>> Welcome everyone to "theCUBE's" coverage of "Compute engineered for your hybrid world," sponsored by HP and Intel. Today we're going to going to discuss how to transform your compute management experience with the new 4th Gen Intel Xeon scalable processors. Hello, I'm John Furrier, host of "theCUBE," and my guests today are Chinmay Ashok, director cloud engineering at Intel, and Koichiro Nakajima, principal product manager, compute at cloud services with HPE. Gentlemen, thanks for coming on this segment, "Transform your compute management experience." >> Thanks for having us. >> Great topic. A lot of people want to see that system management one pane of glass and want to manage everything. This is a really important topic and they started getting into distributed computing and cloud and hybrid. This is a major discussion point. What are some of the major trends you guys see in the system management space? >> Yeah, so system management is trying to help user manage their IT infrastructure effectively and efficiently. So, the system management is evolving along with the IT infrastructures which is trying to accommodate market trends. We have been observing the continuous trends like digital transformation, edge computing, and exponential data growth never stops. AI, machine learning, deep learning, cloud native applications, hybrid cloud, multi-cloud strategies. There's a lot of things going on. Also, COVID-19 pandemic has changed the way we live and work. These are all the things that, given a profound implication to the system design architectures that system management has to consider. Also, security has always been the very important topic, but it has become more important than ever before. Some of the research is saying that the cyber criminals becoming like a $10.5 trillion per year. We all do our efforts on the solution provider size and on the user side, but still cyber criminals are growing 15% year by year. So, with all this kind of thing in the mind, system management really have to evolve in a way to help user efficiently and effectively manage their more and more distributed IT infrastructure. >> Chinmay, what's your thoughts on the major trends in system management space? >> Thanks, John, Yeah, to add to what Koichiro said, I think especially with the view of the system or the service provider, as he was saying, is changing, is evolving over the last few years, especially with the advent of the cloud and the different types of cloud usage models like platform as a service, on-premises, of course, infrastructure is a service, but the traditional software as a service implies that the service provider needs a different view of the system and the context in which we need the CPU vendor, or the platform vendor needs to provide that, is changing. That includes both in-band telemetry being able to monitor what is going on on the system through traditional in-band methods, but also the advent of the out-of-band methods to do this without end user disruption is a key element to the enhancements that our customers are expecting from us as we deploy CPUs and platforms. >> That's great. You know what I love about this discussion is we had multiple generation enhancements, 4th Gen Xeon, 11th Gen ProLiant, iLOs going to come up with got another generation increase on that one. We'll get into that on the next segment, but while we're here, what is iLO? Can you guys define what that is and why it's important? >> Yeah, great question. Real quick, so HPE Integrated Lights-Out is the formal name of the product and we tend to call it as a iLO for short. iLO is HPE'S BMC. If you're familiar with this topic it's a Baseboard Management Controller. If not, this is a small computer on the server mother board and it runs independently from host CPU and the operating system. So, that's why it's named as Lights-Out. Now what can you do with the iLO? iLO really helps a user manage and use and monitor the server remotely, securely, throughout its life from the deployment to the retirement. So, you can really do things like, you know, turning a server power on, off, install operating system, access to IT, firmware update, and when you decide to retire server, you can completely wipe the data off that server so then it's ready to trash. iLO is really a best solution to manage a single server, but when you try to manage hundreds or thousand of servers in a larger scale environment, then managing server one by one by one through the iLO is not practical. So, HPE has two options. One of them is a HPE OneView. OneView is a best solution to manage a very complex, on-prem IT infrastructure that involves a thousand of servers as well as the other IT elements like fiber channel storage through the storage agent network and so on. Another option that we have is HPE for GreenLake Compute Ops Management. This is our latest, greatest product that we recently launched and this is a best solution to manage a distributed IT environment with multiple edge points or multiple clouds. And I recently involved in the customer conversation about the computer office management and with the hotel chain, global hotel chain with 9,000 locations worldwide and each of the location only have like a couple of servers to manage, but combined it's, you know, 27,000 servers and over the 9,000 locations, we didn't really have a great answer for that kind of environment before, but now HPE has GreenLake for computer office management for also deal with, you know, such kind of environment. >> Awesome. We're going to do a big dive on iLO in the next segment, but Chinmay, before we end this segment, what is PMT? >> Sure, so yeah, with the introduction of the 4th Gen Intel Xeon scalable processor, we of course introduce many new technologies like PCI Gen 5, DDR5, et cetera. And these are very key to general system provision, if you will. But with all of these new technologies come new sources of telemetry that the service provider now has to manage, right? So, the PMT is a technology called Platform Monitoring Technology. That is a capability that we introduced with the Intel 4th Gen Xeon scalable processor that allows the service provider to monitor all of these sources of telemetry within the system, within the system on chip, the CPU SOC, in all of these contexts that we talked about, like the hybrid cloud and cloud infrastructure as a service or platform as a service, but both in their in-band traditional telemetry collection models, but also out-of-band collection models such as the ones that Koichiro was talking about through the BMC et cetera. So, this is a key enhancement that we believe that takes the Intel product line closer to what the service providers require for managing their end user experience. >> Awesome, well thanks so much for spending the time in this segment. We're going to take a quick break, we're going to come back and we're going to discuss more what's new with Gen 11 and iLO 6. You're watching "theCUBE," the leader in high tech enterprise coverage. We'll be right back. (light music) Welcome back. We're continuing the coverage of "theCUBE's" coverage of compute engineered for your hybrid world. I'm John Furrier, I'm joined by Chinmay Ashok who's from Intel and Koichiro Nakajima with HPE. We're going to dive deeper into transforming your compute management experience with 4th Gen Intel Xeon scalable processors and HP ProLiant Gen11. Okay, let's get into it. We want to talk about Gen11. What's new with Gen11? What's new with iLO 6? So, NexGen increases in performance capabilities. What's new, what's new at Gen11 and iLO 6 let's go. >> Yeah, iLO 6 accommodates a lot of new features and the latest, greatest technology advancements like a new generation CPUs, DDR5 memories, PCI Gen 5, GPGPUs, SmartNICs. There's a lot of great feature functions. So, it's an iLO, make sure that supports all the use cases that associate with those latest, greatest advancements. For instance, like you know, some of the higher thermal design point CPU SKUs that requires a liquid cooling. We all support those kind of things. And also iLO6 accommodates latest, greatest industry standard system management, standard specifications, for instance, like an DMTF, TLDN, DMTF, RDE, SPDM. And what are these means for the iLO6 and Gen11? iLO6 really offers the greatest manageability and monitoring user experiences as well as the greatest automation through the refresh APIs. >> Chinmay, what's your thoughts on the Gen11 and iLO6? You're at Intel, you're enabling all this innovation. >> Yeah. >> What's the new features? >> Yeah, thanks John. Yeah, so yeah, to add to what Koichiro said, I think with the introduction of Gen11, 4th Gen Intel Xeon scalable processor, we have all of these rich new feature sets, right? With the DDR5, PCI Gen5, liquid cooling, et cetera. And then all of these new accelerators for various specific workloads that customers can use using this processor. So, as we were discussing previously, what this brings is all of these different sources of telemetry, right? So, our sources of data that the system provider or the service provider then needs to utilize to manage the compute experience for their end user. And so, what's new from that perspective is Intel realized that these new different sources of telemetry and the new mechanisms by which the service provider has to extract this telemetry required us to fundamentally think about how we provide the telemetry experience to the service provider. And that meant extending our existing best-in-class, in-band telemetry capabilities that we have today already built into in market Intel processors. But now, extending that with the introduction of the PMT, the Platform Monitoring Technology, that allows us to expand on that in-band telemetry, but also include all of these new sources of telemetry data through all of these new accelerators through the new features like PCI Gen5, DDR5, et cetera, but also bring in that out-of-band telemetry management experience. And so, I think that's a key innovation here, helping prepare for the world that the cloud is enabling. >> It's interesting, you know, Koichiro you had mentioned on the previous segment, COVID-19, we all know the impact of how that changed, how IT at the managed, you know, all of a sudden remote work, right? So, as you have cloud go to hybrid, now we got the edge coming, we're talking about a distributed computing environment, we got telemetry, you got management. This is a huge shift and it's happening super fast. What's the Gen11 iLO6 mean for architects as they start to look at going beyond hybrid and going to the edge, you're going to need all this telemetry. What's the impact? Can you guys just riff and share your thoughts on what this means for that kind of NexGen cloud that we see coming on on which is essentially distributed computing. >> Yeah, that's a great topic to discuss. So, there's a couple of the things. Really, to make sure those remote environment and also the management distributed IT environments, the system management has to reach across the remote location, across the internet connections, and the connectivities. So, the system management protocol, for instance, like traditionally IPMI or SNMP, or those things, got to be modernized into more restful API and those modern integration friendly to the modern tool chains. So, we're investing on those like refresh APIs and also again, the security becomes paramount importance because those are exposed to the bad people to snoop and trying to do some bad thing like men in a middle attacks, things like that. So we really, you know, focus on the security side on the two aspects on the iLO6 and Gen11. One other thing is we continue our industry unique silicon root of trust technology. So, that one is fortunate platform making sure the platform firmware, only the authentic and legitimate image of the firmware can run on HP server. And when you check in, validating the firmware images, the root of the trust reside in the silicon. So, no one can change it. Even the bad people trying to change the root of trust, it's bond in the chips so you cannot really change. And that's why, even bad people trying to compromise, you know, install compromise the firmware image on the HPE servers, you cannot do that. Another thing is we're making a lot of enhancements to make sure security on board our HP server into your network or onto a services like a GreenLake. Give you a couple of example, for instance, like a IDevID, Initial Device ID. That one is conforming to IEEE 802.1AR and it's immutable so no one can change it. And by using the IDevID, you can really identify you are not onboarding a rogue server or unknown server, but the server that you you want to onboard, right? It's absolutely important. Another thing is like platform certificate. Platform certificate really is the measurement of the configuration. So again, this is a great feature that makes sure you receive a server from the factory and no one during the transportation touch the server and alter the configuration. >> Chinmay, what's your reaction to this new distributed NextGen cloud? You got data, security, edge, move the compute to the data, don't move the data around. These are big conversations. >> Yeah, great question, John. I think this is an important thing to consider for the end user, the service provider in all of these contexts, right? I think Koichiro mentioned some of these key elements that go into as we develop and design these new products. But for example, from a security perspective, we introduce the trust domain extensions, TDX feature, for confidential computing in Intel 4th Generation Xeon scalable processors. And that enables the isolation of user workloads in these cloud environments, et cetera. But again, going back to the point Koichiro was making where if you go to the edge, you go to the cloud and then have the edge connect to the cloud you have independent networks for system management, independent networks for user data, et cetera. So, you need the ability to create that isolation. All of this telemetry data that needs to be isolated from the user, but used by the service provider to provide the best experience. All of these are built on the foundations of technologies such as TDX, PMT, iLO6, et cetera. >> Great stuff, gentlemen. Well, we have a lot more to discuss on our next segment. We're going to take a break here before wrapping up. We'll be right back with more. You're watching "theCUBE," the leader in high tech coverage. (light music) Okay, welcome back here, on "theCUBE's" coverage of "Compute engineered for your hybrid world." I'm John Furrier, host of the Cube. We're wrapping up our discussion here on transforming compute management experience with 4th Gen Intel Xeon scalable processors and obviously HPE ProLiant Gen11. Gentlemen, welcome back. Let's get into the takeaways for this discussion. Obviously, systems management has been around for a while, but transforming that experience on the management side is super important as the environment just radically changing for the better. What are some of the key takeaways for the audience watching here that they should put into their kind of tickler file and/or put on their to-do list to keep an eye on? >> Yeah, so Gen11 and iLO6 offers the latest, greatest technologies with new generation CPUs, DDR5, PCI Gen5, and so on and on. There's a lot of things in there and also iLO6 is the most mature version of iLO and it offers the best manageability and security. On top of iLO, HP offers the best of read management options like HP OneView and Compute Ops Management. It's really a lot of the things that help user achieve a lot of the things regardless of the use case like edge computing, or distributed IT, or hybrid strategy and so on and on. And you could also have a great system management that you can unleash all the full potential of latest, greatest technology. >> Chinmay, what's your thoughts on the key takeaways? Obviously as the world's changing, more gen chips are coming out, specialized workloads, performance. I mean, I've never met anyone that says they want to run on slower infrastructure. I mean, come on, performance matters. >> Yes, no, it definitely, I think one of the key things I would say is yes, with Gen11 Intel for gen scalable we're introducing all of these technologies, but I think one of the key things that has grown over the last few years is the view of the system provider, the abstraction that's needed, right? Like the end user today is migrating a lot of what they're traditionally used to from a physical compute perspective to the cloud. Everything goes to the cloud and when that happens there's a lot of just the experience that the end user sees, but everything underneath is abstracted away and then managed by the system provider, right? So we at Intel, and of course, our partners at HP, we have spent a lot of time figuring out what are the best sets of features that provide that best system management experience that allow for that abstraction to work seamlessly without the end user noticing? And I think from that perspective, the 4th Gen Intel Xeon scalable processors is so far the best Intel product that we have introduced that is prepared for that type of abstraction. >> So, I'm going to put my customer hat on for a second. I'll ask you both. What's in it for me? I'm the customer. What's in it for me? What's the benefit to me? What does this all mean to me? What's my win? >> Yeah, I can start there. I think the key thing here is that when we create capabilities that allow you to build the best cloud, at the end of the day that efficiency, that performance, all of that translates to a better experience for the consumer, right? So, as the service provider is able to have all of these myriad capabilities to use and choose from and then manage the system experience, what that implies is that the end user sees a seamless experience as they go from one application to another as they go about their daily lives. >> Koichiro, what's your thoughts on what's in it for me? You guys got a lot of engineering going on in Gen11, every gen increase always is a step function and increase of value. What's in it for me? What do I care? What's in it for me? I'm the customer. >> Alright. Yeah, so I fully agree with Chinmay's point. You know, he lays out the all the good points, right? Again, you know what the Gen11 and iLO6 offer all the latest, greatest features and all the technology and advancements are packed in the Gen11 platform and iLO6 unleash all full potentials for those benefits. And things are really dynamic in today's world and IT system also going to be agile and the system management get really far, to the point like we never imagine what the system management can do in the past. For instance, the managing on-prem devices across multiple locations from a single point, like a single pane of glass on the cloud management system, management on the cloud, that's what really the compute office management that HP offers. It's all new and it's really help customers unleash full potential of the gear and their investment and provide the best TCO and ROIs, right? I'm very excited that all the things that all the teams have worked for the multiple years have finally come to their life and to the public. And I can't really wait to see our customers start putting their hands on and enjoy the benefit of the latest, greatest offerings. >> Yeah, 4th Gen Xeon, Gen11 ProLiant, I mean, all the things coming together, accelerators, more cores. You got data, you got compute, and you got now this idea of security, I mean, you got hitting all the points, data and security big features here, right? Data being computed in a way with Gen4 and Gen11. This is like the big theme, data security, kind of the the big part of the core here in this announcement, in this relationship. >> Absolutely. I believe, I think the key things as these new generations of processors enable is new types of compute which imply is more types of data, more types of and hence, with more types of data, more types of compute. You have more types of system management more differentiation that the service provider has to then deal with, the disaggregation that they have to deal with. So yes, absolutely this is, I think exciting times for end users, but also for new frontiers for service providers to go tackle. And we believe that the features that we're introducing with this CPU and this platform will enable them to do so. >> Well Chinmay thank you so much for sharing your Intel perspective, Koichiro with HPE. Congratulations on all that hard work and engineering coming together. Bearing fruit, as you said, Koichiro, this is an exciting time. And again, keep moving the needle. This is an important inflection point in the industry and now more than ever this compute is needed and this kind of specialization's all awesome. So, congratulations and participating in the "Transforming your compute management experience" segment. >> Thank you very much. >> Okay. I'm John Furrier with "theCUBE." You're watching the "Compute Engineered for your Hybrid World Series" sponsored by HP and Intel. Thanks for watching. (light music)

>>How do y'all, and welcome to Dallas, where we're proud to be live from Supercomputing 2022. My name is Savannah Peterson, joined here by my cohost David on the Cube, and our first guest today is a very exciting visionary. He's a leader at Dell. Please welcome Travis Vhi. Travis, thank you so much for being here. >>Thank you so much for having me. >>How you feeling? >>Okay. I I'm feeling like an exciting visionary. You >>Are. That's, that's the ideas why we tee you up for that. Great. So, so tell us, Dell had some huge announcements Yes. Last night. And you get to break it to the cube audience. Give us the rundown. >>Yeah. It's a really big show for Dell. We announced a brand new suite of GPU enabled servers, eight ways, four ways, direct liquid cooling. Really the first time in the history of the portfolio that we've had this much coverage across Intel amd, Invidia getting great reviews from the show floor. I had the chance earlier to be in the whisper suite to actually look at the gear. Customers are buzzing over it. That's one thing I love about this show is the gear is here. >>Yes, it is. It is a haven for hardware nerds. Yes. Like, like well, I'll include you in this group, it sounds like, on >>That. Great. Yes. Oh >>Yeah, absolutely. And I know David is as well, sew up >>The street. Oh, big, big time. Big time hardware nerd. And just to be clear, for the kids that will be watching these videos Yes. We're not talking about alien wear gaming systems. >>No. Right. >>So they're >>Yay big yay tall, 200 pounds. >>Give us a price point on one of these things. Re retail, suggested retail price. >>Oh, I'm >>More than 10 grand. >>Oh, yeah. Yeah. Try another order of magnitude. Yeah. >>Yeah. So this is, this is the most exciting stuff from an infrastructure perspective. Absolutely. You can imagine. Absolutely. But what is it driving? So talk, talk to us about where you see the world of high performance computing with your customers. What are they, what are they doing with this? What do they expect to do with this stuff in the future? >>Yeah. You know, it's, it's a real interesting time and, and I know that the provenance of this show is HPC focused, but what we're seeing and what we're hearing from our customers is that AI workloads and traditional HPC workloads are becoming almost indistinguishable. You need the right mix of compute, you need GPU acceleration, and you need the ability to take the vast quantities of data that are being generated and actually gather insight from them. And so if you look at what customers are trying to do with, you know, enterprise level ai, it's really, you know, how do I classify and categorize my data, but more, more importantly, how do I make sense of it? How do I derive insights from it? Yeah. And so at the end of the day, you know, you look, you look at what customers are trying to do. It's, it's take all the various streams of data, whether it be structured data, whether it be unstructured data, bring it together and make decisions, make business decisions. >>And it's a really exciting time because customers are saying, you know, the same things that, that, that, you know, research scientists and universities have been trying to do forever with hpc. I want to do it on industrial scale, but I want to do it in a way that's more open, more flexible, you know, I call it AI for the rest of us. And, and, and customers are here and they want those systems, but they want the ecosystem to support ease of deployment, ease of use, ease of scale. And that's what we're providing in addition to the systems. We, we provide, you know, Dell's one of the only providers on the on in the industry that can provide not only the, the compute, but the networking and the storage, and more importantly, the solutions that bring it all together. Give you one example. We, we have what we call a validated design for, for ai. And that validated design, we put together all of the pieces, provided the recipe for customers so that they can take what used to be two months to build and run a model. We provide that capability 18 times faster. So we're talking about hours versus months. So >>That's a lot. 18 times faster. I just wanna emphasize that 18 times faster, and we're talking about orders of magnitude and whatnot up here, that makes a huge difference in what people are able to do. Absolutely. >>Absolutely. And so, I mean, we've, you know, you've been doing this for a while. We've been talking about the, the deluge of data forever, but it's gotten to the point and it's, you know, the, the disparity of the data, the fact that much of it remains siloed. Customers are demanding that we provide solutions that allow them to bring that data together, process it, make decisions with it. So >>Where, where are we in the adoption cycle early because we, we've been talking about AI and ML for a while. Yeah. You, you mentioned, you know, kind of the leading edge of academia and supercomputing and HPC and what that, what that conjures up in people's minds. Do you have any numbers or, you know, any, any thoughts about where we are in this cycle? How many, how many people are actually doing this in production versus, versus experimenting at this point? Yeah, >>I think it's a, it's a reason. There's so much interest in what we're doing and so much demand for not only the systems, but the solutions that bring the systems together. The ecosystem that brings the, the, the systems together. We did a study recently and ask customers where they felt they were at in terms of deploying best practices for ai, you know, mass deployment of ai. Only 31% of customers said that they felt that they self-reported. 31% said they felt that they were deploying best practices for their AI deployments. So almost 70% self reporting saying we're not doing it right yet. Yeah. And, and, and another good stat is, is three quarters of customers have fewer than five AI applications deployed at scale in their, in their IT environments today. So, you know, I think we're on the, you know, if, if I, you think about it as a traditional S curve, I think we're at the first inflection point and customers are asking, Can I do it end to end? >>Can I do it with the best of breed in terms of systems? But Dell, can you also use an ecosystem that I know and understand? And I think that's, you know, another great example of something that Dell is doing is, is we have focused on ethernet as connectivity for many of the solutions that we put together. Again, you know, provenance of hpc InfiniBand, it's InfiniBand is a great connectivity option, but you know, there's a lot of care and feeding that goes along with InfiniBand and the fact that you can do it both with InfiniBand for those, you know, government class CU scale, government scale clusters or university scale clusters and more of our enterprise customers can do it with, with ethernet on premises. It's a great option. >>Yeah. You've got so many things going on. I got to actually check out the million dollar hardware that you have just casually Yeah. Sitting in your booth. I feel like, I feel like an event like this is probably one of the only times you can let something like that out. Yeah, yeah. And, and people would actually know what it is you're working >>With. We actually unveiled it. There was a sheet on it and we actually unveiled it last night. >>Did you get a lot of uz and os >>You know, you said this was a show for hardware nerds. It's been a long time since I've been at a shoe, a show where people cheer and u and a when you take the sheet off the hardware and, and, and Yes, yes, >>Yes, it has and reveal you had your >>Moment. Exactly, exactly. Our three new systems, >>Speaking of u and os, I love that. And I love that everyone was excited as we all are about it. What I wanna, It's nice to be home with our nerds. Speaking of, of applications and excitement, you get to see a lot of different customers across verticals. Is there a sector or space that has you personally most excited? >>Oh, personally most excited, you know, for, for credibility at home when, when the sector is media and entertainment and the movie is one that your, your children have actually seen, that one gives me credibility. Exciting. It's, you can talk to your friends about it at, at at dinner parties and things like that. I'm like, >>Stuff >>Curing cancer. Marvel movie at home cred goes to the Marvel movie. Yeah. But, but, but you know, what really excites me is the variety of applications that AI is being used, used in healthcare. You know, on a serious note, healthcare, genomics, a huge and growing application area that excites me. You know, doing, doing good in the world is something that's very important to Dell. You know, know sustainability is something that's very important to Dell. Yeah. So any application related to that is exciting to me. And then, you know, just pragmatically speaking, anything that helps our customers make better business decisions excites me. >>So we are, we are just at the beginning of what I refer to as this rolling thunder of cpu. Yes. Next generation releases. We re recently from AMD in the near future it'll be, it'll be Intel joining the party Yeah. Going back and forth, back and forth along with that gen five PCI e at the motherboard level. Yep. It's very easy to look at it and say, Wow, previous gen, Wow, double, double, double. It >>Is, double >>It is. However, most of your customers, I would guess a fair number of them might be not just N minus one, but n minus two looking at an upgrade. So for a lot of people, the upgrade season that's ahead of us is going to be not a doubling, but a four x or eight x in a lot of, in a lot of cases. Yeah. So the quantity of compute from these new systems is going to be a, it's gonna be a massive increase from where we've been in, in, in the recent past, like as in last, last Tuesday. So is there, you know, this is sort of a philosophical question. We talked a little earlier about this idea of the quantitative versus qualitative difference in computing horsepower. Do we feel like we're at a point where there's gonna be an inflection in terms of what AI can actually deliver? Yeah. Based on current technology just doing it more, better, faster, cheaper? Yeah. Or do we, or do we need this leap to quantum computing to, to get there? >>Yeah. I look, >>I think we're, and I was having some really interesting conversations with, with, with customers that whose job it is to run very, very large, very, very complex clusters. And we're talking a little bit about quantum computing. Interesting thing about quantum computing is, you know, I think we're or we're a ways off still. And in order to make quantum computing work, you still need to have classical computing surrounding Right. Number one. Number two, with, with the advances that we're, we're seeing generation on generation with this, you know, what, what has moved from a kind of a three year, you know, call it a two to three year upgrade cycle to, to something that because of all of the technology that's being deployed into the industry is almost more continuous upgrade cycle. I, I'm personally optimistic that we are on the, the cusp of a new level of infrastructure modernization. >>And it's not just the, the computing power, it's not just the increases in GPUs. It's not, you know, those things are important, but it's things like power consumption, right? One of the, the, the ways that customers can do better in terms of power consumption and sustainability is by modernizing infrastructure. Looking to your point, a lot of people are, are running n minus one, N minus two. The stuff that's coming out now is, is much more energy efficient. And so I think there's a lot of, a lot of vectors that we're seeing in, in the market, whether it be technology innovation, whether it be be a drive for energy efficiency, whether it be the rise of AI and ml, whether it be all of the new silicon that's coming in into the portfolio where customers are gonna have a continuous reason to upgrade. I mean, that's, that's my thought. What do you think? >>Yeah, no, I think, I think that the, the, the objective numbers that are gonna be rolling out Yeah. That are starting to roll out now and in the near future. That's why it's really an exciting time. Yeah. I think those numbers are gonna support your point. Yeah. Because people will look and they'll say, Wait a minute, it used to be a dollar, but now it's $2. That's more expensive. Yeah. But you're getting 10 times as much Yeah. For half of the amount of power boom. And it's, and it's >>Done. Exactly. It's, it's a >>Tco It's, it's no brainer. It's Oh yeah. You, it gets to the point where it's, you look at this rack of amazing stuff that you have a personal relationship with and you say, I can't afford to keep you plugged in anymore. Yeah. >>And Right. >>The power is such a huge component of this. Yeah. It's huge, huge. >>Our customer, I mean, it's always a huge issue, but our customers, especially in Amia with what's going on over there are, are saying, I, you know, I need to upgrade because I need to be more energy efficient. >>Yeah. >>Yeah. I I, we were talking about 20 years from now, so you've been at Dell over 18 years. >>Yeah. It'll be 19 in in May. >>Congratulations. Yeah. What, what commitment, so 19 years from now in your, in your second Dell career. Yeah. What are we gonna be able to say then that perhaps we can't say now? >>Oh my gosh. Wow. 19 years from now. >>Yeah. I love this as an arbitrary number too. This is great. Yeah. >>38 year Dell career. Yeah. >>That might be a record. Yeah. >>And if you'd like to share the winners of Super Bowls and World Series in advance, like the world and the, the sports element act from back to the future. So we can play ball bets power and the >>Power ball, but, but any >>Point building Yeah. I mean this is what, what, what, what do you think ai, what's AI gonna deliver in the next decade? >>Yeah. I, I look, I mean, there are are, you know, global issues that advances in computing power will help us solve. And, you know, the, the models that are being built, the ability to generate a, a digital copy of the analog world and be able to run models and simulations on it is, is amazing. Truly. Yeah. You know, I, I was looking at some, you know, it's very, it's a very simple and pragmatic thing, but I think it's, it, it's an example of, of what could be, we were with one of our technology providers and they, they were, were showing us a digital simulation, you know, a digital twin of a factory for a car manufacturer. And they were saying that, you know, it used to be you had to build the factory, you had to put the people in the factory. You had to, you know, run cars through the factory to figure out sort of how you optimize and you know, where everything's placed. >>Yeah. They don't have to do that anymore. No. Right. They can do it all via simulation, all via digital, you know, copy of, of analog reality. And so, I mean, I think the, you know, the, the, the, the possibilities are endless. And, you know, 19 years ago, I had no idea I'd be sitting here so excited about hardware, you know, here we are baby. I think 19 years from now, hardware still matters. Yeah. You know, hardware still matters. I know software eats the world, the hardware still matters. Gotta run something. Yeah. And, and we'll be talking about, you know, that same type of, of example, but at a broader and more global scale. Well, I'm the knucklehead who >>Keeps waving his phone around going, There's one terabyte in here. Can you believe that one terabyte? Cause when you've been around long enough, it's like >>Insane. You know, like, like I've been to nasa, I live in Texas, I've been to NASA a couple times. They, you know, they talk about, they sent, you know, they sent people to the moon on, on way less, less on >>Too far less in our pocket computers. Yeah. It's, it's amazing. >>I am an optimist on, on where we're going clearly. >>And we're clearly an exciting visionary, like we said, said the gate. It's no surprise that people are using Dell's tech to realize their AI ecosystem dreams. Travis, thank you so much for being here with us David. Always a pleasure. And thank you for tuning in to the Cube Live from Dallas, Texas. My name is Savannah Peterson. We'll be back with more supercomputing soon.

