(upbeat music) >> Welcome back. We're here at Supercomputing 22 in Dallas. My name's Paul Gill, I'm your host. With me, Dave Nicholson, my co-host. And one thing that struck me about this conference arriving here, was the number of universities that are exhibiting here. I mean, big, big exhibits from universities. Never seen that at a conference before. And one of those universities is Indiana University. Our two guests, Dave Jent, who's the AVP of Networks at Indiana University, Aaron Neal, Deputy CIO at Indiana University. Welcome, thanks for joining us. >> Thank you for having us. >> Thank you. >> I've always thought that the CIO job at a university has got to be the toughest CIO job there is, because you're managing this sprawling network, people are doing all kinds of different things on it. You've got to secure it. You've got to make it performant. And it just seems to be a big challenge. Talk about the network at Indiana University and what you have done particularly since the pandemic, how that has affected the architecture of your network. And what you do to maintain the levels of performance and security that you need. >> On the network side one of the things we've done is, kept in close contact with what the incoming students are looking for. It's a different environment than it was then 10 years ago when a student would come, maybe they had a phone, maybe they had one laptop. Today they're coming with multiple phones, multiple laptops, gaming devices. And the expectation that they have to come on a campus and plug all that stuff in causes lots of problems for us, in managing just the security aspect of it, the capacity, the IP space required to manage six, seven devices per student when you have 35,000 students on campus, has always been a challenge. And keeping ahead of that knowing what students are going to come in with, has been interesting. During the pandemic the campus was closed for a bit of time. What we found was our biggest challenge was keeping up with the number of people who wanted to VPN to campus. We had to buy additional VPN licenses so they could do their work, authenticate to the network. We doubled, maybe even tripled our our VPN license count. And that has settled down now that we're back on campus. But again, they came back with a vengeance. More gaming devices, more things to be connected, and into an environment that was a couple years old, that we hadn't done much with. We had gone through a pretty good size network deployment of new hardware to try to get ready for them. And it's worked well, but it's always challenging to keep up with students. >> Aaron, I want to ask you about security because that really is one of your key areas of focus. And you're collaborating with counties, local municipalities, as well as other educational institutions. How's your security strategy evolving in light of some of the vulnerabilities of VPNs that became obvious during the pandemic, and this kind of perfusion of new devices that that Dave was talking about? >> Yeah, so one of the things that we we did several years ago was establish what we call OmniSOC, which is a shared security operations center in collaboration with other institutions as well as research centers across the United States and in Indiana. And really what that is, is we took the lessons that we've learned and the capabilities that we've had within the institution and looked to partner with those key institutions to bring that data in-house, utilize our staff such that we can look for security threats and share that information across the the other institutions so that we can give each of those areas a heads up and work with those institutions to address any kind of vulnerabilities that might be out there. One of the other things that you mentioned is, we're partnering with Purdue in the Indiana Office of Technology on a grant to actually work with municipalities, county governments, to really assess their posture as it relates to security in those areas. It's a great opportunity for us to work together as institutions as well as work with the state in general to increase our posture as it relates to security. >> Dave, what brings IU to Supercomputing 2022? >> We've been here for a long time. And I think one of the things that we're always interested in is, what's next? What's new? There's so many, there's network vendors, software vendors, hardware vendors, high performance computing suppliers. What is out there that we're interested in? IU runs a large Cray system in Indiana called Big Red 200. And with any system you procure it, you get it running, you operate it, and your next goal is to upgrade it. And what's out there that we might be interested? That I think why we come to IU. We also like to showcase what we do at IU. If you come by the booth you'll see the OmniSOC, there's some video on that. The GlobalNOC, which I manage, which supports a lot of the RNE institutions in the country. We talk about that. Being able to have a place for people to come and see us. If you stand by the booth long enough people come and find you, and want to talk about a project they have, or a collaboration they'd like to partner with. We had a guy come by a while ago wanting a job. Those are all good things having a big booth can do for you. >> Well, so on that subject, in each of your areas of expertise and your purview are you kind of interleaved with the academic side of things on campus? Do you include students? I mean, I would think it would be a great source of cheap labor for you at least. Or is there kind of a wall between what you guys are responsible for and what students? >> Absolutely we try to support faculty and students as much as we can. And just to go back a little bit on the OmniSOC discussion. One of the things that we provide is internships for each of the universities that we work with. They have to sponsor at least three students every year and make that financial commitment. We bring them on site for three weeks. They learn us alongside the other analysts, information security analysts and work in a real world environment and gain those skills to be able to go back to their institutions and do an additional work there. So it's a great program for us to work with students. I think the other thing that we do is we provide obviously the infrastructure that enable our faculty members to do the research that they need to do. Whether that's through Big Red 200, our Supercomputer or just kind of the everyday infrastructure that allows them to do what they need to do. We have an environment on premise called our Intelligent Infrastructure, that we provide managed access to hardware and storage resources in a way that we know it's secure and they can utilize that environment to do virtually anything that they need in a server environment. >> Dave, I want to get back to the GigaPOP, which you mentioned earlier you're the managing director of the Indiana GigaPOP. What exactly is it? >> Well, the GigaPOP and there are a number of GigaPOP around the country. It was really the aggregation facility for Indiana and all of the universities in Indiana to connect to outside resources. GigaPOP has connections to internet too, the commodity internet, Esnet, the Big Ten or the BTAA a network in Chicago. It's a way for all universities in Indiana to connect to a single source to allow them to connect nationally to research organizations. >> And what are the benefits of having this collaboration of university. >> If you could think of a researcher at Indiana wants to do something with a researcher in Wisconsin, they both connect to their research networks in Wisconsin and Indiana, and they have essentially direct