(upbeat music) >> (Narrator) From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> Hello, welcome back to theCUBE's coverage of IBM Think 2021 virtual. Soon we'll be back in person in real life, but this year again it's a virtual conference. I'm John Furrier, your host of the cube for more cube coverage. We've got a great guest here, Debbie Vavangas, Global Garage Lead for IBM Services. Global Garage, great program. Debbie, great to see you. Thanks for coming on theCUBE. >> Thanks for having me. >> So, we've covered the Garage a lot on theCUBE in the past, and a success, everyone loves the Garage. Things are born in the Garage, entrepreneurship, innovation, has been kind of categorically known for, kind of, the Garage startup. >> Absolutely. >> But also, it's become known for, really, agility, which has been a cloud phenomenon, DevOps. Now we're seeing dev SecOps as a big trend this year with hybrid cloud. So, I've got to ask you, how is Garage doing with the pandemic? Obviously, I can almost imagine people at home kind of disrupted from the office, but maybe more creativity, maybe more energy online? What's going on with the Garage? How has your transformation journey been with COVID? >> Well, John, COVID has been the leveler for us all, right? There isn't a person who hasn't had some challenge or some complexity to And that includes our clients. And I'm incredibly proud to be able to say that IBM Garage, because it is so digitally native, when the COVID pandemic has struck around the world every single one of our Garages was able to switch to being virtual without fail, without a single days lost productivity. And that's hugely beneficial to clients who are on an incredibly time-sensitive journey. And so, we've seen as a result of COVID actually there are a huge acceleration in Garages, for two reasons. So, number one, from a virtualization perspective, actually it's much easier when everybodies together in the same space. So everybody's together virtually in the same space, and we've seen, you know, acceleration in our velocity, in our collaboration, because everybody is really learning how to work in that same space. But two, because of the pandemic, because of the pressure on our client's needs to make decisions fast, know not guess, really be focused on their outcomes, not just doing stuff, the Garage really plays to that objective for them. And so we've seen a huge rise, you know, we've gone from in 2019 to just a few hundred garages, to finishing 2020 with over two and a half thousand garages. And it being embedded across services and with the goal of being the primary way our clients experience it. So COVID has been a big accelerator. >> Sorry, Debbie, can you repeat the numbers again? I just want to capture that, I missed that. >> Sure, sure. >> I did a double take on the numbers. (Debbie laughs) >> So then, we finished 2019 with just under 300 garages, and we finished 2020 with just over two and a half thousand. So, we've had a huge growth, and it isn't just the number of garages, it's the range of garages and what we're serving with our clients, and how we're collaborating with our clients, and the topics we're unpacking that has really broadened. >> Yeah, I mean I covered, and we've reported on the Garage on theCUBE and also on www.siliconangle.com in the past things and through your news coverage, but that's amazing growth. I got to believe the tailwind from COVID and just the energy around it has energized you. I want to get your thoughts on that because, you know, what we've reported on in the past has been about design thinking, human-centered design, all of those beautiful things that come with cloud-scale, right? You know, you're moving faster, you're innovating, and so that's been kind of there. But what you're getting at with this growth is, and with COVID has proven, and again, we've been pointing this out, you're seeing the pattern, it's clear. Companies are either retrenching, okay, which is refactoring, redesigning, doing those things to kind of get ready to come out of COVID with a growth strategy, and you're seeing other companies build net new innovations. So, they're building new capabilities, because COVID's shown them, kind of pulled back the curtain if you will on where the action is. So, this means there's two threads going on. You've got, "Okay, I've got to transform my business, and I got to refactor', or 'Hey, we got net new business models'. These are kind of two different things and not mutually exclusive. What's your comment on that? >> And I think that my comment on it is that is the sweet spot that Garage comes into its own, right? You mentioned lots of things in there. You talked about design thinking, and agility, and, you know, these other buzzwords that are used all the time, and Garage of course is synonymous with those. Of course, Garage uses the best design thinking, and AGILE practices, and all of those things that absolutely call to what we do. DevOps, even through down to DesignOps. You know, we have the whole range depending on what the client objective is. But, I think what is really happening now is that innovation being something separate is no longer how to accelerate your outcomes, and your business outcomes. Regardless of whether that is in refactoring and modernizing your existing estate, or diversifying, creating new ecosystems, new platforms, new offerings. Regardless of what that is, you can't do it separate to your core business. I mean, it's a well known fact, John, right? Like 75% of transformation programs fail to deliver an impact to the business performance, right? And in the same period of time there's been huge cuts in innovation funding, and that's because for the same reason, because they don't deliver the impact to the business performance. And that's why Garage is unique, because it is entirely focused on the outcome, right? We're using user research, through design thinking of course, using agile to deliver it at speed, and all of those other things. But, it's focused on value, on benefits realization and driving to your outcome. And we do that by putting that innovation at the heart of your enterprise in order to drive that transformation, rather than it being something separate. >> Debbie, I saw you gave a talk called 'Innovation is Dead'. Obviously, that's a provocative title, that's an attention-getter. Tell me what you mean by that. Because it seems to be a setup. >> I mean, if the innovation is dead, >> Of course. was it with a question mark? Were you, kind of, trying to highlight that innovation is transformation? >> So, the full title was 'Innovation is dead and transformation is pointless'. And, of course, it's meant to be an eye-catching title so people show up and listen to my pitch rather than somebody else's. But, the reality is I mean it most sincerely, it's back to that stat. 75% of these transformation programs fail to deliver the impact, and I speculate that that is for a few reasons. Because, the idea itself wasn't a good one, or wasn't at the right time. Because, you were unable to understand what the measure of good looked like, and therefore just being able to create that path. And, in order to transform a company, you must transform the individuals within a company. And so that way of working becomes incredibly holistic. And it's those three things, that I think amongst the whole myriad of others, that are the primary reasons why those programs fail. And what Garage does, is it breaks that. By putting innovation at the heart of your enterprise, and by using data-driven value orchestration, that means that we don't guess where the value to be gained is, we know. It's no longer chucking ideas at the wall to see what sticks, it's meaningful research. This is my favorite quote from my dear friend, Courtney Noll, who says, "It's not about searching for the innovation needle in the proverbial haystack, it's using your research in order to de-risk your investment, and drive your innovation to enable your outcomes." And so, if you do innovation without a view to how it's going to yield your business outcomes, I agree, I fundamentally agree that it's pointless. >> Yeah, exactly. And, you know, of course we're on the writing side, we love titles like, 'Innovation is dead, long live innovation'. So, it's classic, you know, to get your attention. >> Exactly, exactly. And of course, what I really mean is that innovation is a separate entity. >> Totally. >> There's no longer relevance for a company to make sure they achieve their business outcomes. >> Well, this is what I wanted to just double-click on that with you on is that you look at transformation. You guys are essentially saying transformation meets innovation with the Garage philosophy, if I get that right. >> Yep >> And it's interesting, and we've experienced this here with theCUBE, we're theCUBE virtual, we're not at IBM Think, there is no physical game day like some of us normally do. >> Well, as you can see, I'm at my house. (Debbie laughs) And so, I was talking to a CEO and I said, "Hey, you guys are doing really, really good. We had to pivot with the cube", and he goes, "You guys did a good pivot yourself". He goes, "No, John, we did not pivot. We actually put our business on hold because of the pandemic. We actually created a line extension, so, technically, we're going to bring that business back when COVID has gone and come back to real life, so it's technically not a pivot, we're not pivoting our business, we've created new functionality." Through the innovations that they were doing. So, this is kind of like, this is the real deal here. Share your thoughts on that. >> To me, it's about people get so focused on the output that they lose track of the outcome, right? And so, be really clear on what you're doing, and why. And the outcomes can be really broad, so instead of saying, "We're all going to implement a new ERP, or build a new mobile app". That's not an outcome, right? What we should be saying is, "What we're trying to achieve is a 10 percent growth in net promoter score in China, right? In this group." Or whatever it is we were trying to achieve, right? Or, "We want to make a 25% reduction in our operating cost base by simplifying our estate". Whatever those outcomes are, that's the starting point, and then driving that to use as the vehicle for what is the right innovation, what is going to deliver that value, and fast, right? Garage delivers three to five times faster than other models and at a reduced delivery cost, and so it's all about that speed. Speed of decision, speed of insight, speed of culture and training, speed of new skills, and speed to outcomes. >> Well, Debbie, you did a great job, love what you're doing, and Garage has got a great model. Congratulations on the growth, love this intersection, or transformation meets innovation because innovation is transformation, and vice versa, this interplay going on there. >> Exactly. >> I think COVID has proven that. Let me dig into a little bit more about the garage, what's going on. How many practitioners do you guys have there now at IBM? You've got growth, are you adding more people in? Obviously, Virtual First, COVID, is there still centers of design? Take us through what's going on at Garage. >> Certainly, so like, I think I mentioned it right up front. Our goal is to make IBM Garage the primary way our clients experience us. We've proven in that it delivers higher value to our clients and they get a really rich and broad set of outcomes. And so, in order for us to deliver on that promise we have to be enabled across IBM to deliver to it, right? So, over the last 18 months or so we've had a whole range of training programs in Enable, we've had a whole badging and certification program, we have all the skills, and the pathways, and the career pathways to find. But Garage is for everybody, right? And so, it isn't about creating a select group that can do this across IBM. This is about making all of services capable. So, in 2020 we trained over 28,000 people, in all the different skills that are needed, from selling, to execution, to QA, to user research, whatever it is. And this year we're launching our Garage Skills Academy, which will take that across all of services and make it easily available. So, you know, we've got hundreds of thousands. >> And talk about the footprint on the global side, because, again, not to bring up global, but global is what is in your title. >> Yep. >> Companies need to be global, because now with virtual workforces you're seeing much more tapped creativity and ability to execute from global teams. How does that impact you? >> Well, so it's global in two perspectives, right? So, number one, we have Garages all around the world, right? It isn't just the market of, you know, our most developed nations in Americas and Europe, it is everywhere, we see it in all emerging markets. From Latin America, through to all parts of eastern Europe, which are really beginning to come into their own. So, we see all these different Garages at different scales and opportunities. So, definitely global from that image. But, what virtualization has also enabled is truly global teams. Because, it's really easy to go, "Oh, I need one of those. Okay, I need a supply chain expert, and I need an AI expert, and I need somebody who's got industry experience in whatever it is." And you can quickly gather them around the virtual table, you know, faster than you can in a physical table. But, we still leverage the global communities with those physical. >> It's an expert network. You have an expert network there at IBM. >> We have a huge network, yeah. And both within IBM, and of course a growing network of ecosystem partners that we continue to work with. >> Well, Debbie, I'm really excited. Congratulations on the growth. I'm looking forward to partnering with you on your ecosystem as that develops. I can almost imagine you must be getting a lot of outside IBM practitioners and experts coming in to collaborate in a social construct. >> Absolutely. >> It's a great program, thanks for sharing. >> My pleasure, it's been great to be here, thank you. >> Okay, IBM's Global Garage Lead, Debbie Vavangas, who's here on theCUBE with IBM Services. A phenomenon, it's a social construct that's helping companies with digital transformation. Intersecting, with innovation. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>from around the globe, it's the >>Cube with digital coverage of IBM think 2020 >>one brought to you >>by IBM. Hello, welcome back to the cubes coverage of IBM Think 2021 virtual soon we'll be back in person in real life. But this year again it's a virtual conference. I'm john for your host of the cube for more cube coverage. You got a great guest here Debbie Viviendas Global garage lead for IBM Services Global garage great program. Ah Debbie, great to see you. Thanks for coming on the cube. >>Thanks for having me. >>So we've covered the garage a lot on the cube in the past and the success, Everyone loves the garage things are born in the garage, entrepreneurship innovation has been kind of categorically known for kind of the garage start up um but also it's become um known for really agile agility and which has been a cloud phenomenon, devops and now we're seeing Deb sec apps as a big trend this year with hybrid cloud. So I gotta ask you, how is garage doing with the pandemic? I was I can almost imagine people at home kind of disrupted from the office, but maybe more creativity, maybe more energy online. What's going on with the garage? How has your transformation journey been with Covid? >>Well, don't I mean it's Covid has been the level of for us. All right, there isn't a person who hasn't had some challenge or some complexity to Yeah, and that includes our clients and I'm incredibly proud to be able to say that IBM garage because it is so digitally native. When the covid pandemic has struck around the world, every single one of our garages was able to switch to being virtual without fail without a single days lost productivity. And that I mean that's hugely beneficial to clients who are on an incredible time sensitive journey. And so we've seen as a result of Covid actually there are a huge acceleration in garages from two reasons. The number one from a virtualization perspective. Actually it's much easier when everybody's together in the same space, everybody's together virtually in the same space. And we've seen acceleration in our velocity and our collaboration because everybody is really learning how to work in that century. But to because of the pandemic, because of the pressure on our client's needs to make decisions fast. No, not guess really, be focused on their outcomes, not just doing stuff, the garage really plays to that objective for them. And so we've seen a huge rise. We've gone from 2019 to just a few 100 garages to finishing 2020 with over 2.5 1000 garages and being embedded across services and the goal of being the primary way our clients experiencing COVID has been a big accelerator. >>Sorry Debbie, can you repeat the numbers again? I just want to capture that. I missed that. >>Sure. Sure. So we finished >>training on the numbers. >>Yeah. So that we finished 2019 with just under 300 garages and we finished 2020 with just over 2.5 1000. So we've had a huge growth in the in the rain and it isn't just the number of garages, it's the range of garages and what we're what we're serving with our clients and how we're collaborating with our clients and the topics were unpacking. That is is really broadened. >>Yeah. I mean I I covered and we've reported on the garage on the Cuban also in silicon angle dot com. And the past thinks and through your your news coverage. That's amazing growth. Um I gotta believe the tailwind from Covid and just the energy around it has energized. You wanna get your thoughts on that because you know what we've reported the past, it's been about design, thinking human centered design, all those beautiful things that come with cloud, cloud scale, right? You know, you're moving faster, you're innovating. Um and so that's been kind of there, but what you're getting at with this growth is with and what Covid has proven. And again, we've been pointing this out, you're seeing the pattern, It's clear companies are either retrenching okay. Which is re factoring, redesigning, doing those things to kind of get ready to come out to cope with a growth strategy and you're seeing other companies um build net new innovations so they're building new capabilities because Covid shown them kind of pulled back the curtain if you will on where the action is. So this means there's two threads going on. You got okay, I got to transform my business and I gotta re factor and then, or hey, we got net new business models, these are kind of two different things and not mutually exclusive. What's your comment on that? >>Uh, and I think that my comment on is that is the sweet spot that garage comes into its own right. You mentioned lots of things in that, you talked about design thinking and agility and you know, these other buzzwords that are used all the time and garage of course is synonymous with those of course, you know, it's Gap uses the best design thinking and agile practices and all of those things that absolutely core to what we do, devops, even through down to design up, we have the whole range depending on what the client objective is, but I think what is really happening now is the innovation, you know, being something separate. It is no longer how to accelerate your outcomes and your business outcomes regardless of whether that is in re factoring and modernizing your existing estate or diversifying creating new ecosystems and new platforms and new offerings. Regardless of what that is, you can't do it separate to your, To your core business. I mean it's a well known fact John right, like 75 of transformation programmes failed to deliver an impact on the business performance. Right? And in the same period of time there's been huge cuts in innovation funding and that's because for the same reason because they don't deliver the impact of the business performance and that's why garage is unique because it is entirely focused on the outcome, right? But using user research through design thinking of course using agile to deliver it at speed and all of those other things, but it's focused on value, on benefits, realization and driving to your outcome. And we do that by putting that innovation at the heart of your enterprise in order to drive that transformation rather than it being something separate. >>Debbie, I saw you gave a talk uh called Innovation Is Dead. Um obviously that's a provocative title. That's an attention getter. Um tell me what you mean by that because it seems to be a setup. I mean many mentions dead. Was it with a question mark? What you're kind of trying to highlight that innovation is transformation? Or were you trying >>to do the full title? The full title was Innovation is Dead and transformation is pointless. And of course, it's meant to be an eye catching title. So people show up and listen to my pitch rather than somebody else's. But But the reality is I mean that most sincerely it's back to that step, 75 of these transformation programmes failed to deliver the impact. And I and I speculate that that is for a few reasons because the idea itself wasn't a good one or wasn't at the right time because you were unable to understand what the measure of good looked like and therefore him just be able to create that path. And in order to transform a company, you must transform the individuals within a company. And so that way of working becomes incredibly holistic and it's those three things, I think amongst the whole myriad of others are the primary reasons why those programs fail. And what garage does is it breaks this by putting innovation at the heart of your enterprise and by using data driven value orchestration. That means that we don't no, we don't guess where the value to be gained is. We know it's no longer checking ideas at the wall to see what sticks it's meaningful research. It's not searching. This is my favorite quote from my dear friend Courtney, know, who says it's not about searching for the innovation needle in the proverbial haystack. It's using your research in order to de risk your investment and drive your innovation to enable your outcomes. So if you do innovation without a view to how it's going to yield your business outcomes, I agree. I fundamentally agree that it's pointless. >>Exactly. Of course, we're on the writing side. We love titles like innovation is dead long live innovation, so that's classic. Get your attention. But I think >>Exactly, and of course what I really mean is that innovation is a separate entity, >>totally. >>There is no longer relevant for company to make sure they achieve their business >>outcome. Well, this is what I wanted to just double click on that with you on is that you look at transformation, you guys essentially saying transformation meets innovation with the garage philosophy if I get that right. Um, and, and, and it's interesting I had, and we've experienced here with the cube where the cube virtual, we're not at IBM think there is no physical game day, like >>my house. >>And, and so I was talking to a Ceo and he said, I said, hey you guys are doing really, really good. You know, we had to pivot with the cube and he goes, you guys did a good pivot yourself because no, john we did not pivot, we actually put our business on hold because of the pandemic. We actually created a line extension. So technically we're going to bring that business back when Covid is gone and we come back to real life. So it's technically not a pivot. We're not pivoting our business. We've created new functionality through the innovations that they were doing. So this is kind of like, this is the real deal here. This is like depends proven what's your share your thoughts on that? >>Well, it's just to me it's about people get so focused on the output that they lose track of the outcome, right? And so being really clear on what you're doing and why and the outcomes can be really broad that, you know, so instead of saying, you know, we're all going to implement the new E. R. P. Or build a new mobile app. That's that's that's not an outcome, right? What we should be saying is what we're trying to achieve is a 10% growth in net promoter score in china, Right in this group or whatever it is we were trying to achieve right, we want to make a 25 reduction in our operating cost base by simplifying our estate whatever those outcomes are. I mean that's the starting point and then driving that use to use as the vehicle for what is the right innovation, what is going to deliver that value and fast right garage delivers 3-5 times faster than other models and reduced delivery costs. And so it's all about that speed, speed of decision, speed of insight, speed of culture and training, speed of new skills and speed to outcomes. >>You got a great job, love what you're doing in Karaj got a great model, congratulations on the growth. Love this intersection or transformation meets innovation because innovation is transformation advice versus interplay going on there I think has proven that. Let me dig into a little bit more about the garage. What's going on? How many practitioners you guys have there now at IBM? Um, you've got growth. Are you adding more people in? I'll see virtual first. Covid. Is there still centers of design take us through what's going on at garage? >>Certainly. So I think I mentioned it right up front. Right. So our goal is to make IBM guards the primary way our clients experiences. We've proven that it delivers higher value to our clients and they get really rich and broad set of outcomes. And so in order for us to deliver on that promise, we have to be unable to cross IBM to deliver to it. Right? So over the last 18 months or so we've had a whole range of training programs and enable we have a whole badging and certification program. We have all the skills and the pathways and the career pathways to find. But garages for everybody. Right? And so it isn't about creating a selected group that can do this across IBM, this is about making all of services capable. So in 2020 we we trained over 28,000 people right? In in all the different skills that are needed from selling to execution to QA to use a research, whatever it is. And this year we're launching our garage skills academy which will take that across all of services and make it easily available. So we will, you've got to >>talk about the footprint of the global side because again, not to bring up global, but global is what yours in your title companies need to be global because now with virtual workforce is you're seeing much more tapped creativity and execution ability to execute from global teams. How does that impact you? >>Well, so garages as in its global in two perspectives. Right, So number one, we have garages all around the world. Right? It isn't it isn't just the market of you are most developed nations in the Americas and europe. It is everywhere. We see it in all emerging markets, from latin America through to you all parts of eastern europe which are really beginning to come into their own. So we see all these different garages of different different scales and opportunity. So definitely global from that image. But what what what virtualization has also enabled these truly global teams because it's really easy to go, I need one of those. Okay, I need a supply chain expert and I need an Ai expert and I need somebody who's got industry experience in whatever it is and you can quickly gather them around the virtual table faster than you can in a physical table. But we still leverage the global community >>for the network. You have an expert network there at IBM. >>You have a huge network. Yeah. And both both within IBM and of course a growing network of ecosystem partners that we continue to work >>with. Debbie. I'm really excited. Congratulations. Growth. I'm looking forward to partnering with you on your ecosystem as that develops. I can almost imagine you must be getting a lot of outside IBM practitioners and experts coming in to collaborate. It is a social construct. It's a great program. Thanks for sharing >>my pleasure. It's been great to be here. Thank >>you. Okay, IBM's global garage. Lee Debbie Vegas who's here on the queue with IBM services, a phenomenon. This is social construct is helping companies with digital transformation intersecting with innovation. I'm john for your host. Thanks for watching

