>>Good afternoon from the Venetian Expo, center, hall, whatever you wanna call it, in Las Vegas. Lisa Martin here. It's day four. I'm not sure what this place is called. Wait, >>What? >>Lisa Martin here with Dave Ante. This is the cube. This is day four of a ton of coverage that we've been delivering to you, which, you know, cause you've been watching since Monday night, Dave, we are almost at the end, we're almost at the show wrap. Excited to bring back, we've been talking about security, a lot about security. Excited to bring back a, an alumni to talk about that. But what's your final thoughts? >>Well, so just in, in, in the context of security, we've had just three in a row talking about cyber, which is like the most important topic. And I, and I love that we're having Palo Alto Networks on Palo Alto Networks is the gold standard in security. Talk to CISOs, they wanna work with them. And, and it was, it's interesting because I've been following them for a little bit now, watch them move to the cloud and a couple of little stumbling points. But I said at the time, they're gonna figure it out and, and come rocking back. And they have, and the company's just performing unbelievably well despite, you know, all the macro headwinds that we love to >>Talk about. So. Right. And we're gonna be unpacking all of that with one of our alumni. As I mentioned, Anker Shaw is with us, the SVP and GM of Palo Alto Networks. Anker, welcome back to the Cub. It's great to see you. It's been a while. >>It's good to be here after a couple years. Yeah, >>Yeah. I think three. >>Yeah, yeah, for sure. Yeah. Yeah. It's a bit of a blur after Covid. >>Everyone's saying that. Yeah. Are you surprised that there are still this many people on the show floor? Cuz I am. >>I am. Yeah. Look, I am not, this is my fourth, last year was probably one third or one fourth of this size. Yeah. But pre covid, this is what dream went looked like. And it's energizing, it's exciting. It's just good to be doing the good old things. So many people and yeah. Amazing technology and innovation. It's been incredible. >>Let's talk about innovation. I know you guys, Palo Alto Networks recently acquired cyber security. Talk to us a little bit about that. How is it gonna compliment Prisma? Give us all the scoop on that. >>Yeah, for sure. Look, some of the recent, the cybersecurity attacks that we have seen are related to supply chain, the colonial pipeline, many, many supply chain. And the reason for that is the modern software supply chain, not the physical supply chain, the one that AWS announced, but this is the software supply chain is really incredibly complicated, complicated developers that are building and shipping code faster than ever before. And the, the site acquisition at the center, the heart of that was securing the entire supply chain. White House came with a new initiative on supply chain security and SBO software bill of material. And we needed a technology, a company, and a set of people who can really deliver to that. And that's why we acquired that for supply chain security, otherwise known as cicd, security, c >>IDC security. Yeah. So how will that complement PRIs McCloud? >>Yeah, so look, if you look at our history lease over the last four years, we have been wanting to, our mission mission has been to build a single code to cloud platform. As you may know, there are over 3000 security vendors in the industry. And we said enough is enough. We need a platform player who can really deliver a unified cohesive platform solution for our customers because they're sick and tired of buying PI point product. So our mission has been to deliver that code to cloud platform supply chain security was a missing piece and we acquired them, it fits right really nicely into our portfolio of products and solution that customers have. And they'll have a single pin of glass with this. >>Yeah. So there's a lot going on. You've got, you've got an adversary that is incredibly capable. Yeah. These days and highly motivated and extremely sophisticated mentioned supply chain. It's caused a shift in, in CSO strategies, talking about the pandemic, of course we know work from home that changed things. You've mentioned public policy. Yeah. And, and so, and as well you have the cloud, cloud, you know, relatively new. I mean, it's not that new, but still. Yeah. But you've got the shared responsibility model and not, not only do you have the shared responsibility model, you have the shared responsibility across clouds and OnPrem. So yes, the cloud helps with security, but that the CISO has to worry about all these other things. The, the app dev team is being asked to shift left, you know, secure and they're not security pros. Yeah. And you know, kind audit is like the last line of defense. So I love this event, I love the cloud, but customers need help in making their lives simpler. Yeah. And the cloud in and of itself, because, you know, shared responsibility doesn't do that. Yeah. That's what Palo Alto and firms like yours come in. >>Absolutely. So look, Jim, this is a unable situation for a lot of the Cisco, simply because there are over 26 million developers, less than 3 million security professional. If you just look at all the announcement the AWS made, I bet you there were like probably over 2000 features. Yeah. I mean, they're shipping faster than ever before. Developers are moving really, really fast and just not enough security people to keep up with the velocity and the innovation. So you are right, while AWS will guarantee securing the infrastructure layer, but everything that is built on top of it, the new machine learning stuff, the new application, the new supply chain applications that are developed, that's the responsibility of the ciso. They stay up at night, they don't know what's going on because developers are bringing new services and new technology. And that's why, you know, we've always taken a platform approach where customers and the systems don't have to worry about it. >>What AWS new service they have, it's covered, it's secured. And that's why the adopters, McCloud and Palo Alto Networks, because regardless what developers bring, security is always there by their side. And so security teams need just a simple one click solution. They don't have to worry about it. They can sleep at night, keep the bad actors away. And, and that's, that's where Palo Alto Networks has been innovating in this area. AWS is one of our biggest partners and you know, we've integrated with, with a lot of their services. We launch about three integrations with their services. And we've been doing this historically for more and >>More. Are you still having conversations with the security folks? Or because security is a board level conversation, are your conversations going up a stack because this is a C-suite problem, this is a board level initiative? >>Absolutely. Look, you know, there was a time about four years ago, like the best we could do is director of security. Now it's just so CEO level conversation, board level conversation to your point, simply because I mean, if, if all your financial stuff is going to public cloud, all your healthcare data, all your supply chain data is going to public cloud, the board is asking very simple question, what are you doing to secure that? And to be honest, the question is simple. The answer's not because all the stuff that we talked about, too many applications, lots and lots of different services, different threat vectors and the bad actors, the bad guys are always a step ahead of the curve. And that's why this has become a board level conversation. They wanna make sure that things are secure from the get go before, you know, the enterprises go too deep into public cloud adoption. >>I mean there, there was shift topics a little bit. There was hope or kinda early this year that that cyber was somewhat insulated from the sort of macro press pressures. Nobody's safe. Even the cloud is sort of, you know, facing those, those headwinds people optimizing costs. But one thing when you talk to customers is, I always like to talk about that, that optiv graph. We've all seen it, right? And it's just this eye test of tools and it's a beautiful taxonomy, but there's just too many tools. So we're seeing a shift from point tools to platforms because obviously a platform play, and that's a way. So what are you seeing in the, in the field with customers trying to optimize their infrastructure costs with regard to consolidating to >>Platforms? Yeah. Look, you rightly pointed out one thing, the cybersecurity industry in general and Palo Alto networks, knock on wood, the stocks doing well. The macro headwinds hasn't impacted the security spend so far, right? Like time will tell, we'll, we'll see how things go. And one of the primary reason is that when you know the economy starts to slow down, the customers again want to invest in platforms. It's simple to deploy, simple to operationalize. They want a security partner of choice that knows that they, it's gonna be by them through the entire journey from code to cloud. And so that's why platform, especially times like these are more important than they've ever been before. You know, customers are investing in the, the, the product I lead at Palo Alto network called Prisma Cloud. It's in the cloud network application protection platform seen app space where once again, customers that investing in platform from quote to cloud and avoiding all the point products for sure. >>Yeah. Yeah. And you've seen it in, in Palo Alto's performance. I mean, not every cyber firm has is, is, >>You know, I know. Ouch. CrowdStrike Yeah. >>Was not. Well you saw that. I mean, and it was, and and you know, the large customers were continuing to spend, it was the small and mid-size businesses Yeah. That were, were were a little bit soft. Yeah. You know, it's a really, it's really, I mean, you see Okta now, you know, after they had some troubles announcing that, you know, their, their, their visibility's a little bit better. So it's, it's very hard to predict right now. And of course if TOMA Brava is buying you, then your stock price has been up and steady. That's, >>Yeah. Look, I think the key is to have a diversified portfolio of products. Four years ago before our CEO cash took over the reins of the company, we were a single product X firewall company. Right. And over time we have added XDR with the first one to introduce that recently launched x Im, you know, to, to make sure we build an NextGen team, cloud security is a completely net new investment, zero trust with access as workers started working remotely and they needed to make sure enterprises needed to make sure that they're accessing the applications securely. So we've added a lot of portfolio products over time. So you have to remain incredibly diversified, stay strong, because there will be stuff like remote work that slowed down. But if you've got other portfolio product like cloud security, while those secular tailwinds continue to grow, I mean, look how fast AWS is growing. 35, 40%, like $80 billion run rate. Crazy at that, that scale. So luckily we've got the portfolio of products to ensure that regardless of what the customer's journey is, macro headwinds are, we've got portfolio of solutions to help our customers. >>Talk a little bit about the AWS partnership. You talked about the run rate and I was reading a few days ago. You're right. It's an 82 billion arr, massive run rate. It's crazy. Well, what are, what is a Palo Alto Networks doing with aws and what's the value in it to help your customers on a secure digital transformation journey? >>Well, absolutely. We have been doing business with aws. We've been one of their security partners of choice for many years now. We have a presence in the marketplace where customers can through one click deploy the, the several Palo Alto Networks security solutions. So that's available. Like I said, we had launch partner to many, many new products and innovation that AWS comes up with. But always the day one partner, Adam was talking about some of those announcements and his keynote security data lake was one of those. And they were like a bunch of others related to compute and others. So we have been a partner for a long time, and look, AWS is an incredibly customer obsessed company. They've got their own security products. But if the customer says like, Hey, like I'd like to pick this from yours, but there's three other things from Palo Alto Networks or S MacCloud or whatever else that may be, they're open to it. And that's the great thing about AWS where it doesn't have to be wall garden open ecosystem, let the customer pick the best. >>And, and that's, I mean, there's, there's examples where AWS is directly competitive. I mean, my favorite example is Redshift and Snowflake. I mean those are directly competitive products, but, but Snowflake is an unbelievably great relationship with aws. They do cyber's, I think different, I mean, yeah, you got guard duty and you got some other stuff there. But generally speaking, the, correct me if I'm wrong, the e the ecosystem has more room to play on AWS than it may on some other clouds. >>A hundred percent. Yeah. Once again, you know, guard duty for examples, we've got a lot of customers who use guard duty and Prisma Cloud and other Palo Alto Networks products. And we also ingest the data from guard duty. So if customers want a single pane of glass, they can use the best of AWS in terms of guard duty threat detection, but leverage other technology suite from, you know, a platform provider like Palo Alto Networks. So you know, that that, you know, look, world is a complicated place. Some like blue, some like red, whatever that may be. But we believe in giving customers that choice, just like AWS customers want that. Not a >>Problem. And at least today they're not like directly, you know, in your space. Yeah. You know, and even if they were, you've got such a much mature stack. Absolutely. And my, my frankly Microsoft's different, right? I mean, you see, I mean even the analysts were saying that some of the CrowdStrike's troubles for, cuz Microsoft's got the good enough, right? So >>Yeah. Endpoint security. Yeah. And >>Yeah, for sure. So >>Do you have a favorite example of a customer where Palo Alto Networks has really helped them come in and, and enable that secure business transformation? Anything come to mind that you think really shines a light on Palo Alto Networks and what it's able to do? >>Yeah, look, we have customers across, and I'm gonna speak to public cloud in general, right? Like Palo Alto has over 60,000 customers. So we've been helping with that business transformation for years now. But because it's reinvented aws, the Prisma cloud product has been helping customers across different industry verticals. Some of the largest credit card processing companies, they can process transactions because we are running security on top of the workloads, the biggest financial services, biggest healthcare customers. They're able to put the patient health records in public cloud because Palo Alto Networks is helping them get there. So we are helping accelerated that digital journey. We've been an enabler. Security is often perceived as a blocker, but we have always treated our role as enabler. How can we get developers and enterprises to move as fast as possible? And like, my favorite thing is that, you know, moving fast and going digital is not a monopoly of just a tech company. Every company is gonna be a tech company Oh absolutely. To public cloud. Yes. And we want to help them get there. Yeah. >>So the other thing too, I mean, I'll just give you some data. I love data. I have a, ETR is our survey partner and I'm looking at Data 395. They do a survey every quarter, 1,250 respondents on this survey. 395 were Palo Alto customers, fortune 500 s and P 500, you know, big global 2000 companies as well. Some small companies. Single digit churn. Yeah. Okay. Yeah. Very, very low replacement >>Rates. Absolutely. >>And still high single digit new adoption. Yeah. Right. So you've got that tailwind going for you. Yeah, >>Right. It's, it's sticky because especially our, our main business firewall, once you deploy the firewall, we are inspecting all the network traffic. It's just so hard to rip and replace. Customers are getting value every second, every minute because we are thwarting attacks from public cloud. And look, we, we, we provide solutions not just product, we just don't leave the product and ask the customers to deploy it. We help them with deployment consumption of the product. And we've been really fortunate with that kind of gross dollar and netten rate for our customers. >>Now, before we wrap, I gotta tease, the cube is gonna be at Palo Alto Ignite. Yeah. In two weeks back here. I think we're at D mgm, right? We >>Were at D MGM December 13th and >>14th. So give us a little, show us a little leg if you would. What could we expect? >>Hey, look, I mean, a lot of exciting new things coming. Obviously I can't talk about it right now. The PR Inc is still not dry yet. But lots of, lots of new innovation across our three main businesses. Network security, public cloud, security, as well as XDR X. Im so stay tuned. You know, you'll, you'll see a lot of new exciting things coming up. >>Looking forward to it. >>We are looking forward to it. Last question on curf. You, if you had a billboard to place in New York Times Square. Yeah. You're gonna take over the the the Times Square Nasdaq. What does the billboard say about why organizations should be working with Palo Alto Networks? Yeah. To really embed security into their dna. Yeah. >>You know when Jim said Palo Alto Networks is the gold standard for security, I thought it was gonna steal it. I think it's pretty good gold standard for security. But I'm gonna go with our mission cyber security partner's choice. We want to be known as that and that's who we are. >>Beautifully said. Walker, thank you so much for joining David in the program. We really appreciate your insights, your time. We look forward to seeing you in a couple weeks back here in Vegas. >>Absolutely. Can't have enough of Vegas. Thank you. Lisa. >>Can't have in Vegas, >>I dunno about that. By this time of the year, I think we can have had enough of Vegas, but we're gonna be able to see you on the cubes coverage, which you could catch up. Palo Alto Networks show Ignite December, I believe 13th and 14th on the cube.net. We want to thank Anker Shaw for joining us. For Dave Ante, this is Lisa Martin. You're watching the Cube, the leader in live enterprise and emerging tech coverage.