connection. There's no commodity internet, there's no throttling of of capacity. Both networks and the interconnects because we use internet too, are essentially UNT throttled access for the researchers to do anything they need to do. It's secure, it's fast, easy to use, in fact, so easy they don't even know that they're using it. It just we manage the networks and organize the networks in a way configure them that's the path of least resistance and that's the path traffic will take. And that's nationally. There are lots of these that are interconnected in various ways. I do want to get back to the labor point, just for a moment. (laughs) Because... >> You're here to claim you're not violating any labor laws. Is that what you're going to be? >> I'm here to hopefully hire, get more people to be interested to coming to IU. >> Stop by the booth. >> It's a great place to work. >> Exactly. >> We hire lots of interns and in the network space hiring really experienced network engineers, really hard to do, hard to attract people. And these days when you can work from anywhere, you don't have to be any place to work for anybody. We try to attract as many students as we can. And really we're exposing 'em to an environment that exists in very few places. Tens of thousands of wireless access points, big fast networks, interconnections and national international networks. We support the Noah network which supports satellite systems and secure traffic. It really is a very unique experience and you can come to IU, spend lots of years there and never see the same thing twice. We think we have an environment that's really a good way for people to come out of college, graduate school, work for some number of years and hopefully stay at IU, but if not, leave and get a good job and talk well about IU. In fact, the wireless network today here at SC was installed and is managed by a person who manages our campus network wireless, James Dickerson. That's the kind of opportunity we can provide people at IU. >> Aaron, I'd like to ask, you hear a lot about everything moving to the cloud these days, but in the HPC world I don't think that move is happening as quickly as it is in some areas. In fact, there's a good argument some workloads should never move to the cloud. You're having to balance these decisions. Where are you on the thinking of what belongs in the data center and what belongs in the cloud? >> I think our approach has really been specific to what the needs are. As an institution, we've not pushed all our chips in on the cloud, whether it be for high performance computing or otherwise. It's really looking at what the specific need is and addressing it with the proper solution. We made an investment several years ago in a data center internally, and we're leveraging that through the intelligent infrastructure that I spoke about. But really it's addressing what the specific need is and finding the specific solution, rather than going all in in one direction or another. I dunno if Jet Stream is something that you would like to bring up as well. >> By having our own data center and having our own facilities we're able to compete for NSF grants and work on projects that provide shared resources for the research community. Just dream is a project that does that. Without a data center and without the ability to work on large projects, we don't have any of that. If you don't have that then you're dependent on someone else. We like to say that, what we are proud of is the people come to IU and ask us if they can partner on our projects. Without a data center and those resources we are the ones who have to go out and say can we partner on your project? We'd like to be the leaders of that in that space. >> I wanted to kind of double click on something you mentioned. Couple of things. Historically IU has been I'm sure closely associated with Chicago. You think of what are students thinking of doing when they graduate? Maybe they're going to go home, but the sort of center of gravity it's like Chicago. You mentioned talking about, especially post pandemic, the idea that you can live anywhere. Not everybody wants to live in Manhattan or Santa Clara. And of course, technology over decades has given us the ability to do things remotely and IU is plugged into the globe, doesn't matter where you are. But have you seen either during or post pandemic 'cause we're really in the early stages of this. Are you seeing that? Are you seeing people say, Hey, thinking about their family, where do I want to live? Where do I want to raise my family? I'm in academia and no, I don't want to live in Manhattan. Hey, we can go to IU and we're plugged into the globe. And then students in California we see this, there's some schools on the central coast where people loved living there when they were in college but there was no economic opportunity there. Are you seeing a shift, are basically houses in Bloomington becoming unaffordable because people are saying, you know what, I'm going to stay here. What does that look like? >> I mean, for our group there are a lot of people who do work from home, have chosen to stay in Bloomington. We have had some people who for various reasons want to leave. We want to retain them, so we allow them to work remotely. And that has turned into a tool for recruiting. The kid that graduates from Caltech. Doesn't want to stay in Caltech in California, we have an opportunity now he can move to wherever between here and there and we can hire him do work. We love to have people come to Indiana. We think it is a unique experience, Bloomington, Indianapolis are great places. But I think the reality is, we're not going to get everybody to come live, be a Hoosier, how do we get them to come and work at IU? In some ways disappointing when we don't have buildings full of people, but 40 paying Zoom or teams window, not kind the same thing. But I think this is what we're going to have to figure out, how do we make this kind of environment work. >> Last question here, give you a chance to put in a plug for Indiana University. For those those data scientists those researchers who may be open to working somewhere else, why would they come to Indiana University? What's different about what you do from what every other academic institution does, Aaron? >> Yeah, I think a lot of what we just talked about today in terms of from a network's perspective, that were plugged in globally. I think if you look beyond the networks I think there are tremendous opportunities for folks to come to Bloomington and experience some bleeding edge technology and to work with some very talented people. I've been amazed, I've been at IU for 20 years and as I look at our peers across higher ed, well, I don't want to say they're not doing as well I do want brag at how well we're doing in terms of organizationally addressing things like security in a centralized way that really puts us in a better position. We're just doing a lot of things that I think some of our peers are catching up to and have been catching up to over the last 10, 12 years. >> And I think to sure scale of IU goes unnoticed at times. IU has the largest medical school in the country. One of the largest nursing schools in the country. And people just kind of overlook some of that. Maybe we need to do a better job of talking about it. But for those who are aware there are a lot of opportunities in life sciences, healthcare, the social sciences. IU has the largest logistics program in the world. We teach more languages than anybody else in the world. The varying kinds of things you can get involved with at IU including networks, I think pretty unparalleled. >> Well, making the case for high performance computing in the Hoosier State. Aaron, Dave, thanks very much for joining you making a great case. >> Thank you. >> Thank you. >> We'll be back right after this short message. This is theCUBE. (upbeat music)

