hello welcome everyone to thecube's coverage here in monaco i'm john furrier host of thecube the monaco crypto summit is happening we're here for the full day and tonight at the yacht club for special presentations crypto team is here digital bits and the industry's gathering and we get some great guests lined up throughout the day our first guest is michael gord co-founder and ceo of gda capital michael welcome to thecube cube great lunch on so we're kicking off the day here we got a lot of a lot of commentary around crypto and also we're in monaco so kind of a special inaugural event why this event why are people gathering here in monaco monaco has traditionally been a top financial jurisdiction but and there has been crypto events here before but never with participation from from prince albert so this being the first event first blockchain focus event in monaco that has participation from prince albert has brought a has brought a global audience and the fact that digital bets is intending to there's a a lot of excitement and and what uh what digital bits is going to be coming to market with yeah and i think i talked to alberto the founder and ceo of digitalbits um i've known him for many years he's a tech guy by heart but he's been in the trenches doing a lot of work over the years in crypto and one of the things i think digital bits has nailed this first the name's amazing but they got real deals i saw our announcement a couple days ago less than 48 hours roma soccer team has a new player they brought the big roll out digitalbits is on the uniform on the front of it huge crowd great visibility so this is a real trend where the the assets of physical and digital coming together there's certainly a lot of hype and a lot of kind of like cleaning up right now in the market but this train is definition is happening training has left the station there's been a lot of over the past decade a lot of startups building in the on blockchains and some of those startups have become big companies but big traditional enterprises have been slow to adopt digital assets and uh digitalbits is really well positioned to bring a lot of those and bring a lot of enterprise participation to the blockchain yeah i mean we met a couple days ago and we were talking in um at the hotel um you're you've been at this for a while you got some great successes talk about your firm what are you guys doing gda what are some of the things you're working on uh you're doing some investment what are some of the angles you're taking bets you've made things you're looking at yeah so i'm a serial entrepreneur and investor i've been focused on the mainstream adoption digital assets for the last decade went about that in in various different ways as i have as i've matured but the way our business looks now is uh is focused on bridging the gap between institutional capital markets and the blockchain and helping institutional capital participate in the market um so we help digital assets with their with their public offering we've gotten into traditional public markets through uh the blockchain moon acquisition corp spac that one of my co-founders is director of we have a brokerage business that does a few hundred million dollars about the transaction volume collateralized lending business we just started some some funds principal investments and then we incubate our own companies internally in category new categories like the metaverse nfts and um other things like that so pretty diversified across the boxing cabinet market at this point and in general looking to create solutions to um help the traditional capital market and the boxing cabinet market get get deeper exposure here you know it's interesting i hear you're speaking about the um how you guys are handling your your view of the landscape multiple moving parts on the investment thesis a lot of integration of instruments and vehicles it's a new creative structural change i mean if you look at just the money how crypto and the future of money this this cultural shift it's also some structural change on how to invest how to manage the investments how to bring on like incubation into most capital public private at the same time on the other side of the coin you have the entrepreneurial energy of um a lot of entrepreneurial ideas you see a lot of creative artists the creator culture has emerged in the past year and a half as a massive wave but to me that's just an application on top of the new infrastructure if you look at all the big investment houses that are pouring billions of whether it's industrial horowitz or other big vcs moving and shifting it's all the same game it's the infrastructure platform applications and it's but it's different it's not what we used to see because it decentralized how do you react to that what's your view on that concept you see it the same way yeah i think that there's everything with blockchains is novel but almost all of it we've seen before so um we've had games before now with the blockchain we have the ability to earn income by playing games we've had exchanges before but they've always been a centralized organization that everything that is now built on blockchains exists in the traditional internet or capital market or game industry or or whatever uh that you know there has been art for generations there's been uh now the ability to have art on the blockchain with provable nft like every everything is innovative because of the decentralization aspect but it's not it's not the first thing the first time that we've seen any of this stuff it's almost interesting you're seeing it recycling all the same concepts on the old web kind of come in the new web and there's also a gen z angle especially the metaverse metaverse the constant theme i'm seeing is hey you want to watch sports you can watch in the metaverse and do it differently and not have to attend so you know the whole pandemic has shown us that hybrid virtual and hybrid is coming together and so i see a huge tsunami of innovation coming from just the tailwind post pandemic i think still massive value in a real event like this us being able to sit in front of each other as real people is uh not replicatable in the metaverse but to be in monaco is not possible for everyone because uh visa reasons because they have something you know it's just you have to be here today is not possible for a hundred percent of the world or for a sports game or for a concert or for a music premiere movie premiere really anything that's happening in the real world is not the metaverse is not gonna replace the real world but it is gonna create a massive additional audience to anything that's happening in the real world that anyone around the world can participate and how amazing would it be for uh for someone from zimbabwe someone from sydney and someone from brazil to all be interested in what digital bits is doing in monaco and what prince albert is you know how how how how the monarchical crypto summit is looking to position monaco in the future of cryptocurrency the kind of theme of this event and they have the amazing fortune to meet in the metaverse it doesn't replace well i mean i think i mean i think this is a great point this to me is going to be the holy grail in my opinion i agree if you look at the notion of presence we're face to face we're here there's people here so we peace we see each other in the lobby maybe he's out sightseeing at dinners so when you have that face to face that's the scarce resource right that's going to be the intimacy sometimes it's not even just to learn about what the pro what's going on but if we're present here how do we create that same experience when you have presence not just some icon chatting but like just movement knowing that you're there connected to people first party is going to be no one's really done it well i think the metaverse is to me is showing the path to being a first-class citizen digitally with a real-time event it's new so it is possible to communicate in the metaverse through through a microphone so if if you're beside someone then similar to the real world you can say you know hey how's it going what do you think about the presentation or or whatever you want and if you're speaking in a conversational way then the person beside you will hear what the person down the hall might might not um it's also that i've i've seen new features in certain like experiences that are coming to market that kind of take the google hangout or skype yeah like video infrastructure and put that in so we could choose to have our cameras on which is it's getting better but it of course doesn't replace real presence there's no doubt in my mind that in near future soon sooner or later there's gonna be a guest sitting right next to you that's not here okay there will be a hologram model where people will be interviewed will have capability to visualize that person they'll be in a metaverse they'll be queuing up for interviews this is a game this is a mind-blowing thing i mean if you just think about that concept that we could have participation in real time here with expressions with their with their digital expression their icon whatever whatever their nfts are so i think this is going to be the blending of how communities gather and i think ultimately how truth and and journalism and news is going to change so to me yeah we're super excited we're here obviously because we want to get the stories and you know we love what digital bits is doing prince albert certainly a relevant figure on the global stage um i think this is a signal for a lot of things to come indeed indeed all right so final question before we move on what's your hottest thing you got going on what are you looking at what are you most excited about um well just just this conference um we've got quite a lot of of companies we have exposure in that are that are presenting and a lot of them are coining new new new niches of the market so um we have uh um we've spoken about a lot about the metaverse we have you know i'm and i think the metaverse is probably the the thing that i'm overall most excited about i think it's the next multi-trillion dollar market that feels like bitcoins in but in addition to that we have the first regenerative finance platform that is that is presenting here that's using decentralized finance and and blockchain technology to create a model that people can earn income while mining carbon credits essentially with an objective of having first boxing all blocking protocols but eventually creating a leader board of carbon positive businesses where businesses will challenge their competitors to be more carbon positive in a way that actually earns them earn some income outside of the potential value what's the name of that company that's kyoto protocol uh we have the first entertained to earn a company that is is presenting here it's playgood um the first uh e-commerce metaverse platform so integrated directly into e-commerce without needing to i think the future of the metaverse is is social links you have you know finest in the metaverse and you have all of the all the logos of metaverses that you have experiences in which is cool yeah that that's uh but then you're you're going out of the native website instead of having a um instead of you know native to the to the website having a metabolism experience so they're doing that um yeah really cool awesome final question one more final question i got for you because you made me think of it so metaverse obviously hot is there going to be an open metaverse you start to see walled gardens and you got facebook they got slam dunk by the u.s uh in terms of monopolistic move for buying a exercise act which you know i can i i don't think that was a good move by the u.s i think i let him do that but but there they're they're kind of the wall garden model the old facebook i mean decentralized about open yeah historically if we go back in time there's always open and closed infrastructure in the internet um there was there is companies building open infrastructure companies building closed infrastructure and we could have been talking in 1992 about whether the private intranet will create mass adoption or the open internet will create mass adoption and not that the the intranet is probably is even today still a multi-billion dollar per year business but it's not a multi-trillion dollar per year per year you know infrastructure like the public internet same with the blockchain in 2012 2013 um private blockchains were all the rage by banking raising hundreds of millions of dollars to build up private boxing infrastructure and private blockchains are generating probably today still multi-billion dollars of revenue annually but they haven't accrued multi-trillion dollars like the public watching has i think the same thing will be in the metaverse there will be open and closed infrastructure um but event and there already is close you know fortnight and and games are are essentially closed metaverses just without ownable land um i always look at the i'm old school i look at aol they had they monopolized dial up internet like where the hell did that go you know history so again yeah we don't know it's going to be maybe a connection a connection point between these open metaverses we'll see maybe i'm investment update michael thanks for coming on thecube appreciate you kicking off the event here monaco crypto summit powered by digital bits presented by digital bits uh the company really and behind all the innovation here and the companies i'm john furrier with more coverage after this short break thanks john [Music] you

(upbeat intro jingle) >> 'Kay, welcome back everyone to theCUBE's coverage of AWS re:Invent, 2021. I'm John Furrier, your host of theCUBE, with your Matt Morgan, Vice President of Cloud Infrastructure Business Group of VMware, CUBE alumni. Matt, great to see you. Can't wait to see you in person, but thanks for coming in remotely for the virtual now hybrid CUBE for re:Invent. >> It's good to see you too, John. Thanks for having us. You know, it's our ninth year covering re:Invented, Remember the first year we went there, it was all developers, right? >> Right. >> And reminds me of the story that you guys have with AWS, you know, VMware Cloud, and VMware with vSphere pioneered operations in IT, you know, vSphere workloads, but now you move that all in the cloud. I remember Ragu when he announced that deal with Pat Gelsinger and Andy Jassy, we covered it extensively. People were like "What are they doing here? This is interesting". Boy- >> Yeah, you- >> The pundits all get it wrong. Their relationship has been blossoming. It's been really powerful, take us through the history here. >> Thanks, John, I mean, you're absolutely right. We have a phenomenal relationship with Amazon Web Services. The value of our partnership has been realized by customers all over the world, in every industry, as they embrace the seamless hybrid cloud experience powered by VMware, vSphere, and of course VM-ware Cloud Stack. Of course, we've recently expanded our operations here, including Japan and the launch of the Soccer Regions. And we're fully open for business with the U.S. Federal Government with VMware Cloud on AWS Gov Cloud. There's strong alignment across the field with new go-to-market teams on both sides and a powerful resell agreement that enables AWS sellers to take VMware Cloud on AWS and all the associated VMware services, such as VMware cloud disaster recovery, NSX vRealize Cloud Management, to their enterprise customers. And we couldn't be doing better. >> Yeah, and you brought up a lot of things there. You mentioned Outpost, mentioned Gov Cloud, you mentioned Marketplace, which means you mentioned the acronym, which is basically, I think it's called EDP Credits, which essentially the enterprise, Amazon's Salesforce working together. So, essentially full business model and technical integrations with Amazon. So, success certainly being demonstrated there. So congratulations, that being said, there's still more to do. We got this whole big wave coming on, you see the edge, you seeing multicloud, you seeing hybrid becoming the operational model, both on premises and in the cloud. And so, customers really are asking themselves "Okay, I got VMware, I got AWS Cloud, I got to secure these clouds now. I got to start putting the business model together on top of the technical architecture". You know, microservices, Kubernetes, Tansu, all the things you guys are doing, but customers want to ask you "What about securing the cloud?", this is the number one question, what's your reaction to that? >> Yeah, it's a great topic, John, at the end of the day, this is about evolving the hybrid cloud. And if you think about it, originally, the hybrid cloud was about unifying both infrastructure and operations between the on-premises world, and the public cloud world. And now what's happening, is we are seeing people embrace that in spades, and as a result of that, their Tier 1 applications are running both on-premises and in the public cloud. And with our new announced local cloud capabilities with VMware Cloud on AWS Outpost, it's leading to this whole new enterprise architecture, which we call the distributed cloud. When you look at deploying enterprise applications in a distributed cloud environment, the conversation starts with consistent networking and importantly security. So, let's talk about that for a moment. Customers are asking us "How do we secure our data when we start having infrastructure in a variety of locations? Are our applications and networks... Are they really secure when they run in these completely different environments? And importantly, when we move an application, we take it from our on-premise data center, we move it to the public cloud are the security policies... Are they moving with it? Do I need to re-architect for that?". And the real question, all of this boils down to "Are we expanding that attack surface when we move to VMware Cloud on AWS?". And so we have to come back to what do we do here to really alleviate these concerns? With data security, it's all about encryption, universal insights. We have the super root capability within our platform to ensure that everything is measured, every message from an application, every data, it's great for Chain Of Custody, Audit. Of course we have backup DR Ransomware. On the application side, of course, segmentation is super important with application centric firewalls, VPNs, tunneling, EDR, IDS, IPS. And of course, none of that matters if you have to reset everything up every time an application moves. And this is a real unique value proposition for us, it's about portability. We deliver portable security. We can move an application, the APIs are standard. You can move it up to the public cloud, your policies, your integrations, even if it's third-party integrations, they're maintained. And that really delivers the ability to say "Look, we can make sure your attack surface is not expanding, it's a controlled environment for you". And that really shrinks the risk factors associated with moving to this distributed cloud environment. >> You know, that's the really, I think the key point, I think that you brought up this infrastructure, kind of, table stakes. Which keeps rising because security's, honestly is now there's no... There's a huge... There's no perimeter. It's huge surface area. Everything has to be secured and locked down. And the big theme at re:Invent this year is data, right? So, you know, data and security all go hand in hand. And so that brings up the aspect of the edge. The edge is now booming, you seeing 5G again, you're here hearing it here at reinvent again, more and more 5G. You mentioned local services, Outpost is evolving. This is kind of the new area, and certainly, attack factor as well. So, you mentioned this whole local services. Take me through that because this becomes interesting because this is an architectural issue for enterprises to figure out, "Okay, I got to distribute a computing architecture, it's called The Cloud and multiple clouds. Now, I've got this edge, whole 'nother opening opens up the case for the architecture conversation". What's the strategy? How do you guys view the case? How do you make the case for local services? >> So, we were super excited to announce VMware Cloud on AWS Outpost. This is a local cloud as a service offering. So, let me break that down a little bit. Of course, compute at the edge is nothing new, but the problem with traditional approaches is typically edge locations may lack IT excellence. Which means there's no one there to manage the service. VMware Cloud on AWS outposts is that local cloud as a service, meaning it's fully managed and at the edge, that's a perfect fit. It's hand in glove for those types of workloads that are out, pushed all the way out, whether it's part of an agricultural deployment or an energy production facility or retail store, where there isn't that typical IT excellence. VMware cloud on AWS outposts enables customers to deploy the same Cloud instance as they're running VMware Cloud on AWS, but be able to do it out at that edge environment. And when you look at the overall value of VMware Cloud on AWS Outpost, it's about delivering a simpler, cost effective, consistent cloud experience for those on-prem environments that matches the operating model of the public cloud. Think of the places that you really want to have cloud infrastructure, where it's critical. Going back to your point on data, getting real time insights on that data, to be able to process that, we call those perishable insights. The value is the immediacy understanding that value specific to the moment it's being captured. Think about the different types of sensor environments, where data's coming off expensive equipment, that's measuring temperature and speed. Understanding that value back to the operator - really, really important. You don't have time to pipe that data up to a cloud process and send the results back down. Edge environments require that real-time stuff. So, together with AWS, we jointly deliver a fully managed service right down to the AWS hardware on which we built the VMware cloud instance. We think about where we're seeing the most interest here. You can look across all kinds of industries and use cases, and we're seeing it specifically in healthcare, out of the hospital, manufacturing for equipment monitoring, government, higher education, where those end points are typically virtualized. There are others, but these are the big ones so far. >> You know, I was just talking to an AMD executive or product marketing person on the gaming side. And they're living this right now because they're putting all the virtual collaboration in the cloud, all the data, because they have so much data and they have so much need for these special instances, whether it's GPUs, and CPUs, a mix and match. So, as instances become more special purposed, that's going to enable them to have more productivity. But then, when you have that baseline in the cloud, the edge also has processing power. So, I think people are starting to see this notion of "Okay, I'm in the cloud, but I can also have that cloud edge without moving data back to the centralized cloud and processing it at the edge with software". >> Yeah, that's true. >> This is real. >> It's super real. And the one that really resonates with customers, is one that we all understand and that's healthcare. Anytime you're in a regional environment where you're at a hospital, think of an ICU, the criticality of that data being processed, providing the insights, this is more mission critical than any other environment, because we're dealing with human lives, think about the complex compute requirements of that environment. And then look at the beauty and elegance of this system, a cloud-based system on premises, doing that compute, providing those insights, giving reality back to the clinician, so they can make those decisions. Healthcare is super, super important. And we see customers across the spectrum, looking at what's happening at the edge and embracing it, whether it's healthcare or other industries. And again, it's a perfect fit for them. >> Yeah, real quick, before we move on to what's new, I'm want to get to that, the Tansu stuff as well. What other industries are popping out? Obviously, manufacturing. What can you talk with some industries and some verticals that are really primed for this local cloud service? >> So, let's talk about manufacturing for a moment. Manufacturing is another facility oriented compute requirement that is perfectly fit, from a system and solution way like VMware cloud on AWS Outposts. Within the manufacturing environment, there's tons of very critical machines. There's inventory management, there's a combination of time management, people management, bringing it all together to ensure that process lines are moving as required, that inventory is provided at the specific moment it's needed, and to make sure that everything, especially in today's supply chain world is provided when is required. This type of capability allows an organization to bring in that sensor data, bring in that inventory data, produce applications that manage that in real time, delivering that compute. And in the manufacturing floor, again, limited IT excellence. So, this provides that capability. Another one is energy production. Think about energy production that's out in the field in North Dakota, or out on an oil rig that might be in the Gulf of Mexico. Not only are you dealing with lack of IT excellence, you're also dealing with limited connectivity. This equipment needs to be monitored and censored and the data from those sensors help drive critical decisions. And with limited connectivity, I mean, you may not even have an LTE signal, the need to do that real time is paramount, local cloud provides that. >> Yeah, and I'd also just add, because we're going to move on, but higher ED is going to be completely transformed. Well, I think that's going to be kind of like a pleat revamp. Let's get into what's new on VMware Cloud on AWS give us the update on the new things that people should know about. That's important that they should review, take us through that, what's new? >> Yeah, absolutely. So, the first is the integration with the AWS console. This is a big thing that we're delivering because VMware Cloud on AWS is a native service of AWS. I have to kind of say that twice, it's a native service of AWS. And because of that, we get the same operational and commerce experience for VMware Cloud instances as customers do with traditional AWS services. This means customers now have a choice between AWS centric operating model, which is highly relevant to DevOps and developers, or VMware centric operating model, which is very relevant to traditional operators, and IT users. VMware Cloud on AWS Gov Cloud is expanded to the U.S., East Virginia Region, and achieved aisle five certification. This new region will make the service more relevant for the Eastern Seaboard where much of the Federal Government resides. And of course with aisle five, it opens up VMware Cloud on AWS to the U.S. military and defense contractors, which is huge because there's massive cloud transformation contracts currently in play. And of course, VMware Cloud on AWS Gov Cloud provides the most secure enterprise cloud for those DOD customers, especially when they focus on those critical Tier 1 workloads. >> It's been three years since the GA of the VMware cloud on AWS, has been earlier, since you announced it> You're pumping on all cylinders, as we had predicted, others didn't, just FYI for the folks watching. What's the final vibe? End the segment with your view of what's going on with VMware Cloud on AWS? What's the bumper sticker? >> So, at the end of the day, every customer is looking to migrate and modernize their workloads. And VMWare cloud gives them that capability to do it faster than anyone else. Customers take their applications, tier 1 applications, move it to that secure distributed cloud construct, that idea of having VMware Cloud on AWS, sharing all those security policies, all of that consistent infrastructure and operations. And then they can modernize those applications, using all of those cloud services and the ability to use Tansu to containerize where applicable. We're excited about these capabilities, and our customers are adopting it faster each and every year. And we're thrilled about the traction we're had. And we're thrilled about the partnership we have with Amazon Web Services. So, lots more to come in this space. >> Lot of great stuff, people moving up the stack on the cloud, you're seeing more refactoring in the cloud. Matt Morgan, great to see you. We've been talking 'about this for years on theCUBE. Great to come on and give some insights. All happening. Infrastructure is code. And everyone's winning with containers and microservices. So, great stuff. Thanks for coming on. >> Thanks a lot, John, take care. >> Okay, Matt Morgan, the VP of Cloud Infrastructure Business Group of VMware. This theCUBE's coverage of AWS re:Invent, 2021. I'm John Furrier, your host. Thanks for watching. (upbeat outro jingle)