(soft music) >> Welcome back to Sin City, guys and girls we're glad you're with us. You've been watching theCUBE all week, we know that. This is theCUBE's live coverage of AWS re:Invent 22, from the Venetian Expo Center where there are tens of thousands of people, and this event if you know it, covers the entire strip. There are over 55,000 people here, hundreds of thousands online. Dave, this has been a fantastic show. It is clear everyone's back. We're hearing phenomenal stories from AWS and it's ecosystem. We got a great customer story coming up next, featured on the main stage. >> Yeah, I mean, you know, post pandemic, you start to think about, okay, how are things changing? And one of the things that we heard from Adam Selipsky, was, we're going beyond digital transformation into business transformation. Okay. That can mean a lot of things to a lot of people. I have a sense of what it means. And I think this next interview really talks to business transformation beyond digital transformation, beyond the IT. >> Excellent. We've got two guests. One of them is an alumni, Scott Mullins joins us, GM, AWS Worldwide Financial Services, and Brad Peterson is here, the EVP, CIO and CTO of NASDAQ. Welcome guys. Great to have you. >> Hey guys. >> Hey guys. Thanks for having us. >> Yeah >> Brad, talk a little bit, there was an announcement with NASDAQ and AWS last year, a year ago, about how they're partnering to transform capital markets. It was a highlight of last year. Remind us what you talked about and what's gone on since then. >> Yeah, so, we are very excited. I work with Adena Friedman, she's my boss, CEO of NASDAQ, and she was on stage with Adam for his first Keynote as CEO of AWS. And we made the commitment that we were going to move our markets to the Cloud. And we've been a long time customer of AWS and everyone said, you know the last piece, the last frontier to be moved was the actual matching where all the messages, the quotes get matched together to become confirmed orders. So that was what we committed to less than a year ago. And we said we were going to move one of our options markets. In the US, we have six of them. And options markets are the most challenging, they're the most high volume and high performance. So we said, let's start with something really challenging and prove we can do it together with AWS. So we committed to that. >> And? Results so far? >> So, I can sit here and say that November 7th so we are live, we're in production and the MRX Exchange is called Mercury, so we shorten it for MRX, we like acronyms in technology. And so, we started with a phased launch of symbols, so you kind of allow yourself to make sure you have all the functionality working then you add some volume on it, and we are going to complete the conversion on Monday. So we are all good so far. And I have some results I can share, but maybe Scott, if you want to talk about why we did that together. >> Yeah. >> And what we've done together over many years. >> Right. You know, Brian, I think it's a natural extension of our relationship, right? You know, you look at the 12 year relationship that AWS and NASDAQ have had together, it's just the next step, in the way that we're going to help the industry transform itself. And so not just NASDAQ's business transformation for itself, but really a blueprint and a template for the entire capital markets industry. And so many times people will ask me, who's using Cloud well? Who's doing well in the Cloud? And NASDAQ is an easy example to point to, of somebody who's truly taking advantage of these capabilities because the Cloud isn't a place, it's a set of capabilities. And so, this is a shining example of how to use these capabilities to actually deliver real business benefit, not just to to your organization, but I think the really exciting part is the market technology piece of how you're serving other exchanges. >> So last year before re:Invent, we said, and it's obvious within the tech ecosystem, that technology companies are building on top of the Cloud. We said, the big trend that we see in the 2020s is that, you know, consumers of IT, historically, your customers are going to start taking their stacks, their software, their data, their services and sassifying, putting it on the Cloud and delivering new services to customers. So when we saw Adena on stage last year, we called it by the way, we called it Super Cloud. >> Yeah. >> Okay. Some people liked the term but I love it. And so yeah, Super Cloud. So when we saw Adena on stage, we said that's a great example. We've seen Capital One doing some similar things, we've had some conversations with US West, it's happening, right? So talk about how you actually do that. I mean, because you've got a lot, you've got a big on-premises stay, are you connecting to that? Is it all in the Cloud? Paint a picture of what the architecture looks like? >> Yeah. And there's, so you started with the business transformation, so I like that. >> Yeah. >> And the Super Cloud designation, what we are is, we own and operate exchanges in the United States and in Europe and in Canada. So we have our own markets that we're looking at modernizing. So we look at this, as a modernization of the capital market infrastructure, but we happen to be the leading technology provider for other markets around the world. So you either build your own or you source from us. And we're by far the leading provider. So a lot of our customers said, how about if you go first? It's kind of like Mikey, you know, give it to Mikey, let him try it. >> See if Mikey likes it. >> Yeah. >> Penguin off the iceberg thing. >> Yeah. And so what we did is we said, to make this easy for our customers, so you want to ask your customers, you want to figure out how you can do it so that you don't disrupt their business. So we took the Edge Compute that was announced a few years ago, Amazon Outposts, and we were one of their early customers. So we started immediately to innovate with, jointly innovate with Amazon. And we said, this looks interesting for us. So we extended the region into our Carteret data center in Northern New Jersey, which gave us all the services that we know and love from Amazon. So our technical operations team has the same tools and services but then, we're able to connect because in the markets what we're doing is we need to connect fairly. So we need to ensure that you still have that fairness element. So by bringing it into our building and extending the Edge Compute platform, the AWS Outpost into Carteret, that allowed us to also talk very succinctly with our regulators. It's a familiar territory, it's all buttoned up. And that simplified the conversion conversation with the regulators. It simplified it with our customers. And then it was up to us to then deliver time and performance >> Because you had alternatives. You could have taken a more mature kind of on-prem legacy stack, figured out how to bolt that in, you know, less cloudy. So why did you choose Outposts? I am curious. >> Well, Outposts looked like when it was announced, that it was really about extending territory, so we had our customers in mind, our global customers, and they don't always have an AWS region in country. So a lot of you think about a regulator, they're going to say, well where is this region located? So finally we saw this ability to grow the Cloud geographically. And of course we're in Sweden, so we we work with the AWS region in Stockholm, but not every country has a region yet. >> And we're working as fast as we can. - Yes, you are. >> Building in every single location around the planet. >> You're doing a good job. >> So, we saw it as an investment that Amazon had to grow the geographic footprint and we have customers in many smaller countries that don't have a region today. So maybe talk a little bit about what you guys had in mind and it's a multi-industry trend that the Edge Compute has four or five industries that you can say, this really makes a lot of sense to extend the Cloud. >> And David, you said it earlier, there's a trend of ecosystems that are coming onto the Cloud. This is our opportunity to bring the Cloud to an ecosystem, to an existing ecosystem. And if you think about NASDAQ's data center in Carteret, there's an ecosystem of NASDAQ's clients there that are there to be with NASDAQ. And so, it was actually much easier for us as we worked together over a really a four year period, thinking about this and how to make this technological transition, to actually bring the capabilities to that ecosystem, rather than trying to bring the ecosystem to AWS in one of our public regions. And so, that's been our philosophy with Outpost all along. It's actually extending our capabilities that our customers know and love into any environment that they need to be able to use that in. And so to Brad's point about servicing other markets in different countries around the world, it actually gives us that ability to do that very quickly, very nimbly and very succinctly and successfully. >> Did you guys write a working backwards document for this initiative? >> We did. >> Yeah, we actually did. So to be, this is one of the fully exercised. We have a couple of... So by the way, Scott used to work at NASDAQ and we have a number of people who have gone from NASDAQ data to AWS, and from AWS to NASDAQ. So we have adopted, that's one of the things that we think is an effective way to really clarify what you're trying to accomplish with a project. So I know you're a little bit kidding on that, but we did. >> No, I was close. Because I want to go to the like, where are we in the milestone? And take us through kind of what we can expect going forward now that we've worked backwards. >> Yep, we did. >> We did. And look, I think from a milestone perspective, as you heard Brad say, we're very excited that we've stood up MRX in production. Having worked at NASDAQ myself, when you make a change and when you stand up a market that's always a moment where you're working with your community, with your clients and you've got a market-wide call that you're working and you're wanting to make sure that everything goes smoothly. And so, when that call went smoothly and that transition went smoothly I know you were very happy, and in AWS, we were also very happy as well that we hit that milestone within the timeframe that Adena set. And that was very important I know to you. >> Yeah. >> And for us as well. >> Yeah. And our commitment, so the time base of this one was by the end of 2022. So November 7th, checked. We got that one done. >> That's awesome. >> The other one is we said, we wanted the performance to be as good or better than our current platform that we have. And we were putting a new version of our derivative or options software onto this platform. We had confidence because we already rolled it to one market in the US then we rolled it earlier this year and that was last year. And we rolled it to our nordic derivatives market. And we saw really good customer feedback. So we had confidence in our software was going to run. Now we had to marry that up with the Outpost platform and we said we really want to achieve as good or better performance and we achieved better performance, so that's noticeable by our customers. And that one was the biggest question. I think our customers understand when we set a date, we test them with them. We have our national test facility that they can test in. But really the big question was how is it going to perform? And that was, I think one of the biggest proof points that we're really proud about, jointly together. And it took both, it took both of us to really innovate and get the platform right, and we did a number of iterations. We're never done. >> Right. >> But we have a final result that says it is better. >> Well, congratulations. - Thank you. >> It sounds like you guys have done a tremendous job. What can we expect in 2023? From NASDAQ and AWS? Any little nuggets you can share? >> Well, we just came from the partner, the partner Keynote with Adam and Ruba and we had another colleague on stage, so Nick Ciubotariu, so he is actually someone who brought digital assets and cryptocurrencies onto the Venmo, PayPal platform. He joined NASDAQ about a year ago and we announced that in our marketplace, the Amazon marketplace, we are going to offer digital custody, digital assets custody solution. So that is certainly going to be something we're excited about in 2023. >> I know we got to go, but I love this story because it fits so great at the Super cloud but we've learned so much from Amazon over the years. Two pieces of teams, we talked about working backwards, customer obsession, but this is a story of NASDAQ pointing its internal capabilities externally. We're already on that journey and then, bringing that to the Cloud. Very powerful story. I wonder what's next in this, because we learn a lot and we, it's like the NFL, we copy it. I think about product market fit. You think about scientific, you know, go to market and seeing that applied to the financial services industry and obviously other industries, it's really exciting to see. So congratulations. >> No, thank you. And look, I think it's an example of Invent and Simplify, that's another Amazon principle. And this is, I think a great example of inventing on behalf of an industry and then continually working to simplify the way that the industry works with all of us. >> Last question and we've got only 30 seconds left. Brad, I'm going to direct it to you. If you had the opportunity to take over the NASDAQ sign in Times Square and say a phrase that summarizes what NASDAQ and AWS are doing together, what would it say? >> Oh, and I think I'm going to put that up on Monday. So we're going to close the market together and it's going to say, "Modernizing the capital market's infrastructure together." >> Very cool. >> Excellent. Drop the mic. Guys, this was fantastic. Thank you so much for joining us. We appreciate you joining us on the show, sharing your insights and what NASDAQ and AWS are doing. We're going to have to keep watching this. You're going to have to come back next year. >> All right. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (soft music)

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies customers. Earth Camp. Joining Me is built sharp, the senior VP of product development and strategy from Earth Camp Phil, Welcome to the Cube. >>Thank you so much. >>So talk to me a little bit. About what Earth Cam does this very interesting Web can technology? You guys have tens of thousands of cameras and sensors all over the globe give her audience and understanding of what you guys are all about. >>Sure thing. The world's leading provider of Webcam technologies and mentioned content services were leaders and live streaming time lapse imaging primary focus in the vertical construction. So a lot of these, the most ambitious, largest construction projects around the world, you see, these amazing time lapse movies were capturing all of that imagery. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating these time lapse movies from it. >>You guys, you're headquartered in New Jersey and I was commenting before we went live about your great background. So you're actually getting to be on site today? >>Yes, Yes, that's where lives from our headquarters in Upper Saddle River, New Jersey. >>Excellent. So in terms of the types of information that you're capturing. So I was looking at the website and see from a construction perspective or some of the big projects you guys have done the Hudson Yards, the Panama Canal expansion, the 9 11 Museum. But you talked about one of the biggest focus is that you have is in the construction industry in terms of what type of data you're capturing from all of these thousands of edge devices give us a little bit of insight into how much data you're capturing high per day, how it gets from the edge, presumably back to your court data center for editing. >>Sure, and it's not just construction were also in travel, hospitality, tourism, security, architectural engineering, basically, any any industry that that need high resolution visualization of their their projects or their their performance or of their, you know, product flow. So it's it's high resolution documentation is basically our business. There are billions of files in the isil on system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that air up to 30 giga pixel, sometimes typically around 1 to 2 giga pixel. But that composite imagery Eyes represents millions of images per per month coming into the storage system and then being, uh, stitched together to those those composites >>the millions of images coming in every month. You mentioned Isil on talk to me a little bit about before you were working with Delhi, EMC and Power Scale. How are you managing this massive volume of data? >>Sure we had. We've used a number of other enterprise storage systems. It was really nothing was as easy to manage Azazel on really is there was there was a lot of a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, you to manage that, uh, and and it's interesting with the amount of data that we handle. This is being billions of relatively small files there there, you know, half a megabyte to a couple of megabytes each. It's an interesting data profile, which, which isil on really is well suited for. >>So if we think about some of the massive changes that we've all been through the last in 2020 what are some of the changes that that Earth Kemp has seen with respect to the needs for organizations? Or you mentioned other industries, like travel hospitality? Since none of us could get to these great travel destinations, Have you seen a big drive up in the demand and the need to process data more data faster? >>Yeah, that's an injury interesting point with with the Pandemic. Obviously we had to pivot and move a lot of people toe working from home, which we were able to do pretty quickly. But there's also an interesting opportunity that arose from this, where so many of our customers and other people also have to do the same. And there is an increased demand for our our technology so people can remotely collaborate. They can. They can work at a distance. They can stay at home and see what's going on in these projects sites. So we really so kind of an uptick in the in the need for our products and services. And we've also created Cem basically virtual travel applications. We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people can kind of virtually travel when they can't really get out there. So it's, uh, we've been doing kind of giving back Thio to people that are having having some issues with being able to travel around. We've done the fireworks of the Washington Mall around the Statue of Liberty for the July 4th, and this year will be Webcasting and New Year's in Times Square