers, and that's really helping them take their workloads and put them on on Not just, you know, um, a source platform, but actually take their workloads, bring them into production and enterprise way. That's really ready for day two. And that's that's why we called it data. >>And let's unpack the day to, cause I think some people are really familiar with the concept of day two. And for some people, they probably never heard it. But it's a pretty interesting concept, and I think it packs a lot of meaning in it. A number of letters. I think you >>can kind of just think about it if you were writing software, right? I mean, Day zero is okay. We're gonna design it. We're gonna start playing with some ideas. We're gonna pull into different technologies. We're gonna do a POC. We're gonna build our skateboards. So to say, that's kind of your day. Zero. What do we want? Okay, we're gonna build a Data Analytics pipeline. We want spark. We're going to store data. Cassandra, we're gonna use cough. Go to pass it around. We're gonna run our containers on top of communities. That's just kind of your day. Zero idea. You get it working, you slap it on a cluster. Things are good right? Day one might be okay. Let's actually do a beta put in production in some kind of way. You start getting customers using it. But now, in Day two, after all that's done, you're like, Wait a second. Things were going wrong. Where's our monitoring? We didn't set that up. Where's our logging? Oh, I don't know. Like, >>who do we >>call this? Our container Run time, we think has above. Who do we call like? Oh, I don't know What support contract that we cut, Right? So that's the things that we want to help customers with. We want to help them in the whole journey, getting to Day two. But once they're there, we want them to be ready for day two, right? And that's what we do. >>I love it because one of my favorite quotes I've used it 1000 times. I'll do 2001 right? Is that open source is free like a puppy. Exactly for you. When you leave you guys, you're not writing a check necessarily to the to the shelter, But there's a whole lot of other check. You got a right and take care of. And I think that's such a key piece. Thio Enterprise, right. They need somebody to call when that thing breaks. >>Yeah. I mean, I haven't come from enterprise company. I was actually a customer basis Fear before I joined. Yeah, that's exactly why we're customers that we wanted. Not only that, insurance policy, but someone that partner with us as we start figuring this out, you know? I mean, just picking. You know what container run time do I want to use with communities? That one decision could take months if you're not familiar with it. And you you put a couple of your best architects on it. Go research container. You go research, cryo go research doctor. Tell me what's what's the best one we should use with kubernetes. Whereas if you're going, if you have a partnership with a company like day two, you can say, Look, I trust these. You know this company, they they're they're experts of this and they see a lot of this. Let's go with their recommendation. It's >>okay. So you got you got your white board. You've got a whole bunch of open source things going on, right? And you've got a whole bunch of initiatives and the pressure's coming down from from on high to get going, you've got containers, Asian and Cloud native and hybrid Cloud all the stuff. And then you've got some port CEO on his team trying to figure it out. You guys have a whole plethora of service is around some of these products. So as you try it and then you got the journey right and you don't start from from a standing start. You gotta go. You gotta go. So how do you map out the combination of how people progress through their journey? What are the different types of systems that they want to put in place and into, prioritize and have some type of a logical successful implementation and roll out of these things from day zero day 132? No, it's >>a great question. I think that's actually how we formed our product. Strategy is we've been doing this for a while now and we've we've gone. We've gone on this journey with really big advanced customers like ride sharing companies and large telcos customers like that. We've also gone on this journey with smaller, less sophisticated customers like, you know, industrial customers from the Midwest. Right? And those are two very, very different customers. But what's similar is they're both going on the same journey we feel like, but they're just at different places. So we wanted to build products, find the customer where they're at in their journey, and the way we see it really is just at the very beginning. It's just training, right? So we have, ah, bunch of support. We're sorry. Service is around training. Help you understand? Not just kubernetes, but the whole cloud native ecosystem. So what is all this stuff? How does it work? How does it fit together? How do I just deploy simple app to right? That's the beginning of it. We also have some products in that area as well, to help people scale their training across the whole whole organization. So that's really exciting for us once once, once that customer has their training down there like Okay, look, get I need a cluster now, like I need a destroyer of sorts and criminals itself is great, but it needs a lot of pieces to actually get it ready for prime time. And that's where we build a product called Convoy Say Okay, here is your enterprise great. Ready to go kubernetes destro right out of the box. And that product is really it's what you could use to just fiddle around with communities. It's also what you put into production right on the game. That's that's been scale tested, security tests and mixed workload tested. It's everything. So that's that's kind of our communities. Destro. So you've gotten your training. You have your destro and now you're like, OK, I actually wanna want to run some applesauce. >>Let me hold there. Is it Is it open corps? Or, you know, there's a lot of conversation in the way the boys actually >>the way we built convoy. It's a great question. The way we build convoys said, Okay, we don't We want to pick the best of breed from each of these. Have you seen the cloud native ecosystem kind of like >>by charter, high charter, whatever it is, where they have all the logos and all the different spiral thing. So it's crazy. Got thousands of logos, right? And >>we said, Look, we're gonna navigate this for you. What's the best container run time to pick. And it's It's almost as if we were gonna build this for ourselves using all open source technology. So convoys completely opens. Okay, um, there's some special sauce that we put in on how to bring these things together. Install it. But all the actual components itself is open source. Okay, so that's so if you're a customer, you're like, OK, I want open source. I don't want to be tied to any specific vendor. I want to run on Lee open. So >>yeah, I was just thinking in terms of you know, how Duke is a reference right. And you had, you know, the Horton worst cloud there and map our strategies, which were radically different in the way they actually packaged told a dupe under the covers. Yeah, >>you can think of it similar. How Cloudera per ship, Possibly where they had cdh. And they brought in a lot of open source. But they also had a lot of proprietary components to see th and what we've tried to get away from it is tying someone in tow. Us. I know that sounds counterintuitive from a business perspective, but we don't want customers to feel like if I go with D to like you. I