(upbeat intro jingle) >> 'Kay, welcome back everyone to theCUBE's coverage of AWS re:Invent, 2021. I'm John Furrier, your host of theCUBE, with your Matt Morgan, Vice President of Cloud Infrastructure Business Group of VMware, CUBE alumni. Matt, great to see you. Can't wait to see you in person, but thanks for coming in remotely for the virtual now hybrid CUBE for re:Invent. >> It's good to see you too, John. Thanks for having us. You know, it's our ninth year covering re:Invented, Remember the first year we went there, it was all developers, right? >> Right. >> And reminds me of the story that you guys have with AWS, you know, VMware Cloud, and VMware with vSphere pioneered operations in IT, you know, vSphere workloads, but now you move that all in the cloud. I remember Ragu when he announced that deal with Pat Gelsinger and Andy Jassy, we covered it extensively. People were like "What are they doing here? This is interesting". Boy- >> Yeah, you- >> The pundits all get it wrong. Their relationship has been blossoming. It's been really powerful, take us through the history here. >> Thanks, John, I mean, you're absolutely right. We have a phenomenal relationship with Amazon Web Services. The value of our partnership has been realized by customers all over the world, in every industry, as they embrace the seamless hybrid cloud experience powered by VMware, vSphere, and of course VM-ware Cloud Stack. Of course, we've recently expanded our operations here, including Japan and the launch of the Soccer Regions. And we're fully open for business with the U.S. Federal Government with VMware Cloud on AWS Gov Cloud. There's strong alignment across the field with new go-to-market teams on both sides and a powerful resell agreement that enables AWS sellers to take VMware Cloud on AWS and all the associated VMware services, such as VMware cloud disaster recovery, NSX vRealize Cloud Management, to their enterprise customers. And we couldn't be doing better. >> Yeah, and you brought up a lot of things there. You mentioned Outpost, mentioned Gov Cloud, you mentioned Marketplace, which means you mentioned the acronym, which is basically, I think it's called EDP Credits, which essentially the enterprise, Amazon's Salesforce working together. So, essentially full business model and technical integrations with Amazon. So, success certainly being demonstrated there. So congratulations, that being said, there's still more to do. We got this whole big wave coming on, you see the edge, you seeing multicloud, you seeing hybrid becoming the operational model, both on premises and in the cloud. And so, customers really are asking themselves "Okay, I got VMware, I got AWS Cloud, I got to secure these clouds now. I got to start putting the business model together on top of the technical architecture". You know, microservices, Kubernetes, Tansu, all the things you guys are doing, but customers want to ask you "What about securing the cloud?", this is the number one question, what's your reaction to that? >> Yeah, it's a great topic, John, at the end of the day, this is about evolving the hybrid cloud. And if you think about it, originally, the hybrid cloud was about unifying both infrastructure and operations between the on-premises world, and the public cloud world. And now what's happening, is we are seeing people embrace that in spades, and as a result of that, their Tier 1 applications are running both on-premises and in the public cloud. And with our new announced local cloud capabilities with VMware Cloud on AWS Outpost, it's leading to this whole new enterprise architecture, which we call the distributed cloud. When you look at deploying enterprise applications in a distributed cloud environment, the conversation starts with consistent networking and importantly security. So, let's talk about that for a moment. Customers are asking us "How do we secure our data when we start having infrastructure in a variety of locations? Are our applications and networks... Are they really secure when they run in these completely different environments? And importantly, when we move an application, we take it from our on-premise data center, we move it to the public cloud are the security policies... Are they moving with it? Do I need to re-architect for that?". And the real question, all of this boils down to "Are we expanding that attack surface when we move to VMware Cloud on AWS?". And so we have to come back to what do we do here to really alleviate these concerns? With data security, it's all about encryption, universal insights. We have the super root capability within our platform to ensure that everything is measured, every message from an application, every data, it's great for Chain Of Custody, Audit. Of course we have backup DR Ransomware. On the application side, of course, segmentation is super important with application centric firewalls, VPNs, tunneling, EDR, IDS, IPS. And of course, none of that matters if you have to reset everything up every time an application moves. And this is a real unique value proposition for us, it's about portability. We deliver portable security. We can move an application, the APIs are standard. You can move it up to the public cloud, your policies, your integrations, even if it's third-party integrations, they're maintained. And that really delivers the ability to say "Look, we can make sure your attack surface is not expanding, it's a controlled environment for you". And that really shrinks the risk factors associated with moving to this distributed cloud environment. >> You know, that's the really, I think the key point, I think that you brought up this infrastructure, kind of, table stakes. Which keeps rising because security's, honestly is now there's no... There's a huge... There's no perimeter. It's huge surface area. Everything has to be secured and locked down. And the big theme at re:Invent this year is data, right? So, you know, data and security all go hand in hand. And so that brings up the aspect of the edge. The edge is now booming, you seeing 5G again, you're here hearing it here at reinvent again, more and more 5G. You mentioned local services, Outpost is evolving. This is kind of the new area, and certainly, attack factor as well. So, you mentioned this whole local services. Take me through that because this becomes interesting because this is an architectural issue for enterprises to figure out, "Okay, I got to distribute a computing architecture, it's called The Cloud and multiple clouds. Now, I've got this edge, whole 'nother opening opens up the case for the architecture conversation". What's the strategy? How do you guys view the case? How do you make the case for local services? >> So, we were super excited to announce VMware Cloud on AWS Outpost. This is a local cloud as a service offering. So, let me break that down a little bit. Of course, compute at the edge is nothing new, but the problem with traditional approaches is typically edge locations may lack IT excellence. Which means there's no one there to manage the service. VMware Cloud on AWS outposts is that local cloud as a service, meaning it's fully managed and at the edge, that's a perfect fit. It's hand in glove for those types of workloads that are out, pushed all the way out, whether it's part of an agricultural deployment or an energy production facility or retail store, where there isn't that typical IT excellence. VMware cloud on AWS outposts enables customers to deploy the same Cloud instance as they're running VMware Cloud on AWS, but be able to do it out at that edge environment. And when you look at the overall value of VMware Cloud on AWS Outpost, it's about delivering a simpler, cost effective, consistent cloud experience for those on-prem environments that matches the operating model of the public cloud. Think of the places that you really want to have cloud infrastructure, where it's critical. Going back to your point on data, getting real time insights on that data, to be able to process that, we call those perishable insights. The value is the immediacy understanding that value specific to the moment it's being captured. Think about the different types of sensor environments, where data's coming off expensive equipment, that's measuring temperature and speed. Understanding that value back to the operator - really, really important. You don't have time to pipe that data up to a cloud process and send the results back down. Edge environments require that real-time stuff. So, together with AWS, we jointly deliver a fully managed service right down to the AWS hardware on which we built the VMware cloud instance. We think about where we're seeing the most interest here. You can look across all kinds of industries and use cases, and we're seeing it specifically in healthcare, out of the hospital, manufacturing for equipment monitoring, government, higher education, where those end points are typically virtualized. There are others, but these are the big ones so far. >> You know, I was just talking to an AMD executive or product marketing person on the gaming side. And they're living this right now because they're putting all the virtual collaboration in the cloud, all the data, because they have so much data and they have so much need for these special instances, whether it's GPUs, and CPUs, a mix and match. So, as instances become more special purposed, that's going to enable them to have more productivity. But then, when you have that baseline in the cloud, the edge also has processing power. So, I think people are starting to see this notion of "Okay, I'm in the cloud, but I can also have that cloud edge without moving data back to the centralized cloud and processing it at the edge with software". >> Yeah, that's true. >> This is real. >> It's super real. And the one that really resonates with customers, is one that we all understand and that's healthcare. Anytime you're in a regional environment where you're at a hospital, think of an ICU, the criticality of that data being processed, providing the insights, this is more mission critical than any other environment, because we're dealing with human lives, think about the complex compute requirements of that environment. And then look at the beauty and elegance of this system, a cloud-based system on premises, doing that compute, providing those insights, giving reality back to the clinician, so they can make those decisions. Healthcare is super, super important. And we see customers across the spectrum, looking at what's happening at the edge and embracing it, whether it's healthcare or other industries. And again, it's a perfect fit for them. >> Yeah, real quick, before we move on to what's new, I'm want to get to that, the Tansu stuff as well. What other industries are popping out? Obviously, manufacturing. What can you talk with some industries and some verticals that are really primed for this local cloud service? >> So, let's talk about manufacturing for a moment. Manufacturing is another facility oriented compute requirement that is perfectly fit, from a system and solution way like VMware cloud on AWS Outposts. Within the manufacturing environment, there's tons of very critical machines. There's inventory management, there's a combination of time management, people management, bringing it all together to ensure that process lines are moving as required, that inventory is provided at the specific moment it's needed, and to make sure that everything, especially in today's supply chain world is provided when is required. This type of capability allows an organization to bring in that sensor data, bring in that inventory data, produce applications that manage that in real time, delivering that compute. And in the manufacturing floor, again, limited IT excellence. So, this provides that capability. Another one is energy production. Think about energy production that's out in the field in North Dakota, or out on an oil rig that might be in the Gulf of Mexico. Not only are you dealing with lack of IT excellence, you're also dealing with limited connectivity. This equipment needs to be monitored and censored and the data from those sensors help drive critical decisions. And with limited connectivity, I mean, you may not even have an LTE signal, the need to do that real time is paramount, local cloud provides that. >> Yeah, and I'd also just add, because we're going to move on, but higher ED is going to be completely transformed. Well, I think that's going to be kind of like a pleat revamp. Let's get into what's new on VMware Cloud on AWS give us the update on the new things that people should know about. That's important that they should review, take us through that, what's new? >> Yeah, absolutely. So, the first is the integration with the AWS console. This is a big thing that we're delivering because VMware Cloud on AWS is a native service of AWS. I have to kind of say that twice, it's a native service of AWS. And because of that, we get the same operational and commerce experience for VMware Cloud instances as customers do with traditional AWS services. This means customers now have a choice between AWS centric operating model, which is highly relevant to DevOps and developers, or VMware centric operating model, which is very relevant to traditional operators, and IT users. VMware Cloud on AWS Gov Cloud is expanded to the U.S., East Virginia Region, and achieved aisle five certification. This new region will make the service more relevant for the Eastern Seaboard where much of the Federal Government resides. And of course with aisle five, it opens up VMware Cloud on AWS to the U.S. military and defense contractors, which is huge because there's massive cloud transformation contracts currently in play. And of course, VMware Cloud on AWS Gov Cloud provides the most secure enterprise cloud for those DOD customers, especially when they focus on those critical Tier 1 workloads. >> It's been three years since the GA of the VMware cloud on AWS, has been earlier, since you announced it> You're pumping on all cylinders, as we had predicted, others didn't, just FYI for the folks watching. What's the final vibe? End the segment with your view of what's going on with VMware Cloud on AWS? What's the bumper sticker? >> So, at the end of the day, every customer is looking to migrate and modernize their workloads. And VMWare cloud gives them that capability to do it faster than anyone else. Customers take their applications, tier 1 applications, move it to that secure distributed cloud construct, that idea of having VMware Cloud on AWS, sharing all those security policies, all of that consistent infrastructure and operations. And then they can modernize those applications, using all of those cloud services and the ability to use Tansu to containerize where applicable. We're excited about these capabilities, and our customers are adopting it faster each and every year. And we're thrilled about the traction we're had. And we're thrilled about the partnership we have with Amazon Web Services. So, lots more to come in this space. >> Lot of great stuff, people moving up the stack on the cloud, you're seeing more refactoring in the cloud. Matt Morgan, great to see you. We've been talking 'about this for years on theCUBE. Great to come on and give some insights. All happening. Infrastructure is code. And everyone's winning with containers and microservices. So, great stuff. Thanks for coming on. >> Thanks a lot, John, take care. >> Okay, Matt Morgan, the VP of Cloud Infrastructure Business Group of VMware. This theCUBE's coverage of AWS re:Invent, 2021. I'm John Furrier, your host. Thanks for watching. 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>>Bach from Las Vegas. It's the cube covering AWS executive summit brought to you by extension. >>Welcome back and good morning. Welcome to the cubes live coverage of the Accenture executive summit here at AWS reinvent. I'm your host, Rebecca Knight. We are joined by pre-saved Sanker and he is the senior managing director global lead intelligent cloud infrastructure. Welcome back on the queue. Thanks so much for coming on. And Larry soccer, global managing director infrastructure services domains and strategy. Thank you so much. So we're talking today about hybrid cloud, the best of both worlds. As we say on the cube. It's a multicloud world. But I want to start with you Prisaad. What is driving clients to move to the cloud? What are you hearing from CEOs? What keeps them up at night? >>So I think, you know, you've got to a point with our clients where they're really trying to get the power of the cloud to the enterprises. So there are multiple things that they're trying to do. First, they're trying to really get the innovation that cloud provides in driving their digital transformation. The second is taking advantage of the cost savings that cloud can provide. So there are multiple aspects of how they use cloud. The first would be using SAS type applications. So for example it is faults.com or Workday and things like that. The second is using the power of providers like AWS really to drive what they can do from cloud native perspective in building new applications. And the third is just taking existing applications that aren't legacy and either re hosting them or refactoring them as an adjusting them to some extent and then hosting them on again, clouds like AWS. So that is a multiyear journey that all our clients are on. And you know, as Accenture we're helping them identify what needs to go first, what needs to go next and help them in that journey. >>And what is, what is the compelling force? Is it that they want to save cut costs? Is it that they want to be more innovative and they view this innovation in the cloud as the key to it? >>It's a combination of both. It's innovation is absolutely number one. Secondly, it is speed to market. The ability to get your product out there very quickly is second and the third is at the end of the day, you know data centers are going to go away and we're going to all recite in the cloud in some form or fashion, whether it's public or private. And which is why this whole topic of hybrid and multi-cloud is becoming so important today. >>So Larry, why hybrid cloud though? Why? Why can't we just go all in on private and public? Sorry. >>Interesting. I mean the, the real driver and Prisaad touched on it there. The real driver to get to, to AWS and to, to the hyperscalers really is around the innovation cycles. You know the passes, the services that they can do and that drives innovation. The speed to, to get there as important, it gives you a way of quickly scaling, which you know, if I really want to build out an application fast, a great way to get there and is obviously the consumption economics. How do I shift from cap ex topics? So that's driving the cloud native that push into, into the wallet of public. At the same time, our clients do have a number of requirements that really make them look and rethink and figure out how to evolve their data centers first. The first ones were regulatory, so you think about when you were the pharmaceutical GXP compliance, HIPAA in the healthcare side of things, you know, GDPR, so I've got companies that regulate that regulatory. >>What was perceived as a barrier, particularly in some of the more difficult regulatory environment and while the public providers are really evolving and starting to get better regulatory posture is at the same time a lot of our clients were making investment and decide, Hey, how do I really build my private cloud? I'm another big driver that people continue to look at private clouds, whether they're in their data centers or increasingly moving Nikolas is to scale up architectures like high terabyte HANA. If I want a 64 terabyte HANA deployment, while while the AWS footprints get bigger and bigger, sometimes just performance and tuning for those high scale an environment is a big deal. Article article racks, a great example where it's not only do you have something in a highly tuned environment but be given, given some of the licensing arbitrage stuff, it makes it extremely difficult. >>And in a big part of it is data gravity. If I've got these big data sets, if you think about fraud analysis in Hadoop clusters for credit card processing, where I've got high terabyte HANA database, I've got 20 or 30 applications that need to access that data. I can't really put it over a wide area network due to latency. And you know, the cost of moving data around. So what you ultimately end up with is applications clustered around lakes, big pockets of data. And I think that's where we're ending up. And that will be across a hybrid architecture. So that's, we call it, you know, as you look at balancing your apps and your data across public private solutions. That's why we view hybrid as the way that most of our clients are going, given the scale and the amount of applications and data they have. So that's what we refer to as the best of both worlds for hybrid. >>So I saw you nodding a lot. Allow what he was saying for sod. How do you, I mean, as you said, it's a balancing act in terms of how you set your strategy. How do you recommend companies go about thinking in terms of how they allocate their, their cloud? >>Yeah, so I think, you know, we have particular really, really take an application centric approach. Um, you know, I'll go back a couple of years, uh, when our clients were really looking to, uh, really use the public cloud and then they've signed up with one or more public cloud providers and then they, you know, move some applications and then some of them have actually taken a step back. In fact, there's a very global investment bank that I've been working with, um, who, who are taking that approach initially. And now what they've done in working with us is taking a very application centric approach. So we studied their entire suite of applications, understand from a regulatory perspective, from a compliance perspective, from a perspective of security. And this is a global bank. So there are different rules in Europe versus the United States. And so on. So based upon all that we come up with an approach on ward should reside in a private cloud as opposed to orchard resided in a public cloud. >>And you know, obviously there are multiple providers that are reasons to go with more than one cloud from a public perspective, et cetera. So we advise them on that. And then once we're able to do that, then we chart the journey on, you know, what application gets moved and when, and certain applications are very important to them from a performance perspective and they need to scale up and so on. So in those cases, you know, we treat them differently in certain other application cases, you know, we moved them onto a pass and in some other cases, you know, we just move these application into new what's available from a SAS perspective. So really it's a very much an application centric approach on where the workloads, >>is it a living and breathing thing. I mean, we talk about cloud being this journey. It's not a destination. Does this change over time? I mean, it absolutely does. I mean, it's constantly evolving. You know? Did you date or patterns are coming out there? And it was interesting like if you have, a couple of years ago, all we talked about was the app. How are you going to modernize this six or seminars when you get re-imagined in there. But all of a sudden a lot of the conversation shifting, shifting to my data strategy, where did this data reside? Particularly as the data sets get larger and that's moving from what was very centralized in the public data centers into much more distributed architecture. So we see it evolving very quickly. And even with a single application, a great example with a global hotel, they had a reservation system that was strategic application running on an old IBM mainframe. >>They were finding it was just taking too long to get innovative and agile to really support the new mobile applications, their web channels. So they looked at the, Hey, if I were just re-imagine, rewrite this, you know, go put it up in Amazon hot, how long would it take? How much would it cost? They found it was even going to be two or three years and they didn't have the luxury to wait, so they basically wrap that application with API APIs. They expose it with microservices and then developed a cloud native front end with the database cache new technologies that they can now drop every three or four days. They can now get a new mobile application, so you've got agile delivery, they've still got the legacy stuff there. The data's still there now. Now when you go against their web and mobile application, you, you're actually browsing against that new cloud native, you know the database cache. >>When you look at rates and rooms, et cetera, it's only when you transact and reserve the room that it goes back to the main frame. So you agile delivery, they get a lot of the benefits there and then they can offload a lot of the processing on the mainframe. It's only read all the reads up front now and slowly deprecate that over time. Now they, they're now that very interesting hybrid deployment architectures. They, they have a container approach. This database caches, they have two clusters. Those containers running on private cloud, on VMware, and then one running in AWS so they're now can optimize across the public and or hybrid footprint. You know how they do this over the time they then look to evolve that application and started an introduce serverless. So starting to take advantage of Lambda. So there even within a single application you can see it's constantly evolving. They starting in an advantage of the next evolution. So the next one to move beyond containers into more serverless. >>So I mean you both have just given me terrific examples. Your years in financial services, years as a, as a hotel chain. How do you develop best practices? I mean when you're working with clients or really is it case by case? I mean you talked about the decisions and the allocation of cloud. Of course there are regulatory constraints for each company, but are there industry wide best practices? Absolutely. Absolutely. >>I do want to hear, sure. So I'll start with financial services as an example. So if you take the world of banking or insurance, there's obviously, you know, regulatory requirements, compliance requirements that they have to look at. And also if you look at, for example, in banking, a lot of it is very mainframe based. You know, they have very old core banking systems, which is not possible to really move all of that onto public cloud necessarily. And maybe the business case isn't even there. So we have to take a digital decoupling type of approach and figure out, you know, how do we take some of the best bits and then put that on public cloud while still keeping some of the major amount of data is still on the legacy side. So I think it's really an industry specific approach, which on top of which you have to layer on what the local requirements are. >>Yeah, absolutely. I mean, so I spent a lot of work with the life sciences department ceuticals and GXP compliance rules the day there. So how did they start to, they started off, you know, how do we get to private clouds that look and feel a lot like public and then start to move and test the test the waters as they start to migrate into the AWS as a little world. So it is very regular industry specific. So we've, we basically developed a library of this is how you know, this is how you solve for banking, you know, the retail bank on a mainframe, how do I do similar pattern to that hotel front ends. Reinsurance is actually very similar to a lot of, lot of the logic on mainframe. It doesn't make sense to rewrite. There's not enough value in that if I can wrap it in microservices and move out. So that clouds, I mean their roadmaps, obviously every given the scale and complexity of our clients, everyone's going to be slightly different. But they're patterns that are very industry specific. And it gets getting even more interesting as we start to get in, move into IOT and edge. Because when you get into like the, the connected oil and gas or connected mine, all of a sudden it becomes extremely verticalized in industry and industry. >>So the hybrid Creek solves a lot of problems, but I imagine it also creates some other challenges too. What are the challenges and how do you counteract them? Do you want to start Larry? >>Yeah, sure. I mean it's not just hybrid. I think it's, we have much more complex architectures. So with all the power of digital decoupling, you know, creating microservices architectures, being able to pick best of breed services as everything becomes much more dynamic and ephemeral. So we moving for a world where a server in a virtual machine would be up for like 12 months or 15 months to containers that have lifespans of minutes or hours or even seconds now managed in a much more dynamic way with Kubernetes. So that that hotel was 15,000 containers managed by Kubernetes optimized over that environment and even more with serverless. So you've got a very complex environment to manage. And I think as our clients start to really evolve that the application portfolios, you know, SAS cloud, native wrapping legacy, the ability to then seamlessly manage across that environment and optimize and, and even the optimization in the old days we used to optimize around one or two dimensions was a cost or performance and SLA is now we've got to simultaneously take this very complex environment and optimize across performance, service levels, security, compliance and cost. >>So it's not just about cost optimization and they offset each other. If I'm optimizing for performance and service levels, my cost probably goes up. So it becomes a very complex problem. So spend a lot of time looking first. First starting with the operating model, you'd got to operate differently. How do you really get dev sec ops involved so that operations and security baked in, right? When you do the analysis so you're not building something that can't be secure operated and then really transforming the people, right? I mean the one of the hardest thing and place where our clients struggle the most is how do they upskill their organization, change the culture, the behavior. Because great example is we look at how we operate cloud and infrastructure. We want to turn all operators from eyes on glass looking at consoles into developers who are writing the next analytic algorithms to figure out predictive operation, to doing the automation script, to take, to remove mistakes, streamline drive agility, and reduce costs. And ultimately to tune the AI engines that are going to need it to do that. Complex optimization across very, very, very complex architectures. >>So you're talking about the people and the change management. I want to ask about innovation within Accenture and AWS. We heard Andy Jassy this morning in his fireside chat talk about how the company is now obviously a ginormous company, but how it really still has this startup mentality and how and how and what he does to ensure that innovation is still a priority, a relentless focus on the customer, de-centralized nature of the organization. Really focusing on what you're good at, knowing what's in your wheelhouse, how do you think about innovation and hadn't then how do you help clients make sure that they are bringing people along and as you said, give, giving them the sort of developer mindset. Do you want to, >>yeah, so that's absolutely important for us, innovation is very much part of our culture. When I look at my own group, which is intelligent cloud and infrastructure, what we do is we have 30,000 people who are working everyday at our clients and in many ways they understand the client's landscape even better than anybody else because they're working there every day. So some of the things that we do with our clients, our innovation forums where our people bring forward ideas to suggest to clients. So this is something that we do on a regular basis, take it to our clients, CEOs, and invariably several of these ideas actually get put into their whole plan for the following year. So we go innovate with our clients. We've created several digital studios and liquid studios in multiple locations. We've got several of them across the globe. So we bring our clients in for design thinking workshops, suggest ideas and you know, and await with them on things that we can take forward. >>Yeah, and just just to further that, I mean we've always driven it an innovation agenda. We always try and figure out what's next, where do we invest? What's the next bet? I mean, even going back to our cloud first days, I mean we've always been pushing the envelope. I think what Prisaad hit on is a really a step change for us in how we engage. I think in the past we'd come in as the consultants present, et cetera. And I think our clients are bright people. So actually engaging with them and more design thinking. I, we acquired a bunch of digital studios, Fjord and stuff, and I think they've infused this culture of coasts, co-sourcing, collaboration, and creating together. And the dynamic just changes and it's fantastic when the client then is in the room. They own it with you, they close, co-creating. And I think we've been spending a lot of time on how do we transform how we interact and engage with our clients in a much more collaborative way. And it's really changed the changes your relationship with them >>and the technology enables that to Larry and Priscilla, thank you so much for coming on the cube. A really great conversation. Thank you, Rebecca. I'm Rebecca night's stay tuned for more of the cubes live coverage of the Accenture executive summit. Coming up in just a little bit.