>> Announcer: From Miami Beach, Florida, it's The Cube, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Welcome back everyone. We are here with The Cube coverage for two days. We're wrapping up, getting down on day one in the books for the Acronis Global Cyber Summit 2019. I'm John Furrier, your host of The Cube. We are in Miami Beach, the Fontainebleau Hotel. I'm personally excited for this next guest because I'm a huge Red Sox fan, even though I got moved out to California. Giants is in a different area. National League is different than American League, still my heart with the Red Sox. And we're here with an industry veteran, seasoned professional in IT and data, Brian Shield. Boston Red Sox Vice President of Technology and IT. Welcome to The Cube, thanks for joining us. >> Thank you. It's great to be here. >> John: So congratulations on the rings. Since I moved out of town, Red sox win their World Series, break the curse of the Bambino. >> Hey we appreciate that. Thank you. >> My family doesn't want me back. You got to show >> Yeah, maybe I'll put this one up for the, maybe someone can zoom in on this. Which camera is the good one? This one here? So, there ya go. So, World Series champs for at least for another week. (laughter) >> Bummer about this year. Pitching just couldn't get it done. But, good team. >> Happens. >> Again, things move on, but you know. New regime, new GM going to come on board. >> Yup. >> So, but in general, Red Sox, storied franchise. Love it there. Fenway Park, the cathedral of baseball parks. >> Brian: Defnitely. >> And you're seeing that just play out now, standard. So just a great place to go. We have tickets there. So, I got to ask you. Technology, sports, really is modernized faster than I think any category. And certainly cyber security forced to modernize because of the threats. But sports, you got a business to run, not just IT and making the planes run on time. >> Sure. >> Scouts, money, whatever. >> Fans. >> You got fan experience. >> Stadium opportunities. >> Club management, scouts are out there. So you got business, team, fans. And data's a big part of it. That's part of your career. Tell us what the cutting edge innovation is at the Red Sox these days. >> I think baseball in general, as you indicated, it's a very evolving kind of environment. I mean historically I think people really sort of relish the nostalgia of sports and Fenway Park being as historic as it is, was probably the pinnacle of that, in some respects. But Red Sox have always been leaders and baseball analytics, you know. And everyone's pretty familiar with "Moneyball" and Brad Pitt. >> John: Is that a true story, he turned down the GM job? >> I'm told it is. (laughter) I don't know if I fully vetted that question. But over the last six, seven years, you know we've really turned our attention to sort of leveraging sort of technology across the businesses, right? Not just baseball and analytics and how we do scouting, which continues to evolve at a very rapid pace. But also as you pointed out, running a better business, understanding our fans, understanding fan behavior, understanding stadiums. There's a lot of challenges around running an effective stadium. First and foremost to all of us is really ensuring it's a great fan experience. Whether it's artificial intelligence, or IoT technologies or 5G or the latest Wifi, all those things are coming up at Fenway Park. You and I talked earlier about we're about to break ground for a new theater, so a live theater on the outside, beyond the bleachers type of thing. So that'll be a 5,400-seat arena, 200 live performances a year, and with e-sports, you know, complementing it. It just gives you an example of just how fast baseball is sort of transitioning. >> And the theater, is that going to be blown out from where that parking garage is, structure and going towards >> So the corner of Landsdown and Ipswich, if you think of that sort of corner back there, for those that are familiar with the Fenway area. So it's going to be a very big change and you'll see the difference too from within the ballpark. I think we'll lose a couple of rows of the bleachers. That'll be replaced with another gathering area for fans and things like that, on the back end of that theater. So build a great experience and I think it really speaks to sort of our ability to think of Fenway as more of a destination, as a venue, as a complementary experience. We want people to come to the area to enjoy sports and to enjoy entertainment and things. >> You know Brian, the consumerization of IT has been kicked around. Last decade, that was a big buzzword. Now the blending of a physical event and digital has certainly consumed the world. >> Absolutely. >> And we're starting to see that dynamic. You speak to a theater. That's a physical space. But digital is also a big part of kind of that complementary. It's not mutually exclusive for each other. They're integrated business models. >> Absolutely. >> So therefore, the technology has to be seamless. The data has to be available. >> Yup. >> And it's got to be secure. >> Well the data's got to be ubiquitous, right? I mean you don't want to, if we're going to have fans attending theater and then you're going to go to Fenway Park or they leave a game and then go to some other event or they attend a tour of Fenway Park, and beyond maybe the traditional what people might think about, is certainly when you think about baseball and Fenway Park. You know we have ten to twelve concerts a year. We'll host Spartan games, you know. This Christmas, I'm sorry, Christmas 2020 we now have sort of the Fenway Bowl. So we'll be hosting the AAC ACC championship games there with ESPN. >> John: Hockey games? >> Hockey games. Obviously we have Liverpool soccer being held there so it's much more of a destination, a venue for us. How we leverage all the wonderful things about Fenway Park and how we modernize, how we get basically the best of what makes Fenway Park as great as it is, yet as modern as we can make it, where appropriate to create a great fan experience. >> It's a tough balance between balancing the brand and having things on brand as well. >> Sure. >> Does that come into your job a lot around IT? Saying being on brand, not kind of tearing down the old. >> Yeah absolutely. I think our CEOs and leadership team, I mean it's not success for us if you pan to the audience and everyone is looking at their phone, right? That's not what we aspire to. We aspire to leverage technology to simplify people's experience of how do you get to the ballpark, how do I park, how do I get if I want to buy concessions or merchandise, how do I do it easily and simply? How do we supplement that experience with maybe additional data that you may not have had before. Things like that, so we're doing a lot of different testing right now whether it's 4D technologies or how we can understand, watch a play from different dimensions or AI and be able to perhaps see sort of the skyline of Boston since 1912, when Fenway Park launched... And so we sort of see all these technologies as supplemental materials, really kind of making it a holistic experience for fans. >> In Las Vegas, they have a section of Las Vegas where they have all their test beds. 5G, they call it 5G, it's really, you know, evolution, fake 5G but it's a sandbox. One of the challenges that you guys have in Boston, I know from a constraint standpoint physically, you don't have a lot of space. How do you sandbox new technologies and what are some of the things that are cool that people might not know about that are being sandboxed? So, one, how do you do it? >> Yeah. >> Effectively. And then what are some of the cool things that you guys are looking at or things they might not know about that would be interesting. >> Sure. Yeah so Fenway Park, we struggle as you know, a little bit with our footprint. You know, honestly, I walk into some of these large stadiums and I get instant jealousy, relative to just the amount of space that people have to work with and things. But we have a great relationship with our partners so we really partner, I think, particularly well with key partners like Verizon and others. So we now have 5G partially implemented at Fenway Park. We expect to have it sort of fully live come opening day next year. So we're really excited about that. We hope to have a new version of Wifi, the latest version of Wifi available, for the second half of the year. After the All-Star Break, probably after the season's over. But before our bowl game hopefully. We're looking at some really interesting ways that we can tease that out. That bowl game, we're really trying to use that as an opportunity, the Fenway Bowl, as an opportunity to make it kind of a high-tech bowl. So we're looking at ways of maybe doing everything from hack-a-thons to a pre-egaming sort of event to some interesting fan experiential opportunities and things like that. >> Got a lot of nerds at MIT, Northeastern, BU, Bentley, Babson, all the schools in the area. >> Yeah, so we'll be reaching out to colleges and we'll be reaching out to our, the ACC and AACs as well, and see what we can do to kind of create sort of a really fun experience and capitalize on the evolving role of e-sports and the role that technology can play in the future. >> I want to get to the e-sports in a second but I want to just get the plug in for Acronis. We're here at their Global Cyber Summit. You flew down for it, giving some keynote speeches and talks around security. It's a security company, data protection, to cyber protection. It is a data problem, not a storage appliance problem. It's a data problem holistically. You get that. >> Sure. Sure. >> You've been in the business for a long time. What is the security kind of posture that you guys have? Obviously you want to protect the data, protect privacy. But you got to business. You have people that work with you, supply chain, complex but yet dynamic, always on environment. >> That's a great question. It's evolving as you indicated. Major League Baseball, first and foremost, does an outstanding job. So the last, probably last four plus years, Major League Baseball has had a cyber security program that all the clubs partake in. So all 30 clubs are active participants in the program. They basically help build out a suite of tools as well as the ability to kind of monitor, help participate in the monitoring, sort of a lot of our cyber security assets and logs And that's really elevated significantly our posture in terms of security. We supplement that quite a bit and a good example of that is like Acronis. Acronis, for us, represents the ability for us to be able to respond to certain potential threats like ransom-ware and other things. As well as frankly, what's wonderful about a tool like this is that it allows us to also solve other problems. Making our scouts more efficient. We've got these 125 scouts scattered around the globe. These guys are the lifeblood of our, you know, the success of our business. When they have a problem, if they're in Venezuela or the Dominican or someplace else, in southeast Asia, getting them up and running as quickly as we can, being able to consume their video assets and other things as they're scouting prospects. We use Acronis for those solutions. It's great to kind of have a partner who can both double down as a cyber partner as well as someone who helps drive a more efficient business. >> People bring their phone into the stadiums too so those are end points now connecting to your network. >> Definitely. And as you pointed out before, we've got great partnerships. We've got a great concession relationship with Aramark and they operate, in the future they'll be operating off our infrastructure. So we're in the point of rolling out all new point-of-sale terminals this off-season. We're excited about that 'cause we think for the first time it really allows us to build a very comprehensive, very secure environment for both ourselves and for all the touchpoints to fans. >> You have a very stellar career. I noticed you were at Scudder Investments back in the '80s, very cutting-edge firm. FTD that set the whole standard for connecting retailers. Again, huge scale play. Can see the data kind of coming out, they way you've been a CIO, CTO. The EVP CIO at The Weather Channel and the weather.com again, first mover, kind of pioneer. And then now the Red Sox, pioneering. So I got to ask you the modernization question. Red Sox certainly have been cutting-edge, certainly under the last few owners, and the previous Henry is a good one, doing more and more, Has the business model of baseball evolved, 'cause you guys a franchise. >> Sure. >> You operate under the franchisor, Major League Baseball, and you have jurisdictions. So has digital blurred the lines between what Advanced Media unit can do. You got communities developing outside. I watch the games in California. I'm not in there but I'm present digitally. >> Sure. Sure. >> So how has the business model flexed with the innovation of baseball? >> That's a great question. So I mean, first off, the relationship between clubs like ours and MLB continue to evolve. We have a new commissioner, relatively new commissioner, and I think the whole one-baseball model that he's been promoting I think has been great. The boundaries sometimes between digital assets and how we innovate and things like that continues to evolve. Major League Baseball and technology groups and product groups that support Major League Baseball have been a fantastic partner of ours. If you look at some of the innovations with Statcast and some of the other types of things that fans are now becoming more familiar with. And when they see how fast a runner goes or how far a home run goes and all those sort of things, these kinds of capabilities are on the surface, but even like mobile applications, to make it easy for fans to come into ballparks and things like that really. What we see is really are platforms for the future touchpoints to all of our customers. But you're right, it gets complicated. Streaming videos and people hadn't thought of before. >> Latin America, huge audience for the Red Sox. Got great players down there. That's outside the jurisdiction, I think, of the franchise agreement, isn't it? (laughs) >> Well, it's complicated. As this past summer, we played two games in England, right? So we enjoy two games in London, sadly we lost to the Yankees in both of those, but amazing experience and Major League Baseball really hats off to those guys, what they did to kind of pull that together. >> You mentioned Statcast. Every year when I meet with Andy Jassy at AWS, he's a sports fan. We love to talk sports. That's a huge, kind of shows the power of data and cloud computing. >> No doubt. >> How do you guys interface with Statcast? Is that an Amazon thing? Do they come to you? Are they leveraging dimensions, camera angles? How does that all work? Are you guys involved in that or? >> Brian: Oh yeah, yeah. >> Is that separate? >> So Statcast is just one of many data feeds as you can imagine. One of the things that Major League Baseball does is all that type of data is readily available to every club. So every club has access to the data. The real competitive differentiator, if you will, is how you use it internally. Like how your analysts can consume that data. We have a baseball system we call Beacon. We retired Carmine, if you're familiar with the old days of Carmine. So we retired Carmine a few years ago with Beacon. And Beacon for us represents sort of our opportunity to effectively collapse all this information into a decision-making environment that allows us to hopefully to kind of make the best decisions to win the most games. >> I love that you're answering all these questions. I really appreciate it. The one I really want to get into is obviously the fan experience. We talked about that. No talent on the field means no World Series so you got to always be constantly replenishing the talent pool, farm system, recruiting, scouting, all these things go on. They're instrumental. Data's a key driver. What new innovations that the casual fan or IT person might be interested in what's going on around scouting and understanding the asset of a human being? >> Right. Sure. I mean some of this gets highly confidential and things, but I think at a macro level, as you start to see both in the minor leagues and in some portions of the major leagues, wearable technologies. I think beyond just sort of player performance information that you would see traditionally with you might associate it with like Billy Beane, and things like that with "Moneyball" which is evolved obviously considerably since those days. I mean understanding sort of player wellness, understanding sort of how to get the most out of a player and understanding sort of, be able to kind of predict potential injuries and accelerate recoveries and being able to use all of this technology where appropriate to really kind of help sort of maximize the value of player performance. I mean, David Ortiz, you know, I don't know where we would have been in 2018 without, you know, David. >> John: Yeah. >> But like, you know >> Longevity of a player. >> Absolutely. >> To when they're in the zone. You wear a ring now to tell you if you're sleeping well. Will managers have a visual, in-the-zone, don't pull 'em out, he can go an extra inning? >> Well, I mean they have a lot of data. We currently don't provide all that data to the clubhouse. I mean, you know, and so If you're in the dugout, that information isn't always readily available type of thing. But players know all this information. We continue to evolve it. At the end of the day though, it's finding the balancing act between data and the aptitudes of our coaching staff and our managers to really make the wise decisions. >> Brian, final question for you. What's the coolest thing you're working on right now? Besides the fan having a great experience, 'cause that's you kind of touched on that. What's the coolest thing that you're excited about that you're working on from a tech perspective that you think is going to be game-changing or interesting? >> I think our cloud strategy coming up in the future. It's still a little bit early stage, but our hope would be to kind of have clarity about that in the next couple months. I think is going to be a game-changer for us. I think having, you know, we enjoy a great relationship with Dell EMC and yet we also do work in the cloud and so being able to leverage the best of both of those to be able to kind of create sort of a compelling experience for both fans, for both player, baseball operations as well as sort of running an efficient business, I think is really what we're all about. >> I mean you guys are the poster child for hybrid cloud because you got core, data center, IoT, and >> No doubt. So it's exciting times. And we're very fortunate that with our relationship organizations like Dell and EMC, we have leading-edge technologies. So we're excited about where that can go and kind of what that can mean. It'll be a big step. >> Okay two personal questions from me as a fan. One is there really a money-counting room like in the movie "The Town"? Where they count a big stack of dollar bills. >> Well, I'm sure there is. I personally haven't visited it. (laughs) I know it's not in the room that they would tell you it is on the movie. (laughter) >> And finally, can The Cube get press passes to cover the games, next to NESN? Talk tech. >> Yeah, we'll see what we can do. >> They can talk baseball. We can talk about bandwidth. Right now, it's the level five conductivity. We're looking good on the pipes. >> Yeah we'll give you a tech tour. And you guys can sort of help us articulate all that to the fans. >> Thank you so much. Brian Shield, Vice President of Technology of the Boston Red Sox. Here talking about security and also the complications and challenges but the mega-opportunities around what digital and fan experiences are with the physical product like baseball, encapsulates kind of the digital revolution that's happening. So keep covering it. Here in Miami, I'm John Furrier. We'll be right back after this short break. (techno music)

(upbeat music) >> Hello everyone. I'm John Furrier here in Palo Alto at our CUBE headquarters. We're here with John Del Santo, Senior Managing Director at Accenture for a Cube Conversation. John, welcome to theCUBE. Good to see you. >> Thanks, John. Great to be here. >> So we just talked before we came on camera about Accenture and all the stuff you guys are doing. You guys are in the cloud heavily. We've been following, you guys have probably one of the most comprehensive analytics teams out there. And global SI market and just, the world's changing. So it's pretty fun. I'm looking forward to this conversation. So I got to ask you first, before we get started. I want to jump in with a ton of questions. What is your role at Accenture? You're in the Bay Area. Take a minute to explain what you do for Accenture and what's your territory. >> I've got the best job at Accenture. So, Accenture's got close to half a million people right now and my job is, I'm responsible for our business on the West Coast, across all of our industries, et cetera. I've been here 32 years, so I've seen a lot of things happen in the Bay Area. And I now have the responsibility of making sure that we're doing great work for our clients. And we're doing great work in the community. And then we're providing great opportunities to the thousands of people that work for us here in the Bay Area and across the West Coast. So it's a lot of fun. >> Obviously, West Coast is booming. And for tech it's been a hotbed. And obviously the industry's across the board now is global. I got to ask you because, you know, you've been around multiple waves of innovation. And Accenture's been, had their hands in enabling a lot of value creation for clients. You guys have a great reputation. There's a lot of smart people. But the waves are always kind of different in their own way, but sometimes it's the same. What's different about the way we're living now? Because you can almost look back and see the major inflection points. Obviously the PC revolution, client server, interoperability, networking stacks went standard. Then you saw the Internet come. Now you've got Web 2.0. And now you got the whole global, you got things like cryptocurrency and blockchain. You have multiple clouds. You have a whole new game-changing dynamic going on with IT infrastructure combined with opensource at a whole 'nother level. So how is this wave different? Is it like the, how would you compare? >> Well, I think all the technologies that have waved through my career, at least, have been real enablers for the business model that the companies had at the time, and that they evolved. What we see now is epic disruption, right? So, the waves now are, we have digital native companies that are just disrupting the heck out of the industry or the company that we're trying to help. And so it's now about pulling all of those technologies together, and really figuring out a new business model for a client. Figuring out a new distribution channel, a new product that's maybe natively digital. And so it's very, very different, I feel, then it was five, 10, 15, 20 years ago, through some of the other waves. >> Talk about the things going on in the Bay Area before we get more in the global themes, because I think the Bay Area is always kind of a leading indicator. I call it a bellwether. Some cool things happened. You've got things like the Golden State Warriors got a stadium that's being built. I'm watching the World Series with the Red Sox, and you see Amazon stat cast, you're seeing overlays, you're seeing rosserial. All these things are changing the work and play. The Bay Area's got a lot of leading indicators. What are some of the projects that you've been involved in? What's happening now that you think is worth noting, that's exciting, that piques your interest? >> Yeah, I mean, we work across every industry, and we do a ton of work in tech, but I actually find some of the more interesting projects are the ones we're doing for healthcare companies in the Bay Area, some of the utilities in the Bay Area, some of the big resource companies, some of the financial services institutions, 'cause, like I said before, all of those industries have disruption coming or have been disrupted, and so we're doing some work right now around patient services in healthcare and in pharma that is really interesting. It's meant to change the experience that a patient has, that you and I have when we interact with our healthcare providers or, you know, the whole industry. And so those kinds of projects are real interesting cause a lot of these industries are old and sort of have a big legacy estate and model that they need to transform from. So they need to move fast, and we kind of describe it as a wise pivot. They sort of need to move, but they need to make sure they're moving at the right time. They can't hurt their existing business, but they got to pivot to the next business model, and that's happening in lots of places. And you're right, I think it is happening a lot in the Bay Area and the West Coast as sort of a bellwether. >> I want to get your thoughts on some of the moments that are going on in tech. You mentioned prior, before we came on camera, you worked for Apple in the old days. Tim Cook was just recently tweeting yesterday, and that tweet's going around, privacy. He was at this big GDPR conference. The role of regulatory now is changing some of the West Coast dynamics. Used to be kind of fast and loose West Coast, innovate, and then it gets operationalized globally with tech, tech trends. What's the tech enablers now that you see that are involved that actually have to deal with regulatory, and is regulatory an opportunity? You're mentioning utilities, finance, those are two areas you can jump out and say okay, we see something there. Privacy is another one. So you have a perfect storm with tech and regulatory frameworks. How has that impacted your job in the West Coast? >> Well, I mean, GDBR, we live with everyday. And clearly we're doing a ton of work in Europe. And I think that's one of the advantages Accenture has of being a big global company, and being able to take lessons learned from other parts of the world that are likely to come to the United States, et cetera, so, but I think the combination of tech and regulatory are going to be merging together here pretty quickly, especially when you talk about AI and data privacy, and that sort of thing. But it's definitely been an evolution. Great to hear Tim's point of view on what Apple thinks. And it's been really fun in my life to see Apple in the 80s when I worked there. They were a client of mine in the 80s. I worked with NEXT Computing in the 90s. And then obviously they're a big partner of ours now, so it's been a really interesting evolution. >> What are some of the growth accomplishments you guys have in the Bay Area? Obviously there's been growth here for you guys. Obviously, we've been seeing it. >> Well, I think the amount of tech-driven disruption, or digital transformation, we call it, is growing like crazy. So, you know, 20 years ago we were doing a lot of eCommerce work. We kind of shied away from doing Y2K work and a lot of our competitors saw that as a big opportunity. We didn't think it was a lot of value for our clients, fixing the old systems. And so we pivoted to eCommerce in a very aggressive way. And I would say now that's evolved even further, where more than close to 2/3 of our business here on the West Coast is what we call the new, which is clouds, security, digital analytics. And I really think it gets down to, we were talking a little bit earlier, about the data. And so we have more data scientists than we've ever had. We're probably hiring one or two every day out here on the West Coast. And it's about the data. Data is driving our consulting business. It's driving our technology business. It's driving what we're doing with AI, obviously, and things like that, so. The transformation's been pretty tremendous. >> So take a minute to explain the difference (mumbles), data, you mentioned a lot of things, you got data in there, you got cloud, and you mentioned earlier you got kind of cloud first companies, got born in the cloud, born in AI, AI first, data first, these new companies that are essentially disrupting incumbents, also your clients, that are kind of born before the cloud. And they got to transform. Is digital transformation one of those things or both of those things? How does digital transformation translate to the clients that you guys work with? >> Well, every client has a unique set of needs depending on where they came from. We do a lot of work with the digital natives. We do a lot of work with the unicorns out here on the West Coast. And their needs are different. You know, they need to learn how to scale globally. They need help in the back office. They need help sort of maturing their business model. We do a lot of work with legacy financial services companies, healthcare companies, that sort of thing. They need to figure out how to sort of, you know, pivot to digital products or digital interactions with their customers. We have a very large business now in Accenture Interactive around helping to find customer experiences for clients. And we think our mission is sort of help our clients really redefine that relationship with their customer, their supplier, their supply chain, and the experience is a key part of that. Given expectations means a lot. >> We have a lot of CUBE Conversations around IT transformation as well. And I had a CIO, big time firm, we won't say the name cause it'll out em, but he said, "We've been outsourcing IT for so many years, but now we got to build the core competency internally because now it's a competitive advantage." And they have to ramp up pretty quickly. Cloud helps them there, and they need partners that can help them move the needle on the top line. That this is not just cost control and operational scale or whether it's horizontally scalable scale-out or whatnot. Top line revenue. This is where the bread and butter of the companies are. >> Right. >> So how are you guys engaging with the clients? Give some examples of how you're helping them with the digital transition to drive their business, how do you engage them? Do you do the standard sales calls engagements? You bring them to a technology center? As the world starts to change, how do you guys help those clients meet those top lines? >> Well, a perfect client for us, you know, we're really good at helping clients cut costs and get really efficient and be good with their peers on cost structure. We love a client where they want us to help em with that and they want to pivot the savings to the new part. The way, one of the things that triggered a thought when you mentioned that was we like to bring our clients into our innovation hubs, so we've had labs here on the West Coast for a long time. We now have 10 innovation hubs in the U.S. We have a very large one in San Francisco now, and so we'll bring a client into our innovation hub and really roll up our sleeves with the client and over a week or two weeks or three period of time, we really brainstorm on envisioning their future for their company, build a minimal viable product if we have to out of our rapid prototyping capability and really envision what the target and state of their business could be, of their product could be or their customer interaction and we'll model it. Rather than sort of do a study, do another study, do a PowerPoint presentation, it's let's roll up our sleeves and figure out how to really pivot your business to the new and then take it from there. >> And they come to your location Absolutely. >> For an extended period of time? >> Yeah, so we'll have, any given day we'll have at least two different clients in our location doing either a couple a day workshop, a multi-week workshop, and it's co-creation. It's us collaborating with our client to figure out a solution. A good example is we had one of our large clients from the West Coast in there recently and we were trying to figure out how to use drone technology to drive analytics in, you know, over a geography to