>>You can put this in a conference. >>Good morning and welcome back to Dallas. Ladies and gentlemen, we are here with the cube Live from, from Supercomputing 2022. David, my cohost, how you doing? Exciting. Day two. Feeling good. >>Very exciting. Ready to start off the >>Day. Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >>Having us, >>For having us. I'm excited that you're starting off the day because we've been hearing a lot of rumors about ethernet as the fabric for hpc, but we really haven't done a deep dive yet during the show. Y'all seem all in on ethernet. Tell us about that. Armando, why don't you start? >>Yeah. I mean, when you look at ethernet, customers are asking for flexibility and choice. So when you look at HPC and you know, infinite band's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial and their enterprise customers. And not everybody wants to be in the top 500. What they want to do is improve their job time and improve their latency over the network. And when you look at ethernet, you kinda look at the sweet spot between 8, 12, 16, 32 nodes. That's a perfect fit for ethernet and that space and, and those types of jobs. >>I love that. Pete, you wanna elaborate? Yeah, yeah, >>Yeah, sure. I mean, I think, you know, one of the biggest things you find with internet for HPC is that, you know, if you look at where the different technologies have gone over time, you know, you've had old technologies like, you know, atm, Sonic, fitty, you know, and pretty much everything is now kind of converged toward ethernet. I mean, there's still some technologies such as, you know, InfiniBand, omnipath that are out there. Yeah. But basically there's single source at this point. So, you know, what you see is that there is a huge ecosystem behind ethernet. And you see that also, the fact that ethernet is used in the rest of the enterprise is using the cloud data centers that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia, you know, into, you know, into enterprise, into cloud service providers is much easier to integrate it with the same technology you're already using in those data centers, in those networks. >>So, so what's this, what is, what's the state of the art for ethernet right now? What, you know, what's, what's the leading edge, what's shipping now and what and what's in the near future? You, you were with Broadcom, you guys design this stuff. >>Yeah, yeah. Right. Yeah. So leading edge right now, I got a couple, you know, Wes stage >>Trough here on the cube. Yeah. >>So this is Tomahawk four. So this is what is in production is shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 tets per second. Okay. Which matches any other technology out there. Like if you look at say, infin band, highest they have right now that's just starting to get into production is 25 point sixt. So state of the art right now is what we introduced. We announced this in August. This is Tomahawk five. So this is 51.2 terabytes per second. So double the bandwidth have, you know, any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, it's actually winds up being a factor of six efficiency. Wow. Cause if you want, I can go into that, but why >>Not? Well, I, what I wanna know, please tell me that in your labs you have a poster on the wall that says T five with, with some like Terminator kind of character. Cause that would be cool if it's not true. Don't just don't say anything. I just want, I can actually shift visual >>It into a terminator. So. >>Well, but so what, what are the, what are the, so this is, this is from a switching perspective. Yeah. When we talk about the end nodes, when we talk about creating a fabric, what, what's, what's the latest in terms of, well, the kns that are, that are going in there, what's, what speed are we talking about today? >>So as far as 30 speeds, it tends to be 50 gigabits per second. Okay. Moving to a hundred gig pan four. Okay. And we do see a lot of Knicks in the 200 gig ethernet port speed. So that would be, you know, four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon. 800 gig in the future. But say state of the art right now, we're seeing for the end nodes tends to be 200 gig E based on 50 gig pan four. Wow. >>Yeah. That's crazy. Yeah, >>That is, that is great. My mind is act actively blown. I wanna circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen. Where do you think we are on the adoption curve and sort of in that cycle? Armand, do you wanna go? >>Yeah, yeah. Well, if you look at the market research, they're actually telling it's 50 50 now. So ethernet is at the level of 50%. InfiniBand is at 50%. Right. Interesting. Yeah. And so what's interesting to us, customers are coming to us and say, Hey, we want to see, you know, flexibility and choice and hey, let's look at ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we have our switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially mpi. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, Hey, I've been in fbe, but now I want to go ethernet, you know, there's gonna be some learning curves there. And so what we wanna do is really simplify that so that we can make it easy to install, get the cluster up and running, and they can actually get some value out of the cluster. >>Yeah. Peter, what, talk about that partnership. What, what, what does that look like? Is it, is it, I mean, are you, you working with Dell before the, you know, before the T six comes out? Or you just say, you know, what would be cool, what would be cool is we'll put this in the T six? >>No, we've had a very long partnership both on the hardware and the software side. You know, Dell has been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, you know, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group within Broadcom, we've then gotten, you know, very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, you know, Dell can take it and, you know, deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again in both the hardware and the software. >>So, so I, I'm, I'm just, I'm fascinated by, I I, I always like to know kind like what Yeah, exactly. Exactly right. Look, you, you start talking about the largest super supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be 2 million CPUs, 2 million CPU cores, yeah. Ex alop of, of, of, of performance. What are the, what are the outward limits of T five in switches, building out a fabric, what does that look like? What are the, what are the increments in terms of how many, and I know it, I know it's a depends answer, but, but, but how many nodes can you support in a, in a, in a scale out cluster before you need another switch? What does that increment of scale look like today? >>Yeah, so I think, so this is 51.2 terras per second. What we see the most common implementation based on this would be with 400 gig ethernet ports. Okay. So that would be 128, you know, 400 giggi ports connected to, to one chip. Okay. Now, if you went to 200 gig, which is kind of the state of the art for the Nicks, you can have double that. Okay. So, you know, in a single hop you can have 256 end nodes connected through one switch. >>So, okay, so this T five, that thing right there inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what is, what does that, what's the form factor look like for that, for where that T five sits? Is there just one in a chassis or you have, what does that look >>Like? It tends to be pizza boxes these days. Okay. What you've seen overall is that the industry's moved away from chassis for these high end systems more towards pizza, pizza boxes. And you can have composable systems where, you know, in the past you would have line cards, either the fabric cards that the line cards are plugged into or interface to these days, what tends to happen is you'd have a pizza box, and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the, the line card. >>Okay. >>So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a two R U with 64 OSF P ports. And often each of those OSF p, which is an 800 gig e or 800 gig port, we've broken out into two 400 gig quarts. Okay. So yeah, in two r u you've got, and this is all air cooled, you know, in two re you've got 51.2 T. We do see some cases where customers would like to have different optics, and they'll actually deploy a four U just so that way they have the face place density, so they can plug in 128, say qsf P one 12. But yeah, it really depends on which optics, if you wanna have DAK connectivity combined with, with optics. But those are the two most common form factors. >>And, and Armando ethernet isn't, ethernet isn't necessarily ethernet in the sense that many protocols can be run over it. Right. I think I have a projector at home that's actually using ethernet physical connections. But what, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center ethernet, or, or is this, you know, RDMA over converged ethernet? What, what are >>We talking about? Yeah, so our rdma, right? So when you look at, you know, running, you know, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on ethernet, if you look at NPI is officially, you know, built to, Hey, it was designed to run on InfiniBand, but now what you see with Broadcom and the