>> from atop the Salesforce Tower in downtown San Francisco. It's the Q covering Accenture Innovation Date brought to you by ex center >> Hey, welcome back Your body jefe Rick here from the Cube were high atop San Francisco in the essential innovation hub. It's in the middle of the Salesforce Tower. It's a beautiful facility. They think you had it. The grand opening about six months ago. We're here for the grand opening. Very cool space. I got maker studios. They've got all kinds of crazy stuff going on. But we're here today to talk about Cloud in this continuing evolution about cloud in the enterprise and hybrid cloud and multi cloud in Public Cloud and Private Cloud. And we're really excited to have a couple of guys who really helping customers make this journey, cause it's really tough to do by yourself. CEOs are super busy. They worry about security and all kinds of other things. So centers, often a trusted partner. We got two of the leaders from center joining us today's Prasad Sankaran. He's the senior managing director of Intelligent Cloud infrastructure for Center Welcome and Larry Soccer, the global managing director. Intelligent cloud infrastructure offering from central gentlemen. Welcome. I love it. It intelligent cloud. What is an intelligent cloud all about? Got it in your title. It must mean something pretty significant. >> Yeah, I think First of all, thank you for having us, but you're absolutely Everything's around becoming more intelligent around using more automation. And the work that, you know we delivered to our clients and cloud, as you know, is the platform to which all of our clients are moving. So it's all about bringing the intelligence not only into infrastructure, but also into cloud generally. And it's all driven by software, >> right? It's just funny to think where we are in this journey. We talked a little bit before we turn the cameras on and there you made an interesting comment when I said, You know, when did this cloud for the Enterprise start? And you took it back to sass based applications, which, >> you know, you were sitting in the sales force builder. >> That's true. It isn't just the tallest building in here, and everyone all right, everyone's >> had a lot of focus on AWS is rise, etcetera. But the real start was really getting into sass. I mean, I remember We used to do a lot of Siebel deployments for CR M, and we started to pivot to sales, for some were moving from remedy into service. Now I mean, we went through on premise collaboration, email todo 360 5 So So we've actually been at it for quite a while in the particularly the SAS world. And it's only more recently that we started to see that kind of push to the, you know, the public pass, and it's starting to cloud native development. But But this journey started, you know, it was that 78 years ago that we really start to see some scale around it >> and tell me if you agree. I think really, what? The sales forces of the world and the service now is of the world off. 3 65 kind of broke down some of those initial barriers which were all really about security and security. Security secure. It's always too here where now security is actually probably an attribute >> and loud can brink Absolutely. In fact, I'm in those barriers took years to bring down. I still saw clients where they were forcing salesforce tor service. Now to put you know instances on Prime, and I think I think they finally woke up toe. You know, these guys invested ton in their security organizations. You know, there's a little of that needle in the haystack. You know, if you breach a data set, you know what you're getting after. But when you happen to sail sports, it's a lot harder. And so you know. So I think that security problems, I've certainly got away. We still have some compliance, regulatory things, data sovereignty. But I think security and not not that it's all by any means that you know, it's always giving an ongoing problem. But I think they're getting more comfortable with their data being up in the public domain, right? Not public. >> I think it also help them with their progress towards getting cloud native. So, you know, you pick certain applications which were obviously hosted by sales force and other companies, and you did some level of custom development around it. And now I think that's paved the way for more complex applications and different workloads now going into, you know, the public cloud and the private cloud. But that's the next part of the journey, >> right? So Let's back up 1/2 a step cause then, as you said, a bunch of stuff then went into Public Cloud, right? Everyone's putting in AWS and Google. Um, IBM has got a public how there was a lot more. They're not quite so many as there used to be. Um, but then we ran into a whole new home, Those of issues, right, Which is kind of opened up this hybrid cloud. This multi cloud world, which is you just can't put everything into a public clouds there certain attributes that you need to think about and yet from the application point of view, before you decide where you deploy that. So I'm just curious. If you can share now, would you guys do with clients? How should they think about applications? How, after they think about what to deploy where I >> think I'll start in the, You know, Larry has a lot of expertise in this area. I think you know, we have to obviously start from an application centric perspectives. You got to take a look at you know where your applications have to live water. What are some of the data implications on the applications or do you have by way of regulatory and compliance issues? Or do you have to do as faras performance because certain applications have to be in a high performance environment? Certain other applications don't think a lot of these factors will then drive where these applications need to recite. And then what we're seeing in today's world is really accomplish. Complex, um, situation where you have a lot of legacy, but you also have private as well as public cloud. So you approach it from an application perspective. >> Yeah. I mean, if you really take a look at Army, you look at it centers clients, and we were totally focused on up into the market Global 2000 savory. You know, clients typically have application portfolios ranging from 520,000 applications. And really, I mean, if you think about the purpose of cloud or even infrastructure for that, they're there to serve the applications. No one cares if your cloud infrastructure is not performing the absolute. So we start off with an application monetization approach and ultimately looking, you know, you know, with our tech advisory guys coming in, there are intelligent engineering service is to do the cloud native and at mod work our platforms. Guys, who do you know everything from sales forward through ASAP. They should drive a strategy on how those applications going to evolve with its 520,000 and determined hey, and usually using some like the six orders methodology. And I'm I am I going to retire this Am I going to retain it? And I'm gonna replace it with sass. Am I gonna re factor in format? And it's ultimately that strategy that's really gonna dictate a multi in and, you know, hybrid cloud story. So it's based on the applications data, gravity issues where they gonna reside on their requirements around regulatory, the requirements for performance, et cetera. That will then dictate the cloud strategies. I'm you know, not a big fan of going in there and just doing a multi hybrid cloud strategy without a really good up front application portfolio approach, right? How we're gonna modernize that >> it hadn't had a you segment. That's a lot of applications. And you know, how do you know the old thing? How do you know that one by that time, how do you help them pray or size? Where they should be focusing on. Yes, >> it. Typically, what we do is work with our clients to do a full application portfolio analysis, and then we're able to then segment the applications based on, you know, important to the business and some of the factors that both of us mentioned. And once we have that, then we come up with an approach where certain sets of applications have moved to sass certain other applications you moved past. So you know, you're basically doing the re factoring and the modernization, and then certain others, you know, you can just, you know, lift and shift. So it's really a combination off both modernization as well as migration. It's a combination off that, but to do that, you have initially look at the entire set of applications and come up with that approach. >> I'm just curious where within that application assessment, where is cost savings? Where is, uh, this is just old and where is opportunities to innovate faster? Because we know a lot of lot of talk really. Days has cost savings, but what the real advantages is execution speed if you can get it. >> If >> you could go back three or four years and we had there was a lot of CEO discussions around cost savings. I'm not really have seen our clients shift. It costs never goes away, obviously right. But there's a lot greater emphasis now on business agility. You know, howto innovate faster, get, get new capabilities, market faster to change my customer experience. So it's really I t is really trying to step up and, you know, enabled the business toe to compete in the marketplace. So we're seeing a huge shift in emphasis or focus at least starting with, you know, how do I get better business agility outta leverage to cloud and cloud native development to get there upper service levels? Actually, we started seeing increase on Hey, you know, these applications need to work. It's actress, So obviously cost still remains a factor, but we seem much more, you know, much more emphasis on agility, you know, enabling the business on giving the right service levels of right experience to the user. Little customers. Big pivot there, >> Okay. And let's get the definitions out because you know a lot of lot of conversation about public clouds. Easy private clouds, easy but hybrid cloud and multi cloud and confusion about what those are. How do you guys define them? How do you help your customers think about the definition? Yes, >> I think it's a really good point. So what we're starting to see is there were a lot of different definitions out there. But I think as I talk to my clients and our partners, I think we're all starting to come toe. You know, the same kind of definition on multi cloud. It's really about using more than one cloud. But hybrid, I think, is a very important concept because hybrid is really all about the placement off the workload or where your application is going to run on. And then again, it goes to all of these points that we talked about data, gravity and performance and other things. Other factors. But it's really all about where do you place the specific workload >> if you look at that, so if you think about public, I mean obviously gives us the innovation of the public providers. You look at how fast Amazon comes out with new versions of Lambda etcetera, so that's the innovations. There obviously agility. You could spend up environments very quickly which is, you know, one of the big benefits of it. The consumption economic models. So that is the number of drivers that are pushing in the direction of public. You know, on the private side, they're still it's quite a few benefits that don't get talked about as much. Um, so you know, if you look at it performance, you know, if you think the public world, you know, although they're scaling up larger T shirts, et cetera, they're still trying to do that for a large array of applications on the private side, you can really Taylor somethingto very high performance characteristics. Whether it's you know, 30 to 64 terabyte Hana, you can get a much more focused precision environment for business critical workloads like that article, article rack. You know, the Duke clusters everything about fraud analysis. So that's a big part of it. Related to that is the data gravity that Prasad just mentioned. You know, if I've got a 64 terrified Hana database, you know, sitting in my private cloud, it may not be that convenient to go and put get that data shared up in red shift or in Google's tensorflow. So So there's some data gravity out. Networks just aren't there. The Laden sea of moving that stuff around is a big issue. And then a lot of people of investments in their data centers. I mean, the other piece, that's interesting. His legacy, you know, You know, as we start to look at the world a lot, there's a ton of Could still living in, You know, whether it's you, Nick system, that IBM mainframes. There's a lot of business value there, and sometimes the business cases aren't aren't necessarily there toe to replace them. Right. And in world of digital, the decoupling where I can start to use micro service is we're seeing a lot of trends. We worked with one hotel to take the reservation system. You know, Rapid and Micro Service is, um, we then didn't you know, open shift couch base, front end. And now when you go against, you know, when you go and browsing properties, you're looking at rates you actually going into distributed database cash on, you know, in using the latest cloud native technologies that could be dropped every two weeks or every three or four days for my mobile application and It's only when it goes, you know, when the transaction goes back, to reserve the room that it goes back there. So we're seeing a lot of power with digital decoupling, but we still need to take advantage of, you know, we've got these legacy applications. So So the data centers air really were trying to evolve them. And really, just, you know, how do we learn everything from the world of public and struck to bring those saints similar type efficiencies to the to the world of private? And really, what we're saying is this emerging approach where I can start to take advantage of the innovation cycles that land is that you know, the red shifts the azure functions of the public world. But then maybe keep some of my more business critical regulated workloads. You know, that's the other side of the private side, right? I've got G X p compliance. If I've got hip data that I need to worry about GDP are you know, the whole set of regular two requirements Over time, we do anticipate the public guys will get much better and more compliant. In fact, they made great headway already, but they're Still not a number of clients are still, you know, not 100% comfortable from rail client's perspective. >> Gotta meet Teresa Carlson. She'll change him. Who runs that AWS Public Sector is doing amazing things, obviously with big government contracts. But but you raise real inching point later. You almost described what I would say is really a hybrid application in this thing. This hotel example that you use because it's is, you know, kind of break in the application and leveraging micro service is to do things around the core that allowed to take advantage of some this agility and hyper fast development, yet still maintain that core stuff that either doesn't need to move Works fine. Be too expensive. Drea Factor. It's a real different weight. Even think about workloads and applications into breaking those things into bits. >> And we see that pattern all over the place. I'm gonna give you the hotel Example Where but finance, you know, look at financial service. Is retail banking so open banking a lot. All those rito applications are on the mainframe. I'm insurance claims and and you look at it, the business value, replicating a lot of like the regulatory stuff, the locality stuff. It doesn't make sense to write it. There's no rule inherent business values of I can wrap it, expose it and you know the micro service's architecture now. D'oh cloud native front end. That's gonna give me a 360 view a customer, Change the customer experience. You know, I've got a much you know, I can still get that agility. The the innovation cycles by public. Bye bye. Wrapping my legacy environment >> in person, you rated jump in and I'll give you something to react to, Which is which is the single glass right now? How do I How did I manage all this stuff now? Not only do I have distributed infrastructure now, I've got distributed applications and the thing that you just described and everyone wants to be that single pane of glass Everybody wants to be the app that's upon everybody. Screen. How are you seeing people deal with the management complexity of these kind of distributed infrastructures? If you will Yeah, >> I think that that's that's an area that's, ah, actually very topical these days because, you know, you're starting to see more and more workers. Goto private cloud and so you've got a hybrid infrastructure you're starting to see move movement from just using the EMS to, you know, the cantinas and Cuban Edie's. And, you know, we talked about Serval s and so on. So all of our clients are looking for a way, and you have different types of users as well. Yeah, developers. You have data scientists. You have, you know, operators and so on. So they're all looking for that control plane that allows them access and a view toe everything that is out there that is being used in the enterprise. And that's where I think you know, a company like Accenture were able to use the best of breed toe provide that visibility to our clients. >> Yeah. I mean, you hit the nail on the head. It's becoming, you know, with all the promise of cloud and all the power. And these new architectures is becoming much more dynamic, ephemeral, with containers and kubernetes with service computing that that one application for the hotel, they're actually started, and they've got some actually, now running a native us of their containers and looking at serverless. So you gonna even a single application can span that and one of things we've seen is is first. You know, a lot of our clients used to look at, you know, application management, you know, different from their their infrastructure. And the lines are now getting very blurry. You need to have very tight alignment. You take that single application. You know, if any my public side goes down or my mid tier with my you know, you know, open shipped on VM where it goes down on my back and mainframe goes down. Or the networks that connected to go down the devices that talked it. It's a very well, despite the power, very complex environment. So what we've been doing is first we've been looking at, you know, how do we get better synergy across what we you know, application service is teams that do the application manager an optimization cloud infrastructure, you know, how do we get better alignment that are embedded security, You know, how do you know what are managed to Security Service's and bringing those together? And then what we did was we looked at, you know, we got very aggressive of cloud for a strategy and, you know, how do we manage the world of public. But when looking at the public providers of hyper scale er's and how they hit incredible degrees of automation, we really looked at, said and said, Hey, look, you gotta operate differently in this new world. What can we learn from how the public guys they're doing that? We came up with this concept We call it running different. You know, how do you operate differently in this new multi speed? You know, you know, hot, very hybrid world across public, private demon, legacy environment and started looking say OK, what is it that they do? You know, first they standardize, and that's one of the big challenges you know, going to almost all of our clients in this a sprawl. And you know, whether it's application sprawl, its infrastructure, sprawl and >> my business is so unique. The Larry no business out there has the same process that we have. So we started make you know how to be >> standardized like center hybrid cloud solution apart with HP. Envy em where we, you know, how do we that was an example. So we can get thio because you can't automate unless you standardise. So that was the first thing you know, standardizing service catalog. Standardizing that, um, you know, the next thing is the operating model. They obviously operate differently. So we've been putting a lot of time and energy and what I call a cloud and agile operating model. And also a big part of that is truly you hear a lot about Dev ops right now, but truly putting the security and and operations into Deb set cops of bringing, you know, the development in the operations much tied together. So spending a lot of time looking at that and transforming operations re skilling the people you know, the operators of the future aren't eyes on glass there. Developers, they're writing the data ingestion, the analytic algorithms, you know, to do predictive operations. They're writing the automation script to take work, you know, test work out. Right. And over time, they'll be tuned in the air. Aye, aye. Engines to really optimize the environment and then finally has presided. Looted thio. Is that the platforms that control planes? That doing that? So, you know, we What we've been doing is we've had a significant investments in the eccentric cloud platform, our infrastructure automation platforms and then the application teams with it with our my wizard framework, and we've been starting to bring that together. You know, it's an integrated control plane that can plug into our clients environments to really manage seamlessly, you know, and provide, you know, automation Analytics. Aye, aye. Across APS, cloud infrastructure and even security. Right. And that, you know, that really is a iob is right. I mean, that's delivering on, you know, as the industry starts toe define and really coalesce around, eh? I ops, that's what we use. >> So just so I'm clear that so it's really your layer your software layer kind of management layer that that integrates all these different systems and provides kind of a unified view. Control, I reporting et cetera. Right >> Exactly. Then can plug in and integrate, you know, third party tools. I had to do some strategic function. >> I'm just I'm just >> curious is one of the themes that we here out in the press right now is this is this kind of pull back of public cloud app. Some of them are coming back. Or maybe it was, you know, kind of a rush. Maybe a little bit too aggressively. What are some of the reasons why people are pulling stuff back out of public clouds, that just with the wrong it was just the wrong application? The costs were not what we anticipated to be. We find it, you know, what are some of the reasons that you see after coming back in house? Yeah, >> I think it's >> a variety of factors. I mean, it's certainly cost, I think is one. So as there are multiple private options and you know, we don't talk about this, but the hyper skills themselves are coming out with their own different private options, like Aunt Ours and out pulls and other stack and on. And Ali Baba has obsessed I and so on. So you see a proliferation of that and you see many more options around private cloud. So I think the cost is certainly a factor. The second is I think data gravity is, I think, a very important point because as you're starting to see how different applications have to work together, then that becomes a very important point. The third is just about compliance, and, you know, the regulatory environment. As we look across the globe, you know, even outside the U. S. We look at Europe and other parts of Asia as clients and moving more to the cloud. You know, that becomes an important factor. So as you start to balance these things, I think you have to take a very application centric view. You see some of those some some maps moving back, and and I think that's the part of the hybrid world is that you know, you can have a nap running on the private cloud and then tomorrow you can move this. Since it's been containerized to run on public and it's, you know, it's all managed that look >> e. I mean, cost is a big factor if you actually look at it. Most of our clients, you know, they typically you were big cap ex businesses, and all of a sudden they're using this consumption consumption model. And they weren't really They didn't have a function to go and look at the thousands or millions of lines of it, right? You know, as your statement, exactly think they misjudged, you know, some of the scale on B e e. I mean, that's one of the reasons we started. It's got to be an application lead modernization that really that will dictate that. And I think in many cases, people didn't may not have thought through which application. What data? There The data, gravity data. Gravity's a conversation I'm having just by with every client right now. You know, I've got a 64 terabyte hana, and that's the core. My crown jewels. That data, you know, how do I get that to tensorflow? How'd I get that >> right? But if Andy was >> here, though, Andy would say, we'll send down the snow. The snow came from which virgin snow plows Snowball snowball. Well, they're snowballs. But we've seen the >> hold of a truck killer >> that comes out and he'd say, Take that and stick it in the cloud. Because if you've got that data in a single source right now, you can apply multitude of applications across that thing. So they you know they're pushing. Get that date end in this single source course than to move it, change it, you know you run it. All these micro lines of billing statement take >> the hotel. I mean, their data stolen the mainframe. So if they may want need to expose it? Yeah, they have a database cash, and they move it out. You know, the particulars of data sets get larger, it becomes, you know, the data. Gravity becomes a big issue. Because no matter how much you know, while Moore's law might be might have elongated from 18 to 24 months, the network will always be the bottle, Mac. So ultimately, we're seeing, you know, a CZ. We proliferate more and more data, all data sets get bigger and better than network becomes more of a bottleneck. Conned. That's a lot of times you gotta look at your applications. They have. I've got some legacy database I need to get. Thio. I need this to be approximately somewhere where I don't have, you know, high bandwith o r. Right Or, you know, highlight and see type or so egress costs a pretty big deals. My date is up in the cloud, and I'm gonna get charged for pulling it off. You know that That's been a big issue. >> You know, it's funny, I think, and I think a lot of the issue, obviously complexity building. It's a totally different building model, but I think to a lot of people will put stuff in a public cloud and then operated as if they bought it. And they're running in the data center in this kind of this. Turn it on, turn it off when you need it. Everyone turns. Everyone loves to talk about the example turning it on when you need it. But nobody ever talks about turning it off when you don't. But but the kind of clothes on our conversation I won't talk about a I and applied a I. CoSine is a lot of talk in the market place, but a time machine learning. But as you guys know pride better than anybody, it's the application of a I and specific applications, which really on unlocks the value. And as we're sitting here talking about this complexity, I can't help but think that, you know, applied a I in a management layer like your run differently, set up to actually know when to turn things on, when to turn things off when you moved in but not moved, it's gonna have to be machines running that right cause the data sets and the complexity of these systems is going to be just overwhelming. Yeah, yeah, >> absolutely completely agree with you in fact. Ah, essential. We actually referred to the Seoul area as Applied intelligence. Ah, and that's our guy, right? And, uh, it is absolutely to add more and more automation Move everything Maur toe where it's being run by the machine rather than, you know, having people really working on these things >> yet, e I mean, if you think you hit the nail on the head, we're gonna a eyes e. I mean, given how things getting complex, more ephemeral, you think about kubernetes et cetera. We're gonna have to leverage a humans or not to be able to get, you know, manage this. The environment is important, right? What's interesting way we've used quite effectively for quite some time. But it's good at some stuff, not good at others. So we find it's very good at, like, ticket triage, like ticket triage, chicken routing, et cetera. You know, any time we take over account, we tune our AI ai engines. We have ticket advisers, etcetera. That's what probably got the most, you know, most bang for the buck. We tried in the network space. Less success to start even with, you know, commercial products that were out there. I think where a I ultimately bails us out of this is if you look at the problem. You know, a lot of times we talked about optimizing around cost, but then performance. I mean, and it's they they're somewhat, you know, you gotta weigh him off each other. So you've got a very multi dimensional problem on howto I optimize my workloads, particularly. I gotta kubernetes cluster and something on Amazon, you know, sums running on my private cloud, etcetera. So we're gonna get some very complex environment. And the only way you're gonna be ableto optimize across multi dimensions that cost performance service levels, you know, and then multiple options don't do it public private, You know, what's my network costs etcetera. Isn't a I engine tuning that ai ai engines? So ultimately, I mean, you heard me earlier on the operators. I think you know, they write the analytic albums, they do the automation scripts, but they're the ultimate ones who then tune the aye aye engines that will manage our environment, right. And I think it kubernetes will be interesting because it becomes a link to the control plane optimize workload placement between >> when the best thing to you. Then you have dynamic optimization can. You might be up to my tanks at us right now, but you might be optimizing for output the next day. So exists really a you know, kind of Ah, never ending >> when you got you got to see them >> together with it. And multi dimension optimization is very difficult. So I mean, you know, humans can't get their head around. Machines can, but they need to be trained. >> Well, Prasad, Larry, Lots of great opportunities for for centuries bring that expertise to the table. So thanks for taking a few minutes to walk through some of these things. Our pleasure. Thank you. Raise Prasad is Larry. I'm Jeff. You're watching the Cube. We are high above San Francisco in the Salesforce Tower. Theis Center. Innovation have in San Francisco. Thanks for watching. We'll see you next time