for our 25th year, actually. So again, helping people travel virtually and be, uh, maintain can be collectivity with with each other and with their projects, >>which is so essential during these times, where for the last 67 months everyone is trying to get a sense of community, and most of us just have the Internet. So I also heard you guys were available on Apple TV, someone to fire that up later and maybe virtually travel. Um, but tell me a little bit about how working in conjunction with Delta Technologies and Power Cell How is that enabled you to manage this massive volume change you've experienced this year? Because, as you said, it's also about facilitating collaboration, which is largely online these days. >>Yeah, I mean, the the great things they're working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we worked with in the past we've always found ourselves kind of second guessing. Obviously, resolutions are increasing. The camera performance is increasing. Streaming video is everything is is constantly getting bigger and better, faster. Maurits And we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at the second guess we're at with it With with this, this did L infrastructure. That's been it's been fantastic. We don't really have to think about that as much. We just continue innovating everything scales as we needed to dio. It's it's much easier to work with, >>so you've got power scale at your core data center in New Jersey. Tell me a little bit about how data gets from thes tens of thousands of devices at the edge, back to your editors for editing and how power scale facilitates faster editing, for example. >>Basically, you imagine every one of these cameras on It's not just camera. We have mobile applications. We have fixed position of robotic cameras. There's all these different data acquisition systems were integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the Internet, so these are all endpoints in our network. Eso that's that's constantly being ingested into our network and say WTO. I salon the big the big thing that's really been a timesaver Working with the video editors is, instead of having to take that content, move it into an editing environment where we have we have a whole team of award winning video editors. Creating these time lapse is we don't need to keep moving that around. We're working natively on Iselin clusters. They're doing their editing, their subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all it happened right there on that live environment on the retention. Is there if we have to go back later on all of our customers, data is really kept within that 11 area. It's consolidated, its secure. >>I was looking at the Del Tech website. There's a case study that you guys did earth campaign with Deltek saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I could imagine what the volumes changing so much now but on Li not only is huge for your business, but to the demands that your customers have as well, depending on where there's demands are coming from >>absolutely and and just being able to do that a lot faster and be more nimble allows us to scale. We've added actually against speaking on this pandemic, we've actually added person who we've been hiring people. A lot of those people are working remotely, as as we've stated before on it's just with the increase in business. We have to continue to keep building on that on this storage environments been been great. >>Tell me about what you guys really kind of think about with respect to power scale in terms of data management, not storage management and what that difference means to your business. >>Well, again, I mean number number one was was really eliminating the amount of resource is amount of time we have to spend managing it. We've almost eliminated any downtime of any of any kind. We have greater storage density, were able to have better visualization on how our data is being used, how it's being access so as thes as thes things, a revolving. We really have good visibility on how the how the storage system is being used in both our production and our and also in our backup environments. It's really, really easy for us Thio to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >>And you mentioned hiring folks during the pandemic, which is fantastic but also being able to do things much in a much more streamlined way with respect to managing all of this data. But I am curious in terms of of innovation and new product development. What have you been able to achieve because you've got more resource is presumably to focus on being more innovative rather than managing storage >>well again? It's were always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 Giga pixel. You know, people are talking about megapixel images were stitching hundreds of these together. We've we're just really changing the way imagery is used, uh, both in the time lapse and also just in archival process. Ah, lot of these things we've done with the interior. You know, we have this virtual reality product where you can you can walk through and see in the 3 60 bubble. We're taking that imagery, and we're combining it with with these been models who are actually taking the three D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress and different things that are happening on the site. Look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people, time and money on these construction sites. We've also introduced a I machine learning applications into directly into the workflow in this in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure, it really is seamless and working with YSL on now. >>Imagine, by being able to infuse AI and machine learning, you're able to get insight faster to be ableto either respond faster to those construction customers, for example, or alert them. If perhaps something isn't going according to plan. >>A lot of it's about schedule. It's about saving money about saving time and again, with not as many people traveling to the sites, they really just have have constant visualization of what's going on. Day to day, we're detecting things like different types of construction equipment and things that are happening on the side. We're partnering with people that are doing safety analytics and things of that nature. So these these are all things that are very important to construction sites. >>What are some of the things as we are rounding out the calendar year 2020? What are some of the things that you're excited about going forward in 2021? That Earth cam is going to be able to get into and to deliver >>it, just MAWR and more people really, finally seeing the value. I mean, I've been doing this for 20 years, and it's just it's it's It's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with things they can do with this imagery. That's what we're all about that's really exciting to us in a very challenging environment right now is that people are are recognizing the need for this technology and really starting to put it on a lot more projects. >>Well, it's You can kind of consider an essential service, whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget on schedule, as you said, Or maybe even just the essential nous of helping folks from any country in the world connect with a favorite favorite travel location or sending the right to help. From an emotional perspective, I think the essential nous of what you guys are delivering is probably even more impactful now, don't you think? >>Absolutely and again about connecting people and when they're at home. And recently we we webcast the president's speech from the Flight 93 9 11 observation from the memorial. There was something where the only the immediate families were allowed to travel there. We webcast that so people could see that around the world we have documented again some of the biggest construction projects out there. The new rate years greater stadium was one of the recent ones, uh, is delivering this kind of flagship content. Wall Street Journal is to use some of our content recently to really show the things that have happened during the pandemic in Times Square's. We have these cameras around the world. So again, it's really bringing awareness of letting people virtually travel and share and really remain connected during this this challenging time on and again, we're seeing a really increase demand in the traffic in those areas as well. >>I can imagine some of these things that you're doing that you're achieving now are going to become permanent, not necessarily artifacts of Cove in 19 as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value off this type of video to be able to reach consumers that they probably could never reach before. >>Yeah, I think the whole nature of business and communication and travel on everything is really going to be changed from this point forward. It's really people are looking at things very, very differently and again, seeing the technology really can help with so many different areas that, uh, that it's just it's gonna be a different kind of landscape out there we feel on that's really, you know, continuing to be seen on the uptick in our business and how many people are adopting this technology. We're developing a lot more. Partnerships with other companies were expanding into new industries on again. You know, we're confident that the current platform is going to keep up with us and help us, you know, really scale and evolved as thes needs air growing. >>It sounds to me like you have the foundation with Dell Technologies with power scale to be able to facilitate the massive growth that you're saying and the skill in the future like you've got that foundation. You're ready to go? >>Yeah, we've been We've been We've been using the system for five years already. We've already added capacity. We can add capacity on the fly, Really haven't hit any limits. And what we can do, It's It's almost infinitely scalable, highly redundant. Gives everyone a real sense of security on our side. And, you know, we could just keep innovating, which is what we do without hitting any any technological limits with with our partnership. >>Excellent. Well, Bill, I'm gonna let you get back to innovating for Earth camp. It's been a pleasure talking to you. Thank you so much for your time today. >>Thank you so much. It's been a pleasure >>for Bill Sharp and Lisa Martin. You're watching the cubes. Digital coverage of Dell Technologies World 2020. Thanks for watching. Yeah,

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Dell Technologies World, digital experience. Brought to you by Dell Technologies. >> Welcome to theCUBE's coverage of Dell Technologies World 2020, the digital coverage. I'm Lisa Martin, and I'm excited to be talking with one of Dell Technologies' customers EarthCam. Joining me is Bill Sharp, the senior VP of product development and strategy from EarthCam. Bill, welcome to theCUBE. >> Thank you so much. >> So talk to me a little bit about what EarthCam does. This is very interesting webcam technology. You guys have tens of thousands of cameras and sensors all over the globe. Give our audience an understanding of what you guys are all about. >> Sure thing. The world's leading provider of webcam technologies, you mentioned content and services, we're leaders in live streaming, time-lapse imaging, primary focus in the vertical construction. So with a lot of these, the most ambitious, largest construction projects around the world that you see these amazing time-lapse movies, we're capturing all of that imagery basically around the clock, these cameras are sending all of that image content to us and we're generating these time-lapse movies from it. >> You guys are headquartered in New Jersey. I was commenting before we went live about your great background. So you're actually getting to be onsite today? >> Yes, yes. We're live from our headquarters in upper Saddle River, New Jersey. >> Excellent, so in terms of the types of information that you're capturing, so I was looking at the website, and see from a construction perspective, some of the big projects you guys have done, the Hudson Yards, the Panama Canal expansion, the 9/11 museum. But you talked about one of the biggest focuses that you have is in the construction industry. In terms of what type of data you're capturing from all of these thousands of edge devices, give us a little bit of an insight into how much data you're capturing per day, how it gets from the edge, presumably, back to your core data center for editing. >> Sure, and it's not just construction. We're also in travel, hospitality, tourism, security, architecture, engineering, basically any industry that need high resolution visualization of their projects or their performance or their product flow. So it's high resolution documentation is basically our business. There are billions of files in the Isilon system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that are up to 30 gigapixel sometimes. Typically around one to two gigapixel but that composite imagery represents millions of images per month coming into the storage system and then being stitched together to those composites. >> So millions of images coming in every month, you mentioned Isilon. Talk to me a little bit about before you were working with Dell EMC and PowerScale, how were you managing this massive volume of data? >> Sure, we've used a number of other enterprise storage systems. It was really nothing was as easy to manage as Isilon really is. There was a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, to manage that. And it's interesting with the amount of data that we handle, being billions of relatively small files. They're, you know, a half a megabyte to a couple of megabytes each. It's an interesting data profile which Isilon really is well suited for. >> So if we think about some of the massive changes that we've all been through in the last, in 2020, what are some of the changes that EarthCam hasn't seen with respect to the needs for organizations, or you mentioned other industries like travel, hospitality, since none of us can get to these great travel destinations, have you seen a big drive up in the demand and the need to process more data faster? >> Yeah, that's an interesting point with the pandemic. I mean, obviously we had to pivot and move a lot of people to working from home, which we were able to do pretty quickly, but there's also an interesting opportunity that arose from this where so many of our customers and other people also have to do the same. And there is an increased demand for our technology. So people can remotely collaborate. They can work at a distance, they can stay at home and see what's going on in these project sites. So we really saw kind of an uptick in the need for our products and services. And we've also created some basically virtual travel applications. We have an application on the Amazon Fire TV which is the number one app in the travel platform, and people can kind of virtually travel when they can't really get out there. So it's, we've been doing kind of giving back to people that are having some issues with being able to travel around. We've done the fireworks at the Washington Mall around the Statue of Liberty for July 4th. And this year we'll be webcasting New Years in Times Square for our 25th year, actually. So again, helping people travel virtually and maintain connectivity with each other, and with their projects. >> Which is so essential during these times where for the last six, seven months, everyone is trying to get a sense of community and most of us just have the internet. So I also heard you guys were available on the Apple TV, someone should fire that up later and maybe virtually travel. But tell me a little bit about how working in conjunction with Dell Technologies and PowerScale. How has that enabled you to manage this massive volume change that you've experienced this year? Because as you said, it's also about facilitating collaboration which is largely online these days. >> Yeah, and I mean, the great things of working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we've worked with in the past we've always found ourselves kind of second guessing. We're constantly innovating. Obviously resolutions are increasing. The camera performance is increasing, streaming video is, everything is constantly getting bigger and better, faster, more, and we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at them, second guess where we're at with it. With the Dell infrastructure it's been fantastic. We don't really have to think about that as much. We just continue innovating, everything scales as we need it to do. It's much easier to work with. >> So you've got PowerScale at your core data center in New Jersey. Tell me a little bit about how data gets from these tens of thousands of devices at the edge, back to your editors for editing, and how PowerScale facilitates faster editing, for example. >> Well, basically you can imagine every one of these cameras, and it's not just cameras. It's also, you know, we have 360 virtual reality kind of bubble cameras. We have mobile applications, we have fixed position and robotic cameras. There's all these different data acquisition systems we're integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the internet. So these are all endpoints in our network. So that's constantly being ingested into our network and saved to Isilon. The big thing that's really been a time saver working with the video editors is instead of having to take that content, move it into an editing environment where we have a whole team of award-winning video editors creating these time lapses. We don't need to keep moving that around. We're working natively on Isilon clusters. They're doing their editing there, and subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all, can happen right there on that live environment. And the retention is there. If we have to go back later on, all of our customers' data is really kept within that one area, it's consolidated and it's secure. >> I was looking at the Dell Tech website, and there's a case study that you guys did, EarthCam did with Dell Tech saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I can imagine with the volumes changing so much now, not only is huge to your business but to the demands that your customers have as well, depending on where those demands are coming from. >> Absolutely. And just being able to do that a lot faster and be more nimble allows us to scale. We've added actually, again, speaking of during this pandemic, we've actually added personnel, we've been hiring people. A lot of those people are working remotely as we've stated before. And it's just with the increase in business, we have to continue to keep building on that, and this storage environment's been great. >> Tell me about what you guys really