great work they're doing now, we can make that work on ethernet and get, you know, it's same performance. So that's huge for customers. >>Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a, a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of ethernet in hpc in terms of AI and ml. Where, where do you think we're gonna be next year or 10 years from now? >>You wanna go first or you want me to go first? I can start. >>Yeah. Pete feels ready. >>So I mean, what I see, I mean, ethernet, I mean, is what we've seen is that as far as on the starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. That's >>Impressive. >>Yeah. So nicely >>Done, casual, humble brag there. That was great. That was great. I love that. >>I'm here for you. I mean, I think that's one of the benefits of, of Ethan is like, is the ecosystem, is the trajectory, the roadmap we've had, I mean, you don't see that in any other networking technology >>More who, >>So, you know, I see that, you know, that trajectory is gonna continue as far as the switches, you know, doubling in bandwidth. I think that, you know, they're evolving protocols. You know, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on rdma, you know, for the supercomputing, the a AIML workloads. But we do see that, you know, as you have, you know, a mix of the applications running on these end nodes, maybe they're interfacing to the, the CPUs for some processing, you might use a different mix of protocols. So I'd say it's gonna be doubling a bandwidth over time evolution of the protocols. I mean, I expect that Rocky is probably gonna evolve over time depending on the a AIML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-pack optics. So, you know, right now this chip is all, all the balls in the back here, there's electrical connections. How >>Many are there, by the way? 9,000 plus on the back of that >>352. >>I love how specific it is. It's brilliant. >>Yeah. So we get, so right now, you know, all the thirties, all the signals are coming out electrically based, but we've actually shown, we have this, actually, we have a version of Hawk four at 25 point sixt that has co-pack optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk five Nice. Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. Wow. Cool. So I see, you know, there's, you know, the bandwidth, there's radis increasing protocols, different physical connectivity. So I think there's, you know, a lot of things throughout, and the protocol stack's also evolving. So, you know, a lot of excitement, a lot of new technology coming to bear. >>Okay. You just threw a carrot down the rabbit hole. I'm only gonna chase this one. Okay. >>All right. >>So I think of, I think of individual discreet physical connections to the back of those balls. Yeah. So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that in many optical connections? What's, what's, what's the mapping there? What does that, what does that look like? >>So what we've announced for TAMA five is it would have fr four optics coming out. So you'd actually have, you know, 512 fiber pairs coming out. So you'd have, you know, basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the, the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because >>It's, it's miraculous, essentially. It's, I know. Yeah, yeah, yeah, yeah. Yeah. So, so, you know, a lot of people are gonna be looking at this and thinking in terms of InfiniBand versus versus ethernet. I think you've highlighted some of the benefits of specifically running ethernet moving forward as, as hpc, you know, which is sort of just trails slightly behind supercomputing as we define it, becomes more pervasive AI ml. What, what are some of the other things that maybe people might not immediately think about when they think about the advantages of running ethernet in that environment? Is it, is it connecting, is it about connecting the HPC part of their business into the rest of it? What, or what, what are the advantages? >>Yeah, I mean, that's a big thing. I think, and one of the biggest things that ethernet has again, is that, you know, the data centers, you know, the networks within enterprises within, you know, clouds right now are run on ethernet. So now if you want to add services for your customers, the easiest thing for you to do is, you know, the drop in clusters that are connected with the same networking technology, you know, so I think what, you know, one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to ethernet. So now you've got to train your technicians, you train your, your assist admins on two different network technologies. You need to have all the, the debug technology, all the interconnect for that. So here, the easiest thing is you can use ethernet, it's gonna give you the same performance. And actually in some cases we seen better performance than we've seen with omnipath than, you know, better than in InfiniBand. >>That's awesome. Armando, we didn't get to you, so I wanna make sure we get your future hot take. Where do you see the future of ethernet here in hpc? >>Well, Pete hit on a big thing is bandwidth, right? So when you look at train a model, okay, so when you go and train a model in ai, you need to have a lot of data in order to train that model, right? So what you do is essentially you build a model, you choose whatever neural network you wanna utilize, but if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the cpu. And essentially, if you're gonna do it maybe on CPU only, but if you do it on accelerators, well guess what? You need a big pipe in order to get all that data through. And here's the deal. The bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage. Maybe it's some new way you design a product, but that's a benefit of speed you want faster, faster, faster. >>It's all about making it faster and easier. It is for, for the users. I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas Stakes, there's a lot going on with with that making >>Me hungry. >>I know exactly. I'm sitting up here thinking, man, I did not have a big enough breakfast. How do you come up with the name Tomahawk? >>So Tomahawk, I think you just came, came from a list. So we had, we have a tri end product line. Ah, a missile product line. And Tomahawk is being kinda like, you know, the bigger and batter missile, so, oh, okay. >>Love this. Yeah, I, well, I >>Mean, so you let your engineers, you get to name it >>Had to ask. It's >>Collaborative. Oh good. I wanna make sure everyone's in sync with it. >>So just so we, it's not the Aquaman tried. Right, >>Right. >>The steak Tomahawk. I >>Think we're, we're good now. Now that we've cleared that up. Now we've cleared >>That up. >>Armando P, it was really nice to have both you. Thank you for teaching us about the future of ethernet N hpc. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to the Cube Live from Dallas. We're here talking all things HPC and Supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us.

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital experience Brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes Coverage of Del Tech World 2020. With me is Jeff Boudreau, the president general manager of Infrastructure Solutions group Deltek. Jeff, always good to see you, my friend. How you doing? >>Good. Good to see you. >>I wish we were hanging out a Sox game or a pat's game, but, uh, I guess this will dio But, you know, it was about a year ago when you took over leadership of I s G. I actually had way had that sort of brief conversation. You were in the room with Jeff Clark. I thought it was a great, great choice. How you doing? How you feeling Any sort of key moments the past 12 months that you you feel like sharing? >>Sure. So I first I want to say, I do remember that about a year ago. So thank you for reminding me. Yeah, it's, uh it's been a very interesting year, right? It's been it's been one year. It was in September was one year since I took over I s G. But I'm feeling great. So thank you for asking. I hope you're doing the same. And I'm really optimistic about where we are and where we're heading. Aziz, you know, it's been an extremely challenging year in a very unpredictable year, as we've all experienced. And I'd say for the, you know, the first part of the year, especially starting in March on I've been really focused on the health and safety of our, you know, the families, our customers and our team members of the team on a lot of it's been shifting, you know, in regards to helping our customers around, you know, work from home or education and learn from home. And, you know, during all this time, though, I'll tell you, as a team, we've accomplished a lot. There's a handful of things that I'm very proud of, you know, first and foremost, that states around the customer experience we have delivered on our best quality in our product. NPS scores in our entire history. So something I'm extremely proud of during this time around our innovation and innovation engine, we part of the entire portfolio which you're well aware of. We had nine launches in nine weeks back in that May in June. Timeframe. So something I'm really proud of the team on, uh, on. Then last, I'd say it's around the team and right, we shifted