>>Yeah, yeah. >>Welcome back to session, too. Thoughts about everywhere. Unlock new revenue streams with embedded search and I Today we're joined by our senior director of Global Oh am Rick Dimel, along with speakers from our thoughts about customer Hayes to discuss how thought spot is open for everyone by unlocking unprecedented value through data search in A I, you'll see how thoughts about compound analytics in your applications and hear how industry leaders are creating new revenue streams with embedded search and a I. You'll also learn how to increase app stickiness on how to create an autonomous this experience for your end users. I'm delighted to introduce our senior director of Global OPM from Phillips Spot, Rick DeMARE on then British Ramesh, chief technology officer, and Leon Roof, director of product management, both from Hayes over to you. Rick, >>Thank you so much. I appreciate it. Hi, everybody. We're here to talk to you about Fox Spot everywhere are branded version of our embedded analytics application. It really our analytics application is all about user experience. And in today's world, user experience could mean a lot of things in ux design methodologies. We want to talk about the things that make our product different from an embedded perspective. If you take a look at what product managers and product design people and engineers are doing in this space, they're looking at a couple of key themes when they design applications for us to consume. One of the key things in the marketplace today is about product led growth, where the product is actually the best marketing tool for the business, not even the sales portion or the marketing department. The product, by the word of mouth, is expanding and getting more people onto the system. Why is that important? It's important because within the first few days of any application, regardless of what it is being used binding users, 70% of those users will lose. Interest will stop coming back. Why do they stop coming back? Because there's no ah ha moment through them. To get engaged within the technology, today's technologies need to create a direct relationship with the user. There can't be a gatekeeper between the user and the products, such as marketing or sales or information. In our case. Week to to make this work, we have toe leverage learning models in leverage learning as it's called Thio. Get the user is engaged, and what that means is we have to give them capabilities they already know how to use and understand. There are too many applications on the marketplace today for for users to figure out. So if we can leverage the best of what other APS have, we can increase the usage of our systems. Because in today's world, what we don't want to do from a product perspective is lead the user to a dead end or from a product methodology. Our perspective. It's called an empty state, and in our world we do that all the time. In the embedded market place. If you look at at the embedded marketplace, it's all visualizations and dashboards, or what I call check engine lights in your application's Well, guess what happens when you hit a check engine life. You've got to call the dealer to get more information about what just took place. The same thing happens in the analytic space where we provide visualizations to users. They get an indicator, but they have to go through your gatekeepers to get access to the real value of that data. What am I looking at? Why is it important the best user experiences out on the marketplace today? They are autonomous. If we wanna leverage the true value of digital transformation, we have to allow our developers to develop, not have them, the gatekeepers to the rial, content to users want. And in today's world, with data growing at much larger and faster levels than we've ever seen. And with that shelf life or value of that data being much shorter and that data itself being much more fragmented, there's no developer or analysts that can create enough visualizations or dashboards in the world to keep the consumption or desire for these users to get access to information up to speed. Clients today require the ability to sift through this information on their own to customize their own content. And if we don't support this methodology, our users are gonna end up feeling powerless and frustrated and coming back to us. The gatekeepers of that information for more information. Loyalty, conversely, can be created when we give the users the ability toe access this information on their own. That is what product like growth is all about in thought spot, as you know we're all about search. It's simple. It's guided as we type. It gives a super fast responses, but it's also smart on the back end handling complexities, and it's really safe from a governance and as well as who gets access to what perspective it's unknown learned environment. Equally important in that learned environment is this expectation that it's not just search on music. It's actually gonna recommend content to me on the fly instantly as I try content I might not even thought of before. Just the way Spotify recommends music to us or Netflix recommends a movie. This is a expected learned behavior, and we don't want to support that so that they can get benefit and get to the ah ha moments much quicker. In the end, which consumption layer do you want to use, the one that leads you to the Dead End Street or the one that gets you to the ah ha moment quickly and easily and does it in an autonomous fashion. Needless to say, the benefits of autonomous user access are well documented today. Natural language search is the wave of the future. It is today. By 2004 75% of organizations are going to be using it. The dashboard is dead. It's no longer going to be utilized through search today, I if we can improve customer satisfaction and customer productivity, we're going to increase pretensions of our retention of our applications. And if we do that just a little bit, it's gonna have a tremendous impact to our bottom line. The way we deploy hotspots. As you know, from today's conversations in the cloud, it could be a manage class, not offering or could be software that runs in your own VPC. We've talked about that at length at this conference. We've also talked about the transformation of application delivery from a Cloud Analytics perspective at length here it beyond. But we apply those same principles to your product development. The benefits are astronomical because not only do you get architectural flexibility to scale up and scale down and right size, but your engineers will increase their productivity because their offerings, because their time and effort is not going to be spent on delivering analytics but delivering their offerings. The speed of innovation isn't gonna be released twice a year or four times a year. It's gonna It can happen on a weekly basis, so your time to market in your margins should increase significantly. At this point, I want a hand. The microphone over to Revert. Tesche was going to tell you a little bit about what they're doing. It hes for cash. >>Thanks, Rick. I just want to introduce myself to the audience. My name is Rotational. Mention the CTO Europe ace. I'm joined my today by my colleague Gillian Ruffles or doctor of product management will be demoing what we have built with thoughts about, >>um but >>just to my introduction, I'm going to talk about five key things. Talk about what we do. What hes, uh we have Really, um what we went through the select that spot with other competitors What we have built with that spot very quickly and last but not least, some lessons learned during the implementation. So just to start with what we do, uh, we're age. We are health care compliance and revenue integrity platform were a saas platform voter on AWS were very short of l A. That's it. Use it on these around 1 50 customers across the U. S. On these include large academic Medical Insight on. We have been in the compliant space for the last 30 plus years, and we were traditionally consulting company. But very recently we have people did more towards software platform model, uh, in terms off why we chose that spot. There were three business problems that I faced when I took this job last year. At age number one is, uh, should be really rapidly deliver new functionality, nor platform, and he agile because some of our product development cycles are in weeks and not months. Hey had a lot of data, which we collected traditionally from the SAS platform, and all should be really create inside stretch experience for our customers. And then the third Big one is what we saw Waas large for customers but really demanding self service capabilities. But they were really not going for the static dash boats and and curated content, but instead they wanted to really use the cell service capabilities. Thio mind the data and get some interesting answers during their questions. So they elevated around three products around these problems statements, and there were 14 reasons why we just start spot number one wars off course. The performance and speed to insights. Uh, we had around 800 to a billion robot of data and we wanted to really kind of mind the data and set up the data in seconds on not minutes and hours. We had a lot of out of the box capabilities with that spot, be it natural language search, predictive algorithms. And also the interactive visualization, which, which was which, Which gave us the agility Thio deliver these products very quickly. And then, uh, the end user experience. We just wanted to make sure that I would users can use this interface s so that they can very quickly, um, do some discovery of data and get some insights very quickly. On last but not least, talksport add a lot of robust AP ice around the platform which helped us embed tot spot into are offering. But those are the four key reasons which we went for thoughts part which we thought was, uh, missing in in the other products we evaluated performance and search, uh, the interactive visualization, the end user experience, and last but not least flexible AP ice, which we could customize into our platform in terms of what we built. We were trying to solve to $50 billion problem in health care, which is around denials. Um so every year, around 2, 50 to $300 billion are denied by players thes air claims which are submitted by providers. And we built offering, which we called it US revenue optimizer. But in plain English, what revenue optimizer does is it gives the capability tow our customers to mind that denials data s so that they can really understand why the claims were being denied. And under what category? Recent reasons. We're all the providers and quarters who are responsible for these claims, Um, that were dryland denials, how they could really do some, uh, prediction off. It is trending based on their historical denial reasons. And then last but not least, we also build some functionality in the platform where we could close the loop between insights, action and outcome that Leon will be showing where we could detect some compliance and revenue risks in the platform. On more importantly, we could, uh, take those risks, put it in a I would say, shopping card and and push it to the stakeholders to take corrective action so the revenue optimizer is something which we built in three months from concept to lunch and and that that pretty much prove the value proposition of thoughts. But while we could kind of take it the market within a short period of time Next leopard >>in terms >>off lessons learned during the implementation thes air, some of the things that came to my mind asses, we're going through this journey. The first one is, uh, focus on the use case formulation, outcomes and wishful story boarding. And that is something that hot spot that's really balance. Now you can you can focus on your business problem formulation and not really focus on your custom dash boarding and technology track, etcetera. So I think it really helped our team to focus on the versus problem, to focus on the outcomes from the problem and more importantly, really spend some time on visualizing What story are we say? Are we trying to say to our customers through revenue optimizer The second lesson learned first When we started this implementation, we did not dualistic data volume and capacity planning exercise and we learned it our way. When we are we loaded a lot of our data sets into that spot. And then Aziz were doing performance optimization. XYZ. We figured out that we had to go back and shot the infrastructure because the data volumes are growing exponentially and we did not account for it. So the biggest lesson learned This is part of your architectural er planning, exercise, always future proof your infrastructure and make sure that you work very closely with the transport engineering team. Um, to make sure that the platform can scale. Uh, the last two points are passport as a robust set of AP Ice and we were able to plug into those AP ice to seamlessly ended the top spot software into a platform. And last but not least, one thing I would like to closest as we start these projects, it's very common that the solution design we run into a lot of surprises. The one thing I should say is, along those 12 weeks, we very closely work with the thoughts, part architecture and accounting, and they were a great partner to work with us to really understand our business problem, and they were along the way to kind of government suggested, recommends and workarounds and more importantly, also, helpers put some other features and functionality which you requested in their engineering roadmap. So it's been a very successful partnership. Um, So I think the biggest take of it is please make sure that you set up your project and operating model value ember thoughts what resources and your team to make sure that they can help you as you. It's some obstacles in the projects so that you can meet your time ones. Uh, those are the key lessons learned from the implementation. And with that, I would pass this to my colleague Leon Rough was going to show you a demo off what we go. >>Thanks for Tesh. So when we were looking Thio provide this to our customer base, we knew that not everyone needed do you access or have available to them the same types of information or at the same particular level of information. And we do have different roles within RMD auto Enterprise platform. So we did, uh, minimize some roles to certain information. We drew upon a persona centric approach because we knew that those different personas had different goals and different reasons for wanting to drive into these insights, and those different personas were on three different levels. So we're looking at the executive level, which is more on the C suite. Chief Compliance Officer. We have a denial trending analyses pin board, which is more for the upper, uh, managers and also exact relatives if they're interested. And then really, um, the targeted denial analysis is more for the day to day analysts, um, the usage so that they could go in and they can really see where the trends are going and how they need to take action and launch into the auditing workflow so within the executive or review, Um, and not to mention that we were integrating and implementing this when everyone was we were focused on co vid. So as you can imagine, just without covert in the picture, our customers are concentrated on denials, and that's why they utilize our platform so they could minimize those risks and then throw in the covert factor. Um, you know, those denial dollars increase substantially over the course of spring and the summer, and we wanted to be able to give them ah, good view of the denials in aggregate as well as's we focus some curated pin boards specific to those areas that were accounting for those high developed denials. So on the Executive Overview Board, we created some banner tiles. The banner tiles are pretty much a blast of information for executives thes air, particular areas where there concentrating and their look looking at those numbers consistently so it provides them away to take a good look at that and have that quick snapshot. Um, more importantly, we did offer as I mentioned some curated pin boards so that it would give customers this turnkey access. They wouldn't necessarily have to wonder, You know, what should I be doing now on Day one, but the day one that we're providing to them these curated insights leads the curiosity and increases that curiosity so that they can go in and start creating their own. But the base curated set is a good overview of their denial dollars and those risks, and we used, um, a subject matter expert within our organization who worked in the field. So it's important to know you know what you're targeting and why you're targeting it and what's important to these personas. Um, not everyone is necessarily interests in all the same information, and you want to really hit on those critical key point to draw them and, um, and allowed them that quick access and answer those questions they may have. So in this particular example, the curated insight that we created was a monthly denial amount by functional area. And as I was mentioning being uber focused on co vid, you know, a lot of scrutiny goes back to those organizations, especially those coding and H i M departments, um, to ensure that their coding correctly, making sure that players aren't sitting on, um, those payments or denying those payments. So if I were in executive and I came in here and this was interesting to me and I want to drill down a little bit, I might say, You know, let me focus more on the functional area than I know probably is our main concern. And that's coating and h i M. And because of it hit in about the early winter. I know that those claims came in and they weren't getting paid until springtime. So that's where I start to see a spike. And what's nice is that the executive can drill down, they may have a hunch, or they can utilize any of the data attributes we made available to them from the Remittance file. So all of these data, um, attributes are related to what's being sent on the 8 35 fear familiar with the anti 8 35 file. So in particular, if I was curious and had a suspicion that these were co vid related or just want to concentrate in that area, um, we have particular flag set up. So the confirmed and suspected cases are pulling in certain diagnosis and procedure codes. And I might say 1.27 million is pretty high. Um, toe look at for that particular month, and then they have the ability to drill down even further. Maybe they want to look at a facility level or where that where that's coming from. Furthermore, on the executive level, we did take advantage of Let me stop here where, um also provided some lagged a so leg. This is important to organizations in this area because they wanna know how long does it take before they re submit a claim that was originally denied before they get paid industry benchmark is about 10 days of 10 days is a fairly good, good, um, basis to look at. And then, obviously anything over that they're going to take a little bit more scrutiny on and want to drill in and understand why that is. And again, they have that capabilities in order to drill down and really get it. Those answers that they're looking for, we also for this particular pin board. And these users thought it would be helpful to utilize the time Siri's forecasting that's made available. So again, thes executives need thio need to keep track and forecast where they're trends were going or what those numbers may look like in the future. And we thought by providing the prediction pins and we have a few prediction pins, um would give them that capability to take a look at that and be able to drill down and use that within, um, certain reporting and such for their organization. Another person, a level that I will go to is, um, Mawr on the analyst side, where those folks are utilizing, um, are auditing workflow and being in our platform, creating audits, completing audits, we have it segregated by two different areas. And this is by claim types so professional or institutional, I'm going to jump in here. And then I am going to go to present mode. So in this particular, um, in this particular view or insight, we're providing that analysts view with something that's really key and critical in their organization is denials related Thio HCC s andi. That's a condition category that kind of forecast, the risk of treatment. And, you know, if that particular patient is probably going to be seen again and have more conditions and higher costs, higher health care spending. So in this example, we're looking at the top 15 attending providers that had those HCC denials. And this is, um, critical because at this point, it really peaks in analyst curiosity. Especially, You know, they'll see providers here and then see the top 15 on the top is generating Ah, hide denial rate. Hi, denial. The dollars for those HCC's and that's a that's a real risk to the organization, because if that behavior continues, um, then those those dollars won't go down. That number won't go down so that analysts then can go in and they can drill down um, I'm going to drill down on diagnosis and then look at the diagnosis name because I have a suspicion, but I'm not exactly sure. And what's great is that they can easily do this. Change the view. Um, you know, it's showing a lot of diagnoses, but what's important is the first one is sepsis and substance is a big one. Substances something that those organizations see a lot of. And if they hover, they can see that 49.57 million, um, is attributed to that. So they may want to look further into that. They'd probably be interested in closing that loop and creating an audit. And so what allowed us to be able to do that for them is we're launching directly into our auditing workflow. So they noticed something in the carried insight. It sparked some investigation, and then they don't have to leave that insight to be able to jump into the auditing workflow and complete that. Answer that question. Okay, so now they're at the point where we've pulled back all the cases that attributed to that dollar amount that we saw on the Insight and the users launching into their auditing workflow. They have the ability Thio select be selective about what cases they wanna pull into the audit or if they were looking, um, as we saw with sepsis, they could pull in their 1600 rose, but they could take a sampling size, which is primarily what they would do. They went audit all 1600 cases, and then from this point in they're into, they're auditing workflow and they'd continue down the path. Looking at those cases they just pulled in and being able Thio finalized the audit and determine, you know, if further, um, education with that provider is needed. So that concludes the demo of how we integrated thought spot into our platform. >>Thank you, LeAnn. And thank you. Re test for taking the time to walk us through. Not only your company, but how Thought spot is helping you Power analytics for your clients. At this point, we want to open this up for a little Q and A, but we want to leave you with the fact that thought spot everywhere. Specifically, it cannot only do this for Hayes, but could do it for any company anywhere they need. Analytical applications providing these applications for their customers, their partners, providers or anybody within their network for more about this, you can see that the website attached below >>Thanks, Rick and thanks for tests and Leon that I find it just fascinating hearing what our customers are doing with our technology. And I certainly have learned 100% more about sepsis than I ever knew before this session. So thank you so much for sharing that it's really is great to see how you're taking our software and putting it into your application. So that's it for this session. But do stay tuned for the next session, which is all about getting the most out of your data and amplifying your insights. With the help of A, I will be joined by two thought spot leaders who will share their first hand experiences. So take a quick breather and come right back

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back to the Cube. Lisa Martin at AWS Reinvent 19. This is Day three of the Cubes coverage. We have two sets here. Lots of cute content are joined by Justin Warren, the founder and chief analyst at Pivot nine. Justin. How's it going? Great, right? You still have a voice? Three days? >>Just barely. I've been I've been trying to take care of it. >>Impressed. And you probably have talked to at least half of the 65,000 attendees. >>I'm trying to talk to as many as I can. >>Well, we're gonna talk to another guy here. Joining us from data ICU is well, Novak, the solutions architect will be the Cube. >>Thanks for having me. >>You have a good voice too. After a three day is that you >>have been doing the best I can. >>Yeah, he's good. So did ICU. Interesting name. Let's start off by sharing with our audience. Who did a coup is and what you guys do in technology. >>Yes. So the Entomology of date ICU. It's like hi cooze for data. So we say we take your data and, you know, we make poetry out of it. Make your data so beautiful. Wow, Now, But for those who are unaware Day like it was an enterprise data science platform. Eso we provide a collaborative environment for we say coders and clickers kind of business analyst and native data scientists to make use of organizations, data bill reports and Bill productive machine learning base models and deploy them. >>I'm only the guy's been around around for eight years. Eight years. Okay, >>so start up. Still >>mourning the cloud, the opportunity there That data is no longer a liability. It's an asset or should be. >>So we've been server based from the start, which is one of our differentiators. And so by that we see ourselves as a collaborative platform. Users access it through a Web browser, log into a shared space and share code, can share visual recipes, as we call them to prepare data. >>Okay, so what customers using the platform to do with machine learning is pretty hot at the moment. I think it might be nearing the peak of the life cycle pretty hot. Yeah, what a customer is actually actually doing on the platform, >>you know, So we really focus on enabling the enterprise. So, for example, G has been a customer for some time now, and Sergey is a great prototypical example on that. They have many disparate use cases, like simple things like doing customer segmentation for, you know, marketing campaigns but also stuff like Coyote predicted maintenance. So use cases kind of run the gamut, and so did ICU. Based on open source, we're enabling all of G's users to come into a centralized platform, access their data manipulated for whatever purposes. Maybe >>nobody talked about marketing campaigns for a second. I'm wondering. Are, is their integration with serum technologies? Or how would a customer like wanting to understand customer segmentation or had a segment it for marketing campaign? How would they work in conjunction with a serum and data ICU, for example? >>It's a great question. So again, us being a platform way sit on a single server, something like an Amazon ec2 instance, and then we make connections into an organization's data sources. So if using something like Salesforce weaken seamlessly, pull in data from Salesforce Yuka manipulated in date ICU, but the same time. Maybe also have some excel file someone you know me. I can bring that into my data to work environment. And I also have a red shift data table. All those things would come into the same environment. I can visualize. I can analyze, and I can prepare the data. I see. >>So you tell you it's based on open source? I'm a longtime fan of over. It's always been involved in it for longer than I care to remember. Actually, that's an interesting way t base your product on that. So maybe talk us through how you how you came to found the company based on basic an open source. What? What led to that choice? What? What was that decision based on? >>Yeah, for sure. So you talked about how you know the hype cycle? A. I saw how hot is a I and so I think again, our founders astutely recognize that this is a very fast moving place to be. And so I'm kind of betting on one particular technology can be risky. So instead, by being a platform, we say, like sequel has been the data transformation language do jour for many days now. So, of course, that you can easily write Sequel and a lot of our visual data Transformations are based on the sequel language, but also something like Python again. It's like the language de jour for machine law machine learning model building right now, so you can easily code in python. Maintain your python libraries in date, ICU And so by leveraging open source, we figured we're making our clients more future proof as long as they're staying in date ICU. But using data ICU to leverage the best in breed and open source, they'll always be kind of where they want to be in the technological landscape by supposed to locked into some tech that is now out of date. >>What's been the appetite for making data beautiful for a legacy enterprise, like a G E that's been around for a very long time versus a more modern either. Born in the Cloud er's our CEO says, reborn in the cloud. What are some of the differences but also similarities that you see in terms of we have to be able to use emerging tech. Otherwise someone's gonna come in behind us and replace us. >>Yeah, I mean, I think it's complicated in that there's still a lot of value to be had in someone says, like a bar chart you can rely on right, So it's maybe not sexy. But having good reporting and