always have to go with me to like you. I have to drink the Kool Aid, and I'm never gonna be able to get off. >>Kind of not. Doesn't really go with the open source. Exactly this stuff. It's not >>right for our customers, right? A lot of our customers want that optionality, and they don't want to feel locked in. And so when we built convoy, he said, Look, you know, if we were to start our own company, not not an infrastructure coming that we are right now, but just a software company build any kind of ab How would we approach it? And that was one of the problems we saw for We don't wanna feel like we're tied into any. >>Right. Okay, so you got to get the training, you got the products. What's >>next? What's next is if you think about the journey, you're like, OK, a lot. What we've found and this may or may not be totally true is one of the first things people like to run on committees is actually they're builds. So see, I see. And we said, How can we help with this. We looked around the market and there's a lot of great see, I see products out there right now. There's get lab, which is great partner of ours. It's a great product. There's there's your older products. Like Jenkins. There's a bunch of sass products, Travis. See all these things. But what we we wanted to do if we were customers of our own products is something that was native to Kubernetes. And so we started looking at projects like tectonic and proud. Some of these projects, right? And we said, How can we do the same thing we did with convoy where we bring these projects together and make it easy for someone to adopt these kubernetes native. See, I see tools. And we did some stuff there that we think is pretty innovative as well. And that's what that's the product we call dispatch. >>Okay. What do you got? More than just products. You've got profession service. That's right. So now >>you need help setting all this up. How do you actually bring your legacy applications to this new platform? How do you get your legacy builds onto these new build systems That that's where our service is coming the plate and kind of steer you through this whole journey. Lastly, what we next in the journey, though? Those service's compliment Really? Well, with with the kind of the rest of the product suite, right? And we didn't just stop with C i c. He said, what is the next type of work that we want to run here? Okay, so there we looked at things like red hat operators. Right? And we said, Look, red hats doing really cool thing here with this operator framework, how can we simplify it? We learn we've done a lot of this before with D. C. O s, where we built what we called the DCS sdk to help people bring advanced complex workloads onto that platform. And we saw a lot of similarities with operators to our d c West sdk. We said, How can we bring some of our understanding and knowledge to that world? And we built this open source product called kudo. Okay, people are free to go check that out. And that's how we bring more advanced workload. So if you think about the journey back to the journey again, you got some training you have your have your cluster, you put your builds on it. Now you want to run some advance work logs? That's where Kudo comes. >>Okay? And then finally, at the end of the trail is 1 800 I need help. Well, almost into the trail. We're not there yet. There was one thing they're still moving with one more step right on >>the very last one. Actually, we said, Okay, what's next in this journey? And that's running multiple clusters of the same. Okay, so that's kind of the scale. That's the end of the journey from for us, for our proxy as it stands right now. And that's where you build a product called Commander. And that's really helping us launch and manage multiple >>companies clusters at the same time. >>So it's so great that you have the perspective of a customer and you bring that directly in two. You know what you want because you just have gone through this this journey. But I'm just curious, you know, if you put your old hat on, you know, kind of c i o your customer. You know, you just talked about the cake chart with Lord knows how many logos? How do you help people even just begin to think about about the choices and about the crazy rapid change in what? That I mean? Kubernetes wasn't a thing four years ago to help them stay on top of it to help them, you know, both kind of have a night to the vision, you know, make sure you're delivering today on not just get completely distracted by every bright, shiny object that happens to come along. Yeah, no, >>I think it's really challenging for the buyers. You know, I think there's a, especially as the industry continues to make sure there's a new concept that gets thrown at all times. Service Manager. You know, some new, cool way to do monitoring or logging right? And you almost feel like a dinosaur. If you're not right on top of these things to go to a conference in, are you using? You know, you know B P f. Yet what is that? You didn't feel right? Exactly. I think I think most importantly, what customers want is the ability what, the ability to move their technology and their platforms as their business has the need. If the need isn't there for the business, and the technology is running well. There shouldn't be a reason to move to a new platform. Our new set of technologies, in fact, with dese us with Mason charities. To us, we have a lot of happy customers that are gonna be moving crib. Amazing if they wanted to anytime soon. Do you see What's that? Something's that criminal is currently doesn't do. It may never do because the community is just not focused on it that DCS is solving. And those customers just want to see that will continue to support them in the journey that they're on with their their business. And I think that's what's most important is just really understanding our customer's understanding their business, understand where they wanna go. What are their goals, So to say, for their technology platforms and and making sure you were always one step ahead >>of them, that's a >>good place to be one step ahead of demand. All right, well, thanks for for taking a few minutes and sharing the story. Appreciate it. Okay. Thank you. All right. Thanks. Chandler. I'm Jeff. You're watching >>the Cube. Where? Day two. I >>Q in downtown San Francisco. Thanks for watching. We'll see you next time

(upbeat music) >> Announcer: Live from Las Vegas, it's theCUBE! Covering UIPath FORWARD America's 2019. Brought to you by UIPath. >> Welcome back, everyone, to theCUBE's live coverage of UIPath FORWARD, here in Sin City, Las Vegas Nevada. I'm your host, Rebecca Knight, co-hosting alongside Dave Velante. We have two guests for this segment. We have Kurt Carlson, Associate Dean for faculty and academic affairs of the Mason School of Business at the college of William and Mary. Thanks for coming on the show. >> Thanks you for having me. >> Rebecca: And we have Tom Clancy, the SVP of learning at UIPath, thank you so much. >> Great to be here. >> You're a Cube alum, so thank you for coming back. >> I've been here a few times. >> A Cube veteran, I should say. >> I think 10 years or so >> So we're talking today about a robot for every student, this was just announced in August, William and Mary is the first university in the US to provide automation software to every undergraduate student, thanks to a four million dollar investment from UIPath. Tell us a little bit about this program, Kurt, how it works and what you're trying to do here. >> Yeah, so first of all, to Tom and the people at UIPath for making this happen. This is a bold and