provide better data for them to minimize risk. And we've got a number of co-creation projects now going on with them to figure out how do we take that into a solution that not only helps their business but maybe it is a commercially available system. >> Yeah, our Wikibon research team brings us all the time with IOT and security you're starting to see companies leverage their existing assets, which is physical as well as digital and then figure out a model that makes them work together because these new use cases are springing up. So what if some of those use cases that you guys see happening, because you mentioned drones, cause that's an IOT device, right, essentially. There's all these new scenarios that are emerging and the speed is critical. It's not like, you can't do a study. There's no time to do a study. There's no time to do these things. You got to get some feet on the ground. You got to have product in market, you got to iterate. This is devops culture. >> Right. >> What is an example? >> So we did a project for a big ag company and not actually a West Coast based company but they came to our labs to look at it. And basically what we did was, we covered an area that's basically the size of Delaware in terms of drone video and we were able to drive analytics from that and ten times faster figure out for them where the forest was weak and where it wasn't. where they ought to worry about vegetation, where they might have disease issues or other risks that were facing them. And those analytics we were able to drive a lot faster and so rather than manually going around this huge square mile set of geography, they were able to sort of do it through technology a lot faster. >> Yeah, just a side note. I was talking to Paul Daugherty and interviewed him. We were celebrating, covering the celebration, your 30th anniversary of your labs. And one of the interviews I did was a wacky idea which made total sense, was during like a car accident or scene where there's been a car accident, they send drones in first and they map out the forensics- >> Sure. >> First. And you think, okay, who would have thought of that? I mean, these are new things that are happening that are changing the game on the road because they'll open up faster. They get the data that they need. They don't have to spend all that physical time laying things out. This is not just a one-off, this is like in every industry. Is there an industry that's hotter than another for you guys? (mumbles) oil and gas, utilities, financial services is kind of the big ones. What are some of the hot areas that you guys see the most activity on, on this kind of new way of taking existing industries and transforming them? >> I don't know if I could pinpoint an industry, I really don't. I mean, because I see what we're trying to do with anti-money laundering and banking is really moving the ball forward. What we're doing with patient services and pharma in health care is pretty aggressive. Even some of the things that we're doing for some of the states and governments around citizen services to make sure that ... Cause all of us have expectations now on how we want to interact with government and our expectations are not being met in just about every department, right? So we're doing a lot of work with states around how to provide a better experience to citizens. So I don't know if I could pinpoint an actual industry. One of the fun ones that we just, that we're involved with our here in our patch is one of the big gaming companies in Vegas. We are doing a lot of video analytics and technology and again, it's something like 20 times faster being able to detect fraud, being able to figure out what's going on on a gaming table and how to provide rewards quicker to their customers, keep em at the table faster or longer- >> He's got to nice stack of chips. Oh, he's going down. (laughs) Give him a comp through, he's feeling down. Look at his facial expression. I can (mumbles) imagine, I mean, this is the thing. I would agree. I think this every vertical we see is being disrupted. Just mentioned public sectors. Interesting. We were riffing at an Amazon event one time around who decides with the self-driving cars? These towns and cities don't have the budget or the bandwidth to figure out and reimagine the public services that they have, they're offering the citizens. The consumerization of IT hits the public sector. >> For sure. >> And they need help. So again every industry is going on. Okay, well I want to step back and get some time in for analytics because you guys have been investing as a company heavily in analytics in the past 10 years. Past, I think, seven years, you guys have been really, really ramping up the investment on data science, analytics. Give us an update on that. How is that going? How's that changed? And what's the update today? >> Yeah, and it's a good point. I mean, and again, you mentioned those labs being here for 30 years. A lot of our data scientists and big machine learning and big data folks frankly started at the labs here years and years ago and so, we've now got one of the largest analytics capabilities, I think, of any services company globally. We called it applied intelligence. It's a combination of our analytics capability and artificial intelligence, and we basically have an analytics capability that's built into all the different services that we provide. So we think it's, everything's about analytics just about. I mean, clearly you can't do a consulting project unless you've really got a unique analytical point of view and unique data around assessing a client's problem. You really can't really do a project or implement a system without a heavy data influence. So we are adding, frankly, I think every day I'm approving more analytics head count into our team on the West Coast in lots of different practices. And so it disbands industries, it spans all the platform sets, that sort of thing, but we're the largest of most of the big data players. >> I think one of the consistent trends with AI, which is now being the word artificial intelligence, AI, is kind of encapsulated the whole big data world because big data's now AI is the implementation of it. You're seeing everything from fraud. You mentioned anti-money laundering, know your customer, these kind of dynamics. But you get the whole dark web phenomenon going out there with fraud. All kinds of underground economies going on. So AI is a real value driver across all industries around one, understanding what's happening, >> Sure. >> And then how to figure out how to applications development could be smarter. >> Right. >> This is kind of relatively new concept for these scale out applications, which is what businesses do. So how is that going? Any color commentary on the impact of AI specifically around how companies are operationally changing and re-imagining their businesses? >> Well, I think it's very early days for most of our clients, most big companies. I think, we've done some recent surveys that say something like 78% of our clients believe that AI's really, really important and they're not at all prepared to deal with or apply it to their business. So I think it's relatively early days. There's a huge fight for skills, so we're building our team and that sort of thing. We're also classic Accenture. We grow skills pretty well too through both on-the-job training and real training. And so I think we're seeing sort of baby steps with AI. There's a lot of great vended solutions out there that we're able to apply to business problems as well. But I think we're in relatively early days. >> It's almost as if, you know, the old black-box garbage in, garbage out. You have good data, >> Exactly. >> and you got to understand data differently, and I think what I'm seeing is a lot of data architects going on, figuring out how do we take the role of data and put it in a position to be successful. It's kind of like, cause then you use AI and you go, that's great, but what about, oh, we missed this data set. >> Right. >> You'll have fully exposed data sets, so this is all new dynamics. >> So you have to iterate through it and you'll have to (mumble) solutions that'll start and restart. >> All right, so final question for you. Talk about this technology hubs again. So you have the labs, get that. So how many hubs do you have, technology hubs? >> Well, in the U.S., there's 10. But I would say in the West Coast it's really San Francisco and Seattle right now, with San Francisco being our flagship and frankly it's a flagship in the U.S. We've had the 30 year presence of our labs here on the West Coast and we've had design studios on the West Coast. We've had our what we call liquid studios, which is a big rapid prototyping sort of capability. We've had our research, et cetera. We've pulled all of those locations, so our lab started in Palo Alto, went to San Jose and is now in San Francisco. We've pulled all those locations together into what we're calling the innovation hub for the West Coast and it's a five-story marquee building in San Francisco and it's where we bring our clients and we expect to have literally, I think last year we had 200 and something client workshops and co-creation sessions there. This year we think the number's going to go to 400 and so it's really becoming a fabric of all our practices. >> How important is the co-creation, because you have a physical presence here and it's the flagship for the innovation hub and it's an accumulation of a lot of work you guys have done across multiple things you've done. Labs, liquid labs, all that stuff coming together. How important is the co-creation part as a mechanism for fostering collaboration with your clients? Co-creation's certainly hot. Your thoughts on co-creation. >> Great question, and I would tell you Accenture's kind of gone through waves as technology's gone through waves and so we were always an enabler for a client's projects and we did a lot of project work. I think we're in a wave now where we're going to be the innovation partner. We continue to sort of be named the innovation partner or the digital partner for certain clients. And we're going to do that through co-creating with them, and it's not just at their site, et cetera. It's going to be co-creation in our labs where we're taking advantage of the hundreds of data scientists and computer researchers and technical architects that we have in our labs to create something that's new and fresh and purpose-built for their particular business model. So we think co-creation is a huge part of the formula for us being successful with our clients over the next 10 years. And so that's why we've put this infrastructure in place, expect it to expand and to be sold out and that sort of thing. But it's a good way for us to build capability and really, really viable solutions for our clients going forward. >> So it's not just a sales development initiative. It's an operationalized engagement and delivery mechanism for you guys. >> Exactly, exactly. It's not, I mean it has, it self markets but it's not about marketing. It's about, we'll have tours and we'll have a little tourism through our center and so clients'll say, Wait, look at that maker lab. Look what you're doing with that client. I want one of those, right? I need to do that in my business, even though I'm in a different industry. So it's not really a marketing tool per se, it's a way for us to interact and engage with our clients. >> Well, it's a showcase in the sense of where you can showcase what you have and if clients see value, they can go to the next step. It's an accelerated path to outcomes re-imagining businesses. Okay, final question. What have you learned from all this? Because now you guys have a state of the art engagement model, delivery model, around cloud, all these things coming together, perfect storm for what you guys do. As you guys look back and see what you've built and where it's going to go, what are the key learnings that you guys came out of the West Coast team around pulling it all together over the years? What's the key learnings? >> Well, I think that our clientele is just thirsty for innovation and innovation now. It's now about sort of let's envision the future and we'll get to it some other day. It's what can we do right now and what journey, what glide path are we on to change our business? So the pace is just radically different than it used to be. And so it's about changing, rapidly changing, putting real innovation on it, and collaborating with clients in a pace that we've never seen before. I mean, I've been here 32 years and I've just never the pace of change. >> That's great, John. So (mumbles), really appreciate it. We'll get a quick plug in. What's coming up for you guys? What's going on in the West Coast? What's happening? >> Well, we're in event season right now, so we just finished all the ... We're wrapping up Oracle Open World. We just won five awards at Oracle Open World. We just did an acquisition on the West Coast to beef up our Oracle capabilities. We've got ReInvent and we have all kinds of events coming up but it's a, it's been a pretty busy season. >> So cloud and data have certainly helped rise the tide for your business. >> 100%. I mean, cloud is taking Accenture from kind of in the back of the office and put us into the front office over the last 10 years. >> Well, certainly it's awesome, (mumbles), leveling the playing field, allowing companies to scale out very rapidly, bringing a devops culture, new kinds of modern application developments, real value being created, super exciting time. Thanks for coming in and sharing your time. John Del Santo here in theCube for Cube Conversation, senior managing director at Accenture. I'm John Furrier here in theCube studios for Cube Conversation. Thanks for watching. (upbeat music)

(pulsating electronic music) >> Welcome everybody, this is theCube, I'm Paul Gillin. Physical security and cybersecurity have traditionally been sort of isolated worlds, they didn't talk to each other. But in the age of the Internet of Things we now have unprecedented opportunities to unite these two traditionally separate areas. Armored Things is a startup out of Boston and is doing some very interesting work in using intelligent devices to make decisions and to intuit patterns in crowd behavior which has applications in cybersecurity, crowd management, traffic management, a lot of different potential uses of this technology. With me are Julie Johnson the co-founder and President of Armored Things, and Chris Lord, the Chief Technology Officer, Welcome. >> Thank you. >> Why don't you describe in a nutshell, let's start out, what you do Julie. >> Great, Armored things is building software to do next generation incident response. We're using the IOT devices and their data to power decisions across large environments used for safety. So for example the data that we're collecting can be used to get better situational awareness within seconds and drive incident response in seconds instead tens of minutes, which is the state of the art today. >> And so it's sounds like, is security the primary target area or are there others? >> That's right, we sit at the intersection of physical and cybersecurity. This information can also be used to drive additional value over time but right now we're really focused on achieving that mission, using these devices, this technology to improve both the physical and cyber realms for Internet of Things. >> Chris why don't you give us an example of how your technology might be applied? >> Sure, so a very common one is, you know active shooter. People are very concerned about active shooter, and so how can you leverage all the data that you have across different devices, different systems that you have out there, in order to understand what happened, and get people the right information at the right time. A more commonplace example might be something like a protest formation. So if you look at a university campus where you might have a controversial group meeting on campus and you need to get early warning when there's a protest forming on the other side. Our technology will allow you to see that before it's gotten to a critical proportion or before it's marching down the street. >> So why don't you take a deeper dive and talk about what, how are you federating these devices? How are you using these multiple devices together? >> Well that's exactly what we are. So we're a data analytics layer across all the silos of data that you already have in your environment. So as you look around you might have motion sensors in your environment, you might have access control systems in your environment, you have wireless infrastructure in your environment, all these things are used for specific purposes now but nothings really trying to correlate and connect the data across all of them. So Armored Things builds a layer across all of them, brings that data together to give you better understanding of what's going on in your environment, people and your physical space. >> Julie talk about how the company came about, what are the origins? >> Sure, so I started working with Charles Curran our CEO about two years ago at Qualcomm. We were really focused on understanding the security portion of the IOT layer and how to manage these things in enterprise. So if you're familiar with IOT in the household there's been a lot of proliferation around turning your lights on, understanding who's at your front door, but in enterprise it's been much slower to adopt. Fundamentally we believe that part of that was because management took a lot of time. Every time you provisioned a device it took a number of minutes and because there was an intrinsic lack of security on each of the devices. So we went around and started talking to different potential customer groups about what it would look like to bring more IOT into their environments. And we really got pulled into universities, and large sporting and entertainment venues, who we're still working with as our primary customers today. Because they saw a desperate need for IOT, not only to save time on managing these devices, and to make sure that they're secure in their environments, but also to use them for physical security. So now that we've spent, you know $15 million in selling IP video cameras, or a few million dollars in selling access control systems, how do we actually elevate their use from what they were initially intended for. That spend has a secondary use when it comes to physical security. That ability to, you know quickly get cameras on the scene of an incident. That ability to harness data coming off of motion sensors or environmental sensors. How do we use all of that information to drive an awareness of our environments day-to-day and then use it in critical emergencies for a better response. >> I understand you're working with some sports teams right now. Can you describe a scenario in which you might be able to help them manage crowds more effectively? >> So there was a great example we heard about two weeks ago from a top team, who's recently hosted some World Series events. They had a unfortunate incident where they were watching, they were hosting a watch party for the World Series in their venue during an away game, and they handed about 40,000 paper tickets out. They got a great turnout, 20,000 people came to the venue. But in the seventh inning of the game the other 20,000 people decided that they also wanted to be in the venue in order to celebrate. That was a pretty unanticipated event. Usually in the fifth or sixth inning you start to consolidate your entrances, you start to consolidate your security personnel and send them to other parts of the venue, and the net result of that was they ended up closing the doors, not allowing additional entrance in, and tweeting that there wouldn't be additional people allowed to enter. There were a lot of security issues with letting 20,000 people in, in the seventh inning, not of the least is you don't know where they're coming from, and you don't really know what their intent is in coming so late to that venue. But there's patterns in the data that we could've seen sooner. So hypothetically, understanding that a normal game day has a couple hundred people entering in the fifth, sixth, seventh innings. Seeing a significant uptick in that number of people coming into your environment should immediately say, what's unique, you know what's different about this situation? Now how do I tie in my resources, my security personnel, my responders, and just maybe notify people who are in charge of making these types of decisions, so that we're not closing the gate and tweeting out to our fans that there's no more entries. >> And getting back to the technical nuances of this situation, how might your technology detect this crowd assembling before it was even visually apparent? >> Good question, so there's many, many different things. So part of what we do is rely on diversity of data from different sources. So that might be mobile devices. That might be from wireless. That might be from cameras that you have there and doing occupancy counts on those cameras. It might be from other, you know motion sensors you have in your environment. All this data gets aggregated so that we can come up with a good understanding of population and flow within your environment. So we would have early indications and bring that awareness to people that have to respond, people who might be sitting in a network operations center, and looking at other cameras but not seeing the information. So we can bring the information right there, notify them that there's a problem forming before it's gotten to critical proportions. >> Fantastic. >> One more thought on that is there's kind of a unique advantage in data to go beyond what humans can perceive. When we're looking at these knocks, you know they have thousands of video cameras potentially united in one central screen. It takes not only having the right camera up but also noticing a degree of difference that might be quite minute, to actually see it as an anomaly in real-time. So you can imagine, you know a university campus where people are walking through the campus at a certain pace every single day. One day everyone's walking just 30% faster, not running just walking, why? You know is there a suspicious package? Is there someone gathered there that you know is attracting people that they don't necessarily want to be associated with, or end up in a vulnerable position? How can we see that in the data faster than someone in the control room might notice it and alert people to respond. >> And with machine learning, of course now we have the means to do that. Chris, talk about the, it strikes me that there must be a lot of complexity involved. You've got a great diversity of devices out there you have to connect to. Every institution would have a different fabric. How are you technically pulling this all together? >> Well the nice thing about a lot of these technologies is there is standardization across many of these different types of devices, and there are, you know there are tiers of players right. And so we do have to be selective about who we integrate with. We are integrated with the top-tier players in all these categories, and we'll prioritize other integrations over time based on our customers and our market so. >> And Julie, what are your plans for deployment? What's your timeframe? >> We're looking to rollout our first generation of product in the next nine to twelve months. That really drives home at that situational awareness piece. So before we even get to building through incident response at scale, the ability to give people very specific cues during a critical emergency. How do we start with getting more information to the people who are there? So getting occupancy, flow, the dynamics of movement around a campus or a large venue. How do we start equipping the police personnel, and security personnel to make better decisions and drive value from there. >> I understand there's no shortage of demand for your solution. >> We do have some top-tier universities, and pro-sporting and entertainment venues who we're working with to build the right solution not just the solution that we think is needed, but the solution that they're telling us, "Hey we would really like to use something like this." >> I also understand you've pulled together a team, kind of a dream team, talk about some of the people that you've brought on board for this operation which few people have even heard of. >> Yeah so I think the first of those you're seeing here, so Chris joined us as co-founder and CTO and has been really an asset to this team given his background in cybersecurity from Carbon Black and before that. And you know if you want to add more to that please feel free to. >> No thanks. >> We've also brought in, I would call it two pillars of our strategy. One one the physical security side and one on the machine learning data analytics side, and those two women are Elizabeth Carter. Who came to us from Apple, where she led crisis management for the Americas. She previously worked at Chertoff Group where she sat at the intersection of physical and cybersecurity, and before that actually worked for the city of New York, where she understood weapons of mass destruction, different types of biological and chemical weapons response planning. So she's kind of the pillar of our physical security response understanding and driving product. The other woman, her name is Clare Bernard and she recently joined us from another Boston startup called Tamr where she was running product and engineering for them. Clare's background is actually in particle physics. She was BU and John's Hopkins, and happened to work with the team that discovered the God particle while she was getting her PhD. So we' think she's as smart as you can find, and is going to help us think about these data challenges, the analytics piece at a scale that, you know we think has the potential to really improve physical security and cybersecurity. I would be remiss if I didn't mention the rest of our team. Our CEO Charles comes from a background in the venture capital community and is just incredibly knowledgeable about the process of building a company from the ground up, and has many skills when it comes to recruiting as well. Really helped drive some of these hires forward and the rest of the team is the next generation of rising stars, people from Oracle, HP Vertica, other Carbon Black individuals. People who just have experience from across the board that's going to help us build the right solution. >> And you know at a time when diversity has been a major issue for tech companies, I understand your team is unusually well represented. >> I think our executive team is about 60% women, which we're very proud of. I think our team in general might actually be, >> About that too, yup. >> About 60% women, which we're also very proud of. And I'd like to say that that's organic. That we've worked with some great advisors and potential customers, and I do think that from my perspective, it's been helpful to have younger women coming in who see a path forward for senior women in executive roles in their company. I think that's something that can't be underestimated. >> Where do you stand in funding right now? >> We just closed our first institutional capital about a week and a half ago. We're still finishing the close of that round but we have a Boston based partner who's very focused on machine learning and analytics, and also has been a well recognized investor in the cyber security realm. So we're very fortunate to have this investor as our partner, and excited to keep working with them. >> Chris, as someone whose background is in cybersecurity how do you see the security landscape changing now with the IOT coming on and the possibility of really transforming the way organizations look at their physical and cybersecurity operations? >> Good question, so over time they're converging, and they're converging I think more rapidly than we expected, so now I'm going to step back a little bit and say that there's a lot of parallels. Cybersecurity I think is probably about five years ahead of physical security in terms of maturity of technology and approaches to problems. And then so what we're seeing right now, and we're part of the force behind that, is taking the learnings from cyber security and applying them to physical security right. So when we talk about situational awareness, when we talk about the data analytics that supports that, and when we talk about incident response and orchestration automation. All of those are core to taking cybersecurity and applying it to physical security. In terms of convergence, we're seeing many cases, and this is going back a number of years, where people are using cyber events to create physical problems right. Stuxnet is a classic example, but you can do the same thing by taking over something and instilling panic in a stadium, and causing you know, all sorts of grief, cyber driving physical. You can also see cases where people who are running cybersecurity operation centers want access to physical knowledge of their environment in order to do their job better. Whether it is a malicious insider that they suspect, whether it's an infection that occurs on a particular machine, being able to pull up the cameras, know who was there at the time, bringing all that information together, is again necessary in order to understand their perception of situational awareness. So two converging towards one, we're going to be building towards that goal from our perspective. >> Now the flip side of federating IOT devices is that the bad guys can do the same thing. So you potentially have a much broader attack surface. That has to be factoring into your thinking. What is the embedded security in your platform? >> So, we're not going to address fully that right now, but so we take advantage of best in breed security principles in our design both for security and for privacy. But in terms of the dependency we have on a lot of IOT devices and IOT systems, part of what helps us is diversity of data across those, and diversity of devices right. And so while you might have compromises in specific cases, the fact that you are dealing with so many, and so many different categories at the same time, allows you to maintain and fulfill your mission, and deliver what you're trying to do regardless of some of those individual compromises. We're also in a unique vantage point where we can actually see the operational integrity of what's going on. So when you look across all those different categories and you look at the data that we're collecting, whether it's malicious or not, we're able to identify a failure, and bring that to the attention of the people who are dependent on those systems. So we could be an early morning to cyber events, malicious or not. >> Julie, entrepreneurs love to dream. I'm sure you are thinking big, beyond the immediate cybersecurity applications. Where could Armored Things eventually go? >> That's a great question. The dream is that we become not only the dominant solution for physical and cyber security for schools and large venues. But we bring our solution into K, 12 where some of this is desperately needed. That's kind of the mission orientation of our team. How do we start to drive value in a way that we can get to every school in the country sooner. In the longer term though, I think there's a lot of opportunities with IOT and we're still kind of at the tip of the iceberg here. We're going to see all sorts of new devices come online over the next two, five, 10 years. The growth of these devices is incredible. And the question is how do we continue this challenge of solving the data at scale in a way that continues to drive value, not just for some of the first use cases, which are often around marketing, and understanding an environment in that sense, but also continuing that physical cybersecurity angle. >> Enormous potential and hope you stay based in Boston. We can use more companies like that. Chris Lord and Julie Johnson, thanks very much for joining us today on theCUbe. >> Thanks Paul. >> Thank you. >> Armored Things, keep your eye on them. You're going to be hearing a lot more about this company in the months to come. I'm Paul Gillin, this is theCube.