analytics is something that both you know, 200 year old enterprise organizations and data native organizations startups needs. At the same time, building predicted machine learning models and deploying those is rest a p i n points that developers can use in your organization to provide a data driven product for your consumers. Like that's amore advanced use case that everyone kind of wants to be a part of again data. Who's a nice tool, which says Maybe you don't have developers who are very fluent in turning out flashed applications. We could give you a place to build a predictive model and deploy that predictive model, saving you time to write all that code on the back end. >>One of the themes of the show has been transformation, so it sounds like data ICU would be It's something that you can dip your toes in and start to get used to using. Even if you're not particularly familiar with Time machine learning model a model building. >>Yeah, that's exactly right. So a big part of our product and encourage watchers to go try it out themselves and go to our website. Download a free version pretrial, but is enablement. So if you're the most sophisticated applied math PhD there is, like, Who's a great environment for you to Code and Bill predictive models. If you never built the machine learning model before you can use data ICU to run visual machine learning recipes, we call them, and also we give you documentation, which is, Hey, this is a random forest model. What is a random forest model? We'll tell you a little bit about it. And that's another thing that some of these enterprises have really appreciated about date I could. It is helping up skill there user base >>in terms of that transformation theme that Justin just mention which we're hearing a lot about, not visit this show. It's a big thing, but we hear it all the time, right? But in terms of customers transformation, journey, whatever you wanna call it, cloud is gonna be an essential enabler of being able to really love it value from a I. So I'm just wondering from a strategic positioning standpoint. Is did ICU positioned as a facilitator or as fuel for a cloud transformation that on enterprise would undergo >>again? Yes, great point. So for us, I can't take the credit. This credit goes to our founders, but we've thought from the start the clouds and exciting proposition Not everyone is. They're still in 2019. Most people, if not all of them, want to get there. Also, people want too many of our clients want the multi cloud on a day. Like who says, If you want to be on prim, if you want to be in a single cloud subscription. If you want to be multi cloud again as a platform, we're just gonna give you connection to your underlying infrastructure. You could use the infrastructure that you like and just use our front end to help your analyst get value. They can. I >>think I think a lot of vendors across the entire ecosystem around to say the customer choice is really important, and the customers, particularly enterprise customers, want to be able to have lots of different options, and not all of them will be ready to go completely. All in on cloud today. They made it may take them years, possibly decades, to get there. So having that choice is like it's something that it would work with you today and we'll work with you tomorrow, depending on what choices you make. >>It's exactly right. Another thing we've seen a lot of to that day, like who helps with and whether it's like you or other tools. Like, of course, you want best in breed, but you also want particularly for a large enterprise. You don't want people operating kind of in a wild West, particularly in like the ML data science space. So you know we integrate with Jupiter notebooks, but some of our clients come to us initially. Just have I won't say rogues that has a negative connotation. But maybe I will say Road road data Scientists are just tapping into some day the store. They're using Jupiter notebooks to build a predictive model, but then to actually production allies that to get sustainable value out of it like it's to one off and so having a centralized platform like date ICU, where you can say this is where we're going to use our central model depository, that something where businesses like they can sleep easier at night because they know where is my ML development happening? It's happening in one ecosystem. What tools that happening with, well, best in breed of open source. So again, you kind of get best of both worlds like they like you. >>It sounds like it's more about the operations of machine learning. It is really, really important rather than just. It's the pure technology. Yes, that's important as well, and you need to have the data Sinus to build it, but having something that allows you to operationalize it so that you can just bake it into what we do every day as a business. >>Yeah, I think in a conference like this all about tech, it's easy to forget what we firmly believe, which is a I and maybe tech. More broadly, it's still human problems at the core, right? Once you get the tech right, the code runs corrected. The code is written correctly. Therefore, like human interactions, project management model deployment in an organization. These are really hard, human centered problems, but so having tech that enables that human centric collaboration helps with that, we find >>Let's talk about some of the things that we can't ever go to an event and not talk about. Nut is respected data quality, reliability and security. Understood? I could facilitate those three cornerstones. >>Yeah, sure. So, again, viewers, I would encourage you to check out the date. ICU has some nice visual indications of data quality. So an analyst or data scientists and come in very easily understand, you know, is this quality to conform to the standards that my organization has set and what I mean by standards that could be configured. Right? So does this column have the appropriate schema? Does it have the appropriate carnality? These are things that an individual might decide to use on then for security. So Data has its own security mechanisms. However, we also to this point about incorporating best Retek. We'll work with whatever underlying security mechanisms organizations organizations have in place. So, for instance, if you're using a W s, you have, I am rolls to manage your security. Did ICU comport those that apply those to the date ICU environment or using something like on prime miss, uh, duke waken you something like Kerberos has the technology to again manage access to resources. So we're taking the best in breed that this organization already has invested time, energy and resources into and saying We're not trying to compete with them but rather were trying to enable organizations to use these technologies efficiently. >>Yeah, I like that consistency of customer choice. We spoke about that just before. I'm seeing that here with their choices around. Well, if you're on this particular platform will integrate with whatever the tools are there. People underestimate how important that is for enterprises, that it has to be ahead. Virginia's environment, playing well with others is actually quite important. >>Yeah, I don't know that point. Like the combination of heterogeneity but also uniformity. It's a hard balance to strike, and I think it's really important, giving someone a unified environment but still choice. At the same time. A good restaurant or something like you won't be able to pick your dish, but you want to know that the entire quality is high. And so having that consistent ecosystem, I think, really helps >>what are, in your opinion, some of the next industries that you see there really right to start Really leveraging machine learning to transfer You mentioned g e a very old legacy business. If we think of you know what happened with the ride hailing industry uber, for example, or fitness with Saletan or pinchers with visible Serge, what do you think is the next industry? That's like you guys taking advantage of machine learning will completely transform this and our lives. >>I mean, the easy answer that I'll give because it's easy to say it's gonna transform. But hard to operationalize is health care, right? So there is structured data, but the data quality is so desperate and had a row genius s, I think you know, if organizations in a lot of this again it's a human centered problem. If people could decide on data standards and also data privacy is, of course, a huge issue. We talked about data security internally, but also as a customer. What day to do I want you know, this hospital, this health care provider, to have access to that human issues we have to result but conditional on that being resolved that staring out a way to anonymous eyes data and respect data privacy but have consistent data structure. And we could say, Hey, let's really set these a I M L models loose and figure out things like personalized medicine which were starting to get to. But I feel like there's still a lot of room to go. That >>sounds like it's exciting time to be in machine learning. People should definitely check out products such as Dead Rock you and see what happens. >>Last question for you is so much news has come out in the last three days. It's mind boggling sum of the takeaways, that of some of the things that you've heard from Andy Jassy to border This'll Morning. >>Yeah, I think a big thing for me, which was something for me before this week. But it's always nice to hear an Amazon reassures the concept of white box. Aye, aye. We've been talking about that a date ICU for some time, but everyone wants performance A. I R ml solutions, but increasing. There's a really appetite publicly for interpret ability, and so you have to be responsible. You have to have interpret belay I and so it's nice to hear a leader like Amazon echo that day like you. That's something we've been talking about since our start. >>A little bit validating them for data ICU, for sure, for sure. Well, thank you for joining. Just to be on the kid, the suffering. And we appreciate it. Appreciate it. All right. For my co host, Justin Warren, I'm Lisa Martin and your work to the Cube from Vegas. It's AWS reinvent 19.

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas. Lisa Martin with John Ferrier. You're watching the Cube live at Del Technologies World twenty nineteen. This is our second full day of Double Cube set coverage. We've got a couple of we're gonna really cool conversation coming up for you. We've got Robbie Pender County, one of our alumni on the cue back as VP product management server solutions. Robbie, Welcome back. >> Thank you, Lisa. Much appreciated. >> And you brought some Hollywood? Yes. Glenn Glenn ER, president of Sony Innovation Studios. Glenn and welcome to the Cube. >> Thank you very much. It's great to be here. >> So you are love this intersection of Hollywood and technology. But you're a filmmaker. >> Yeah. I have been filming movies for many years. Uh, I started off making motion pictures for many years. Executive produced him and over so production for them at one of our movie labels called Screen Gems, which is part of Sony Pictures. >> Wait a tremendous amount of evolution of the creative process being really fueled by technology and vice versa. Sony Innovation Studios is not quite one year old. This is a really exciting venture. Tell us about that and and what the the impetus was to start this company. >> You know that the genesis for it was based out of necessity because I looked at a nice Well, you know, I love making movies were doing it for a long time. And the challenge of making good pictures is resource is and you never get enough money believing not you never get enough money and never get enough time. That's everybody's issue, particularly time management. And I thought, Well, you know, we got a pretty good technology company behind us. What if we looked inward towards technology to help us find solutions? And so innovation studios is born out of that idea on what was exciting about it was to know that we had, uh, invited partners to the game right here with Del so that we could make movies and television shows and commercials and even enterprise solutions leaning into state of the art and cutting edge technology. >> And what some of the work prize and you guys envision coming out this mission you mentioned commercials. TV is it going to be like an artist's studio actor? Ackerson Ball is Take us through what this is going to look like. How does it get billed out? >> I lean into my career as a producer. To answer that one and say is going to enable that's one of the greatest things about being a producer is enabling stories, uh, inspiring ideas to be Greenland. That may not have been able to be done so before. And there's a key reason why we can't do that, because one of our key technologies is what we call the volumetric image acquisition. That's a lot of words. You probably say. What the heck is that? But a volumetric image acquisition is our ability to capture a real world, this analog world and digitize it, bring it into our servers using the power of Del and then live in that new environment, which is now a virtual sets. And that virtual set is made out of billions and trillions in quadrillions of points, much like the matter around us. And it's a difference because many people use pixels, which is interpretation of like worry, using points which is representative of the world around us, so it's a whole revolutionary way of looking at it. But what it allows us to do is actually film in it in a thirty K moving volume. >> It's like a monster green screen for the world. Been away >> in a way, your your your your action around it because you have peril X so these cameras could be photographing us. And for all you know, we may not be here. Could be at stage seven at Innovation Studios and not physically here, but you couldn't tell it. If >> this is like cloud computing, we talking check world, you don't the provisional these resource is you just get what you want. This is Hollywood looking at the artistry, enabling faster, more agile storytelling. You don't need to go set up a town and go get the permit. All the all the heavy lifting you're shooting in this new digital realm. >> That's right. Exactly. Now I love going on location on. There's a lot to celebrate about going on location, but we can always get to that location. Think of all the locations that we want to be in that air >> base off limits. Both space, the one I >> haven't been, uh, but but on said I've been I've walked on virtual moons and I've walked on set moons. But what if we did a volumetric image acquisition of someone set off the moon? Now we have that, and then we can walk around it. Or what if there's a great club, a nightclub? This says guys want you shoot here, but we have performances Monday night, Tuesday night, Wednesday night there. You know they have a job. What if we grab that image, acquired it, and then you could be there anytime you want. >> Robbie, we could go for an hour here. This is just a great comic. I >> completely agree with you. >> The Cube. You could. You could sponsor a cube in this new world. We could run the Q twenty four seven. That's absolutely >> right. And we don't even have >> to talk about the relationship with Dale because on Del Technologies, because you're enabling new capabilities. New kind of artistry was just totally cool. Want to get back to the second? But you guys were involved. What's your role? How do you get involved? Tell the story about your >> John. I mean, first and foremost one of things that didn't Glendon mention is he's actually got about fifty movies to his credit. So the guy actually knows this stuff, so which is absolutely fantastic. So we said, How do you go take average to the next level? So what else is better than trying to work something out, wherein we together between what Glenn and Esteem does at the Sony Innovation Labs for Studio Sorry. And as in Dead Technologies could do is to try and actually stretch the boundaries of our technology to a next tent that when he talks about kazillion bytes of data right one followed the harmony of our zeros way have to be able to process the data quickly. We have to be able to go out and do their rendering. We probably have to go out and do whatever is needed to make a high quality movie, and that, I think, in a way, is actually giving us an opportunity to go back and test the boundaries of their technology. They're building, which we believe this is the first of its kind in the media industry. If we can go learn together from this experience, we can actually go ahead and do other things in other industries. To maybe, and we were just talking about how we could also take this. He's got his labs here in Los Angeles, were thinking maybe one of the next things we do based on the learnings we get, we probably could take it to other parts of the world. And if we are successful, we might even take it to other industries. What if we could go do something to help in this field of medicine? >> It's just thinking that, right? Yes. >> Think about it. Lisa, John. I mean, it's phenomenal. I mean, this is something Michael always talks about is how do we as del technologies help in progress in the human kind? And if this is something that we can learn from, I think it's going to be phenomenal. >> I think I think that's so interesting. Not only is that a good angle for Del Technologies, the thing that strikes me is the access toe artist trees, voices, new voices that may be missed in the prop the vetting process the old way. But, you know, you got to know where we're going. No, in the Venture Capital way seen this with democratization of seed labs and incubators, where, if you can create access to the story, tells on the artists we're gonna have one more exposure to people might have missed. But also as things change, like whether it's Ray Ray beaming and streaming, we saw in the gaming side to pull a metric or volumetric things. You're gonna have a better canvas, more paint brushes on the creative side and more. Artist. Is that the mission to get AC, get those artists in there? Is it? Is that part of the core mission submission? Because you're going to be essentially incubating new opportunities really fast. >> It's, uh, it's very important to me. Personally. I know it speaks of the values of both Sony and L. I like to call it the democratization of storytelling. You know, I've been very blessed again, a Hollywood producer, and we maybe curate a certain kind of movie, a certain kind of experience. But there's so many voices around the world that need to be hurt, and there are so many stories that otherwise can't be enabled. Imagine a story that perhaps is a unique >> special voice but requires distance. It requires five disparate locations Perhaps it's in London, Piccadilly Circus and in Times Square. And perhaps it's overto Abu Dhabi on DH Libya somewhere because that's part of the story. We can now collapse geography and bring those locations to a central place and allow a story to be told that may not otherwise have been able to be created. And that's vital to the fabric of storytelling worldwide's >> going change the creative process to you don't have to have that waterfall kind of mentality like we don't talk about intact. You're totally distributed content, decentralized, potentially the creative process going change with all the tools and also the visual tools. >> That's right. It's >> almost becoming unlimited. >> You wanted to be unlimited. You want the human spirit to be unlimited. You want to be able to elevate people on. That's the great thing about what we're trying to achieve and will achieve. >> It is your right. I mean, it is interesting, you know, we were just talking about this, too. Uh, we're in, you know, as an example. Shock tank. Yes, right. I mean, they obviously did it. The filming and stuff, and then they don't have the access. Let's say to the right studio. But the fact is, they had all this done. Andi, you know, they had all the rendering they had captured. Already done. You could now go out and do your chute without having all the space you needed. >> That's right. In the case of Shark Tank, which shoots a Sony Pictures studios, they knew they had a real estate issue. The fact of the matter is, there's a limited amount of sound stages around the world. They needed to sound stages and only had access to one. So we went in and we did a volumetric image acquisition of their exit interview stage. They're set. And then when it came time to shoot the second half a season ten, one hundred contestants went into a virtual set and were filmed in that set. And the funny thing is, one of the guys in the truck you know how you have the camera trucks and, you know, off offstage, he leaned into the mike. Is that you guys, could you move that plant a couple inches to the left and somebody said, Uh, I don't think we can do it right now, he said, We're on a movie lot. You could move a plant. They said No, it's physically not there. We're on innovation studios goes Oh, that's right. It's virtual mind. >> So he was fooled. >> He was pulled. In a way, we're >> being hashing it out within a team. When we heard about some of the things you know Glenn and Team are doing is think about this. If you have to teach people when we are running short of doctors, right? Yeah, if you could. With this technology and the learnings that come from here, if you could go have an expert surgeon do surgery once you're captured, it would be nice. Just imagine, to take that learning, go to the new surgeons of the future and trained them and so they can get into the act without actually doing it. So my point and all this is this is where I think we can take technology, that next level where we can not only learn from one specific industry, but we could potentially put it to human good in terms of what we could to and not only preparing the next of doctors, but also take it to the next level. >> This was a great theme to Michael Dell put out there about these new kinds of use case is that the time is now to do before. Maybe you could get there technology, but maybe aspirational. Hey, let's do it. I could see that, Glenn, I want to ask you specifically. The time is now. This is all kind of coming together. Timing's pretty good. It's only gonna get better. It's gonna be good Tech, Tech mojo Coming for the creative side. Where were we before? Because I can almost imagine this is not a new vision for you. Probably seen it now that this house here now what was it like before for, um and compare contrast where you were a few years ago, maybe decades. Now what's different? Why? Why is this so important >> for me? There's a fundamental change in how we can create content and how we can tell stories. It used to be the two most expensive words in the movie TV industry were what if today that the most important words to me or what if Because what if we could collapse geography? What if we could empower a new story? Technology is at a place where, if we can dream it. Chances are we can make it a reality. We're changing the dynamics of how we may content. He used to be lights, action camera. I think it's now lights, action, compute power action, you know, is that kind of difference. >> That is an amazing vision. I think society now has opportunities to kind of take that from distance learning to distance connections, the distance sharing experiences, whether it's immersion, virtual analog face, the face could really be powerful. Yeah, >> and this is not even a year old. >> That's right. >> So if you look at your your launch, you said, I think let june fourth twenty eighteen. What? Where do you go from here? I mean, like we said, this is like, unlimited possibilities. But besides putting Robbie in the movie, naturally, Yes, of course I have >> a star here >> who? E. >> So I got to say he's got star power. >> What's what's next year? Exactly? >> Very exciting. I will say we have shark tank Thie Advanced Imaging Society gives an award for being the first volume met you set ever put out on the airwaves. Uh, for that television show is a great honor. We have already captured uh, men in black. We captured a fifty thousand square foot stage that had the men in black headquarters has been used for commercials to market the film that comes out this June. We have captured sets where television shows >> and in hopes, that they got a second season and one television show called up and said, Guys, we got the second season so they don't have to go back to what was a very expensive set and a beautiful set >> way captured that set. It reminds me of a story of productions and a friend of mine said, which is every year. The greatest gift I have is building a beautiful set and and to me, the biggest challenges. When I say, remember that sent you built four years ago? I need that again. Now you can go >> toe. It's hard to replicate the exact set. You capture it digitally. It lives. >> That's exactly it. >> And this is amazing. I mean, I'd love to do a cube set into do ah, like a simulcast. Virtually. >> So. This is the next thing John and Lisa. You guys could be sitting anywhere going forward >> way. You don't have to be really sitting here >> you could be doing. What do you have to do? And, you know, you got everything rendered >> captured. We don't have to come to Vegas twenty times a year. >> We billed upset once. You >> know you want to see you here believing that So I'LL take that >> visual is a really beautiful thing. So if we can with hologram just seeing people doing conscious with Hollywood. Frank Zappa just did a concert hologram concert, but bringing real people and from communities around the world where the localization diversity right into a content mixture is just so powerful. >> Actually, you said something very interesting, John, which is one of the other teams to which is, if you have a globally connected society and he wanted try and personalize it to that particular nation ethnicity group. You can do that easily now because you can probably pop in actors from the local area with the same. Yeah, think about it. >> It's surely right. >> There's a cascade of transformations that that this is going Teo to generate. I mean just thinking of how different even acting schools and drama schools will be well, teaching people how to behave in these virtual environments, right? >> How to immerse themselves in these environments. And we have tricks up our sleeves that Khun put the actor in that moment through projection mapping and the other techniques that allow filmmakers and actors to actually understand the world. They're about to stepped in rather than a green screen and saying, OK, there's going to be a creature over here is gonna be blue Water falls over there will actually be able to see that environment because that environment will exist before they step on the stage. >> Well, great job the Del Partnership. On my final question, Glenn, free since you're awesome and got a great vision so smart, experienced, I've been really thinking a lot about how visualization and artistry are coming together and how disciplines silo disciplines like music. They do great music, but they're not translating to the graphics. It was just some about Ray tracing and the impact with GP use for an immersive experiences, which we're seeing on the client side of the house. It del So you got the back and stuff you metrics. And so, as artist trees, the next generation come up. This is now a link between the visual that audio the storytelling. It's not a siloed. >> It is not >> your I want to get your vision on. How do you see this playing out and your advice for young artists? That might be, you know, looked as country. What do you know? That's not how we do it. >> Well, the beautiful thing is that there are new ways to tell stories. You know, Hollywood has evolved over the last century. If you look at the studios and still exist, they have all evolved, and that's why they do exist. Great storytellers evolved. We tell stories differently, so long as we can emotionally relate to the story that's being told. I say, Do it in your own voice. The cinematic power is among us. We're blessed that when we look back, we have that shared experience, whether it's animate from Japan or traditional animation from Walt Disney everybody, she shares a similar history. Now it's opportunity to author our new stories, and we can do that and physical assets and volumetric assets and weaken blend the real and the unreal. With the compute power. The world is our oyster. >> Wow, >> What a nice >> trap right there. >> Exactly. That isn't my job. The transformation of of Hollywood. What it's really like the tip of the iceberg. Unlimited story potential. Thank you, Glenn. Thank you. This has been a fascinating cannot wait to hear, See and feel and touch What's next for Sony Animation studios With your technology power, we appreciate your time. >> Thank you. Thank you both. Which of >> our pleasure for John Carrier? I'm Lisa Martin. You're watching the Cube lie from Del Technologies World twenty nineteen We've just wrapped up Day two we'LL see you tomorrow.