incredible initiative, one that, frankly, when we had it initially, we thought that maybe we could get a robot for every student, we weren't sure that other people would be willing to go along with that, but UIPath was, they see the vision, and so it was really a meeting of the minds on a common purpose. The idea was pretty simple, this technology is transforming the world in a way that students, we think it's going to transform the way that students actually are students. But it's certainly transforming the world that our students are going into. And so, we want to give them exposure to it. We wanted to try and be the first business school on the planet that actually prepares students not just for the way RPA's being used today, but the way that it's going to be used when AI starts to take hold, when it becomes the gateway to AI three, four, five years down the road. So, we talked to UIPath, they thought it was a really good idea, we went all in on it. Yeah, all of our starting juniors in the business school have robots right now, they've all been trained through the academy live session putting together a course, it's very exciting. >> So, Tom, you've always been an innovator when it comes to learning, here's my question. How come we didn't learn this school stuff when we were in college? We learned Fortran. >> I don't know, I only learned BASIC, so I can't speak to that. >> So you know last year we talked about how you're scaling, learning some of the open, sort of philosophy that you have. So, give us the update on how you're pushing learning FORWARD, and why the College of William and Mary. >> Okay, so if you buy into a bot for every worker, or a bot for every desktop, that's a lot of bots, that's a lot of desktops, right? There's studies out there from the research companies that say that there's somewhere a hundred and 200 million people that need to be educated on RPA, RPA/AI. So if you buy into that, which we do, then traditional learning isn't going to do it. We're going to miss the boat. So we have a multi-pronged approach. The first thing is to democratize RPA learning. Two and a half years ago we made, we created RPA Academy, UIPath academy, and 100% free. After two and a half years, we have 451,000 people go through the academy courses, that's huge. But we think there's a lot more. Over the next next three years we think we'll train at least two million people. But the challenge still is, if we train five million people, there's still a hundred million that need to know about it. So, the second biggest thing we're doing is, we went out, last year at this event, we announced our academic alliance program. We had one university, now we're approaching 400 universities. But what we're doing with William and Mary is a lot more than just providing a course, and I'll let Kurt talk to that, but there is so much more that we could be doing to educate our students, our youth, upscaling, rescaling the existing workforce. When you break down that hundred million people, they come from a lot of different backgrounds, and we're trying to touch as many people as we can. >> You guys are really out ahead of the curve. Oftentimes, I mean, you saw this a little bit with data science, saw some colleges leaning in. So what lead you guys to the decision to actually invest and prioritize RPA? >> Yeah, I think what we're trying to accomplish requires incredibly smart students. It requires students that can sit at the interface between what we would think of today as sort of an RPA developer and a decision maker who would be stroking the check or signing the contract. There's got to be somebody that sits in that space that understands enough about how you would actually execute this implementation. What's the right buildout of that, how we're going to build a portfolio of bots, how we're going to prioritize the different processes that we might automate, How we're going to balance some processes that might have a nice ROI but be harder for the individual who's process is being automated to absorb against processes that the individual would love to have automated, but might not have as great of an ROI. How do you balance that whole set of things? So what we've done is worked with UIPath to bring together the ideas of automation with the ideas of being a strategic thinker in process automation, and we're designing a course in collaboration to help train our students to hit the ground running. >> Rebecca, it's really visionary, isn't it? I mean it's not just about using the tooling, it's about how to apply the tooling to create competitive advantage or change lives. >> I used to cover business education for the Financial Times, so I completely agree that this really is a game changer for the students to have this kind of access to technology and ability to explore this leading edge of software robotics and really be, and graduate from college. This isn't even graduate school, they're graduating from college already having these skills. So tell me, Kurt, what are they doing? What is the course, what does it look like, how are they using this in the classroom? >> The course is called a one credit. It's 14 hours but it actually turns into about 42 when you add this stuff that's going on outside of class. They're learning about these large conceptual issues around how do you prioritize which processes, what's the process you should go through to make sure that you measure in advance of implementation so that you can do an audit on the backend to have proof points on the effectiveness, so you got to measure in advance, creating a portfolio of perspective processes and then scoring them, how do you do that, so they're learning all that sort of conceptual straight business slash strategy implementation stuff, so that's on the first half, and to keep them engaged with this software, we're giving them small skills, we're calling them skillets. Small skills in every one of those sessions that add up to having a fully automated and programmed robot. Then they're going to go into a series of days where every one of those days they're going to learn a big skill. And the big skills are ones that are going to be useful for the students in their lives as people, useful in lives as students, and useful in their lives as entrepreneurs using RPA to create new ventures, or in the organizations they go to. We've worked with UIPath and with our alums who've implement this, folks at EY, Booz. In fact, we went up to DC, we had a three hour meeting with these folks. So what are the skills students need to learn, and they told us, and so we build these three big classes, each around each one of those skills so that our students are going to come out with the ability to be business translators, not necessarily the hardcore programmers. We're not going to prevent them from doing that, but to be these business translators that sit between the programming and the decision makers. >> That's huge because, you know, like, my son's a senior in college. He and his friends, they all either want to work for Amazon, Google, an investment bank, or one of the big SIs, right? So this is a perfect role for a consultant to go in and advise. Tom, I wanted to ask you, and you and I have known each other for a long time, but one of the reasons I think you were successful at your previous company is because you weren't just focused on a narrow vendor, how to make metrics work, for instance. I presume you're taking the same philosophy here. It transcends UIPath and is really more about, you know, the category if you will, the potential. Can you talk about that? >> So we listen to our