about 90% of our workforce from the office tow home, you know, from an engineering team. That could be, you know, 85% of my team is engineers and writing code. And so, you know, people were concerned about that. But we didn't skip a beat, so, you know, pretty impressed by the team and what they've done there. So, you know, the strategy remains unchanged. Uh, you know, we're focused on our customers integrating across the entire portfolio and the businesses like VM ware and really focused on getting share. So despite all the uncertainty in the market, I'm pretty pleased with the team and everything that's been going on. So uh, yeah, it's it's been it's been an interesting year, but it's really great. I'm really optimistic about what we have in front of us. >>Yeah, I mean, there's not much you could do a control about the macro condition on it, you know it. Z dealt to us and we have to deal with it. I mean, in your space. It's the sort of countervailing things here one is. Look, you're not selling laptops and endpoint security. That's not your business right in the data center. Eso. But the flip side of that is you mentioned your portfolio refresh. You know, things like Power Store. You got product cycles now kicking in. So that could be, you know, a buffer. What are you seeing with Power Store and what's the uptake look like? They're >>sure. Well, specifically, let me take a step back and the regards the portfolio. So first, you know, the portfolio itself is a direct reflection in the feedback from all our partners and our customers over the last couple of years on Day two, ramp up that innovation. I spent a lot of time in the last few years simplifying under the power brands, which you're well aware of, right? So we had a lot of for a legacy EMC and Legacy dollars. Really? How do we simplify under a set of brands really over delivering innovation on a fewer set of products that really accelerating in exceeding customer needs? And we did that across the board. So from power edge servers, you know, power Max, the high end storage, the Powerball, all that we didn't hear one. And just most recently. And, you know, it's part of the big launches. We had power scale. We have power flex for software to find. And, of course, the new flagship offer for the mid range, which is power store. Um, Specifically, the policy of the momentum has been building since our launch back in May. And the feedback from our partners and our customers has been fantastic. And we've had a lot of big wins against, you know, a lot of a lot of our core competitors. A couple examples one is Arrow Electronics SAA, Fortune 500 Global Elektronik supplier. They leverage power Store to provide, you know, basically both, you know, enterprise computing and storage needs for their for their broader bases around the world on there, really taking advantage of the 41 data reduction, really helping them simplify their capacity planning and really improve operational efficiencies specifically without impacting performance. So it's it's one. We're given the data reductions, but there's no impact on performance, which is a huge value proffer for arrow another big customers tickets and write a global law firm on their reporting to us that over 90 they've had a 90% reduction in their rack space, and they've had over five times two performance over a core competitors storage systems azi. They've deployed power store around the world, really, and it's really been helping them. Thio easily migrate workloads across, so the feedback from the customers and partners has been extremely positive. Um, there really citing benefits around the architecture, the flexibility architecture around the micro services, the containers they're loving, the D M or integration. They're loving the height of the predictable data reduction capabilities in line with in line performance or no performance penalties with data efficiencies, the workload support, I'd say the other big things around the anytime upgrades is another big thing that customers we're really talking about so very excited and optimistic in regards as we continue to re empower store the second half of the year into next year really is the full full year for power store. >>So can I ask you about that? That in line data reduction with no performance hit is that new ipe? I mean, you're not doing some kind of batch data reduction, right? >>No, it's It's new, I p. It's all patented. We've actually done a lot of work in regards to our technologies. There's some of the things we talk about GPS and deep use and smart Knicks and things like that. We've used some offload engines to help with that. So between the software and the hardware, we've had leverage new I. P. So we can actually provide that predictable data reduction. But right with the performance customers need, So we're not gonna have a trade off in regards. You get more efficiencies and less performance or more performance and less efficiency. >>That's interesting. Yeah, when I talked to the chip guys, they talk about this sort of the storage offloads and other offloads we're seeing. These alternative processors really start to hit the market videos. The obvious one. But you're seeing others. Aziz. Well, you're really it sounds like you're taking advantage of that. >>Yeah, it's a huge benefit. I mean, we should, you know, with our partners, if it's Intel's and in videos and folks like that broad comes, it's really leveraging the great innovation that they do, plus our innovation. So if you know the sum of the parts, can you know equal Mauritz a benefit to our customers in the other day? That's what it's all about. >>So it sounds like Cove. It hasn't changed your strategy. I was talking toe Dennis Hoffman and he was saying, Look, you know, fundamentally, we're executing on the same strategy. You know, tactically, there's things that we do differently. But what's your summarize your strategy coming in tow 2021. You know, we're still early in this decade. What are you seeing is the trends that you're trying to take advantage of? What do you excited about? Maybe some things that keep you up at night? >>Yeah, so I'd say, you know, I'll stay with what Dennis said. You know, it's our strategy is not changing its a company. You probably got that from Michael and from job, obviously, Dennis just recently. But for me, it's a two pronged approach. One's all about winning the consolidation in the core infrastructure markets that we could just paid in today. So I think Service Storage Network, we're already clear leader across all those segments that we serve in our you know, we'll continue to innovate within our existing product categories. And you saw that with the nine launches in the nine weeks in my point on that one is we're gonna always make sure that we have best debris offers. If it's a three tier, two tier or converge or hyper converged offer, we wanna make sure that we serve that and have the best innovation possible. In addition to that, though, the secondary piece of the strategy really is around. How do we differentiate value across or innovating across I S G? You know, Dell Technologies and even the broader ecosystems and some of the examples I'll give you right now that we're doing is if you think about innovating across icy, that's all about providing improved customer experience, a set of solutions and offers that really helped simplify customer operations, right? And really give them better T CEOs or better. S L. A. An example of something like that's cloud like it's a SAS based off of that we have. That really helps provide great insights and telemetry to our customers. That helps them simplify their I T operations, and it's a major step forward towards, you know, autonomous infrastructure which is really what they're asking for. Customers of a very happy with the work we've done around Day one, you know, faster, time to value. But now it's like Day two and beyond. How do you really helped me Kinda accelerate the operations and really take that away from a three other big pieces innovating across all technologies. And you know, we do this with VM Ware now live today, and that's just writing. So things like VX rail is an example where we work together and where the clear leader in H C I. Things like Delta Cloud Uh, when we built in V M V C F A, B, M or cloud foundation in Tan Xue delivering an industry leading hybrid cloud platform just recently a VM world. I'm sure you heard about it, but Project Monterey was just announced, and that's an effort we're doing with VM Ware and some other partners. They're really about the next generation of infrastructure. Um, you know, I guess taking it up a notch out of the infrastructure and I've g phase, you know, some of the areas that we're gonna be looking at the end to end solutions to help our customers around six key areas. I'm sure John Rose talking about the past, but things like cloud Edge five g A i m l data management security. So those will be the big things. You'll see us lean into a Z strategies consistent. Some big themes that you'll see us lean into going into next year. >>Yeah, I mean, it is consistent, right? You guys have always tried to ride the waves, vector your portfolio into those waves and add value. I'm