>> Announcer: Live from Chicago, Illinois, it's theCUBE! Covering VeeamON 2018. Brought to you by Veeam. >> Welcome back to Day Two of VeeamON 2018 in Chicago. My name is Dave Vellante, and I'm here with Stu Miniman. You're watching theCUBE, the leader in live tech coverage. Dante Orsini is here. He's the Senior Vice President of Biz Dev at iland. CUBE alum. Good friend of theCUBE. Great to see you again. >> Great to see ya. >> Thanks for coming on. >> Yeah, thanks for having me. >> What's happening with iland these days, in the world of cloud service providers? >> Well Dave, it's been insane for us. Obviously Veeam's a huge partner of ours. We've been working together for what, seven years now I think. And it's just amazing to see the growth of this company. Right? We've integrated Veeam -- our relationship. We started off basically providing managed backup many, many moons ago. But six years ago we started to build our own platform, on top of Veeam, on top of Cisco, on top of HPE. Customers really wanted to see more control. They wanted greater levels of security. They really wanted a true enterprise cloud. To do that we had to enhance the VMware stack. We had chose to take Veeam and integrate them via their API. Today if somebody deploys anything in the world with iland, it's automatically backed up by Veeam. If you fast forward a bit, as you see what Veeam's done to innovate with cloud and multi cloud, they've really helped build our business. >> Dante, if you go and look back before the whole cloud wave, the typical service provider. They would have one of everything. You'd walk down the aisles and there'd be whatever it was. An EMC box. A digital box. Whatever it was. Did virtualization change that? Were you able to consolidate? Create a platform. Create a simpler environment to manage. Or is there still a lot of bespoke infrastructure lying around? >> Yeah, that's a great question. For us, I'd love to tell you we hit it right the first time twelve years ago. But no. Just like you said. There's all sorts of different technologies right? But I think what we've done is we quickly standardized. We leverage Cisco UCS from a compute perspective. We leverage some of their storage platforms for the things that we do with Veeam Cloud Connect Backup. We actually help them drive the validation of that product before it came to market. We operate at scale with them. Same thing with Veeam. We're their the largest cloud provider in the world right now. As far as leveraging Veeam technologies. In addition to that on the storage front, we also because of the demands of the environment, we really want to deliver a secure cloud service. Encryption is table stakes, and has been for years. HPE Nimble plays a critical role for us there. That's really our stack. Cisco from a network and a compute perspective, VMware with the hypervisor, and HPE from a storage perspective. >> It's sounds like you've taken some very cost effective platforms. Nimble, Veeam, etc. And then architected an enterprise class solution. You guys are adding value around that as an integrator and obviously a service provider. >> Yup, correct. And I think the market is demanding more and more from a cloud provider. People want true transparency. They want control over the infrastructure. For us it's like, how can we develop an API? So we can make this platform extensible. And then still work with the customers that are struggling with the promise of cloud. And Stu, you see this all the time, right? >> Yeah, and Dante, one of the things we're discussing here is it's a very hybrid world. As Veeam said, customers are doing lots of SAAS. They're using service providers. They have their own data centers. They're using a few public clouds. One of the things I've been watching real closely is companies like iland and the other cloud service providers Amazon and Microsoft aren't the enemy anymore. It's, well we actually have to partner with them on some services. We do some things locally. Maybe give us your viewpoint on how that's changed in the last couple of years. >> Yeah, great question. I would tell you that we're not quite there yet, Stu. From my perspective. You guys know, we're known best for providing disaster recovery as a service. That's where we've made a name in the space. But the irony is we've really focused on building this cloud infrastructure. So an I as platform. And ironically that's the majority of our revenue. When we look at public, clearly it is a hybrid world. Where we spend a lot of time, is investing in how can we highly automate the integration? Because we know that people are going to have workloads everywhere. The idea is, think about it from a recovery perspective. If I'm protecting your traditional workloads. And you've got a dev team that's using various different services that are proprietary to a public cloud, that stuff's got to talk to each other in a true resiliency capacity. We wanted to make sure that people could actually highly automate and orchestrate a failover to us, a test to us. But also integrate the connectivity portion of that. Right? Making sure that all these things can talk together is important. You understand as well as I do, as these cloud architectures change, become more modern, and they're more service driven. The traditional, I'm going to move from point A to point B is no longer in play. It's how can I have more diversity amongst my vendor base? If I'm using containers. You've got a globally distributed architecture. If I can deploy some of that with iland, and some of that maybe using Kubernetes, that gives me diversity for recovery. >> Dante, you've hit one of the key things we've been as an industry struggling with. That pace of change is just so rapid. How do you internally deal with that pace of change? As to I architected something today, and tomorrow there's something new. Tell us what you're hearing from your customers as to how they make their decisions and sort through this constantly changing Rubrik? >> Well it's definitely insane. We see all sorts of various different use cases, depending on the industry. And that pressure to innovate at the speed of light is, really people struggle with it. I think from our perspective, there's a couple things that we're doing. One, we actually wrote our own assessment application. We call it iland Catalyst. This was really designed to help both our customers as well as our partners. Cause we go to market through a lot of partners as well, to help streamline this pre-sales process for a customer. Again, we focus squarely on the VMware infrastructure stack. Being able to pull an inventory of what somebody has in their environment. And then go through and select resource pools and VM's, for whatever the purpose. Whether they're looking to work and shift workloads. Or whether they're looking to protect them from a backup or DR perspective, we're able to mitigate all the challenges associated with that. To your point. As people are looking at cloud, it's like okay. Is this cloud thing real? And how's it apply to my business? What can I really do with this? And by the way, I got to deal with my budget also. What's this stuff cost? We've got some really smart people. But you can't scale our smartest people globally. We wanted to really drive that into an application. It's really helped get people to outcomes much quicker. So do it right first. >> Dante, if you reverse back a few years ago, VMware was calling Amazon a book seller. Amazon was calling guys like VMware the old guard. The old way. They kissed and hugged last year. You must've loved that first of all. Because it was like, great, VMware specialist. We'll just drive truck through that opportunity, because we get service provision, cloud, VMware stack, boom. Now fast forward. They've got this little kumbaya thing going on. How do you now differentiate from that? >> Yeah, that's a great question. First of all, VMware, obviously a very strategic partner. I think they've got a long road ahead of them. On some of the things that they're doing. I think the promise of where they're going is great. But I still think there's a lot of folks that struggle with the idea. Think about co-mingling my traditional workloads. And then trying to integrate cloud native services on top of it. I think it's a tall order. We'll see where it goes. We're keeping a close eye on it. But in the interim for us, we continue to see folks that are saying, look I want to get out of the data center business. I've built my data center on VMware. I need to have much greater levels of control and visibility. And you need to make this easy on me. From that perspective, we've been able to do really, really well. We work with a lot of service providers that are looking for that level of a consultative approach. But also want to realize the benefits of a cloud. The point being is, I want a great cloud but it needs to be enterprise class. And I also need to know that I might need help architecting that migration. >> Well that's the key, right? You're not going to get that from an Amazon. They're not going to come into your shop. They're not going to hold your hand through it. They're not going to help you build the architecture route. And help you manage it on an ongoing basis. >> Dante, it's May 2018, so I'd be remiss if I didn't ask about GDPR. >> Hey Stu, I love you man! This is great. You guys know we operate globally, and have for over a decade. GDPR we were way out in front of this. I'm not sure if you follow, The BSI just came out with a new standard. 10012, I believe. I think our Compliance and DPO Officer would be pretty proud of me for remembering that one. >> Dave: I'm proud of ya. >> It's tailor made for GDPR. We've been pre-certified, one of four companies that did it. We do a ton in the security side and the compliance side. And I know they go hand in hand. We went through a global audit last year. On the back of some of the ISO work we do with the CSA, the Cloud Security Alliance. And actually came out with a gold star certification. Sounds juvenile, right? A gold star, woo hoo! But it's a big deal. Only iland and Microsoft have actually achieved that level of certification. Yeah. On the compliance side we're way out in front of GDPR. We're doing a lot from a thought leadership perspective in educating both the partners and the marketplace. I think it's going to see what happens with Brexit also. I think you'll see the rest of the world kind of find their way to their own type of regulation. >> What do all those acronyms mean for your customers in terms of GDPR compliance? How does that turn into value for them, and make their life easier? Can you explain? >> I think right now the whole market's been in my opinion has been ill prepared for this. You see a lot of people scrambling. Being able to identify what data is going to fall under that regulation. How you treat the data. How you're able to account for the data. And also destroy the data. And validate that. Is frankly I see some of the biggest sweeping change in marketing. I see marketing people really scrambling. Because they have to make sure that they double-opt in. Cause the fines for breaching this are unbelievable. I think you're going to see the regulators make an example out of certain people. >> No doubt. >> Quickly. >> There's going to be some examples. They're going to go after the guys with deep pockets first. But the fines are... What are the fines? Four, is it 10% of the turnover? No, 4% of turnover. >> 4% of your previous year's turnover. >> Which is insane. >> Yep, yep. >> That's going to hurt. >> Or something like 20 million pounds, something like that. >> Which ever is greater. >> Which ever is greater. Yes! Yes, exactly. Yup. >> It's pretty onerous. Dante, VeeamON 2018, we'll give you closing thoughts. >> Fantastic event, right. Just super appreciative for our relationship with Veeam. They've been behind us. They've been behind this whole cloud provider community. I mean guys, you know this. Raat Mere and team had the ability to go take this stuff to a public cloud many moons ago. They chose to enable a managed cloud provider market first. We are very grateful for that. >> Awesome. Hey thanks so much for coming on theCUBE. Great to see you. >> My pleasure. >> As always. >> Yup, go Yankees! >> Oh whoa, time out. >> Go Yankees. >> While we're on the topic. Listen, you can't beat the Red Sox in April. Okay, you know that, right? >> Yeah, here we go. >> So it's going to be interesting to see. I mean I have predicted the Yankees take the east, and they go to the World Series. But you got to be excited as a Yankees fan. >> Could be a good year. >> I've always liked Brian Cashman. I think he's one of the best GM's in the business. Watch his moves at the trading deadline. He's going to beef up the bullpen. I hope the Sox can hang tough with him because anything can happen. >> It's true, anything can happen. >> Hey, great to see ya. >> Great to see you guys, thank you. >> Go Sox. >> Dig it. >> Keep it right there everybody. We'll be back with our next guest right after this short break.

>> 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.

>> Announcer: Live from Las Vegas, it's theCUBE, covering Veritas Vision 2017. Brought to you by Veritas. (rippling music) >> Welcome back to The Aria in Las Vegas, everybody. This is theCUBE, the leader in live tech coverage. We go out to the events and extract the signal from the noise. My name is Dave Vellante, I'm here with Stuart Miniman, who's my cohost for the week. Bob Swanson is here, he's the head of sales for dcVAST out of Chicago. Bob, thanks for coming on theCUBE! >> Thanks for having me, guys. >> So, well first of all, the show, how's it going for you? We've now got enough data, it's been a couple of days, a few days perhaps for you. What's the vibe like, what are the conversations like? >> Yeah, it's been a great week. This is the very tail end of the event, so a little exhausted. But it's been exciting, there's been a good buzz at the event and we get a lot of our customers here, and just kind of seeing the buzz and the pace of innovation that's goin on here with Veritas, you know, it has been exciting. >> Tell us more about dcVAST. You're focused on IT infrastructure services, but dig a little deeper. Right, yep, so we're headquartered in Chicago, Illinois, and you're right, we do infrastructure and cloud services. So we do support-type services with a seven by 24 call center, have different managed service offerings, different cloud offerings, and certainly do consulting and project work as well. >> Yeah, and Bob, so what does multi-cloud mean to your customers? (chuckles) >> It's only natural that if they're not there today, then they're going to be multi-cloud at some point. So, Veritas here is pretty uniquely positioned. to be able to get customers there. It's all about flexibility and data portability. So, I think where infrastructure and storage and data protection is sometimes not that exciting of a conversation, now kind of changing the conversation, the data management, 'cause everybody needs their data to become more productive for them. It changes the conversation, has a little more sizzle. >> Okay, but you know, your primary area of focus is infrastructure services, so that means first and foremost, every year you got to help me lower my costs, right, you've been hearing that, I'm sure, for years, and help me improve my operational efficiency. And you do that, and really attack my labor problem, IT labor problem so I can focus on my business, right? Are those still the big overriding themes? Oh, yeah, there's no question. I mean, I think the public cloud has been probably the most disruptive thing in our space since the internet. And it's making customers re-evaluate all cost and really how they're doing things, and different consumption and financial models. So, the technology is cool, and we like that conversation, but it naturally brings a big financial and cost savings, and do-more-with-less element to all the conversations. >> So what are the big trends that you're seeing in marketplace, what are the conversations like with your customers? >> Yeah, and I'll give you an example. I think customers have different approaches to cloud, right, some cloud-first, everything's got to go. Others maybe want to keep more of their workloads on premise. And in one customer example, where they said, hey, we want to move all non-production out to the cloud and it was a single cloud provider. And they got about 40% of what they were looking to move out there and they reached what they thought their estimated budget was going to be. So at that point, having that portability and having the tool sets to be able to move those workloads around becomes very important from a financial standpoint. >> So, I wonder if we can unpack those. Cloud first, and then these other guys on-prem. The motivation for cloud first, and the type of company. Do they tend to be a smaller companies, or do you see larger companies saying hey, we're going all in? I mean, you've seen some stories in the press, you know, large company, GE's going all to the cloud, okay I'm sure there's still a lot of on-prem going on there. What do you see? >> Yeah, you're right. A lot of small business is certainly, it makes sense for them, any startups too are pretty much born in the cloud now. You're not going to have too many financial backers that are going to want a startup to be spending too much money on data center, or buying hardware. But the established large enterprises, too, are kind of all over the map, but there are already some of them that are taking this cloud first approach. But, the large enterprises and companies that have been around, where it's not kind of a clean slate, naturally it's going to be hybrid and ultimately there's probably a lot of predictable static workloads that are, at the end of the day, going to be cheaper to run on-prem than they are out in the public cloud. Public cloud's great for the stuff that's not predictable, or is very dynamic, so we're seeing, and I am from Chicago and so we say the coasts move faster, maybe, than the Midwest does as well, but we're seeing varying degrees of adoption and strategy. >> But the business in the data center's good right now, I mean, the market's sort of booming, but if you roll back a few years, you guys must have thought, and maybe you're still thinking it, okay, see this cloud that's coming. Like you said, it's one of the most disruptive, if not the most disruptive in a while, and it's aiming right at the heart of your business, infrastructure services. So how have you responded to that, you must be riding the wave now of data center growth and investment, but strategically, what are you thinking about in your firm? >> Yeah, I mean, there's no question. We've had to pivot. But it does create opportunity. And we do need to help our customers be able to be most cost-effectively managing their workload, right, helping them with that. So where there's challenge and change, there's certainly inopportunity. And we've seen it. >> So, but my understanding, your firm also offers managed cloud offerings. That's been one of the things we've looked at is the channel, can they get on board, can they offer that, how is it working with the big cloud providers, and yeah, let's start there. >> Yeah, that's a good question, and a lot of people have a misperception that the cloud is kind of the easy button. (laughter) But at the end of the day-- >> Stu: Maybe 10 years ago we thought that-- >> Dave: You have your hoodie. >> Right, but I mean, people need to realize the same architecture and security considerations are there as they are for on-premise, so it's not the easy button, and you can just kind of set it and forget it. So some people that are underestimating that still need help from a third party like ourselves to be able to help them manage it. >> Could you speak about the maturation of your support services? >> Yeah, we started doing a lot of hardware support years ago when the business was founded in 1989. And at that time, it was a lot of Unix-based engineering workstations and kind of morphed into servers and storage and other data center equipment, and then started doing a lot more software support, which all can be delivered remotely, for the most part. From time to time, you may need to be onsite for something, so that kind of changes the logistical model, and now with the cloud as well, we've just kind of evolved in that direction. >> And how about the Veritas relationship? What's that been like, you know, the Symantec sale, any comments on how that's evolved, and where do you see that going? >> Yeah, we've been a long-time Veritas partner, and really the reason why we first got started with them was because they were relatively platform-agnostic, and supported and endorsed heterogeneity. And in the old Foundation Suite days, which now their InfoScale product, it's obviously had some name changes, it didn't matter what operating system, didn't matter which array vendor you used. And it's good to have friends in the industry and alliances, but there's also some benefit of staying relatively agnostic like Veritas has, and that message resonates now more than ever with all the different cloud providers out there, and just being able to be interoperable with a lot of different technologies. >> What's your customer's reaction been to all the announcements that Veritas has been making here? >> Yeah, yeah, everyone's excited. Now it's getting the word out. And I mentioned pace of innovation earlier, and it seems to have gone from zero to 100, really, really fast. So, that's exciting. It shows commitment, I think, from the new executive leadership team at Veritas, and their backers at Carlyle as well. So, you know, I think it's an exciting time for Veritas, and for us as a partner as well, and our customers. >> And anything you want to see out of those guys? From your perspective, in the partner standpoint, in the voice of the customer, what's on their to-do list? >> Yeah, and I mean, the concept of data management, looking at it holistically is important. After people and intellectual property, data's the most valuable asset a company has, and a lot of the intellectual property resides in the form of data as well. So, it's an exciting place to be as we kind of see the industry shift. >> Dave: Cubs or White Sox? >> Bob: Cubbies! >> Hey, well, congratulations on that! >> Yeah, it's been a-- >> Really, really Cubbies, not just White Sox, oh, the Cubbies won it? >> No, Cubbies all the way. >> Hardcore Cubbies fan. >> Diehard, absolutely, yep. >> Well, you're welcome for Theo Epstein. We gave Theo, and Lester, you know. And Lackey. (laughs) >> You know, Theo seems to have the Midas touch, you know, and it's interesting too, you can use sports analogies for a lot of things, and Theo's a guy who was a little disruptive by using data and analytics in his approach to managing a baseball team. >> Right, right, well, good. That's great. It was an exciting World Series last year. Hope it can be as exciting again. Must have been insane in Chicago. >> Absolutely, yep, getting ready for another run this year, hopefully. >> Excellent, well, Bob, thanks very much for coming on theCUBE. Really appreciate it. >> Thanks again, gentlemen. >> You're welcome, all right, keep it right there, buddy, we'll be back to wrap up Vision 2017. This is theCUBE. (rippling music)

>> Announcer: Live from Las Vegas, its theCUBE, covering Veritas Vision 2017. Brought to you by Veritas. >> Welcome back the the Aria in Las Vegas, everybody. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with Stu Miniman. Angelo Sciascia is here, big Tom Brady fan, Senior Vice President of NetX Information Systems, from Brooklyn, New York, I don't think so. >> Not a Tom Brady fan. >> Thanks for coming on theCUBE do you think it matters, how much it airs at a football. >> No, not at all, Tom Brady doesn't care about that. >> No, well, listen, thanks for coming on. We have a great conversation, we love talking sports on the Cube. So welcome, how's the show going for you? >> Ah, it's fantastic, you know, lots of great material Veritas has been talking about. 360 Data Management, obviously we all know the benefits of that by now. So we have a lot of customers here so I'm glad they they got to see it from a senior leadership perspective, rather than our sales guys and sales engineers going in there and talking to them, and seeing Veritas executives really getting behind what we're talking about. So it backs up our story and, you know, our customers are pretty excited about it, actually. >> What's the nature of your relationship with Veritas. I know you have a relationship, and maybe still do, with Symantec. How's that all, how did it all evolve? >> Yeah, so we are a Veritas Platinum Partner, we would be, what we consider, a solution-provider type partner. A lot of our business today is either directly or indirectly tied to Veritas, which was kind of funny because we started as a security company, so our roots are systems management, you know. That's where we were in 2005 when I joined NetX, that's where we were for many, many years after Symantec acquired a company called Altiris. We just stayed in that vein, you know, managing endpoints, securing endpoints, encrypting data. And then, somewhere in 2013, we said hey, you know, let's try to diversify the portfolio a little bit. And we used to manufacture an endpoint management appliance for Altiris so we said hey, Symantec's got these things called NetBackup Appliances, let's check it out. It's a formed fact that we know how to sell and, shoot, four years later it's been a great partnership for us, great partnership, I'm sure, for Veritas, and for our customers and that's a lot of our business today. >> So, I mean, it's hot market, you know. Data protection is exploding, and security. I mean, you're in two of the sweet spots in the market right now. So how do you approach the business with customers? Do you, are you a specialist around data protection? You deliver services around them. Maybe you can explain it on the model? >> Yeah, you know, that's actually a good question, because it's evolved quite a bit, right? So, you know, when you had a limited portfolio of just one or two products that you can sell to a customer, you're really doing a product sale, right, which, I would say that was probably the most difficult transition from the split from Symantec to Veritas, because at Symantec we had thousands of products in the portfolio, or hundreds of products in the portfolio that we could actually talk to. And for a little while, really we had a handful, you know, we had NetBackup Appliances, Enterprise Vault and ancillary things to bulk on to that, like Clearwell. I think one of the most exciting things for us, as a reseller, is to now be able to go have a discussion with our customers that we were never able to have before. And rather than sit there and try to sell them a backup product or a storage solution, we could sell them a platform that solves many problems for them, right? Rather than sitting there and trying to sell one-off. So, our conversations are significantly more strategic now then they've ever been, and frankly I speak for myself and my whole team, I know everyone enjoys the conversation more now that we have a portfolio to talk about, than just a handful of products. >> Angelo, you've got an interesting viewpoint on this split off of Aritas from Symantec. What have your customers said about it? What's been your interaction with the organization? What can you tell us about kind of the inside going on? >> Yeah, look, I've lived firsthand on a Symantec acquisition of a company, okay. I was, we were not a Symantec partner when they acquired Veritas. Funny enough, I was actually doing Veritas consulting, you know, on my own on the side prior to Symantec purchasing Veritas. So I really, I'd made my career on two products; Veritas for backup and Altiris for systems management. Symantec bought Veritas and I was like okay, you know, I'm just going to stay with Altiris. Symantec bought Altiris and here we are now, so we can talk about all of them. The thing I noticed was Symantec was always going to be a security company, right, and they weren't going to change that no matter how much they try to integrate it. It's two radically different stories. You know, and for many, many years, things that we look at as new products today were kind of already there in the Symantec portfolio, but buried underneath other products that really never saw the light of day because when you have hundreds or thousands of products, like I said earlier, you know, the ones that are going to move the most are the ones that are going to get the attention. So I think the benefit of the split is that it really allowed Veritas to focus on what they do well, which is managing data, and Symantec to do what they do well, which is securing your infrastructure and securing your data. From my perspective, our customers really appreciated that. Sure, a couple of them were a little annoyed that they had to now split contracts and deal with that kind of stuff, but I think that was a momentary blip and for the most part, it's been well-received from everyone we've spoken to. >> Angelo, you said you're having, your conversations are evolving. Who are you talking to? And maybe take us inside some of those conversations. What are the big challenges they're having? >> Yeah, a year ago, a year and a half ago I was talking to either somebody who was on the messaging side and needed to archive emails or IMs, or on the backup side and they just wanted to be able to meet their backup windows and maybe to get some better d dub rates, right. Fun conversation to have, bit mundane. It's not really solving problems as much as backing up data or archiving data. Today, we're having overarching conversations at a C-level, or a senior VP level, or a director level, and talking about dramatic changes to the way they do business, and how we can do business with them. Six months ago, NetX, we weren't doing anything in the Cloud, you know. We were selling to some customers' Vdub space to the Cloud, and that's about it. We weren't talking Cloud strategy with them. Today we're talking to our customers about moving workloads to the Cloud, doing it in a way that's predictable for them, and doing it with Veritas. >> That's a really interesting point. I have to imagine that changed who you're talking with inside the company. Can you walk us through kind of a typical customer's, you know, and how you kind of move up into a more strategic discussion for Cloud strategy? >> You know, so for full transparency, that whole thing's still evolving, right. 