customers and now we listen to the universities too, and they're going to help guide us to where we need to go. Most companies in tech, you work with marketing, and you work with engineering, and you build product courses. And you also try to sell those courses, because it's a really good PNL when you sell training. We don't think that's right for the industry, for UIPath, or for our customers, or our partners. So when we democratize learning, everything else falls into place. So, as we go forward, we have a bunch of ideas. You know, as we get more into AI, you'll see more AI type courses. We'll team with 400 universities now, by end of next year, we'll probably have a thousand universities signed up. And so, there's a lot of subject matter expertise, and if they come to us with ideas, you mentioned a 14 hour course, we have a four hour course, and we also have a 60 hour course. So we want to be as flexible as possible, because different universities want to apply it in different ways. So we also heard about Lean Six Sigma. I mean, sorry, Lean RPA, so we might build a course on Lean RPA, because that's really important. Solution architect is one of the biggest gaps in the industry right now so, so we look to where these gaps are, we listen to everybody, and then we just execute. >> Well, it's interesting you said Six Sigma, we have Jean Younger coming on, she's a Six Sigma expert. I don't know if she's a black belt, but she's pretty sure. She talks about how to apply RPA to make business processes in Six Sigma, but you would never spend the time and money, I mean, if it's an airplane engine, for sure, but now, so that's kind of transformative. Kurt, I'm curious as to how you, as a college, market this. You know, you're very competitive industry, if you will. So how do you see this attracting students and separating you guys from the pack? >> Well, it's a two separate things. How do we actively try to take advantage of this, and what effects is it having already? Enrollments to the business school, well. Students at William and Mary get admitted to William and Mary, and they're fantastic, amazingly good undergraduate students. The best students at William and Mary come to the Raymond A. Mason school of business. If you take our undergraduate GPA of students in the business school, they're top five in the country. So what we've seen since we've announced this is that our applications to the business school are up. I don't know that it's a one to one correlation. >> Tom: I think it is. >> I believe it's a strong predictor, right? And part because it's such an easy sell. And so, when we talk to those alums and friends in DC and said, tell us why this is, why our students should do this, they said, well, if for no other reason, we are hiring students that have these skills into data science lines in the mid 90s. When I said that to my students, they fell out of their chairs. So there's incredible opportunity here for them, that's the easy way to market it internally, it aligns with things that are happening at William and Mary, trying to be innovative, nimble, and entrepreneurial. We've been talking about being innovative, nimble, and entrepreneurial for longer than we've been doing it, we believe we're getting there, we believe this is the type of activity that would fit for that. As far as promoting it, we're telling everybody that will listen that this is interesting, and people are listening. You know, the standard sort of marketing strategy that goes around, and we are coordinating with UIPath on that. But internally, this sells actually pretty easy. This is something people are looking for, we're going to make it ready for the world the way that it's going to be now and in the future. >> Well, I imagine the big consultants are hovering as well. You know, you mentioned DC, Booz Allen, Hughes and DC, and Excensior, EY, Deloitte, PWC, IBM itself. I mean it's just, they all want the best and the brightest, and now you're going to have this skill set that is a sweet spot for their businesses. >> Kurt: That's the plan. >> I'm just thinking back to remembering who these people are, these are 19 and 20 year olds. They've never experienced the dreariness of work and the drudge tasks that we all know well. So, what are you, in terms of this whole business translator idea, that they're going to be the be people that sit in the middle and can sort of be these people who can speak both languages. What kind of skills are you trying to impart to them, because it is a whole different skill set. >> Our vision is that in two or three years, the nodes and the processes that are currently... That currently make implementing RPA complex and require significant programmer skills, these places where, right now, there's a human making a relatively mundane decision, but it's sill a model. There's a decision node there. We think AI is going to take over that. The simple, AI's going to simply put models into those decision nodes. We also think a lot of the programming that takes place, you're seeing it now with studio X, a lot of the programming is going to go away. And what that's going to do is it's going to elevate the business process from the mundane to the more human intelligent, what would currently be considered human intelligence process. When we get into that space, people skills are going to be really important, prioritizing is going to be really important, identifying organizations that are ripe for this, at this moment in time, which processes to automate. Those are the kind of skills we're trying to get students to develop, and what we're selling it partly as, this is going to make you ready of the world the way we think it's going to be, a bit of a guess. But we're also saying if you don't want to automate mundane processes, then come with us on a different magic carpet ride. And that magic carpet ride is, imagine all the processes that don't exist right now because nobody would ever conceive of them because they couldn't possibly be sustained, or they would be too mundane. Now think about those processes through a business lens, so take a business student and think about all the potential when you look at it that way. So this course that we're building has that, everything in the course is wrapped in that, and so, at the end of the course, they're going to be doing a project, and the project is to bring a new process to the world that doesn't currently exist. Don't program it, don't worry about whether or not you have a team that could actually execute it. Just conceive of a process that doesn't currently exist and let's imagine, with the potential of RPA, how we would make that happen. That's going to be, we think we're going to be able to bring a lot of students along through that innovative lens even though they are 19 and 20, because 19 and 20 year olds love innovation, while they've never submitted a procurement report. >> Exactly! >> A innovation presentation. >> We'll need to do a Cube follow up with that. >> What Kurt just said, is the reason why, Tom, I think this market is being way undercounted. I think it's hard for the IDCs and the forces, because they look back they say how big was it last year, how fast are these companies growing, but, to your point, there's so much unknown processes that could be attacked. The TAM on this could be enormous. >> We agree. >> Yeah, I know you do, but I think that it's a point worth mentioning because it touches so many different parts of every organization that I think people perhaps don't realize the impact that it could have. >> You know, when listening to you, Kurt, when you look at these young kids, at least compared to me, all the coding and setting up a robot, that's the easy part, they'll pick that up right away. It's really the thought process that goes into identifying new opportunities, and that's, I think, you're challenging them to do that. But learning how to do robots, I think, is going to be pretty easy for this new digital generation. >> Piece of cake. Tom and Kurt, thank you so much for coming on theCUBE with a really fascinating conversation. >> Thank you. >> Thanks, you guys >> I'm Rebecca Knight, for Dave Velante, stay tuned for more of theCUBEs live coverage of UIPath FORWARD. (upbeat music)