kind of think about with respect to PowerScale in terms of data management, not storage management, and what that difference means to your business. >> Well, again, I mean, number one was really eliminating the amount of resources. The amount of time we have to spend managing it. We've almost eliminated any downtime of any kind. We have greater storage density, we're able to have better visualization on how our data is being used, how it's being accessed. So as these things are evolving, we really have good visibility on how the storage system is being used in both our production and also in our backup environments. It's really, really easy for us to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >> And you mentioned hiring folks during the pandemic, which is fantastic, but also being able to do things in a much more streamlined way with respect to managing all of this data. But I am curious in terms of innovation and new product development, what have you been able to achieve? Because you've got more resources presumably to focus on being more innovative rather than managing storage. >> Well, again, it's, we're always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 gigapixels, people are talking about megapixel images, we're stitching hundreds of these together. We're just really changing the way imagery is used both in the time lapse and also just in archival process. A lot of these things we've done with the interior, we have this virtual reality product where you can walk through and see in a 360 bubble, we're taking that imagery and we're combining it with these BIM models. So we're actually taking the 3D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress, and different things that are happening on the site, look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people time and money on these construction sites. We've also introduced AI and machine learning applications directly into the workflow in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure. It really is seamless and working with Isilon now. >> I imagine by being able to infuse AI and machine learning, you're able to get insights faster, to be able to either respond faster to those construction customers, for example, or alert them if perhaps something isn't going according to plan. >> Yeah, a lot of it's about schedule, it's about saving money, about saving time. And again, with not as many people traveling to these sites, they really just have to have constant visualization of what's going on day to day. We're detecting things like different types of construction equipment and things that are happening on the site. We're partnering with people that are doing safety analytics and things of that nature. So these are all things that are very important to construction sites. >> What are some of the things as we are rounding out the calendar year 2020, what are some of the things that you're excited about going forward in 2021, that EarthCam is going to be able to get into and to deliver? >> Just more and more people really finally seeing the value. I mean I've been doing this for 20 years and it's just, it's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with the things they can do with this imagery. That's what we're all about. And that's really exciting to us in a very challenging environment right now is that people are recognizing the need for this technology and really starting to put it on a lot more projects. >> Well, you can kind of consider it an essential service whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget, on schedule, as you said, or maybe even just the essentialness of helping folks from any country in the world connect with a favorite travel location, or (indistinct) to help from an emotional perspective. I think the essentialness of what you guys are delivering is probably even more impactful now, don't you think? >> Absolutely. And again about connecting people when they're at home, and recently we webcast the president's speech from the Flight 93 9/11 observation from the memorial, there was something where only the immediate families were allowed to travel there. We webcast that so people could see that around the world. We've documented, again, some of the biggest construction projects out there, the new Raiders stadium was one of the recent ones, just delivering this kind of flagship content. Wall Street Journal has used some of our content recently to really show the things that have happened during the pandemic in Times Square. We have these cameras around the world. So again, it's really bringing awareness. So letting people virtually travel and share and really remain connected during this challenging time. And again, we're seeing a real increased demand in the traffic in those areas as well. >> I can imagine some of these things that you're doing that you're achieving now are going to become permanent not necessarily artifacts of COVID-19, as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value of this type of video to be able to reach consumers that they probably could never reach before. >> Yeah, I think the whole nature of business and communication and travel and everything is really going to be changed from this point forward. It's really, people are looking at things very, very differently. And again, seeing that the technology really can help with so many different areas that it's just, it's going to be a different kind of landscape out there we feel. And that's really continuing to be seen as on the uptick in our business and how many people are adopting this technology. We're developing a lot more partnerships with other companies, we're expanding into new industries. And again, you know, we're confident that the current platform is going to keep up with us and help us really scale and evolve as these needs are growing. >> It sounds to me like you have the foundation with Dell Technologies, with PowerScale, to be able to facilitate the massive growth that you were saying and the scale in the future, you've got that foundation, you're ready to go. >> Yeah, we've been using the system for five years already. We've already added capacity. We can add capacity on the fly, really haven't hit any limits in what we can do. It's almost infinitely scalable, highly redundant. It gives everyone a real sense of security on our side. And you know, we can just keep innovating, which is what we do, without hitting any technological limits with our partnership. >> Excellent, well, Bill, I'm going to let you get back to innovating for EarthCam. It's been a pleasure talking to you. Thank you so much for your time today. >> Thank you so much. It's been a pleasure. >> For Bill Sharp, I'm Lisa Martin, you're watching theCUBE's digital coverage of Dell Technologies World 2020. Thanks for watching. (calm music)

(upbeat techno music) >> Announcer: From New York City, it's theCUBE, covering New Relic FutureStack 2019, brought to you by the New Relic. >> Hi, I'm Stu Miniman and this is theCUBE's exclusive coverage of New Relic's Futurestack 2019 here in New York City, seventh year of the show. Our first year here, about 600 or so in attendance, and real excited, because we've had some of the users here to help kick off our coverage. And joining us, first time guest on the program, Josh Biggely is a senior engineer of Enterprise Monitoring, with Cardinal Health coming to us from a little bit further north and east than I do, Prince Edward Island, thank you so much for coming here to New York City and joining me on the program. >> Yeah, thanks for having me Stu, I'm excited to be here. I haven't been in New York, it's probably been more two decades. So it's nice to be back in a big city, I live in a very small place. >> Yeah, so if you go to Times Square, it's now Disneyland, is what we call it. It's not the 42nd street that it might've been a couple of decades ago. I grew up about 45 minutes from here, so it's gone through a lot, love the city, especially gorgeous weather we're having here in the fall. >> I'm excited for it. >> All right, so Josh, Cardinal Health, health is in the name so we think we understand a little bit about it, but tell us a little bit about the organization itself and how it's going through changes these days. >> Sure, so Cardinal Health is a global healthcare solutions provider. We are essential to care, which means we deliver the products and solutions that your healthcare providers need to literally cure disease, keep people healthy. So we're in 85% of the hospitals in the United States, 26,000 pharmacies, about 3,000,000 different home healthcare users receive products from us. Again we're global, so we're based in Dublin, Ohio, just outside of Columbus. But obviously, I live in Canada so I work for the Cardinal Health Canada Division. We've got acquisitions around the world. So yeah, it's an exciting company. We've recently gone through a transformation not only as a company, but from a technology side where we've shifted one of our data centers entirely into the cloud. >> All right, and Josh, your role inside the company, tell us a little bit about, you said it's global, what's under your purview? >> So my team is responsible for Enterprise Monitoring, and that means that we develop, deploy, support and integrate solutions for monitoring both infrastructure applications and digital experience for our customers. We have a number of tools, including New Relic, that we use. But it's a broad scope for a small team. >> Stu: Okay, and you've talked about that transformation. Walk us through a little bit about that, what led to, as you said, some big moves into public cloud? >> Yeah, our team is part of an overall effort to allow Cardinal Health to be more adaptive, to be more agile. The move to cloud allows teams that are developing applications and platforms to make a decision how to respond to the needs of their customers more rapidly. Gone are the days of, "I need a new server, "I need to predict six months from now "that I'm going to need a new server, "put the order in, get it delivered, "get it racked, get it wired." We watch a lot of people, the provision on demand. I mean, our senior vice president, or my senior vice president, likes to say, "I want you to fail fast, fail cheap." He does not say fail often. Although sometimes I do that, but that's okay. As long as you recognize that you're failing and can roll that back, redeploy, It's been really transformative for my team in particular, who was very infrastructure focused when I started with the company five years ago. >> Stu: All right, and can you bring us inside from your application portfolio, was it a set of applications, was it an entire data center? What moved over, how long did it take, and can you share what cloud you're using? >> Sure, so it's been about a two year journey. We're actually a multicloud company. We've got a small footprint in Azure, small footprint in AWS, but we're primarily in Google Cloud. We are shutting down one data center, we are minimizing another data center, and we've moved everything. We've moved everything from small bespoke applications that are targeted on team to entire ecommerce platforms and we've done everything from lift and shift, which I know you don't like to hear. But we've done lift and shift, we've done rehosting, we've done refactoring and we have re-architected entire platforms. >> Yeah, so if you could expand a little bit when we say lift and shift, I'm fine with lift and shift as long as there's another word or plan after that which I'm expecting you do have. >> Josh: Yeah, absolutely. So the lift and shift was, "Hey, let's move from our data centers into GCP. "Let's give teams the visibility, the observability "that they need so that they can make the decisions on "what they need to do best." In a lot of cases, or in fact, in 15% of the 6,500 severs that we touch, we actually full out decommed the instance. Teams had them, they were running at our data centers but they weren't actually providing any value to the company. >> So you said your team before was mostly concerned about infrastructure and a lot of what you did is now on GCP so you fired the entire team and you hired a bunch of PhDs to be able to manage Google environments? >> Absolutely not. (laughter) The principals of enterprise monitoring as a practice still apply in a cloud. We are, at heart, data geeks. And I would fair say that we're actually data story tellers. Our job is to give tools and methodologies to application teams who know what the data means in context, but we give the tools to provide that data to them. >> Stu: All right, love that. I believe I've actually seen data geek shirts at the the New Relic shows itself. But data story tellers, that was kind of thing that you heard, "I have a data scientist "that's going to help us to do this." Is that data scientist in New York or are you actually enabling who is able to tell those data stories today? >> So that is the unique part. Data story telling is not a data science. I wish that I could be a data scientist, I like math, but I'm not nearly that good at it. A data story teller takes the data and the narrative of the business, and weaves them together. When you tell someone, "Here's some data." They will look at it and they will develop their own narrative around it. But as a story teller you help craft that narrative for them. They're going to look at that data and they're going to feel it, They're going to understand it and it's going to motivate them to act in a way that is aligned with what the business objectives are. So data story tellers come in all forms. They come as monitoring engineers, they're app engineers, but they're also people who are facing the customer, they're business leaders, they're people in our distribution centers who are trying to understand the impacts of orders in their order flow, in their personnel that they have. It is a discipline that anyone can engage in if we're willing to give them the right tools. >> All right, so Josh, you got rid of a data center, you're minimizing a data center, you're shifting to cloud, you're making a lot of changes and now being able to tell data stories. Can you tell us organizationally everything goes smoothly or are their anythings that you learned along the way that maybe you could share with your peers to help them along that journey? And any rough spots, with hindsight being what it is, that you might be able to learn from? >> Yeah, so hindsight definitely 20/20. The one thing that I would say to folks is get your data right. Metadata, trusting your data is key, it's absolutely vital. We talk a lot about automation and automation is one of those things that the cloud enables very nicely. If you automate on garbage data, you are going to automate garbage generation. That was one of our struggles but I think that every organization struggles with data fidelity. But teams need to spend more time in making sure that their data, specifically their metadata, around, "Hey is this prod, is it non-prod, "what stack is this running, who built it?" Those things definitely need to be sorted out. >> Okay, talk about the observability and the monitoring that you do, how long have you been using New Relic and what products? And tell us a little about that journey. >> Sure, so we've been using New Relic for about two years. It was a bit of a slow run up to its adoption. We are a multi-tool company so we have a number of tools. Some of them are focused primarily on our network infrastructure, our on-prem storage. Although Cardinal had moved predominantly to the cloud, we have distribution centers, nuclear pharmacies all around the world. And those facilities have not gone into the cloud. So you've got network connectivity. New Relic for us has filled our cloud niche and observability, as Lou announced, is going to give us context to things that we're after. You hear the term dark data, we call them obs logs. It's data that we want to have, we only need it for a very short period of time to help us do post-op or RCAs as well as to look at, overall in our organization, the performance of the applications. For us, New Relic is going to give us an option to put data for observability. Observability is really about high fidelity data. In its world of cloud, everyone wants everything right now. And they also want it down to the millisecond. A platform that can pull that off, that's a remarkable thing. >> Yeah, Veruca Salt had it right, "I want it now." So are you using New Relic One yet? >> We have been using New Relic One for at least a couple of months going back into March this year. It's exciting, we're one of those companies that Lou talked about in his key note, we have hundreds of sub accounts. And we did so very intentfully, but it was a bit of a nightmare before we got to New Relic One. That ability for a platform team to see across multiple sub accounts, really powerful. >> Okay, so you saw a lot of announcements this morning. Anything particular that jumped out, you were excited? Because Lou kept saying over and over, and if you're using New Relic One, "This is free, this is free, this is free." That platform where it's all available for you now. >> I think the programmability is one of the things that really got me excited. One of the engineers on my team had a chance to go and sit with Lou and team, two weeks ago, and was part of that initial Hackathon. Made some really interesting things. That's exciting so shout out to Zack and the work he did. Logging, for me, is something that is huge. I know we've got data that we should have in context. So that Lou announced five terabytes of ingestion for free, all I could do was tap my fingers together and think, "Oh, okay. You're asking for it, Lou. Challenge accepted." (laughter) >> Stu: That's exciting, right. So you feel that you're going to be building apps, it sounds like already, at the FutureHack. That you're starting to move down that path. >> Definitely, and I'm really excited. Not to necessarily give it to my team. We build the patterns for teams that needs patterns, but there are so many talented individuals at Cardinal Health who, if we give them the patterns to follow, they're just going to go execute. Open sourcing that is a brilliant idea and really crowd sourcing development is the way to go. >> Yeah, I think you bring up a really interesting point. So even though your team might be the one that provides the platform, you're giving that programmability, sensibility to a broader audience inside the team and democratizing the data that you have in there. >> Yes, you keyed in on one of the things I love to talk about which is democratized access to data. Over and over again you'll hear me preach that, "I know what I know but I also know what I don't know "and more particular I don't know what I don't know. "I need other people to help me recognize that." >> We've really talked about that buzzword out there about digital transformation. When it is actually being happened, it goes from, "Oh, somebody had an opinion," to, "Wait, I actually now can actually get to the data, "and show you the data and leverage the data "to be able to take good actions on that." >> That's right, data driven decision making is not just just an idiom. It's not something that is a buzzword, it is a practice that we all need to follow. >> Stu: All right, so Josh, you're speaking here at the show. Give our audience just a quick taste, if you will, about what you're going to be sharing with your peers here at the show. >> We've actually talked about a lot of it already so I hope that people are not going to watch this session before my session later. But it really is around the power of additional transformation, the power of observability, what happens when you do things right, and the way the cloud makes teams more nimble. I won't give you it all because then people won't watch my session on Replay but, yeah, it'll be good. >> Well, definitely they should check that out. I'm hoping New Relic has that available on Replay. Give the final word here, what you're really hoping to come out of this week. Sounds like your team's deeply engaged, you've done the Hackathon, you're working with the executive teams. So FutureStack 2019, what are you hoping to walk away with? >> For me, it's about developing patterns. My team, in addition to our enterprise architecture team, is responsible for mapping out what we're going to do and how we're going to do it. Teams want to go fast and if we're not going to lay down the foundation for them to move quickly, especially in the realm of enterprise monitoring, they're going to try do it themselves. Which may or may not work. We don't want to turn teams away from using specific tools if it fits, but if there's a platform that will allow them to execute and to keep all that data centralized, that is really the key to observability. Having that high fidelity data, but then being able to ask questions, not just of the data you put in, but the data that put in maybe by a platform team or by a team that supported Kubernetes or PCF. >> All right, well, Josh Biggely, thank you so much for sharing all that you've been going through in Cardinal Health's transformation. Great to talk to you. >> Thanks so much, Stu. >> All right, lots more here at New Relic's FutureStack 2019. I'm Stu Miniman and as always, thank you for watching theCUBE. (light techno music)