>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue. Here's your host. Still Minutemen. >> Hi, I'm Stew Minutemen. And welcome to a special cute conversation here in our Boston Areas studio where in spring 2019 whole lot of shows where the cubes gonna be on going to lots of events so many different technologies were covering on one of the areas we always love to be able to dig into is what's happening with the users. Many of these shows, we go to our user conferences as well as the community. Really happy to Boca Burger. Believe first time on the program. Steve Methodists famous. Who is the newly elected president of the mug s. So I think most of Ronan should know the V mug organization to the VM where User group. We've done cube events at, you know, the most related events. Absolute talked about the mug we've had, you know, the CEO of the mug on the program. And of course, the VM were Community 2019 will be the 10th year of the Cube at VM World. Still figuring out if we should do a party and stuff like that. We know all the ins and outs of what happened at that show. But you know the V mugs itself? I've attended many. Your Boston V mug is one that I've been, too. But before we get into the mug stuff, Steve could just give us a little bit of your back, because you are. You're practicing your user yourself. >> Yeah, well, first thanks for having me. You know what? I've been watching the cube for years, and it's ah, it's great to be on this side of the of the screen, right? So, yes. So I'm Steve. I think I, you know, show up every day as the associate chief information officer of the University of Massachusetts. Little just for 95 here, and that's my day job. That's my career, right? But what? You know what? I'm excited to be here to talk about what I'm excited in general with the mug is it's a community organization. And so it's a volunteer gig, and that's true of all of our leadership, right? So the from the president of the board of directors to our local leaders around the world, they're all volunteers, and that's I think, what makes it special is We're doing this because we're excited about it. We're passionate about it. >> Yeah, you know the mugs, It's, you know, created by users for user's. You go to them, talk a little bit. It's evolved a lot, you know, It started as just a bunch of independent little events. Is now you know, my Twitter feed. I feel like constantly every day. It's like, Oh, wait, who is at the St Louis? The Wisconsin one? I'll get like ads for like, it's like a weight is the Northeast one. I'm like, Oh, is that here in New England that I don't know about? No, no, no. It's in the UK on things like that. So I get ads and friends around the world and I love seeing the community. So, boy, how do you guys keep it all straight? Man, is that allow both the organic nature as well as some of the coordination and understanding of what's going on. How do you balance that? >> Yeah, that's a great question. And you know, So I was a V mug member for many, many years before I ever got interested in becoming a leader, and you're right it when it started, it was 10 of us would get around with a six pack of beer and a box of pizza, right? And we'd be talking shop and that, you know, that was awesome. And that's what would that was, how it started. But you get to a certain scale when you start talking about having 50,000 now, over 125,000 members around the world. You gotta coordinate that somehow you're right on the money with that. And so that's why you know, we have, you know, a strong, um, coordination effort that is our offices down in Nashville, Tennessee, and their their role is to enable our leaders to give back to their community and take the burden out of running these things. You know, sourcing venues and, you know, working with hotels and stuff. That is effort that not everybody wants to do all the time. And so to do that for them lets them focus on the really cool stuff which is the tech and connecting users. >> Yeah. Can you speak a little bit too? You know what were some of the speeds and feed to the event? How many do you have How much growing, you know, Like I'm signed up. I get the newsletter for activities as well as you know, lots of weapons. I've spoken on some of the webinars too. >> Yeah, well, first thanks for that s o. We have over 30 user cons around the world on three continents. >> In fact, what's the user cough? >> Great questions. So user kind is user conference, you know, consolidated into user Connery. And those are hundreds of end users getting together around the world were on three continents. In fact, I was fortunate enough in March, I went to Australia and I spoke at Sydney and Melbourne on That was awesome, getting to meet users literally, almost a sw far away from Boston. As you can get having the same challenges in the office day today, solving the same business problems with technology. So that was exciting. And so we've got those all over. We also have local meetings which are, you know, smaller in scope and often more focused on content. We've got 235 or Maur local chapters around the world. They're talking about this, and so we're really engaged at multiple levels with this and like you talk about. We have the online events which are global in scope. And we do those, you know, we time so that people in our time zone here in the States could get to them as well as folks in, you know, e m b A and a factory. >> Yeah, and I have to imagine the attendees have to vary. I mean, is it primarily for, you know, Sylvie, um, where admin is the primary title there up to, you know, people that are CEOs or one of the CEOs? >> Yes. So that actually we've seen that change over the past couple years, which is exciting for me being in the role that I'm in is you're right historically was vey Sphere admits, right? And we're all getting together. We're talking about how do we partition our lungs appropriately, right? And now it has switched. We see a lot more architect titles. We Seymour director titles coming in because, you know, I said the other day I was in Charlotte talking and I said, You know, business is being written in code, right? And so there's a lot more emphasis on what it's happening with V m wearing his VM worth portfolio expands. We've got a lot of new type of members coming into the group, which is exciting. >> Yeah, And what about the contents out? How much of it is user generated content versus VM were content and then, you know, I understand sponsorships or part of it vendors. The vendor ecosystem, which vm where has a robust ecosystem? Yes, you know, help make sure that it's financially viable for things to happen and as well as participate in the contest. >> Yes, I feel like I almost planted that question because it's such a good one. So, you know, in 2018 we started putting a strong emphasis on community content because we were, you know, we heard from remembers that awesome VM were content, awesome partner content. But we're starting to miss some of the user to user from the trenches, battle war stories, right? And so we put an emphasis on getting that back in and 2018 we've doubled down in 2019 in a big way, so if you've been to a user kind yet in 2019 but we've limited the number of sponsors sessions that we have, right so that we have more room for community content. We're actually able to get people from around the world to these events. So again, me and a couple folks from the States went toe