>> from Seattle WASHINGTON. It's the Q covering AWS Imagine brought to you by Amazon Web service is >> Hey, welcome back, You're ready. Geoffrey here with the Cube were in Seattle, >> Washington downtown, right next to the convention center for the AWS. Imagine e d. You show. It's a second year of the show found by Andrew Cohen. His crew, part of Theresa's public sector group, really focused on education. Education means everything from K through 12 higher education and community college education, getting out of the military and retraining education. It's ah, it's a really huge category, and it's everything from, you know, getting the colleges to do a better job by being on cloud infrastructure, innovating and really thinking outside the box are really excited to have the man who's doing a lot of the work on the curriculum development in the education is Ken Eisner is the director of worldwide education programs for AWS. Educate can great to see you. Thank you so much for having absolutely nice shot out this morning by Theresa, she said. She just keeps asking you for more. So >> you want to deliver for Theresa? Carl says she is. She is a dynamo and she drives us >> all she does. So let's dive into it a little bit. So, you know, there was, Ah, great line that they played in the keynote with Andy talking about, You know, we cannot be protecting old institutions. We need to think about the kids is a story I hear all the time where somebody came from a time machine from 17 76 and landed here today. It wouldn't recognize how we talk, how we get around, but they would recognize one thing, and unfortunately, that's the school house down at the end of the block. So you guys are trying to change that. You're really trying to revolutionize what's happening in education, give us a little bit of background on some of the specific things that you're working on today. >> Yeah, I I think Andy, one of the things that he mentioned at that time was that education is really in a crisis on. We need to be inventing at a rapid rate. We need to show that invented simplify inside that occassion. Andi, he's incredibly, he's correct. The students are our customers, and we've got to be changing things for them. What we've been really excited to see is that with this giant growth in cloud computing A W S. It was the fastest I T vendor to ever hit $10,000,000,000 a year. The run rate We're now growing at a 42% or 41% year over year growth Ray and $31,000,000,000 a year Lee company. It's creating this giant cloud computing opportunity cloud computing in the number one Lincoln Skill for the past four years in Rome, when we look at that software development to cloud architecture to the data science and artificial intelligence and data analytics and cyber security rules. But we're not preparing kids for this. Market Gallop ran a study that that showed about 11% of business executives thought that students were prepared for their jobs. It's not working, It's gotta change. And the exciting thing that's happening right now is workforce development. Governments are really pushing for change in education, and it's starting to happen >> right? It's pretty amazing were here last year. The team last year was very much round the community college releases and the certification of the associate programs and trial down in Southern California, and this year. I've been surprised. We've had two guests on where it's the state governor has pushed these initiatives not at the district level, the city level, but from the state winning both Louisiana as well as Virginia. That's pretty amazing support to move in such an aggressive direction and really a new area. >> Yeah, I was actually just moderating a panel where we had Virginia, Louisiana, in California, all sitting down talking about that scaling statewide strategy. We had announcements from the entire CUNY and Sunni or City University of New York and State University of New York system to do both two and four year programs in Cloud Computing. And Louisiana announced it with their K 12 system, their community college system and their four year with Governor John Bel Edwards making the announcement two months ago. So right we are seeing this scaling consortium, a play where institutions are collaborating across themselves. They're collaborating vertically with your higher ed and K 12 and yet direct to the workforce because we need to be hiring people at such a rapid ray that we we need to be also putting a lot of skin in the game and that story that happened so again, I agree with Andy said. Education is at a crisis. But now we're starting to see change makers inside of education, making that move right. It's interesting. I wonder, >> you know, is it? Is it? I don't want to say second tier, that's the wrong word, but kind of what I'm thinking, you know, kind of these other institutions that the schools that don't necessarily have the super top in cachet, you know who are forced to be innovative, right? We're number two. We try harder. As they used to say in the in the Hertz commercial. Um, really a lot of creativity coming out of again the community colleges last year in L. A. Which I was, I was blown away, that kind of understand cause that specifically to skill people up to get a job. But now you're hearing it in much more kind of traditional institutions and doing really innovative things like the thing with the the Marines teaching active duty Marines about data science. >> Yeah, who came up with that idea that phenomenal Well, you know, data permeates every threat. It's not just impure data science, jobs and machine learning jobs. There's air brilliantly important, but it's also in marketing jobs and business jobs. And so on. Dad Analytics, that intelligence, security, cybersecurity so important that you think, God, you Northern Virginia Community College in U. S. Marine Corps are working for to make these programs available to their veterans and active military. The other thing is, they're sharing it with the rest of the student by. So that's I think another thing that's happening is this sharing this ability, all of for this cloud degree program that AWS educate is running. All these institutions are sharing their curricula. So the stuff that was done in Los Angeles is being learned in Virginia is the stuff that the U. S Marine Corps is doing is being available to students. Who are you not in military occupations? I think that collaboration mode is is amazing. The thing they say about community colleges and just this new locus of control for education on dhe. Why it's changing community colleges. You're right there. They're moving fast. These institutions have a bias for action. They know they have to. You change the r A. Y right? It's about preventing students for this work for, but they also serve as a flywheel to those four year institutions back to the 12 into the into the workforce and they hit you underserved audience. Is that the rest? So that you were not all picking from the same