360 Data Management is still fairly new. So what we're seeing, the conversations turned, it would start, again we're talking to somebody that we've been talking to historically in the backup side or architecture side, and we talk to them about wanting to do better things than what their backup is, and start to talk about, hey this is what 360 Data Management is. What's relevant to that person he's going to want to talk about but then there's going to be things in there that are not relevant to him. So he'll make that introduction and he'll get other stakeholders in the boat with him. And that's something we've really appreciated because the people you used to talk to are now bringing in stakeholders to offset their own desires and their own budgets, so want to bring in other technology. And typically, when we get to that point when we're starting to talk about strategic pricing, is when you're getting that C-level person to really have that aha moment, and say wow, we're offsetting costs here, we're doing things like truly getting rid of tape, or moving to the Cloud and things like that, and it's a conversation that really evolves and it's still starts at the bottom. But we're figuring out ways to start it at a higher point. >> Well, those strategies are still evolving for most customers; the roles of those people that might have had one role definitely are changing. I'm curious, one of the big transition points, especially for a company like Veritas, is going from licenses to some kind of more of a subscription model. Any commentary you have on your customers; their embrace, or like, dislike of some of those transitions? >> I think the one thing the Cloud has done is it's opened up a different avenue of how people consume IT, right. Cloud is very much consumption-based billing, and while that can complicate our lives from a reseller perspective in terms of how to collect and track monthly billing and things like that, they like it because they feel like, and it's the truth, they're only paying for what they're truly using, rather than paying for products or infrastructure that they're only using part of the day, or software that they're only using for a particular project. A lot of our healthcare systems might have a research project that their going on, and they might like to scale up for some backup licensing and scale back down once that project is done. Consumption licensing allows that, versus having to go to them and saying, hey, well now you got to buy 200 terabytes of perpetual licensing, and justify that capital expense, rather than having an operational expense on just that one particular workload that you have to back up for that one period of time. >> Angelo, Stu and I are always interested in the human capital management aspects of things, and you talked about, you went from sort of talking about having a conversation around email archiving or backup, to one about the Cloud, Cloud strategies. From your internal organization perspective, how did you manage that? Are you rescaling, are you retraining? Is it just you got really supersmart people that can adapt? >> We definitely have supersmart people, because they're all over there, that's right. But I definitely have supersmart people. But, you know, it's a little bit of both. It's a little bit of, you know, you take one of our data protection projects; see Christian Muma, you know, he's been in the data center for god knows how many years, he has seen technology evolve. It was a natural fit to look at Cloud infrastructure. Started taking some classes, consumed it, all the information he could, and now we're out there actively selling it. In some other respects, we had to hire from outside and bring in some services ourselves to actually use, maybe some third party partnerships to help us better understand how we price out Cloud for our customers. So it's a little bit of everything, and I think that that's what's exciting about it, because I think for the first time in a long time, everyone's learning something new at the same time, because, I don't care what anyone said about the Cloud years ago; it's different today, it's going to be different in six months, it's going to be different in nine months. And I think that that's exciting, and I've been in this industry since 1996. I've seen a lot of really cool things come and go. I just think that there's still infancy in the Cloud and I think it's exciting because everyone's still learning. And any time you can still learn, I think that's, I think an important part of your job. >> So when you think about your, sort of, near-term and midterm and long-term plan for the company, how do you sort of describe that? Where do you want to take this thing? >> Near-term, I want to have a solid end of the quarter. >> Business is good, right, I mean market's booming right now. >> Business is very good. Veritas will tell me it's not good enough but they're just never happy. No, business is, business is very good. I think, near-term for us, you said hey, how do we get our head around it? Near-term for us is, as we're absorbing all this information, is start to really figure out what our path is going to be. So near-term, I think we still have to identify other ancillary partners that we need to bring to the table. We've got our partnerships with Azure, Microsoft Azure, and our partnerships with AWS. We'll probably have to look at Google and IBM and see what they're doing, and then we have to look at other partnerships that are not related to Veritas but still drive that home. We maybe look at a different colo partnership or partnerships around outsourcing billing, things like that, that we can make where it's easier for our customers to consume the technology. So I think six to nine months from now if we were to have the same conversation, everything that we're doing today is probably going to be somewhat different. But I just think that there's still a lot of planning to do. >> Angelo, any feedback from your customers on what there's still on the to-do list from the vendors? We talked, you know, the strategy, Cloud's changing a lot, you know. What are some of the pinpoints that they said hey, if we could get this into the offering from Veritas or some of the others it would make our lives a lot easier. >> I mean, that's a tough question, because we're going to them now and changing the conversation already. You know, obviously they're always asking for different features, but I don't like to get into a feature conversation with the customers. I try to solve the problem. >> Dave: You're leading that conversation, is what you're saying. >> Yeah, I don't want to get into the weeds of talking about well, this widget does it at 50% and you do it at 48%. You know, I try to sit a little bit more macro. I think that one of the things our customers have asked us to do a better job at is figure out better ways to make it easier to consume the technology from budget perspective. So we're trying to figure that out now; 360 Data Management is a subscription, Veritas would like them sold in three years, we're trying to figure out ways to get creative with our customers on that. What's the right bundle, what's not the right bundle. One thing that I've noticed, and Veritas have been great at it, is we have to have some flexibility in terms of adding things in and make it seem like it's all part of that bundle. There's been some flexibility and I think that, because of that, we haven't hit that roadblock yet where, well, we really want this product in the bundle. Reality is that we'll work through that and try to add it in there, some way, shape or form, even if behind the scenes. >> The customers see you as the experts, and what we often see is that technology is the technology; it's pretty much understood. What's not understood by the customers is how to apply it to their business, and their business is changing so fast that it seems like they're looking to organizations like yours saying okay, here's our business challenge. How can you help me? You tell me, and then the best answer is somebody he'll be able to work with. Is that a valid, sort of, premise? >> Yeah, it is, it certainly is and I think we're really uniquely positioned in the fact that, here we've got, we've got our partnership with Veritas and we're 100% focused to everything in the Veritas portfolio so we don't compete from within. That's the same thing that we could say, basically, on Symantec and some of our traditional storage partners as well. That'll change most likely, on our storage partners, especially because of what Veritas have been releasing with Access and some of the other software providing storage technology. When we're brought in, we're brought in as the experts in that finite area, so we're not brought in as a generalist-type of reseller. We're brought in as, hey, I've got a data management problem, I've got a data security problem, or I'm trying to do some high-performance workloads on storage. So yeah, we are the experts, but at the same time we're being brought in for those handfuls of things, so we're not having these, hey, can you maximize my span on anti-virus software because I want to sell you commoditized software. It's just not us, it's not our thing. We're not adding any value to the customers, or the poor owners for that matter. >> Angelo, curious that there's a lot of startups in the data protection space. What do you here, your customers asking you about them? You know, what's your thoughts there? >> I guess I got to be nice, right? Because I'm being streamed everywhere. >> Stu: They're not listening, go ahead, be a New Yorker. >> Listen, I challenge Rubrik at any point of time, you know, those guys, Rubrik, Cohesity, those guys, they're new, they're the shiny new toy. The problem, the problem is they have their messaging out there, and the problem we have is that they're the shiny new toy. But when the rubber hits the road and when it's time to actually go and prove out what the technology can do, we'll win all the time. We will win ten out of ten times if we get the seat at the table, right. The problem is is because we were a limited portfolio, a limited product, limited integration type of company before, we weren't getting that seat at the table. I think they see it now, I think they're starting to get a little concerned about, hey, you know what, if this 360 Data Management is what it's going to be, and we all know it is, I think they're going to be concerned. They're new, and they're going to get attention. My honest opinion: I'm glad they came out, I'm glad that Rubrik and Cohesity and all these guys came out and did all this different ways to go to market, because I think it really forced all of us to say hey, we got some real tough decisions to make here, the competition has caught up, in certain ways. Let's change the game, and 360 Data Management does that. I think they should take as much business as they can right now, because it's going to be short-lived. >> You said it makes you rethink your strengths, and like you said, change the game. >> Yeah, it changes the game. >> Yeah. Uh, okay, predictions on the MLB? Yankees won their getaway game today to put the pressure on the Red Sox, two and a half to two and a half games back. You know, the Indians are looking good, my man, Terry Francona. What's your prediction for it? >> The Sox fan's outnumbered two to one here, so go ahead. >> You know, so I shouldn't say that the Yankees are going to win the World Series? >> No, he's a Yankees fan. >> I'm a Yankee fan, too. >> Honestly, as a Yankee fan, I think we all know that they weren't supposed to be this team, so I think this is, that's the team to look out for. >> Dave: Maybe this is their year. >> I think this is the year that they're going to challenge people, I mean, are they going to win? It's Cleveland, do you really think Cleveland's going to win anything? They won one thing in the last, what, 30 years. >> That's what they used to say about us in Boston. Angelo, thanks so much coming on, really appreciate it. Keep right there, buddy, we'll be back with our next guest right after this short break. We're live from-- (electronic music)

(mid-tempo music) >> Announcer: Live, from Los Angeles, it's The Cube. Covering Open source Summit North America 2017. Brought to you by the Linux Foundation and Red Hat. >> Okay welcome back and we're live in Los Angeles for The Cube's exclusive coverage of the Open Source Summit North America. I'm John Furrier, my co-host Stu Miniman, Our next guest is Steve Pousty, who's the Director of Developer Advocacy for Red Hat, Cube alumni, we last spoke at the Cisco Devnet Create, which is their new kind of cloud-native approach. Welcome Back. >> Thank you, thank you, glad to be here. >> We're here at the Open Source Summit, which is a recognition that of all these kind of ... With LinuxCon, all these things, coming events, it's a big ten event, love the direction, >> Yeah Validation to what's already happened and the recognition of open source, where Linux is at the heart of all that, Red Hat also you guys are the Linux standard, and gold standard, but there's more- >> We like to think of it that way, but- >> But there's more than Linux on top of it now, so this is a recognition that open source is so much more. >> For sure, I'm mean you can even see ... Who would've thought that there'd be a whole huge hubbub about Facebook doing a separate license for their react libraries and all the interactions with Apache, the Apache Foundation. Open source is so much ... it's the mainstream now. Like, basically, it's very hard to release a proprietary product right now and come up with some justification about why you have to do it. >> And why, and can it even be as good. >> Steve: Right. >> There's two issues, justification and performance. >> Yeah, quality, all that stuff. And also, customers' acceptability of that. Like, "Oh wait, you mean I can't actually even see the code? "I can't modify the code, I can't pay you to modify the code "and share it with everybody else?" I think customers have come to a whole ... Users of open source stuff, it's so permeated now that I think it's hard to be in the market without ... I mean, look at everybody who's here. Some of the people that are here were some of the biggest closed source people before. >> John: Microsoft is here. >> Exactly. >> John: IBM is here, although they've always been open, they were big on Linux early on. >> Yes. >> But now you're seeing the ecosystem grow, so we see some scale coming, but there's still a lot of work that needs to get done. We see greatness, like Kubernetes and Serverless offering great promise and hope for either multi-cloud workflow, workload management, all those cool stuff. But there's still some work to be done. >> Steve: For sure. >> What's your take on progress, where are we, what's the ... some of those under the hood things that need to get worked on? >> Well so, progress, I think ... the funny part is our expectations have changed so much over time that, so Kubernetes is about a little over two years old, and we're all like, oh it's moving so s-- why is it not doing this, this, and this? Whereas if this was like 10 years ago, the rate at which Kubernetes is moving is phenomenal. So, basically, every quarter there is a new release of Kubernetes, and we basically built OpenShift as a distribution on top of Kubernetes, and so we're delivering to our customers every quarter as well, and a bunch of them are like, "This is too fast, this is too fast, "like, we can't integrate all these changes." But at the same time, they say, "But don't slow down." Because, "Oh, next release we're going to get this thing "that we want and we know we want to go to that release." So, I think Kubernetes definitely has more growing room, but the thing is, how much it's already being seen as the standard, it's the ... so the way I like to talk about it, and I'll talk about this in my talk later, I think for Red Hat, Kubernetes is the cloud Linux kernel. It's the exact same story all over again. It's this infrastructure that everybody's going to build on. Now there are people who are standing up OpenStack on Kubernetes, or on OpenShift. So basically saying, "I don't want to install and manage "my Openstack, it's too difficult. "Give me some JSON and some components "and I'll just use Kubernetes as my operating plane." >> We saw Kubernetes right out of the gates, Stu and I, at the first Cube-Con, we were present at creation, and just on the doorstep of that thing just unfolded, and we saw the orchestration piece is huge, but I want to get your take if you can share with the audience, why Kubernetes has taken the world by storm. Why is it so relevant? What's all the hubbub about with Kubernetes? Share your opinion. >> Okay, so remember ... okay so this is a red shirt, and remember I work at Red Hat, so this obviously a biased opinion. I want to be up front about that. >> John: In your biased opinion ... >> Right, well as opposed to a neutral opinion, right, we definitely, so, I say that in front of my audiences just so that ... go do your own research, but from my perspective and what I've seen in the market place, there was a lot of orchestration and scheduling out there, then it kind of narrowed down, there's three players I would say right now. The three players all end with Kubernetes, but I just started with it (laughs). There's Kubernetes, there is Mesosphere, and there's Docker Swarm. I see those as being the three that are out there right now. And I think the reason we're ... So I won't talk about the others, but I see those ... Why Kubernetes has won is, one, community. So they have done a great job of being upstream, working with all people, being a very open community, open to working with others, not trying to make things just so it benefits Google's business but to benefit everybody. The other reason is the size of that community, right, everybody working together. The third is I think they, so some of it's luck, right? >> John: Yeah, timing is everything. >> Timing is everything. >> John: You're on a wave, and you're on your board and a big wave comes, you surf it, right? >> That's exactly, so I think what happened with Mesosphere is they're a great scheduler, and a lot of people said they were the best scheduler to start with. But they didn't really focus on containers to start with and it seems like they missed ... Like, Kubernetes said, "No, it's all about containers "and we're going to focus on container workload." And that's right where everybody else was. And so it was like, "I don't want to write "all that extra stuff from Mesosphere. "I want to do it with Kubernetes 'cause that's containers." And so that's the bit of luck lining up with the market. So I'd say it's the community but also recognizing that it's about containers to start with and containers are kind of taking over. >> Yeah, Steve, take us inside containers. You're wearing a shirt that says "Linux is Containers" on the front, if our audience could see the back it says "Containers are Linux." >> Steve: Exactly. >> Of course, Red Hat heavily involved. You're in the weeds, dealing with things that we're doing to make stability of containers, make sure it works in other environments. Tell us some of the things you're working on, some of the projects, and the like. >> So, some of the projects I'll be showing today, one is based off of OpenScap, Open S-C-A-P, it's another open consortium for scanning for vulnerabilities. We've written something called Atomic Scan, so it can take any OpenScap provider, plug it in to Atomic Scan, and you can scan a container image without having to actually run it. So, you don't actually have to start it up, it actually just goes in. The other thing I'm going to be talking about today is Bilda, this is part of the CNCF stuff. This is the ability to actually build a runC-compatible container without ever using Docker or MOBI. The way, a totally different approach to it, what you basically do is you say, "I want this container from this other container, or from blank," then you have a container there and then you actually mount the file system. So rather than actually booting a container and doing all sorts of steps in the container itself, you actually mount the file system, do normal operations on your machine like it was your normal file system, and then actually commit at the end. So it's another way for some of our customers that really like that idea of how they want to build and manage containers. But also, there's a bunch more. There's Kryo, which is the common runtime interface, and the implementation of it, so that Kubernetes can now run on an alternative container technology. This is Kryo, it's agnostic. If you looked at Kelsey Hightower's latest "Kubernetes and Anger," I think, or "Kubernetes the Hard Way" or something. His latest is building it all on Kryo. So rather than running on Docker, it runs ... All your container running happens on Kryo. I'm not trying to say, well of course I think it's better, but I think the point that we're really seeing is, by everything moving in to CNCF and the Linux Foundation and getting around standards, it's really enabled the ecosystem to take off. Like, TekTonic and CoreOS have done that with Rocket. We're going to see a lot more blossoming. The fertilizer has been applied, back from our ... >> Yeah, CNCF of two years old, I mean their fertilizer down big time, you got the manure and all the thousand flowers are blooming from that. >> Yeah, between Prometheus, I mean just, Prometheus, Istio, there's just ... I can't even keep track of it all. >> So Steve, you were talking earlier. Customers are having a hard time with that quarterly release. >> Steve: Yes. >> How do they keep up with all these projects, I mean you know, we rattled through all of 'em. You've got 'em all down pat, but the typical customer, do they need to worry about what do they have to focus on, how do they keep up with the pace change, how do they absorb all of this? >> Okay so it highly depends on the customer. There are some customers who are not our customers, I'll just say users, who are advanced enough on their own, who they're out there basically just, they're consuming the tip of what's coming out of CNCF. All that stuff, and they're picking and choosing and they're doing that all. For Red Hat, a lot of our customers are, "I like all that technology, you're our trusted advisor, "when you release it as a product "and I know I can sit on it for three years, "because you'll support it for at least three years, "maybe five years, then I'm going to start to consume it "and you'll actually probably put it into a more usable form for me." 'Cause a lot of the upstream stuff isn't necessary made direct for consumption. >> How are you guys dealing with the growth prospects. We've been talking about this all morning, this has been the big theme of this show is, not only just the renaming of a variety of different events, LinuxCon, but Open Source Summit is an encapsulation of all the projects that are blossoming across the board. So, the scale issues, and as a participant, Red Hat, >> Steve: Yes. >> Your biased opinion, but you're also incentive and you guys are active in the community. The growth that's coming is going to put pressure on the system. It may change the relationship between communities and vendors and how they're all working together, so again, to use the river analogy, there's a lot of water going to be pumping through the system. And so how's that going to impact the ecosystem, is it going to be the great growth that could flood everything, is there a potential for that, I mean you're an ecosystem guy, so the theory is there, it's like, Jim's stepping up with the Linux Foundation. I talked to him yesterday and he recognizes it. >> Steve: Yeah. >> But he also doesn't want to get in the way, either. >> Steve: No, no, no- >> So there's a balance of leadership that's needed. Your thoughts. >> So, I mean I think one of the things ... So I mean you know the Linux kernel has its benevolent dictator, and that works well for that one community, but then you'll see something like Kubernetes, where it's much more of a community base, there is no benevolent dictator for life on Kubernetes. I think one of the nice things that the Linux Foundation has done, and which Red Hat has acknowledged is, you know, let the community govern the way that works for that community. Don't try to force necessarily one model on it. In terms of the flood part that you were talking about, I think, if you want to go back to rivers, there's cycles in terms of 10 year floods, 100 year floods, I think what we're seeing right now is a big flood, and then what'll happen out of this is some things will shake out and other things won't. I don't expect every vendor that's here to be here next year. >> And find the high ground, I mean, I mean the numbers that Jim shared in his opening keynote is by 2026, 400 million libraries are going to be out there versus today's 64 roughly million. >> Steve: Right. >> You know, Ed from Cisco thinks that's understated, but now there's more code coming in, more people, and so a new generation is coming on board. This is going to be the great flood in open source. >> I also think it's a great opportunity for some companies. I mean, I'm not high enough in Red Hat to know what we're doing in that space, but it's also a great opportunity for some companies to help with that. Like, I think, that's one of the other things that Linux Foundation did was set up the Javascript Foundation. That is a community that-- >> But that doesn't have Node.js, it's a little bit separate. >> No, I know, but think-- >> You're talking about the js, okay. >> But I'm talking about, but if you think about the client-side javascript, forget Node. Just think about client-side javascript and how many frameworks are coming up all the time, and new libraries. >> Stu: That's a challenge. >> So I think actually that community could be one that could be good to maybe gain some lessons from, as things happen more in open source. I think there are other open source communities. Like, I'm wondering like GNOME-- >> But the feedback on the js community is that there's a lot of challenges in the volume of things happening. >> And that's coming for us though, right? >> Yeah. >> That's what's coming, that's what's going to come for this larger ecosystem, so I think maybe there's market opportunity, maybe there's new governance models, maybe there's ... I mean, this is where innovation comes from. There's a new problem that's come, it's a good problem. >> Your next point of failure is your opportunity to innovate. >> Exactly, and it's a good problem to have, right, as opposed to, we have too few projects, and we don't really, no one really likes them. Instead, now it's like, we've got so many projects and people are contributing, and everybody's excited, how do we manage that excitement? >> So another dynamic that we're observing, and again we're just speculating, we're pontificating, we're opining ... is fashion. Fashion, fashionable projects. Never fight fashion, my philosophy is. In marketing, don't fight the fashion. >> Steve: Right. >> CNCF is fashionable right now, people love it. It's popular, it's trendy, it's the hip new night club if you will in open source. Other projects are just as relevant. So, relevance and trending sometimes can be misconstrued. How do you guys think about that, because this is a dynamic, everyone wants to go to the best party. There a fear of missing out, I'm going to go check out Kubernetes, but also relevance matters. >> Yes. >> John: Your thoughts. >> So I've seen this discussion internally in engineering all the time, when we're talking about, 'cause you know OpenShift is trying to build a real distribution, not like, "Oh here is Kubernetes," but a real distribution. Like when Red Hat ships you the Linux, gives you Linux, we don't just say, "Here's the Linux kernel, have a good time." We put a whole bunch of stuff around it, and we're trying to do that with Kubernetes as well, so we're constantly evaluating all the like, "Should we switch to Prometheus now, "when's the time to switch to Prometheus? "Oh it's trending really hot. "Oh but does it give us the features?" >> John: It's a balance. >> It's a balance, it's going to have to come down to, I hate to say it-- >> It's a community, people vote with their code, so if something has traction, you got to take a look at it. >> But I would say, and this has been going on for a while, and I've seen other people talk about it, if you are the lead on an open source project, and you want a lot of community, you have to get into marketing. You can't just do-- >> John: You got to market the project. >> You got to, and not in the nasty term of market, which is that I'm going to lie to you and like, what a lot of developers think about like, "Oh I'm just going to give you bullshit and lie to you, "and it's not going to be helpful." No, market in terms of just getting your word out there so at least people know about it. Lead with all your-- >> John: Socialize it. >> Yeah. I mean, that's what you got to get it, so there is a lot of chatter now. How do you get it noticed as a Twitter person, right? You have to do some, it's the same, it's going to be more like that for open source projects. >> John: So we're doing our share to kind of extract the signal from some of the noise out there, and it's great to talk to you about it because you help give perspective. And certainly Red Hat, you're biased, that's okay, you're biased. Now, take your Red Hat off. >> Okay. >> Hat off. Take your Red Hat hat off >> Steve: Propaganda hat off. >> and put your neutral hat on. An observation of Open Source Summit, I'll see that name change kind of significance in the sense it's a big ten event. This event here, what's your thoughts on what it means? >> Hey c'mon Steve, you've got a PhD in ecology, so we want some detailed analysis as to how this all goes together. >> I mean it's good marketing, Open Source Summit, good name change, little bit broader. >> I'm actually glad for it. So, I've gone to some other smaller events, and I actually like this, because it was hard for me to get to the smaller events, or to get quite enough people. Like this actually builds a critical mass, and more cross-fertilization, right, so it's much easier for me to talk to containers to car people. 