Australia to share our story and then user story, right? And at the end of the day, we used to have sponsored sessions to sort of close it out. Now we have a community, our right, and Sophie Mug provides food and beverages and a chance to get together a network. And so that is a great community. Our and you know, I was at one recently and I was able to watch Ah, couple folks get to them. We're talking about different problems. They're having this and let me get your card so we can touch base on this later, which at the end of the day, that's what gets me motivated. That's what >> it's about. It's Steve. I won't touch on that for a second. You know what? Get you motivated. You've been doing this for years. You're, you know, putting your time in your president. I know. When I attended your Boston V mark the end of the day, it was a good community member talking about career and got some real good, you know, somebody we both know and it really gets you pumped up in something very, a little bit different from there. So talk a little bit without kind of your goals. For a CZ president of Emma, >> Sure eso I get excited about Vima because it's a community organization, right? And because, you know, I've said this a bunch of times. But for me, what excites me is it's a community of people with similar interests growing together right and reinforcing each other. I know for a fact that I can call ah whole bunch of people around the world and say, Hey, I'm having a problem technically or hey, I'm looking for some career advice or hey, one of my buddies is looking for work. Do you know of any opening somewhere? And that's really powerful, right? Because of the end of the day, I think the mug is about names and people and not logos, right? And so that's what it motivates me is seeing the change and the transformation of people and their career growth that V mug can provide. In fact, I know ah ton of people from Boston. In fact, several of them have. You know, they were administrators at a local organization. Maybe they moved into partners. Maybe they moved into vendors. Maybe they stay where they are, and they kept accelerating their growth. But I've seen tons of career growth and that that gets me excited watching people take the next step to be ableto to build a >> career, I tell you, most conferences, I go to the kind of jobs take boards, especially if you're kind of in the hot, cool new space they're all trying to hire. But especially when you go to a local on the smaller events, it's so much about the networking and the people. When I go to a local user, event it. Hey, what kind of jobs you hiring for who you're looking for and who do I know that's looking for those kind of things and trying to help connect? You know, people in cos cause I mean, you know, we all sometime in our career, you know we'll need help alone those lines that I have, something that's personally that you know, I always love to help >> you. I have a friend who said it. I think best, and I can't take credit for this, right? But it's It can be easy to get dismissed from your day job, right? One errant click could be the career limiting click. It is nigh impossible to be fired from the community, right? And that that, to me, is a powerful differentiator for folks that are plugged into a community versus those that are trying to go it >> alone. Yeah, there are some community guidelines that if you don't follow, you might be checking for sure, but no, if if we're there in good faith and we're doing everything like out, tell me it's speaking. You know, this is such, you know, change. Is this the constant in our world? You know, I've been around in the interview long enough. That's like, you know, I remember what the, um where was this tiny little company that had, you know, once a week, they had a barbecue for everybody in the company because they were, like, 100 of them. And, you know, you know, desktop was what they started working on first. And, you know, we also hear stories about when we first heard about the emotion and the like. But, you know, today you know Veum world is so many different aspects. The community is, you know, in many ways fragmented through so many different pieces. What are some of the hot, interesting things? How does seem a deal with the Oh, hey, I want the Aye Aye or the Dev Ops or the you know where where's the vmc cloud versus all these various flavors? How do you balance all that out? All these different pieces of the community? >> Yeah, it's an interesting question. And to be fair with you, I think that's an area that were still getting better at. And we're still adapting to write. You know, if you look at V mug Five years ago, we were the V's fear, sort of first, last and always right. And now you know, especially is VM. Where's portfolio keeps increasing and they keep moving into new areas. That's new areas for us, too. And so, you know, we've got a big, uh, initiative over the next year to really reach out and and see where we can connect with, you know, the kubernetes environment, right? Cause that the hefty oh acquisition is a really big deal. and I think fundamentally changes or potential community, right? And so you know, we've launched a bunch of special interest groups over the span of the past couple years, and I think that's a big piece of it, which is, if you're really interested in networking and security, here's an area that you can connect in and folks that are like minded. If you're really interested in and user computing, here's what you can connect into. And so I think, you know, as we continue to grow and you know, we're, you know, hundreds of thousands of people now around the world so that you can be a challenge. But I think it's It's also a huge opportunity for us to be ableto keep building that connection with folks and saying, Hey, you know, as you continue to move through your career, it's not always gonna be this. You're right. Change is constant. So hey, what's on the horizon for >> you? When I look at like the field organization for being where boy, I wonder when we're gonna have the sand and NSX user groups just because there's such a strong emphasis on the pieces, the business right now? Yeah, All right, Steve, let's change that for a second. Sure said, You know, you're you got CEO is part of your title, their eyes, what you're doing. Tell me about your life these days and you know the stresses and strains And what what's changing these days and what's exciting? You >> sure? So you know, it's exciting to have moved for my career because I'm an old school admin, right? I mean, that's my background. Uh, so, you know, as I've progressed, you know, I keep getting different things in my portfolio, right? So it started out as I was, you know, I was the admin, and then I was managing the systems engineering team. And then they added desktop support that was out of necessity was like, I'm not really a dustup person, right? So something new you need to learn. But then you start seeing where these synergies are, right? Not to hate, like the words energies. But the reality is that's where we launched our VD. I project at U Mass. Lowell, and that has been transformative for how we deliver education. And it has been a lot of ways. Reduced barriers to students to get access to things they couldn't before. So we had engineering students that would have to go out and finance a 3 $4000 laptop to get the horsepower to do their work. Now, that can use a chromebook, right? They don't have to have that because we do that for them and just they have to have any device t get access via via where horizon. Right, So that happened, and then, you know, then they moved in. Our service is