>> Hey, welcome back. Your body, Jefe Rick here from the Cube were high atop San Francisco in the century innovation hub. It's in the middle of the Salesforce Tower. It's a beautiful facility. They think you had it. The grand opening about six months ago. We're here for the grand opening. Very cool space. I got maker studios. They've got all kinds of crazy stuff going on. But we're here today to talk about Cloud in this continuing evolution about cloud in the enterprise and hybrid cloud and multi cloud in Public Cloud and Private Cloud. And we're really excited to have a couple of guys who really helping customers make this journey, cause it's really tough to do by yourself. CEOs are super busy. There were about security and all kinds of other things, so centers, often a trusted partner. We got two of the leaders from center joining us today's Prasad Sankaran. He's the senior managing director of Intelligent Cloud infrastructure for Center Welcome and Larry Soccer, the global managing director. Intelligent cloud infrastructure offering from central gentlemen. Welcome. I love it. It intelligent cloud. What is an intelligent cloud all about? Got it in your title. It must mean something pretty significant. >> Yeah, I think First of all, thank you for having us, but yeah, absolutely. Everything's around becoming more intelligent around using more automation. And the work that, you know we delivered to our clients and cloud, as you know, is the platform to reach. All of our clients are moving. So it's all about bringing the intelligence not only into infrastructure, but also into cloud generally. And it's all driven by software, >> right? It's just funny to think where we are in this journey. We talked a little bit before we turn the cameras on and there you made an interesting comment when I said, You know, when did this cloud for the Enterprise start? And you took it back to sass based applications, which, >> you know you were sitting in the sales force builder. >> That's true. It isn't just the tallest building in >> everyone's, you know, everyone's got a lot of focus on AWS is rise, etcetera. But the real start was really getting into sass. I mean, I remember we used to do a lot of Siebel deployments for CR M, and we started to pivot to sales, for some were moving from remedy into service now. I mean, we've went through on premise collaboration, email thio 3 65 So So we've actually been at it for quite a while in the particularly the SAS world. And it's only more recently that we started to see that kind of push to the, you know, the public pass, and it's starting to cloud native development. But But this journey started, you know, it was that 78 years ago that we really started. See some scale around it. >> And I think and tell me if you agree, I think really, what? The sales forces of the world and and the service now is of the world office 3 65 kind of broke down some of those initial beers, which are all really about security and security, security, security, Always to hear where now security is actually probably an attributes and loud can brink. >> Absolutely. In fact, I mean, those barriers took years to bring down. I still saw clients where they were forcing salesforce tor service Now to put, you know, instances on prime and I think I think they finally woke up toe. You know, these guys invested ton in their security organizations. You know there's a little of that needle in the haystack. You know, if you breach a data set, you know what you're getting after. But when Europe into sales force, it's a lot harder. And so you know. So I think that security problems have certainly gone away. We still have some compliance, regulatory things, data sovereignty. But I think security and not not that it sold by any means that you know, it's always giving an ongoing problem. But I think they're getting more comfortable with their data being up in the in the public domain, right? Not public. >> And I think it also helped them with their progress towards getting cloud native. So, you know, you pick certain applications which were obviously hosted by sales force and other companies, and you did some level of custom development around it. And now I think that's paved the way for more complex applications and different workloads now going into, you know, the public cloud and the private cloud. But that's the next part of the journey, >> right? So let's back up 1/2 a step, because then, as you said, a bunch of stuff then went into public cloud, right? Everyone's putting in AWS and Google. Um, IBM has got a public how there was a lot more. They're not quite so many as there used to be, Um, but then we ran into a whole new host of issues, right, which is kind of opened up this hybrid cloud. This multi cloud world, which is you just can't put everything into a public clouds. There's certain attributes is that you need to think about and yet from the application point of view before you decide where you deploy that. So I'm just curious. If you can share now, would you guys do with clients? How should they think about applications? How should they think about what to deploy where I think >> I'll start in? The military has a lot of expertise in this area. I think you know, we have to obviously start from an application centric perspective. You go to take a look at you know where your applications have to live water. What are some of the data implications on the applications, or do you have by way of regulatory and compliance issues, or do you have to do as faras performance because certain applications have to be in a high performance environment. Certain other applications don't think a lot of these factors will. Then Dr where these applications need to recite and then what we think in today's world is really accomplish. Complex, um, situation where you have a lot of legacy. But you also have private as well as public cloud. So you approach it from an application perspective. >> Yeah. I mean, if you really take a look at Army, you look at it centers clients, and we were totally focused on up into the market Global 2000 savory. You know how clients typically have application portfolios ranging from 520,000 applications? And really, I mean, if you think about the purpose of cloud or even infrastructure for that, they're there to serve the applications. No one cares if your cloud infrastructure is not performing the absolute. So we start off with an application monetization approach and ultimately looking, you know, you know, with our tech advisory guys coming in, there are intelligent engineering service is to do the cloud native and at mod work our platforms, guys, who do you know everything from sales forward through ASAP. They should drive a strategy on how those applications gonna evolve with its 520,000 and determined hey, and usually using some, like the six orders methodology. And I'm I am I going to retire this Am I going to retain it? And, you know, I'm gonna replace it with sass. Am I gonna re factor in format? And it's ultimately that strategy that's really gonna dictate a multi and, you know, every cloud story. So it's based on the applications data, gravity issues where they gonna reside on their requirements around regulatory, the requirements for performance, etcetera. That will then dictate the cloud strategies. I'm you know, not a big fan of going in there and just doing a multi hybrid cloud strategy without a really good up front application portfolio approach, right? How we gonna modernize that >> it had. And how do you segment? That's a lot of applications. And you know, how do you know the old thing? How do you know that one by that time, how do you help them pray or size where they should be focusing on us? >> So typically what we do is work with our clients to do a full application portfolio analysis, and then we're able to then segment the applications based on, you know, important to the business and some of the factors that both of us mentioned. And once we have that, then we come up with an approach where certain sets of applications he moved to sass certain other applications you move to pass. So you know, you're basically doing the re factoring and the modernization and then certain others you know, you can just, you know, lift and shift. So it's really a combination off both modernization as well as migration. It's a combination off that, but to do that, you have to initially look at the entire set of applications and come up with that approach. >> I'm just curious where within that application assessment, um, where is cost savings? Where is, uh, this is just old. And where is opportunities to innovate faster? Because we know a lot of lot of talk really. Days has cost savings, but what the real advantages is execution speed if you can get it. If >> you could go back through four years and we had there was a lot of CEO discussions around cost savings, I'm not really have seen our clients shift. It costs never goes away, obviously right. But there's a lot greater emphasis now on business agility. You know, howto innovate faster, get getting your capabilities to market faster, to change my customer experience. So So it's really I t is really trying to step up and, you know, enabled the business toe to compete in the marketplace. We're seeing a huge shift in emphasis or focus at least starting with, you know, how'd I get better business agility outta leverage to cloud and cloud native development to get their upper service levels? Actually, we started seeing increase on Hey, you know, these applications need to work. It's actress. So So Obviously, cost still remains a factor, but we seem much more for, you know, much more emphasis on agility, you know, enabling the business on, given the right service levels of right experience to the user, little customers. Big pivot there, >> Okay. And let's get the definitions out because you know a lot of lot of conversation about public clouds, easy private clouds, easy but hybrid cloud and multi cloud and confusion about what those are. How do you guys define him? How do you help your customers think about the definition? Yes, >> I think it's a really good point. So what we're starting to see is there were a lot of different definitions out there. But I think as I talked more clients and our partners, I think we're all starting to, you know, come to ah, you know, the same kind of definition on multi cloud. It's really about using more than one cloud. But hybrid, I think, is a very important concept because hybrid is really all about the placement off the workload or where your application is going to run on. And then again, it goes to all of these points that we talked about data, gravity and performance and other things. Other factors. But it's really all about where do you place the specific look >> if you look at that, so if you think about public, I mean obviously gives us the innovation of the public providers. You look at how fast Amazon comes out with new versions of Lambda etcetera. So that's the innovations there obviously agility. You could spend up environments very quickly, which is, you know, one of the big benefits of it. The consumption, economic models. So that is the number of drivers that are pushing in the direction of public. You know, on the private side, they're still it's quite a few benefits that don't get talked about as much. Um, so you know, if you look at it, um, performance if you think the public world, you know, Although they're scaling up larger T shirts, et cetera, they're still trying to do that for a large array of applications on the private side, you can really Taylor somethingto very high performance characteristics. Whether it's you know, 30 to 64 terabyte Hana, you can get a much more focused precision environment for business. Critical workloads like that article, article rack, the Duke clusters, everything about fraud analysis. So that's a big part of it. Related to that is the data gravity that Prasad just mentioned. You know, if I've got a 64 terabyte Hana database you know, sitting in my private cloud, it may not be that convenient to go and put get that data shared up in red shift or in Google's tensorflow. So So there's some data gravity out. Networks just aren't there. The laden sea of moving that stuff around is a big issue. And then a lot of people of investments in their data centers. I mean, the other piece, that's interesting. His legacy, you know, you know, as we start to look at the world a lot, there's a ton of code still living in, You know, whether it's you, nick system, just IBM mainframes. There's a lot of business value there, and sometimes the business cases aren't aren't necessarily there toe to replace them. Right? And in world of digital, the decoupling where I can start to use micro service is we're seeing a lot of trends. We worked with one hotel to take their reservation system. You know, Rapid and Micro Service is, um, we then didn't you know, open shift couch base, front end. And now, when you go against, you know, when you go and browsing properties, you're looking at rates you actually going into distributed database cash on, you know, in using the latest cloud native technologies that could be dropped every two weeks or everything three or four days for my mobile application. And it's only when it goes, you know, when the transaction goes back, to reserve the room that it goes back there. So we're seeing a lot of power with digital decoupling, But we still need to take advantage of, you know, we've got these legacy applications. So So the data centers air really were trying to evolve them. And really, just, you know, how do we learn everything from the world of public and struck to bring those saints similar type efficiencies to the to the world of private? And really, what we're seeing is this emerging approach where I can start to take advantage of the innovation cycles. The land is that, you know, the red shifts the functions of the public world, but then maybe keep some of my more business critical regulated workloads. You know, that's the other side of the private side, right? I've got G X p compliance. If I've got hip, a data that I need to worry about GDP are there, you know, the whole set of regular two requirements. Now, over time, we do anticipate the public guys will get much better and more compliant. In fact, they made great headway already, but they're still not a number of clients are still, you know, not 100% comfortable from my client's perspective. >> Gotta meet Teresa Carlson. She'll change him, runs that AWS public sector is doing amazing things, obviously with big government contracts. But but you raise real inching point later. You almost described what I would say is really a hybrid application in this in this hotel example that you use because it's is, you know, kind of breaking the application and leveraging micro service is to do things around the core that allowed to take advantage of some this agility and hyper fast development, yet still maintain that core stuff that either doesn't need to move. Works fine, be too expensive. Drea Factor. It's a real different weight. Even think about workloads and applications into breaking those things into bits. >> And we see that pattern all over the place. I'm gonna give you the hotel Example Where? But finance, you know, look at financial service. Is retail banking so open banking a lot. All those rito applications are on the mainframe. I'm insurance claims and and you look at it the business value of replicating a lot of like the regulatory stuff, the locality stuff. It doesn't make sense to write it. There's no rule inherent business values of I can wrap it, expose it and in a micro service's architecture now D'oh cloud native front end. That's gonna give me a 360 view a customer, Change the customer experience. You know, I've got a much you know, I can still get that agility. The innovation cycles by public. Bye bye. Wrapping my legacy environment >> and percent you raided, jump in and I'll give you something to react to, Which is which is the single planet glass right now? How do I How did I manage all this stuff now? Not only do I have distributed infrastructure now, I've got distributed applications in the and the thing that you just described and everyone wants to be that single pane of glass. Everybody wants to be the app that's upon everybody. Screen. How are you seeing people deal with the management complexity of these kind of distributed infrastructures? If you will Yeah, >> I think that that's that's an area that's, ah, actually very topical these days because, you know, you're starting to see more and more workers go to private cloud. And so you've got a hybrid infrastructure you're starting to see move movement from just using the EMS to, you know, cantinas and Cuba needs. And, you know, we talked about Serval s and so on. So all of our clients are looking for a way, and you have different types of users as well. Yeah, developers. You have data scientists. You have, you know, operators and so on. So they're all looking for that control plane that allows them access and a view toe everything that is out there that is being used in the enterprise. And that's where I think you know, a company like Accenture were able to use the best of breed toe provide that visibility to our clients, >> right? Yeah. I mean, you hit the nail on the head. It's becoming, you know, with all the promises, cloud and all the power. And these new architectures is becoming much more dynamic, ephemeral, with containers and kubernetes with service computing that that that one application for the hotel, they're actually started in. They've got some, actually, now running a native us of their containers and looking at surveillance. So you're gonna even a single application can span that. And one of things we've seen is is first, you know, a lot of our clients used to look at, you know, application management, you know, different from their their infrastructure. And the lines are now getting very blurry. You need to have very tight alignment. You take that single application, if any my public side goes down or my mid tier with my you know, you know, open shipped on VM, where it goes down on my back and mainframe goes down. Or the networks that connected to go down the devices that talk to it. It's a very well. Despite the power, it's a very complex environment. So what we've been doing is first we've been looking at, you know, how do we get better synergy across what we you know, Application Service's teams that do that Application manager, an optimization cloud infrastructure. How do we get better alignment that are embedded security, You know, how do you know what are managed to security service is bringing those together. And then what we did was we looked at, you know, we got very aggressive with cloud for a strategy and, you know, how do we manage the world of public? But when looking at the public providers of hyper scale, er's and how they hit Incredible degrees of automation. We really looked at, said and said, Hey, look, you gotta operate differently in this new world. What can we learn from how the public guys we're doing that We came up with this concept. We call it running different. You know, how do you operate differently in this new multi speed? You know, you know, hot, very hybrid world across public, private demon, legacy, environment, and start a look and say, OK, what is it that they do? You know, first they standardize, and that's one of the big challenges you know, going to almost all of our clients in this a sprawl. And you know, whether it's application sprawl, its infrastructure, sprawl >> and my business is so unique. The Larry no business out there has the same process that way. So >> we started make you know how to be standardized like center hybrid cloud solution important with hp envy And where we how do we that was an example of so we can get to you because you can't automate unless you standardise. So that was the first thing you know, standardizing our service catalog. Standardizing that, um you know, the next thing is the operating model. They obviously operate differently. So we've been putting a lot of time and energy and what I call a cloud and agile operating model. And also a big part of that is truly you hear a lot about Dev ops right now. But truly putting the security and and operations into Deb said cops are bringing, you know, the development in the operations much tied together. So spending a lot of time looking at that and transforming operations re Skilling the people you know, the operators of the future aren't eyes on glass there. Developers, they're writing the data ingestion, the analytic algorithms, you know, to do predictive operations. They're riding the automation script to take work, you know, test work out right. And over time they'll be tuning the aye aye engines to really optimize environment. And then finally, has Prasad alluded to Is that the platforms that control planes? That doing that? So, you know what we've been doing is we've had a significant investments in the eccentric cloud platform, our infrastructure automation platforms, and then the application teams with it with my wizard framework, and we started to bring that together you know, it's an integrated control plane that can plug into our clients environments to really manage seamlessly, you know, and provide. You know, it's automation. Analytics. Aye, aye. Across APS, cloud infrastructure and even security. Right. And that, you know, that really is a I ops, right? I mean, that's delivering on, you know, as the industry starts toe define and really coalesce around, eh? I ops. That's what we you A ups. >> So just so I'm clear that so it's really your layer your software layer kind of management layer that that integrates all these different systems and provides kind of a unified view. Control? Aye, aye. Reporting et cetera. Right? >> Exactly. Then can plug in and integrate, you know, third party tools to do straight functions. >> I'm just I'm just curious is one of the themes that we here out in the press right now is this is this kind of pull back of public cloud app, something we're coming back. Or maybe it was, you know, kind of a rush. Maybe a little bit too aggressively. What are some of the reasons why people are pulling stuff back out of public clouds that just with the wrong. It was just the wrong application. The costs were not what we anticipated to be. We find it, you know, what are some of the reasons that you see after coming back in house? Yeah, I think it's >> a variety of factors. I mean, it's certainly cost, I think is one. So as there are multiple private options and you know, we don't talk about this, but the hyper skills themselves are coming out with their own different private options like an tars and out pulls an actor stack and on. And Ali Baba has obsessed I and so on. So you see a proliferation of that, then you see many more options around around private cloud. So I think the cost is certainly a factor. The second is I think data gravity is, I think, a very important point because as you're starting to see how different applications have to work together, then that becomes a very important point. The third is just about compliance, and, you know, the regulatory environment. As we look across the globe, even outside the U. S. We look at Europe and other parts of Asia as clients and moving more to the cloud. You know that becomes an important factor. So as you start to balance these things, I think you have to take a very application centric view. You see some of those some some maps moving back, and and I think that's the part of the hybrid world is that you know, you can have a nap running on the private cloud and then tomorrow you can move this. Since it's been containerized to run on public and it's, you know, it's all managed. That left >> E. I mean, cost is a big factor if you actually look at it. Most of our clients, you know, they typically you were a big cap ex businesses, and all of a sudden they're using this consumption, you know, consumption model. And they went, really, they didn't have a function to go and look at be thousands or millions of lines of it, right? You know, as your statement Exactly. I think they misjudged, you know, some of the scale on Do you know e? I mean, that's one of the reasons we started. It's got to be an application led, you know, modernization, that really that will dictate that. And I think In many cases, people didn't. May not have thought Through which application. What data? There The data, gravity data. Gravity's a conversation I'm having just by with every client right now. And if I've got a 64 terabyte Hana and that's the core, my crown jewels that data, you know, how do I get that to tensorflow? How'd I get that? >> Right? But if Andy was here, though, and he would say we'll send down the stove, the snow came from which virgin snow plows? Snowball Snowball. Well, they're snowballs. But I have seen the whole truck killer that comes out and he'd say, Take that and stick it in the cloud. Because if you've got that data in a single source right now, you can apply multitude of applications across that thing. So they, you know, they're pushing. Get that date end in this single source. Of course. Then to move it, change it. You know, you run into all these micro lines of billing statement, take >> the hotel. I mean, their data stolen the mainframe, so if they anyone need to expose it, Yeah, they have a database cash, and they move it out, You know, particulars of data sets get larger, it becomes, you know, the data. Gravity becomes a big issue because no matter how much you know, while Moore's Law might be might have elongated from 18 to 24 months, the network will always be the bottle Mac. So ultimately, we're seeing, you know, a CZ. We proliferate more and more data, all data sets get bigger and better. The network becomes more of a bottleneck. And that's a It's a lot of times you gotta look at your applications. They have. I've got some legacy database I need to get Thio. I need this to be approximately somewhere where I don't have, you know, high bandwith. Oh, all right. Or, you know, highlight and see type. Also, egress costs a pretty big deals. My date is up in the cloud, and I'm gonna get charged for pulling it off. You know, that's being a big issue, >> you know, it's funny, I think, and I think a lot of the the issue, obviously complexity building. It's a totally from building model, but I think to a lot of people will put stuff in a public cloud and then operated as if they bought it and they're running in the data center in this kind of this. Turn it on, Turn it off when you need it. Everyone turns. Everyone loves to talk about the example turning it on when you need it. But nobody ever talks about turning it off when you don't. But it kind of close on our conversation. I won't talk about a I and applied a Iot because he has a lot of talk in the market place. But, hey, I'm machine learning. But as you guys know pride better than anybody, it's the application of a I and specific applications, which really on unlocks the value. And as we're sitting here talking about this complexity, I can't help but think that, you know, applied a I in a management layer like your run differently, set up to actually know when to turn things on, when to turn things off when you moved in but not moved, it's gonna have to be machines running that right cause the data sets and the complexity of these systems is going to be just overwhelming. Yeah, yeah, >> absolutely. Completely agree with you. In fact, attack sensual. We actually refer to this whole area as applied intelligence on That's our guy, right? And it is absolutely to add more and more automation move everything Maur toe where it's being run by the machine rather than you know, having people really working on these things >> yet, e I mean, if you think you hit the nail on the head, we're gonna a eyes e. I mean, given how things getting complex, more ephemeral, you think about kubernetes et cetera. We're gonna have to leverage a humans or not to be able to get, you know, manage this. The environments comported right. What's interesting way we've used quite effectively for quite some time. But it's good at some stuff, not good at others. So we find it's very good at, like, ticket triage, like ticket triage, chicken rounding et cetera. You know, any time we take over account, we tune our AI ai engines. We have ticket advisers, etcetera. That's what probably got the most, you know, most bang for the buck. We tried in the network space, less success to start even with, you know, commercial products that were out there. I think where a I ultimately bails us out of this is if you look at the problem. You know, a lot of times we talked about optimizing around cost, but then performance. I mean, and it's they they're somewhat, you know, you gotta weigh him off each other. So you've got a very multi dimensional problem on howto I optimize my workloads, particularly. I gotta kubernetes cluster and something on Amazon, you know, sums running on my private cloud, etcetera. So we're gonna get some very complex environment. And the only way you're gonna be ableto optimize across multi dimensions that cost performance service levels, you know, And then multiple options don't do it public private, You know, what's my network costs etcetera. Isn't a I engine tuning that ai ai engines? So ultimately, I mean, you heard me earlier on the operators. I think you know, they write the analytic albums, they do the automation scripts, but they're the ultimate one too. Then tune the aye aye engines that will manage our environment. And I think it kubernetes will be interesting because it becomes a link to the control plane optimize workload placement. You know, between >> when the best thing to you, then you have dynamic optimization. Could you might be optimizing eggs at us right now. But you might be optimizing for output the next day. So exists really a you know, kind of Ah, never ending when you got me. They got to see them >> together with you and multi dimension. Optimization is very difficult. So I mean, you know, humans can't get their head around. Machines can, but they need to be trained. >> Well, Prasad, Larry, Lots of great opportunities for for centuries bring that expertise to the tables. So thanks for taking a few minutes to walk through some of these things. Our pleasure. Thank you, Grace. Besides Larry, I'm Jeff. You're watching the Cube. We are high above San Francisco in the Salesforce Tower, Theis Center, Innovation hub in San Francisco. Thanks for watching. We'll see you next time.