>> Live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas. Lisa Martin with John Ferrier. You're watching the Cube live at Del Technologies World twenty nineteen. This is our second full day of Double Cube set coverage. We've got a couple of we got a really cool conversation coming up for you. We've got Robbie Pender County, one of our alumni on the cue back as VP product management server solutions. Robbie, Welcome back. >> Thank you, Lisa. Much appreciated. >> And you brought some Hollywood? Yes, Glenn Glenn er, president of Sony Innovation Studios. Glenn and welcome to the Cube. >> Thank you very much. It's great to be here. >> So you are love this intersection of Hollywood and technology. But you're a filmmaker. >> Yeah, I have been filming movies for many years. I started off making motion pictures for many years. Executive produced him and oversaw production for them at one of our movie labels called Screen Gems, which is part of Sony Pictures. >> Wait a tremendous amount of evolution of the creative process being really fueled by technology and vice versa. Sony Innovation Studios is not quite one year old. This is a really exciting venture. Tell us about that and and what the The impetus was to start this company. >> You know that the genesis for it was based out of necessity because I looked at a nice Well, you know, I love making movies were doing it for a long time. And the challenge of making good pictures is resource is and you never get enough money. Believe or not, you never get enough money and never get enough time. That's everybody's issue, particularly time management. And I thought, Well, you know, we got a pretty good technology company behind us. What if we looked inward towards technology to help us find solutions? And so innovation studios is born out of that idea on what was exciting about it was to know that we had, uh, invited partners to the game right here with Del so that we could make movies and television shows and commercials and even enterprise solutions leaning into state of the art and cutting edge technology. >> And what some of the work private you guys envision coming out this mission you mentioned commercials TV. Is it going to be like an artist's studio actor actress in ball is take us through what this is going to look like. How does it get billed out? >> I lean into my career as a producer. To answer that one and say is going to enable that's one of the greatest things about being a producer is enabling stories, uh, inspiring ideas to be green lit that may not have been able to be done so before. And there's a key reason why we can't do that, because one of our key technologies is what we call the volumetric image acquisition. That's a lot of words. You probably say. What the heck is that? But a volumetric image acquisition is our ability to capture a real world, this analog world and digitize it, bring it into our servers using the power of Del and then live in that new environment, which is now a virtual sets. And that virtual set is made out of billions and trillions in quadrillions of points, much like the matter around us. And that's a difference because many people use pixels, which is interpretation of like we're using points which is representative of the world around us, so it's a whole revolutionary way of looking at it. But what it allows us to do is actually film in it in a thirty K moving volume. >> It's like a monster green screen for the world. Been away >> in a way, you're you're you're interaction around it because you have peril X, so these cameras could be photographing us. And for all you know, we may not be here. Could be at stage seven at Innovation Studios and not physically here, but you couldn't tell the >> difference. This is like cloud computing. We talking check world, you don't the provisional these resource is you just get what you want. This is Hollywood looking at the artistry, enabling faster, more agile storytelling. You don't need to go set up a town and go get the permit. All the all the heavy lifting you're shooting in this new digital realm. >> That's right. Exactly. Now I love going on location on There's a lot to celebrate about going on location, but we can always get to that location. Think of all the locations that we want to be in that air >> base off limits. Both space, the one I >> haven't been, uh, but but on said I've been I've walked on virtual moons and I've walked on set moons. But what if we did a volumetric image acquisition of someone set off the moon? Now we have that, and then we can walk around it. Or what if there's a great club, a nightclub? This says guys and wanted to shoot here. But we have performances Monday night, Tuesday night, Wednesday night there. You know they have a job. What? We grabbed that image acquired it. And then you could be there anytime you want. >> Robbie, we could go for an hour here. This is just a great comic. I >> completely agree with >> you. The Cube. You could You could sponsor a cube in this new world. We could run the Q twenty four seven is absolutely >> right. And we don't even have >> to talk about the relationship with Dale because on Del Technologies, because you're enabling new capabilities. New kind of artistry, just totally cool. Want to get back to the second? But you guys were involved. What's your role? How do you get involved? Tell the story about your >> John. I mean, first and foremost one of the things didn't Glendon mention is he's actually got about fifty movies to his credit. So the guy actually knows this stuff. So which is absolutely fantastic. So we said, How do you go take coverage to the next level? So what else is better than trying to work something out, wherein we together between what Glenn and Esteem does at the Sony Innovation Labs for Studio Sorry. And as in Dead Technologies could do is to try and actually stretch the boundaries of our technology to a next tent that when he talks about kazillion bytes of data right one followed by harmony, our zeros. We have to be able to process the data quickly. We have to be able to go out and do their rendering. We probably have to go out and do whatever is needed to make a high quality movie, and that, I think, in a way, is actually giving us an opportunity to go back and test the boundaries of their technology. They're building, which we believe this is the first of its kind in the media industry. If we can go learn together from this experience, we can actually go ahead and do other things in other industries do. Maybe. And we were just talking about how we could also take this. He's got his labs here in Los Angeles, were thinking maybe one of the next things we do based on the learning to get. We probably could take it to other parts of the world. And if we are successful, we might even take it to other industries. What if we could go do something to help in this field of medicine? >> It's just thinking that, right? Yes. Think >> about it. Lisa, John. I mean, it's phenomenal. I mean, this is something Michael always talks about is how do we as del technologies help in progress in the human kind? And if this is something that we can learn from, I think it's going to be phenomenal. >> I think I think that's so interesting. Not only is that a good angle for Del Technologies, the thing that strikes me is the access to artist trees, voices, new voices that may be missed in the prop the vetting process the old way. But, you know, you got to know where we're going. No, in the venture, cobble way seen this with democratization of seed labs and incubators where, if you can create access to the story, tells on the artists we're gonna have one more exposure to people might have missed. But also as things change, like whether it's Ray Ray beaming and streaming we saw in the gaming side to volumetric or volumetric things, you're gonna have a better canvas, more paint brushes on the creative side and more action. Is that the mission to get AC Get those artists in there? Is it? Is that part of the core mission submission? Because you're going to be essentially incubating new opportunities really fast. >> It's, uh, it's very important to me. Personally. I know it speaks of the values of both Sony and L. I like to call it the democratization of storytelling. You know, I've been very blessed again, a Hollywood producer, and we maybe curate a certain kind of movie, a certain kind of experience. But there's so many voices around the world that need to be hurt, and there are so many stories that otherwise can't be enabled. Imagine a story that perhaps is >> a unique special voice but requires distance. It requires five disparate locations. Perhaps it's in London Piccadilly Circus and in Times Square. And perhaps it's overto Abu Dhabi on DH Libya somewhere because that's part of the story. We can now collapse geography and bring those locations to a central place and allow a story to be told that may not otherwise have been able to be created. And that's vital to the fabric of storytelling. Worldwide >> is going to change the creative process to You don't have to have that waterfall kind of mentality like we don't talk about intact. You're totally distributed content, decentralized, potentially the creative process going change with all the tools and also the visual tools. >> That's right. It's >> almost becoming unlimited. >> You want it to be unlimited. You want the human spirit to be unlimited. You want to be able to elevate people on. That's the great thing about what we're trying to achieve and will achieve. >> It is your right. I mean, it is interesting, you know, we were just talking about this too. We're in, you know, as an example, shock tank. Yes, right. I mean, they obviously did it the filming and stuff, and then they don't have the access, let's say to the right studio, but The fact is, there had all this done on DH. No, they had all the rendering. They had the captured already done. You could now go out and do your chute without having all the space you needed. >> That's right. In the case of Shark Tank, which shoots a Sony Pictures studios, they knew they had a real estate issue. The fact of the matter is, there's a limited amount of sound stages around the world. They needed to sound stages and only had access to one. So we went in and we did a volumetric image acquisition of their exit interview stage. They're set. And then when it came time to shoot the second half a season ten, one hundred contestants went into a virtual set and were filmed in that set. And the funny thing is, one of the guys in the truck you know how you have the camera trucks and, you know, off offstage, he leaned into the mike. Is that you guys, could you move that plant a couple inches to the left and somebody said, Uh, I don't think we can do it right now, he said. We're on a movie lot. You could move a plant. They said, No, it's physically not there. We're on innovation studios goes Oh, that's right. It's virtual mind. >> So he was fooled. >> He was pulled. In a way, we're >> being hashing it out within a team. When we heard about some of the things you know Glenn and Team are doing is think about this. If you have to teach people when we are running short of doctors, right? Yeah, if you could. With this technology and the learnings that come from here, if you could go have an expert surgeon do surgery once you're captured, it would be nice. Just imagine, to take that learning, go to the new surgeons of the future and trained them and so they can get into the act without actually doing it. So my point in all this is this is where I think we can take technology, that next level where we can not only learn from one specific industry, but we could potentially put it to human good in terms of what we could to and not only preparing the next of doctors, but also take it to the next level. >> This was a great theme to Michael Dell put out there about these new kinds of use case is that the time is now to do before. Maybe you couldn't get there with technology, but maybe aspirational, eh? Let's do it. I could see that. Glenn, I want to ask you specifically. The time is now. This is all kind of coming together. Timing's pretty good. It's only gonna get better. It's gonna be good. Tech, Tech mojo Coming for the creative side. Where were we before? Because I could almost imagine this is not a new vision for you. Probably seen it now that this house here now what was it like before for, um and compare contrast where you were a few years ago, maybe decades. Now what's different? Why? Why is this so important? >> You know, for me, there's a fundamental change in how we can create content and how we can tell stories. It used to be the two most expensive words in the movie TV industry were what if today that the most important words to me or what if Because what if we could collapse geography? What if we could empower a new story? Technology is at a place where if we can dream it. Chances are we can make it a reality. We're changing the dynamics of how we may content. He used to be lights, action, camera. I think it's now lights, action, compute power action, you know, is that kind of difference. >> That is an amazing vision. I think society now has opportunities to kind of take that from distance learning to distance connections, the distance sharing experiences, whether it's immersion, virtual analog face the face. I could really be powerful. Yeah, >> and this is not even a year old. >> That's right. >> So if you look at your your launch, you said, I think let june fourth twenty eighteen. What? Where do you go from here? I mean, like we said, this is like, unlimited possibilities. But besides putting Robbie in the movie, naturally, Yes, of course I have >> a star here >> who video. >> So I got to say he's got star power. >> What's what. The next year? Exactly. >> Very exciting. I will say we have shark tank Thie Advanced Imaging Society gives an award for being the first volume metric set ever put out on the airwaves. Uh, for that television show was a great honor. Uh, we have already captured, uh, men in black. We captured a fifty thousand square foot stage that had the men in black headquarters has been used for commercials to market the film that comes out this June. We have captured sets where television >> shows and in the in hopes that they got a second season and one television show called up and said, Guys, we got the second season so they don't have to go back to what was a very expensive set and a beautiful set >> Way captured that set. It reminds me of a story of productions and a friend of mine said, which is every year. The greatest gift I have is building a beautiful set and and to me, the biggest challenges. When I say, remember that sent you built four years ago. I need that again. Now you can go >> toe hard, replicate the exact set, you capture it digitally. It lives. >> That's exactly it. >> And this is amazing. I mean, I'd love to do a cube set into do ah, like a simulcasts. Virtually. >> So. This is the next thing John and Lisa. You guys could be sitting anywhere going forward. We don't have to be really sitting here you could be doing. What do you have to do? And, you know, you got everything rendered >> captured. We don't have to come to Vegas twenty times a year. >> We billed upset once >> You want to see you here believing that So I'LL take that >> visual is a really beautiful thing. So if we can with hologram just seeing people doing conscious. But Hollywood Frank Zappa just did a concert hologram concert, but bringing real people and from communities around the world where the localization diversity right into a content mixture is just so powerful. >> Actually, you said something very interesting, John, which is one of the other teams to which is, if you have a globally connected society and he wanted try and personalize it to that particular nation ethnicity group. You can do that easily now because you can probably pop in actors from the local area with the same city. Yeah, think about it. >> It's surely right. >> There's a cascade of transformations that that this is going Teo to generate. I mean just thinking of how different even acting schools and drama schools will be well, teaching people how to behave in these virtual environments, right? >> How to immerse themselves in these environments. And we have tricks up our sleeves that Khun put the actor in that moment through projection mapping and the other techniques that allow filmmakers and actors to actually understand the world. They're about to stepped in rather than a green screen and saying, OK, there's going to be a creature over here is gonna be blue Water Falls over there will actually be able to see that environment because that environment will exist before they step on the stage. >> Well, great job the Dale Partnership On my final question, Glenn free since you're awesome and got a great vision so smart, experienced, I've been really thinking a lot about how visualization and artistry are coming together and how disciplines silo disciplines like music. They do great music, but they're not translating to the graphics. It was just some about Ray tracing and the impact with GP use for immersive experiences, which was seeing on the client side of the house. It del So you got the back and stuff, but you metrics. And so, as artist trees, the next generation come up. This is now a link between the visual that audio, the storytelling. It's not a siloed. >> It is not >> your I want to get your vision on. How do you see this playing out and your advice for young artists? That might be, you know, looked as country. What do you know? That's not how we do it. >> Well, the beautiful thing is that there are new ways to tell stories. You know, Hollywood has evolved over the last century. If you look at the studios and still exist, they have all evolved, and that's why they do exist. Great storytellers evolved. We tell stories differently, so long as we can emotionally relate to the story that's being told. I say Do it in your own voice. The cinematic power is among us. We're blessed that when we look back, we have that shared experience, whether it's animate from Japan or traditional animation from Walt Disney, everybody shares a similar history. Now it's opportunity to author our new stories and we can do that and physical assets and volumetric assets and weakened blend the real and the unreal. With the compute power. The world is our oyster. >> Wow, >> What a nice >> trap right there. >> Exactly that is, um I dropped the transformation of Hollywood. What? And it's really think the tip of the iceberg. Unlimited story potential. Thank you, Glenn. Thank you. This has been a fascinating cannot wait to hear, See and feel and touch What's next for Sony Animation studios With your technology power We appreciate your time. >> Yeah, Thank you. Thank you both of >> our pleasure for John Farrier. I'm Lisa Martin. You're watching the Cube lie from Del Technologies World twenty nineteen We've just wrapped up Day two we'LL see you tomorrow.

>> (narrator) Live from Las Vegas, it's the Cube, covering VMWorld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back to Las Vegas everybody. You're watching the Cube, the leader in live-tech coverage. My name is Dave Vellante and I'm here with my co-host, David Floyer. Good to see you again David. VMWorld day three, wall to wall coverage. We got sets going on. 94 guests. Patrick Osborne is here, he's the Vice President of Big Data and Secondary Storage at Hewlett Packard Enterprise. Patrick, it's great to see you again. >> Always a pleasure to be on the Cube. >> Big quarter, Antonio Neri early into his tenure. >> Yes. The earnings, raise guidance, great to see that. Got to feel good. Give us the update, VMworld 2018, what's happening with you guys? >> So Q3 was bang up quarter, for all segments of the business. It was great, you know. Obviously it's the kind of earnings you want to have from a CEO in a second quarter. Steering the ship here. I think everyone's jazzed up. He's brought a lot of new life to the company, in terms of technology leadership. He's someone who's certainly grown up, from the grounds up, starting off his career at HPE. So for us who have started off as a Product Manager, an individual contributor, making your way up to CEO is definitely possible. So that's been great and I think it's favorable micro economics and we're taking advantage of that. VMworld's been awesome. I think this whole story around Multicloud and obviously we talk about hybrid IT at HPE, so it fits very well. VMware Technology, partner of the year, again. Four years running, so it's been a really good show for us. >> As last year, data protection is the single, hottest topic. Data protection, obviously Cloud, The Edge, but The Edge is kind of new and it's hot, it's sexy. But in terms of actual business that's getting done, companies that are getting funded, companies getting huge raises, throwing big parties. We saw you back to back nights at Omnia, it's a lot happening in data protection. HPE has got a whole new strategy around data protection. Maybe talk about that a little bit and how it's going. >> So it's going really well, like you said, that part of the market, it's pretty hot right now. I think there's a couple of things playing into that, certainly this new style of IT, like applied to secondary storage. We saw that with primary storage the last few years. Multicloud, the move to all flash, low-latency workloads. And then, certainly a lot of the things, in that area, are disrupting secondary storage. People want to do it different ways, they want to be able to simplify this area. It's a growing area for data, in general. They want to make that data work for them. Test, Dev, workload placement, intelligent placement of data, for secondary and even tertiary storage in the cloud. So a lot of good things happening, from an HPE perspective. >> So not just back up? >> No, not just back up. >> I want more out of my insurance policy. >> Exactly. Something in the past that was moving from purely a TCO type of conversation. My examples are always like, who likes to pay their life insurance premium, right? Because at the end of the day, I'm not going to derive any utility from that payment. So now, it's moving into more ROI. So we have things like, the Hybrid Flash Array, from Nimble, for example. It allows you to put your workloads to work. We have a great cloud service, called HPE Cloud Volumes, that we use for our customers to be able to do intelligent DR, as a service, and be able to apply Cloud compute to your data. So there's a lot of things going on, in the space, that's just outside of your traditional move data from point A to point B. Now you want to make it work for you. >> And what about the big data portfolio? You hear a lot about data. You don't hear a ton about the Big Data, Hadoop piece of the world. I know Hadoop, nobody seems to be talking about that anymore. But everybody's talking about AI, Machine-Learning, Deep-Learning. Certainly The Edge is all about data. What's the Big Data story? >> So at HPE, we're definitely focused on the whole Edge to Core analytic story. So we have a great story and you can see in the numbers from Q3, The Edge business, The Edge line servers, Aruba, driving a lot of growth in the company, where a lot of that data is being created. And then back into the Core, so for Big Data, we see a number of customers, who are using these tools to affect digital transformation. They're doing it, we're doing it to ourselves. So they're moving from batch oriented, to now fast data, so streaming analytics. And then, incorporating concepts of AI and ML to provide better service or better experience for their customers. And we're doing that with, for example, InfoSight. So we have a great product, Nimble, 3PAR. And then we provide a service, on top of that, which is a SAS based service. It has predictive analytics and Machine Learning. And we're able to do that, by using Big Data analytics. >> You're offering that as a service, as a SAS service to your customers? >> Absolutely. And the way we're able to provide those predictive analytics and be able to provide those recommendations and that Machine-Learning across a entire portfolio and be able to scale that service, because it's a service, we got tens of thousands of users using the service on a daily basis, is moving from an ERP system, data warehouse, to batch analytics, to now we're doing Elasticsearch and Kafka and all these really cool techniques, so it's really helped us unlock a lot of value for our customers. >> So, the Nimble acquisision is interesting, it's bringing that sort of Machine-Learning and AI to infrastructure. You got a lot of automation in the portfolio and you can't really talk about Cloud without talking about automations. So talk a little about automation. >> In particular, even at the show here this week, we are a premier technology partner with VMware and I think more that you see in the VMware Ecosystem is all around Cloud and automation. That's really where they're going. And we've been day-zero partners on a lot of different fronts. So VMware Cloud Foundation integration, we do things on the storage level with Vvols and SRM and all these things that allow customers to essentially program that infrastucture and get out of the mundane tasks of having to do this manually. So for us, automation is key part of our story here. Especially with VMware. >> So going a little bit further with that, what sort of examples, what benefit is this to your customers? How are they justifying putting all this in? >> It's a hybrid world, so our customers are going to expect, from us, as a portfolio vendor, the ability to provide an automated solution, on premises, as automated as what you'd get in the cloud. So for us, the ability to have a sourcing experience, that we call GreenLake, so you can buy everything from us, from a solution perspective, in a pay-as-you-go elastic model where you can flex-up, flex-down. And then being able to, essentially provide a different view, depending on what persona you're coming from. Obviously we've been focused on the infrastructure persona, more often, we're getting into the DevOps persona, the Cloud engineer persona, providing all of our infrastructure, whether it's computer networking or storage, that plugs into all these frameworks. Whether it's Ansible, Chef and all these things that we do around our automation ecosystem, it's pretty ubiquitous. >> You're touching on all the Cloud basis and you're seeing a lot of discussion around that. What are you hearing from customers? Sometimes we have to squint through this, a lot of the guys here, we always like to say, move at the speed of the CIO, which sometimes is slow. At the same time, they're all afraid they're going to get disrupted. HPE, over the last two or three years, has really brought in and partnered with some of the guys your talking about. Whether it's containers and companies that do those types of offerings. How fast do the customers actually adopting, where they adopting them, how are they handling, you talked about a hybrid world; How are they bridging the old and the new? >> That's a great question. For a lot of our customers, it's always a brown field conversation. You do have these mission critical workloads that have to run, so there's no Edge to Core without your core ERP system, right? Your Core Oracle System or for smaller customers that are running their businesses on SQL and other things. But what we're seeing is that, by shoring up that Core and we provide a set of services and products that we feel are the best in the industry for that. And then allow them to provide adjacent services on top of that, it's exactly like the same example we had with InfoSight, where those systems use to call home, right now we're taking that data, we're providing a whole ancillary set of services and functions around it and our customers are doing that. Enormous customers, like British Telecom, folks like Wayfair, for example, they're doing this on premises and their disrupting their competitors, in the mean time. >> What do you make of some of the announcements we've heard this week? Obviously VMware making a big deal with what's going on with AWS. We're seeing AWS capitulate, David Floyer you made the call. Got to have an on-prem strategy. Many said no, that'll never happen. They just want to sweep the floor. So that's a tip to the hybrid cap. What are your thoughts on what's going on there? How does HPE sort of participate in those trends? >> I'd say it's, instead of battle and capitulate, we've been very laser-focused on the customers and helping them, along their way, on the journey. So you see a lot of acquisitions we've done around services, advisory service. CTP is a perfect example. So CTP has a whole cadre of experts who understand AGER, who understand ECS and all the services and functions that go along with them And we're able to help people, right size, right place, whatever you want to call it, within their infrastructure. Because we know, we've been in business for 75+ years and have a very loyal customer base, and we're going to help them along their maturity curve and certainly everyone's not on the same path, in the same race. It's been pretty successful so far. >> You guys tend to connect the dots between your HPE Discover in U.S., in Las Vegas and HPE Discover in December. So June to December, you're on these six month cycles, U.S. focus and Europe focus, Decembers in Madrid, again. Second year of Madrid. U.S. is always Vegas, like most of these conferences, what's the cadence that your on? What was the vibe like at Discover? What should we expect leading up to Q4, calendar Q4 in Madrid? >> I'd say that Discover was a big success in Vegas, always fun to spend time here. In Madrid, you'll see a focus around the value part of our business. So we've been growing in automation, we talked about hybrid IT, certainly the Core around storage. We're really focusing and very heavily invested in, not just storage, but intelligent data management. So we really feel that our offerings, especially doubling down and offering more services around InfoSight and some of those predictive and Cloud-ready user stories for our customers is something that definitely differentiates ourselves in the market. So we'll be very focused on the data plan, the data layer and helping customers transform in that area. >> So let's talk some tenor sax. >> (David laughs) >> This is not New Orleans. When we were down in New Orleans, we were at VeeamON, I think you had your sax with you, you jumped in. >> That's right, I played with the Soul Rebels. >> Playing with the Soul Rebels, you were awesome. Leonard, a big jazz man. Love it. I'm a huge TOP fan. What's new in that world? Are you still active? Are you still playing? >> Yeah, the band's still playing. Shout out to my buddies in Jolpe, sitting in with some friends at a Dead cover band coming up, in a couple weeks. So, should be fun. We're going to reenact The Grateful Dead and Branford Marsalis. >> That's wonderful. >> It should be fun. >> We've been getting a big dose of hip-hop this week. >> Yeah. But the new thing is that, in hip-hop, it's getting back to it's original roots, so a lot of folks in the jazz world, collaborating with the folks in the hip-hop world, so not very commercial, definitely underground, but pretty cool. >> I love it. That's right Leonard, you pointing out Miles Davis was one of the first to make that transformation. >> Yeah >> Good call. >> I'm going to get the numbers wrong, but it's about five percent technique and 95 percent attitude. (multiple laughs) >> Jazz, like hip-hop, there's a lot guys just doing their own thing. And somehow it all comes together. >> Absolutely. >> Okay Patrick, great to see you. >> Great to see you guys. Thank you Dave. Yeah, good to see you guys. >> Always a pleasure, go Sox. >> We got some time for talk stocks? >> Alright. >> What do you think? It's getting a little nerve wrecking. >> #Bucky Dent is trending in my Twitter. That's my problem, so hopefully we can..., I definitely don't want to be limping into the playoffs, and still not a fan of this one team wild card playoff, but I think we'll be alright. >> If we go deep... It's a great time to be a Boston fan. >> Celtics. >> Football starting, Celtics are coming in November, so awesome. Great to see you man. >> Thanks for having me. >> Keep it right there everybody, we'll be right back with our next guest. You're watching the Cube, live. Day three at VMWorld 2018, we'll be right back. (techno music)