crew? You cannot keep going to just your lead institutions and recruit. We have to grow that pipeline. So you thank thank these places for moving quick brand operating for their student, right? >> Right, And and And that's where the innovation happens, right? I mean, that's that's, uh, that that's goodness. And the other thing that that was pretty interesting was, um, you know, obviously Skilling people up to get jobs. You need to hire him. That's pretty. That's pretty obvious and simple, but really bringing kind of big data attitude analytics attitude into the universities across into the research departments and the medical schools. And you think at first well, of course, researchers are data centric, right? They've been doing it that way for a long time, but they haven't been doing it and kind of the modern big, big data, real time analytics, you know, streaming data, not sampling data, all the data. So so even bringing that type of point of view, I don't know mindset to the academic institutions outside of what they're doing for the students. >> Absolutely. The machine learning is really changing the game. This notion of big data, the way that costs have gone down in terms of storing and utilizing data and right, it's streaming data. It's non Columbia or down, as opposed to yeah, the old pure sequel set up right that that is a game changer. No longer can you make just can you make a theory and tested out theories air coming streaming by looking at that data and letting it do some work for you, which is kind of machine learning, artificial intelligence path, and it's all becoming democratized. So, yes, researchers need to need learn these new past two to make sense and tow leverage. This with that big data on the medical center site, there are cures that can be discerned again. Some of our most pressing diseases by leveraging data way gonna change. And we, by the way, we gotta change that mindset, not just yeah, the phD level, but actually at the K 12 levels. Are kids learning the right skills to prepare them for you this new big data world once they get into higher ed, right? And then the last piece, which again we've seen >> on the Enterprise. You've kind of seen the movie on the enterprise side in terms of of cloud adoption. What AWS has done is at first it's a better, more efficient way to run your infrastructure. It's, you know, there's a whole bunch of good things that come from running a cloud infrastructure, but >> that's not. But that's not the end, right? The answer to the question >> is the innovation right? It's It's the speed of change, of speed development and some of the things that we're seeing here around the competitive nature of higher education, trying to appeal to the younger kids because you're competing for their time and attention in there. And they're dollar really interesting stuff with Alexa and some of these other kind of innovation, which is where the goodness really starts to pay off on a cloud investment. >> Yeah, without a doubt, Alexa Week AWS came up with robo maker and Deep Racer on our last reinvent, and there's there's organizations at the K 12 level like First Robotics and Project lead. The way they're doing really cool stuff by making this this relevant it you education becomes more relevant when kids get to do hands on stuff. A W S lowers the price for failure lowers the ability you can just open a browser and do real world hands on bay hands on stuff robotics, a rvr that all of these things again are game changers inside the classroom. But you also have to connect it to jobs at the end, right? And if your educational institutions can become more relevant to their students in terms of preparing them for jobs like they've done in Santa Monica College and like they're doing in Northern Virginia Community College across the state of Louisiana and by May putting the real world stuff in the hands of their kids, they will then start to attract assumes. We saw this happen in Santa Monica. They opened up one class, a classroom of 35 students that sold out in a day. They opened another co ward of 35 sold out in another day or two. The name went from 70 students. Last year, about 325 they opened up this California cloud workforce project where they now have 825 students of five. These Northern Virginia Community College. They're they're cloud associate degree that they ran into tandem with AWS Educate grew from 30 students at the start of the year to well over 100. Now the's programs will drive students to them, right and students will get a job at the end. >> Right? Right, well and can. And can the school support the demand? I mean, that's That's a problem we see with CS, right? Everyone says, Tell your kids to take CS. They want to take CS. Guess what? There's no sections, hope in C. S. So you know, thinking of it in a different way, a little bit more innovative way providing that infrastructure kind of ready to go in a cloud based way. Now we'll hopefully enable them to get more kids and really fulfill the demand. >> Absolutely. There's another thing with professional development. I think you're hitting on, so we definitely have a shortage in terms of teachers who are capable to teach about software development and cloud architecture and data sciences and cybersecurity. So we're putting AWS educators putting a specific focus on professional development. We also want to bring Amazonian, Tze and our customers and partners into the classroom to help with that, because the work based learning and the focus on subject matter expert experts is also important. But we really need to have programs both from industry as well as government out support new teachers coming into this field and in service training for existing teachers to make sure, because yes, we launch those programs and students will come. We have to make sure that were adequately preparing teachers. It's not it's not. It's not easy, but again, we're seeing whether it's Koda Cole out of yeah out of, uh, Roosevelt High School. Are the people that were working with George Mason University and so on were seeing such an appetite for making change for their students? And so they're putting in those extra hours they're getting that AWS certification, and they're getting stronger, prepared to teach inside the clients. >> That's amazing, cause right. Teachers have so many conflict ing draws on their time, many of which have nothing to do with teaching right whether it's regulations. And there's just so many things the teachers have to deal with. So you know the fact that they're encouraged. The fact that they want t to spend and invest in this is really a good sign and really a nice kind of indicator to you and the team that, you know, you guys were hitting something really, really positive. >> Yeah, I think we've had its this foam oh fear of missing out opportunity. There's the excitement of the cloud. There's the excitement of watching your kids. You're really transformed their lives. And it could be Alfredo Cologne who came over from Puerto Rico after Hurricane Maria. You wiped out his economic potential and started taking AWS educate. And you're learning some of these pathways and then landing a job as the Dev Ops engineered. When you see the transformation in your students, no matter what their background is, it is. It is a game changer. This has got to be you. Listen, I love watching that women's team when I win the World Cup, and that the excitement cloud is like the new sport. Robotics is the new sport for these kids. They'll bring them on >> pathways to career, right. We'll take for taking a few minutes in The passion comes through, Andrew Koza big passion guy. And we know Teresa is a CZ Well, so it shines through and keep doing good work. >> Thank you so much for the time. Alright, he's can on Jeff. You're watching the cube. We're in downtown Seattle. A aws. Imagine e d. Thanks for watching. >> We'll see you next time.