>> Hey, welcome back already, Jeffrey. Here with the Cube, we are high top San Francisco in the Salesforce Tower in the newest center offices. It's really beautiful and is part of that. They have their San Francisco innovation hubs, so it's five floors of maker's labs and three D printing and all kinds of test facilities and best practices Innovation theater and in this studio, which is really fun to be at. So we're talking about hybrid cloud in the development of cloud and multi cloud. And, you know, we're, you know, continuing on this path. Not only your customers on this path, but everyone's kind of on this path is the same kind of evolved and transformed. We're excited. Have a couple experts in the field. We got Larry Soccer. He's the global managing director of Intelligent Cloud Infrastructure Service's growth and strategy at a center. Very good to see you again. Great to be here. And the Jay Patel. He's the senior vice president and general manager, cloud provider, software business unit, being where enemies of the people are nice. Well, so, uh so first off, how you like the digs appear >> beautiful place and the fact we're part of the innovation team. Thank you for that. It's so let's just >> dive into it. So a lot of crazy stuff happening in the market place a lot of conversations about hybrid cloud, multi cloud, different cloud, public cloud movement of Back and forth from Cloud. Just wanted. Get your perspective a day. You guys have been in the Middle East for a while. Where are we in this kind of evolution? It still kind of feeling themselves out. Is it? We're kind of past the first inning, so now things are settling down. How do you kind of you. Evolution is a great >> question, and I think that was a really nice job of defining the two definitions. What's hybrid worse is multi and simply put hybrid. We look at hybrid as when you have consistent infrastructure. It's the same infrastructure, regardless of location. Multi is when you have disparate infrastructure. We're using them in a collective. So just from a level setting perspective, the taxonomy starting to get standardized industry starting to recognize hybrid is a reality. It's not a step in the long journey. It is an operating model that's gonna be exists for a long time, so it's no longer about location. It's a lot harder. You operate in a multi cloud and a hybrid cloud world and together, right extension BM would have a unique opportunity. Also, the technology provider Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid multicolored world, because workloads are driving decisions right and one of the year in this hybrid medical world for many years to come. But >> do I need another layer of abstraction? Cause I probably have some stuff that's in hybrid. I probably have some stuff in multi, right, because those were probably not much in >> the way we talked a lot about this, and Larry and I were >> chatting as well about this. And the reality is, the reason you choose a specific cloud is for those native different share capability. Abstraction should be just enough so you can make were close portable, really use the caper berry natively as possible right, and by fact, that we now with being where have a native VM we're running on every major hyper scaler, right? And on. Prem gives you that flexibility. You want off not having to abstract away the goodness off the cloud while having a common and consistent infrastructure. What tapping into the innovations that the public cloud brings. So it is a evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center to really make it operating model. That's independent location, right? >> Solarium cures your perspective. When you work with customers, how do you help them frame this? I mean, I always feel so sorry for corporate CEOs. I mean, they got >> complexities on the doors are already going on >> like crazy that GDP are now, I think, right, The California regs. That'll probably go national. They have so many things to be worried about. They got to keep up on the latest technology. What's happening in containers away. I thought it was Dr Knight. Tell me it's kubernetes. I mean, it's really tough. So how >> do you help them? Kind of. It's got a shot with the foundation. >> I mean, you look at cloud, you look at infrastructure more broadly. I mean, it's there to serve the applications, and it's the applications that really drive business value. So I think the starting point has to be application lead. So we start off. We have are intelligent. Engineering guys are platform guys. You really come in and look And do you know an application modernisation strategy? So they'll do an assessment. You know, most of our clients, given their scale and complexity, usually have from 520,000 applications, very large estates, and they got to start to freak out. Okay, what's my current application's? You know, you're a lot of times I use the six R's methodology, and they say, OK, what is it that I I'm gonna retire. This I'm no longer needed no longer is business value, or I'm gonna, you know, replace this with sass. Well, you know, Yeah, if I move it to sales force, for example, or service now mattress. Ah, and then they're gonna start to look at their their workloads and say OK, you know, I don't need to re factor reform at this, you know, re hosted. You know, when one and things obviously be Emily has done a fantastic job is allowing you to re hosted using their softer to find a data center in the hyper scale er's environments >> that we called it just, you know, my great and then modernized. But >> the modern eyes can't be missed. I think that's where a lot of times you see clients kind of getting the trap Hammer's gonna migrate and then figure it out. You need to start tohave a modernisation strategy and then because that's ultimately going to dictate your multi and your hybrid cloud approaches, is how they're zaps evolve and, you know, they know the dispositions of those abs to figure out How do they get replaced? What data sets need to be adjacent to each other? So >> right, so a j you know, we were there when when Pat was with Andy and talking about, you know, Veum, Where on AWS. And then, you know, Sanjay has shown up, but everybody else's conferences a Google cloud talking about you know, Veum. Where? On Google Cloud. I'm sure there was a Microsoft show I probably missed. You guys were probably there to know it. It's kind of interesting, right from the outside looking in You guys are not a public cloud per se. And yet you've come up with this great strategy to give customers the options to adopt being We're in a public hot. And then now we're seeing where even the public cloud providers are saying here, stick this box in your data center and Frank, this little it's like a little piece of our cloud of floating around in your data center. So talk about the evolution of the strategy is kind of what you guys are thinking about because you know, you're cleared in a leadership position, making a lot of interesting acquisitions. How are you guys see this evolving? And how are you placing your bets? >> You know, that has been always consistent about this. Annie. Any strategy, whether it's any cloud, was any device, you know, any workload if you will, or application. And as we started to think about it, right, one of the big things be focused on was meeting the customer where he's out on its journey. Depending on the customer, let me simply be trying to figure out looking at the data center all the way to how the drive in digital transformation effort in a partner like Accenture, who has the breadth and depth and something, the vertical expertise and the insight. That's what customers looking for. Help me figure out in my journey. First tell me where, Matt, Where am I going and how I make that happen? And what we've done in a clever way, in many ways is we've created the market. We've demonstrated that VM where's the omen? Consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I You know, I often say hybrids a two way street. Now, which is you're bringing Maur more hybrid Cloud service is on Prem. And where is he? On Premise now the edge. I was talking to the centering folks and they were saying the mitral edge. So you're starting to see the workloads, And I think you said almost 40 plus percent off future workers that are gonna be in the central cloud. >> Yeah, actually, is an interesting stat out there. 20 years 2020 to 70% of data will be produced and processed outside the cloud. So I mean, the the edges about, you know, as we were on the tipping point of, you know, I ot finally taking off beyond, you know, smart meters. You know, we're gonna see a huge amount of data proliferate out there. So, I mean, the lines between public and private income literary output you look at, you know, Anthony, you know, as your staff for ages. So you know, And that's where you know, I think I am where strategy is coming to fruition >> sometime. It's great, >> you know, when you have a point of view and you stick with it >> against a conventional wisdom, suddenly end up together and then all of a sudden everyone's falling to hurt and you're like, This is great, but I >> hit on the point about the vertical ization. Every one of our client wth e different industries have very different has there and to the meeting that you know the customer, you know, where they're on their journey. I mean, if you talk to a pharmaceutical, you know, geekspeak compliance. Big private cloud started to dip their toes into public. You know, you go to minds and they're being very aggressive public. So >> every manufacturing with EJ boat back in >> the back, coming to it really varies by industry. >> And that's, you know, that's a very interesting here. Like if you look at all the ot environment. So the manufacturing we started see a lot of end of life of environment. So what's that? Next generation, you know, of control system's gonna run on >> interesting on the edge >> because and you've brought of networking a couple times where we've been talking it, you know, and as as, ah, potential gate right when I was still in the gates. But we're seeing Maura where we're at a cool event Churchill Club, when they had Xilinx micron and arm talking about, you know, shifting Maur that compute and store on these edge devices ti to accommodate, which you said, you know, how much of that stuff can you do at the adverse is putting in. But what I think is interesting is how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of you're looting and security times many, many thousands of these devices all over the place. >> You might have heard >> recent announcements from being where around the carbon black acquisition right that combined with our work space one and the pulse I ot well, >> I'm now >> giving you a management framework with It's what people for things or devices and that consistency. Security on the client tied with the network security with NSX all the way to the data center, security were signed. A look at what we call intrinsic security. How do we bake and securing the platform and start solving these end to end and have a park. My rec center helped design these next generation application architectures are distributed by design. Where >> do you put a fence? You're you could put a fence around your data center, >> but your APP is using service now. Another SAS service is so hard to talk to an application boundary in the sea security model around that. It's a very interesting time. >> You hear a lot of you hear a >> lot about a partnership around softer to find data center on networking with Bello and NSX. But we're actually been spending a lot of time with the i o. T. Team and really looking at and a lot of our vision, the lines. I mean, you actually looked that they've been work similarly, agent technology with Leo where you know, ultimately the edge computing for io ti is gonna have to be containerized because you can need multiple middleware stacks supporting different vertical applications, right? We're actually you know what we're working with with one mind where we started off doing video analytics for predictive, you know, maintenance on tires for tractors, which are really expensive. The shovels, It's after we started pushing the data stream up it with a video stream up into azure. But the network became a bottleneck looking into fidelity. So we gotta process there. They're not looking autonomous vehicles which need eight megabits low laden C band with, you know, sitting at the the edge. Those two applications will need to co exist. And you know why we may have as your edge running, you know, in a container down, you know, doing the video analytics. If Caterpillar chooses, you know, Green Grass or Jasper that's going to co exist. So you see how the whole container ization that were started seeing the data center push out there on the other side of the pulse of the management of the edge is gonna be very difficult. I >> need a whole new frontier, absolutely >> moving forward. And with five g and telco. And they're trying to provide evaluated service is So what does that mean from an infrastructure perspective. Right? Right, Right. When do you stay on the five g radio network? Worse is jumping on the back line. And when do you move data? Where's his process? On the edge. Those all business decisions that need to be doing to some framework. >> You guys were going, >> we could go on. Go on, go. But I want to Don't fall upon your Segway from containers because containers were such an important part of this story and an enabler to the story. And, you know, you guys been aggressive. Move with hefty Oh, we've had Craig McCloskey, honor. He was still at Google and Dan great guys, but it's kind of funny, right? Cause three years ago, everyone's going to Dr Khan, right? I was like that were about shows that was hot show. Now doctors kind of faded and and kubernetes has really taken off. Why, for people that aren't familiar with kubernetes, they probably here to cocktail parties. If they live in the Bay Area, why's containers such an important enabler? And what's so special about Coburn? 80 specifically. >> Do you wanna go >> on the way? Don't talk about my products. I mean, if you >> look at the world is getting much more dynamics on the, you know, particularly you start to get more digitally to couple applications you started. You know, we've gone from a world where a virtual machine might have been up for months or years. Toe, You know, obviously you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. That's essential. Kubernetes does is just really starts to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need it for performance, etcetera. So kubernetes an incredible technology that allows you really to optimize, you know, the placement of that. So just like the virtual machine changed, how we compute containers now gives us a much more flexible portable. You know that, you know you can run on anything infrastructure, any location, you know, closer to the data, et cetera. To do that. And I >> think the bold movie >> made is, you know, we finally, after working with customers and partners like century, we have a very comprehensive strategy. We announced Project Enzo, a philosophy in world and Project tansy really focused on three aspects of containers. How do you build applications, which is pivotal in that mansion? People's driven around. How do we run these arm? A robust enterprise class run time. And what if you could take every V sphere SX out there and make it a container platform? Now we have half a million customers. 70 million be EMS, all of sudden that run time. We're continue enabling with the Project Pacific Soviets. Year seven becomes a commonplace for running containers, and I am so that debate of'em czar containers done gone well, one place or just spin up containers and resource is. And then the more important part is How do I manage this? You said, becoming more of a platform not just an orchestration technology, but a platform for how do I manage applications where I deploy them where it makes most sense, right? Have decoupled. My application needs from the resource is, and Coburn is becoming the platform that allows me to port of Lee. I'm the old job Web logic guy, right? >> So this is like distributed Rabb logic job on steroids, running across clouds. Pretty exciting for a middle where guy This is the next generation and the way you just said, >> And two, that's the enabling infrastructure that will allow it to roll into future things like devices. Because now you've got that connection >> with the fabric, and that's working. Becomes a key part of one of the key >> things, and this is gonna be the hard part is optimization. So how do we optimize across particularly performance, but even costs? >> You're rewiring secure, exact unavailability, >> Right? So still, I think my all time favorite business book is Clayton Christians. An innovator's dilemma. And in one of the most important lessons in that book is What are you optimizing four. And by rule, you can't optimize for everything equally you have to you have to rank order. But what I find really interesting in this conversation in where we're going in the complexity of the throughput, the complexity of the size of the data sets the complexity of what am I optimizing for now? Just begs for applied a I or this is not This is not a people problem to solve. This is this >> is gonna be all right. So you look at >> that, you know, kind of opportunity to now apply A I over the top of this thing opens up tremendous opportunity. >> Standardize infrastructural auditory allows you to >> get more metrics that allows you to build models to optimize infrastructure over time. >> And humans >> just can't get their head around me because you do have to optimize across multiple mentions. His performances cost, but then that performances gets compute. It's the network, I mean. In fact, the network's always gonna be the bottlenecks. You look at it even with five G, which is an order of magnitude, more bandwidth from throughput, the network will still lag. I mean, you go back to Moore's Law, right? It's Ah, even though it's extended to 24 months, price performance doubles. The amount of data potentially can kick in and you know exponentially grow on. Networks don't keep pays, so that optimization is constantly going to be tuned. And as we get even with increases in network, we have to keep balancing that right. >> But it's also the business >> optimization beyond the infrastructure optimization. For instance, if you're running a big power generation field of a bunch of turbines, right, you may wanna optimize for maintenance because things were running at some steady state. But maybe there's oil crisis or this or that. Suddenly the price, right? You're like, forget the maintenance. Right now we've got you know, we >> got a radio controlled you start about other >> than a dynamic industry. How do I really time change the behavior, right? Right. And more and more policy driven. Where the infrastructure smart enough to react based on the policy change you made. >> That's the world we >> want to get to. And we're far away from that, right? >> Yeah. I mean, I think so. Ultimately, I think the Cuban honeys controller gets an A I overlay and the operators of the future of tuning the Aye aye engines that optimizing, >> right? Right. And then we run into the whole thing, which we've talked about many times in this building with Dr Room, A child re from a center. Then you got the whole ethics overlay on top of the thing. That's a whole different conversation from their day. So before we wrap kind of just want to give you kind of last thoughts. Um, as you know, customers Aaron, all different stages of their journey. Hopefully, most of them are at least at least off the first square, I would imagine on the monopoly board What does you know, kind of just top level things that you would tell people that they really need just to keep always at the top is they're starting to make these considerations, starting to make these investments starting to move workloads around that they should always have kind of top >> of mind. For me, it's very simple. It's really about focused on the business outcome. Leverage the best resource for the right need and design. Architectures are flexible that give you a choice. You're not locked in and look for strategic partners with this technology partners or service's partners that alive you to guide because the complexities too high the number of choices that too high. You need someone with the breath in depth to give you that platform in which you can operate on. So we want to be the digital kind of the ubiquitous platform. From a software perspective, Neck Centuries wants to be that single partner who can help them guide on the journey. So I think that would be my ask. It's not thinking about who are your strategic partners. What is your architecture and the choices you're making that gave you that flexibility to evolve. Because this is a dynamic market. What should make decisions today? I mean, I'll be the one you need >> six months even. Yeah. And And it's And that that dynamic that dynamics is, um is accelerating if you look at it. I mean, we've all seen change in the industry of decades in the industry, but the rate of change now the pace, you know, things are moving so quickly. >> I mean, little >> respond competitive or business or in our industry regulations, right. You have to be prepared for >> Yeah. Well, gentlemen, thanks for taking a few minutes and ah, great conversation. Clearly, you're in a very good space because it's not getting any less complicated in >> Thank you. Thank you. All right. Thanks, Larry. Ajay, I'm Jeff. You're watching the Cube. >> We are top of San Francisco in the Salesforce Tower at the center Innovation hub. Thanks for watching. We'll see next time. Quick

>> Hey, welcome back. Your body, Jefe Rick here from the Cube were high atop San Francisco in the century innovation hub. It's in the middle of the Salesforce Tower. It's a beautiful facility. They think you had it. The grand opening about six months ago. We're here for the grand opening. Very cool space. I got maker studios. They've got all kinds of crazy stuff going on. But we're here today to talk about Cloud in this continuing evolution about cloud in the enterprise and hybrid cloud and multi cloud in Public Cloud and Private Cloud. And we're really excited to have a couple of guys who really helping customers make this journey, cause it's really tough to do by yourself. CEOs are super busy. There were about security and all kinds of other things, so centers, often a trusted partner. We got two of the leaders from center joining us today's Prasad Sankaran. He's the senior managing director of Intelligent Cloud infrastructure for Center Welcome and Larry Soccer, the global managing director. Intelligent cloud infrastructure offering from central gentlemen. Welcome. I love it. It intelligent cloud. What is an intelligent cloud all about? Got it in your title. It must mean something pretty significant. >> Yeah, I think First of all, thank you for having us, but yeah, absolutely. Everything's around becoming more intelligent around using more automation. And the work that, you know we delivered to our clients and cloud, as you know, is the platform to reach. All of our clients are moving. So it's all about bringing the intelligence not only into infrastructure, but also into cloud generally. And it's all driven by software, >> right? It's just funny to think where we are in this journey. We talked a little bit before we turn the cameras on and there you made an interesting comment when I said, You know, when did this cloud for the Enterprise start? And you took it back to sass based applications, which, >> you know you were sitting in the sales force builder. >> That's true. It isn't just the tallest building in >> everyone's, you know, everyone's got a lot of focus on AWS is rise, etcetera. But the real start was really getting into sass. I mean, I remember we used to do a lot of Siebel deployments for CR M, and we started to pivot to sales, for some were moving from remedy into service now. I mean, we've went through on premise collaboration, email thio 3 65 So So we've actually been at it for quite a while in the particularly the SAS world. And it's only more recently that we started to see that kind of push to the, you know, the public pass, and it's starting to cloud native development. But But this journey started, you know, it was that 78 years ago that we really started. See some scale around it. >> And I think and tell me if you agree, I think really, what? The sales forces of the world and and the service now is of the world office 3 65 kind of broke down some of those initial beers, which are all really about security and security, security, security, Always to hear where now security is actually probably an attributes and loud can brink. >> Absolutely. In fact, I mean, those barriers took years to bring down. I still saw clients where they were forcing salesforce tor service Now to put, you know, instances on prime and I think I think they finally woke up toe. You know, these guys invested ton in their security organizations. You know there's a little of that needle in the haystack. You know, if you breach a data set, you know what you're getting after. But when Europe into sales force, it's a lot harder. And so you know. So I think that security problems have certainly gone away. We still have some compliance, regulatory things, data sovereignty. But I think security and not not that it sold by any means that you know, it's always giving an ongoing problem. But I think they're getting more comfortable with their data being up in the in the public domain, right? Not public. >> And I think it also helped them with their progress towards getting cloud native. So, you know, you pick certain applications which were obviously hosted by sales force and other companies, and you did some level of custom development around it. And now I think that's paved the way for more complex applications and different workloads now going into, you know, the public cloud and the private cloud. But that's the next part of the journey, >> right? So let's back up 1/2 a step, because then, as you said, a bunch of stuff then went into public cloud, right? Everyone's putting in AWS and Google. Um, IBM has got a public how there was a lot more. They're not quite so many as there used to be, Um, but then we ran into a whole new host of issues, right, which is kind of opened up this hybrid cloud. This multi cloud world, which is you just can't put everything into a public clouds. There's certain attributes is that you need to think about and yet from the application point of view before you decide where you deploy that. So I'm just curious. If you can share now, would you guys do with clients? How should they think about applications? How should they think about what to deploy where I >> think I'll start in? The military has a lot of expertise in this area. I think you know, we have to obviously start from an application centric perspective. You go to take a look at you know where your applications have to live water. What are some of the data implications on the applications, or do you have by way of regulatory and compliance issues, or do you have to do as faras performance because certain applications have to be in a high performance environment. Certain other applications don't think a lot of these factors will. Then Dr where these applications need to recite and then what we think in today's world is really accomplish. Complex, um, situation where you have a lot of legacy. But you also have private as well as public cloud. So you approach it from an application perspective. >> Yeah. I mean, if you really take a look at Army, you look at it centers clients, and we were totally focused on up into the market Global 2000 savory. You know how clients typically have application portfolios ranging from 520,000 applications? And really, I mean, if you think about the purpose of cloud or even infrastructure for that, they're there to serve the applications. No one cares if your cloud infrastructure is not performing the absolute. So we start off with an application monetization approach and ultimately looking, you know, you know, with our tech advisory guys coming in, there are intelligent engineering service is to do the cloud native and at mod work our platforms, guys, who do you know everything from sales forward through ASAP. They should drive a strategy on how those applications gonna evolve with its 520,000 and determined hey, and usually using some, like the six orders methodology. And I'm I am I going to retire this Am I going to retain it? And, you know, I'm gonna replace it with sass. Am I gonna re factor in format? And it's ultimately that strategy that's really gonna dictate a multi and, you know, every cloud story. So it's based on the applications data, gravity issues where they gonna reside on their requirements around regulatory, the requirements for performance, etcetera. That will then dictate the cloud strategies. I'm you know, not a big fan of going in there and just doing a multi hybrid cloud strategy without a really good up front application portfolio approach, right? How we gonna modernize that >> it had. And how do you segment? That's a lot of applications. And you know, how do you know the old thing? How do you know that one by that time, how do you help them pray or size where they should be focusing on us? >> So typically what we do is work with our clients to do a full application portfolio analysis, and then we're able to then segment the applications based on, you know, important to the business and some of the factors that both of us mentioned. And once we have that, then we come up with an approach where certain sets of applications he moved to sass certain other applications you move to pass. So you know, you're basically doing the re factoring and the modernization and then certain others you know, you can just, you know, lift and shift. So it's really a combination off both modernization as well as migration. It's a combination off that, but to do that, you have to initially look at the entire set of applications and come up with that approach. >> I'm just curious where within that application assessment, um, where is cost savings? Where is, uh, this is just old. And where is opportunities to innovate faster? Because we know a lot of lot of talk really. Days has cost savings, but what the real advantages is execution speed if you can get it. If >> you could go back through four years and we had there was a lot of CEO discussions around cost savings, I'm not really have seen our clients shift. It costs never goes away, obviously right. But there's a lot greater emphasis now on business agility. You know, howto innovate faster, get getting your capabilities to market faster, to change my customer experience. So So it's really I t is really trying to step up and, you know, enabled the business toe to compete in the marketplace. We're seeing a huge shift in emphasis or focus at least starting with, you know, how'd I get better business agility outta leverage to cloud and cloud native development to get their upper service levels? Actually, we started seeing increase on Hey, you know, these applications need to work. It's actress. So So Obviously, cost