particularly impressed with your focus on customer experience, and I think that's a huge deal. You know, in the past, a lot of companies yours included your predecessor. You see, Hey, throwing so many products at me, I can't I don't understand the portfolio. So I mean, focusing on that I think is huge right now because people want that experience, you know, to be mawr cloudlike. And that's that's what you got to deliver. What about any news from from Dell Tech world? Any any announcements that you you wanna highlight that we could talk about? >>Sure. And actually, just touching back on the point you had no about the simplification that is a major 10 of my in regards the organization. So there's three key components that I drive once around customer focus, and that's keeping customers first and foremost. And everything we do to is around axillary that innovation. Engine three is really bringing everything together as one team. So we provide a better outcome to our customers. You know, in that simplification after that you talk about is court toe what we're driving. So I want to do less things, I guess better in the notion of how we do that. What that means to me is, as I make decisions that want to move away from other technologies and really leverage our best of breed type shared type, that's technology. I p people I p I can, you know, e can exceed customer needs in those markets that were serving. So it's actually allows me to x Sorry, my innovation engine, because I shift more and more resource is onto the newer stock now for Del Tech world. Yes, We got some cool stuff coming. You probably heard about a few of them. Uh, we're gonna be announcing a project project Apex. Hopefully you've been briefed on that already. This isn't new news or I'll be in trouble. But that's really around. Our strategy about delivering, simple, consistent as a service experiences for our customers bringing together are dealt technology as a service offering and our cloud strategy together. Onda also our technology offerings in our go to market all under a single unified effort, which Ellison do would be leading. Um, you know, on behalf of our executive leadership team s, that's one big area. And there is also another big one that I'll talk about a sui expand our as a service offers. And we think there's a big power to that in regards to our Dell Technologies. Cloud console solving will be launching a new cloud console that will provide uniformed experience across all the resources and give users and ability toe instantly managed every aspect of their cloud journey with just a few clicks. So going back to your broader point, it's all about simplicity. >>Yeah, we definitely all over Apex. That's something I wanted to ask you about this notion of as a service, really requiring it could have a new mindset, certainly from a pricing and how you talk about the customer experience that it's a whole new customer experience. Your you're basically giving them access. Thio What I would consider more of a platform on giving them some greater flexibility. Yeah, there's some constraints in there, but of course, you know the physical only put so much capacity and before him. But the idea of being ableto dial up, dial down within certain commitments is, I think, a powerful one. How does it change the way in which you you think about how you go about developing products just in terms of you know, this AP economy Infrastructure is code. How how you converse about those products internally and externally. How would you see that shaking >>out Dave? That's an awesome question. And it's actually for its front center. For everything we do, obviously, customers one choice and flexibility what they do. And to your point as we evolved warm or as a service, no specific product and product brands and logos on probably the way of the future. It's the services. It's the experience that you provide in regards to how we do that. So if you think about me, you know, in in infrastructure making infrastructure as a service, you really want to define what that customer experiences. That s L. A. That they're trying toe realize. And then how do we make sure that we build the right solutions? Products feature functions to enable that a law that goes back to the core engineering stuff that we need to dio right now, a lot of that stuff is about making sure that we have the right things around. If it's around developer community. If it's around AP rich, it's around. SdK is it's all about how do we leverage if it's internal source or external open source, if you will. It's regards to How do we do that? No. A thing that I think we all you know what you're well aware but we ought to keep in mind is that the cloud native applications are really relevant. Toe both the on premises, wealthy off premise. So think about things around portability reusability. You know, those are some great examples of just kind of how we think about this as we go forward. But those modern applications were required modern infrastructure, and regardless of how that infrastructure is abstracted now, just think about things like this. Aggregation or compose ability or Internet based computing. It's just it's a huge trend that we have to make sure we're thinking of. So is we. We just aggregate between the physical layers to the software layers and how we provide that to a service that could be think of a modern container based asset that could be repurposed. Either could be on a purpose built thing. It could be deployed in a converge or hyper converged. Or it could be two points a software feature in a cloud. Now, that's really how we're thinking about that, regards that we go forward. So we're talking about building modern assets or components That could be you right once we used many type model, and we can deploy that wherever you want because of some of the abstraction of desegregation that we're gonna do. >>E could see customers in the in the near term saying, I don't care so much about the product. I want the fast one all right with the cheaper one e. >>It's kind of what you talking about, that I talked about the ways. If you think about that regards, you know, maybe it's on a specific brand or portfolio. You look into and you say, Hey, what's the service level that I'd wanted to your point like Hey, for compute or for storage, it's really gonna end up being the specific S l A. And that's around performance or Leighton see, or cost or resiliency they want. They want that experience in that that you know, And that's why they're gonna be looking for the end of the end state. That's what we have to deliver is an engineering. >>So there's an opportunity here for you guys that I wonder if you could comment on. And that's the storage admin E. M. C essentially created. You know, you get this army of people that you know pretty good of provisioning lungs, although that's not really that's a great career path for folks. But program ability is, and this notion of infrastructure is code as you as you make your systems more programmable. Is there a skill set opportunity to take that army of constituents that you guys helped train and grow and over their careers and bring them along into sort of the next decade? This new era? >>I think the the easy answer is yes, I obviously that's a hard thing to do and you go forward. But I think embracing the change in the evolution of change, I think is a great opportunity. And I think there is e mean if you look step back and you think about data management, right? And you think about all the you know all data is not created equal and you know, and it has a life cycle, if you will. And so if it's on edge to Korda, Cloward, depending think about data vaults and data mobility and all that stuff. There's gonna be a bunch of different personas and people touching data along the way. I think the I T advance and the storage admin. They're just one of those personas that we have to help serve and way talk about How do we make them heroes, if you will, in regards to their broader environment. So if they're providing, if they evolve and really helped provide a modern infrastructure that really enables, you know infrastructure is a code or infrastructure as a service, they become a nightie hero, if you will for the rest of team. So I think there's a huge opportunity for them to evolve as the technology evolves. >>Yeah, you talked about you know, your families, your employees, your team s o. You obviously focused on them. You got your products going hitting all the marks. How are you spending your time these days? >>Thes days right now? Well, we're in. We're in our cycle for fiscal 22 planning. Right? And right now, a lot of that's above the specific markets were serving. It's gonna be about the strategy and making sure that we have people focused on those things. So it really comes back to some of the strategy tents were driving for next year. Now, as I said, our focus big time. Well, I guess for the for this year is one is consolidation of the core markets. Major focus for May 2 is going to be around winning in storage, and I want to be very specific. It's winning midrange storage. And that was one of the big reasons why Power Store came. That's gonna be a big focus on Bennett's really making sure that we're delivering on the as a service stuff that we just talked about in regards to all the technology innovation that's required to really provide the customer experience. And then, lastly, it's making sure that we take advantage of some of these growth factors. So you're going to see a dentist. Probably talked a lot about Telco, but telco on edge and as a service and cloud those things, they're just gonna be key to everything I do. So if you think about from poor infrastructure to some of these emerging opportunities Z, I'm spending all my time. >>Well, it's a It's a big business and a really important one for Fidel. Jeff Boudreau. Thanks so much for coming back in the Cube. Really a pleasure seeing you. I hope we can see each other face to face soon. >>You too. Thank you for having me. >>You're very welcome. And thank you for watching everybody keep it right there. This is Dave Volonte for the Cube. Our continuing coverage of Del Tech World 2020. We'll be right back right after this short break