operation, right? So what I'm interested now is how do we deliver applications seamlessly to users to give them the best possible experience without needing to think about it? Because if you and I have been around long enough that it used to be a hassle to figure out okay, I need to get this done. That means they need to get this new applications I have to go to I t there and I have my laptop. Now it's the expectation is just like you and I really want to pull out my phone now and go to the APP store and get it right. So how do we enable that to make it very seamless and remove any friction to people getting their work >> done? Yeah, absolutely. That the enterprise app store is something we've talked about is not just the Amazon marketplace these days. >> In some ways, it is so not all applications rate. Some applications are more specific to platforms. And so that's a challenge, which is, you know, I'm a professor. I really like my iPad. Well, how do I get S P ss on that? Okay, well, let me come up with some solutions. >> Yeah, it's interesting. I'm curious if you have any thoughts just from the education standpoint, how that ties into i t. Personally myself, I think I was in my second job out of school before I realized I was in the i t industry because I studied engineering they didn't teach us about. Oh, well, here's the industry's You're working. I knew tech, and I knew various pieces of it and, you know, was learning networking and all these various pieces there. But, you know, the industry viewpoint as a technology person wasn't something. I spend a lot of time. I was just in a conference this week and they were talking about, you know, some of the machine learning pieces. There was an analyst got up on stage is like here I have a life hack for you, he said. What you need to do is get a summer intern that's been at least a junior in college that studied this stuff, and they can educate you on all these cool new things because those of us have been here a while that there's only tools and they're teaching them at the universities. And therefore that's one of those areas that even if you have years, well, if you need to get that retraining and they can help with that >> no, that's that to me is one of most exciting parts about working in education is that our faculty are constantly pushing us in new directions that we haven't even contemplated yet. So we were buying GPU raise in order to start doing a I. Before I even knew why we were doing and there was like, Hey, I need this and I was like, Are you doing like a quake server? Like they were mining Bitcoins? I don't think so, but it was, you know, that was that was that was an area for us and now we're old. Had it this stuff, right? And so that is a exciting thing to be able to partner with people that are on the bleeding edge of innovation and hear about the work that they're doing and not just in in the tech field, but how technology is enabling Other drew some groundbreaking research in, you know, the life sciences space that the technology is enabling in a way that it wasn't possible before. In fact, I had one faculty member tell me, Geez, maybe six months ago. That said, the laboratory of the past is beakers and Silla scopes, right? The laboratory of the future is how many cores can you get? >> Yeah, all right, So next week is Del Technologies world. So you know the show. The combination of what used to be A M, C World and Del World put together a big show expecting around 15,000 people in Las Vegas to be the 10th year actually of what used to be M. C world. We actually did a bunch of dead worlds together. For me personally, it's like 17 or 18 of the M C world that I've been, too, just because disclaimer former emcee employees. So V mugs there on dhe, Maybe explain. You know, the mugs roll there. What you're looking to accomplish what you get out of a show like that. >> Sure. So V mug is a part of the affiliation of del Technologies user communities. Right? And what I love about user communities is they're not mutually exclusive, right? You absolutely can. Being a converged and Avi mug and a data protection user group. It's all about what fits your needs and what you're doing back in the office. And, you know, we're excited to be there because there's a ton of the move members that are coming to Deltek World, right? And so we're there to support our community and be a resource for them. And that's exciting for us because, you know, Del Del Technologies World is a whole bunch of really cool attack that were that were seeing people run vm were on Ray. We're seeing via more partner with, and so that's exciting for us. >> Yeah, and it's a try. Hadn't realized because, like, I've been to one of the converted user group events before, didn't realize that there was kind of an affiliation between those but makes all the sense in the world. >> Yeah, right. And it's, you know, again, it's an open hand thing, right? Beaten and one being the other. You realize them both. For what? They're what They're great at connecting with people that are doing the same thing. There's a ton of people running VM wear on. Ah, myriad. Like you talked about earlier VM Where's partner? Ecosystem is massive, right? But many, many, many in fact, I would say a huge majority of converged folks are running VM we're >> on it. All right. So, Steve want to give you the final word? What's the call to action? Understand? A lot of people in the community, but always looking from or always, ways for people to get involved. So where do they go? What? What would you recommend? >> Yeah, thanks. So if if you are not plugged into user community now, when you're in the tech field, I would strongly encourage you to do so. Right? V mug, obviously, is the one that's closest to my heart, right? If you're in that space, we'd love to have you as part of our community. And it's really easy. Go to V mug. dot com and sign up and see where the next meet up is and go there, right? If you're not into the VM where space and I know you have lots of folks that air, they're doing different things. Go check out your community, right? But I tell you, the career advantages to being in a user community are immense, and I frankly was able to track my career growth from admin to manager to director to associate CEO, right alongside my community involvement. And so it's something I'm passionate about, and I would encourage everybody to check out. >> Yeah, it's Steve. Thank you so much for joining us. Yeah, I give a personal plug on this. There are a lot of communities out there, the virtual ization community, especially the VM. One specifically is, you know, a little bit special from the rest. You know, I've seen it's not the only one, but is definitely Maur of. It's definitely welcoming. They're always looking for feedback, and it's a good collaborative environment. I've done surveys in the group that you get way better feedback than I do in certain other sectors in just so many people that are looking to get involved. So it's one that you know, I'm not only interviewing, but, you know, I can personally vouch for its steeple. Thank you. Thank you so much. Always a pleasure to see you. >> Thanks for having me. >> Alright. And be sure to check out the cube dot net. Of course, we've got dealt technologies world in the immediate future. Not that long until we get to the end of summer. And vm World 2019 back in San Francisco, the Q will be there. Double set. So for both del world del Technologies world and VM World. So come find us in Las Vegas. If you're Adele or Mosconi West in the lobby is where will be for the emerald 2019 and lots and lots of other shows. So thank you so much for watching. Thank you.