still remains a factor, but we seem much more for, you know, much more emphasis on agility, you know, enabling the business on, given the right service levels of right experience to the user, little customers. Big pivot there, >> Okay. And let's get the definitions out because you know a lot of lot of conversation about public clouds, easy private clouds, easy but hybrid cloud and multi cloud and confusion about what those are. How do you guys define him? How do you help your customers think about the definition? Yes, >> I think it's a really good point. So what we're starting to see is there were a lot of different definitions out there. But I think as I talked more clients and our partners, I think we're all starting to, you know, come to ah, you know, the same kind of definition on multi cloud. It's really about using more than one cloud. But hybrid, I think, is a very important concept because hybrid is really all about the placement off the workload or where your application is going to run on. And then again, it goes to all of these points that we talked about data, gravity and performance and other things. Other factors. But it's really all about where do you place the specific look >> if you look at that, so if you think about public, I mean obviously gives us the innovation of the public providers. You look at how fast Amazon comes out with new versions of Lambda etcetera. So that's the innovations there obviously agility. You could spend up environments very quickly, which is, you know, one of the big benefits of it. The consumption, economic models. So that is the number of drivers that are pushing in the direction of public. You know, on the private side, they're still it's quite a few benefits that don't get talked about as much. Um, so you know, if you look at it, um, performance if you think the public world, you know, Although they're scaling up larger T shirts, et cetera, they're still trying to do that for a large array of applications on the private side, you can really Taylor somethingto very high performance characteristics. Whether it's you know, 30 to 64 terabyte Hana, you can get a much more focused precision environment for business. Critical workloads like that article, article rack, the Duke clusters, everything about fraud analysis. So that's a big part of it. Related to that is the data gravity that Prasad just mentioned. You know, if I've got a 64 terabyte Hana database you know, sitting in my private cloud, it may not be that convenient to go and put get that data shared up in red shift or in Google's tensorflow. So So there's some data gravity out. Networks just aren't there. The laden sea of moving that stuff around is a big issue. And then a lot of people of investments in their data centers. I mean, the other piece, that's interesting. His legacy, you know, you know, as we start to look at the world a lot, there's a ton of code still living in, You know, whether it's you, nick system, just IBM mainframes. There's a lot of business value there, and sometimes the business cases aren't aren't necessarily there toe to replace them. Right? And in world of digital, the decoupling where I can start to use micro service is we're seeing a lot of trends. We worked with one hotel to take their reservation system. You know, Rapid and Micro Service is, um, we then didn't you know, open shift couch base, front end. And now, when you go against, you know, when you go and browsing properties, you're looking at rates you actually going into distributed database cash on, you know, in using the latest cloud native technologies that could be dropped every two weeks or everything three or four days for my mobile application. And it's only when it goes, you know, when the transaction goes back, to reserve the room that it goes back there. So we're seeing a lot of power with digital decoupling, But we still need to take advantage of, you know, we've got these legacy applications. So So the data centers air really were trying to evolve them. And really, just, you know, how do we learn everything from the world of public and struck to bring those saints similar type efficiencies to the to the world of private? And really, what we're seeing is this emerging approach where I can start to take advantage of the innovation cycles. The land is that, you know, the red shifts the functions of the public world, but then maybe keep some of my more business critical regulated workloads. You know, that's the other side of the private side, right? I've got G X p compliance. If I've got hip, a data that I need to worry about GDP are there, you know, the whole set of regular two requirements. Now, over time, we do anticipate the public guys will get much better and more compliant. In fact, they made great headway already, but they're still not a number of clients are still, you know, not 100% comfortable from my client's perspective. >> Gotta meet Teresa Carlson. She'll change him, runs that AWS public sector is doing amazing things, obviously with big government contracts. But but you raise real inching point later. You almost described what I would say is really a hybrid application in this in this hotel example that you use because it's is, you know, kind of breaking the application and leveraging micro service is to do things around the core that allowed to take advantage of some this agility and hyper fast development, yet still maintain that core stuff that either doesn't need to move. Works fine, be too expensive. Drea Factor. It's a real different weight. Even think about workloads and applications into breaking those things into bits. >> And we see that pattern all over the place. I'm gonna give you the hotel Example Where? But finance, you know, look at financial service. Is retail banking so open banking a lot. All those rito applications are on the mainframe. I'm insurance claims and and you look at it the business value of replicating a lot of like the regulatory stuff, the locality stuff. It doesn't make sense to write it. There's no rule inherent business values of I can wrap it, expose it and in a micro service's architecture now D'oh cloud native front end. That's gonna give me a 360 view a customer, Change the customer experience. You know, I've got a much you know, I can still get that agility. The innovation cycles by public. Bye bye. Wrapping my legacy environment >> and percent you raided, jump in and I'll give you something to react to, Which is which is the single planet glass right now? How do I How did I manage all this stuff now? Not only do I have distributed infrastructure now, I've got distributed applications in the and the thing that you just described and everyone wants to be that single pane of glass. Everybody wants to be the app that's upon everybody. Screen. How are you seeing people deal with the management complexity of these kind of distributed infrastructures? If you >> will Yeah, I think that that's that's an area that's, ah, actually very topical these days because, you know, you're starting to see more and more workers go to private cloud. And so you've got a hybrid infrastructure you're starting to see move movement from just using the EMS to, you know, cantinas and Cuba needs. And, you know, we talked about Serval s and so on. So all of our clients are looking for a way, and you have different types of users as well. Yeah, developers. You have data scientists. You have, you know, operators and so on. So they're all looking for that control plane that allows them access and a view toe everything that is out there that is being used in the enterprise. And that's where I think you know, a company like Accenture were able to use the best of breed toe provide that visibility to our clients, >> right? Yeah. I mean, you hit the nail on the head. It's becoming, you know, with all the promises, cloud and all the power. And these new architectures is becoming much more dynamic, ephemeral, with containers and kubernetes with service computing that that that one application for the hotel, they're actually started in. They've got some, actually, now running a native us of their containers and looking at surveillance. So you're gonna even a single application can span that. And one of things we've seen is is first, you know, a lot of our clients used to look at, you know, application management, you know, different from their their infrastructure. And the lines are now getting very blurry. You need to have very tight alignment. You take that single application, if any my public side goes down or my mid tier with my you know, you know, open shipped on VM, where it goes down on my back and mainframe goes down. Or the networks that connected to go down the devices that talk to it. It's a very well. Despite the power, it's a very complex environment. So what we've been doing is first we've been looking at, you know, how do we get better synergy across what we you know, Application Service's teams that do that Application manager, an optimization cloud infrastructure. How do we get better alignment that are embedded security, You know, how do you know what are managed to security service is bringing those together. And then what we did was we looked at, you know, we got very aggressive with cloud for a strategy and, you know, how do we manage the world of public? But when looking at the public providers of hyper scale, er's and how they hit Incredible degrees of automation. We really looked at, said and said, Hey, look, you gotta operate differently in this new world. What can we learn from how the public guys we're doing that We came up with this concept. We call it running different. You know, how do you operate differently in this new multi speed? You know, you know, hot, very hybrid world across public, private demon, legacy, environment, and start a look and say, OK, what is it that they do? You know, first they standardize, and that's one of the big challenges you know, going to almost all of our clients in this a sprawl. And you know, whether it's application sprawl, its infrastructure, sprawl >> and my business is so unique. The Larry no business out there has the same process that way. So >> we started make you know how to be standardized like center hybrid cloud solution important with hp envy And where we how do we that was an example of so we can get to you because you can't automate unless you standardise. So that was the first thing you know, standardizing our service catalog. Standardizing that, um you know, the next thing is the operating model. They obviously operate differently. So we've been putting a lot of time and energy and what I call a cloud and agile operating model. And also a big part of that is truly you hear a lot about Dev ops right now. But truly putting the security and and operations into Deb said cops are bringing, you know, the development in the operations much tied together. So spending a lot of time looking at that and transforming operations re Skilling the people you know, the operators of the future aren't eyes on glass there. Developers, they're writing the data ingestion, the analytic algorithms, you know, to do predictive operations. They're riding the automation script to take work, you know, test work out right. And over time they'll be tuning the aye aye engines to really optimize environment. And then finally, has Prasad alluded to Is that the platforms that control planes? That doing that? So, you know what we've been doing is we've had a significant investments in the eccentric cloud platform, our infrastructure automation platforms, and then the application teams with it with my wizard framework, and we started to bring that together you know, it's an integrated control plane that can plug into our clients environments to really manage seamlessly, you know, and provide. You know, it's automation. Analytics. Aye, aye. Across APS, cloud infrastructure and even security. Right. And that, you know, that really is a I ops, right? I mean, that's delivering on, you know, as the industry starts toe define and really coalesce around, eh? I ops. That's what we you A ups. >> So just so I'm clear that so it's really your layer your software layer kind of management layer that that integrates all these different systems and provides kind of a unified view. Control? Aye, aye. Reporting et cetera. Right? >> Exactly. Then can plug in and integrate, you know, third party tools to do straight functions. >> I'm just I'm just curious is one of the themes that we here out in the press right now is this is this kind of pull back of public cloud app, something we're coming back. Or maybe it was, you know, kind of a rush. Maybe a little bit too aggressively. What are some of the reasons why people are pulling stuff back out of public clouds that just with the wrong. It was just the wrong application. The costs were not what we anticipated to be. We find it, you know, what are some of the reasons that you see after coming back in house? Yeah, I think it's >> a variety of factors. I mean, it's certainly cost, I think is one. So as there are multiple private options and you know, we don't talk about this, but the hyper skills themselves are coming out with their own different private options like an tars and out pulls an actor stack and on. And Ali Baba has obsessed I and so on. So you see a proliferation of that, then you see many more options around around private cloud. So I think the cost is certainly a factor. The second is I think data gravity is, I think, a very important point because as you're starting to see how different applications have to work together, then that becomes a very important point. The third is just about compliance, and, you know, the regulatory environment. As we look across the globe, even outside the U. S. We look at Europe and other parts of Asia as clients and moving more to the cloud. You know that becomes an important factor. So as you start to balance these things, I think you have to take a very application centric view. You see some of those some some maps moving back, and and I think that's the part of the hybrid world is that you know, you can have a nap running on the private cloud and then tomorrow you can move this. Since it's been containerized to run on public and it's, you know, it's all managed. That >> left E. I mean, cost is a big factor if you actually look at it. Most of our clients, you know, they typically you were a big cap ex businesses, and all of a sudden they're using this consumption, you know, consumption model. And they went, really, they didn't have a function to go and look at be thousands or millions of lines of it, right? You know, as your statement Exactly. I think they misjudged, you know, some of the scale on Do you know e? I mean, that's one of the reasons we started. It's got to be an application led, you know, modernization, that really that will dictate that. And I think In many cases, people didn't. May not have thought Through which application. What data? There The data, gravity data. Gravity's a conversation I'm having just by with every client right now. And if I've got a 64 terabyte Hana and that's the core, my crown jewels that data, you know, how do I get that to tensorflow? How'd I get that? >> Right? But if Andy was here, though, and he would say we'll send down the stove, the snow came from which virgin snow plows? Snowball Snowball. Well, they're snowballs. But I have seen the whole truck killer that comes out and he'd say, Take that and stick it in the cloud. Because if you've got that data in a single source right now, you can apply multitude of applications across that thing. So they, you know, they're pushing. Get that date end in this single source. Of course. Then to move it, change it. You know, you run into all these micro lines of billing statement, take >> the hotel. I mean, their data stolen the mainframe, so if they anyone need to expose it, Yeah, they have a database cash, and they move it out, You know, particulars of data sets get larger, it becomes, you know, the data. Gravity becomes a big issue because no matter how much you know, while Moore's Law might be might have elongated from 18 to 24 months, the network will always be the bottle Mac. So ultimately, we're seeing, you know, a CZ. We proliferate more and more data, all data sets get bigger and better. The network becomes more of a bottleneck. And that's a It's a lot of times you gotta look at your applications. They have. I've got some legacy database I need to get Thio. I need this to be approximately somewhere where I don't have, you know, high bandwith. Oh, all right. Or, you know, highlight and see type. Also, egress costs a pretty big deals. My date is up in the cloud, and I'm gonna get charged for pulling it off. You know, that's being a big issue, >> you know, it's funny, I think, and I think a lot of the the issue, obviously complexity building. It's a totally from building model, but I think to a lot of people will put stuff in a public cloud and then operated as if they bought it and they're running in the data center in this kind of this. Turn it on, Turn it off when you need it. Everyone turns. Everyone loves to talk about the example turning it on when you need it. But nobody ever talks about turning it off when you don't. But it kind of close on our conversation. I won't talk about a I and applied a Iot because he has a lot of talk in the market place. But, hey, I'm machine learning. But as you guys know pride better than anybody, it's the application of a I and specific applications, which really on unlocks the value. And as we're sitting here talking about this complexity, I can't help but think that, you know, applied a I in a management layer like your run differently, set up to actually know when to turn things on, when to turn things off when you moved in but not moved, it's gonna have to be machines running that right cause the data sets and the complexity of these systems is going to be just overwhelming. >> Yeah, yeah, absolutely. Completely agree with you. In fact, attack sensual. We actually refer to this whole area as applied intelligence on That's our guy, right? And it is absolutely to add more and more automation move everything Maur toe where it's being run by the machine rather than you know, having people really working on these things >> yet, e I mean, if you think you hit the nail on the head, we're gonna a eyes e. I mean, given how things getting complex, more ephemeral, you think about kubernetes et cetera. We're gonna have to leverage a humans or not to be able to get, you know, manage this. The environments comported right. What's interesting way we've used quite effectively for quite some time. But it's good at some stuff, not good at others. So we find it's very good at, like, ticket triage, like ticket triage, chicken rounding et cetera. You know, any time we take over account, we tune our AI ai engines. We have ticket advisers, etcetera. That's what probably got the most, you know, most bang for the buck. We tried in the network space, less success to start even with, you know, commercial products that were out there. I think where a I ultimately bails us out of this is if you look at the problem. You know, a lot of times we talked about optimizing around cost, but then performance. I mean, and it's they they're somewhat, you know, you gotta weigh him off each other. So you've got a very multi dimensional problem on howto I optimize my workloads, particularly. I gotta kubernetes cluster and something on Amazon, you know, sums running on my private cloud, etcetera. So we're gonna get some very complex environment. And the only way you're gonna be ableto optimize across multi dimensions that cost performance service levels, you know, And then multiple options don't do it public private, You know, what's my network costs etcetera. Isn't a I engine tuning that ai ai engines? So ultimately, I mean, you heard me earlier on the operators. I think you know, they write the analytic albums, they do the automation scripts, but they're the ultimate one too. Then tune the aye aye engines that will manage our environment. And I think it kubernetes will be interesting because it becomes a link to the control plane optimize workload placement. You know, between >> when the best thing to you, then you have dynamic optimization. Could you might be optimizing eggs at us right now. But you might be optimizing for output the next day. So exists really a you know, kind of Ah, never ending when you got me. They got to see them >> together with you and multi dimension. Optimization is very difficult. So I mean, you know, humans can't get their head around. Machines can, but they need to be trained. >> Well, Prasad, Larry, Lots of great opportunities for for centuries bring that expertise to the tables. So thanks for taking a few minutes to walk through some of these things. Our pleasure. Thank you, Grace. Besides Larry, I'm Jeff. You're watching the Cube. We are high above San Francisco in the Salesforce Tower, Theis Center, Innovation hub in San Francisco. Thanks for watching. We'll see you next time.

>> All right, Cool. We'll go with the first round of this, and we'll see how the central roles perfect. Uh, three, two and one. All right, I'm here with our guests. Chase Phelps, the director of sports science at Stanford University. Chase has an amazing background, and I was fortunate enough to work underneath him at Stanford. Chase is more than versatile. He has a deep understanding in regards to human physiology, but also the technology involved in monitoring athletes and performance in general. So, Chase, I'll let you take it away here, and I can't talk about yourself and the journey that you tell to get to where you are. I personally heard it multiple times. It's quite interesting. And for those listeners out there is going to be a good experience to hear exactly how someone chases esteem, Got to where he is, how the road's not always quite a straight line. >> Well, I appreciate you having me on II. You must be getting the checks in the mail to have that type of intro because that's way over the top on how good I am with my job. But I appreciate it. Um, so I think for me. You know, it started, I think, for a lot of us being in the gym as an athlete, Uh, you know, kind of being one of those guys has gotta work harder. Teo, you know, catch up with the other people who are coming naturally talented. So I started office of your general meathead in the gym in high school, doing all the dumb lab bench incline bench declined, bench checked back into, you know, all the flies, you, Khun Dio, and kind of started to figure out that, ah, I needed, you know, um or scientific way, I guess toe train myself and started out going to a velocity sports informants and, you know, one of those big kind of box performance gyms and got hooked up really, really lucky. Got hooked up with some people who at the time, I didn't know where were ahead of the game, but kind of started giving me the wise behind, you know, all the things I was doing in the gym and sort of kind of carbon that path for laying the foundation. So to say so I went to Undergrad, play the cross in college, Um, and they're so science piece started the internships to be a traditional sec coach on the floor, huh? I did. Let's see. Old Dominion. Radford, Virginia Attack. I AMG performance. Um, you know, just kind of laying the coaching trenches, laying down in the trenches, trying tow, kind of get myself the experience necessary to move ahead of Attritional SEC coach. So I got really lucky and that I got a job at Hampton University is an assistant. And within about seven months of being there, the director at the time up and left and they had nobody to help out with football, they have to take over. And really at an age that was way too young for me to be in that role, and so that was kind of my first, you know, probably fire experience, being twenty three years old, heading up, you know, the one double a football for him, still division one football team where I >> it >> was pretty pretty novice at the time. And while I didn't mess anything up to bad, it was definitely I would change a lot of what I did at the time. So I looked back on an experience that was extremely valuable. But from there, I actually had a stent where I was unemployed. So ah, little life lesson is, I took somebody's word on a job without having it written out and quit my job at Hampton, thinking I had this position set up and literally it fell through. The guy was like, Hey, listen, it's not gonna happen. I don't know what to tell you. I'm really sorry. So for seven months, I worked at local gyms, private personal training, training athletes on the side. You're basically doing anything I needed to do. Teo maintain coaching, but also keeping income going. Ah, and it's kind of funny because a lot of people don't appreciate that type of setting and the personal training. You're either strength coach. It's not personal training, you know. And, ah, a lot of the stuff that I do now, I still you know, I remember picking out because I was working with the client with rheumatoid arthritis, right? So, like your ability to to regress and a purple issues exercise selections for somebody who's sixty years old and is not very mobile translates very well to return to play in an athlete who just had maybe on a C L surgery on. So I looked back on that time is kind of a weird one in my life, but it was extremely valuable, you know, and my experiences. So I got really lucky. And the networking piece fell together and ended up working with the Naval Special Operations and kind of finding a role in the humor for men's branch. There, Bro is there for a little over three years. I >> it >> was just incredibly lucky to work with some of the people there, Mark Stevenson and and a lot of other guys who are still working there. They're still there now, but they're just they're pushing the field for doing a lot of things behind the scenes that I think really kind of kicked off the sports science. See Dick in the in the U. S and the last, you know, six to eight years on DH. So I was really fortunate toe kind of diversify. My experiences there really start looking at performance and training. I don't want to say like that buzzword of holistic, but just how my diversifying my ability to understand which discipline is doing, whether it's a mental performance coach, our nutritionist or sex, our physical therapist. But how can I better understand those fields, too? Then, you know, make sure that everything I'm doing is complimenting what they're doing on DH. So I was able to land the job at Stanford initially just to run the sports science department. But I also got a little coaching duties. On the side is I work with men soccer. So it's been, Ah, it's been all over the place, you know, traditionally in athletics, but, you know, a little bit of Gen file here. Besides, well, >> so Chase bast fully passed over Hiss lacrosse career, right? And how many was that? Multi time All America. Is that correct? >> I had a couple of years where else? Pretty successful. So, uh, >> and I think that's extremely important to highlight because being an athlete, you deal with all these departments firsthand. You see it from their perspective. And so one thing that Chase has really taught me, I was going forward learning about how you contain to challenge yourself, to put yourself into positions that other people are end. And how do you then think about your actions and what you're going to do as a sports scientist in regard to how and not on ly influences the athlete but the coaches and other staff around him and being an athlete, you firsthand get to experience how it is to have someone else trying to intervene on your daily routine. And that's also mention that Chase is now someone who on what level of ju jitsu he's in. But I know he's tough enough to beat the daylights out of me. And that's something as well has taught me. Is that put yourself in situations where you have to be a beginner again and challenge yourself to have tto learn from Square one. We get caught in these ruts of progress, progress, progress. You go from a beginner. When you first learned how to swing a baseball bat to now you're planned higher level travelling. Baseball is part of your life for myself. Basketball, the chase has taught me, is really embrace those opportunities of struggle and whatever way that comes in its shape and form and put you in those positions. So you have the ability to actually learn from that. And now mention that chase in regards to beat an athlete I think there's many things that we overlook as coaches. We apply the idea of an external load, right. We give them sets and wraps and weights and we write out these long workout for next six months what someone was going to dio. We can't predict the internal load and be an athlete. You understand how it is to not sleep, how it is to maybe stay out a little too late with some of your friends, but how that affects you in regards and athletic setting to reach the goals that you want to reach. So I want to dive in the topic a little bit about internal versus external load. That's something that you really challenged myself to learn about when I was with you. We talked about that in regards to H R V sleep and all the above said, I want to hear a little bit about your take on internal versus external load. What specifically is at turns >> out someone, he said, is being an athlete. I think that goes, You know, it's It's almost like every year that you are in the field. You separate yourself from what it feels like to go through the workouts and the daily grind. So to say right, it's really easy to write up a bard and have no thought process about how somebody feels on day six of a week where they've been pulling all day school two and a half hour, three hour practice our weights and you're like, Oh, man, we got a great dynamic effort. Lower body session finished office. Um, you know, if our glory body squats like you know it's It's just really easy to forget how how things can accumulate and how you know you're just trying to kind of that times get through it all and you head above water. Whereas we're thinking about optimizing, for they may be thinking about Hey, I just need to know what my head down and get through today. So I think it was a great point. But I think going on to the external love peace, obviously the U. S. In the last, you know, six, seventy nine years has exploded trying to catch up, maybe with Australian, The Europe of the world have been, um, really kind on the forefront of this, uh, objective collection of needs analysis for sport. You know, whether that's an external load of what they're doing, the mechanical demands of the sports. So how far they're running? What are the physical characteristics that you see? See environmental capabilities, as in, you know, beads with velocities, where they simply gotta Iran hominy times that they're going to change direction, really understanding the demands of the sport versus the internal loading piece, which you're going to be Howard, these individuals responding to those demands and I think the key word there being individual, we know that certain athletes are always going to be pushed and filtered into sports that there, uh, naturally, good at right. Like, I think we all tend a favor, things that we've been successful at. And as we kind of go up through our broken physical education system, we