>> from Seattle WASHINGTON. It's the Q covering AWS Imagine brought to you by Amazon Web service is >> Hey, welcome back, everybody. Jeffrey here with the Cube were in Seattle, >> Washington downtown, right next to the convention center for the AWS. Imagine e d. You show. It's a second year of the show found by Andrew Cohen. His crew, part of Theresa's public sector group, really focused on education. Education means everything from K through 12 higher education, community college education, getting out of the military and retraining education. It's ah, it's a really huge category, and it's everything from, you know, getting the colleges to do a better job by being on cloud infrastructure, innovating and really thinking outside the box are really excited to have the man who's doing a lot of the work on the curriculum development in the education is Ken Eisner is the director of worldwide education programs for AWS. Educate can Great to see you. Thank you so much for having absolutely nice shot out this morning by Theresa, she said. She just keeps asking you for more. So >> you want to deliver for Theresa. Carl says she is. She is a dynamo, and she drives us >> all she does, so just dive into it a little bit. So, you know, there was, Ah, great line that they played in the keynote with Andy talking about, You know, we cannot be protecting old institutions. We need to think about the kids is a story I hear all the time where somebody came from a time machine from 17 76 and landed here today. It wouldn't recognize how we talk, how we get around, but they would recognize one thing, and unfortunately, that's the school house down at the end of the block. So you guys are trying to change that. You're really trying to revolutionize what's happening in education, give us a little bit of background on some of the specific things that you're working on today. >> Yeah, I think Andy, one of the things that he mentioned at that time was that education is really in a crisis on. We need to be inventing at a rapid rate. We need to show that invented, simple, fine inside education, and he's incredibly, he's correct. The students are our customers and we've got to be changing things for them. What we've been really excited to see is that with this giant growth in cloud computing a W. S. It was the fastest I T vendor to ever a $10,000,000,000 a year. The run rate. We're now growing at a 42% or 41% year over year growth Ray and $31,000,000,000 a year Lee company. It's creating this giant cloud computing opportunity, cloud computing in the number one linked in skill for the past four years in Rome. When we look at that software development to cloud architecture to the data science and artificial intelligence and data analytics and cyber security rules. But we're not preparing kids for this. Market Gallop ran a study that that showed about 11% of business executives thought that students were prepared for their jobs. It's not working, It's gotta change. And the exciting thing that's happening right now is workforce development. Governments are really pushing for change in education, and it's starting to happen right? It's pretty amazing were here last year. >> The team last year was very much round the community college releases and the certification of the associate programs and trial down in Southern California, and this year I've been surprised. We've had two guests on where it's the state governor has pushed these initiatives not at the district level, the city level, but from the state winning both Louisiana as well as Virginia. That's pretty amazing support to move in such an aggressive direction and really a new area. >> Yeah, I was actually just moderating a panel where we had Virginia, Louisiana, in California, all sitting down talking about that scaling statewide strategy. We had announcements from the entire CUNY and Sunni or City University of New York and State University of New York system to do both to end four year programs in Cloud Computing. And Louisiana announced it with their K 12 system, their community college system and their four year with Governor John Bel Edwards making the announcement two months ago. So right, we are seeing this scaling consortium, a play where institutions are collaborating across themselves. They're collaborating vertically with your higher ed and K 12 and yet direct to the workforce because we need to be hiring people at such a rapid ray that we we need to be also putting a lot of skin in the game. And that story that happened So again, I agree with Andy said. Education is at a crisis. But now we're starting to see change makers inside of education, making that move right. It's interesting. I wonder, >> you know, is it is it? I don't want to say second tier, that's the wrong word, but kind of what I'm thinking, you know, kind of these other institutions that the schools that don't necessarily have the super top in cachet, you know who are forced to be innovative, right? We're number two. We try harder. As they used to say in the in the Hertz commercial. Um, really a lot of creativity coming out of again the community colleges last year in L. A. Which I was, I was blown away, that kind of understand cause that specifically to skill people up to get a job. But now you're hearing it in much more kind of traditional institutions and doing really innovative things like the thing with the the Marines teaching active duty Marines about data science. >> Yeah, who came up with that idea that phenomenal Well, you know, data permeates every threat. It's not just impure data science, jobs and machine learning jobs. There's air brilliantly important, but it's also in marketing jobs and business jobs. And so on. Dad Analytics that intelligence, security, cybersecurity so important that you think, God, you Northern Virginia Community College in U. S. Marine Corps are working for to make these programs available to their veterans and active military. The other thing is, they're sharing it with the rest of the student by. So that's I think another thing that's happening is this. Sharing this ability all of for this cloud degree program that AWS educate is running. All these institutions are sharing their curricula. So the stuff that was done in Los Angeles is being learned in Virginia's stuff the U. S. Marine Corps is doing is being available to students. Who are you not in military occupations? I think that collaboration mode is is amazing, the thing they say about community colleges and just this new locus of control for education on dhe. Why it's changing community colleges. You're right there. They're moving fast. These institutions have a bias for action. They know they have to. You change the r A. Y right. It's about preventing students for this work for, but they also serve as a flywheel to those four year institutions back to the 12 into the into the workforce and they hit you underserved audience is that the rest is so that you were not all picking from the same crew. You cannot keep going to just share lead institutions and recruit. We have to grow that pipeline. So you thank thank these places for moving quick and operating for their student, right? >> Right, And and And that's where the innovation happens, right? I mean, that's that's, ah, that that's goodness. And the other thing that that was pretty interesting was