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios for a Cube Conversation as part of the Western Digital Data Makes Possible program it's very gracious of Western Digital to sponsor us to go out and talk to a lot of different companies that are doing a lot of cool innovations. At the end of the day it's all powered by data, at the end of the day all software is just an algorithm sitting on data with a nice display for a specific solution. But this one we're diving into sports and there's so much going on with sports and technology and this is a great company that's actually been kind of flying under the radar for 10 years unless you're into the space. But we're happy to have them as GumGum and we're joined here by Brian Kim he's a senior vice president of Product from GumGum. Brian, great to see you. >> Thanks for having me Jeff. Appreciate it. >> Absolutely. So for the folks that aren't familiar with GumGum give 'em kind of the quick overview. >> Sure, so GumGum's a artificial intelligence company with a expertise in computer vision. And what that means in kind of common language is that we focus on building algorithms that allows computers to identify what's happening in imagery. And then we apply that into different businesses that we feel like the usage of computer vision could essentially automate scale or drive significant value to those different businesses themselves. >> Right, but you guys have been at this for awhile you're almost 10 years old, like you said your 10 year anniversary's coming up. >> Yeah we were founded in 2008 and a lot of the core team still together from the original team that worked there. And we were doing computer vision before computer vision was probably sexy. >> Right right. >> So. We've been working on it for a long time we've built a lot of expertise around that and with a lot of the improvements that have happened in machine learning and A.I. these days it's just been kind of the big, hot thing that has continued to accelerate and helped us grow significantly over the last few years. >> Yeah all of our social media feeds are filled with the picture with the chihuahuas and the blueberries right? Trying to figure out which is which. But you guys have a very different approach then kind of what we read about now in the popular press. You built computer vision capabilities but you built them for a very specific application not just as a generic kind of computer vision so I wonder if you can tell us a little bit about your strategy and how you guys got started in the ad space. >> Sure, so you know, to your point Jeff I think a lot of companies have focus on what we like to call A.I. as a service and what that means is that they build the capability of using computer vision but it's really up to the end user to build it use it as a tool in to their actual business and figure out where it'll actually apply. Our path forward has been focusing on building what we like to call full stack vertical solutions meaning that we decide to go into a specific industry or division like ads for example. We build an ad solution that's using computer vision itself and we actually sell that ourselves to agencies and brands direct and then we work with the publishers on the other side of the coin to actually deliver those ad experiences and continue to kind of build our businesses around that model. >> Right, and how is a computer vision, A.I. driven ad experience different than the alternative? >> Well I think there's a few things that we've focused on that make it different. The biggest I would say is the placement of where the ads actually are themselves. So compared to your standard display ads that are around the content on a web page what we've focused on is actually building ad experiences that are within the content itself. What we call in-image ads and that's an ad that overlays on top of that photo editorial content that's on a publisher website. And what we've done there is we've applied our computer vision to understand what's going on in the actual images. And we leverage that tech to be able to then contextually match the ads from our clients to the actual pages themselves so that they're completely relevant to the page. And we make them look very slick and that's kind of the design of it so that it feels very sticky and natural to the way that the page is actually designed. >> Right >> And built. >> So what's different just so I'm clear is that unlike a typical kind of an ad placement in a page which is going into a dedicated spot as soon as the page loads it takes some data. >> Yep. >> And goes to auction. You guys are actually looking at the page as I the consumer of the page am looking at the page, and based on the things that are loaded regardless of where I am on the page: top, middle, bottom, that's the stuff that drives your ad placement. >> Yeah, perfect example would be you might be reading an article about cats and, you know, PetSmart wants to advertise with us so we'll understand that the image itself is about cats and put like a cat food, PetSmart ad that's placed within the actual image itself you might scroll down a little bit farther and find another article that's about a completely different topic. And we would actually match that to the relevant advertiser that fits that example as well. >> So how are advertisers measuring the delta in the value? I mean some of the stuff researching for this I saw a great quote that you guys in your ad "Delivery want to be seen and frequent and respectful" which I think is a really interesting way to take a point of view because clearly ads run a risk of being way too obtrusive, popovers and popunders and popups. I go to a page, I'm a huge customer in two seconds they're asking me if I want to buy something, I'm already your biggest customer! (Brian laughs) So how are the content publishers measuring this different type of an engagement with an ad presentation the way you guys do it? >> Yeah I think what you talked about is the right approach of how we want to go to market which is that we want to provide a premium offering that's high impact but not obtrusive to the end customer, and provides value to both sides of the ecosystem. So to the advertiser it might be a premium CPM that they're paying in order for that ad placement but because of how relevant it is and how viewable it is 'cause if you think about where your eyes are on the webpage, you're not looking at scanning the sides of the page you're focusing on the content that's going down the image is right in the middle of that. So if an ad pops up right in the middle of that image its much more viewable than say all the ads that are typically around the content itself. And on the publishers side to kind of end frequency we don't want to blast an ad on every single image we want to match it to the most contextual and right placement and then therefore to the publisher when they get an ad, that ad is actually paying out a pretty significant price point back to them. And then they're happy with that experience because their users not saying I'm seeing 50 ads on one page which is kind of the traditional problem that they deal with right now. >> So that's a ad tech market and you guys have been doing that for awhile but the reason we reached out to you specifically is your activity in sports which is a relatively new business for you guys. So how did you get into sports, how did you guys identify this opportunity? And then we'll dig into it a little deeper. >> Yeah, so GumGum as a whole has always been looking at different ways that we think computer vision can be applied in a myriad of different industries. And the opportunity that about 18 months ago we identified was that there was a very legacy business that was being built around providing media evaluation around sports sponsorships. So what I mean by that is how do you quantify the value of a sponsor showing up, State Farm for example showing up in a basketball game. Whether that's an LED placement there on the basket stanchion arm, or they're part of the half-time show. And the measurement that was being done traditionally was essentially done by people. So, people were watching those clips, they were timing how long the different brands were showing up, they were measuring how big those actual placements were. And they were then calculating some value off of it. And we really thought that computer vision can one, automate that entire process, so take the humans out of the loop and get it to a point that its completely automated and you don't have to have people involved. We can deliver it faster, so a computer can do something a 1000 times faster than a human being can probably analyze it. And your providing much more accuracy and efficiency of the actual data that you're providing back. You know that exact dimensions pixel by pixel that a computer is telling you versus a human being trying to eyeball where and when certain ads are showing up. So, what we've done there is then built a business that is now called GumGum Sports, where we provide media evaluation to sports sponsorships so both on the team side, which we call rights holders, and on the brand side and we're essentially the middle man who's providing third party reporting to both sides so that they understand the value of what they're getting across their sports sponsorships, both on digital which is essentially broadcast T.V. but also across social media which has been a huge gap that nobody's addressed to date. >> So just before we go into the impact before it was just a person, they're watching the game and every time that State Farm ad pops up on the stanchion they're writing down approximately how big was it could I see it, was it blurry, was it moving? Was it in the center or the side? You guys, obviously that's just right for algorithmic treatment 'cause you know, like you said you know, the pixels. You're doing time, you're doing placement, you're doing quality, you've added a number of things beyond just simple, that is there. >> [Brian] Correct. >> In terms of metrics to measure. >> Yeah so we look at things like where's the action happening? So if we can identify in a basketball game where the actual ball is that's probably where people are focused on 'cause the action's happening near that ball. So the closer you are to the ball the better score that you'll possibly get. To your point, how clear is it if you're panning back and forth through a game of your logo showing up? How big is it, how prominent is it and we've factored that into what we call our MVP factor of media value percentage which helps calculate what that end value looks like to the client. >> And was the demand driven on the suppliers side or the buyers side, were they looking for validation of this money. >> I think both. >> Value or were you saying it was both really? >> Yeah sorry to interrupt you. >> No, it's okay. >> Both sides were looking for somebody who's not favoring one or the other to give you validation so they wanted an arbitrator who would basically say this is what I think the value is in the ecosystem so that both sides know how to negotiate when they want to put together their deals next year. >> [Jeff] Right. >> You know the value to the teams are the more value you can generate obviously the higher that they can increase their price points. And I think to the brands what they're focused on is how do I optimize my ROI? Buy the placements that are generating the most value for me and not waste my money in other placements that don't generate value for them. >> Right, any big surprises in terms of the value of a stanchion versus the value of a half-time show versus a electronic thing on the scoreboard? >> So I think more than the placements themselves I think the biggest surprise that we found was how big social media actually has become in the valuation of all of media in general. You know, there's a lot of talk about subscribers numbers going down on T.V. That broadcast is declining, that nobody's watching. >> Super Bowl was down I think this year? Which doesn't happen very often. >> You know, live sports is just not what it used to be. But the reality is I think consumers have just changed their habits of where they're consuming that content. So instead of having to sit in front of a T.V. for two hours they might go check the highlights on YouTube, they might go look at their Instagram stream and see a bunch of posts that are coming from fan accounts that they're following that give you the highlight clip. So, being able to measure that piece of it that nobody's done before what we found is that that value is actually as big if not bigger than the broadcast side of it. Which nobody has really quantified to this date. >> That's really interesting. So you're what sniffing hashtags or something around a particular event to grab that data how are you grabbing all the social data around say a basketball game or whatever? >> So that's where the computer vision actually gets applied so we don't even need to look at specific hashtags or specific accounts. We can look at the full stream literally all of social media that's available publicly and we're able to sift. >> [Jeff] Just plug into the API. >> Yes sift through all that with computer vision and say oh this is not a sport, oh this one happens to be a sport now I know that it's NFL, now I know it's tied to the Super Bowl. And then you now classify all that data and then figure out the actual post that you want to analyze and the ones you don't want to analyze. >> It's so interesting I can't help but think back to like the Grateful Dead back in the day they were the only band that would allow people to record at the concerts, right. There was this huge, you can't record and no pictures! And then they would trade the tapes in the parking lot before the game and you saw that too with a lot of professional teams, no phones, I was at a concert the other day and they're like no phones! No phones, I mean that is the way that people experience and expand and amplify these live events. And it sounds like what you guys are doing is really validating how important that is to all the people that are participating in that live event. >> I'll give you a perfect example with the NBA all-star week just happened in Los Angeles recently. If you look at the slam dunk contests half of the all-stars that were in the crowd had their phones up (Jeff laughs) and are basically recording something that they're probably going to post on their social media account later. >> With massive, massive, massive followers. >> Each one of them might have two to 10 million people who follow them so you multiply all that and that's probably a bigger audience then actually who'll tune in to TNT or whichever channel that happened to be watching live the actual slam dunk contest itself. >> That's crazy. So, I'm curious to know what the response is as you come back to this data obviously it's great news for the publishers right a bunch of value that they didn't even know they were delivering no one's even capturing. At the same time I would imagine the advertisers are thrilled to actually to see that they are getting this whole nother traunch of activation that they had no clue or at least no way to measure. >> Correct. I think that's been the biggest surprise to everybody is how much value has been unlocked to them and both sides are thrilled about it because now they can start to measure that on a consistent basis and then moving forward they can figure out how that fits into their overall plan for whether they want to charge more for their sponsorships or whether they want to price certain things in like social media that they never did before. >> Right right. So, my mind is going all kind of places, so could you on sniffing that feed find say the State Farm logo stay on the same thing. Where's State Farm logo showing up in a billboard that's on the 101 that happens to be in front of a pretty spot where people take bike paths. Are you seeing or even attempting to look for other kind of secondary social impacts of other forms of advertising outside of your core solution within the sports? >> Yeah, I mean we've started to get feedback of people who are interested in solutions like that whether it's digital out of home different kind of businesses that have built themselves around wanting to track this type of ROI and we've looked at a few use cases and talked to a couple clients that we're starting to dabble in now that might be interesting for us to build new businesses around. Just like the use case that you talked about with the digital out of home example. >> Right, another one of my favorite lines that gets thrown around a lot these days is in God we trust everybody else better bring data. So I'm just curious as to the feedback you're getting from both sides of that equation within the sports application of now we have this data I mean how is that impacting peoples evaluations, how is that impacting their business decision, just kind of generally how does moving from I think this is a good value, we bought it last year we're going to re-up this year, to here's all the impressions you got the quality of the impressions, a score, plus we've uncovered all this additional value I would imagine data driven decision making has got to be so refreshing in these environments. >> It is and I think the challenge that a lot of them had was that they were getting the data six to eight weeks later, so if you think of it from a brand prospective I'm already off to my next sponsorship six to eight weeks later I can't even think about what I previously did so for us to be able to give them a solution where they can get their data back in a week or less really helps them make smarter decisions to your point about taking data driven decision making and figure out real time how they want to adjust to how their audience is adjusting. >> And do they make a lot of real time corrections in those types of packages or are those like annual deals I would imagine in the sports thing. >> Yeah I think a lot of them at this point are still annual deals the way that they sign up for it but I think now that they're having access to this data they're starting to rethink that model and trying to figure out how do we need to change the way that we purchase these things in the future to better fit how they're getting the data around it. >> Has anyone repriced the inventory based on the data that's come out of the research. They increase the price of a stanchion and decrease, I'm just making stuff up, decrease the price of some other ad unit within the stadium based on some of the data that's come out of your system. >> We've had a lot of our clients talk about their plans of how they plan to go do that. I think we're only 18 months into this business so a lot of them are still in the first season or maybe halfway through the second season of working with us so they're still trying to figure out how to message that properly and what the right channel is for them to recoup those gains but I think the ability for them to start those conversations is something they've never had before so exposing that to them now allows them to really rethink how their business model is. >> It's such a cool example of how data actually allows both halves of the equation to do a better job It's really beneficial to everybody right it's not just one sided information that's giving somebody a big advantage over the other one. >> Exactly. >> All right so Brian before I let you go we're in 2018 still hard to believe I can't believe we're almost through the first quarter, we're ripping through it. Some of your priority's for 2018 what is GumGum working on what are you excited about if we sit down a year from now what are we going to be talking about? >> Yeah I mean we've been doing this advertising business since 2011 it's our most mature business so I'm definitely continually scaling that business from a automation standpoint and continually growing that particularly internationally has been one of our main goals for this year. As I said GumGum Sports is a pretty new business to us but we're expecting that to start to bring in significant revenue for us this year and want to see that growth happen. And we're also looking into new emerging areas where we potentially think computer vision can be applied just like we did in GumGum Sports. It could be the medical space it could be television there's a lot of different applications there that we haven't quite tapped into yet but we're starting to noodle around what are the right ways that we want to go after that and potentially where we want to invest in with how successful we've been so far. >> Yeah, the exciting opportunity ahead. >> Yeah. >> All right. All right Brian, he's Brian Kim, he's the senior vice president of Product from GumGum. Thanks for taking a few minutes out of your day and stopping by. >> Thanks Jeff. >> All right, pleasure. I'm Jeff Frick you're watching theCUBE catch ya next time, thanks for watching. (instrumental music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Narrator: From San Francisco, it's theCUBE covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Welcome back everyone. Live here in San Francisco at Moscone West is the exclusive coverage from theCUBE SiliconANGLE Media of the SDC 2017. I'm John Furrier the co-founder of SiliconANGLE Media and the co-host of theCUBE. My next guest is Amy Jo Kim who is the CEO of Shufflebrain. It's the parent company of gamethinking.io, a variety of other projects, and expert in the convergence of design, gaming, computer science, and et cetera. Welcome to theCUBE. >> It's a pleasure to be here. >> Thanks for coming on. Obviously we've been seeing the trend, the convergence trend for a while certainly in the tech industry. Computer science and social science coming together, that was our motto when we started our company eight years ago. But really to me the flashpoint was Steve Jobs had the technology-liberal arts crossroads. That really kind of spawned the beginning of a creative generation start thinking about the devices, how it all intersects, and not the pure play handheld. So gamers here at Samsung Development Conference and developers bring game mechanics in. That's communities, gamification, games themselves, user interface. What's your reaction to all this? You've designed a great bunch of interfaces. >> I'm, I think it's fantastic. I think what we're seeing is really a flashpoint that has several trends converging. One of the trends we have is developers, the folks here, you know are right here at this wonderful conference, they've grown up with games. They're familiar with the lexicon of games, with how games work. And so it's very natural for them when they start to build their own apps and say what will make this engaging to turn to games and look for inspiration in games? So that's been going on for a while and it's accelerating. We're also seeing that mobile technology, mobile phones, have become so ubiquitous that most of the traffic coming in on many people's experiences 70%, I recently ran a promotion for Shufflebrain, 70% of our traffic was mobile total traffic. So the ubiquity of mobile phones means that everybody's got a potential gaming machine or a machine where they can have a light, fun, engaging experience right in their pocket. So as you noted, we've moved away from single purpose game consoles, handheld or otherwise they still exist, but more and more what we see is the best games and the best game like experiences that might not be games but they the feel and the pull of games. Those are showing up on mobile phones like Samsung. >> And the screens are awesome. I'll say my Note 8 here is awesome and bigger and better and the graphics. But it's a generational shift too. Like my son was, we're designing a new app and we're kind of sitting at the drawing board and he's like, "Dad, you're a search generation. "No one searches anymore. "You actually type on the keyboard, that's like so old." So he brings up a point which is illuminated here. Which is you see voice touch, voice activation. Harman's got now the kind of interface with this audio. You're seeing cars all over the air with software. This is really the computer science, computer engineering culture interfacing with art. Where new user experiences are coming that quite frankly don't look the same. >> Exactly that's such a good point. So what's happening is that a lot of the user experiences, the back end neural networks, the AI, the sophisticated bots that we've been seeing in gaming for the last five or six years are trickling into the mainstream. And that's what you always see. Gaming is the canary in a coal mine. What we see now happening in games and what we saw a few years ago is becoming more mainstream. So if we look now at what's happening in gaming, that gives us a clue to 18 to 24 months out for app developers. >> Yeah we brought this up on day one. You nailed it. It's an early indicator. >> That's