'Cause automotive Linux is here as well, right? >> John: You can't avoid it, you see 'em in the hallways, you can say, "Hey, let's chat." >> "Let's talk about that stuff," whereas in the small ... So, you know, this is more about conferences. There's a good side and a bad side to everything, just like I tell my kids, "When you pick up a stick, you also have to pick up the other end of the stick." You can't just like have, "Oh this is a great part," but you don't get the bad part. So the great part about this, really easy to see a lot of people, see a lot of interesting things that are happening. Bad part about this, it's going to be hard, like if this was just CNCF, everybody here would be CNCF, all the talks would be CNCF, it's like you could deep dive and really go. So, I think this is great that they have this. I don't think this gets, should get rid of smaller, more focused events. >> Well at CubeCon, our CubeCon, the CNCF event in Austin, we'll be there for The Cube. That will be CNCF all the time. >> Steve: Exactly. I'm glad they're still doing that. >> So they're going to have the satellite event, and I think that's the best way to do it. I think a big ten event like this is good because, this is small even today, but with the growth coming, it'll be convention hall size in a matter of years. >> Well, exactly, and the fact that you made it into a big, and the fact that you've made it into this cohesive event, rather than going to somebody and saying, "Hey, sponsor these five events." Like, No. Sponsor this one big event, and then we'll get most of the people here for you. >> It's also a celebration, too. A lot of these big ten events are ... 'Cause education you can get online, there are all kinds of collaboration tools online, but when you have these big ten events, one of the rare things is it's the face to face, people-centric, in the moment, engagement. So you're learning in a different way. It's a celebration. So I think open source is just too important right now, that this event will grow in my opinion. >> Steve: For sure. >> Bring even thousands and thousands of people. >> I mean, another way-- >> John: 30,000 at some point, easily. >> Yeah, I think definitely it's theirs to lose, let's put it that way. >> John: (laughing) I'll tell that to Jim "Hey, don't screw it up!" >> Don't screw it up. I think the way that you could almost think of this is OSS-Con, right, instead of Comic-Con, this is like, this can become OSS-Con, which is like, they should probably ... In the same way that the Kubernetes Foundation works and grows with a lot of other people, it would be great if they could bring in other Foundations as well to this. I know this is being run by the Linux, but it'd be great if we could get some Apache in here, some Eclipse in here, I mean that would just be-- >> John: A total home run. >> Those foundations bringing it in-- >> That would truly make it an open source summit. >> Yeah, exactly, as opposed to the World Series that's only in the United States. >> Yeah. (laughing) >> Although you know, I was at a hotel recently, and they had baseball on, it was little league baseball though. Their World Series is actually, Little League World Series is actually the World Series. >> John: It's a global World Series. >> Yeah, like their-- >> John: It's the world. >> Yeah, as opposed to the MLB, right? >> Alright, Steve, great to have you on, any final thoughts on interactions you've had, things you've learned from this event you'd like to share and pass on? >> No, I just think the space is great, I'm really excited to be in it. I'm starting to move a little bit more up to the application tier at my role at the company and I'm excited about that, to actually ... So I've been working down at the container tier, and orchestration tier for a while, and now I'm excited to get back to like, "Well now let's actually build some cool stuff "and see what this enables on the up--" >> And DevOps is going mainstream, which is a great trend, you're starting to see that momentum beachhead on the enterprises, so-- >> Oh, one takeaway message, for microservices people, please put an Ops person on your microservice team. Usually they start with the DBA, and then they say the middle person and the front-end people. I really want to make sure that they start including Ops in your microservice teams-- >> John: And why is that, what'd you learn there? >> Well because if you're going to do microservices, you're going to be, the team's going to end up doing Ops-y work. And it's kind of foolish not to bring in someone who already knows ... The reason you want all the team together is because they're going to own that. And you also want them to share knowledge. So, if I was on a microservice team, I would definitely want an Ops person teaching me how to do Ops for our stuff. I don't want to reinvent that myself. >> You got to have the right core competencies on that team. >> Steve: Yeah. It's like having the right people in the right position. >> Steve: Exactly. >> Skill player. >> Steve: Yeah, exactly. Okay we're here live in Los Angeles, The Cube's coverage of Open Source Summit North America. I'm John Furrier, Stu Miniman. More live coverage after this short break. (electronic music)

(upbeat techno music) >> Announcer: Good morning ladies and gentlemen. This is Lenovo Transform. Please welcome to the stage Lenovo's Rod Lappin. (upbeat instrumental) >> Alright, ladies and gentlemen. Here we go. I was out the back having a chat. A bit faster than I expected. How are you all doing this morning? (crowd cheers) >> Good? How fantastic is it to be in New York City? (crowd applauds) Excellent. So my name's Rod Lappin. I'm with the Data Center Group, obviously. I do basically anything that touches customers from our sales people, our pre-sales engineers, our architects, et cetera, all the way through to our channel partner sales engagement globally. So that's my job, but enough of that, okay? So the weather this morning, absolutely fantastic. Not a cloud in the sky, perfect. A little bit different to how it was yesterday, right? I want to thank all of you because I know a lot of you had a lot of commuting issues getting into New York yesterday with all the storms. We have a lot of people from international and domestic travel caught up in obviously the network, which blows my mind, actually, but we have a lot of people here from Europe, obviously, a lot of analysts and media people here as well as customers who were caught up in circling around the airport apparently for hours. So a big round of applause for our team from Europe. (audience applauds) Thank you for coming. We have some people who commuted a very short distance. For example, our own server general manager, Cameron (mumbles), he's out the back there. Cameron, how long did it take you to get from Raleigh to New York? An hour-and-a-half flight? >> Cameron: 17 hours. >> 17 hours, ladies and gentleman. That's a fantastic distance. I think that's amazing. But I know a lot of us, obviously, in the United States have come a long way with the storms, obviously very tough, but I'm going to call out one individual. Shaneil from Spotless. Where are you Shaneil, you're here somewhere? There he is from Australia. Shaneil how long did it take you to come in from Australia? 25 hour, ladies and gentleman. A big round of applause. That's a pretty big effort. Shaneil actually I want you to stand up, if you don't mind. I've got a seat here right next to my CEO. You've gone the longest distance. How about a big round of applause for Shaneil. We'll put him in my seat, next to YY. Honestly, Shaneil, you're doing me a favor. Okay ladies and gentlemen, we've got a big day today. Obviously, my seat now taken there, fantastic. Obviously New York City, the absolute pinnacle of globalization. I first came to New York in 1996, which was before a lot of people in the room were born, unfortunately for me these days. Was completely in awe. I obviously went to a Yankees game, had no clue what was going on, didn't understand anything to do with baseball. Then I went and saw Patrick Ewing. Some of you would remember Patrick Ewing. Saw the Knicks play basketball. Had no idea what was going on. Obviously, from Australia, and somewhat slightly height challenged, basketball was not my thing but loved it. I really left that game... That was the first game of basketball I'd ever seen. Left that game realizing that effectively the guy throws the ball up at the beginning, someone taps it, that team gets it, they run it, they put it in the basket, then the other team gets it, they put it in the basket, the other team gets it, and that's basically the entire game. So I haven't really progressed from that sort of learning or understanding of basketball since then, but for me, personally, being here in New York, and obviously presenting with all of you guys today, it's really humbling from obviously some of you would have picked my accent, I'm also from Australia. From the north shore of Sydney. To be here is just a fantastic, fantastic event. So welcome ladies and gentlemen to Transform, part of our tech world series globally in our event series and our event season here at Lenovo. So once again, big round of applause. Thank you for coming (audience applauds). Today, basically, is the culmination of what I would classify as a very large journey. Many of you have been with us on that. Customers, partners, media, analysts obviously. We've got quite a lot of our industry analysts in the room. I know Matt Eastwood yesterday was on a train because he sent a Tweet out saying there's 170 people on the WIFI network. He was obviously a bit concerned he was going to get-- Pat Moorhead, he got in at 3:30 this morning, obviously from traveling here as well with some of the challenges with the transportation, so we've got a lot of people in the room that have been giving us advice over the last two years. I think all of our employees are joining us live. All of our partners and customers through the stream. As well as everybody in this packed-out room. We're very very excited about what we're going to be talking to you all today. I want to have a special thanks obviously to our R&D team in Raleigh and around the world. They've also been very very focused on what they've delivered for us today, and it's really important for them to also see the culmination of this great event. And like I mentioned, this is really the feedback. It's not just a Lenovo launch. This is a launch based on the feedback from our partners, our customers, our employees, the analysts. We've been talking to all of you about what we want to be when we grow up from a Data Center Group, and I think you're going to hear some really exciting stuff from some of the speakers today and in the demo and breakout sessions that we have after the event. These last two years, we've really transformed the organization, and that's one of the reasons why that theme is part of our Tech World Series today. We're very very confident in our future, obviously, and where the company's going. It's really important for all of you to understand today and take every single snippet that YY, Kirk, and Christian talk about today in the main session, and then our presenters in the demo sections on what Lenovo's actually doing for its future and how we're positioning the company, obviously, for that future and how the transformation, the digital transformation, is going ahead globally. So, all right, we are now going to step into our Transform event. And I've got a quick agenda statement for you. The very first thing is we're going to hear from YY, our chairman and CEO. He's going to discuss artificial intelligence, the evolution of our society and how Lenovo is clearly positioning itself in the industry. Then, obviously, you're going to hear from Kirk Skaugen, our president of the Data Center Group, our new boss. He's going to talk about how long he's been with the company and the transformation, once again, we're making, very specifically to the Data Center Group and how much of a difference we're making to society and some of our investments. Christian Teismann, our SVP and general manager of our client business is going to talk about the 25 years of ThinkPad. This year is the 25-year anniversary of our ThinkPad product. Easily the most successful brand in our client branch or client branch globally of any vendor. Most successful brand we've had launched, and this afternoon breakout sessions, obviously, with our keynotes, fantastic sessions. Make sure you actually attend all of those after this main arena here. Now, once again, listen, ask questions, and make sure you're giving us feedback. One of the things about Lenovo that we say all the time... There is no room for arrogance in our company. Every single person in this room is a customer, partner, analyst, or an employee. We love your feedback. It's only through your feedback that we continue to improve. And it's really important that through all of the sessions where the Q&As happen, breakouts afterwards, you're giving us feedback on what you want to see from us as an organization as we go forward. All right, so what were you doing 25 years ago? I spoke about ThinkPad being 25 years old, but let me ask you this. I bet you any money that no one here knew that our x86 business is also 25 years old. So, this year, we have both our ThinkPad and our x86 anniversaries for 25 years. Let me tell you. What were you guys doing 25 years ago? There's me, 25 years ago. It's a bit scary, isn't it? It's very svelte and athletic and a lot lighter than I am today. It makes me feel a little bit conscious. And you can see the black and white shot. It shows you that even if you're really really short and you come from the wrong side of the tracks to make some extra cash, you can still do some modeling as long as no one else is in the photo to give anyone any perspective, so very important. I think I might have got one photo shoot out of that, I don't know. I had to do it, I needed the money. Let me show you another couple of photos. Very interesting, how's this guy? How cool does he look? Very svelte and athletic. I think there's no doubt. He looks much much cooler than I do. Okay, so ladies and gentlemen, without further ado, it gives me great honor to obviously introduce our very very first guest to the stage. Ladies and gentlemen, our chairman and CEO, Yuanqing Yang. or as we like to call him, YY. A big round of applause, thank you. (upbeat techno instrumental) >> Good morning everyone. Thank you, Rod, for your introduction. Actually, I didn't think I was younger than you (mumbles). I can't think of another city more fitting to host the Transform event than New York. A city that has transformed from a humble trading post 400 years ago to one of the most vibrant cities in the world today. It is a perfect symbol of transformation of our world. The rapid and the deep transformations that have propelled us from the steam engine to the Internet era in just 200 years. Looking back at 200 years ago, there was only a few companies that operated on a global scale. The total value of the world's economy was around $188 billion U.S. dollars. Today, it is only $180 for each person on earth. Today, there are thousands of independent global companies that compete to sell everything, from corn and crude oil to servers and software. They drive a robust global economy was over $75 trillion or $1,000 per person. Think about it. The global economy has multiplied almost 450 times in just two centuries. What is even more remarkable is that the economy has almost doubled every 15 years since 1950. These are significant transformation for businesses and for the world and our tiny slice of pie. This transformation is the result of the greatest advancement in technology in human history. Not one but three industrial revolutions have happened over the last 200 years. Even though those revolutions created remarkable change, they were just the beginning. Today, we are standing at the beginning of the fourth revolution. This revolution will transform how we work (mumbles) in ways that no one could imagine in the 18th century or even just 18 months ago. You are the people who will lead this revolution. Along with Lenovo, we will redefine IT. IT is no longer just information technology. It's intelligent technology, intelligent transformation. A transformation that is driven by big data called computing and artificial intelligence. Even the transition from PC Internet to mobile Internet is a big leap. Today, we are facing yet another big leap from the mobile Internet to the Smart Internet or intelligent Internet. In this Smart Internet era, Cloud enables devices, such as PCs, Smart phones, Smart speakers, Smart TVs. (mumbles) to provide the content and the services. But the evolution does not stop them. Ultimately, almost everything around us will become Smart, with building computing, storage, and networking capabilities. That's what we call the device plus Cloud transformation. These Smart devices, incorporated with various sensors, will continuously sense our environment and send data about our world to the Cloud. (mumbles) the process of this ever-increasing big data and to support the delivery of Cloud content and services, the data center infrastructure is also transforming to be more agile, flexible, and intelligent. That's what we call the infrastructure plus Cloud transformation. But most importantly, it is the human wisdom, the people learning algorithm vigorously improved by engineers that enables artificial intelligence to learn from big data and make everything around us smarter. With big data collected from Smart devices, computing power of the new infrastructure under the trend artificial intelligence, we can understand the world around us more accurately and make smarter decisions. We can make life better, work easier, and society safer and healthy. Think about what is already possible as we start this transformation. Smart Assistants can help you place orders online with a voice command. Driverless cars can run on the same road as traditional cars. (mumbles) can help troubleshoot customers problems, and the virtual doctors already diagnose basic symptoms. This list goes on and on. Like every revolution before it, intelligent transformation, will fundamentally change the nature of business. Understanding and preparing for that will be the key for the growth and the success of your business. The first industrial revolution made it possible to maximize production. Water and steam power let us go from making things by hand to making them by machine. This transformed how fast things could be produced. It drove the quantity of merchandise made and led to massive increase in trade. With this revolution, business scale expanded, and the number of customers exploded. Fifty years later, the second industrial revolution made it necessary to organize a business like the modern enterprise, electric power, and the telegraph communication made business faster and more complex, challenging businesses to become more efficient and meeting entirely new customer demands. In our own lifetimes, we have witnessed the third industrial revolution, which made it possible to digitize the enterprise. The development of computers and the Internet accelerated business beyond human speed. Now, global businesses have to deal with customers at the end of a cable, not always a handshake. While we are still dealing with the effects of a digitizing business, the fourth revolution is already here. In just the past two or three years, the growth of data and advancement in visual intelligence has been astonishing. The computing power can now process the massive amount of data about your customers, suppliers, partners, competitors, and give you insights you simply could not imagine before. Artificial intelligence can not only tell you what your customers want today but also anticipate what they will need tomorrow. This is not just about making better business decisions or creating better customer relationships. It's about making the world a better place. Ultimately, can we build a new world without diseases, war, and poverty? The power of big data and artificial intelligence may be the revolutionary technology to make that possible. Revolutions don't happen on their own. Every industrial revolution has its leaders, its visionaries, and its heroes. The master transformers of their age. The first industrial revolution was led by mechanics who designed and built power systems, machines, and factories. The heroes of the second industrial revolution were the business managers who designed and built modern organizations. The heroes of the third revolution were the engineers who designed and built the circuits and the source code that digitized our world. The master transformers of the next revolution are actually you. You are the designers and the builders of the networks and the systems. You will bring the benefits of intelligence to every corner of your enterprise and make intelligence the central asset of your business. At Lenovo, data intelligence is embedded into everything we do. How we understand our customer's true needs and develop more desirable products. How we profile our customers and market to them precisely. How we use internal and external data to balance our supply and the demand. And how we train virtual agents to provide more effective sales services. So the decisions you make today about your IT investment will determine the quality of the decisions your enterprise will make tomorrow. So I challenge each of you to seize this opportunity to become a master transformer, to join Lenovo as we work together at the forefront of the fourth industrial revolution, as leaders of the intelligent transformation. (triumphant instrumental) Today, we are launching the largest portfolio in our data center history at Lenovo. We are fully committed to the (mumbles) transformation. Thank you. (audience applauds) >> Thanks YY. All right, ladies and gentlemen. Fantastic, so how about a big round of applause for YY. (audience applauds) Obviously a great speech on the transformation that we at Lenovo are taking as well as obviously wanting to journey with our partners and customers obviously on that same journey. What I heard from him was obviously artificial intelligence, how we're leveraging that integrally as well as externally and for our customers, and the investments we're making in the transformation around IoT machine learning, obviously big data, et cetera, and obviously the Data Center Group, which is one of the key things we've got to be talking about today. So we're on the cusp of that fourth revolution, as YY just mentioned, and Lenovo is definitely leading the way and investing in those parts of the industry and our portfolio to ensure we're complimenting all of our customers and partners on what they want to be, obviously, as part of this new transformation we're seeing globally. Obviously now, ladies and gentlemen, without further ado once again, to tell us more about what's going on today, our announcements, obviously, that all of you will be reading about and seeing in the breakout and the demo sessions with our segment general managers this afternoon is our president of the data center, Mr. Kirk Skaugen. (upbeat instrumental) >> Good morning, and let me add my welcome to Transform. I just crossed my six months here at Lenovo after over 24 years at Intel Corporation, and I can tell you, we've been really busy over the last six months, and I'm more excited and enthusiastic than ever and hope to share some of that with you today. Today's event is called "Transform", and today we're announcing major new transformations in Lenovo, in the data center, but more importantly, we're celebrating the business results that these platforms are going to have on society and with international supercomputing going on in parallel in Frankfurt, some of the amazing scientific discoveries that are going to happen on some of these platforms. Lenovo has gone through some significant transformations in the last two years, since we acquired the IBM x86 business, and that's really positioning us for this next phase of growth, and we'll talk more about that later. Today, we're announcing the largest end-to-end data center portfolio in Lenovo's history, as you heard from YY, and we're really taking the best of the x86 heritage from our IBM acquisition of the x86 server business and combining that with the cost economics that we've delivered from kind of our China heritage. As we've talked to some of the analysts in the room, it's really that best of the east and best of the west is combining together in this announcement today. We're going to be announcing two new brands, building on our position as the number one x86 server vendor in both customer satisfaction and in reliability, and we're also celebrating, next month in July, a very significant milestone, which will we'll be shipping our 20 millionth x86 server into the industry. For us, it's an amazing time, and it's an inflection point to kind of look back, pause, but also share the next phase of Lenovo and the exciting vision for the future. We're also making some declarations on our vision for the future today. Again, international supercomputing's going on, and, as it turns out, we're the fastest growing supercomputer company on earth. We'll talk about that. Our goal today that we're announcing is that we plan in the next several years to become number one in supercomputing, and we're going to put the investments behind that. We're also committing to our customers that we're going to disrupt the status quo and accelerate the pace of innovation, not just in our legacy server solutions, but also in Software-Defined because what we've heard from you is that that lack of legacy, we don't have a huge router business or a huge sand business to protect. It's that lack of legacy that's enabling us to invest and get ahead of the curb on this next transition to Software-Defined. So you're going to see us doing that through building our internal IP, through some significant joint ventures, and also through some merges and acquisitions over the next several quarters. Altogether, we're driving to be the most trusted data center provider in the industry between us and our customers and our suppliers. So a quick summary of what we're going to dive into today, both in my keynote as well as in the breakout sessions. We're in this transformation to the next phase of Lenovo's data center growth. We're closing out our previous transformation. We actually, believe it or not, in the last six months or so, have renegotiated 18,000 contracts in 160 countries. We built out an entire end-to-end organization from development and architecture all the way through sales and support. This next transformation, I think, is really going to excite Lenovo shareholders. We're building the largest data center portfolio in our history. I think when IBM would be up here a couple years ago, we might have two or three servers to announce in time to market with the next Intel platform. Today, we're announcing 14 new servers, seven new storage systems, an expanded set of networking portfolios based on our legacy with Blade Network Technologies and other companies we've acquired. Two new brands that we'll talk about for both data center infrastructure and Software-Defined, a new set of premium premiere services as well as a set of engineered solutions that are going to help our customers get to market faster. We're going to be celebrating our 20 millionth x86 server, and as Rod said, 25 years in x86 server compute, and Christian will be up here talking about 25 years of ThinkPad as well. And then a new end-to-end segmentation model because all of these strategies without execution are kind of meaningless. I hope to give you some confidence in the transformation that Lenovo has gone through as well. So, having observed Lenovo from one of its largest partners, Intel, for more than a couple decades, I thought I'd just start with why we have confidence on the foundation that we're building off of as we move from a PC company into a data center provider in a much more significant way. So Lenovo today is a company of $43 billion in sales. Absolutely astonishing, it puts us at about Fortune 202 as a company, with 52,000 employees around the world. We're supporting and have service personnel, almost a little over 10,000 service personnel that service our servers and data center technologies in over 160 countries that provide onsite service and support. We have seven data center research centers. One of the reasons I came from Intel to Lenovo was that I saw that Lenovo became number one in PCs, not through cost cutting but through innovation. It was Lenovo that was partnering on the next-generation Ultrabooks and two-in-ones and tablets in the modem mods that you saw, but fundamentally, our path to number one in data center is going to be built on innovation. Lastly, we're one of the last companies that's actually building not only our own motherboards at our own motherboard factories, but also with five global data center manufacturing facilities. Today, we build about four devices a second, but we also build over 100 servers per hour, and the cost economics we get, and I just visited our Shenzhen factory, of having everything from screws to microprocessors come up through the elevator on the first floor, go left to build PCs and ThinkPads and go right to build server technology, means we have some of the world's most cost effective solutions so we can compete in things like hyperscale computing. So it's with that that I think we're excited about the foundation that we can build off of on the Data Center Group. Today, as we stated, this event is about transformation, and today, I want to talk about three things we're going to transform. Number one is the customer experience. Number two is the data center and our customer base with Software-Defined infrastructure, and then the third is talk about how we plan to execute flawlessly with a new transformation that we've had internally at Lenovo. So let's dive into it. On customer experience, really, what does it mean to transform customer experience? Industry pundits say that if you're not constantly innovating, you can fall behind. Certainly the technology industry that we're in is transforming at record speed. 