>>from around the globe. It's the Cube >>with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Hello and welcome back to the VM World 2020 Virtual coverage with the Cube Virtual I'm John for day. Volonte your hosts our 11th year covering VM. We'll get a great guest Greg Lavender, SBP and the CTO of VM. Where, uh, welcome to the Cube. Virtual for VM World 2020 Virtual Great. Thanks for coming on. >>Privileged to be here. Thank you. >>Um, really. You know, one of the things Dave and I were commenting with Pat on just in general start 11th year covering VM world. Uh, a little difference not face to face. But it's always been a technical conference. Always a lot of technical innovation. Project Monterey's out there. It's pretty nerdy, but it's a it's called the catnip of the future. Right? People get excited by it, right? So there's really ah lot of awareness to it because it kinda it smells like a systems overhaul. It smells like an operating system. Feels like a, you know, a lot of moving parts that are, quite frankly, what distributed computing geeks and software geeks love to hear about and to end distributed software intelligence with new kinds of hardware innovations from and video and whatnot. Where's that innovation coming from? Can you share your thoughts on this direction? >>Yeah, I think first I should say this isn't like, you know, something that just, you know, we decided to do, you know, six months ago, actually, in the office of C T 04 years ago, we actually had a project. Um, you know, future looking project to get our core hyper visor technology running on arm processors and that incubated in the office of the CTO for three years. And then last December, move the engineering team that had done that research and advanced development work in the office of the CTO over to our cloud platforms business unit, you know, and smart Knicks, you know, kind of converged with that. And so we were already, you know, well along the innovation path there, and it's really now about building the partnerships we have with smart nick vendors and driving this technology out to the benefit of our customers who don't want to leverage it. >>You get >>Greg, I want if you could clarify something for me on that. So Pat talked about Monterey, a complete re architect ing of the i o Stack. And he talked about it affecting in video. Uh, intel, melon, ox and Sandoz part of that when he talks about the Iot stack, you know, specifically what are we talking about there? >>So you know any any computing server in the data center, you know, in a cola facility or even even in the cloud, you know? Ah, large portion of the, you know CPU resource is, and even some memory resource is can get consumed by just processing. You know, the high volumes of Iot that's going out, you know, storage devices, you know, communicating between the different parts of multi tiered applications. And so there's there's a there's an overhead that that gets consumed in the course server CPU, even if its multi core multi socket. And so by offloading that a lot of that I owe work onto the arm core and taking advantage of the of the hardware offloads there in the smart Knicks, you can You can offload that processing and free up even as much as 30% of the CPU of a server, multi socket, multicourse server, and give that back to the application so that the application gets the benefit of that extra compute and memory resource is >>So what about a single sort of low cost flash tear to avoid the complexities of tearing? Is that part of the equation? >>Well, you know, you can you can, um you know, much storage now is network attached. And so you could if it's all flash storage, you know, using something like envy me fabric over over Ethernet, you can essentially build large scale storage networks more efficiently, you know more cheaply and take advantage of that offload processing, uh, to begin to reduce the Iot Leighton. See, that's required taxes. That network attached storage and not just storage. But, you know, other devices, you know, that you can use you could better network attached. So disaggregated architectures is term. >>Uh, is that a yes? Or is that a stay tuned? >>Yeah, Yeah, yeah, yeah, yes. I mean the storage. You know, more efficient use of different classes of storage and storage. Tearing is definitely a prime use case there. >>Yeah, great. Thank you. Thanks for that. John, >>How could people think about the edge now? Because one of the things that's in this end to end is the edge. Pat brought it up multi cloud and edge or two areas that are extending off cloud and hybrid. What should people think about the innovation equation around those things? Is that these offload techniques? What specifically in the systems architecture? Er, do you guys see as the key keys there? >>So so, you know, edges very diversified, heterogeneous place, Uh, in the architectures of multi cloud services. So one thing we do know is, you know, workload. I would like to say workload follows data, and a lot of the data will be analyzed, the process at the edge. So the more that you can accelerate that data processing at the edge and apply some machine learning referencing at the edge were almost certainly gonna have kubernetes everywhere, including the edge. So I think you're seeing a convergence of the hardware architectures er the kubernetes control plane and services and machine learning workloads. You know, traveling to the edge where the where the data is going to be processed and actions could be taken autonomously at the edge. So I think we're in this convergence point in the industry where all that comes together. >>How important do you >>do you see that? Okay, John, >>how important is the intelligence piece? Because again, the potatoes at the edge. How do you guys see the data architecture being built out there? >>Um, well, again, it's depending on the other. The thick edge of the thin edge. You know, you're gonna have different, different types of data, and and again, a lot of the the inference thing that could happen at the edges. Going to, I think, for mawr, you know, again to take action at the edges, opposed to calling home to a cloud, you know, to decide what to do. So, depending on, you know, the computational power and the problem with its video processing or monitoring, you know, sensors, Aaron, oil. Well, the kind of interesting that will happen at the edge will will be dependent on that data type and what kind of decisions you want to make. So I think data will be moving, you know, from the edge to the cloud for historical analytics and maybe transitional training mechanisms. But, you know, the five G is gonna play heavily into this is well right for the network connectivity. So we read This unique point is often occurs in the industry every few years of all these technology innovations converging to open up an entirely new platform in a new way of computing that happens at the edge, not just in your data center at the cloud. >>So, Greg, you did a fairly major stint at a large bank. What would something you mentioned? You know, like an oil rig. But what would something like these changes mean for a new industry like banking or financial? Uh, will it have an impact there and put on your customer hat for a minute and take us through that >>e? You know, eight machines, you know, branches, chaos. You know, there's all make banks always been a very distributed computing platform. And so, you know, people want to deliver mawr user experience, services, more video services. You know all these things at the edge to interact positively with the customer without using the people in the loop. And so the banking industry has already gone through the SD when, and I want transformation to deliver the bandwidth more capably to the edge. And I just think that they'll just now be able to deliver Mawr Edge services that happened can happen more autonomously at the edge is opposed that having the hairpin home run everything back to the data center. >>Awesome. Well, Pat talks about the modern platform, the modern companies. Greg, I wanna ask you because we're seeing with Kovar, there's to use cases, you know, the people who don't have a tailwind, Um, companies