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by the M wear and its ecosystem partners. >> Welcome back. I'm Stew Minuteman here with John Troyer. We're have three days, Walter Wall coverage here at VM World 2019 with lobbying Mosconi North and happy to welcome to the program. To my right is Tom Burns, who is the senior vice president general manager of networking and Solutions at Delhi Emcee and sitting to his right. Another Tom. We have Tom Gillis, who's the S V p and general manager of networking of Security inside VM wear. So I'm super excited. Go back to my roots of networking. Tom and Tom thanks so much for joining us. >> Thanks for having us. Thanks for All >> right. So, you know, Tom, you and I have talked for years now about you know, it was not just s t n, but you know, the changes in the environment. Of course, you know, networking and compute, you know, smashing together and where the role of software in this whole environment has changed. So, you know, let's start, you know, there's some news. Let's get that cover the hard news first. VM Where has the networking pieces? Dell has some software networking pieces also, and there's some more co mingling of those. So maybe walk us through that. >> Absolutely. I think the story this week is about the collaboration that's happening between Tom's team and my team in kind of innovating and disrupting in the traditional networking world. You know, Tom Sad NSX around micro segmentation network virtualization lot going on with analytics and capability to really see what's going on. The network from Cord Out EJ to cloud the acquisition of RV, which is outstanding. Other things that are going on in Vienna, where deli emcee disrupting around the segregation of hardware and software, giving customers that capability to run the nasty need for the connective ity they need, depending upon where the network is sitting. So this week we got two announcements. One is we've got worldwide shipment of the Delhi M CST Land solutions powered by being more great, you know better than none. Software combined with better than none. Hardware coming from del you see, on a global basis worldwide, you know, secure supply chain plus professional service worldwide is a parameter there, right? >> And Tom, maybe bring us in. You know, we'd watch Fellow Cloud before the acquisition esti weigh on. You know, there's a lot of solutions that fit in a couple of different markets. It's not a homogeneous market there. Maybe give us just kind of the camp point from Avella Clubs. Esty Esty. >> Wind is a white Hart market on because it has the classic combination a better, faster, cheaper. It delivers a better end user experience. It is so easy to deploy this and it saves money, NPLs, circuits and back hauling traffic those that was, ah, 19 nineties idea. It was a good idea back then, but it's time for a different approach. >> And just when I've talked to some customers and talk to them about their multi cloud environment, SD Wind, one of those enabling technologies that you know they will bring up to a mad allowed them to actually do that. >> It was it was the movement really >> office 3 65 and sass applications that drove the best human revolution and that back hauling all this traffic to headquarters and then going out to office for 65 when a user might be in, You know, Des Moines, that doesn't make any sense. And so so with us, the win we intelligently route the traffic where it needs to go delivers a better end user experience, and it saves a bunch of money. It's not hard to imagine that cheap broadband links are on order of magnitude lower than these dedicated mpls circuits. And the interesting math is that you could take two or three low cost links and deliver a better experience than with a single dedicated circuit. >> I'm kind of interested in the balance between hardware and software, right? The family trees of networking and compute kind of were different because if they had specialized needs in silicon, so where are we now? It's 2019. Where are we now? With with line speeds and X 86 then the hardware story. >> I think it'll let Tom join the discussion around speeds and feeds is not dead, but it should be dying to get a quick right. You know, it's around virtual network functions and everything really moving to the software layer. Sitting on top of commoditized X 86 based you know, hardware and the combination of these two factors help our customers a lot more with flexibility, agility, time to deploy, return on investment, all these types of things. But I mean, that's my view is a recurring theme you're gonna hear. Is that in networking? And think you're alluding to this You needed these dedicated kind of magical black boxes that had custom hardware in order to do some pretty basic processing. Whether it be switching, routing, advanced security, you had to run things like, you know, hardware. Regular expression, matching et cetera was about three years ago that Intel introduced a technology called D P D. K, which is an acceleration that allowed VM wear to deliver in software on a single CPU. You know, we could push traffic at line rates, and so so or, you know, faster than one rates. And so that was sort of like there wasn't the champagne didn't go off in the, you know, the bald in drop in Times Square. But it's a really important milestone because all of a sudden it doesn't make any sense to build these dedicated black boxes with custom hardware. Now, general purpose hardware, when you have a global supply chain and logistics partner like Dell, coupled with distributed software, can not only replace these network functions, but we can do things completely differently. And that's really you know, we're just beginning this journey because it's only recently that we've been able to do that. But I think you're gonna see a lot more that in the future. >> So we talked about SD win. Uh, there was a second announcement >> that goes back into the court. You know, the creation of a fabric inside of the data center is still a bit difficult. I mean, I've heard quotes saying It's something like 120 lines of cli, you know, per switch. So let's say 4 to 6 Leafs pitches, switches and two spine switches could take days to set up a fabric. What we've announced is the smart Fabric Director, which is a joint collaboration and development between Veum Wear and Delhi emcee that creates this capability to tightly integrate NSX envy Center into the deli emcee power switch, family of data center switches, really eliminating several cases and in fact, setting up that same fabric in less than two minutes. And we're really happy about not just the initial release. But Tom and I have a lot of plans for this particular product and in the road map for, you know, quarters and years to come about really simplifying again, the network automating it. And then, really, our version of intent based networking is the networking operating the way you configured it, you know, when you set it up and I think not just not just on day one, but two, you know and a N and you know you hit the nail on the head. Networking has changed, is no longer about speeds and feeds. It's about availability and simplicity. And so, you know, Del and GM, where I think are uniquely positions to deliver a level of automation where this stuff just works, right? I don't need to go and configure these magic boxes individually. I want to just right, you know, a line of code where my infrastructure is built into the C I. C. D pipeline. And then when I deploy workload, it just works. I don't need an army of people to go figure that out right, and and I think that's the power of what we're working together to unleash. >> So when something technology comes up like like SD win. Sometimes there's a lot of confusion in the marketplace. Vendors going out one size fits all. This will do everything Course. Where are we in the development of SD win and what is the solution? Who should be looking at taking a look at the solution now? >> SD win market, as I said, is growing depend on whose estimate you look at between 50 and 100% a year. And the reason is better, faster, cheaper. Right? So everyone has figured out, you know, like maybe it's timeto think differently about about architecture and save some money. Eso we just announced it on the PM or side, an important milestone. We have more than 13,000 network virtualization customers that includes our data center as well as yesterday, and we don't report them separately. But 13,000 is, you know, that's almost double where it was a year ago. So significant customer growth we also announced were deployed together with our partner from Del 130,000 branches around the world. So by many metrics, I think of'em, where is the number one vendor in this space to your point it is a crowded, noisy space. Everybody's throwing their hat in the Rangel. >> We do it too. >> But I think the thing that is driving the adoption and the sales of our product is that when you put this thing in, it fundamentally changes the experience for the end user. There's not a lot of networking products that do that. Like I meet customers like this thing is magic. You plug it in and all this and streaming just works, you know, like Google hangouts or Web X is like they just work and they worked seamlessly all the time that there's something there that I think it's still unique to the PM or product, and I think it's gonna continue to drive sales in the future. So I think the other strong differentiation when it comes to Del Technologies bm where in Delhi emcee combined is we have this vision around the cloud. You know, EJ core cloud and you know this hybrid multi cloud approach. And obviously SD Ram plays a critical part as one of the stepping stones as relates toe, you know, creating the environment for this multi cloud environment. So, you know, fantastic market opportunity huge growth. As Tom said, markets probably doubling in size each year. I don't know what the damn numbers are. I hate to quote, but you know, we really feel is, though now having this product in this capability inside a deli emcee, again combining our two assets, it could be the next VX rail. We're really good way. Believe the esteem and it's gonna be a gigantic market. And I think that what's interesting about our partnership is that we can reach different segments of the market in a V M, where we tend to focus on the very high end, large enterprise customers. Technically very sophisticated, delicate, rich customers we don't even know we don't even talk to, And a product is simple enough that it works in all segments. We win the very, very biggest, and we win these. You know, smaller accounts where the simplicity of a one quick deployment really really matters. >> Tom. One of the things that excited me a year ago at this show was the networking vision for a multi cloud world reminded to be of nice syrup. React. You know, when we look at networking today, most remote network admin a lot of the network they need to manage. They don't touch the gear. They don't know where it lives, but they're still responsible. Keep it up and running. And if something goes wrong, it's there. It is the update as to where we stand with that where your >> customers are asking the question, right? So our mantra is infrastructure is code, and so no one should ever have to log in with switch. No one should have to look into a Q. And you know, we should have to be like trying to move packets from here. They're just It's very, very difficult. I'm not really feasible. And so So as networking becomes software and those general purpose processors I talk about are giving us the ability to to think about not just a configuration of the network but the operation of the network in ways that were never before possible. So, for example, we announce that the show today with our monitoring product ve realise network in sight. We call it Bernie, not always such clever with the names that were really good at writing code, Vernon gives us the ability to measure application response time from the data center all the way out to the edge. So a single pane of glass we can show you. Oh, here's where it's broken whether it's in the network, whether it's in the server, whether it's the database, that's that's not responding. And we do this all without agents, right? So it's like when the infrastructure gets smart enough to be able to provide that inside, it changes the way the customer operates on. That translates into real savings and real adoption. And that's what's driving all of this momentum, right? That 7 500 to more than 13,000 customers, something has to be behind that. I think it's It's the simplicity of automation. >> CLI has come up a couple times here, and so that's kind of a dirty word. Maybe even these days, it kind of depends on who you're talking with, I think Veum Way. Rendell both spent a lot of time and effort educating the networking engineering market and also educating the kind of data center you know, the rest of the data center crew about, you know, about each other's worlds. Where again, where are we at now? It sounds like with director on with the innocent. The NSX whole stack? Yes. Uh, the role is changing of a network engineer. But again, where are we in that? In that evolution? >> I think you know, we're early on, but it's moving quite rapidly. I think the traditional network in engineer and networking admin is gonna need to evolve. You know more to this, Dev Ops. How do I bring applications? How do I manage the infrastructure? More like a platform. I mean, Tom and I truly believe that the difference between cute and network infrastructure is really going to start to dissolve over time. And why shouldn't it? I mean, based upon what's happening with the commoditization and speeds of the CPU versus the MP use coming from Mersin silicon, it's really beginning to blur. So I think, you >> know, we're in the early >> stages. I mean, certainly from a deli, see perspective. We still, at times, you know, have those discussions and challenges with traditional networking people. But let's face it, they have a tough job. When something's not working, the network administrator usually gets blamed, And so I think it's a journey, uh, and things such as the del Technology Cloud Open networking, NSX, and now SD when it will continue to drive that. And I think we're going to see a rapid change in networking over the next 12 18 to 24 months. I talked to a number of customers that has said, You know, this journey that Tom was talking about is this is a challenge because the skill set is different. My developers need to learn software, and so what? We're working with the M where is trying t o make that software easier and easier to use it actually approach like English language. So latest versions of NSX have these very simple, declarative AP eyes that you can say, Oh, server A talk to server be but not server see, Click Don Deploy. And now, in our partnership with L, we can take that Paulson push it right down into the metal, right down into the silicon. And so so. Simplification and automation are the name of the game, but it is definitely a fundamental change in the skill set necessary to do Networking. Networking is becoming more like software as opposed to, you know, speeds and feeds and packet sniffers and more the old traditional approaches. >> Tom, I don't want to give you the final word as to Ah, you know what people should be taken away from Dell in and Veum wear in the networking space. Well, >> I think across deli emcee and in being work, there's a great amount of collaboration, whether it's the Del Technology Cloud with of'em were really taking the leadership from from that perspective with this multi hybrid cloud. But in the area of networking, you know, Trudeau. Five years ago, when we announced the desegregation of hardware and software, I am in this to disrupt a networking business and to make networking very different tomorrow and in the future than it has been in the past for our customers around. He's deployment, automation and management, and I think that's a shared vision with Tom and his team and the rest of BM, where >> Tom Gillis, Tom Burns, thank you so much faster. Having eight, we'll be back with more coverage here from VM 2019 for John Troyer on stew. Minutemen as always. Thanks for watching the Cube

>> 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.