right. >> What are you seeing in that area? Because you're in the vanguard of the user interface so you have a computer science background. You understand how communities work. Which by the way, you look at anything from blockchain ICOs to game communities, community is the most important aspect right now in the world. The community role of the people are so important. You don't have a network effect. You don't have input output into the quote neural aspect of the interface because now people are involved. Not just software and data bits. I need a notification from my friend if they're right around the corner from me. So it's the role of people. >> Exactly, so I'm a multiplayer game designer. The teams I work with, because it's always a team effort, are multiplayer games. Rock Band, Covet Fashion is a more recent one. And so we've known for a long time in the gaming industry that if you want to drive deep lasting engagement, you need to create a multiplayer experience and some sort of community around that. What you'll hear gamers say is "You know, I'm kind of tired of that game "but my friends need me. "It's where my friends are, my team needs me." So that's part of what drives long term engagement. >> John: The socialization piece. >> Exactly. What we're seeing now and the opportunity I think for developers even outside of gaming is we're seeing the intersection of gaming, a style of gaming that's sort of I would call them gaming systems versus game mechanics. We're seeing gaming systems find their way into social media. Musical.ly is a great example. And Discord is another example. Discord is a platform started by gamers but now it's merging into just other people. That's for communication. Sort of like a next generation Slack but mobile and for gamers. Covet Fashion, a game I worked on with a brilliant team who actually came up with the idea at CrowdStar, really merged a cooperative game mechanic like you might see in say Portal 2 or Left for Dead with social media and very lightweight voting systems of the users themselves playing a crucial role in what's good or not. Just like in Facebook or in Instagram, your feed is going to show you what gets liked a lot, what gets popular. And games are starting to incorporate this too so that the players themselves become almost like the game pieces and become a big part of what's entertaining. We see networks like Twitch with a huge rush of popularity. That is people delivering entertainment to each other. It's not scripted. So this user generated content, this systems which let people be entertaining to each other, is the huge push that's going on in gaming. And we have, part of what makes a game so exciting, is when the game makes interacting with other people lower friction or more magical but it's still the people that makes it exciting. >> Amy Jo this is amazing. I think that you're right on it. Because remember when I was a gamer, single player game on the computer, you got bored. I mastered it. Then comes multiplayer. But you're bringing up a new dynamic which is the dynamic nature of the people themselves. And I think Twitch had an interesting experiment where the comments, which we know on Twitch are pretty bad, drove the game experience. So now you have the people being part of the input to the game itself. I mean isn't Life a game in a way? >> Sure, you could look at Life the game. I think that that's a semantic issue. There are people that really enjoy looking at life as a game And if you define a game as a structured activity with roles and goals, sure you could look at it that way. What I think is most exciting is not so much what is and isn't a game but the bleeding over of gaming systems into places like digital health and education and enterprise and fashion, and those are, and genealogy. Right now I have a client who's merging a game like experience with a genealogy crowd source experience. So I think what I'd like to leave you with and to understand is the first wave of this we called gamification where people got very excited about the visible markers of progress that are in games like points and badges and leaderboards. And that's a great opening door, but that's not where the magic is. Where the magic is is in the underlying systems that drive you toward mastery of something you care about. And that's the explosion we're seeing now. So you say what am I seeing? I'm seeing clients come to me, a game designer, in all kinds, banking, call centers, SaaS products, change transformation in companies as well as all kinds of consumer products, saying we tried gamification. It just worked in the short term. We want what makes games interesting in the long term. First of all you said the most important thing which is other people. But it's not just other people. It's other people in a playful and mastery based environment that helps you get better at something you care about getting better at. >> So this great so take me through what game system. What I hear you saying is, okay, people think of gamification as a one trick pony, a shortcut to something. You're taking a much more wholistic approach saying the game system. What does that mean? What is a game system? Because you're, what I hear you saying, is that this is like a fabric. It's not like, or an operating system maybe. How should people think of a game... >> It's a methodology or a system. A good way to think about this, are you familiar with design thinking? >> Mm-hmm. >> Are you familiar with an agile approach or Agile Lean UX? Those are systems. Those are methodologies. Those are approaches to creating great products. And they help you. Game thinking is similar. It's got elements of design thinking, elements of Agile, but it adds game design. The difference between strong game design and gamification is game design is about bringing systems to life from the inside out. And so game thinking is as much about how you bring your product to life as it is about anything that you put into the product once it's brought to life. Which is where gamification usually comes in. So it's really about building a learning architecture into the core of your game using feedback loops and using simple systems. And one more thing. Every complex system starts as a simple system that works. So it's really about building core systems and then bringing them to life with the right approach and the right people. >> It's like having a kernel or a small building block. If you overthink it you could get in trouble. >> Right. But you also have to have the right building block so you build a strong foundation. >> Yeah I remember the old days when game engines came out. There was no market for game engines when the first games came out. Then someone said hey why don't we just take the game engine and become a game engine. That was an interesting dynamic that spawned a lot of innovation. Is there an analogy to that happening now where there's new innovations that people can build on top of? Is it open source? Is there an equivalent? I'm trying to figure out where that next level up is going to be because right now we've gone like this and then we see a new level with AR and these new kinds of games and you're bringing this kind of integrated system approach is coming. >> Right so I think there's two thing that have to happen for those to take off. One of which is technology based. You have to have engines. So Unity's rise has been tremendous for the gaming industry. Many many simple game-like experiences are being built in Unity, not from scratch. And other tools like that. And then ARKit from Apple is causing an explosion of really interesting work happening, making it easier to create and experiment with an experience like Pokemon Go. So those are the bottom-up tools based changes that are really accelerating innovation in our industry. Now at the time, none of that will work if you don't have the customer demand and the customer hunger. So the other thing that's happening is that customers are being trained by Pokemon Go and things like that that oh, this is how AR could work. We've seen that VR has kind of stalled out but again, that's a special purpose hardware that's not something easy that you can get on your mobile phone in between all the other things you do. So I think it can't be overstated how powerful it is to have these platforms combined with a huge consumer base on mobile, with phones in their pocket, ready to have a compelling game-like experience that doesn't necessarily have to be a game. The world is waiting for those. >> Yeah and your point about VR, you don't want a build it they will come mentality. You got to focus on the magic formula which is-- >> Customer demand. >> Call it sticky. But some could say look it's got to be a utility and that mastery component is critical whether it's learning, friendship, or some human dopamine effect right. >> Well that's exactly what we do at gamethinking.io. We help teams and companies create a product that customers love and come back to from the ground up using gaming techniques. So anyone who's interested, that's what we do. And the reason we help people do that is it's hard, and it's incredibly high leverage. >> Yeah and you got to have the expertise to do it. And it really is. It sounds like gamethinking.io, you're going to bring architecture. It's not just going to be jump on the grenade that someone throws a project at you. Sure, if it's a big project maybe. But you're kind of train the trainer it sounds like, you're teaching people to fish if you will. >> It's product development. Gamification is often a marketing campaign. We're talking about product development. If you want to build lasting engagement and you're a product leader, then you can use these techniques to build it from the ground up but it's not a silver bullet. >> Give a plug for what you do at Shufflebrain about your company and share some advice for folks watching that might be interested. Like I want to transform my Web 2.0, my 1.0 web responsive app, or my offshore built mobile app that I hired someone to just iOS it and Android it. I want to actually build from the ground up a new architecture that's going to be, have a lot of headroom, I really want to build it from the ground up with good design thinking, game system, game thinking, with the game systems, all the magic potentially in there. What do they do? I don't know do you call the, you know there's no Yellow Pages anymore. Do you Google search it? >> Thank you that was a great setup because that's, I mean I wish that I had had this years ago when I doing a venture funded startup. I needed help. So that's why I do what I do. So what we do is take 20 years of what works and what doesn't in game and product design and turn it into a step by step toolkit with templates, instruction, training, and coaching. And let me give you a specific tip. So there's, it's a whole system we use, but one of the things that you do and if anybody wants to try this it will amaze you if you're able to do it right, one of the things that the greatest game designers, the Will Wrights and folks at CrowdStar and Harmonics, what they do is when they're bringing a new game idea to life, first of all they find out aggressively as much about what's wrong with their ideas, what's right with it, through iterative, low fidelity testing early. Secondly they test it on their superfans that shortcut for high need, high value, early adopters. Not your target market but people that can get you to your target market. Knowing how to find and identify and then leverage your superfans for very early product testing and iteration, that's how you bring your core systems to life. Not with your ultimate target market. Most people don't know this. Knowing this, and then finding those people and leveraging them will turn what's often a failure into success. >> John: That's gold. >> It's complete gold. Let me just tell you why. Because if you're able to ask very product-focused questions, again with my guidance, of these people, you can build your product around what you know they want rather than guessing. >> And you can also help the person, might have blind spot, your customer, understand what superfans are saying. Sometimes it's like they're just giving you the answer right there early on. >> That's such a good point. And when you're inside of it- >> And I have bias. I'm an entrepreneur. Oh no I want to hear what I want to hear. I'm going to change the world. (laughs) Not really. >> That's why when I was an entrepreneur I knew all this stuff but I needed a coach when I was doing this. Because you can't see outside of your bubble and that's part of the value of doing this. >> Amy, the URL is? >> Gamethinking.io. >> Gamthinking.io. Amy Jo is a coach, she is an entrepreneur, venture backed, probably has some scar tissue from that but now she's kicking ass and taking names on gamethinking.io. Great mind. Thank you for sharing an amazing tutorial. You know that's free consulting here on theCUBE right here from and expert. >> It's what I love to do. Thank you for having me. >> Amy Jo here on theCUBE. Live in San Francisco at the Samsung Developer Conference, I'm John Furrier back with more here in theCUBE after this short break. (techno music)

>> Announcer: Live from Las Vegas it's theCUBE, covering NetApp Insight 2017. Brought to you by NetApp. (upbeat techno music) >> Hello, everyone. Welcome to this special CUBE presentation. We are here at the Mandalay Bay in Las Vegas, Nevada for NetApp Insight 2017. I'm John Furrier, your co-host and co-founder of SiliconANGLE Media. Here at theCUBE, here with Keith Townsend for all day today. Keith Townsend at CTO Advisor covering NetApp 2017 here at the Mandalay Bay in Las Vegas. And before we kickoff a long day of great conversations with thought leaders, experts, executives, and also customers of NetApp who are transitioning to a whole digital world, a digital transformation. We can't not address the massacre that happened only a few days ago here in Las Vegas, here at the Mandalay Bay, our second home of theCUBE. If you know theCUBE, you know that we're here all the time. Hits home for us, but that pales in comparison to the families and victims of the 58 dead, 59 total but 58 that have died plus the shooter. Over 500 injured in the heinous cowardly act from the shooter who killed those people. Really I'm trying to kind of hold it together because it really hits home for me because, like 9/11, it's one of those moments that this is planned. This was a coordinated attack, kind of like the Oklahoma bombing, and it reflects on our society. I want to make a comment. And Keith, I'd like to get your thoughts in a minute. But first I would like to say our hearts and prayers are with the victims and families. And want to put a shout out to the first responders because if you look at the Mandalay Bay and what happened here, there could have been a lot more that have died. And that is really a testament to the people who responded, to this unpredictable act. And our prayers go out to the families and victims. And again, a shout out to the law enforcement people. Keith, this is a tragedy that people are trying to make sense out of it. And you know, we have to move on. Obviously, we're here at the NetApp event. A lot of great things to talk about with data and the future and how society will change with technology. But this is a time in history where we're seeing a societal shift. But we got to make sense of it. >> Yeah, you know, John, I'm going to try and keep it together as well. I think this is my seventh time in Vegas this year. And I'm sure every time I've spent at least some time in Mandalay Bay. This event, you know, I had a personal tragedy in my own life of losing my nephew to gun violence. We're all scratching for answers and trying to find a solution to this. And I'm a little bit ... It's a tough moment I think, personally, for us and our friends in the community. But the folks here at NetApp have done a really great job. Not just NetApp but the community in general, here in Las Vegas there's been folks in the community that have organized blood drives. The Red Cross has actually asked us to stop donating blood because of the outpouring of support. And I think that focus of hope in changing the world is what I would like to focus on. >> Well, I mean, take a company like NetApp having their annual customer event, partner event here at Mandalay Bay. It's their big event. And on their doorstep this happens. How they've handled themselves, I think, shows the culture of NetApp. They respect, they took pause. They canceled the first day. They handled it with extreme class. George Kurian put out there a personal story. But this is what it's about. We've got to move on. But I think to me, it's not about politics. It's not about any of that. It's about how do we move forward? And I hate to use a cliché, it's a wake up call. The world has changed in an instant through a prism of a known life. We heard that at 9/11. It's been 16 years. Enough's enough. And here's the deal, we have to be awake. We are realizing that, not the digital transformation for the enterprise, it is a transformation around the world. If you look at geopolitics, or you look at what's happened even today in the news. Even though the President of the United States is here to visit with the families, the Senate Intelligence Committee points out more fake news influenced via social media on Facebook with the Russians hacking the election. They didn't really hack the election, they just used advertising and albatross Facebook among other platforms to manipulate the election. Equifax hack, turns out as I reported originally on theCUBE, it was a state-sponsored activity, it was not a hack. These are new realities. And this is the theme that we see at theCUBE across our events that we go to, the new reality that we are living in a completely different society and it's on us to lean in and be part of the solution. And it's not about being a political solution or saying, "Hey, I'm praying." I mean, we're praying. But you can pray. Praying is what you do, action is another. But it's not about just the gun laws or this or that, it's about the society and the communities. The GoFundMe's are going crazy for the victims, but you can't replace the mother. We had a loss in our community, former Cisco employee lost her life, three kids. The communities have to lean in, individuals have to lean in if they have expertise. I think this is going to be a call to arms that's going to have a revolutionary effect on people. And I think it's an opportunity for the technology industry to lean in, use what we know. We have AI. We got blockchain. We got machine-learning. And this data, the slogan of NetApp couldn't be more perfect. Changing the world with data this is the mandate. >> So, George Kurian gave an ardent, and just compassionate... I had a tough time keeping myself together at the end of yesterday's keynote. George shared how data helped save his son's life. His 13-year-old son comes home every day thankful for technology. And we need to find ways to use AI, use machine learning to impact our communities. While we're talking about the larger, global community, even in my hometown of Chicago that's ravished by violence. You know, there's ways to use social media, data, AI-driven changes to help create policies and to help enable community organizers to understand the source of this nonsense basically. We say this is the new normal, but we should never grow numb to it. >> And I'm grateful -- >> John: No, it's not normal. It's not normal. And this is why I tell my daughter who's the class president of junior high school, Paolo Alto High School, this is not normal. This is not normal. This is not what we want. >> Keith: No! >> You know, you're personal tragedy, hit home with you personally. You had to rationalize it. And you're also a very active participant in the community. This is a new opportunity. The new normal is to behave differently, not the outcome. How do you look at that? Given what you've been through personally and now this, it brings together emotions but then the logic has to kick in. >> Keith: Right. >> You have to execute, actually take action. >> So, it started again Monday when a bunch of us had to make the decision on whether or not we're going to make the trip to Vegas to participate in a enterprise IT show. Your initial gut reaction is, "You know what, so many dead. What does it really matter to go to a conference at this point in time?" And then, you start to rationalize. "You know what? My way of life, our way of life cannot change. We can't allow this tragic event to change how we approach it." And again, NetApp and George did a great job of kicking off the conversation saying that we need to use this as a pivot point to drive the conversation to how us technologists can leverage this. >> Let's take this to where NetApp's living right now. NetApp Insight 2017 is the even we're kicking off here, all day coverage, here on theCUBE with Keith Townsend, expert in the field. Cloud, data, storage, it's all converging. But the reality is is that NetApp has SolidFire. They've bought great company. You're seeing a DNA transfer off of the original DNA of NetApp which has been very innovative culture. They have a very big success story as a start up, went public, and now are continuing to transform. Their customers are transforming but you bring up this new normal that the behavior we want to change and the outcomes that will become of it, speaks to the culture of what we're seeing in the enterprise transformation. A new class of developers are coming in. And the class of developers are about DevOps, their about infrastructure as code. And these new developers, have a new mindset. >> Yeah, so NetApp, a storage company, right? They store bits, retrieve bits. Not so much. They spent a hour on stage yesterday, even before they talked about any products, any architectures, talking about the value of data. Data is the ... And John, you've talked about data for as long as I've known you. Data is the number one asset of any company and NetApp focused not on storage, not on arrays, not on how fast the speeds and feeds go, but the value of data and extracting that value from your subsystems and then going into the conversation around how NetApp can assist in that journey in leveraging data. >> Okay, we're going to kickoff Day One coverage with NetApp Insight 2017 here on theCUBE. Changing the world with data. That is the focus, that is the conversation. And that is an aperture, that's the entire world from how you store the data, how to use the data. How do you to put it to work? How do you create value and transformation? This is theCUBE bringing the action here from the Mandalay Bay in Las Vegas for NetApp Insight 2017. Stay with us. We'll be right back with our next guest after this short break. (upbeat techno music)

>> Narrator: Live from Las Vegas it's The Cube covering Veritas Vision 2017. Brought to you by Veritas. >> Welcome back to Las Vegas, everybody this is The Cube, the leader in live tech coverage. We are here covering Veritas Vision 2017, the hashtag is VtasVision. My name is Dave Vellante, and I'm here with Stuart Miniman my cohost David Noy is here, he's the vice president of product management at Vertias. David, thanks for coming to The Cube. >> Thanks for having me, pretty excited. >> Yes, we enjoyed your keynote today taking us through the new product announcements. Let's unpack it, you're at the center of it all. Actually, let's start with the way you started your keynote is you recently left EMC, came here, why, why was that? >> I talk to lots and lots of customers, hundreds, thousands of customers. They're enterprise customers, they're all trying to solve the same kind of problems, reducing infrastructure costs, moving to commodity based architectures, moving to the cloud, in fact they did move to the cloud in Angara. If you look at the NAS market in 2016 it had been on a nice two percent incline until about the second half of 2016 it basically dove 12% and a big part of that was enterprises who were kicking the tires finally saying we're going to move to cloud and actually doing it as opposed to just talking about it. At EMC and a lot of the other big iron vendors they have a strategy that they discuss around helping customers move to cloud, helping them adopt commodity, but the reality is they make their money, their big margin points, on selling branded boxes, right? And as much as it's lip service, it's really hard to fulfill that promise when that's where you're making your revenue, you have revenue margin targets. Veritas on the other hand, it's a software company. We're here to sell software, we're able to make your data more manageable to understand that it's a truth in information, I don't need to own every bit, and I thought that the company that can basically A, provide the real promise of what software define offers is going to be a software company. Number two is that you can't buck the trend of the cloud it's going to happen, and either you're in the critical path and trying to provide friction, in which case you're going to become irrelevant pretty soon or you enable it and figure out how to partner with the cloud vendors in a nonthreatening way. I found that Veritas, because of its heterogeneity background, hey you want AIX, you want Linux, you want Solaris, great, we'll help you with all those. We can do the same thing with the cloud, and the cloud vendors will partner up with us because they love us for that reason. >> Before we get into the products, let's unpack that a little bit. Why is it that as Veritas you can participate in profit from that cloud migration? We know why you can't as a hardware vendor because ultimately the cloud vendor is going to be providing the box. >> Well, the answer is that, a couple things. One is, we believe and even the cloud vendors believe that you're going to be in a hybrid environment. If you project out for the next ten years, it's likely that a lot of data and applications and workloads will move to cloud, but not all of them will. And you probably end up in about a 50/50 shift. The vendor who can provide the management and intelligence and compliance capabilities, and the data protection capabilities across both your on-prem, and your off-premise state as a single unified product set is going to win, in my opinion, that's number one. Number two is that the cloud vendors are all great, but they specialize in different things. Some are specialized in machine learning, some are really good with visual image recognition, some are really good with mobile applications, and people are, in my opinion, going to go to two, three, four different clouds, just like I would go to contracting agencies, some might be good at giving me engineers, I might go to dice.com for engineers, I might go to something completely different for finance people, and you're going to use the best of breed clouds for specific applications. Being able to actually aggregate what you have in your universe of multicloud, and your hybrid environment and allowing you, as an administrator to be aware of all my assets, is something that as a non-branded box pusher, as a software vendor I can go do with credibility. >> You're a recovering box pusher. >> I'm a recovering box pusher, I'm one month into recovery, so thank you very much. >> And David, one of the things we're trying to understand a little bit, you've got products that live in lots of these environments, why do you have visibility into the data? Is it because they're backup customers, is it other pieces? Help us understand in that multicloud world, what I need to be to get that full. >> That's a great question and I'll bridge into some of the new products too. Number one is that Veritas has a huge amount of data that's basically trapped in repositories because we do provide backup, we're the largest backup vendor. So we have all this data that's essentially sitting inactive you know, Mike talks about it, Mike Palmer our CPO, talks about it as kind of like the Uber, you know, what do you do with your car when it's not being used, or Air BnB if you will, what do you do with your home when it's not being used, is you potentially rent it out. You make it available for other purposes. With all this trapped data, there's tons of information that we can glean that enterprises have been grabbing for years and years and years. So that's number one, we're in a great position 'cause we hold a lot of that data. Now, we have products that have the capabilities through classification engines, through engines that are extending machine learning capabilities, to open that data up and actually figure out what's inside. Now we can do it with the backup