42% of business leaders or CIOs say that digital first is their top priority, but less than 50% actually admit that they have a strategy to get there. So people are looking for a partner to keep pace with that innovation and change, and that's really what we're driving to at Lenovo. So today we're announcing a set of plans to take another step function in customer experience, and building off of our number one position. Just recently, Gartner shows Lenovo as the number 24 supply chains of companies over $12 billion. We're up there with Amazon, Coca-Cola, and we've now completely re-architected our supply chain in the Data Center Group from end to end. Today, we can deliver 90% of our SKUs, order to ship in less than seven days. The artificial intelligence that YY mentioned is optimizing our performance even further. In services, as we talked about, we're now in 160 countries, supporting on-site support, 50 different call centers around the world for local language support, and we're today announcing a whole set of new premiere support services that I'll get into in a second. But we're building on what's already better than 90% customer satisfaction in this space. And then in development, for all the engineers out there, we started foundationally for this new set of products, talking about being number one in reliability and the lowest downtime of any x86 server vendor on the planet, and these systems today are architected to basically extend that leadership position. So let me tell you the realities of reliability. This is ITIC, it's a reliability report. 750 CIOs and IT managers from more than 20 countries, so North America, Europe, Asia, Australia, South America, Africa. This isn't anything that's paid for with sponsorship dollars. Lenovo has been number one for four years running on x86 reliability. This is the amount of downtime, four hours or more, in mission-critical environments from the leading x86 providers. You can see relative to our top two competitors that are ahead of us, HP and Dell, you can see from ITIC why we are building foundationally off of this, and why it's foundational to how we're developing these new platforms. In customer satisfaction, we are also rated number one in x86 server customer satisfaction. This year, we're now incentivizing every single Lenovo employee on customer satisfaction and customer experience. It's been a huge mandate from myself and most importantly YY as our CEO. So you may say well what is the basis of this number one in customer satisfaction, and it's not just being number one in one category, it's actually being number one in 21 of the 22 categories that TBR talks about. So whether it's performance, support systems, online product information, parts and availability replacement, Lenovo is number one in 21 of the 22 categories and number one for six consecutive studies going back to Q1 of 2015. So this, again, as we talk about the new product introductions, it's something that we absolutely want to build on, and we're humbled by it, and we want to continue to do better. So let's start now on the new products and talk about how we're going to transform the data center. So today, we are announcing two new product offerings. Think Agile and ThinkSystem. If you think about the 25 years of ThinkPad that Christian's going to talk about, Lenovo has a continuous learning culture. We're fearless innovators, we're risk takers, we continuously learn, but, most importantly, I think we're humble and we have some humility. That when we fail, we can fail fast, we learn, and we improve. That's really what drove ThinkPad to number one. It took about eight years from the acquisition of IBM's x86 PC business before Lenovo became number one, but it was that innovation, that listening and learning, and then improving. As you look at the 25 years of ThinkPad, there were some amazing successes, but there were also some amazing failures along the way, but each and every time we learned and made things better. So this year, as Rod said, we're not just celebrating 25 years of ThinkPad, but we're celebrating 25 years of x86 server development since the original IBM PC servers in 1992. It's a significant day for Lenovo. Today, we're excited to announce two new brands. ThinkSystem and ThinkAgile. It's an important new announcement that we started almost three years ago when we acquired the x86 server business. Why don't we run a video, and we'll show you a little bit about ThinkSystem and ThinkAgile. >> Narrator: The status quo is comfortable. It gets you by, but if you think that's good enough for your data center, think again. If adoption is becoming more complicated when it should be simpler, think again. If others are selling you technology that's best for them, not for you, think again. It's time for answers that win today and tomorrow. Agile, innovative, different. Because different is better. Different embraces change and makes adoption simple. Different designs itself around you. Using 25 years of innovation and design and R&D. Different transforms, it gives you ThinkSystem. World-record performance, most reliable, easy to integrate, scales faster. Different empowers you with ThinkAgile. It redefines the experience, giving you the speed of Cloud and the control of on-premise IT. Responding faster to what your business really needs. Different defines the future. Introducing Lenovo ThinkSystem and ThinkAgile. (exciting and slightly aggressive digital instrumental) >> All right, good stuff, huh? (audience applauds) So it's built off of this 25-year history of us being in the x86 server business, the commitment we established three years ago after acquiring the x86 server business to be and have the most reliable, the most agile, and the most highest-performing data center solutions on the planet. So today we're announcing two brands. ThinkSystem is for the traditional data center infrastructure, and ThinkAgile is our brand for Software-Defined infrastructure. Again, the teams challenge themselves from the start, how do we build off this rich heritage, expanding our position as number one in customer satisfaction, reliability, and one of the world's best supply chains. So let's start and look at the next set of solutions. We have always prided ourself that little things don't mean a lot. Little things mean everything. So today, as we said on the legacy solutions, we have over 30 world-record performance benchmarks on Intel architecture, and more than actually 150 since we started tracking this back in 2001. So it's the little pieces of innovation. It's the fine tuning that we do with our partners like an Intel or a Microsoft, an SAP, VMware, and Nutanix that's enabling us to get these world-record performance benchmarks, and with this next generation of solutions we think we'll continue to certainly do that. So today we're announcing the most comprehensive portfolio ever in our data center history. There's 14 servers, seven storage devices, and five network switches. We're also announcing, which is super important to our customer base, a set of new premiere service options. That's giving you fast access directly to a level two support person. No automated response system involved. You get to pick up the phone and directly talk to a level two support person that's going to have end-to-end ownership of the customer experience for ThinkSystem. With ThinkAgile, that's going to be completely bundled with every ThinkAgile you purchase. In addition, we're having white glove service on site that will actually unbox the product for you and get it up and running. It's an entirely new set of solutions for hybrid Cloud, for big data analytics and database applications around these engineered solutions. These are like 40- to 50-page guides where we fine-tuned the most important applications around virtual desktop infrastructure and those kinds of applications, working side by side with all of our ISP partners. So significantly expanding, not just the hardware but the software solutions that, obviously, you, as our customers, are running. So if you look at ThinkSystem innovation, again, it was designed for the ultimate in flexibility, performance, and reliability. It's a single now-unified brand that combines what used to be the Lenovo Think server and the IBM System x products now into a single brand that spans server, storage, and networking. We're basically future-proofing it for the next-generation data center. It's a significantly simplified portfolio. One of the big pieces that we've heard is that the complexity of our competitors has really been overwhelming to customers. We're building a more flexible, more agile solution set that requires less work, less qualification, and more future proofing. There's a bunch of things in this that you'll see in the demos. Faster time-to-service in terms of the modularity of the systems. 12% faster service equating to almost $50 thousand per hour of reduced downtime. Some new high-density options where we have four nodes and a 2U, twice the density to improve and reduce outbacks and mission-critical workloads. And then in high-performance computing and supercomputing, we're going to spend some time on that here shortly. We're announcing new water-cooled solutions. We have some of the most premiere water-cooled solutions in the world, with more than 25 patents pending now, just in the water-cooled solutions for supercomputing. The performance that we think we're going to see out of these systems is significant. We're building off of that legacy that we have today on the existing Intel solutions. Today, we believe we have more than 50% of SAP HANA installations in the world. In fact, SAP just went public that they're running their internal SAP HANA on Lenovo hardware now. We're seeing a 59% increase in performance on SAP HANA generation on generation. We're seeing 31% lower total cost to ownership. We believe this will continue our position of having the highest level of five-nines in the x86 server industry. And all of these servers will start being available later this summer when the Intel announcements come out. We're also announcing the largest storage portfolio in our history, significantly larger than anything we've done in the past. These are all available today, including some new value class storage offerings. Our network portfolio is expanding now significantly. It was a big surprise when I came to Lenovo, seeing the hundreds of engineers we had from the acquisition of Blade Network Technologies and others with our teams in Romania, Santa Clara, really building out both the embedded portfolio but also the top racks, which is around 10 gig, 25 gig, and 100 gig. Significantly better economics, but all the performance you'd expect from the largest networking companies in the world. Those are also available today. ThinkAgile and Software-Defined, I think the one thing that has kind of overwhelmed me since coming in to Lenovo is we are being embraced by our customers because of our lack of legacy. We're not trying to sell you one more legacy SAN at 65% margins. ThinkAgile really was founded, kind of born free from the shackles of legacy thinking and legacy infrastructure. This is just the beginning of what's going to be an amazing new brand in the transformation to Software-Defined. So, for Lenovo, we're going to invest in our own internal organic IP. I'll foreshadow: There's some significant joint ventures and some mergers and acquisitions that are going to be coming in this space. And so this will be the foundation for our Software-Defined networking and storage, for IoT, and ultimately for the 5G build-out as well. This is all built for data centers of tomorrow that require fluid resources, tightly integrated software and hardware in kind of an appliance, selling at the rack level, and so we'll show you how that is going to take place here in a second. ThinkAgile, we have a few different offerings. One is around hyperconverged storage, Hybrid Cloud, and also Software-Defined storage. So we're really trying to redefine the customer experience. There's two different solutions we're having today. It's a Microsoft Azure solution and a Nutanix solution. These are going to be available both in the appliance space as well as in a full rack solution. We're really simplifying and trying to transform the entire customer experience from how you order it. We've got new capacity planning tools that used to take literally days for us to get the capacity planning done. It's now going down to literally minutes. We've got new order, delivery, deployment, administration service, something we're calling ThinkAgile Advantage, which is the white glove unboxing of the actual solutions on prem. So the whole thing when you hear about it in the breakout sessions about transforming the entire customer experience with both an HX solution and an SX solution. So again, available at the rack level for both Nutanix and for Microsoft Solutions available in just a few months. Many of you in the audience since the Microsoft Airlift event in Seattle have started using these things, and the feedback to date has been fantastic. We appreciate the early customer adoption that we've seen from people in the audience here. So next I want to bring up one of our most important partners, and certainly if you look at all of these solutions, they're based on the next-generation Intel Xeon scalable processor that's going to be announcing very very soon. I want to bring on stage Rupal Shah, who's the corporate vice president and general manager of Global Data Center Sales with Intel, so Rupal, please join me. (upbeat instrumental) So certainly I have long roots at Intel, but why don't you talk about, from Intel's perspective, why Lenovo is an important partner for Lenovo. >> Great, well first of all, thank you very much. I've had the distinct pleasure of not only working with Kirk for many many years, but also working with Lenovo for many years, so it's great to be here. Lenovo is not only a fantastic supplier and leader in the industry for Intel-based servers but also a very active partner in the Intel ecosystem. In the Intel ecosystem, specifically, in our partner programs and in our builder programs around Cloud, around the network, and around storage, I personally have had a long history in working with Lenovo, and I've seen personally that PC transformation that you talked about, Kirk, and I believe, and I know that Intel believes in Lenovo's ability to not only succeed in the data center but to actually lead in the data center. And so today, the ThinkSystem and ThinkAgile announcement is just so incredibly important. It's such a great testament to our two companies working together, and the innovation that we're able to bring to the market, and all of it based on the Intel Xeon scalable processor. >> Excellent, so tell me a little bit about why we've been collaborating, tell me a little bit about why you're excited about ThinkSystem and ThinkAgile, specifically. >> Well, there are a lot of reasons that I'm excited about the innovation, but let me talk about a few. First, both of our companies really stand behind the fact that it's increasingly a hybrid world. Our two companies offer a range of solutions now to customers to be able to address their different workload needs. ThinkSystem really brings the best, right? It brings incredible performance, flexibility in data center deployment, and industry-leading reliability that you've talked about. And, as always, Xeon has a history of being built for the data center specifically. The Intel Xeon scalable processor is really re-architected from the ground up in order to enhance compute, network, and storage data flows so that we can deliver workload optimized performance for both a wide range of traditional workloads and traditional needs but also some emerging new needs in areas like artificial intelligence. Second is when it comes to the next generation of Cloud infrastructure, the new Lenovo ThinkAgile line offers a truly integrated offering to address data center pain points, and so not only are you able to get these pretested solutions, but these pretested solutions are going to get deployed in your infrastructure faster, and they're going to be deployed in a way that's going to meet your specific needs. This is something that is new for both of us, and it's an incredible innovation in the marketplace. I think that it's a great addition to what is already a fantastic portfolio for Lenovo. >> Excellent. >> Finally, there's high-performance computing. In high-performance computing. First of all, congratulations. It's a big week, I think, for both of us. Fantastic work that we've been doing together in high-performance computing and actually bringing the best of the best to our customers, and you're going to hear a whole lot more about that. We obviously have a number of joint innovation centers together between Intel and Lenovo. Tell us about some of the key innovations that you guys are excited about. >> Well, Intel and Lenovo, we do have joint innovation labs around the world, and we have a long and strong history of very tight collaboration. This has brought a big wave of innovation to the marketplace in areas like software-defined infrastructure. Yet another area is working closely on a joint vision that I think our two companies have in artificial intelligence. Intel is very committed to the world of AI, and we're committed in making the investments required in technology development, in training, and also in R&D to be able to deliver end-to-end solutions. So with Intel's comprehensive technology portfolio and Lenovo's development and innovation expertise, it's a great combination in this space. I've already talked a little bit about HPC and so has Kirk, and we're going to hear a little bit more to come, but we're really building the fastest compute solutions for customers that are solving big problems. Finally, we often talk about processors from Intel, but it's not just about the processors. It's way beyond that. It's about engaging at the solution level for our customers, and I'm so excited about the work that we've done together with Lenovo to bring to market products like Intel Omni-Path Architecture, which is really the fabric for high-performance data centers. We've got a great showing this week with Intel Omni-Path Architecture, and I'm so grateful for all the work that we've done to be able to bring true solutions to the marketplace. I am really looking forward to our future collaboration with Lenovo as we have in the past. I want to thank you again for inviting me here today, and congratulations on a fantastic launch. >> Thank you, Rupal, very much, for the long partnership. >> Thank you. (audience applauds) >> Okay, well now let's transition and talk a little bit about how Lenovo is transforming. The first thing we've done when I came on board about six months ago is we've transformed to a truly end-to-end organization. We're looking at the market segments I think as our customers define them, and we've organized into having vice presidents and senior vice presidents in charge of each of these major groups, thinking really end to end, from architecture all the way to end of life and customer support. So the first is hyperscale infrastructure. It's about 20% on the market by 2020. We've hired a new vice president there to run that business. Given we can make money in high-volume desktop PCs, it's really the manufacturing prowess, deep engineering collaboration that's enabling us to sell into Baidu, and to Alibaba, Tencent, as well as the largest Cloud vendors on the West Coast here in the United States. We believe we can make money here by having basically a deep deep engineering engagement with our key customers and building on the PC volume economics that we have within Lenovo. On software-defined infrastructure, again, it's that lack of legacy that I think is propelling us into this space. We're not encumbered by trying to sell one more legacy SAN or router, and that's really what's exciting us here, as we transform from a hardware to a software-based company. On HPC and AI, as we said, we'll talk about this in a second. We're the fastest-growing supercomputing company on earth. We have aspirations to be the largest supercomputing company on earth, with China and the U.S. vying for number one in that position, it puts us in a good position there. We're going to bridge that into artificial intelligence in our upcoming Shanghai Tech World. The entire day is around AI. In fact, YY has committed $1.2 billion to artificial intelligence over the next few years of R&D to help us bridge that. And then on data center infrastructure, is really about moving to a solutions based infrastructure like our position with SAP HANA, where we've gone deep with engineers on site at SAP, SAP running their own infrastructure on Lenovo and building that out beyond just SAP to other solutions in the marketplace. Overall, significantly expanding our services portfolio to maintain our number one customer satisfaction rating. So given ISC, or International Supercomputing, this week in Frankfurt, and a lot of my team are actually over there, I wanted to just show you the transformation we've had at Lenovo for delivering some of the technology to solve some of the most challenging humanitarian problems on earth. Today, we are the fastest-growing supercomputer company on the planet in terms of number of systems on the Top 500 list. We've gone from zero to 92 positions in just a few short years, but IDC also positions Lenovo as the fast-growing supercomputer and HPC company overall at about 17% year on year growth overall, including all of the broad channel, the regional universities and this kind of thing, so this is an exciting place for us. I'm excited today that Sergi has come all the way from Spain to be with us today. It's an exciting time because this week we announce the fastest next-generation Intel supercomputer on the planet at Barcelona Supercomputer. Before I bring Sergi on stage, let's run a video and I'll show you why we're excited about the capabilities of these next-generation supercomputers. Run the video please. >> Narrator: Different creates one of the most powerful supercomputers for the Barcelona Supercomputer Center. A high-performance, high-capacity design to help shape tomorrow's world. Different designs what's best for you, with 25 years of end-to-end expertise delivering large-scale solutions. It integrates easily with technology from industry partners, through deep collaboration with the client to manufacture, test, configure, and install at global scale. Different achieves the impossible. The first of a new series. A more energy-efficient supercomputer yet 10 times more powerful than its predecessor. With over 3,400 Lenovo ThinkSystem servers, each performing over two trillion calculations per second, giving us 11.1 petaflop capacity. Different powers MareNostrum, a supercomputer that will help us better understand cancer, help discover disease-fighting therapies, predict the impact of climate change. MareNostrom 4.0 promises to uncover answers that will help solve humanities greatest challenges. (audience applauds) >> So please help me in welcoming operations director of the Barcelona Supercomputer Center, Sergi Girona. So welcome, and again, congratulations. It's been a big week for both of us. But I think for a long time, if you haven't been to Barcelona, this has been called the world's most beautiful computer because it's in one of the most gorgeous chapels in the world as you can see here. Congratulations, we now are number 13 on the Top500 list and the fastest next-generation Intel computer. >> Thank you very much, and congratulations to you as well. >> So maybe we can just talk a little bit about what you've done over the last few months with us. >> Sure, thank you very much. It is a pleasure for me being invited here to present to you what we've been doing with Lenovo so far and what we are planning to do in the next future. I'm representing here Barcelona Supercomputing Center. I am presenting high-performance computing services to science and industry. How we see these science services has changed the paradigm of science. We are coming from observation. We are coming from observation on the telescopes and the microscopes and the building of infrastructures, but this is not affordable anymore. This is very expensive, so it's not possible, so we need to move to simulations. So we need to understand what's happening in our environment. We need to predict behaviors only going through simulation. So, at BSC, we are devoted to provide services to industry, to science, but also we are doing our own research because we want to understand. At the same time, we are helping and developing the new engineers of the future on the IT, on HPC. So we are having four departments based on different topics. The main and big one is wiling to understand how we are doing the next supercomputers from the programming level to the performance to the EIA, so all these things, but we are having also interest on what about the climate change, what's the air quality that we are having in our cities. What is the precision medicine we need to have. How we can see that the different drugs are better for different individuals, for different humans, and of course we have an energy department, taking care of understanding what's the better optimization for a cold, how we can save energy running simulations on different topics. But, of course, the topic of today is not my research, but it's the systems we are building in Barcelona. So this is what we have been building in Barcelona so far. From left to right, you have the preparation of the facility because this is 160 square meters with 1.4 megabytes, so that means we need new piping, we need new electricity, at the same time in the center we have to install the core services of the system, so the management practices, and then on the right-hand side you have installation of the networking, the Omni-Path by Intel. Because all of the new racks have to be fully integrated and they need to come into operation rapidly. So we start deployment of the system May 15, and we've now been ending and coming in production July first. All the systems, all the (mumbles) systems from Lenovo are coming before being open and available. What we've been installing here in Barcelona is general purpose systems for our general workload of the system with 3,456 nodes. Everyone of those having 48 cores, 96 gigabytes main memory for a total capacity of about 400 terabytes memory. The objective of this is that we want to, all the system, all the processors, to work together for a single execution for running altogether, so this is an example of the platinum processors from Intel having 24 cores each. Of course, for doing this together with all the cores in the same application, we need a high-speed network, so this is Omni-Path, and of course all these cables are connecting all the nodes. Noncontention, working together, cooperating. Of course, this is a bunch of cables. They need to be properly aligned in switches. So here you have the complete presentation. Of course, this is general purpose, but we wanted to invest with our partners. We want to understand what the supercomputers we wanted to install in 2020, (mumbles) Exascale. We want to find out, we are installing as well systems with different capacities with KNH, with power, with ARM processors. We want to leverage our obligations for the future. We want to make sure that in 2020 we are ready to move our users rapidly to the new technologies. Of course, this is in total, giving us a total capacity of 13.7 petaflops that it's 12 times the capacity of the former MareNostrum four years ago. We need to provide the services to our scientists because they are helping to solve problems for humanity. That's the place we are going to go. Last is inviting you to come to Barcelona to see our place and our chapel. Thank you very much (audience applauds). >> Thank you. So now you can all go home to your spouses and significant others and say you have a formal invitation to Barcelona, Spain. So last, I want to talk about what we've done to transform Lenovo. I think we all know the history is nice but without execution, none of this is going to be possible going forward, so we have been very very busy over the last six months to a year of transforming Lenovo's data center organization. First, we moved to a dedicated end-to-end sales and marketing organization. In the past, we had people that were shared between PC and data center, now thousands of sales people around the world are 100% dedicated end to end to our data center clients. We've moved to a fully integrated and dedicated supply chain and procurement organization. A fully dedicated quality organization, 100% dedicated to expanding our data center success. We've moved to a customer-centric segment, again, bringing in significant new leaders from outside the company to look end to end at each of these segments, supercomputing being very very different than small business, being very very different than taking care of, for example, a large retailer or bank. So around hyperscale, software-defined infrastructure, HPC, AI, and supercomputing and data center solutions-led infrastructure. We've built out a whole new set of global channel programs. Last year, or a year passed, we have five different channel programs around the world. We've now got one simplified channel program for dealer registration. I think our channel is very very energized to go out to market with Lenovo technology across the board, and a whole new set of system integrator relationships. You're going to hear from one of them in Christian's