that are, you know, not doing well because there's no business that you have there modernizing their business while they have some downtime. Other ones have a tailwind. They have a modern app that that takes advantage, this covert situation. So that brings up this idea of what is a modern app look like? Because now, if you're talking about a distributed architecture, some of things you're mentioning around inference, data edge. People are starting to think about these modern naps, and they are changing the game for the business. Now you have vertical industries. You mentioned oil and gas, you got financial services. It used to be you had industry solution. It worked like that and was siloed. Now you have a little bit of a different architectures. If we believe that we're looking up, not down. Does it matter by industry? How should people think about a modern application, how they move faster? Can you share your insights into into some of this conceptual? What is a modern approach and does it doesn't matter by vertical or industry. >>Yes, I mean, certainly over the course of my career, I mean, there's there's a massive diversity of applications. And of course, you know, the explosion of mobile and edge computing is just another sort of sort of use cases that will put demands on the infrastructure in the architecture and the networking. So a modern, a modern app I mean, we historically built sort of these monolithic app. So we sort of built these sort of three tier apse with, you know, sort of the client side, the middleware side. The database back in is the system of record. I mean, this is even being more disaggregated in terms of, you know, the the consumer edges both not just web here, but mobile tear. And, you know, we'll see what emerges out of that. The one thing for sure that is that, um they're becoming less monolithic and mawr a conglomeration of sass and other services that are being brought together, whether it's from the cloud services or whether it's s, you know, SBS delivering, you know, bring your own software. Um, and they're becoming more distributed because people need operated higher degrees of scale. There's a limit to Virgil vertical scaling, so you have to go to horizontal scaling, which is what the cloud is really good at. So I think all these things were driving a whole new set of technologies like next generation AP gateways. Message Busses, service mesh. We're announcing Tanzi's service message being world. Um, you know, this is just allowing allowing that application to be disaggregated and then integrated with other APS assassin services that allow you to get faster time to market. So speed of delivery is everything. So modern C I. C d. Modern software, technology and ability to deploy and run that workload anywhere at the edge of the core in the data center in the cloud. >>So when you do in your re architecture like this, Greg, I mean you've seen over the course of history in our industry you've seen so many companies have hit a wall and in VM, whereas it's just amazing engineering culture. How are you able toe, you know, change the engine mid flight here and avoid like, serious technical debt. And I mean, it took, you know, you said started four years ago, but can you give us a peek inside? You know, that sort of transformation and how you're pulling that off? >>Well, I mean, we're providing were delivered the platform and, you know, spring Buddhas a key, you know, technology that's used widely across the industry already, which is what we've got is part of our pivotal acquisition. And so what we're just trying to do is just keep keep delivering the technology and the platform that allows people to go faster with quality security and safety and resiliency. That's what we do really well at VM ware. So I think you're seeing more people building these APS Cloud native is opposed to, you know, taking an existing legacy app In trying to re factor it, they might do what it called e think somebody's called two speed architectures. Take the user front, end the consumer front in, and put that cloud native in the cloud. But the back end system of record still runs in the private cloud in a highly resilient you know, backed up disaster recovered way. So you're having, I think, brand new cloud native APS we're seeing. And then you're seeing people very carefully because there's a cost to it of looking at How do I basically modernized the front end but maintain the reliability of the scalability of security and the reliability of that sort of system of record back in? So either way, it's it's winning for the companies because they could do faster delivery to their businesses and their clients and their partners. But you have to have the resiliency and reliability that were known for for running those mission critical workloads, >>right? So the scenario is that back end stays on premise on the last earnings call, I think, Pat said, or somebody said that, that I think I just they said on Prem or maybe the man hybrid 30 to 40% cheaper, then doing it in the cloud. I presume they were talking about those kind of back end systems that you know you don't wanna migrate. Can you add some color that again from your customer perspective That the economics? >>Yeah. You know, um, somebody asked me one time what's really a cloud. Greg and I said, automation, automation, automation you can take you can take You can take your current environments and highly automate the release. Lifecycle management develop more agile software delivery methods. And so therefore, you could you could get sort of cloud benefits, you know, from your existing applications by just highly optimizing them and, you know, on the cost of goods and services. And then again, the hybrid cloud model just gives customers more choice, which is okay. I want to reduce the number of data centers I have, but I need to maintain reliability, scalability, etcetera. Take advantage of, you know, the hybrid cloud that we offer. But you'll still run things. Cloud natives. I think you're seeing this true multi cloud technology and paradigm, you know, grow out as people have these choices. And then the question is okay. If you have those choices, how do you maintain security? How do you maintain reliability? How do you maintain up time yet be able to move quickly. And so I think there's different speeds in which those platforms will evolve. And our goal is to give you the ability to basically make those choices and and optimize for economics as well as technical. You know, capability. >>Great. I want to ask you a question with Cove it we're seeing and we've been reporting the Cube virtual evolve because we used to be it at events, but we're not there anymore. But the as everyone has realized with cove it it's exposed some projects that you might not want to double down on or highlighted some gaps in architecture. Er, I mean, certainly who would have forecast of the disruption of 100% work from home VP and provisioning to access and access management security, and it really is exposed. What kind of who's where in the journey, Right in digital transformation. So I gotta ask you, what's the most important story or thing to pay attention, Thio as the smart money and smart customers go, Hey, you know what? I'm gonna double down on that. I'm gonna kill that project or sunset. That or I'm not gonna re factor that I'm gonna contain Arise it and there's probably there's a lot of that going on. In our conversations with customers, they're like it's pretty obvious. It's critical path. It's like we stay in business. We build a modern app, but I'm doubling down. I'm transitioning. It's a whole nother ballgame. What >>is >>the most important thing that you see that people should pay attention to around maintaining an innovation and coming out on the other side? >>Yeah, well, I think I think it just generally goes to the whole thesis of software defined. I mean, you know the idea of taking an appliance physical, You know, you have to order the hardware, get it on your loading dock, install in your data center. You know, go configure it, mapping into the rest of your environment. You know, whereas or you could just spend up new, softer instances of load balancers, firewalls, etcetera. So I think you know what's What's really helped in the covert era is the maturity of software to find everything. Compute storage, networking. Lan really allowed customers and many of our customers toe, you know, rapidly make that pivot. And so you know what? It's the you know, the workspace, the remote workspace. You gotta secure it. That's a key part of it, and you've got to give it. You know, you gotta have the scalability back in your data centers or, if you don't have it, be able to run those virtual desktops you know, in the cloud. And I think so. This ability again to take your current environment and, more importantly, your operating model, which, you know the technology could be agile and fast. But if you're operating models not agile, you know you can't executed Well, One of the best comments I heard from a customer CEO was, you know, for six months we debated, you know, the virtual networking architecture and how to deploy the virtual network. And, you know, when covet hit. We