products, but let's face it, data is stored in a number of different other modaliites, right? So there's blocked data that is sitting at the bottom of containerized private clouds, there are tons and tons of unstructured data sitting in NAS repositories, and growing off-prem, but actually on prem this object storage technology for the set it and forget it long term retention. All of that data has hidden information, all of it can be extracted for more value with our same classification engines that we can run against the net backup estate, we can basically take that and extend that into these new modalities, and actually have compelling products that are not just offering infrastructure, but that are actually offering infrastructure with the promise of making that data more valuable. Make sense? >> It does, I mean it's the holy grail of backup. For years it's been insurance, and insurance is a good business, don't get me wrong, but even when you think about information governance, through sarbanes-oxley and FRCP et cetera, it was always that desire to turn that corpus of data into something more valuable than just insurance, it feels like, like you're saying with automated classification and the machine learning AI, we're sort of at the cusp of that, but we've been disappointed so many times what gives you confidence that this time it'll stick? >> Look, there's some very straightforward things that are happening that you just cannot ignore. GDPR is one, there's a specific timeline, specific rules, specific regulatory requirements that have to be met. That one's a no brainer, and that will drive people to understand that, hey when they apply our policies against the data that they have they'll be able to extract value. That'll be one of many, but that's an extreme proof-point because there's no getting around it, there's no interpretation of that, and the date is a hard date. What we'll do is we'll look quickly at other verticals, we'll look at vertical specific data, whether its in data surveillance, or germain sequencing or what have you, and we'll look at what we can extract there, and we'll partner with ISVs, is a strategy that I learned in my past life, in order to actually bring to market systems or solutions that can categorize specific, vertical industry data to provide value back to the end users. If we just try to provide a blanket, hey, I'm just going to provide data categorization, it's a swiss army knife solution. If we get hyper-focused around specific use cases, workloads and industries now we can be very targeted to what the end users care about. >> If I heard right, it's not just for backup, it's primary and secondary data that you're helping to solve and leverage and put intelligence into these products. >> That's right, initially we have an enormous trapped pool of secondary data, so that's great, we want to turn that trapped pool from just basically a stagnant pool into something that you can actually get value out of. >> That Walking Dead analogy you used. >> The Walking Dead, yeah. We also say that there's a lot of data that sits in primary storage, in fact there's a huge category of archive, which we call active archive, it's not really archive, still wanted on spinning disk or flash. You still want to use it for some purpose but what happens when that data goes out into the environment? I talked to customers in automotive, for example, automotive design manufacturers, they do simulations, and they're consuming storage and capacity all the time, they've got all of these runs, and they're overrunning their budget for storage and they have no idea which of those runs they can actually delete, so they create policies like "well, if it hasn't been touched "in 90 days, I'll delete it," Well, just because it hasn't been touched in 90 days doesn't mean there wasn't good information to be gleaned out of that particular simulation run, right? >> Alright, so I want to get back to the object, but before we go deeper there, block and file, there's market leaders out there that seems that, it's a bit entrenched, if you will, what between the hyperscale product and Veritas access, what's the opportunity that you see that Veritas has there, what differentiates you? >> Sure, well, let's start with block. The one big differentiator we'll have in block storage is that it's not just about providing storage to containerized applications. We want to be able to provide machine learning capabilities to where we can actually optimize the IO path for quality of service. Then, we also want to be able to through machine learning determine whether, if it's how you decide to run your business, you want a burst workloads actually out into the cloud. So we're partnered with the cloud vendors, who are happy to partner with us for the reasons that I described earlier, is that we're very vendor agnostic, we're very heterogeneous. To actually move workloads on-prem and off-prem that's a very differentiated capability. You see with a few of the vendors that are out there, I think Nutanix for example, can do that, but it's not something that everyone's going after, because they want to keep their workloads in their environments, they want to check controls. >> And if I can, that high speed data mover is your IP? >> That's right, that's our IP. Now, on the file system side... >> Just one thing, cloud bursting's one of those things, moving real-time is difficult, physics is still a challenge for us. Any specifics you can give, kind of a customer use case where they're doing that? A lot of times I want this piece of the application here, I want to store the data there, but real time, doing things, I can't move massive amounts of data just 'cause, speed of light. >> If you break it down, I don't think that we're going to solve the use case of, "I'm going to snap my finger "and move the workload immediately offline." Essentially what we'll do is we'll sync the data in the background, once it has been synced we'll actually be able to move the application offline and that'll all come down to one of two things: Either user cases that exceed the capabilities of the current infrastructure and I want to be able to continue to grow without building them into my data center, or I have an end of the month processing. A great case is I have a media entertainment company that I used to work with that was working on a film, and it came close to the release date of that film, and they were asked to go back and recut and reedit that film for specific reasons, a pretty interesting reason actually, it had to do with government pressure. And when they went to go back and edit that film they essentially had a point where like, oh my gosh, all of the servers that were dedicated to render for this film have been moved off to another project. What do we do now, right? The answer is, you got to burst. And if you had cloud burst capabilities you could actually use whatever application and then containerize whether you're running on-prem or off-prem, it doesn't matter, it's containeraized, if we can get the data out there into the cloud through fast pipes then basically you can now finish that job without having to take all those servers back, or repurchase that much infrastructure. So that's a pretty cool use case, that's things that people have been talking about doing but nobody's every successfully done. We're staring to prove that out with some vendors and some partners that potentially even want to embed this in their own solutions, larger technology partners. Now, you wanted to talk about file as well, right, and what makes file different. I spent five years with one of the most successful scale-up file systems, you probably know who they are. But the thing about them was that extracting that file system out of the box and making it available as a software solution that you could layer on any hardware is really hard, because you become so addicted to the way that the behavior of the underlying infrastructure, the behavior of the drives, down to the smart errors that come off the drives, you're so tied into that, which is great because you build a very high performance available product when you do that, but the moment you try to go to any sort of commodity hardware, suddenly things start to fall apart. We can do that, and in fact with our file system we're not saying "hey, you've got to go it on "commodity servers and with DAS drives in them." You could layer it on top of your existing net app, your Isolon, your whatever, you name it, your BNX, encapsulate it, and create policies to move data back and forth between those systems, or potentially even provision them out say, "okay, you know what, this is my gold tier, "my silver tier, my bronze tier." We can even encapsulate, for example, a directory on one file service, like a one file system array, and we can actually migrate that data into an object service, whether its on-prem or off-prem, and then provide the same NFS or SMB connectivity back into that data, for example a home directory migration use case, moving off of a NAS filer onto an object storer, on premise or off premise and to the end user, they don't know that things have actually moved. We think that kind of capability is really critical, because we love to sell boxes, if that's what the customer wants to buy from us, and appliance form factor, but we're not pushing the box as the ultimate end point. The ultimate end point is that software layer on top, and that's where the Veritas DNA really shines. >> That's interesting, the traditional use cases for block certainly, and maybe to a lesser extent file, historically fairly well known an understood. So to your point, you could tune an array specifically for those use cases, but in this day and age the processes, and the new business models that are emerging in the digital economy, very unpredictable in terms of the infrastructure requirements. So your argument is a true software defined capability is going to allow you to adapt much more freely and quickly. >> We've also built and we've demoed at Vision this week machine learning capabilities to actually go in and look at your workloads that are running against those underlying infrastructure and tell you are they correctly positioned or not. Oh, guess what, we really don't think this workload should belong on this particular tier that you've chosen, maybe you ought to consider moving it over here. That's something that historically has been the responsibility of the admin, to go in and figure out where those policies are, and try to make some intelligent decisions. But usually those decisions are not super intelligent, they're just like, is it old, is it not old, do I think it's going to be fast? But I don't really know until runtime, based on actual access patterns whether it's going to be high performance or not. Whether it's going to require moving or aging or not. By using machine learning type of algorithms we can actually look at the data, the access patterns over time, and help the administrators make that decision. >> Okay, we're out of time, but just to summarize, hyperscales, the block, access is the scale out, NAS piece, cloud object... >> Veritas cloud storage we call it. Veritas cloud storage, very similar to the access product is for object storage, but again it's not trying to own the entire object bits, if you will, we'll happily be the broker and the asset manager for those objects, classify them and maintain the metadata catalog, because we think it's the metadata around the data that's critical, whether it lives off-prem, on-prem, or in our own appliance. >> You had a nice X/Y graph, dollars on the vertical axis, high frequency of access to the left part of the horizontal axis, lower SLAs to the right, and you had sort of block, file, object as the way to look at the world. Then you talked about the intelligence you bring to the object world. Last question, and then let's end there. Thoughts on object, Stu and I were talking off camera, it's taken a long time, obviously S3 and the cloud guys have been there, you've seen some take outs of object storage companies. But it really hasn't exploded, but it feels like we're on the cusp. What's your observation about object? >> I think object is absolutely on the cusp. Look, people have put it on the cloud, because traditionally object has been used for keeping deep, and because performance doesn't matter, and the deeper you get, the less expensive it gets. So a cloud provider's great, because they're going to aggrigate capacity across 1,000 or 20,000 or a million customers. They can get as deep as possible, and they can slice it off to you. As a single enterprise, I can never get as deep as a cloud service provider. >> The volume, right? >> But what ends up happening is that more and more workloads are not expecting to hold a connection open to their data source. They're actually looking at packetize, get-put type semantics that you can see in genomic sequencing, you see it in a number of different workloads where that kind of semantic, even in hydoop analytic workloads, where that kind of get-put semantic makes sense, not holding that connection open, and object's perfect for that, but it hasn't traditionally had the performance to be able to do that really well. We think that by providing a high performance object system that also has the intelligence to do that data classification, ties into our data protection products, provides the actionable information and metadata, and also makes it possible to use on-prem infrastructure as well as push to cloud or multicloud, and maintain that single pane of glass for that asset management for the objects is really critical, and again, it's the software that matters, the intelligence we build into it that matters. And I think that the primary workloads in a number of different industries in verticals or in adopting object more and more, and that's going to drive more on premise growth of object. By the way, if you look at the NAS market and the object market, you see the NAS market kind of doing this, and you see the object market kind of doing this, it's left pocket right pocket. >> And that get-put framework is a simplifying factor for organizations so, excellent. David, thank you very much for coming on The Cube. We appreciate it. >> Appreciate it, thanks for having me. >> You're welcome, alright, bringing you the truth from Veritas Visions, this is The Cube. We'll be right back, right after this short break.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017. Brought to by VMware and its ecosystem partners. (upbeat techno music) >> Okay, we're live here at VMworld 2017's theCUBE's coverage of VMworld 2017. I'm John Furrier. My hosts, Dave Vellante and Stu Miniman. We've got two sets kicking off live here in Las Vegas for our eighth year of coverage. Boomy, we're in the broadcast booth at the Mandalay Bay. Guys, we're here to kick off the show. Three days of wall-to-wall coverage. Three days of great keynotes. Today, big surprise, Andy Jassy, the CEO of Amazon Web Services joined Pat Gelsinger on stage in a surprise announcement together, hugging each other before they talked and even after they talked. This partnership is going to be big. We're going to have coverage, in-depth analysis of that. Dave, VMWorld is now the cloud show with re:Invent. If you look at what's going on, Stu, you've been to many, many shows. This is our eighth year. This was the show. Great community. Now re:Invent has been called the new VMWorld. You put 'em both together, it's really the only cloud show that matters. Google does not have yet a presence. Microsoft has all these shows that are kind of spread all over the place. All the top people are here in IT and cloud at VMWorld and at re:Invent coming up in December. >> Well, John, eight years ago we talked about is this the last stop for IT before cloud just decimates it? And if you go back two years ago, VMware was not in favor. The stock was half of what it is today. Licensed revenue was down 1%. Fast forward to today, it's growing at 10 to 12% a year. Licenses up 13%. It's throwing off operating cash flow at $3 Billion a year. The market's booming. Wall Street's talking VMware now being and undervalued stock. The big question is, is this a fundamental shift in customer mindsets? In other words, are they saying, "Hey, we want to bring the cloud operating model to the business and not try to force our business into the cloud." Or, is this the last gap of onprem. >> Stu, I want to get your thoughts cause I want, squinting through the announcements and all the hype and all the posturing from the vendors is I was looking for, where's hybrid in all this? Where's the growth? And, my validation point on the keynote was when we heard very few words hybrid. Private, on premise was the focus. You guys put out at Wikibon a report called True Private Cloud, Market Sizing. Kind of lay out, that's where the growth is. But, I tweeted private cloud is the gateway drug to hybrid. We're seeing customers now wanting to do hybrid, but they got to do their homework first. They got to do the building blocks on premise, and that is what your calling True Private Cloud. Do you agree? And your thoughts. >> Yeah, so, really good points, John. And the nuance here, 'cause if I'm VMware, I've got a great position in the data center. 500,000 customers. Absolutely, the growth is the move from legacy to True Private Cloud. The challenge for VMware is they already have 500,000 customers there. Those are the customers that are making that shift. So it does not increase vSphere. One of the key things for me, is Pat said, "What vSphere had done for the last 20 years, is what NSX is going to do for the next 10 years, or more." Because they're betting on networking, security, some of these multi-cloud services that they announced. How do those expand VMware so that as True Private Cloud grows and they also do public cloud, VMware has a bigger seat at the table, not just saying "Wait, my customers are shifting. Where are they going?" >> Dave, I want to get your thoughts. You and I talk about all the time on camera, and also privately, about waves. We've been through many waves in the industry. We've seen a lot of waves. Pat Gelsinger has seen many waves, too. Let's talk about Pat Gelsinger because, interesting little tidbits inside the stage area. One, he said "I want to thank you for being the CEO of this company." Stu, you made a comment that this is the first VMWorld where there's not a rumor that Pat's not going to be the CEO. He's kind of kickin' ass and takin' names right now. Stock's up and he put the wave slide out there. And wave slides to me, you can tell the senior management's kind of mojo by how well laid out the wave slide is. He put up a slide on one side. Mainframe mini computer cloud. And the other side client server, internet, IoT Edge. He nailed it, I think. Pat Gelsinger is going to go down as being one of the most brilliant stroke of genius by looking at either laying down what looked like a data center position, and some say capitulate, to Jassy, who's smiling up there saying, "Bring those customers to Amazon." But this is a real partnership. So, Pat Gelsinger, go big or go home. You can't be any bigger, bold bet that Pat Gelsinger right now with VMware, and it looks like it's paying out. What's your thoughts on Pat Gelsinger, the wave and his bold bet? >> Well, I think that businesses are configuring the cloud, John, to the realities of the data. And the data, most of the data, is on prem. So the big question I have it, how is Amazon going to respond to this? And Stu, you and Furrier have had debates over the years. Furrier has said flat out, Amazon is going to do a True Private Cloud, just like Azure Stack. You have said, no. But if Amazon doesn't do that, I think that Pat Gelsinger's going to look like a genius. If they do do that, it's going to become an increasingly more competitive relationship than it is right now. >> Yeah, just a little bit of the inside baseball. Kudos to VMware for getting this VMware on AW out. I hear it was a sprint to the finish because taking cloud foundation, which is kind of a big piece. It's got the VSAN, the NSX, all that stuff, and putting it in a virtual private data center. Amazon owns the data center. They give them servers. This was a heavy lift. NSX, some of the pieces are still kind of early, but getting this out the door, limited availability. It's one data center. They're going to roll out services, but to Dave's point, right, where does this go down the road? Is this Amazon sticking a straw into 500,000 data centers and saying, "Come on in. You know that we've got great services, and this is awesome." 'Cause, I don't see Amazon re-writing their linux stuff to be all native VMware, So, where will this partnership mature? Andy said, "We're going to listen to our customers." "We're going to do what you're asking us." And absolutely today VMware and Amazon, two of these strongest players in the ecosystem today, they're going to listen to their customers. Google, Oracle, IBM, Microsoft, all in the wings fighting for these customers, so it's battle royale. >> You know the straw is in there, John, what's your take, and where do the developers fit in this? >> Well Stu wrote a good point, inside baseball, the key is that success with Amazon was critical. Jassy said basically, this is not a Barney deal, which he kind of modernized by saying most deals are optical really hitting at Microsoft on this one and Google. I mean, they're groping for relevance. It's clear that they're way behind. Everyone's trying to follow these guys. But, on the heels of Vcloud Air, it was critical that they get stake in the ground with Amazon. They took a lot of heat for the Vcloud Air, Stu. This had to get done. Now, my take on this is that, I think it's a genius move. I think Pat Gelsinger, by betting the ranch on Amazon, will go down in history as being a great move. You heard that here, 2017. He's so smart, he wants to be a component of the Amazon takeover, which will happen. It'll be a two-cloud game, maybe three, maybe four, we'll see, but mainly two. But the ecosystem partners on this phase one is key. DXT, Deloitte, Accenture, Capgemini, and then you start to see the logos coming in. They have so many logos, you have to break them down. But more importantly the white space. devops, migration cost, network security and data protection are all filled in with plenty more room for more players. I think this is where the ecosystem was lagging just a few years ago. You saw the shift in the tide. Now you're seeing the ecosystem going, "Wow, I get what VMware's doing. I'm doubling down." It's an Amazon Web Services, VMWare world. All the other cloud players, in my opinion, are really fumbling the ball. >> So, I can infer from that, you see this as a balanced partnerhip i.e. that's not like one needs more than the other. I mean, clearly, Amazon needs VMWARE to reach those 500,000 customers, and clearly, VMware needs a cloud strategy because Vcloud Air and many other attempts have failed. Yes, we said that. It's failed, we asked Pat about that. So, you see it as a more balanced partnership. Do you see that balance of power shifting over time a that straw gets bigger and bigger and bigger. >> Well the Walking Dead or as the Game of Thrones reference going on is kind of the Gray War is happening in cloud. And it really is going to become Amazon versus whoever they can partner with, and the rest of the legacy world. I think the wave slide was impressive to me because this is such a shift from just distributed computing now decentralized with blockchain and AI looming as massive disrupts, I think this is only going to get more decentralized. So whoever has tech that's legacy, will ultimately be toast. And I think Gelsinger's smart to see that wave, and I'm starting to see the movement. It's super early, so, no big bets. It's just be directionally correct and ride that wave. >> Yeah, so, one of the things that got me is last year, it kind of went under the radar that VMware is starting to launch some cloud services, and were very direct, today, that they said there are seven, basically SaaS offerings. It's security, it's cost management. Now, VMWare on AWS, little expensive. We're starting to get the data on how much it is per month or per year or for three-year. But going to have the SaaS offerings. We know Vcloud Air failed, also Paul Moritz had played the Microsoft game. We're going to get this suite of applications. We're going to give you email. We're going to give you, you know, social. We're going to give you all these things. They're all gone. Kind of cleared the table of all those. Now they've got these SaaS applications, so how will that play. I kind of like Pat, very up front on security, and said, "As an industry, we have failed you." Dave, you've been looking at this for a long time. It's a board-level discussion. It's a do-over for security. Does VMware have the chops to play in this space, Dave? Do you buy them as a, you know, valid SaaS provider? >> Well, two questions there. One is in the security front that great tech is always going to get beat by bad user behavior. So this is a board-level issue. As far as SaaS, to me, it's a business model issue that VMware is migrating its business to a routable business model, which is smart. I don't see it as SaaS as an applications, but I see it as a monthly fee. Better to get ahead of it now, while you're hot, than get crushed by Wall Street as you're trying to make that transition like many other companies have failed to do. >> Guys, one thing I want to note is that VMware also laid out their strategy. You kind of heard it there even though that Jassy came on stage. A look it, Jassy's not an idiot, he's smart. He knows what's going on. He knows that he has to win VMware over because VMware ... he's got to balance it. Got 'em in the back pocket on one hand, got a great relationship, Stu, 500,000 customers. Remember, VMware is also an arms dealer. They got the ops, IT operations locked down with their customers. So they have other clouds they can go to. SO, the big trend that we didn't hear, that's out there kind of hiding in plain site is multi-cloud. Multi-cloud is ultimately VMware's strategy. He laid out, one, make private cloud easy. You guys reported on that. Two, deep partnerships with major cloud providers. And three, expand the ecosystem. >> John, so I mean a little bit of kind of rumors I heard. They were actually looking to make the partnership not with AWS at first, it was going to be Google. And Michael Dell said, "If we're going to start with a cloud deal, it's going to be Amazon." The right move, absolutely, that's where it's going to be. But you remember last year, we were here. John, you and I, the announcement was with IBM. Now, no offense to SoftLayer, great acquisition. It's doing well, but IBM does not play at the level of an Amazon. They might have the revenue of a Google in cloud, but, you know, very different positioning. They were up on stage talking about security today. Great position there with analytics. But, we'll see, there's two more days of keynotes. I expect we'll see another cloud provider making some announcements with VMware. And VMware absolutely an arms dealer. They put out on the slide all of their service providers. We've got people like CenturyLink and OVH and Rackspace on theCUBE this week, as well as how their going to play with the Microsoft Google. You've got Michael Dell on tomorrow. I know you're going to talk to him about how Dell fits with Azure Stack, and how the Dell whole family is going to play across all of these because at the end of the day, Michael Dell, and Pat working for him, they want to keep getting revenue no matter who's the winner out there. >> Okay final question as we wrap up the segment. Customers are that watching here, it's clear to me that, we even heard from one on stage, saying, "Well, we're taking baby steps." That wasn't her exact words, but, their going slow to hybrid cloud. All the actions on private as you guys pointed out in our True Private Cloud report on Wikibon.com If you haven't seen it, go check it out, it's going viral. But, this is classic slowness of most enterprise customers. When there's doubt, they slow down. And, one of the things that concerns me, Stu, about the cloud guys right now, whether it's AWS, Google, and Microsoft, is the market's moving so fast, that if these clouds aren't dead simple easy to use, the customers aren't going to go to hybrid. They're going to go back to their comfort zone, which is the true private cloud, going to build that base. It's just got to get easier to manage. It's got to get easier to multi-cloud. And the bottom line is that Amazon's clearly in the lead. So, Jassy has a window right now to run the table on enterprise. He's got about 18-24 months, but Google's putting the pedal to the metal. I mean they're pedaling as fast as they can. Microsoft's cobbling together their legacy, okay, running as fast as they can. But there's this economies of scale, Stu, for them. Your thoughts and reactions. >> Yeah, so, I always thought enterprise simplicity is actually an oxymoron, does not exist. This VMware community, one of the things people loved about it, they were builders. They were all like get in there, and I tweak that. Harvard Business School calls it the Ikea effect. If I help build it just enough, I actually love it a little bit more. VMware's not simple. NSX, hitting about a billion dollars when you get into it is not easy. Security and networking are never going to be you know dirt simple. Amazon, we thought it was real simple, now thousands of services. Absolutely, we've been at that ecosystem for many years. It gets tougher and tougher the more you get into it. And, John, some of the builders there, the developers there, they get in. There's lot of room for this ecosystem to build around that. Because one of the things we talk about as VMware goes to some of these clouds. Where do they get that ecosystem? You mentioned some of the systems integrators, but the rest of the channel, where can they make money? And trying to help, because it's not simple, how do they help get opinionated, make those choices, build it all together. There's professional services dollars there. There's ways to help consult with companies there. >> Ecosystem is the key point. Watch the ecosystem and how that's forming around cloud, hybrid cloud, true private cloud, whatever you want to call it. And then, again, the technology's maturing. It's all about the people and the process to actually affect so called cloud, hybrid cloud, bringing the cloud model to the data, not forcing your business into cloud. >> We got to wrap up here. We've also got Lisa Martin and Keith Townsend and John Troyer, and we got some community guests as well, joining like we did last year. So this will be great. But I want to put something out there, guys, so we can hit up tomorrow and tease it out. I worry about when you have these fast waves that are coming through and the velocity is phenomenal right now. Is that, what tends to crumble, Dave, to your ecosystem point, are these foundations. When you have these industry consortiums, it's kind of like it's political. They've got boards and multiple fingers in it. That could be the suffering point, in my opinion. And that points directly at Cloud Foundry. Cloud Foundry, OpenStack, some of these consortium groups are at risk, in my opinion, if it goes too fast. Stu, to your point. Kubernetes has got great traction. You've got Containers. Dockers got a new CEO. Uber's got a new CEO. I mean the world is moving so fast. So, rhetorical question, industry consortiums. Do they suffer, or do they win in this environment? >> Depends on what they're doing, right? If they're low-level technical standards that advance the industry, I think they do win. I think if it's posturing, and co-opetition, and trying to cut off the one vendor at the knees, it loses. >> Stu, real quick, consortiums. Win or lose in this environment? >> Yeah, we've seen some that have done quite well, and some that have been horrific. So, absolutely, if it gets way too political. Open source has done some really good things, but the foundations, once they get in there, it's challenging and, I'd say, more times than not, they don't help. >> Well, we're in theCUBE. We're breaking it down. We're going to be squinting through all the announcements looking at where the meat on the bone is, where the action is and the relevance and the impact to enterprises and emerging tech. This is theCUBE. I'm John Furrier with Stu Miniman and Dave Vellante. We're back with more live coverage. Day one, after this short break. (techy music)