(upbeat music) >> Live from Washington, D.C., it's theCUBE, covering Inforum D.C. 2018, brought to you by Infor. >> Well, back here on theCUBE, we are at Inforum '18. We're in Washington, D.C. here in the Walter Washington Convention Center. Not far from the White House. It's about a mile that way, and Capitol Hill's about a mile that way, I think. I know we're right in here, but I know we are smack dab in the middle of it. Dave Vellante and John Walls and Kevin Curry, who's the SVP of the global public sector at Infor. Good to have you with us. Good to see you, sir. >> Great to be here. Thanks for your time. >> So public sector, you're in the heart of it here, and you were telling us before we went on the air that you've got more than 700 clients here at the show this week? >> We do, we do. It's the best attendance we've had yet for Inforum, and I joined about six and a half years ago. And we built this business pretty much from the ground up. So it's been a great experience, and now we're starting to get a lot of adoption within the government, across the government, from federal to state to locals. >> What's the process been like, especially across those three, because I assume they're all different? You know, local, state, federal, everybody has different pain points and there's different tolerances. >> They do, they do. I mean, there's different micro-verticals within each of those statements. As an example, if you look at local governments, it could be anything from transit agencies to K-12 schools, to public works, to police, to fire. They all have all different requirements. State's the same thing, whether it's Department of Transportation or Department of Health and Human Services. And then when you get the federal side of it, then it's from the intelligence community to Department of Defense, healthcare within Defense, like the VA and DoD and Defense agencies as well. So it's a pretty wide swatch of use cases and business cases that you need to be able to sell to. >> Charles said something interesting in the keynote today. I want to ask you about it. He said, "We made a strategic decision to go to the cloud. "We didn't want to compete with Google "and Amazon and Microsoft for CloudScale. "That didn't make any sense for us." And he said, "When we were an on-prem software vendor, "we weren't managing servers for our customers." Now what struck me there is if you look back at the software company back in the day, they really didn't care about the server, right? It was just sort of infrastructure. It was kind of irrelevant to them. The cloud feels different. It seems like a more strategic relationship with Amazon. You know, we talk about Teresa Carlson and what a force she is in the government. AWS in the GovCloud has been a huge force. They had a giant lead. So have you been able to draft off that or is it just another sort of infrastructure platform? >> No, they're a major strategic partnership there with AWS and NN4. At the company level, and especially for me, with the government, they've made the right investments at the right time, I mean, and they actually have cloud environments that are very specific to different segments of the government and to different geographies. So as an example, in the federal government they have an intelligence cloud called C2S, which we work with them on. There's a very large procurement out right now for the Department of Defense called Jedi, which Amazon's going after, as well as the other larger cloud providers, so we're obviously riding that horse with AWS. And also for local governments, and they've done all of the compliancy for the government, whether it be FedRAMP, whether it be CJIS for those departments that are worried about the justice type of requirements. And as you get outside of the U.S., they're putting clouds and we're a global company as well, putting clouds in all the right places. They have a G-Cloud offering in the U.K. and as we talked about earlier when we sat down, they're opening a cloud in the Middle East right now too, in Bahrain that I think traces on oil over there as we speak. >> Right, right. The first Middle East country to claim cloud first. But it just seems like there's a strategic advantage there. And even with the other cloud suppliers. I mean, you know, Google's got its niche, big niche, you know, Microsoft, with its software state, but it seems like Amazon, they talk about that flywheel effect, brings certain technologies that, you know, when you talk to Soma, you guys have been able to take advantage of. It just feels a lot different than the old traditional server manufacturer. Oh, it's a Unix box and there's no difference between vendor A, B and C. >> Absolutely correct. And for us, we've taken advantage of the tools that Amazon has and obviously, we're doing all the compliancy on our applications and they've got whole the infrastructure piece of it, so the two work very well together. >> And that has allowed you to focus on your knitting, if you will. >> Yes. >> The things that you do best, which is a micro-verticals, suite across the application portfolio, bringing AI to the equation, automation, we heard a lot about robotic process automation, which is probably a hot topic in the government. >> Yes. I mean, Charles famously, he may have had a quote. I'm sure you heard it. It's friends don't let friends build data centers. >> Great quote. >> You know, that's not a business we're in. We're a software company. >> Right. >> So the public sector, obviously a different animal than the private sector. Very different needs, different constituents, you got tax payers, you got all that. When you bring the technology into the public sector, what does that do for it or how does that have to be, I don't know, re-conformed or adapted? And ultimately, what's the payoff, right? What's the return on that investment? >> So it was actually pretty shocking how quickly the government has adopted and moved towards the cloud. Typically, they're laggards. Everything happens in the commercial market and then government's a little bit of a late adopter, right? But we're seeing them very quickly go to the cloud and there's a lot of reasons for that. One being, you have an aging workforce. Okay, so the baby boomers are all retiring so a lot of that intellectual knowledge is going out the door. Two, is there's some economies of scale to be realized by doing that because once you're in the cloud, I mean, it's up to the vendor who's maintaining it to maintain that for you. So, you know, the people behind the scenes, they have to do it. You know, when you upgrade your software to go from one release to the other, it's automatically done for you. I mean, so there's real cost savings to be had, you know, from a care and feeding perspective there as well. Also a lot of the, on the ERP side of the things, a lot of the systems that are out in the marketplace today that governments have bought, like the Oracles or the SAPs, a lot of these systems are at end-of-life and the companies are no longer supporting them. So it's a re-implementation for them. You know, and so now they're looking, okay, if we have to re-implement and we have to look at our new options, we're going to do it in a cloud. >> So when you've been around as long as I have, Kevin, >> Right. >> you've seen the pendulum swing. You don't have to agree so vehemently. (laughing) But from mainframe to client server and so you're back to the cloud, and now with IoT, it seems like the pendulum is swinging back to a distributed environment. So help us understand where IoT fits to the cloud and even your on-prem business. >> Okay, so like I say, cloud is a pretty broad topic, okay? We have multiple applications that would run in that environment. So when I look at IoT, I think of things like our asset management platform. We have a very strong enterprise asset management platform that runs in the cloud or runs on-prem. And if you think about infrastructure as an example, which government has a lot of, okay. Think about the ability to have sensors on different pieces of equipment and being able to read that information. Think about using drone technology, okay, to be able to do physical inspections under bridges, so you're not having people having to climb around underneath there. I mean, so being able to do live feeds of data and be able to streamline the way you do business and have that automatically captured within an application. So yes, that is one area where we see it. I mean, I think you're going to see more and more of robotics and artificial intelligence and all the things come into play. I think you heard a lot about that here and it's here. I mean, they were things we saw in movies before but now the technology's here today. >> Well, the other thing we heard this morning that Charles has always talked a lot about the data. You guys always talked about your data lake. I like to think of it as a data ocean. You think about all the data out of GT Nexus and, you know, your customers that are providing data to inform. The data model starts to really expand and you guys have seemed to really take advantage of that. Talk about the data, the importance of data, the importance of securing data to the government. >> Well, think about that. I mean, there's islands of information that governments have that if they were able to consolidate that data and put some intelligence into it, be able to make business decisions versus, you know, one system sitting over here, one system sitting over here and none of them ever communicating or talking to each other. You know, the ability to, You could do from anything from, just think about crime statistics, okay? The ability to deploy resources where the crime is and then as it moves, be able to further deploy resources. You know, New York, years ago, did things like that with CompStat when they were cleaning up Times Square and so forth. But just think of that as a concept, realtime being able to manage data. >> So you've got, here at the show, we were talking about earlier, 700 and some odd clients, 725. You've got the federal forum for the first time. Why now? And what are you getting out of that or what do you hope to get out of that at the end of the week? >> So the whole executive team and our board of directors have made significant investments in this marketplace because they understand that government is a very large beast, if you will, and there's a lot of opportunity for deployment of our solutions and there's a real need to solve problems for constituents here as well. So they've made very significant investments in things for security like FedRAMP, compliancy. You know, some companies are doing it on some of their solutions. We're doing it across the board on all the products that we take to the government marketplace. So we're invested in it. You've probably heard today, Charles talked about the fact that we're going to have a federal cloud suite, which we are. So that means federal financials, okay? Actually being able to solve all the problems for the federal government and comply to all their needs and all the things that are part of mandated accounting for the federal government. They made all the right investments and human capital management would be another area. If you think about, we've got an application called Talent Science. The ability to hire the right people for the right job and retain those people. Just think about, ICE is a good example. You heard that they have to hire thousands of people to deploy on the borders, right? How do you quickly ramp and hire these right people if you don't have the right tools to do it? >> You were quoted in TIME magazine, Marc Benioff's new publication, about America's crumbling infrastructure. What role do you see technology playing generally and specifically in for software and helping with that problem? >> So we do a lot today around infrastructure. As an example, we have a very strong presence in transit agencies here in the U.S. New York City runs us, amounts to about a trillion dollars worth of assets there. So anything moving in, out or around the city, so subways, buses, trains, tunnels, bridges, Metro-North, Long Island Rail Road. L.A. runs us, San Francisco runs us, Chicago runs us, Dallas runs us and many others. So we're managing all of that infrastructure. So you hear a lot about infrastructure bills coming out of the federal government. And they're right. I mean, a lot of these tunnel, a lot of these bridges and tunnels and even roadways were built back during World War II, right? And they're aged, you know, they are starting to crumble and there's going to be a lot of money spent to do that and when it comes to rebuilding those types of things, there's a lot of assets that are going to need to be managed, you know, to do that. So we think there's a real opportunity for software such as what we bring to the marketplace to help with that process. >> How about talent retention? I mean, obviously, as administrations come and go, you know, people move, but there's been a lot of brain drain. I mean, take the Patent Office, people in commercial industry stealing some of the best and brightest out of government. Can software play a role in helping better retain, train, you know, evolve growth paths and careers? >> Yes. I guess, in a couple different ways. I mean, number one, I think the applications of today versus the applications of yesterday have changed so much. I mean, you look at, you know, the applications you have on your mobile phone. The ability to have that look and feel, I mean, our kids today are going to go into the workforce and they won't settle for anything less. They're going to want to have that look and feel. They're going to want to have those intuitive type of applications that help them do their job. And that's the kind of offering we're bringing to the marketplace. Then from just actually bringing the right people and we have an application called Talent Science, as an example, where actually there's multiple different areas of your personality that it can determine and map it back to your top performers in your company. And determine the right people for the right job where they'll fit into that environment and then they would thrive hopefully. And it should increase retention on the staff. In government, we've actually sold it to Department of Health and Human Services for hiring case workers. Okay? Or to police departments for hiring of law enforcement. So there's a real opportunity to take those types of applications and do some pretty creative things. >> What's, I hate to say, the pain side of it. But dealing with the government obviously contracts is an issue, right? And a challenge sometimes maybe for you. I'm curious, in a quickly evolving space such as yours, how do you help them keep up with you and their regulatory oversight and whatever mandated restrictions they have? All those things, you know, that come with government. It just doesn't square up with what you do. >> It is, it's a very, again, to your point, it's a different, it's a different industry with different requirements. And everything here is very open and above board. It's open procurements. Everything is competitively bid. There are contractual vehicles that you competitively bid for that'll allow you to be able to do business a lot easier in the future. I mean, in the feds you have things like the GSA 70 Schedule. U.K., you have something called the G-Cloud contract. A lot of states have vehicles where you can bid for it, so all states and local can buy off of those contracts without having to go to a competitive offering. So there's ways that the business can get done without having to go through a lot. >> Every hoop and every, yeah, right. >> The major pain process. But then there's also competitive RFPs, which, you know, well, they'll put a bid out, it'll be very detailed. You have to answer 3,000 requirements. And then after that you'll end up going into an orals and a demo process and, you know, nine months later, they're going to pick a winner. (laughs lightly) Then you go through, but then you have to go through a very painful contract negotiation process. >> That's the process I was talking about. (laughing) Exactly what I was talking about, right. >> Right. >> Yeah, yeah. Well, Kevin, thanks for being with us. We appreciate the time. >> It's my pleasure. >> And it sounds impressive, right, with the turnout you had, so I'm sure you're very, very pleased with the response you've had here on the show for so far. >> I am and I thank you for your time and >> You bet. >> have a good show. >> Look forward to seeing you down the road. Alright, sir, thank you. Back with more here live on theCUBE. We're at Inforum '18 and we are in Washington, D.C. >> I'm quite sure they got me pinned up back here, but I can't-- (upbeat music)

>> [Announcer] Live from Times Square in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to theCUBE's special presentation. We're covering IBM's announcements today around AI. IBM, as theCUBE does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on theCUBE, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching theCUBE.