haven't done a really good job. I think in our country of kind of diversifying and scaling appropriate levels to make sure people are developing and multiple ways we kind of just like, Oh, you're good at this sport. Keep at it. You suck. You're out on. And I think if we were to kind of cater developmental, developmentally appropriate skill acquisition techniques and I'm stealing all this from a classmate of mine, Peter Bergen City proud, I think a better job of scaling, you know, developmental levels. I think you would see Maur athletes come out of that. That would be successful instead of just they only go on the tall guy put him under the basket. Um, you know, you would be able to develop more skills, but back to the internal load piece on understanding that, like I work with Ben Soccer Max, we're talking about this maybe your ago. I have a guy who logged twenty thousand meters in a playoff game last year, You know, that's over twelve and a half >> miles on run game. And he >> had played a game two days earlier and had been practicing for four months. And it comes to the question of like, How does somebody do that? Do that? Do you train them to do that? Do they just follow the program and all of us and they could do that. Or did there, I guess, internal demands to the sport over time. It took years. It took decades and in my opinion, took that after we to play the sport of high level, you know, for ten plus years to be able to get that cardiac adaptation of peripheral ability to be so efficient that they can run and change and cutting jump a tte that intensity. And so an athlete like that that that internal load, you know, they're going to be very, very effective and mobilizing energy. They're going to be very good of providing blood and oxygen to the to the outside of the body, whereas, you know, you take, not tow it, almost four. But like softball, that's a completely different athlete. And so if you were to ask them to have, ah, Despaigne similar demands, we know that internal load would be different. They're gonna have an inefficiency that, uh, you know what, I've election, Amy. A struggle to match the requirements of work or mechanical load that you're placing upon the athletes. So I think you know, it's really important as you start to look at that internal versus external. The external is critical, I think, on a lot of sports were just now identifying what is necessary to be successful on the field as and what they're doing. So you can start it that, you know, backwards, design and work. Your program to say here is ultimately what they have to be able to do. This is a worst case scenario on the field. This is how we should cater our return to play protocols so that we know we're working towards ultimately the ideal player. And that's sports and >> interesting. Yeah, not to cut you off. I did make some clarity here in regards to internal versus external loads. We talked about external load. We're talking about the amount of work someone actually does. Yes. So the amount of weight being lifted, how fast someone's running, how many pages someone can read, Right? And we end the guards, student, one intern and what side? Go ahead. >> It's really what is happening. What are you doing? What? How much of something? >> Something you're applying to the body. And then the internal load is the physiological changes that take place. And so the most basic concept is Hey, we're going to give you a weight program. We're gonna lift X amount of weight for X amount of days with the external load, intending to change the internal environment to grow muscle. And then the more muscle you grow, the more internal load you can handle. So you're adaptive capacity, that big bucket of how much you can handle a life. You become very efficient at handling that consistent external load and you increase your ability, whether it be efficiently or the magnitude. Insides that bucket to handle. A larger, I guess, external load in regards to having a larger internal capacity. And so what you're talking about is when our buck it's very specific Say we're playing soccer and we changed, too, you know, let's say tennis or in your case, saw Hall. You mentioned the softball player would struggle with soccer, and the soccer player would struggle with tennis because those external loads are so different than the internal capabilities of that individual. Is that correct? >> Yeah, absolutely. I think I think the higher level you go you definitely see that specificity of coordinated skills really kind of become a guest. Very nish. And what you typically say and I actually kind of think it's funny because I've said it. So then guilty as charged is that you'll look at a soccer player, you know, somebody who can play at the highest level and is sprinting doing all these different, you know, athletic exercises and then we'll be like, Man, they're bad athlete. They can't skip or look at that spa product. It's terrible and you know, you kind of take a step back and you're like, was the gold toe squatters, the gold toe score goals and play soccer? Um, and then some, you know, may argue. It will, you know, had the longevity of peace or they're gonna be in a more front injury, all that on and at the same time. And I think about that subconscious confidence when you put some money in a gym and a, you know, a new environment where they may not have done these things. They're very aware they're consciously in confident. They're sitting there going, I >> suck at this >> and they overthink it, right, and then you ask him to, like, go out on the field and kick a ball around, and they're doing these things. They're changing direction, which is basically a squat with shen angles changed. Uh, yeah, you know these things fluently without even thinking about it. So it's like their ability is there. It's just not in the right contact. >> Interesting. Yes, they bring up the concept of selling, being consciously aware, right? So they might be in a nervous kind of state. They're not familiar with the weight room, and that actually bring some level anxiety, possibly that true. And that itself may make the weight room instead of ah, use dresser, which is something very positive. It might be a distress, sir, and so they see that waiting is negative. And so now they're nervous toe workout and they have to work out, which makes the internal load even larger. So make this environment that kind of gets magnified. In regards to that. What other factors influence your internal load? Something I mentioned was that stress and obviously their external stressors, especially at Stanford, work very intelligent students who are having to go through rigorous testing in school. And it's a very competitive environment, not just athletically, but, um, you know, the education side as well through those stressors and past internal load. And if it does, how does that influenced the amount? External load? As a coach, you might provide? >> Yeah, absolutely. I think it's always going to be multispectral. It's always going to be. It depends on who's who's the athlete. What's their background? And the supporter? The activity. You're asking to dio, um, the daily life of the twenty two hours that they're not with you. Are they hydrated? Are they eating properly? They fuelling for adequate activity. Are they getting enough sleep? Are they, you know, have a test for their psychosocial factors at play? Like their girlfriend or boyfriend just broke up with him. And I think all those things obviously have an impact Has been Aton and ton of focus placed on this type of, I guess, capturing that whole athlete. Whereas maybe, you know, years ago, you would look at tonnage and now people will look tonnage. And what that stress load is, what that academic load is Because, you know, research is coming out. Now that we know that these types of overloaded stressors and stresses the same stress of you know makes you resilient can break you down. So it's really the improper dozing and inability to cope with that load, and that's dressed, it creates the problems. But, um, you know, you look at athletes who are an exam week, there's research talking about that people hell less efficiently. They have immune issues. So you're seeing people get sick. You're seeing that inability to adapt and cope with the demands that are placed on him, being significantly altered by some other type of factor outside of a weight room or a field. Um, you know, I think the the fact that the collegiate environment is being more aware to that and teams they're trying to push practice in the morning. A little later, they're tryingto manipulate schedules so that its aren't just running straight from class. But they have a little time between do get some type of snack and to some moment to themselves toe. Take a couple of rest before they go out on the flip side, right after practice. Are they running directly into Ah, you know, a test or something? Or are they actually will have a little moments of themselves where they can kind of down, regulate, take everything in and then move on? I think that those types of things, well or not, massive are significant because they happened ten to twenty times every day over the span of weeks in years. And that's really the problems, that chronic buildup of a over activated, sympathetic response that maybe exacerbated by an athletes Taipei, their personalities or type a person. Yeah. Hey, I'm driven. I'm a pi performer. This is what I do, or maybe some of the lifestyle stuff. So maybe that there's somebody who you know is just pumping refined sugars and other body and creating a flux and blood sugar regulation that again mobilizes cortisol, a sympathetic response. And next thing you know, you've just in the span of three hours tagged on six different things, albeit slightly different, that had the same outcome on the system. So that internal response becomes very, very sensitive. Teo, everything you're doing because it's that chronic build up that's really taken its toll on it. >> Interesting. So he bring up the idea of the sympathetic nervous system and the sympathetic nervous system being broken down. I guess being partnered with, I should say with the parasympathetic nervous system, right, that makes up your autonomic nervous systems. So for those you're not familiar. Sympathetic nervous systems, your fighter flight. It mobilizes energy. It's looked at to be very important for survival. If we saw a lion during evolutionary times would help us increase our heart rate, Increased auction supply, mobilized energy so we could run away from a lion. But then we had the parasympathetic aspect. That branch would help regulate rest. And I just kind of the repair and rebuild process. Now, with that, you mentioned the hyperactivity of the sympathetic nervous system. Now, does this get out of whack? Sometimes if you're an athlete, your individual were chronically stressed. And if so, does that affect some of your endocrinology? So how your body responds? And what kind of tips can you have No muse with your athletes or yourself to get yourself back into a parasympathetic state? Yes, >> that's a great point. I think the and not tow to correct you. I think what you're saying is absolutely right. I think the key is, is not constantly counter act sympathetic, but is to bring the body back into a more balanced ability to appropriately turn on sympathetic into appropriately eternal in Paris. Sympathetic and what you typically see, and I said it so I think you're totally right, is sympathetic, does become the primary driver, but it isn't all about just turning on sympathetic. It's it's having the ability to use both when you need it. And I think a lot of times the door or the window to that is to drive parasympathetic activity on so that it can kind of restore itself. Ah, and then the goal. Once you're kind of an ability where you have a little bit more of stability and that is, then tow, have access to both. >> So you talk to me about me. Interrupt chase. But this is something to remind me completely where, if someone is chronically sympathetic, let's say they're in a game situation. This can goes back. That being stressed out, they might have hyperactivity, sympathetic nervous system and correct if I'm wrong, this decentralize is sorry. Desensitize is the frontal cortex and reduces some individuals ability to make decisions, especially when fatigue begins to set in. Because you have multiple areas of stress coming to body fatigue, the actual stress emotional of the situation and in the person's internal Billy to regulate that, that's something you talking to me about? Spoken with me about while Stanford. I found that topic to be extremely interesting and do the fact that it's completely universal. Whether you're an athlete or your individual going in for a job interview, they kind of fall under the same umbrella. Is this the case? >> Yes, excuse me. So I think ultimately it's a fine line, right? So I think the sympathetic nervous system actually has been shown to enhance some cognitive activities, right? So it does increase that acute ability, toe recall some information and at the same time and over driven response of it can almost shut everything down. And that's where you see people kind of like getting up hyperventilating and not being able to perform and really kind of altering some type of, um, thoughtful, logical, rational action. So I think it comes down to two primary things. It's a primary and secondary appraisal, and this is a psychology based concept. But I think it applies basically everything in performance and primary, the athlete, the person. Whoever is going to say what is happening, and this is subconscious and happening in different aspects of the Iranian or not I fell. Missed what? Your body goes, What's? What is this? Right? So I looked at the analogy of you walk into a bar. All right, You scan the bar, You have a very, very fast Ah, action arms. Excuse me? Decision about what is in that bar. Is that a threat? Do you see a bunch of hell's angels with guns and, you know, baseball bats sitting there? Or do you see a bunch of friends? Right, So and then it's that same split. Second, a secondary appraisal happens to the primary. That's secondary being. Do I have the resources to cope with this? And that is really what dictates what type of response and house is going to send. Oh, are the brain will send to the body to stimulate what side of the annulment? Nervous system. Right. So if I walk in, I say what? I don't like this. Tio. Hey, I've been in this scenario before. It didn't go well. That's when that sympathetic sent a kick on because I got to get out of here verses. I walk into that same place. It's a bunch of friends, You know, It's my old buddy from college. You're gonna have a completely different mobilization of your transmitters of hormones. Because of your perception of the stressor is completely different. And you mentioned you stress distress. And I think that that's the case for everything, because, uh, not to go on a rant. But if you if you take an athlete who loves running, that stress of running is completely different than an athlete who doesn't like running right. So their perception of an activity, albeit the same activity, will have a different psycho physiological manifestation of stress or load on the body. And so I think, as we talk about mental toughness with our athlete, even all of that ultimately comes down to have you put them in such situations to prepare them, have confidence in them. And that's what's going to dictate some of these positive body responses that you'll see because they'll walk up to that playing go. Yep. Done this a million times, and that is where you kind of have that mental resilience versus I don't know what's gonna happen. I've never done this before. If I miss, it's going to be the game. Aunt. I think when we talk about all of performance in psychology and physiology. It's so intertwined you cannot separate them, and we like to separate things we like to have absolute. We like to wear a monitor on a wrist or a chest that tells us we're tired or that tells us we've been too stressed. But the reality is, is that the individual differences in perception of stress and my ability, my body's ability to adapt to that stress based on what type of internal environment is kind of walking around twenty four hours a day is going to dictate everything. And that's why it's really tough and in a team environment for us to just blast everybody and say We're gonna stress, you know, we're going to internal load monitoring by H. R. V. Well, that's fantastic and I think there's there's marriage of that. So I'm not saying there isn't what. You better make sure you know a lot about your athletes. You better make sure you have the time to learn about their personalities, how they handle things, What type of family experiences, a fat, what type of things go into them making decisions about what they're experiencing. >> Gotcha. So that I couldn't agree more. Yeah, that's beautifully said one things you mentioned. There was the idea of HRT, but also the idea of perception. So H R v being a reflection on Amit nervous system and compared to your own baseline when your H R V numbers lower means you have less variability that, essentially inferring a higher level of sympathetic drive when you're HIV is higher, infers a more balanced eight or more parasympathetic state, essentially less sympathetic, right? Right. And so we start using H R V, and we talk about that as an internal tool. They also mentioned the idea ofthe having individuals be in situations that are similar to that of sport. Do you think there's a time and place for real time H R V feedback and HIV training? And would you possibly put someone in a situation where they're trying to score that goal? Maybe you fatigued them with, say, a sled push or prowler push and then you have there HIV tank. And they have to perform a difficult technical task in attempt to have them auto regulate that H R V. So they can perform that task successfully, making training and skill development much more specific and begin to messed together. >> Yeah, absolutely. I mean, that's biofeedback. Wanna one, right? That's that's ah, thought technology, heart, math. All those companies out there using that with Forman psychologists to see how people a handle the stressors implied on them. But how did they bounce back? So the military has been doing this for years and live monitoring H R V on some of the operators and then watching them perform. You know, they're training, going through selection and training bases where they have Tio ah, handle extremely dynamic and challenging environments where they're under watch, their being scrutinised every step of the way. And so what we've actually seen is that people who on average, you know it's not. There's anomalies of force. People who take the hit right, so you'll see a drop in H R B or increasing sympathetic tone. They will actually bounce back, though, so having a stressor impact your your your body is is normal. But the ability to rebound and kind of come back to those norms within a relatively quick period of time is what is critical for high performance. You know, they talked about having a five minute or a three hundred feet average prior to that activity to get a baseline. What we found in some of the research coming out now you can actually probably cut it down to one two, three minutes. Right? So it becomes much more, I guess. Logistically feasible. Tohave guys sit around for one to three minutes, kind of collect that boarding for baseline and then go about their day. And that's really critical to get that that daily baseline. Because as we talked about, if you're on day six of AH long week, your body is functioning and flowing. Ah, and kind of repair mode. It's trying to keep up with what you've been putting it through. So each day that you wake up, you are gonna be slightly different than what you do where for. So it's not an apples, apples. You gotta look at your ability to flux in that Alice static load and your body's proactive decision making to try and match what it was doing in the prior day's training. Evolution >> Dacha. So H r v itself. I refer to the check engine light because it doesn't necessarily come from one area and come from emotional you, Khun, Stub your toe. You can have a lower H R V. And some of the things I've been reading about lately and talking to you about office, podcast or text message and kindly enough, you respond to my random texts at nine thirty at night with a slew of articles and ten questions, has been a nutritional side right and the idea of low level systemic inflammation or inadequate nutrition. What I mean by that is, I will put in food into her body under the assumption that this is going to give us a positive effect. Really. Sometimes the food that we put into our body are causing a stressor on our system, because either, eh, they're so foreign to us in regards to weigh their process or be too simple sugars. And them and I mean simple in terms of your eating a fruit loop have an effect on our body that can take us down a road that necessarily isn't positive for adaptation. And just like H. R. V. Is affected by your psychological perception, I've been read a little bit about H. R V is a kind of systemic monitor and how it could be influenced by nutrition in regards that nutritional aspect. I know we've talked a little bit about biomarkers and some of the diving deep into internal medicine and understanding that our body is very complex. It's made of of all these subsystems and how one subsystem acts might affect how another subsystem acts. And as we gain these risk factors of an adequate nutrients status, our overall risk profile increases and the idea that we might have an emergent pattern in terms of illness manifests increases. So I want to hear some of your thoughts on some of the internal medicine where that's going in regards to bio markers for athletics, human performance and just general wellness. I know you're not a physician and you're not ordering bloodwork and diagnosing off blood work. But being a sports scientist, I do think it's important to appreciate and understand some of these concepts, and you have a great indepth knowledge in this area. So I love to hear a little more about it. >> Yeah, no, I think that's an area and by no means a mine expert, right? I just read a lot of things and copy what other people say so I have to always say that. No, that's what we always hang her hat on is that if you go through the research, you're basically taking somebody else's thoughts interpreting to your own. So my experiences with this, our personal and what I've seen in a professional setting and all kind of touched on the personal piece because I think you know, as we talked about being an athlete and understanding what people go through, our own experiences can drive a lot of how we make decisions with their athletes or are clients or whoever working with and that basically, for twenty five years of my life I've been on some form of allergy medicine allergies, shot decongestant Z Pac to get rid of a sinus infection, you name it. I had, I had and I had multiple sci affections every year and not one time. I want your nose and throat, Doctor Otto. You know, allergy specialists now, one time to never anyone ever bring up what you're putting in your body. And you know, it took you know, I went toe doctor Dima Val seminar last summer and it took ah, somebody while he's very good, but it took somebody to kind of like, say, Hey, man, like it's not just isolating the symptom and given you an anti histamine or something like that, you got to think that you're in a systemic state of inadequacy. Your body doesn't have the ability to recognize normal nutrients as you eat things. But then also, it doesn't have the ability to recognize, um, some of the I guess the things that are supposed to be normal now become pathological. And it's just complete dysfunctional cycle. And so for me, I literally just He said, Hey, do me a favor. Stop eating dairy. Okay? Yeah, I love cheese, but we'll do that. And I literally and within three to four days, every single allergies symptom. I had one away. I haven't had any issues for seven and a half months. While legal thing, >> I >> haven't had any issues. Haven't got sick once. And it was just one thing come to find out. I have a lactose allergy. And not only does it didn't affect me like g I distress, but it effects chronic states of allergies. So my body was perceiving things as, ah, the enemy and the immune system was essentially creating that inflammatory response to deal with them s So I think that first and foremost, I started just looking at Maybe people are eating things that they may have a low grade flamer. Inflammatory response. Tio, Um, I was taking and sets staking insides like there were Andy since I was sixteen years old. You know, being an athlete, you get off him a practice, your knees hurt, ankle hurts. Whatever happens, you know, you just take him so that you can, um >> you know, keep >> on going toe to practice. The next day, um, I was taking CPAC's >> is >> taking prednisone. All these things basically put my spotty in a state of in a state of shock to a point where it can actually regulate normal. >> So just take that >> into my work and special environment. And we have athletes who were under that significant academic stress, social stress and the physical stress. Well, we also see is they're just like me. And then they were taken and said they were taken. You know, prednisone. They're taking quarter to steroids for asthma, exercise induced asthma. They were taking all these things that basically is driving the body into a state of alarm where it doesn't have a normalcy to it. So we're not seeing the immune system actually do its job. We're seeing chronic sympathetic response basically to everything that's being put into the body. So with that low grade inflammation that's happening over weeks, months, years, you get that inability to handle external loads, then that's where than internal load becomes so critical. But what once is, maybe a resilient person now they're getting the sniffles every three weeks now they're walking around with some type of tell, ephemeral and an itis. Ah, no. I think that we so easily look at Oh, they landed on it funny and practice. Oh, they took a bump or a bruise for somebody. But maybe that is exacerbated. Or maybe that's highly sensitive due to the fact that the body isn't able to function under normal circumstances. >> No, that's there's a lot of topics in that one dive into you. Um, I guess what is immediate topics that's most applicable for individuals, the idea of in said's and how? I mean, when I was in ah, middle school, I must have taken maybe six, four, five before a game when I was playing, and it felt nothing. Elements. I can only imagine what that's doing to my internal, You know, my, my style making my gastric system and how much to chewed up. Yeah, that's a lot of information that's come out regarding tendon healing and the adaptations of it, um, you've taught me well, I think the first one to bring this to my attention on some of the detriments of and said itself and some of the alternative we could possibly have, such as your human and things that don't necessarily tear our system up. Um, you give any thoughts on that and how that might play a role than Okay. We have this functional medicine world. Now, how do we apply that into, you know, physical therapy. And if we're trying to have ten and adaptations in regards to Isometrics, you might be doing them to increase longevity and reduced to an apathy or for film someone up with insides. Are we really getting the bang for the buck we want to get or we just causing more harm than good? >> Yeah, absolutely. I think you know, you said it right there and that. Are you taking that risk reward on using that, like, a short term? Ah, you know that hill, Teo, is it overriding what you truly want in the long term? Okay, so we talked about adaptation you mentioned Well, we've seen that and sides actually have. Ah, a destruction of satellite cells. So when you're normally building muscle and you're having some of these repair sells, Memento help stimulate regeneration and says, Well, actually blunt that response to Seo X one and two being the primary enzymes associated with that, we'll actually get shut down. Ah, And when they dio, you're literally stopping your body from adapting. Growing. So I talked to my soccer team all the time about I'm like, does it. You guys, You want you're wearing the sleepless shirts. You want to fill those things out? Let's not wait from what already isn't there, you know? And I think you know when we start looking at As you mentioned it, healing in the early stage returned to play. And now I'm never going to say, Hey, you know, you shouldn't do that. That's always up to the doctors and the medical professionals. But I think that there is lack of thought for our long term. Ah, mala dictations. So you mentioned, do we alter college and proliferation for the expense of just taking down some swelling and irritation? Maybe that paper's the response can be better handled by Tylenol or whatever else somebody thinks because I think it's critical. Especially, you know, you see the two different primary types intelligent Type one and Type three. They've seen that there is a blunted response and how that tendon regenerates. And so I think, you know, little things like that. Those conversations you have with your athletic trainer or your doctor and be like, Hey, is this absolutely necessary? I'm not questioning your rationale. But does this athlete need that? Or is there something else we can do? Is going to make sure that when I am doing the Afar or whatever before ISOs to maximize ah tended thickness or tendon restructuring or whatever I'm doing. Are we going to the baby? Out with the bath water? Are we gonna hurt something, You know, for the expense of you know what's easy and what we know from a Western medical model. >> Yeah, that's it. Very interesting moment. Thanks. By the way, I wanna clarify For those not familiar with terminology and says or non sorry, chase, I letyou go ahead there up the real quick and sense of things like ibuprofen and Advil around non steroidal anti inflammatory. Um, what's the d stand for? I'm forgetting right now. Feels stupid. Now draw. Go. Okay. There you go. Yeah, perfect things like ibuprofen and no Advil. I should take like six angel's before I play basketball. Because when it came out, I knew no better. It made me feel better and take more than barrier against coming out that we're really tearing up our system. What's interesting is we look at some of the inflammation studies. You look at older adults. It brings up the idea that as we age, we get in such an inflammatory state. We're taking things like insects, which are known to possibly reduce adaptation shins. And individuals were healthy. It actually increases muscle growth and some of the older adults because their level of inflammation, it's so high systemically that taking something as like an insider Advil, which we think is bad, actually increases adaptation. And they just show I just read a paper. Probably thirty men, too. For this that showed Curcumin has a potential effects to do the same, which might be a healthier alternative to end, says regards to reducing inflammation.