discussion, but a whole new set of partnerships to build solutions together with our system integrative partners. And, again, as I mentioned, a brand new leadership team. So look forward to talking about the details of this. There's been a significant amount of transformation internal to Lenovo that's led to the success of this new product introduction today. So in conclusion, I want to talk about the news of the day. We are transforming Lenovo to the next phase of our data center growth. Again, in over 160 countries, closing on that first phase of transformation and moving forward with some unique declarations. We're launching the largest portfolio in our history, not just in servers but in storage and networking, as everything becomes kind of a software personality on top of x86 Compute. We think we're very well positioned with our scale on PCs as well as data center. Two new brands for both data center infrastructure and Software-Defined, without the legacy shackles of our competitors, enabling us to move very very quickly into Software-Defined, and, again, foreshadowing some joint ventures in M&A that are going to be coming up that will further accelerate ourselves there. New premiere support offerings, enabling you to get direct access to level two engineers and white glove unboxing services, which are going to be bundled along with ThinkAgile. And then celebrating the milestone of 25 years in x86 server compute, not just ThinkPads that you'll hear about shortly, but also our 20 million server shipping next month. So we're celebrating that legacy and looking forward to the next phase. And then making sure we have the execution engine to maintain our position and grow it, being number one in customer satisfaction and number one in quality. So, with that, thank you very much. I look forward to seeing you in the breakouts today and talking with many of you, and I'll bring Rod back up to transition us to the next section. Thank you. (audience applauds) >> All right, Kirk, thank you, sir. All right, ladies and gentlemen, what did you think of that? How about a big round of applause for ThinkAgile, ThinkSystems new brands? (audience applauds) And, obviously, with that comes a big round of applause, for Kirk Skaugen, my boss, so we've got to give him a big round of applause, please. I need to stay employed, it's very important. All right, now you just heard from Kirk about some of the new systems, the brands. How about we have a quick look at the video, which shows us the brand new DCG images. >> Narrator: Legacy thinking is dead, stuck in the past, selling the same old stuff, over and over. So then why does it seem like a data center, you know, that thing powering all our little devices and more or less everything interaction today is still stuck in legacy thinking because it's rigid, inflexible, slow, but that's not us. We don't do legacy. We do different. Because different is fearless. Different reduces Cloud deployment from days to hours. Different creates agile technology that others follow. Different is fluid. It uses water-cooling technology to save energy. It co-innovates with some of the best minds in the industry today. Different is better, smarter. Maybe that's why different already holds so many world-record benchmarks in everything. From virtualization to database and application performance or why it's number one in reliability and customer satisfaction. Legacy sells you what they want. Different builds the data center you need without locking you in. Introducing the Data Center Group at Lenovo. Different... Is better. >> All right, ladies and gentlemen, a big round of applause, once again (mumbles) DCG, fantastic. And I'm sure all of you would agree, and Kirk mentioned it a couple of times there. No legacy means a real consultative approach to our customers, and that's something that we really feel is differentiated for ourselves. We are effectively now one of the largest startups in the DCG space, and we are very much ready to disrupt. Now, here in New York City, obviously, the heart of the fashion industry, and much like fashion, as I mentioned earlier, we're different, we're disruptive, we're agile, smarter, and faster. I'd like to say that about myself, but, unfortunately, I can't. But those of you who have observed, you may have noticed that I, too, have transformed. I don't know if anyone saw that. I've transformed from the pinstripe blue, white shirt, red tie look of the, shall we say, our predecessors who owned the x86 business to now a very Lenovo look. No tie and consequently a little bit more chic New York sort of fashion look, shall I say. Nothing more than that. So anyway, a bit of a transformation. It takes a lot to get to this look, by the way. It's a lot of effort. Our next speaker, Christian Teismann, is going to talk a lot about the core business of Lenovo, which really has been, as we've mentioned today, our ThinkPad, 25-year anniversary this year. It's going to be a great celebration inside Lenovo, and as we get through the year and we get closer and closer to the day, you'll see a lot more social and digital work that engages our customers, partners, analysts, et cetera, when we get close to that birthday. Customers just generally are a lot tougher on computers. We know they are. Whether you hang onto it between meetings from the corner of the Notebook, and that's why we have magnesium chassis inside the box or whether you're just dropping it or hypothetically doing anything else like that. We do a lot of robust testing on these products, and that's why it's the number one branded Notebook in the world. So Christian talks a lot about this, but I thought instead of having him talk, I might just do a little impromptu jump back stage and I'll show you exactly what I'm talking about. So follow me for a second. I'm going to jaunt this way. I know a lot of you would have seen, obviously, the front of house here, what we call the front of house. Lots of videos, et cetera, but I don't think many of you would have seen the back of house here, so I'm going to jump through the back here. Hang on one second. You'll see us when we get here. Okay, let's see what's going on back stage right now. You can see one of the team here in the back stage is obviously working on their keyboard. Fantastic, let me tell you, this is one of the key value props of this product, obviously still working, lots of coffee all over it, spill-proof keyboard, one of the key value propositions and why this is the number one laptop brand in the world. Congratulations there, well done for that. Obviously, we test these things. Height, distances, Mil-SPEC approved, once again, fantastic product, pick that up, lovely. Absolutely resistant to any height or drops, once again, in line with our Mil-SPEC. This is Charles, our producer and director back stage for the absolute event. You can see, once again, sand, coincidentally, in Manhattan, who would have thought a snow storm was occurring here, but you can throw sand. We test these things for all of the elements. I've obviously been pretty keen on our development solutions, having lived in Japan for 12 years. We had this originally designed in 1992 by (mumbles), he's still our chief development officer still today, fantastic, congratulations, a sand-enhanced notebook, he'd love that. All right, let's get back out front and on with the show. Watch the coffee. All right, how was that? Not too bad (laughs). It wasn't very impromptu at all, was it? Not at all a set up (giggles). How many people have events and have a bag of sand sitting on the floor right next to a Notebook? I don't know. All right, now it's time, obviously, to introduce our next speaker, ladies and gentlemen, and I hope I didn't steal his thunder, obviously, in my conversations just now that you saw back stage. He's one of my best friends in Lenovo and easily is a great representative of our legendary PC products and solutions that we're putting together for all of our customers right now, and having been an ex-Pat with Lenovo in New York really calls this his second home and is continually fighting with me over the fact that he believes New York has better sushi than Tokyo, let's welcome please, Christian Teismann, our SVP, Commercial Business Segment, and PC Smart Office. Christian Teismann, come on up mate. (audience applauds) >> So Rod thank you very much for this wonderful introduction. I'm not sure how much there is to add to what you have seen already back stage, but I think there is a 25-year of history I will touch a little bit on, but also a very big transformation. But first of all, welcome to New York. As Rod said, it's my second home, but it's also a very important place for the ThinkPad, and I will come back to this later. The ThinkPad is thee industry standard of business computing. It's an industry icon. We are celebrating 25 years this year like no other PC brand has done before. But this story today is not looking back only. It's a story looking forward about the future of PC, and we see a transformation from PCs to personalized computing. I am privileged to lead the commercial PC and Smart device business for Lenovo, but much more important beyond product, I also am responsible for customer experience. And this is what really matters on an ongoing basis. But allow me to stay a little bit longer with our iconic ThinkPad and history of the last 25 years. ThinkPad has always stand for two things, and it always will be. Highest quality in the industry and technology innovation leadership that matters. That matters for you and that matters for your end users. So, now let me step back a little bit in time. As Rod was showing you, as only Rod can do, reliability is a very important part of ThinkPad story. ThinkPads have been used everywhere and done everything. They have survived fires and extreme weather, and they keep surviving your end users. For 25 years, they have been built for real business. ThinkPad also has a legacy of first innovation. There are so many firsts over the last 25 years, we could spend an hour talking about them. But I just want to cover a couple of the most important milestones. First of all, the ThinkPad 1992 has been developed and invented in Japan on the base design of a Bento box. It was designed by the famous industrial designer, Richard Sapper. Did you also know that the ThinkPad was the first commercial Notebook flying into space? In '93, we traveled with the space shuttle the first time. For two decades, ThinkPads were on every single mission. Did you know that the ThinkPad Butterfly, the iconic ThinkPad that opens the keyboard to its size, is the first and only computer showcased in the permanent collection of the Museum of Modern Art, right here in New York City? Ten years later, in 2005, IBM passed the torch to Lenovo, and the story got even better. Over the last 12 years, we sold over 100 million ThinkPads, four times the amount IBM sold in the same time. Many customers were concerned at that time, but since then, the ThinkPad has remained the best business Notebook in the industry, with even better quality, but most important, we kept innovating. In 2012, we unveiled the X1 Carbon. It was the thinnest, lightest, and still most robust business PC in the world. Using advanced composited materials like a Formula One car, for super strengths, X1 Carbon has become our ThinkPad flagship since then. We've added an X1 Carbon Yoga, a 360-degree convertible. An X1 Carbon tablet, a detachable, and many new products to come in the future. Over the last few years, many new firsts have been focused on providing the best end-user experience. The first dual-screen mobile workstation. The first Windows business tablet, and the first business PC with OLED screen technology. History is important, but a massive transformation is on the way. Future success requires us to think beyond the box. Think beyond hardware, think beyond notebooks and desktops, and to think about the future of personalized computing. Now, why is this happening? Well, because the business world is rapidly changing. Looking back on history that YY gave, and the acceleration of innovation and how it changes our everyday life in business and in personal is driving a massive change also to our industry. Most important because you are changing faster than ever before. Human capital is your most important asset. In today's generation, they want to have freedom of choice. They want to have a product that is tailored to their specific needs, every single day, every single minute, when they use it. But also IT is changing. The Cloud, constant connectivity, 5G will change everything. Artificial intelligence is adding things to the capability of an infrastructure that we just are starting to imagine. Let me talk about the workforce first because it's the most important part of what drives this. The millennials will comprise more than half of the world's workforce in 2020, three years from now. Already, one out of three millennials is prioritizing mobile work environment over salary, and for nearly 60% of all new hires in the United States, technology is a very important factor for their job search in terms of the way they work and the way they are empowered. This new generation of new employees has grown up with PCs, with Smart phones, with tablets, with touch, for their personal use and for their occupation use. They want freedom. Second, the workplace is transforming. The video you see here in the background. This is our North America headquarters in Raleigh, where we have a brand new Smart workspace. We have transformed this to attract the new generation of workers. It has fewer traditional workspaces, much more meaning and collaborative spaces, and Lenovo, like many companies, is seeing workspaces getting smaller. An average workspace per employee has decreased by 30% over the last five years. Employees are increasingly mobile, but, if they come to the office, they want to collaborate with their colleagues. The way we collaborate and communicate is changing. Investment in new collaboration technology is exploding. The market of collaboration technology is exceeding the market of personal computing today. It will grow in the future. Conference rooms are being re-imagined from a ratio of 50 employees to one large conference room. Today, we are moving into scenarios of four employees to one conference room, and these are huddle rooms, pioneer spaces. Technology is everywhere. Video, mega-screens, audio, electronic whiteboards. Adaptive technologies are popping up and change the way we work. As YY said earlier, the pace of the revolution is astonishing. So personalized computing will transform the PC we all know. There's a couple of key factors that we are integrating in our next generations of PC as we go forward. The most important trends that we see. First of all, choose your own device. We talked about this new generation of workforce. Employees who are used to choosing their own device. We have to respond and offer devices that are tailored to each end user's needs without adding complexity to how we operate them. PC is a service. Corporations increasingly are looking for on-demand computing in data center as well as in personal computing. Customers want flexibility. A tailored management solution and a services portfolio that completes the lifecycle of the device. Agile IT, even more important, corporations want to run an infrastructure that is agile, instant respond to their end-customer needs, that is self provisioning, self diagnostic, and remote software repair. Artificial intelligence. Think about artificial intelligence for you personally as your personal assistant. A personal assistant which does understand you, your schedule, your travel, your next task, an extension of yourself. We believe the PC will be the center of this mobile device universe. Mobile device synergy. Each of you have two devices or more with you. They need to work together across different operating systems, across different platforms. We believe Lenovo is uniquely positioned as the only company who has a Smart phone business, a PC business, and an infrastructure business to really seamlessly integrate all of these devices for simplicity and for efficiency. Augmented reality. We believe augmented reality will drive significantly productivity improvements in commercial business. The core will be to understand industry-specific solutions. New processes, new business challenges, to improve things like customer service and sales. Security will remain the foundation for personalized computing. Without security, without trust in the device integrity, this will not happen. One of the most important trends, I believe, is that the PC will transform, is always connected, and always on, like a Smart phone. Regardless if it's open, if it's closed, if you carry it, or if you work with it, it always is capable to respond to you and to work with you. 5G is becoming a reality, and the data capacity that will be out there is by far exceeding today's traffic imagination. Finally, Smart Office, delivering flexible and collaborative work environments regardless on where the worker sits, fully integrated and leverages all the technologies we just talked before. These are the main challenges you and all of your CIO and CTO colleagues have to face today. A changing workforce and a new set of technologies that are transforming PC into personalized computing. Let me give you a real example of a challenge. DXC was just formed by merging CSE company and HP's Enterprise services for the largest independent services company in the world. DXC is now a 25 billion IT services leader with more than 170,000 employees. The most important capital. 6,000 clients and eight million managed devices. I'd like to welcome their CIO, who has one of the most challenging workforce transformation in front of him. Erich Windmuller, please give him a round of applause. (audience applauds). >> Thank you Christian. >> Thank you. >> It's my pleasure to be here, thank you. >> So first of all, let me congratulation you to this very special time. By forming a new multi-billion-dollar enterprise, this new venture. I think it has been so far fantastically received by analysts, by the press, by customers, and we are delighted to be one of your strategic partners, and clearly we are collaborating around workforce transformation between our two companies. But let me ask you a couple of more personal questions. So by bringing these two companies together with nearly 200,00 employees, what are the first actions you are taking to make this a success, and what are your biggest challenges? >> Well, first, again, let me thank you for inviting me and for DXC Technology to be a part of this very very special event with Lenovo, so thank you. As many of you might expect, it's been a bit of a challenge over the past several months. My goal was really very simple. It was to make sure that we brought two companies together, and they could operate as one. We need to make sure that could continue to support our clients. We certainly need to make sure we could continue to sell, our sellers could sell. That we could pay our employees, that we could hire people, we could do all the basic foundational things that you might expect a company would want to do, but we really focused on three simple areas. I called it the three Cs. Connectivity, communicate, and collaborate. So we wanted to make sure that we connected our legacy data centers so we could transfer information and communicate back and forth. We certainly wanted to be sure that our employees could communicate via WIFI, whatever locations they may or may not go to. We certainly wanted to, when we talk about communicate, we need to be sure that everyone of our employees could send and receive email as a DXC employee. And that we had a single-enterprise directory and people could communicate, gain access to calendars across each of the two legacy companies, and then collaborate was also key. And so we wanted to be sure, again, that people could communicate across each other, that our legacy employees on either side could get access to many of their legacy systems, and, again, we could collaborate together as a single corporation, so it was challenging, but very very, great opportunity for all of us. And, certainly, you might expect cyber and security was a very very important topic. My chairman challenged me that we had to be at least as good as we were before from a cyber perspective, and when you bring two large companies together like that there's clearly an opportunity in this disruptive world so we wanted to be sure that we had a very very strong cyber security posture, of which Lenovo has been very very helpful in our achieving that. >> Thank you, Erich. So what does DXC consider as their critical solutions and technology for workplace transformation, both internally as well as out on the market? >> So workplace transformation, and, again, I've heard a lot of the same kinds of words that I would espouse... It's all about making our employees productive. It's giving the right tools to do their jobs. I, personally, have been focused, and you know this because Lenovo has been a very very big part of this, in working with our, we call it our My Style Workplace, it's an offering team in developing a solution and driving as much functionality as possible down to the workstation. We want to be able, for me, to avoid and eliminate other ancillary costs, audio video costs, telecommunication cost. The platform that we have, the digitized workstation that Lenovo has provided us, has just got a tremendous amount of capability. We want to streamline those solutions, as well, on top of the modern server. The modern platform, as we call it, internally. I'd like to congratulate Kirk and your team that you guys have successfully... Your hardware has been certified on our modern platform, which is a significant accomplishment between our two companies and our partnership. It was really really foundational. Lenovo is a big part of our digital workstation transformation, and you'll continue to be, so it's very very important, and I want you to know that your tools and your products have done a significant job in helping us bring two large corporations together as one. >> Thank you, Erich. Last question, what is your view on device as a service and hardware utility model? >> This is the easy question, right? So who in the room doesn't like PC or device as a service? This is a tremendous opportunity, I think, for all of us. Our corporation, like many of you in the room, we're all driven by the concept of buying devices in an Opex versus a Capex type of a world and be able to pay as you go. I think this is something that all of us would like to procure, product services and products, if you will, personal products, in this type of a mode, so I am very very eager to work with Lenovo to be sure that we bring forth a very dynamic and constructive device as a service approach. So very eager to do that with Lenovo and bring that forward for DXC Technology. >> Erich, thank you very much. It's a great pleasure to work with you, today and going forward on all sides. I think with your new company and our lineup, I think we have great things to come. Thank you very much. >> My pleasure, great pleasure, thank you very much. >> So, what's next for Lenovo PC? We already have the most comprehensive commercial portfolio in the industry. We have put the end user in the core of our portfolio to finish and going forward. Ultra mobile users, like consultants, analysts, sales and service. Heavy compute users like engineers and designers. Industry users, increasingly more understanding. Industry-specific use cases like education, healthcare, or banking. So, there are a few exciting things we have to announce today. Obviously, we don't have that broad of an announcement like our colleagues from the data center side, but there is one thing that I have that actually... Thank you Rod... Looks like a Bento box, but it's not a ThinkPad. It's a first of it's kind. It's the world's smallest professional workstation. It has the power of a tower in the Bento box. It has the newest Intel core architecture, and it's designed for a wide range of heavy duty workload. Innovation continues, not only in the ThinkPad but also in the desktops and workstations. Second, you hear much about Smart Office and workspace transformation today. I'm excited to announce that we have made a strategic decision to expand our Think portfolio into Smart Office, and we will soon have solutions on the table in conference rooms, working with strategic partners like Intel and like Microsoft. We are focused on a set of devices and a software architecture that, as an IoT architecture, unifies the management of Smart Office. We want to move fast, so our target is that we will have our first product already later this year. More to come. And finally, what gets me most excited is the upcoming 25 anniversary in October. Actually, if you go to Japan, there are many ThinkPad lovers. Actually beyond lovers, enthusiasts, who are collectors. We've been consistently asked in blogs and forums about a special anniversary edition, so let me offer you a first glimpse what we will announce in October, of something we are bring to market later this year. For the anniversary, we will introduce a limited edition product. This will include throwback features from ThinkPad's history as well as the best and most powerful features of the ThinkPad today. But we are not just making incremental adjustments to the Think product line. We are rethinking ThinkPad of the future. Well, here is what I would call a concept card. Maybe a ThinkPad without a hinge. Maybe one you can fold. What do you think? (audience applauds) but this is more than just design or look and feel. It's a new set of advanced materials and new screen technologies. It's how you can speak to it or write on it or how it speaks to you. Always connected, always on, and can communicate on multiple inputs and outputs. It will anticipate your next meeting, your next travel, your next task. And when you put it all together, it's just another part of the story, which we call personalized computing. Thank you very much. (audience applauds) Thank you, sir. >> Good on ya, mate. All right, ladies and gentlemen. We are now at the conclusion of the day, for this session anyway. I'm going to talk a little bit more about our breakouts and our demo rooms next door. But how about the power with no tower, from Christian, huh? Big round of applause. (audience applauds) And what about the concept card, the ThinkPad? Pretty good, huh? I love that as well. I tell you, it was almost like Leonardo DiCaprio was up on stage at one stage. He put that big ThinkPad concept up, and everyone's phones went straight up and took a photo, the whole audience, so let's be very selective on how we distribute that. I'm sure it's already on Twitter. I'll check it out in a second. So once again, ThinkPad brand is a core part of the organization, and together both DCG and PCSD, what we call PCSD, which is our client side of the business and Smart device side of the business, are obviously very very linked in transforming Lenovo for the future. We want to also transform the industry, obviously, and transform the way that all of us do business. Lenovo, if you look at basically a summary of the day, we are highly committed to being a top three data center provider. That is really important for us. We are the largest and fastest growing supercomputing company in the world, and Kirk actually mentioned earlier on, committed to being number one by 2020. So Madhu who is in Frankfurt at the International Supercomputing Convention, if you're watching, congratulations, your targets have gone up. There's no doubt he's going to have a lot of work to do. We're obviously very very committed to disrupting the data center. That's obviously really important for us. As we mentioned, with both the brands, the ThinkSystem, and our ThinkAgile brands now, highly focused on disrupting and ensuring that we do things differently because different is better. Thank you to our customers, our partners, media, analysts, and of course, once again, all of our employees who have been on this journey with us over the last two years that's really culminating today in the launch of all of our new products and our profile and our portfolio. It's really thanks to all of you that once again on your feedback we've been able to get to this day. And now really our journey truly begins in ensuring we are disrupting and enduring that we are bringing more value to our customers without that legacy that Kirk mentioned earlier on is really an advantage for us as we really are that large startup from a company perspective. It's an exciting time to be part of Lenovo. It's an exciting time to be associated with Lenovo, and I hope very much all of you feel that way. So a big round of applause for today, thank you very much. (audience applauds) I need to remind all of you. I don't think I'm going to have too much trouble getting you out there, because I was just looking at Christian on the streaming solutions out in the room out the back there, and there's quite a nice bit of lunch out there as well for those of you who are hungry, so at least there's some good food out there, but I think in reality all of you should be getting up into the demo sessions with our segment general managers because that's really where the rubber hits the road. You've heard from YY, you've heard from Kirk, and you've heard from Christian. All of our general managers and our specialists in our product sets are going to be out there to obviously demonstrate our technology. As we said at the very beginning of this session, this is Transform, obviously the fashion change, hopefully you remember that. Transform, we've all gone through the transformation. It's part of our season of events globally, and our next event obviously is going to be in Tech World in Shanghai on the 20th of July. I hope very much for those of you who are going to attend have a great safe travel over there. We look forward to seeing you. Hope you've had a good morning, and get into the sessions next door so you get to understand the technology. Thank you very much, ladies and gentlemen. (upbeat innovative instrumental)