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

>> Announcer: Live from Las Vegas, it's theCUBE. Covering VMworld 2017, brought to you by VMware and its ecosystem partners. (techno music) >> Good morning, welcome to day three of VMworld 2017. This is theCUBE's continuing coverage of this big event in Las Vegas. I am Lisa Martin with my co-host, Stu Miniman, and we're very excited to be joined by a couple of gals in tech. We have Phoummala Schmitt, you are the infrastructure lead for Independence Blue Cross, and Theresa Miller, the founder of the 24x7 IT Connection. Welcome. >> Thank you. >> Thank you for having us. >> So, Phoummala, let's start with you. You are a very leading female in the technology and software space. Tell us about yourself. What do you do, what inspires you as a female leader in technology? >> I'm currently in infrastructure lead at Independence Blue Cross. I manage unified communications applications, Exchange, Skype for Business. What excites me is the ability to show young women that you can do anything. When I was younger, I was told that I didn't understand math very well, so I couldn't be in IT, well, look at me here. So, it's inspiring. I feel inspired when little girls tell me, I want to do something technical. >> That's awesome, Theresa, what about you? What's your journey been like? >> My journey started over 20 years ago by accident, while I was studying at the University of Wisconsin-Whitewater. There was an opportunity to study IT. It made sense to me, and I dropped my accounting major just like that as soon as IT came to the forefront. I've been doing technology ever since, and today, now, I have 24x7 IT Connection. We do writing, we do webinars, and we also do IT consulting. >> So, you guys work together with the 24x7 IT Connection. Tell us a little bit more about that. I know that, Phoummala, you mentioned Microsoft, and here we are at VMworld. Tell us about what kind of topics you cover on that 24x7 IT Connection. >> We cover a broad range of topics. I usually cover the Microsoft, Exchange, Skype world. I actually blog for Theresa, but we do a podcast together, the Current Status, and we talk about all sorts of technology, storage, networking, and just the current trends. And we actually do it on video, through YouTube, and we have a glass of wine. We make it so it's like a casual conversation with your friends, you know, you're just chattin' and talkin' tech, like here at the conference. >> Uh-- >> Just to add to that, so, from the blogging and like the approach we use, it really is about what's relevant to us. So, like she said, we're covering end-user computing, it might be Microsoft, it might be Linux. It just depends. We have several female writers and one male, and it's really about what's relevant, what's going on in their world, because then you know it's going on in someone else's. >> Yeah, Phoummala, you've been in the center of a really interesting transition we've been seeing in the marketplace. You know, I think about my career in tech, you know, deploying servers for email, and that whole push. Microsoft, huge push to get everybody onto Office 365. >> Phoummala: Yes. >> You know, where that lives, we talk about, you know, software's eating the world. You know, so, give us that journey of applications for you. How's that change your role, some of the dynamics? Sounds like you might need a glass of wine after talking through some of these topics, yeah. >> Yeah, I actually started in the server world, server and infrastructure. I was racking and stacking servers, deploying VMs, and then, at the same time, I was also managing Exchange, but as my career progressed, I kind of left that storage and server background and decided, you know what, applications. I wanted to focus a little bit more and really embrace the application world. Since I had that server background, that was my job, it just seems I could actually deploy these applications a lot better, because I understand the underlying foundations behind it, Exchange, and the cloud, so, right now, you know the push is to be in the cloud. Where I work, and a lot of organizations like ourselves, we don't go to the cloud yet. We're just not there. So, there is a very strong push. Eventually, I suspect we'll all be in the cloud. I mean, that's just, it's not if, it's when. >> Yeah, so, but I want to dig down just a tiny bit more, because, you know, most people in the VMware community know Microsoft pretty well. The relationship with Microsoft and their applications with virtualization, and now with cloud, is a really interest dynamic, so you've gone against some of what Microsoft said in the past, kind of do what's best for your organization, why don't you explain some of that to our audience, yeah. >> Yeah, so, Exchange. The preferred architecture for Exchange is, or Exchange 2016, 2013, is to be physical servers, with DAS, direct-attached storage, which is, you know, not what most people are. I mean, it's a virtualized world now. I don't know any company that isn't virtualized. So, I've taken the approach, what is the best situation or deployment for your organization. Yes, there is the preferred architecture, but it's not, I don't look at it as the Holy Grail, or the Bible. I look at what is best for the organization. What are your requirements? So, if the requirement is to reduce data center cost, reduce some rack space, and you can't go to the cloud yet, due to other requirements, let's look at alternative solutions that still follow some of the guidance. So, you know, yes, I break away from it, but it's what's best for your business, because not everybody can deploy physical. >> Yeah, Theresa, I have to imagine you cover a lot of this. You know, what's really happening with customers versus, you know, no offense to our friends on the vendor side, but, you know, they always think its what's right, as opposed to the person doing it, knows what is right for their environment. That's one of the challenges of IT, right? There is no one way to do things, so. >> Every organization is going to take a different path and journey, and that might be to the cloud, that might not be yet. That might be a combination, that could be hybrid IT, I think is another term that we keep hearing where, maybe I have some applications in the cloud, and some that will always remain on prem, but it has to match the culture and the fit of the business, or you won't be successful with any IT project. >> So, ladies, we're at VMworld 2017, given both of your thoughts in terms of, we need to do what's best for the business, Phoummala, let me start with you. What are some of the things that you're hearing, are you hearing other peers of yours echo the same feelings and sentiments? >> Yes, when we're out in the field, you know, I'm talkin' to people, and it's, yeah, it's