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

>> Live from Times Square in New York City, it's theCUBE. Covering IBMs Change the Game, Winning With AI. Brought to you by, IBM. >> Hi, everybody, we're back. My name is Dave Vellante and you're watching, theCUBE. The leader in live tech coverage. We're here with Scott Hebner who's the VP of marketing for IBM analytics and AI. Scott, it's good to see you again, thanks for coming back on theCUBE. >> It's always great to be here, I love doing these. >> So one of the things we've been talking about for quite some time on theCUBE now, we've been following the whole big data movement since the early Hadoop days. And now AI is the big trend and we always ask is this old wine, new bottle? Or is it something substantive? And the consensus is, it's real, it's real innovation because of the data. What's your perspective? >> I do think it's another one of these major waves, and if you kind of go back through time, there's been a series of them, right? We went from, sort of centralized computing into client server, and then we went from client server into the whole world of e-business in the internet, back around 2000 time frame or so. Then we went from internet computing to, cloud. Right? And I think the next major wave here is that next step is AI. And machine learning, and applying all this intelligent automation to the entire system. So I think, and it's not just a evolution, it's a pretty big change that's occurring here. Particularly the value that it can provide businesses is pretty profound. >> Well it seems like that's the innovation engine for at least the next decade. It's not Moore's Law anymore, it's applying machine intelligence and AI to the data and then being able to actually operationalize that at scale. With the cloud-like model, whether its OnPrem or Offprem, your thoughts on that? >> Yeah, I mean I think that's right on 'cause, if you kind of think about what AI's going to do, in the end it's going to be about just making much better decisions. Evidence based decisions, your ability to get to data that is previously unattainable, right? 'Cause it can discover things in real time. So it's about decision making and it's about fueling better, and more intelligent business processing. Right? But I think, what's really driving, sort of under the covers of that, is this idea that, are clients really getting what they need from their data? 'Cause we all know that the data's exploding in terms of growth. And what we know from our clients and from studies is only about 15% of what business leaders believe that they're getting what they need from their data. Yet most businesses are sitting on about 80% of their data, that's either inaccessible, un-analyzed, or un-trusted, right? So, what they're asking themselves is how do we first unlock the value of all this data. And they knew they have to do it in new ways, and I think the new ways starts to talk about cloud native architectures, containerization, things of that nature. Plus, artificial intelligence. So, I think what the market is starting to tell us is, AI is the way to unlock the value of all this data. And it's time to really do something significant with it otherwise, it's just going to be marginal progress over time. They need to make big progress. >> But data is plentiful, insights aren't. And part of your strategy is always been to bring insights out of that dividend and obviously focused on clients outcomes. But, a big part of your role is not only communicating IBMs analytic and AI strategy, but also helping shape that strategy. How do you, sort of summarize that strategy? >> Well we talk about the ladder to AI, 'cause one thing when you look at the actual clients that are ahead of the game here, and the challenges that they've faced to get to the value of AI, what we've learned, very, very clearly, is that the hardest part of AI is actually making your data ready for AI. It's about the data. It's sort of this notion that there's no AI without a information architecture, right? You have to build that architecture to make your data ready, 'cause bad data will be paralyzing to AI. And actually there was a great MIT Sloan study that they did earlier in the year that really dives into all these challenges and if I remember correctly, about 81% of them said that the number one challenge they had is, their data. Is their data ready? Do they know what data to get to? And that's really where it all starts. So we have this notion of the ladder to AI, it's several, very prescriptive steps, that we believe through best practices, you need to actually take to get to AI. And once you get to AI then it becomes about how you operationalize it in the way that it scales, that you have explainability, you have transparency, you have trust in what the model is. But it really much is a systematical approach here that we believe clients are going to get there in a much faster way. >> So the picture of the ladder here it starts with collect, and that's kind of what we did with, Hadoop, we collected a lot of data 'cause it was inexpensive and then organizing it, it says, create a trusted analytics foundation. Still building that sort of framework and then analyze and actually start getting insights on demand. And then automation, that seems to be the big theme now. Is, how do I get automation? Whether it's through machine learning, infusing AI everywhere. Be a blockchain is part of that automation, obviously. And it ultimately getting to the outcome, you call it trust, achieving trust and transparency, that's the outcome that we want here, right? >> I mean I think it all really starts with making your data simple and accessible. Which is about collecting the data. And doing it in a way you can tap into all types of data, regardless of where it lives. So the days of trying to move data around all over the place or, heavy duty replication and integration, let it sit where it is, but be able to virtualize it and collect it and containerize it, so it can be more accessible and usable. And that kind of goes to the point that 80% of the enterprised data, is inaccessible, right? So it all starts first with, are you getting all the data collected appropriately, and getting it into a way that you can use it. And then we start feeding things in like, IOT data, and sensors, and it becomes real time data that you have to do this against, right? So, notions of replicating and integrating and moving data around becomes not very practical. So that's step one. Step two is, once you collect all the data doesn't necessarily mean you trust it, right? So when we say, trust, we're talking about business ready data. Do people know what the data is? Are there business entities associated with it? Has it been cleansed, right? Has it been take out all the duplicate data? What do you when a situation with data, you know you have sources of data that are telling you different things. Like, I think we've all been on a treadmill where the phone, the watch, and the treadmill will actually tell you different distances, I mean what's the truth? The whole notion of organizing is getting it ready to be used by the business, in applying the policies, the compliance, and all the protections that you need for that data. Step three is, the ability to build out all this, ability to analyze it. To do it on scale, right, and to do it in a way that everyone can leverage the data. So not just the business analysts, but you need to enable everyone through self-service. And that's the advancements that we're getting in new analytics capabilities that make mere mortals able to get to that data and do their analysis. >> And if I could inject, the challenge with the sort of traditional decision support world is you had maybe two, or three people that were like, the data gods. You had to go through them, and they would get the analysis. And it's just, the agility wasn't there. >> Right. >> So you're trying to, democratizing that, putting it in the hands. >> Absolutely. >> Maybe the business user's not as much of an expert as the person who can build theCUBE, but they could find new use cases, and drive more value, right? >> Actually, from a developer, that needs to get access, and analytics infused into their applications, to the other end of the spectrum which could be, a marketing leader, a finance planner, someone who's planning budgets, supply chain planner. Right, so it's that whole spectrum, not only allowing them to tap into, and analyze the data and gain insights from it, but allow them to customize how they do it and do it in a more self-service. So that's the notion of scale on demand insights. It's really a cultural thing enabled through the technology. With that foundation, then you have the ability to start infuse, where I think the real power starts to kick in here. So I mean, all that's kind of making your data ready for AI, right? Then you start to infuse machine learning, everywhere. And that's when you start to build these models that are self-learning, that start to automate the ability to get to these insights, and to the data. And uncover what has previously been unattainable, right? And that's where the whole thing starts to become automated and more real time and more intelligent. And that's where those models then allow you to do things you couldn't do before. With the data, they're saying they're not getting access to. And then of course, once you get the models, just because you have good models doesn't mean that they've been operationalized, that they've been embedded in applications, embedded in business process. That you have trust and transparency and explainability of what it's telling you. And that's that top tier of the ladder, is really about embedding it, right, so that into your business process in a way that you trust it. So, we have a systematic set of approaches to that, best practices. And of course we have the portfolio that would help you step up that ladder. >> So the fat middle of this bell curve is, something kind of this maturity curve, is kind of the organize and analyze phase, that's probably where most people are today. And what's the big challenge of getting up that ladder, is it the algorithms, what is it? >> Well I think it, it clearly with most movements like this, starts with culture and skills, right? And the ability to just change the game within an organization. But putting that aside, I think what's really needed here is an information architecture that's based in the agility of a cloud native platform, that gives you the productivity, and truly allows you to leverage your data, wherever it resides. So whether it's in the private cloud, the public cloud, on premise, dedicated no matter where it sits, you want to be able to tap into all that data. 'Cause remember, the challenge with data is it's always changing. I don't mean the sources, but the actual data. So you need an architecture that can handle all that. Once you stabilize that, then you can start to apply better analytics to it. And so yeah, I think you're right. That is sort of the bell curve here. And with that foundation that's when the power of infusing machine learning and deep learning and neuronetworks, I mean those kind of AI technologies and models into it all, just takes it to a whole new level. But you can't do those models until you have those bottom tiers under control. >> Right, setting that foundation. Building that framework. >> Exactly. >> And then applying. >> What developers of AI applications, particularly those that have been successful, have told us pretty clearly, is that building the actual algorithms, is not necessarily the hard part. The hard part is making all the data ready for that. And in fact I was reading a survey the other day of actual data scientists and AI developers and 60% of them said the thing they hate the most, is all the data collection, data prep. 'Cause it's so hard. And so, a big part of our strategy is just to simplify that. Make it simple and accessible so that you can really focus on what you want to do and where the value is, which is building the algorithms and the models, and getting those deployed. >> Big challenge and hugely important, I mean IBM is a 100 year old company that's going through it's own digital transformation. You know, we've had Inderpal Bhandari on talking about how to essentially put data at the core of the company, it's a real hard problem for a lot of companies who were not born, you know, five or, seven years ago. And so, putting data at that core and putting human expertise around it as opposed to maybe, having whatever as the core. Humans or the plant or the manufacturing facility, that's a big change for a lot of organizations. Now at the end of the day IBM, and IBM sells strategy but the analytics group, you're in the software business so, what offerings do you have, to help people get there? >> Well in the collect step, it's essentially our hybrid data management portfolio. So think DB2, DB2 warehouse, DB2 event store, which is about IOT data. So there's a set of, and that's where big data in Hadoop and all that with Wentworth's, that's where that all fits in. So building the ability to access all this data, virtualize it, do things like Queryplex, things of that nature, is where that all sits. >> Queryplex being that to the data, virtualization capability. >> Yeah. >> Get to the data no matter where it is. >> To find a queary and don't worry about where it resides, we'll figure that out for you, kind of thought, right? In the organize, that is infosphere, so that's basically our unified governance and integration part of our portfolio. So again, that is collecting all this, taking the collected data and organizing it, and making sure you're compliant with whatever policies. And making it, you know, business ready, right? And so infosphere's where you should look to understand that portfolio better. When you get into scale and analytics on demand, that's Cognos analytics, it is our planning analytics portfolio. And that's essentially our business analytics part of all this. And some data science tools like, SPSS, we're doing statistical analysis and SPSS modeler, if we're doing statistical modeling, things of that nature, right? When you get into the automate and the ML, everywhere, that's Watson Studio which is the integrated development environment, right? Not just for IBM Watson, but all, has a huge array of open technologies in it like, TensorFlow and Python, and all those kind of things. So that's the development environment that Watson machine learning is the runtime that will allow you to run those models anywhere. So those are the two big pieces of that. And then from there you'll see IBM building out more and more of what we already have. But we have Watson applications. Like Watson Assistant, Watson Discovery. We have a huge portfolio of Watson APIs for everything from tone to speech, things of that nature. And then the ability to infuse that all into the business processes. Sort of where you're going to see IBM heading in the future here. >> I love how you brought that home, and we talked about the ladder and it's more than just a PowerPoint slide. It actually is fundamental to your strategy, it maps with your offerings. So you can get the heads nodding, with the customers. Where are you on this maturity curve, here's how we can help with products and services. And then the other thing I'll mention, you know, we kind of learned when we spoke to some others this week, and we saw some of your announcements previously, the Red Hat component which allows you to bring that cloud experience no matter where you are, and you've got technologies to do that, obviously, you know, Red Hat, you guys have been sort of birds of a feather, an open source. Because, your data is going to live wherever it lives, whether it's on Prem, whether it's in the cloud, whether it's in the Edge, and you want to bring sort of a common model. Whether it's, containers, kubernetes, being able to, bring that cloud experience to the data, your thoughts on that? >> And this is where the big deal comes in, is for each one of those tiers, so, the DB2 family, infosphere, business analytics, Cognos and all that, and Watson Studio, you can get started, purchase those technologies and start to use them, right, as individual products or softwares that service. What we're also doing is, this is the more important step into the future, is we're building all those capabilities into one integrated unified cloud platform. That's called, IBM Cloud Private for data. Think of that as a unified, collaborative team environment for AI and data science. Completely built on a cloud native architecture of containers and micro services. That will support a multi cloud environment. So, IBM cloud, other clouds, you mention Red Hat with Openshift, so, over time by adopting IBM Cloud Private for data, you'll get those steps of the ladder all integrated to one unified environment. So you have the ability to buy the unified environment, get involved in that, and it all integrated, no assembly required kind of thought. Or, you could assemble it by buying the individual components, or some combination of both. So a big part of the strategy is, a great deal of flexibility on how you acquire these capabilities and deploy them in your enterprise. There's no one size fits all. We give you a lot of flexibility to do that. >> And that's a true hybrid vision, I don't have to have just IBM and IBM cloud, you're recognizing other clouds out there, you're not exclusive like some companies, but that's really important. >> It's a multi cloud strategy, it really is, it's a multi cloud strategy. And that's exactly what we need, we recognize that most businesses, there's very few that have standardized on only one cloud provider, right? Most of them have multiples clouds, and then it breaks up of dedicated, private, public. And so our strategy is to enable this capability, think of it as a cloud data platform for AI, across all these clouds, regardless of what you have. >> All right, Scott, thanks for taking us through the strategies. I've always loved talking to you 'cause you're a clear thinker, and you explain things really well in simple terms, a lot of complexity here but, it is really important as the next wave sets up. So thanks very much for your time. >> Great, always great to be here, thank you. >> All right, good to see you. All right, thanks for watching everybody. We are now going to bring it back to CubeNYC so, thanks for watching and we will see you in the afternoon. We've got the panel, the influencer panel, that I'll be running with Peter Burris and John Furrier. So, keep it right there, we'll be right back. (upbeat music)

>> Live from Times Square in New York City, it's The Cube covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to The Cube's special presentation. We're covering IBM's announcements today around AI. IBM, as The Cube does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on The Cube, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching The Cube.