made the decision that did it all in one week. So the question the CEO asked now is like Well, why do we Why do we have to operate in that six month model going forward? Let's operate in the one week model going forward. E. I think that that z yeah, that's e think that's the big That's a big inflection point is the operating model has to be agile. We got all kinds of agile technology and choices I mentioned it's like, How do you make your organization agile to take advantage of those technological offerings? That's really what I've been doing the last six months, helping our customers achieve. >>I think that's a key point worth calling out and doubling down on day because, you know, whether you talk about our q Q virtual, our operating model has changed and we're doing new things. But it's not bad. It's actually beneficial. We could talk to more people. This idea of virtual ization. I mean pun intended virtual izing workforces face to face interactions air now remote. This is a software defined operating business. This is the rial innovation. I think this is the exposure. As companies wake up and going. Why didn't we do that before? Reminds me of the old mainframe days. Days? You know, why do we have that mainframe? Because they're still clutching and grabbing onto it. They got a transition. So this is the new the new reality. >>We were joking earlier that you know it ain't broke, don't fix it. And all of a sudden Covic broke everything. And so you know, virtualization becomes a fundamental component of of of how you respond. But and I wonder if Greg you could talk about the security. Peace? How how that fits in. You know everybody you know, the bromide, of course, is security can't be a bolt on. It's gotta be designed in from the start, Pat Gelsinger said years ago in the Cube. Security is a do over. You guys have purchased many different security components you've built in. Security comes. So how should we think about? And how are you thinking about designing insecurity across that entire stack without really bolting in, You know, pieces, whether it's carbon, carbon, black or other acquisitions that you've made? >>Yeah, I mean, I think that's that's the key. Inflection point we're in is an industry. I mean, getting back to my banking experience, I was responsible for cybersecurity, engineering the platforms that we engineered and deployed across the bank globally. And the challenge, the challenge. You know, that's I had, you know, 150 plus security products, and you go to bed at night wondering what? Which one did I forget to deploy or what did I get that gap? Do you think you think you're safe by the sheer number, but when you really boil down to it is like, you know, because you have to sort of like both all this stuff together to create a secure environment, you know, on a global level. And so really, our philosophy of VM where is Okay? Well, let's kind of break that model. That's what we call it intrinsic security, which is just, you know, we have the hyper visor. If you're running, the hyper visor is running on most of the service in your data center. If we have your if you have our network virtualization, we see all the traffic going between all those hyper visors and out to the cloud as well hybrid cloud or public cloud with our NSX technology. And then, you know, then you sort of bring into that the load balancers and the software to find firewalls. And pretty soon you have realized Okay, look, we have we have most of the estate. Therefore we could see everything and bring some intelligent machine learning to that and get proactive as opposed to reactive. Because our whole model now is we. All this technology and some alert pops and we get reactive. How about proactively telling me that something nasty is going on. >>I need to ask you a >>question. May be remediated. Sorry, John. It may be remediated at some point anyway. Bring in some machine intelligence tow. So instead of like you said, getting an alert actually tells me what what happened and how it was fixed, you know? Or at least recommending what I should dio, right? >>Yeah. I mean, part of the problem in the historic architectures is it was all these little silos. You know, every business unit had its own sort of technology. And Aziz, you make things virtualized. You you sort of do the virtual networking. The virtual stories of virtual compute all the software. You know, all of a sudden you have you have a different platform, you have lots of standardization. Therefore you don't have your operating model simplifies right and amount of and then it's about just collecting all the data and then making sense of the data. So you're not overwhelming the human's capacity to respond to it. And so I think that's really the fundamental thing we're all trying to get to. But the surface area is enlarged outside the data centers we've discussed out to the edge, whatever the edges, you know, into the cloud hybrid or public. So now you've got this big surface area where you've gotta have all that telemetry and all that visibility again, Back to getting proactive. So you got to do it in Band is opposed out of band. >>Great. I want to ask you a question on cyber security. We have an event on October 4th, the virtual event that Cuba is hosting with Cal Poly around this space and cybersecurity, symposiums, intersection of space and cyber. I noticed VM Ware recently announced last month that the United States Space Force has committed to the Tan Xue platform for for Continuous Dev ops operation for agility. I interviewed Lieutenant General John Thompson, Space Force, and we talked about that. He said quote, it's hard to do break fix in space. Uh, illustrating, really? Just can't send someone to swap out something in space. Not yet, at least. So they're looking at software defined as a key operating reality. Okay, so again, talk about the edge of space Isas edges. You're gonna get it. Need to be completely mad and talk about payloads and data. This >>is kind >>of interesting data point because you have security issues because space is gonna be contested and congested as an edge device. So it's actually the government's interested in that. But fundamentally, the death hops problem that you're you guys are involved in This >>is a >>reality. It's kind of connects this reality idea of operating models based in reality have to be software. What's >>your name? Yeah. I mean, I think the term we use now is def sec ops because you can't just do Dev ops. You have to have the security component in there, So, uh, yeah, the interesting. You know, like, there's a lot of interesting things happen just in fundamental networking, right? I mean, you know, the StarLink, you know, satellites at Testa. His launched Elon musk has launched and, you know, bringing sort of, you know, higher band with laurel agency to those. Yeah, we'll call it near space the and then again, just opens up all new opportunities for what we can dio. And so, Yeah, I think that's the software that the whole the whole saw for development ecosystem again, back to this idea. I think of three things. You gotta have speed. You gotta have scale and you gotta have security. And so that's really the emerging platform, whether it's a terrestrial or in near space, Uh, that's giving us the opportunity, Thio Do new architectures create service measures of services, some terrestrial, some some you know, far remote. And as you bring these new application architectures and system platform architectures together with all the underlying hardware and networking innovations that are occurring, you mentioned flash. But even getting into pmm persistent memory, right? So this this is so much happening that is converging. What's exciting to me about being a TV? Where is the CTO and we partner with all the hardware vendors? We partner with all the system providers, like in video and others. You know, the smart nick vendors. And then we get to come up with software architectures that sort of bring that together holistically and give people a platform. We can run your workloads to get work done wherever you need to land those workloads. And that's really the excitement about >>the candy store. And yet you've got problems hard problems to work on to solve. I mean, this really brings the whole project moderate, full circle because we think about space and networks and all these things you're talking about, You need to have smart everything. I mean, isn't that software? It's a complete tie into the Monterey. >>Yeah, yeah, yeah, Exactly. You're right. It's not just it's not just connecting everything and pushing data around its than having the intelligence to do it efficiently, economically, insecurely. And that's you know. So I see that you don't want to over hype machine learning. I did not to use the term AI, but use the machine learning technologies, you know, properly trained with the proper data sets, you know, and then the proper algorithms. You know that you can then a employee, you know, at the edge small edge, thick edge, you know, in the data center at the cloud is really Then you give the visibility so that we get to that proactive world I was talking about. >>Yeah, great stuff, Greg. Great insight, great conversation. Looking