we're not there yet, we want to go there, yeah, but we can't do it, we don't have the infrastructure, we don't have the resources. And oftentimes, you know, our vendors, they, they forget that budgets, there's constraints, you know, resourcing. So, you know, my word to them is be patient with us. We want to go there, we like your products, but there's so many other factors in play, especially when you work for a large enterprise. You know, there's politics, and large enterprises just take time to do things, especially certain industries, healthcare, financial sectors. You have certain regulations that you have to follow, and in order to get to the cloud, or whatever, you know, the latest trend is, we may have to modify certain policies that are in place and then there's a downward effect, because let's say we want to go to the cloud, then you have to go to, you know, your security department. What regulations or what retention policies do you have to change? And that may, you know, that may take time. So, it's not like it's going to' happen today. >> Theresa, same question, but I guess, maybe, no, maybe a different question. In terms of your podcast, have you heard anything here that's inspired the next conversation that you guys want to have with your glass of wine? >> So, I really think, it's probably going to revolve around cloud again, and in terms of, the other thing I keep seeing is analytics. Everybody's talking about analytics. >> Lisa: Yes. >> And I think that's a really interesting conversation, 'cause it means so many different things. The depth, what are you going to analyze? How do you manage that data? So I could see it being a combination of those topics, and even maybe separate. >> Yeah, yeah, Phoummala, you and I were talking before the interview. Think about this community here. It used to be, you know, it was like hypervisor, virtualizing, we were all in this journey to virtualize. Now, it's a little bit fragmented because there's so many different areas. Analytics, absolutely huge people. Security, lots of people going there. This whole cloud discussion, on all the different apps. What are you seeing in the community? What are the topic areas? Is that, you know, is that a challenge to the community? VMware, the VMworld community was a pretty tight-knit community, and now it feels, you know, while there's great connections and great people, it's broken into a few different pieces. What's your reaction to that? >> I mean, I do feel there's sort of a, not a disconnect, but there's so many different aspects, and I think that's just the evolution of IT. We've evolved to the point where it's beyond IT, it's beyond the technical approaches. It is, um, it's almost like it's, IT's just another business department. We're a business. We provide services to the other business units, and it's just that evolution of, we're service providers, all of us. Whether we are in the data center, or we are an apps develop, we are providing a service to somebody, and we have customers. >> Do you find that that's an advantage? We were talking to some guests earlier this week that, I think it was an analyst from ESG that was saying, you know, you can show that certain problems with storage, certain costs, aren't IT's problem, it's a business problem. Is that an advantage what you just kind of talked about, Phoummala, in terms of getting eyes and ears of the business to provide, okay, this is a business challenge, we need to provide the right expertise, the right funding, to support these services that are needed? >> I definitely think so. I mean, just from my own experience. Understanding what the business wants and needs is huge. And then just puttin' yourself in their shoes. What do they need, what can we do to make their jobs better? So that person, you know, clicking the button of submitting our payroll, or, you know, putting a purchase order in, what can we do to make that better? So, you know, it's one of the things I always do when I'm looking at projects. What value is this going to bring for our business? And, I think, that's just the way IT has evolved to. We're not the programmers in the basement anymore, you know, with the lights turned off and just coding away. We're all business analysts now. Because, at the end of the day, it's our paycheck, too. So, these products that we're hearing about, at the end of the day, it affects us, it affects our business, and the bottom line. >> And what is the website of 24x7 IT Connection that people can see and hear the value that you bring to the community? >> It's 24x7, so that's the 24 by 7, and then itconnection.com. And so, like I said, we share a lot of really great stuff. We have something new every week, so it's definitely worth checking out. >> Well, ladies, thank you so much for joining Stu and myself this morning and sharing your journeys into IT, as well as your insights, what you've learned from the show, what excites you, and where people can go to find more information about the expertise that you bring to the community. We want to thank you for watching again. We are theCUBE live from day three of VMworld 2017. I am Lisa Martin, for my esteemed co-host, Stu Miniman, thanks for watching. We'll be right back. (techno music)

>> Narrator: Live from Las Vegas, it's the Cube, covering Dell EMC world 2017 Brought to you by Dell EMC. >> Welcome back to Dell E, Dell EMC world, I'm your host Rebecca Knight along with my co-host Keith Townsend. We are joined by Fernando Thompson. He is the CIO of University of the Americas, Puebla, Mexico. Thanks so much for joining us, Fernando. >> Thanks Rebecca, to you. >> So I want you to just set the scene a little bit for our viewers and talk a little bit about some of the biggest technology problems you were facing on your campus. >> Well, Universidad de las Américas Puebla is in the state of Puebla. We have around, less than 10,000 students. We have 1,000 employees. And we are full dedicated to research and development. And also bachelor's degrees. I used to work for the federal government and also for the private sector in television and entertainment. But I have never been in such a huge challenge, like, in a university because, you know, it is, it is so difficult to implement the governance but at the same time the freedom and, if you think, well for instance, when, when is a period of time where, where more appears in the world, it's on vacations where the students of the universities have enough time to build that kind of thing. Not very creative people, so in, in the sense that, well we live in an environment where you have to deal, to deliver technology, to protect to millennials that doesn't want to be protected and also, you know, they always ask for more services, no? And now is coming the generation C, so, it's going to be real tough for the