>> Live from Times Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI, brought to you by IBM. >> Hi everybody, welcome back to theCUBE's special presentation. We're here at the Western Hotel and the theater district covering IBM's announcements. They've got an analyst meeting today, partner event. They've got a big event tonight. IBM.com/winwithAI, go to that website, if you're in town register. You can watch the webcast online. You'll see this very cool play of Vince Lombardy, one of his famous plays. It's kind of a power sweep right which is a great way to talk about sort of winning and with X's and O's. So anyway, Daniel Hernandez is here the vice president of IBM analytics, long time Cube along. It's great to see you again, thanks for coming on. >> My pleasure Dave. >> So we've talked a number of times. We talked earlier this year. Give us the update on momentum in your business. You guys are doing really well, we see this in the quadrants and the waves, but your perspective. >> Data science and AI, so when we last talked we were just introducing something called IBM Club Private for data. The basic idea is anybody that wants to do data science, data engineering or building apps with data anywhere, we're going to give them a single integrated platform to get that done. It's going to be the most efficient, best way to do those jobs to be done. We introduced it, it's been a resounding success. Been rolling that out with clients, that's been a whole lot of fun. >> So we talked a little bit with Rob Thomas about some of the news that you guys have, but this is really your wheelhouse so I'm going to drill down into each of these. Let's say we had Rob Beerden on yesterday on our program and he talked a lot about the IBM Red Hat and Hortonworks relationship. Certainly they talked about it on their earnings call and there seems to be clear momentum in the marketplace. But give us your perspective on that announcement. What exactly is it all about? I mean it started kind of back in the ODPI days and it's really evolved into something that now customers are taking advantage of. >> You go back to June last year, we entered into a relationship with Hortonworks where the basic primacy, was customers care about data and any data driven initiative was going to require data science. We had to do a better job bringing these eco systems, one focused on kind of Hadoop, the other one on classic enterprise analytical and operational data together. We did that last year. The other element of that was we're going to bring our data science and machine learning tools and run times to where the data is including Hadoop. That's been a resounding success. The next step up is how do we proliferate that single integrated stack everywhere including private Cloud or preferred Clouds like Open Shift. So there was two elements of the announcement. We did the hybrid Cloud architecture initiative which is taking the Hadoop data stack and bringing it to containers and Kubernetes. That's a big deal for people that want to run the infrastructure with Cloud characteristics. And the other was we're going to bring that whole stack onto Open Shift. So on IBM's side, with IBM Cloud Private for data we are driving certification of that entire stack on OpenShift so any customer that's betting on OpenShift as their Cloud infrastructure can benefit from that and the single integrated data stack. It's a pretty big deal. >> So OpenShift is really interesting because OpenShift was kind of quiet for awhile. It was quiest if you will. And then containers come on the scene and OpenShift has just exploded. What are your perspectives on that and what's IBM's angle on OpenShift? >> Containers of Kubernetes basically allow you to get Cloud characteristics everywhere. It used to be locked in to kind of the public Cloud or SCP providers that were offering as a service whether PAS OR IAS and Docker and Kubernetes are making the same underline technology that enabled elasticity, pay as you go models available anywhere including your own data center. So I think it explains why OpenShift, why IBM Cloud Private, why IBM Club Private for data just got on there. >> I mean the Core OS move by Red Hat was genius. They picked that up for the song in our view anyway and it's really helped explode that. And in this world, everybody's talking about Kubernetes. I mean we're here at a big data conference all week. It used to be Hadoop world. Everybody's talking about containers, Kubernetes and Multi cloud. Those are kind of the hot trends. I presume you've seen the same thing. >> 100 percent. There's not a single client that I know, and I spend the majority of my time with clients that are running their workloads in a single stack. And so what do you do? If data is an imperative for you, you better run your data analytic stack wherever you need to and that means Multi cloud by definition. So you've got a choice. You can say, I can port that workload to every distinct programming model and data stack or you can have a data stack everywhere including Multi clouds and Open Shift in this case. >> So thinking about the three companies, so Hortonworks obviously had duped distro specialists, open source, brings that end to end sort of data management from you know Edge, or Clouds on Prim. Red Hat doing a lot of the sort of hardcore infrastructure layer. IBM bringing in the analytics and really empowering people to get insights out of data. Is that the right way to think about that triangle? >> 100 percent and you know with the Hortonworks and IBM data stacks, we've got our common services, particularly you're on open meta data which means wherever your data is, you're going to know about it and you're going to be able to control it. Privacy, security, data discovery reasons, that's a pretty big deal. >> Yeah and as the Cloud, well obviously the Cloud whether it's on Prim or in the public Cloud expands now to the Edge, you've also got this concept of data virtualization. We've talked about this in the past. You guys have made some announcements there. But let's put a double click on that a little bit. What's it all about? >> Data virtualization been going on for a long time. It's basic intent is to help you access data through whatever tools, no matter where the data is. Traditional approaches of data virtualization are pretty limiting. So they work relatively well when you've got small data sets but when you've got highly fragmented data, which is the case in virtually every enterprise that exists a lot of the undermined technology for data virtualization breaks down. Data coming through a single headnote. Ultimately that becomes the critical issue. So you can't take advantage of data virtualization technologies largely because of that when you've got wide scale deployments. We've been incubating technology under this project codename query plex, it was a code name that we used internally and that we were working with Beta clients on and testing it out, validating it technically and it was pretty clear that this is a game changing method for data virtualization that allows you to drive the benefits of accessing your data wherever it is, pushing down queries where the data is and getting benefits of that through highly fragmented data landscape. And so what we've done is take that extremely innovated next generation data virtualization technology include it in our data platform called IBM Club Private for Data, and made it a critical feature inside of that. >> I like that term, query plex, it reminds me of the global sisplex. I go back to the days when actually viewing sort of distributed global systems was very, very challenging and IBM sort of solved that problem. Okay, so what's the secret sauce though of query plex and data virtualization? How does it all work? What's the tech behind it? >> So technically, instead of data coming and getting funneled through one node. If you ever think of your data as kind of a graph of computational data nodes. What query plex does is take advantage of that computational mesh to do queries and analytics. So instead of bringing all the data and funneling it through one of the nodes, and depending on the computational horsepower of that node and all the data being able to get to it, this just federates it out. It distributes out that workload so it's some magic behind the scenes but relatively simple technique. Low computing aggregate, it's probably going to be higher than whatever you can put into that single node. >> And how do customers access these services? How long does it take? >> It would look like a standard query interface to them. So this is all magic behind the scenes. >> Okay and they get this capability as part of what? IBM's >> IBM's Club Private for Data. It's going to be a feature, so this project query plex, is introduced as next generation data virtualization technology which just becomes a part of IBM Club Private for Data. >> Okay and then the other announcement that we talked to Rob, I'd like to understand a little bit more behind it. Actually before we get there, can we talk about the business impact of query plex and data virtualization? Thinking about it, it dramatically simplifies the processes that I have to go through to get data. But more importantly, it helps me get a handle on my data so I can apply machine intelligence. It seems like the innovation sandwich if you will. Data plus AI and then Cloud models for scale and simplicity and that's what's going to drive innovation. So talk about the business impact that people are excited about with regard to query plex. >> Better economics, so in order for you to access your data, you don't have to do ETO in this particular case. So data at rest getting consumed because of this online technology. Two performance, so because of the way this works you're actually going to get faster response times. Three, you're going to be able to query more data simply because this technology allows you to access all your data in a fragmented way without having to consolidate it. >> Okay, so it eliminates steps, right, and gets you time to value and gives you a bigger corporate of data that you can the analyze and drive inside. >> 100 percent. >> Okay, let's talk about stack overflow. You know, Rob took us through a little bit about what that's, what's going on there but why stack overflow, you're targeting developers? Talk to me more about that. >> So stack overflow, 50 million active developers each month on that community. You're a developer and you want to know something, you have to go to stack overflow. You think about data science and AI as disciplines. The idea that that is only dermained to AI and data scientists is very limiting idea. In order for you to actually apply artificial intelligence for whatever your use case is instead of a business it's going to require multiple individuals working together to get that particular outcome done including developers. So instead of having a distinct community for AI that's focused on AI machine developers, why not bring the artificial intelligence community to where the developers already are, which is stack overflow. So, if you go to AI.stackexchange.com, it's going to be the place for you to go to get all your answers to any question around artificial intelligence and of course IBM is going to be there in the community helping out. >> So it's AI.stackexchange.com. You know, it's interesting Daniel that, I mean to talk about digital transformation talking about data. John Furrier said something awhile back about the dots. This is like five or six years ago. He said data is the new development kit and now you guys are essentially targeting developers around AI, obviously a data centric. People trying to put data at the core of the organization. You see that that's a winning strategy. What do you think about that? >> 100 percent, I mean we're the data company instead of IBM, so you're probably asking the wrong guy if you think >> You're biased. (laughing) >> Yeah possibly, but I'm acknowledged. The data over opinions. >> Alright, tell us about tonight what we can expect? I was referencing the Vince Lombardy play here. You know, what's behind that? What are we going to see tonight? >> We were joking a little bit about the old school power eye formation, but that obviously works for your, you're a New England fan aren't you? >> I am actually, if you saw the games this weekend Pat's were in the power eye for quite a bit of the game which I know upset a lot of people. But it works. >> Yeah, maybe we should of used it as a Dallas Cowboy team. But anyways, it's going to be an amazing night. So we're going to have a bunch of clients talking about what they're doing with AI. And so if you're interested in learning what's happening in the industry, kind of perfect event to get it. We're going to do some expert analysis. It will be a little bit of fun breaking down what those customers did to be successful and maybe some tips and tricks that will help you along your way. >> Great, it's right up the street on the west side highway, probably about a mile from the Javis Center people that are at Strata. We've been running programs all week. One of the themes that we talked about, we had an event Tuesday night. We had a bunch of people coming in. There was people from financial services, we had folks from New York State, the city of New York. It was a great meet up and we had a whole conversation got going and one of the things that we talked about and I'd love to get your thoughts and kind of know where you're headed here, but big data to do all that talk and people ask, is that, now at AI, the conversation has moved to AI, is it same wine, new bottle, or is there something substantive here? The consensus was, there's substantive innovation going on. Your thoughts about where that innovation is coming from and what the potential is for clients? >> So if you're going to implement AI for let's say customer care for instance, you're going to be three wrongs griefs. You need data, you need algorithms, you need compute. With a lot of different structure to relate down to capture data wasn't captured until the traditional data systems anchored by Hadoop and big data movement. We landed, we created a data and computational grid for that data today. With all the advancements going on in algorithms particularly in Open Source, you now have, you can build a neuro networks, you can do Cisco machine learning in any language that you want. And bringing those together are exactly the combination that you need to implement any AI system. You already have data and computational grids here. You've got algorithms bringing them together solving some problem that matters to a customer is like the natural next step. >> And despite the skills gap, the skill gaps that we talked about, you're seeing a lot of knowledge transfer from a lot of expertise getting out there into the wild when you follow people like Kirk Born on Twitter you'll see that he'll post like the 20 different models for deep learning and people are starting to share that information. And then that skills gap is closing. Maybe not as fast as some people like but it seems like the industry is paying attention to this and really driving hard to work toward it 'cause it's real. >> Yeah I agree. You're going to have Seth Dulpren, I think it's Niagara, one of our clients. What I like about them is the, in general there's two skill issues. There's one, where does data science and AI help us solve problems that matter in business? That's really a, trying to build a treasure map of potential problems you can solve with a stack. And Seth and Niagara are going to give you a really good basis for the kinds of problems that we can solve. I don't think there's enough of that going on. There's a lot of commentary communication actually work underway in the technical skill problem. You know, how do I actually build these models to do. But there's not enough in how do I, now that I solved that problem, how do we marry it to problems that matter? So the skills gap, you know, we're doing our part with our data science lead team which Seth opens which is telling a customer, pick a hard problem, give us some data, give us some domain experts. We're going to be in the AI and ML experts and we're going to see what happens. So the skill problem is very serious but I don't think it's most people are not having the right conversations about it necessarily. They understand intuitively there's a tech problem but that tech not linked to a business problem matters nothing. >> Yeah it's not insurmountable, I'm glad you mentioned that. We're going to be talking to Niagara Bottling and how they use the data science elite team as an accelerant, to kind of close that gap. And I'm really interested in the knowledge transfer that occurred and of course the one thing about IBM and companies like IBM is you get not only technical skills but you get deep industry expertise as well. Daniel, always great to see you. Love talking about the offerings and going deep. So good luck tonight. We'll see you there and thanks so much for coming on theCUBE. >> My pleasure. >> Alright, keep it right there everybody. This is Dave Vellanti. We'll be back right after this short break. You're watching theCUBE. (upbeat music)