obviously Skilling people up to get jobs, you need to hire him. That's pretty. That's pretty obvious and simple, but really bringing kind of big data attitude analytics attitude into the universities across into the research departments and the medical schools. And you think at first, of course, researchers are data centric, right? They've been doing it that way for a long time, but they haven't been doing it in kind of the modern big, big data. Real time analytics, you know, streaming data, not sampling data, all the data. So so even bringing that type of point of view, I don't know, mindset to the academic institutions outside of what they're doing for the students. >> Absolutely. The machine learning is really changing the game. This notion of big data, the way that costs have gone down in terms of storing and utilizing data and right, it's streaming data. It's non Columbia or down, as opposed to yeah, the old pure sequel set up right that that is a game changer. No longer can you make just can you make a theory and tested out theories air coming streaming by looking at that data and letting it do some work for you, which is kind of machine learning, artificial intelligence path, and it's all becoming democratized. So, yes, researchers need to need learn these new past two to make sense and tow leverage. This with that big data on the medical center site, there are cures that could be discerned again some of our most pressing diseases by leveraging data, way gonna change. And we, by the way, we gotta change that mindset, not just yeah, the phD level, but actually at the K 12 levels. Are kids learning the right skills to prepare them for you? This new big data world once they get into higher ed, right? And then the last piece, which again we've seen >> on the Enterprise. You've kind of seen the movie on the enterprise side in terms of of cloud adoption. What AWS has done is at first it's a better, more efficient way to run your infrastructure. It's, you know, there's a whole bunch of good things that come from running a cloud infrastructure, but >> that's not. But that's not the end, right? The answer to the question >> is the innovation right? It's It's the speed of change, of speed, a development and some of the things that we're seeing here around the competitive nature of higher education, trying to appeal to the younger kids because you're competing for their time and attention in there. And they're dollar really interesting stuff with Alexa and some of these other kind of innovation, which is where the goodness really starts to pay off on a cloud investment. >> Yeah, without a doubt, Alexa Week AWS came up with robo maker and Deep Racer on our last reinvent, and there's there's organizations at the K 12 level like First Robotics and project lead the way they're doing really cool stuff by making this this relevant you education becomes more relevant when kids get to do hands on stuff. A W S lowers the price for failure lowers the ability you can just open a browser and do real world hands on bay hands on stuff. Robotics, A R V R. That all of these things again are game changers inside the classroom. But you also have to connect it to jobs at the end, right? And if your educational institutions can become more relevant to their students in terms of preparing them for jobs like they've done in Santa Monica College and like they're doing in Northern Virginia Community College across the state of Louisiana and by May putting the real world stuff in the hands of their kids, they will then start to attract assumes. We saw this happen in Santa Monica. They opened up one class, a classroom of 35 students that sold out in a day. They opened another co ward of 35 sold out in another day or two. The name went from 70 students. Last year, about 325 they opened up this California Cloud Workforce Project, where they now have 825 students of five. These Northern Virginia Community College. They're they're cloud associate degree that they ran in tandem with AWS Educate grew from 30 students at the start of the year to well over 100. Now these programs will drive students to them right and students will get a job at the end. >> Right? Right, well in Ken. And can the schools sports a demand? That's that's a problem we see with CS, right? Everyone says, Tell your kids to take CS. They want to take CS. Guess what? There's no sections, hope in C. S. So you know, thinking of it in a different way, a little bit more innovative way providing that infrastructure kind of ready to go in a cloud based way. Now we'll hopefully enable them to get more kids and really fulfill the demand. >> Absolutely. There's another thing with professional development. I think you're hitting on, so we definitely have a shortage in terms of teachers who are capable to teach about software development and cloud architecture and data sciences and cybersecurity. So we're putting a W. C. Educate is putting a specific focus on professional development. We also want to bring Amazonian, Tze and our customers and partners into the classroom to help with that, because the work based learning and the focus on subject matter expert experts is also important. But we really need to have programs both from industry as well as government out support new teachers coming into this field and in service training for existing teachers to make sure, because yes, we launch those programs and students will come. We have to make sure that were adequately preparing teachers. It's not, it's not. It's not easy, but again, we're seeing whether it's Koda Cole out of out of, uh Roosevelt High School. Are the people that were working with George Mason University and so on were seeing such an appetite >> for >> making change for their students? And so they're putting in those extra hours they're getting that AWS certification, and they're getting stronger, prepared to teach inside the class. That's >> amazing, cause right. Teachers have so many conflict ing draws on their time, many of which have nothing to do with teaching right whether it's regulations and there's just so many things the teachers have to deal with. So you know the fact that they're encouraged the fact that they want t to spend and invest in this is really a good sign and really a nice kind of indicator to you and the team that, you know, you guys were hitting something really, really positive. >> Yeah, I think we've had its this foam oh fear of missing out opportunity. There's the excitement of the cloud. There's the excitement of watching your kids. You're really transformed their lives. And it could be Alfredo Cologne who came over from Puerto Rico after Hurricane Maria. You wiped out his economic potential and started taking AWS educate and you're learning some of these pathways and then landing a job has the Dev ops engineer to Michael Brown, who went through that Santa Monica problem and >> landed an >> internship with Annika. When you see the transformation in your students, no matter what their background is, it is. It is a game changer. This has got to be you. Listen, I love watching that women's team when I win the World Cup, and that the excitement cloud is like the new sport. Robotics is the new sport for these kids. They'll bring them on >> pathways to career, right, well, take for taking a few minutes in The passion comes through Andrew Koza, Big passion guy. And we know Teresa is as well. So it shines through and keep doing good work. >> Thank you so much for the time. Alright, He's Can I'm Jeff, You're watching the Cube. We're in downtown Seattle. A aws. Imagine E d. Thanks for >> watching. We'll see you next time.