next years. >> So, so, so set the scene and talk to us about the kinds of things that you are trying to deliver. Better products, better speed, better services to both students, perspective students, faculty and staff. Talk about what some of the needs were. >> Well, we have to think that, actually, technology is very important in the university because we have to prepare our students to give them the tools to face the future. The world is changing. And, in Mexico right now, we have a huge challenge because we're competing against China, Brazil, and India. We used to have an advantage with, with the price, and people that is very well prepared and with their salaries, but not any longer. So, we have to give this advantage to our students. So, nine years ago, when I arrived to university, for instance, we have the subscription system, and it was awful, no? Because it was very slow with the performance and not very reliable, so the people was really complaining, no? Because we're a private, private university. You always suspect, you know, good performance for a private university. And especially in subscription where your main customer, that is a student. So, we start to work to fix the main system and it take us years, years, no? So, we passed through availability and relability but bad performance. Now, performance, but since we implement XtremIO and we changed our data center with Dell products, this very year, in, in, in the spring, in the spring semester, we make a huge change cause the subscription system now became, you know, one of the biggest transformation tools in the service for the students. What happened was that, during the subscription, we announced on a special day, and a special hour, 8:30, and if you are okay with your administrative stuff and qualifications, you can get into the system and subscribe. Four years ago, that takes, like, one hour or 45 minutes. But with the change that we made, now it is a matter of two minutes. So-- >> Wow. It was a change of 100 degrees, no? What happened was that, yes, we changed the application, but also, you know, with this Flash technology, we use it because at the end, what we want to make was, you know, to impact to the student, in order that they can receive the schedule. He can take faster decisions. They move very fast with their computers and with their devices, so they needed the applications, so, we build it, we test it, and it worked fantastic. And let me tell you something, no? How I measure, I don't have a business analytic tool or a business intelligence tool. What I have was, you know, access to Facebook, Instagram, and Snapchat. And, man, they gave us such a compliment, no? Like, hey TI finally did something well for us also and it was thousands of comments, no? >> Members was waiting. >> A lot of thumbs up. >> Yeah, yeah. Members was waiting, you know, to attack us, like, you shall not pass, and stuff like that. Like past years. But not this year. So, it was, it was, it was a successful story and now we are thinking to implement all these changes that we made in an ordered space itself, of the university. >> So let's talk about that a little bit. So that's, I think, is a great example of digital transformation. I don't need to ask you if you believe in digital transformation. That was a digital transformation to your business. Specifically, I'm interested in research. What type of research does the university do and how does your group play a role in enabling that? >> Yeah, basically, for instance, we have four programs of PSD with specialization in technology and also in environment, no? One of the top challenge that we are facing in Latin America. So we have to supply the technology that our researchers need. And it has to be at the same level of the United States. We belong to the Southern Association of Colleges and Schools in the United States. So, it's not United States soil, but we belong to that association. >> Reporter: So you have the same SLEs? >> Yeah, basically, basically, you know? And we have to supply, you know, all the bandwidth and performance because they're discussing, at the same time, with people in Texas or Wisconsin or Alaska or Brazil and they need, you know, for instance, high-performance computing, storage, cloud tools, and for them, have to be, you know, pretty clear and transparent. What I mean is that they don't care, and they don't have to care about where it's working, of it is expensive or not, no? Do you have, do you have to supply what they need? Let me give you an example. For instance, a fractal. If you send the information of a fractal, we don't have a super-computer in our university, but we have a deal with another university and they have this super-computer. So, what we do is to supply the connection of the computer to send information and to receive information immediately, in order than the graphic can have information in real time. And people are taking decision in different parts of the, of the country with that information that we're sending. Sometimes, could be, you know, if we're facing a hurricane or we're going to have a problem with water or if we want to avoid, you know, you know, something that can happen with an earthquake or something like that. And with that kind of information, now, our researchers certainly can know what is happening. So, we give the info, we give the technology for them. We make a mixture between cloud-computing services and also our data center. Then that a wonderful tool because with XtremIO and with our new servers, if you can go to our new data center, you will realize that it's only Dell and EMC right there, no? And something funny is that we have a wonderful data center, but to be honest with you, we only use, right now, only three racks. So, the space that we are using right now with this new disk is, the space that we are saving is amazing, no? Because if you can, if you can see our racks, you will see that we have, for instance, Clarion, and DMX and another technology. Right now, we shut down all those racks. And right now, we are using just like, 40 inches of disk. And, man, you know, I have more performance, I have savings on air, savings on electricity. The people think then, right, we are having, you know, more space with more racks and more this. But not any longer. So, I think what is going to happen with Dell and EMC in the future, I think that they are going to deliver, for instance, 100 terabytes in just in a USB or something like that, it's the future. So, there's not going to be need over that, that's good, no? >> Hit it on the head of a, on the head of a pin. >> Yeah. >> Fernando, thank you so much for joining us. >> No, thank you to you. Thank you Rebecca, thank you. >> I'm Rebecca Knight for Keith Townsen. We will have more from Dell EMC world after this. (progressive music)