>> Announcer: Live from Las Vegas, it's The Cube, covering HPE Discover, 2017 brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're here live in Las Vegas for our exclusive three day coverage from The Cube Silicon Angle media's flagship program. We go out to events, talk to the smartest people we can find CEOs, entrepreneurs, R&D lab managers and of course we're here at HPE Discover 2017 our next two guests, Andrew Wheeler, the Fellow, VP, Deputy Director, Hewlett Packard Labs and Kirk Bresniker, Fellow and VP, Chief Architect of HP Labs, was on yesterday. Welcome back, welcome to The Cube. Hewlett Packard Labs well known you guys doing great research, Meg Whitman really staying with a focused message and one of the comments she mentioned at our press analyst meeting yesterday was focusing on the lab. So I want ask you where is that range in the labs? In terms of what you guys, when does something go outside the lines if you will? >> Andrew: Yeah good question. So, if you think about Hewlett Packard Labs and really our charter role within the company we're really kind of tasked for looking at things that will disrupt our current business or looking for kind of those new opportunities. So for us we have something we call an innovation horizon and you know it's like any other portfolio that you have where you've got maybe things that are more kind of near term, maybe you know one to three years out, things that are easily kind of transferred or the timing is right. And then we have kind of another bucket that says well maybe it's more of a three to five year kind of in that advanced development category where it needs a little more incubation but you know it needs a little more time. And then you know we reserve probably you know a smaller pocket that's for more kind of pure research. Things that are further out, higher risk. It's a bigger bet but you know we do want to have kind of a complete portfolio of those, and you know over time throughout our history you know we've got really success stories in all of those. So it's always finding kind of that right blend. But you know there's clearly a focus around the advanced development piece now that we've had a lot of things come from that research point and really one of the... >> John: You're looking for breakthroughs. I mean that's what you're... Some-- >> Andrew: Clearly. >> Internal improvement, simplify IT all that good stuff, you guys still have your eyes on some breakthroughs. >> That's right. Breakthroughs, how do we differentiate what we're doing so but yeah clearly, clearly looking for those breakthrough opportunities. >> John: And one of the things that's come up really big in this show is the security and chip thing was pretty hot, very hot, and actually wiki bonds public, true public cloud report that they put out sizing up on prem the cloud mark. >> Dave: True private cloud. >> True private cloud I'm sorry. And that's not including hybrids of $265 billion tam but the notable thing that I want to get your thoughts on is the point they pushed was over 10 years $150 billion is going to shift out of IT on premise into other differentiated services. >> Andrew: Out of labor. >> Out of labor. So this, and I asked them what that means, as he said that means it's going to shift to vendor R&D meaning the suppliers have to do more work. So that the customers don't have to do the R&D. Which we see a lot in cloud where there's a lot of R&D going on. That's your job. So you guys are HP Labs, what's happening in that R&D area that's going to off load that labor so they can move to some other high yield tasks. >> Sure. Take first. >> John: Go ahead take a stab at it. >> When we've been looking at some of the concepts we had in the memory driven computing research and advanced development programs the machine program, you know one of the things that was the kick off for me back in 2003 we looked at what we had in the unix market, we had advanced virtualization technologies, we had great management of resources technologies, we had memory fabric technologies. But they're all kind of proprietary. But Silicon is thinking and back then we were saying how does risk unix compete with industry standards service? This new methodology, new wave, exciting changing cost structures. And for us it was that it was a chance to explore those ideas and understand how they would affect our maintaining the kind of rich set of customer experiences, mission criticality, security, all of these elements. And it's kind of funny that we're sort of just coming back to the future again and we're saying okay we have this move we want to see these things happen on the cloud and we're seeing those same technologies, the composable infrastructure we have in synergy and looking forward to see the research we've done on the machine advanced development program and how will that intersect hardware composability, converged infrastructure so that you can actually have that shift, those technologies coming in taking on more of that burden to allow you freedom of choice, so you can make sure that you end up with that right mix. The right part on a full public cloud, the right mix on a full private cloud, the right mixing on that intelligent edge. But still having the ability to have all of those great software development methodologies that agile methodology, the only thing the kids know how to do out of school is open source and agile now. So you want to make sure that you can embrace that and make sure regardless of where the right spot is for a particular application in your entire enterprise portfolio that you have this common set of experiences and tools. And some of the research and development we're doing will enable us to drive that into that existing, conventional, enterprise market as well as this intelligent edge. Making a continuum, a continuum from the core to the intelligent edge. And something that modern computer science graduates will find completely comfortable. >> One attracting them is going to be the key, I think the edge is kind of intoxicating if you think about all the possibilities that are out there in terms of what you know just from a business model disruption and also technology. I mean wearables are edge, brain implants in the future will be edge, you know the singularities here as Ray Kersewile would say... >> Yeah. >> I mean but, this is the truth. This is what's happened. This is real right now. >> Oh absolutely. You know we think of all that data and right now we're just scratching the surface. I remember it was 1994 the first time I fired up a web server inside of my development team. So I could begin thinning out design information on prototype products inside of HP, and it was a novelty. People would say "What is that thing "you just sent me an email, W W whatever?" And suddenly we went from, like almost overnight, from a novelty to a business necessity, to then it transformed the way that we created the applications for the... >> John: A lot of people don't know this but since you brought it up this historical trivia, HP Labs, Hewlett Packard Labs had scientists who actually invented the web with Tim Berners-Lee, I think HTML founder was an HP Labs scientist. Pretty notable trivia. A lot of people don't know that so congratulations. >> And so I look at just what you're saying there and we see this new edge thing is it's going to be similarly transformative. Now today it's a little gimmicky perhaps it's sort of scratching the surface. It's taking security and it can be problematic at times but that will transform, because there is so much possibility for economic transformation. Right now almost all that data on the edge is thrown away. If you, the first person who understands okay I'm going to get 1% more of that data and turn it into real time intelligence, real time action... That will unmake industries and it will remake new industries. >> John: Andrew this the applied research vision, you got to apply R&D to the problem... >> Andrew: Correct. >> That's what he's getting at but you got to also think differently. You got to bring in talent. The young guns. How are you guys bringing in the young guns? What's the, what's the honeypot? >> Well I think you know for us it's, the sell for us, obviously is just the tradition of Hewlett Packard to begin with right? You know we have recognition on that level even it's not just Hewlett Packard Labs as well it's you know just R&D in general right? Kind of it you know the DNA being an engineering company so... But it's you know I think it is creating kind of these opportunities and whether it's internship programs you know just the various things that we're doing whether it's enterprise related, high performance computing... I think this edge opportunity is a really interesting one as a bridge because if you think about all the things that we hear about in enterprise in terms of "Oh you know I need this deep analytics "capability," or you know even a lot of the in memories things that we're talking about, real time response, driving information, right? All of that needs to happen at the edge as well for various opportunities so it's got a lot of the young graduates excited. We host you know hundreds of interns every year and it's real exciting to see kind of the ideas they come in with and you know they're all excited to work in this space. >> Dave: So Kirk you have your machine button, three, of course you got the logo. And then the machine... >> I got the labs logo, I got the machine logo. >> So when I first entered you talked about in the early 1980s. When I first got in the business I remembered Gene Emdall. "The best IO is no IO." (laughter) >> Yeah that's right. >> We're here again with this sort of memory semantics, centric computing. So in terms of the three that Andrew laid out the three types of sort of projects you guys pursue... Where does the machine fit? IS it sort of in all three? Or maybe you could talk about that a little bit. >> Kirk: I think it is, so we see those technologies that over the last three years we have brought so much new and it was, the critical thing about this is I think it's also sort of the prototyping of the overall approach our leaning in approach here... >> Andrew: That's right. >> It wasn't just researchers. Right? Those 500 people who made that 160 terabyte monster machine possible weren't just from labs. It was engineering teams from across Hewlett Packard Enterprise. It was our supply chain team. It was our services team telling us how these things fit together for real. Now we've had incredible technology experiences, incredible technologist experiences, and what we're seeing is that we have intercepts on conventional platforms where there's the photonics, the persistent memories. Those will make our existing DCIG and SDCG products better almost immediately. But then we also have now these whole cloth applications and as we take all of our learnings, drive them into open source software, drive them into the genesys consortium and we'll see you know probably 18, 24 months from now some of those first optimized silicon designs pop out of that ecosystem then we'll be right there to assemble those again, into conventional systems as well as more expansive, exo-scale computing, intelligent edge with large persistent memories and application specific processing as that next generation of gateways, I think we can see these intercept points at every category Andrew talked about. >> Andrew: And another good point there that kind of magnifies the model we were talking about, if we were sitting here five years ago, we would talking about things like photonics and non-volatile memory as being those big R projects. Those higher risk, longer term things, that right? As those mature, we make more progress innovation happens, right? It gets pulled into that shorter time frame that becomes advanced development. >> Dave: And Meg has talked about that... >> Yeah. >> Wanting to get more productivity out of the labs. And she's also pointed out you guys have spent more on R&D in the last several years. But even as we talked about the other day you want to see a little more D and keep the R going. So my question is, when you get to that point, of being able to support DCIG... Where do you, is it a hand off? Are you guys intimately involved? When you're making decisions about okay so member stir for example, okay this is great, that's still in the R phase then you bring it in. But now you got to commercialize this and you got 3D nan coming out and okay let's use that, that fits into our framework. So how much do you guys get involved in that handoff? You know the commercialization of this stuff? >> We get very involved. So it's at the point where when we think have something that hey we think you know maybe this could get into a product or let's see if there's good intercept here. We work jointly at that point. It's lab engineers, it's the product managers out of the group, engineers out of the business group, they essentially work collectively then on getting it to that next step. So it's kind of just one big R&D effort at that point. >> Dave: And so specifically as it relates to the machine, where do you see in the next in the near term, let's call near term next three years, or five years even, what do you see that looking like? Is it this combination of memory width capacitors or flash extensions? What does that look like in terms of commercial terms that we can expect? >> Kirk: So I really think the palette is pretty broad here. That I can see these going into existing rack and tower products to allow them to have memory that's composable down to the individual module level. To be able to take that facility to have just the right resources applied at just the right time with that API that we have in one view. Extend down to composing the hardware itself. I think we look at those edge line systems and want to have just the right kind of analytic capability, large persistent memories at that edge so we can handle those zeta bytes and zeta bytes of data in full fidelity analyzed at the edge sending back that intelligence to the core but also taking action at the edge in a timeframe that matters. I also see it coming out and being the basis of our exoscale high performance computing. You know when you want to have a exoscale system that has all of the combined capacity of the top 500 systems today but 1/20th of their power that is going to take rather novel technologies and everything we've been working on is exactly what's feeding that research and soon to be advanced development and then soon to be production in supply chain. >> Dave: Great. >> John: So the question I have is obviously we saw some really awesome Gen 10 stuff here at this show you guys are seeing that obviously you're on stage talking about a lot of the cool R&D, but really the reality is that's multiple years in the works some of this root of trust silicon technology that's pretty, getting the show buzzed up everyone's psyched about it. Dreamworks Animation's talking about how inorganic opportunities is helping their business and they got the security with the root of trust NIST certified and compliant. Pretty impressive. What's next? What else are you working on because this is where the R&D is on your shoulders for that next level of innovation. Where, what do you guys see that? Because security is a huge deal. That's that great example of how you guys innovated. Cause that'll stop the vector of a tax in the service area of IOT if you can get the servers to lock down and you have firmware that's secure, makes a lot of sense. That's probably the tip of the iceberg. What else is happening with security? >> Kirk: So when we think about security and our efforts on advanced development research around the machine what you're seeing here with the proliance is making the machines more secure. The inherent platform more secure. But the other thing I would point to you is the application we're running on the prototype. Large scale graph inference. And this is security because you have a platform like the machine. Able to digest hundreds and hundreds of tera bytes worth of log data to look for that fingerprint, that subtle clue that you have a system that has been compromised. And these are not blatant let's just blast everything out to some dot dot x x x sub domain, this is an advanced persistent thread by a very capable adversary who is very subtle in their reach out from a system that has been compromised to that command and control server. The signs are there if you can look at the data holistically. If you can look at that DNS log, graph of billions of entries everyday, constantly changing, if you can look at that as a graph in totality in a timeframe that matters then that's an empowering thing for a cyber defense team and I think that's one of the interesting things that we're adding to this discussion. Not only protect, detect and recover, but giving offensive weapons to our cyber defense team so they can hunt, they can hunt for those events for system threats. >> John: One of the things, Andrew I'll get your thoughts and reaction to this because Ill make an observation and you guys can comment and tell me I'm all wet, fell off the deep end or what not. Last year HP had great marketing around the machine. I love that Star Trek ad. It was beautiful and it was just... A machine is very, a great marketing technique. I mean use the machine... So a lot of people set expectations on the machine You saw articles being written maybe these people didn't understand it. Little bit pulled back, almost dampered down a little bit in terms of the marketing of the machine, other than the bin. Is that because you don't yet know what it's going to look like? Or there's so many broader possibilities where you're trying to set expectations? Cause the machine certainly has a lot of range and it's almost as if I could read your minds you don't want to post the position too early on what it could do. And that's my observation. Why the pullback? I mean certainly as a marketer I'd be all over that. >> Andrew: Yeah, I think part of it has been intentional just on how the ecosystem, we need the ecosystem developed kind of around this at the same time. Meaning, there are a lot of kind of moving parts to it whether it's around the open source community and kind of getting their head wrapped around what is this new architecture look like. We've got things like you know the Jin Zee Consortium where we're pouring a lot of our understanding and knowledge into that. And so we need a lot of partners, we know we're in a day and an age where look there's no single one company that's going to do every piece and part themselves. So part of it is kind of enough to get out there, to get the buzz, get the excitement to get other people then on board and now we have been heads down especially this last six months of... >> John: Jamming hard on it. >> Getting it all together. You know you think about what we showed first essentially first booted the thing in November and now you know we've got it running at this scale, that's really been the focus. But we needed a lot of that early engagement, interaction to get a lot of the other, members of the ecosystem kind of on board and starting to contribute. And really that's where we're at today. >> John: It's almost you want it let it take its own course organically because you mentioned just on the cyber surveillance opportunity around the crunching, you kind of don't know yet what the killer app is right? >> And that's the great thing of where we're at today now that we have kind of the prototype running at scale like this, it is allowing us to move beyond, look we've had the simulators to work with, we've had kind of emulation vehicles now you've got the real thing to run actual workloads on. You know we had the announcement around DZ and E as kind of an early early example, but it really now will allow us to do some refinement that allows us to get to those product concepts. >> Dave: I want to just ask the closing question. So I've had this screen here, it's like the theater, and I've been seeing these great things coming up and one was "Moore's Law is dead." >> Oh that was my session this morning. >> Another one was block chain. And unfortunately I couldn't hear it but I could see the tease. So when you guys come to work in the morning what's kind of the driving set of assumptions for you? Is it just the technology is limitless and we're going to go figure it out or are there things that sort of frame your raison d'etre? That drive your activities and thinking? And what are the fundamental assumptions that you guys use to drive your actions? >> Kirk: So what's been driving me for the last couple years is this exponential growth of information that we create as a species. That seems to have no upper bounding function that tamps it down. At the same time, the timeframe we want to get from information, from raw information to insight that we can take action on seems to be shrinking from days, weeks, minutes... Now it's down to micro seconds. If I want to have an intelligent power grid, intelligent 3G communication, I have to have micro seconds. So if you look at those two things and at the same time we just have to be the lucky few who are sitting in these seats right when Moore's Law is slowing down and will eventually flatten out. And so all the skills that we've had over the last 28 years of my career you look at those technologies and you say "Those aren't the ones that are going "to take us forward." This is an opportunity for us to really look and examine every piece of this, because if was something we could of just can't we just dot dot dot do one thing? We would do it, right? We can't just do one thing. We have to be more holistic if we're going to create the next 20, 30, 40 years of innovation. And that's really what I'm looking at. How do we get back exponential scaling on supply to meet this unending exponential demand? >> Dave: So technically I would imagine, that's a very hard thing to balance because the former says that we're going to have more data than we've ever seen. The latter says we've got to act on it fast which is a great trend for memory but the economics are going to be such a challenge to meet, to balance that. >> Kirk: We have to be able to afford the energy, and we have to be able to afford the material cost, and we have to be able to afford the business processes that do all these things. So yeah, you need breakthroughs. And that's really what we've been doing. And I think that's why we're so fortunate at Hewlett Packard Enterprise to have the labs team but also that world class engineering and that world class supply chain and a services team that can get us introduced to every interesting customer around the world who has those challenging problems and can give us that partnership and that insight to get those kind of breakthroughs. >> Dave: And I wonder if there will be a tipping point, if the tipping point will be, and I'm sure you've thought about this, a change in the application development model that drives so much value and so much productivity that it offsets some of the potential cost issues of changing the development paradigm. >> And I think you're seeing hints of that. Now we saw this when we went from systems of record, OLTP systems, to systems of engagement, mobile systems, and suddenly new ways to develop it. I think now the interesting thing is we move over to systems of action and we're moving from programmatic to training. And this is this interesting thing if you have those data bytes of data you can't have a pair of human eyeballs in front of that, you have to have a machine learning algorithm. That's the only thing that's voracious enough to consume this data in a timely enough fashion to get us answers, but you can't program it. We saw those old approaches in old school A.I., old school autonomous vehicle programs, they go about 10 feet, boom, and they'd flip over, right? Now you know they're on our streets and they are functioning. They're a little bit raw right now but that improvement cycle is fantastic because they're training, they're not programming. >> Great opportunity to your point about Moore's Law but also all this new functionality that has yet been defined, is right on the doorstep. Andrew, Kirk thank you so much for sharing. >> Andrew: Thank you >> Great insight, love Hewlett Packard Labs love the R&D conversation. Gets us a chance to go play in the wild and dream about the future you guys are out creating it congratulations and thanks for spending the time on The Cube, appreciate it. >> Thanks. >> The Cube coverage will continue here live at Las Vegas for HPE Discover 2017, Hewlett Packard Enterprises annual event. We'll be right back with more, stay with us. (bright music)