>> Live from Bahrain, it's theCUBE. Covering AWS Summit Bahrain. Brought to you by Amazon Web Services. >> Hello everyone, welcome to theCUBE's exclusive coverage. We are here in Bahrain in the Middle East for exclusive coverage of AWS's new region in the area. I'm John Furrier, cohost of theCUBE. It's our first time in the Middle East, as we go out into the world and expand theCUBE's mission of bringing you the best content, extracting the signal from the noise, meeting new people, connecting with thought leaders, people creating innovation, creating a new cultural shift with cloud computing. It's a societal global phenomenon, it's a change that's going to impact society, culture, economics, and humans. And this is theCUBE coverage, we're going to continue with that we are excited to have Khalid Al Rumaihi who is the CEO of the Bahrain Economic Development Board. He's the man, and responsible with his team for all the success and vision of bringing an Amazon region into the area. Here in Bahrain, Amazon has announced a region that's going to come in. And we expect to see economic revitalization. We expect to see an amplification of culture. Welcome to theCUBE, thank you for joining me. >> Thanks for having me John. >> Thanks for inviting us, and thanks for having us here. Here in the middle of all the action. Teresa Carlson from Amazon had a vision and you aligned with that vision, you guys are like-minded individuals. You saw something special with digital. >> Right. >> And this is not new. It's not like you woke up one morning and said, hey, let's bring Amazon in. Take us through the history of how we got here with Amazon about to launch a region early 2019 in Bahrain. You guys have had a vision, take us through that. >> You know, I started in my position about three years ago. I remember March 2015, a little more than three years ago. And my first week on the job, was joining his highness the crown prince in a meeting with Teresa. And so, in that meeting, that's what kicked it really off. Teresa heard form his highness, who is the chairman of the Bahrain Economic Development board, the vision for the country. We deregulated our telecom sector about 13, 14 years ago. We were the first country to do that in the Middle East. Which meant that we introduced competition on broadband, on mobile. It dropped prices by about 50%. On connectivity in the country. That attracted Amazon. When they looked at the region, they said, here's a government that's allowing true competition and for a data center obviously broadband communication, and the competitiveness of that price is key. And she was also impressed with his royal highness's vision for the country going forward. We want to become a digital economy, we want to transform this economy from an oil-based economy, to one that is based on information. And so we had a common view. And we determined, at that point, that we were going to do everything in our power to translate the conversation we had there to a reality. And here we are, almost three years later, almost to have a region here. >> And you know, people know my rant and rave, I always talk about, data is the new oil, information is the new oil. In that data and information, digital assets are digital. It a life-blood now of society. Citizen are reacting. Everyone's now connected with mobile devices, you're starting to see autonomous vehicles, you're starting to see a cultural blending between the old world, and then digital. And citizens can get new services, there's more efficiencies but there's actually a better opportunity for the citizens. And also in general. How do you guys look at that when you guys have your meetings, and you're looking at the vision of the future, the citizen benefits. Whether it's an entrepreneur or someone who's just living life. >> Well you know, when we had this discussion with Amazon, we decided to do what we call a cloud first policy. And we decided that we were going to move the government work loads to the cloud. We were going to actually, challenge any government institution, why they're not using the cloud. And it's been phenomenal. Now, it's been phenomenal from a cost saving perspective, which we want to pass on to the citizens. So for the citizens, for be for them to be able to get government services on their mobile phone, to pay their electricity bill to do get their license. And the government, if it reduces its cost can pass that on to that citizen. But more importantly, it's going to allow innovation to take place in the government. We're going to be able to have our education data in the Ministry of Education, communicate with our labor data. We're going to be able to do education in a new way. So it is going to unleash innovation in the government and the way it offers its services. We think it's going to do the same for businesses and for startups. >> We didn't get a chance to film it yesterday, but we were part of with Teresa Carlson's team with you and your startup Bahrain. All the entrepreneurs from the community, very vibrant, talking General Keith Alexander was there, knows a thing or two about cyber and then we had an entrepreneur visionary in John Wood, who's been in the business, but he's also a visionary. He made a comment and you reacted to that around the impact of the AWS region coming here. He was almost like, there's a storm of innovation coming and you align with that. You said, you kind of reacted at dinner last night about it. What is your feeling of what this will bring to the region? 'Cause Amazon has proven that when they put a region out, there's unexpected consequences sometimes like things you might not see. What are you expecting for the impact. For AWS? >> I think it's a game changer. I mean, you said data is the new oil. If we think back to the 30s, this country was the first country to discover oil. When, at that time, Texaco and So Cal started a refinery and started extracting oil, all the industries that developed around it refineries, oilfield engineering, oilfield services. You know, I think we're seeing we're going to see that in the new digital economy with data. Amazon coming here is going to do several things. Number one, it's going to unleash this innovation, it's going to reduce latency for people who are storing data looking to retrieve that. It's going to create new jobs, data scientists. We estimate 10,000 jobs are going to come on the back of this, that is going to be for the entire region. And I get it, I emphasize this is going to be a game changer, not just for the kingdom of Bahrain, but for the entire Middle East. We're already seeing startups who are getting educated about what the cloud can do for them, and the scale, the scale that they can reach by going to the cloud early on, we've seen them in the United States. Why can't this region see a unicorn that is able to be a global leader, just by virtue of, going to the cloud and learning from Amazon. And Amazon, AWS shares our passion for the startup community and what this can do for that. >> I want to get to the what's going to attract business to come into Bahrain. But first about what startup impact Amazon has proven and I heard a comment from one of the startups, Amazon Web Services is for big companies. Whoa, whoa, yeah, big companies are using Amazon now, but they won, they were built on the backs of startups. When Amazon first started and startups still use Amazon. It is a dream for a startup, the cost to get a company up off the ground, the speed of innovation with Amazon has proven startups, this is a big opportunity. And so this is going to impact how you set policy and get out of the way entrepreneurs, do you help them? As you look at policy, is that almost a tough decision on your part? 'Cause you guys are used to helping entrepreneurs, very entrepreneur friendly, but almost do you get out of their way, do you help them? What's the strategy for the startups? How do you look at this, because if the acceleration comes in and the training kicks in, you're going to see a renaissance of entrepreneurs, >> Right? >> What do you do, get out of their way, help them out? What's that? >> You got to balance it. I think, you can't coddle them. You can't do everything for the entrepreneur, there's got to be that grit, the resilience, that hunger at the entrepreneur. I was an entrepreneur before I took this role, and I think you've really got to have that fire in your belly. So what we want to do is we want to create an ecosystem, but we don't want to spoon feed them. So what we've done is for instance, we launched a $100 million venture capital fund of funds. And we said, the government shouldn't invest in startups but let's create a fund of funds that will invite venture capitalists to base themselves here, but we're not going to tell these venture capitalist how to invest. So each startup has to pitch itself to these venture capitalists and make sure that there's justification for it. We're going to create, you know, training, we're going to create elements, the regulation. We introduced a bankruptcy law this year, that is going to allow people to fail and to restructure. So we're going to put the policy in place. We're going to allow capital to be there, we're going to look at our training and education. But again, it really is down to the entrepreneur, to, so you've got to mix you've got to balance it. You've got to say, the burden is also on you to think about what's the market opportunity. Here is what the country will do, but then the rest is up to you. And I think, we're going to see our young youth in the region. We're doing this because this region is transforming. This region needs to create jobs. There's about a 100 million jobs you need to create in the Middle East over the next couple years. You're not going to be able to create that in the normal way. So we want people to become employers become entrepreneurs, rather than just employees and looking for a nine to five job. So it's integral to the vision of the region. >> Entrepreneurship is the engine of innovation. All right, let's talk about the region. You know, we're first out here so I'm kind of new, fresh eyes and you see Dubai out there, you got Asia, China and all these in Hong Kong and Singapore. So you guys have a unique opportunity. Dubai is kind of like a New York, it's hustle bustle is built out. You guys have this feeling like a Silicon Valley vibe. >> Right? >> It feels very open, very friendly, so you don't have to compete with each other. And New York does things, Silicon Valley does things. So you have this entrepreneurial culture. The key is a global co-creation a connection. How are you going to attract businesses? Because there is demand in the US for domiciling in places outside the United States. There's been a lot of competition. >> Sure. >> So are you prepared for companies to come here work with you? I know you guys are doing a lot of work. What do you say to the folks out there saying, I need to have a presence. Can I domicile in Bahrain? What's it like? What's the opportunities for me to connect into a growing ecosystem around Bahrain? >> So I'd say first of all, on the region, I mean, just like in Asia, just like in the US, you can have multiple hubs. So you know Bahrain will be a hub alongside a Dubai or a Riyadh or a Kuwait and so forth or a Abu Dhabi. And our niche is, as a small country, we're going to be very agile. One of the reasons why Amazon chose Bahrain is because we have a team Bahrain approach. And I, you know, I came from the private sector, when you're talking to General Electric, you're not talking to one department in General Electric, especially if you're a large customer. The whole company's going to rally around you and bring a solution to you as a customer. We're going to do that as a country. So with Amazon we got all the various ministries and we took a team Bahrain approach and we said we're going to solve through the economic development board, we're going to solve for your problem. Mondelez, which chose to locate their $100 million facility in Bahrain, built a facility about 30 soccer pitches, and they did it within a year and a half. We reclaimed land and had the land ready for them. They called it 'cause they make Oreos, they call it turning ocean to Oreos. >> Yeah. >> And so it's that agility that is going to differentiate us. In terms of niche, we're very interested in FinTech. We think we're going to take a leadership position not only regionally, but globally in FinTech. We have exciting announcements that we're going to make in FinTech. It's a small country, we can be nimble, agile, startup friendly, and kind of innovate. And so we're determined to carve a niche in open banking, in crypto currency exchanges, interesting innovation areas that we think we can excel at. >> Cloud computing certainly is a driver, artificial intelligence, obviously clearly. The fodder for entrepreneurship because it allows you to do things with data at a scale with a cloud engine, talk about FinTech and banking you can't ignore blockchain and crypto currency, which is bubble-ish right now, and then was kind of cleaning itself out, sorting itself out, but when that starts to settle and it becomes legitimate in the sense of a global access to digital money, or software defined money. >> Right. >> And data, that could be an integral part. How do you guys look at that? I know that's something that everyone's talking about. People are looking to do token kind of business models and there's really hasn't been any leadership globally at all on. >> Right. >> This is a place people can domicile, here Malta, here, there and there. So how do you guys look at that market, are you thinking about it, are you kicking the tires, what's happening? >> We're looking at FinTech and saying, really, beyond all the logos and all that. We're looking to reduce the friction for a customer doing the simple things. Looking at aggregating your accounts, understanding how you're spending money, looking at how to transfer money, looking at how to raise capital. If we can look at reducing the friction for people around these challenges, these day to day challenges and use our country as a pilot for doing that. Then imagine the potential that once you illustrate the potential here, you could go replicate it elsewhere. So we're very interested in blockchain. So you talk about crypto currencies, I think the real interesting element is the blockchain opportunity in FinTech and beyond. How can you allow the distributed ledger to have multiple applications. We're going to introduce issuing car licensing by a blockchain. Land, real estate transactions via blockchain. In addition to that, we're looking at open banking and allowing open banking to be prevalent here and allowing entrepreneurs to plug in and get access to that data and innovate around that. So that's how we're thinking about innovation in FinTech. >> Really, thanks for coming on and spending the time. I know you're super busy, and thanks for hosting us with theCUBE as part of the Amazon contingent. I give you the final word for the folks watching out there. What should they know about Bahrain that they might not know about it? And how do they engage with you guys? What are you guys doing? How should someone contact you? How do we engage? And what's the secret sauce of the Bahrain plan? >> Well, first of all, I'm going to plug my institution. It's simple, look at bahrainedb.com. It's on the internet. It's going to give you everything you need about what Bahrain. And what I'd say is, this is a small, but you know in this, in today's world, a global world and interconnected world, small is beautiful. So we're a small, forward thinking country. We're in a region that is about $1.5 trillion in terms of just the Gulf Cooperation Council. And here is a great gateway for tapping into that opportunity. We're about 30 minutes from the kingdom of Saudi Arabia which is doing wonderful things with Vision 2030, and you can be in Bahrain accessing that opportunity. And so I'd invite you to come, look at our website and the Bahrainedb will help you translate that kind of opportunity to a reality. >> Khalid, Chief Executive of Economic Development Board in Bahrain. Bold move congratulations. Bold moves have bold payoffs. Big bet with Amazon. >> Thanks, for having me John. >> Thanks for coming on. It's theCUBE here, we're live in Bahrain here at the Ritz Carlton for AWS summit 2018 here in the Middle East. I'm John Furrier. We'll be back with more coverage after this short break. (upbeat music)

>> Live from New York City, it's theCUBE, covering Lenovo Transform 2.0 brought to you by Lenovo. >> Welcome back to theCUBE's live coverage of Lenovo Transform here in New York City. I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Melissa Massa. She is the Executive Director of Hyperscale Sales. Thanks so much for coming on theCUBE. >> Thank you, thank you for having me. It's quite exciting. >> It is, it is very exciting. You're a cube newbie. >> I'm a cube newbie, yes. >> So this is very exciting. I'm sure it's the first of many visits. So Melissa we're at this real inflection point in technology and in AI as AI is ushering in this new wave with increasing use of big data and analytics and machine learning. All this means hyperscale is increasingly important. Can you just set the stage for our viewers a little bit about where we are in this-- >> Absolutely, yeah the transformation is really taking place in this industry that we know and love. And it's really amazing at how fast rapid the change is coming so if you look at in the past traditional one U two U type compute were the standard requirements right and today it's much more complex. It's becoming a much faster paced and you look at some of the big guys out there right from the top ten space. They're really helping to evolve AI and machine learning much faster as it's part of the cloud now and it's centric from the cloud space. So it's making things whether it's for personal use, for play, for business or for good humanity type areas. It's really helping involve and change the space altogether. >> One of the themes we've talked about in our kickoff there is Lenovo has a global presence, but it's also through a lot of partnerships. So Intel, Nvidia of course has to be very important in the AI space, you know, people like Microsoft and VMware. That's very much you know, some of those last ones especially look like Microsoft and VMware very much on the enterprise side. The cloud, the hyperscale, you mentioned the top 10 providers. What are the pieces, what are they looking for? What's the expertise that Lenovo brings that helps you fight in this very competitive real tight margin and very demanding ever-changing marketplace? >> You know this marketplace well? You sum it up very well, but in this in this marketplace, when you look at what the big guys are doing right and then you talk about partnerships, in our space, we don't come in and we don't have predisposition in terms of what we're going to. It's really through understanding what they're trying to do with technology and the direction they're going and it's interesting because at Lenovo we have several hundred engineers now dedicated just in our hyperscale organization, but we have 2000 engineers across the globe. So this really allows us to tap into this expertise in our organization, everything from even HPC aspects to multi socket boxes to different types of platforms, you look at ARM, you can look at AMD, look at Intel. So we don't really try to be one provider. We try to be the provider for our customers, and what their needs and where their requirements are going. >> So where have you seen the most success and we're looking forward do you see the growth coming from? >> Yeah we've started out a little bit different in this space. I think a lot of companies take a while getting their name out and getting traction, trying to grow up in what I'll call more that tier two that tier three space. Lenovo really has come into the tier one space. We're very fortunate in that aspect that we kind of are doing more of a top-down trajectory, so we've been very successful. I think you've heard Kirk talk about and you'll hear us continue to talk about the partnerships we have today with ten of the largest, truth be known, I've got pilots going on with the others. I think in a very short period of time we'll be talking about what we're doing across all of the top ten that is really unique to Lenovo, but again I think one of the reasons there's been success there is there's an availability of an engineer to engineer relationship we bring to the table that is really unique and allows our customers as they're going through this evolution with this change in the cloud space, they're realizing that there's not always the expertise they need in house. They've got to go outside and external and look for help in certain areas. One of the areas is we have an eight socket box and it's a great box with an incredibly high memory footprint and there's not a reference architecture on that box in the marketplace. Lenovo really helped develop it. So that's been a great platform for us to be able to have conversations with clients around for SAP hosting, HANA hosting and whatnot. >> Can you talk a little bit about this kind of the scale and investment Lenovo needs to have to be successful in this space? For those of us that track the hyperscales it's like you know there's tens of billions of dollars a year that they're investing in people, plant, and infrastructure. Kirk mentioned in the keynote, what was it? 42 soccer field size manufacturing facility. Is that only for hyperscale? Is it used for some of the other businesses? Help us unpack that a little yeah. >> So that's great, great question. To be in this business, you have to be incredibly committed into this business right, and I can say from YY on down through our entire leadership organization, there is a passion around this space from a hyperscale compute perspective in ensuring our success. In order to do that it really comes with making those right investments, so we can take care of these customers both near-term and long-term. This is not a short-term thing. This is an incredibly long-term plan for us and I will tell you the growth numbers they've given me over the course of the next years so that we have to make these types of investments right, so not only do we leverage our own manufacturing plants, but fortunately for Lenovo, we own. So it really helps minimize margin stacking but I've got great manufacturing facilities around the world and also now as you heard today, and the 42 football fields, we have started our own motherboard lines in our Hefei China Factory. So we'll be producing over 40,000 boards there a year with the two lines we have and then we're going to continue to grow well beyond that. >> So you are a tech veteran. You've been, at this is not your first rodeo here at Lenovo. How would you describe, I mean talking about YY's vision and the commitment he has made to hyperscale, what do you think it is that differentiates Lenovo in this very crowded and competitive tech world? >> I came from a couple of different places before Lenovo. So I had seen the OEM, I had seen the ODM aspect. And I was nervous when we launched this out of Lenovo as to how well is the market going to receive it. It's a crowded place and then you've started to see some of the other players that have been there, have faded off right. So what's really interesting about Lenovo when people ask us about what is your strategy, it's really we call it our ODM plus model and what does that mean? Well it means I'm taking the best parts of an OEM from a size, the global perspective of the markets I can get into for my clients are incredible and for an export of record, being able to get them into markets that are very challenging for others, I have a global services organization. So if you do need me to happen to come into your data center and help with other things, we have that capability too. And then also, but because I own my own manufacturing and I don't outsource anything, I keep relatively low costs to do business with. I can compete with more of that traditional ODM size and now you take the full vertical integration we have and you bring that to the table with being able to we manufacture all of our own motherboards, all the way up through our systems, it's a pretty powerful story, and I think from what we've seen the clients have really resonated with this story. They like what they're seeing from the benefits. >> Yeah it's so much we can learn, maybe you talk so much about scale, I think first of all the customer base that you talk about, 5000 servers or more is kind of the entry level for that, and just the speed that they're changing. A question we get all the time is how do people keep up with this? Give us a little bit of insight as to what you're hearing from your customers in the hyperscale market? How are they keeping to innovate, keeping to grow and how can everybody deal with kind of the pace of change today? >> It's unbelievable, I mean you look around it's immersive data. It's the network you got all this data now and you've got to get it through a pipe right and so there's all these different aspects coming. I've always told our customers look if there are areas that I can't help you with in, I'm going to tell you. I'm going to be more what's right up the middle for you guys, so we really focus on where are you going, where are you evolving, where do you need help from, how can we help to get you? I don't know if Kirk or anybody at the team has talked about it, but really breaking news for you guys because I was going to announce it in pitch today is that we are actually going to build our own white box networking products, and we're going to leave them open source from an OS perspective for our customers too, because we feel this is going to be a very key area for them. We've got the in-house talent. We've actually moved a number of engineers on our networking team directly into our hyperscale organization to get this started. >> Okay is this announcement which, congratulations by the way, is this, are you hearing that demand from the hyperscalers? Some of the hyperscalers have-- >> Absolutely. >> Kind of dipped their toe in there. I know you've been at the OCP events where we see some of the big players like Microsoft and Google. How do they fit, how does that compete against Cisco, so yeah how much of that is kind of a requirement to the customers? >> It is a requirement. I think if you're going to be all-in with these customers because we happen to have a great investment in the networking space already. Also you see Lenovo I think we're a company that we don't come with 50 years of habits right? We come as a fresh company. I never hear inside the company oh we tried that 10 years ago, and we don't want to do it again. We come with a fresh perspective and approach to building our business. We've got the networking organization inside of our company. Why not proliferate it in the next generation and why does that matter? Open matters right? Everything look at what's coming today. Open BMC, open OS. I have major customers coming into Raleigh and sitting down and talking to us about where we going from a security perspective, and how we're going to bring open security standards into this market? >> The other thing when I think about you know, YY mentioned it. Cloud network and device kind of things like IOT and the global device because everybody, AI and IOT everybody's going there. How does that play in your space? >> It just continues, the data just continues to double in massive size and scale, and there are new technologies out. People are learning to use things like the FPGA is a lot smarter and you look at like what they're able to do today from that technology and deliver one server that can take the compute power of four now. So all of that is helping to evolve this rapid pace and where we're going. >> Finally what we'll be talking about next year? I mean perhaps inked deals with the remaining four players that you are in pilot programs with. What other things are most exciting to you? >> Yeah so I think in what you're going to find is I'm launching a team that's going to go after the tier II and tier III market. And we're going to really start to invest in this space. We're going to really start to proliferate. Paul and I, you saw up on the screen. We have 33 custom boards in design today. We have a factory that we need to fill right, so we're going to continue to really push the envelope on everything we're going to be developing from a custom perspective. I think you're going to see it evolve with quite a number of products, maybe even more so beyond just your traditional server approach. We're there to help clients in other areas where they also need to manufacture maybe a part or what could be a commodity for them. And they need special attention in that particular space. We're going to continue to work with them, but I would say the biggest thing. When I'm sitting here next year is going to be the sheer size of where this hyperscale team is going and the revenue and the growth that's bringing in to Lenovo overall. >> Great well thank you so much for coming into theCUBE Melissa. >> It was nice talking to you. >> I appreciate it. Thank you. >> I'm Rebecca Knight for Stu Miniman. We will have more from theCUBE live at Lenovo Transform in just a little bit. (upbeat music)

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. 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>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.