forward to talking mawr Tech with you. Obviously you are in the right spot was in the center of all the action across the board final point. If you could just close it out for us. What is the most important story at VM World 2020 this year. >>Um, well, I think you know, I like to say that I have the best job. I think you know that I've had in my career. I've had some great ones is you know, we get to be disruptive innovators, and we have a culture of perpetual innovation and really being world for us, Aly employees and all the people that work together to put it together is we get to showcase. You know, some of that obviously have more up our sleeves for the future. But, you know, being world is are, you know, coming coming out out show of the latest set of innovations and technologies. So there's going to be so much I have, ah, vision and innovation. Keynote kickoff, right. Do some lightning demos. And actually, I talk about work we're doing in sustainability, and we're putting a micro grid on our campus in Palo Alto and partnership with City of Palo Alto so that when the wildfires come through or there is power outages, you know we're in oasis of power generating capacity with our solar in our batteries. And so the city of Palo Alto could take their emergency command vehicles and plug into our batteries when the power is out in Palo Alto and operate city services and city emergency services. So we're not just innovating, you know, in cortex we're innovating to become a more, you know, sustainable company and provide sustainable, you know, carbon neutral technology for our customers to adopt. And I think that's an area we wanna talk about me. We talk about it next time, but I think you know our innovations. We're gonna basically help change the world with regard to climate as well. >>Let's definitely do that. Let's follow up for another in depth conversation on the societal impact. Of course, VM Ware VM Ware's VM World's 2020 is virtual is a ton of sessions. There's a Cloud City portion. Check out the 60 solution demos. Of course, they ask the expert, Greg, you're in there with Joe Beta Raghu, all the experts, um, engage and check it out. Thank you so much for the insight here on the Cube. Virtual. Thanks for coming on. >>Appreciate the opportunity. Great conversation and good questions. >>Great stuff. Thank you very much. Innovation that vm where it's the heart of their missions always has been, but they're doing well on the business side, Dave. Okay. The cube coverage. They're not there in person. Virtual. I'm John for day. Volonte. Thanks for watching.

>> Hey, welcome back, everybody. Jeffrey here with the Cube were Rhetorical Park in San Francisco on the shores of McCovey Cove. I just love saying that we >> haven't been here since >> 2014. We're excited to be back for a really interesting event is called Sports Tech Tokyo World Demo Day. This next guest has been at it for a number of years. A really cool technology. We're excited for the conversation and to welcome Mark Philip. He's the founder and CEO of Are >> You watching this Mark? >> Great to see you. Good to see you, too. Absolutely. So, first off, you've been Thio Park before. Here I have. It's been way too long. >> There are >> a few iconic stadiums in the world, and this has got to be one of the great. So let's get into it. So what is are you watching this all about? >> We are the best friend that is >> giving the digital tap on the shoulder when it's time to run to the couch. We monitor pitch by pitch, shot by shot data to figure out when the game gets exciting. I love my Yankees till death, but the >> Yankees Red Sox occasionally tend to >> take over my entire night when they play each other. So being able to get that tap on the shoulder saying, Hey, it's time to tune in or stop raking the leaves, there's a no hitter through eight. Okay, that's what we try to do. Okay, so let's break it down before we get some of the applications into which actor doing So You guys air, You're actively watching these games. You've got some type of an algorithm based on scoring plays. Pitch count. Are we? What are some of the things that drive? Whether this is an exciting game or not, it's a great question. The easiest way to think about it is if you imagine what a win probability graph looks like. So game probably starts off in the middle. Might go up or down based on who's winning, the more violently that graph goes up and down generally, the more exciting the game is, so when probability is a big factor. But also you think about rarity whether it's we had a no hitter last night, we had the Astros with a four picture no hitter a few weeks ago. You know, those sort of things that you don't see often, even if the game's nine nothing, even if the wind probabilities and changing. If that's a no hitter, that's something you want to turn into, right? And so are you tapping into just kind of some of the feeds that are out there in terms of what's happening in the game or you actually watching and using a I in terms of actually looking at a screen and making judgments? Sure, thankfully, I'm not watching or else I would never leave the house. But for us, it's about getting that real time live data. Okay, so I can see balls and strikes on my servers faster than I can see it on live TV, which is a little bit mind bending of time. So we work with the the official data sources. So whether it's a company like sport radar or stats or opt or Abels and pretty much anyone around the globe, we pull in that real time data so we can give people that tap on. The show says Hey, run to the couch. Run to the bar, tune in. Something interesting is about to happen, right? But what's entering your B to B play. So your customers are not me. Jeff, go to the couch. You're working through other people that might be motivated to have me run to the count. So how does your business model work? Who are some of your customers? What are some of the ways that they use your service? >> I'm I'm the guy behind the guy. I'm behind the >> Red Curtain, pulling the strings, you know, for us not to paint with an overly broad brush. But we're based in Austin, Texas, and one of the big things about a city like ours versus the city like this is that our companies tend to skew very B to B versus the Bay Area, which generally excuse a lot more B to C. So pitching to the cable companies, the sports providers, probably CBS Sports is our oldest customer right now. We work with small startups, more established folks, and everyone uses this differently. But the goal is the vision. Is that whether it's your DVR recording automatically when the game gets good or just making sure that, you know, maybe you want to place a bet on the Giants or if you are, ah, glutton for punishment my lowly Knicks if the if the spreads. Good enough, you know, getting that nudge when games get exciting is an accelerant. Not just for watching in, but I think, for fandom. Yeah, well, when Kevin Durant comes back, you'll get a bit more exciting >> Nets, not Nick's. I'm gonna give you one free one. So we had a conversation >> before we turn the cameras on about, you know, kind of this. This never ending attention span competition and the never ending shrinking of consumable media. And how you guys really play an interesting role in that evolution, where if you can give us a little bit deeper background, >> I think it's fascinating. You look at >> the N B A. That really any league. If you rewind five years ago, you have to pay to 5300 bucks to get access to anything digitally, and then you got access to everything, and then the NBA's said, Well, maybe just want to buy one team, so we'll let you pay things around 80 bucks and they just want to watch. One game will sell it to you for eight. I just want 1/4 with such for dollar 99 if you just want a few minutes with silty for 99 >> cents, and now they've done that really, really quietly. >> But I think it's seismic because I think all leagues we're gonna have to follow and do this. So if you look at these snack passes and especially as thes NFL rights are coming up, I could easily imagine someone like a YouTube or, I should say, a Google if they were to grab these rights, how easy would be to go to YouTube and get a game for a few bucks and how well their entire infrastructure would work. But rewind to today when you have 10 to 20 states that are online. As far as gambling goes, you take gambling. You take excitement analytics and you take the snack passes and you kind of mix him up in a pot and you get this vision of I can send you a Texas is Hey, LeBron has 60 points with 3/4. Do you want to pay 99 cents tow, Watch the finish, or do you want, let's say, place a wager on if he's gonna be Kobe's 81 point Lakers record and then we'll let you watch for free. And so getting both sides of that equation, whether your die hard or casual fan, it's hard to say no to both those options, right? And do you see within your customer base that drive to the smaller segmentation of snack packs? Is that driven by customer demand, or are they trying to get ahead of it a little bit and offer, you know, kind of different sizes of consumption, I guess, would be the right. >> Sure, I think the horse is out of the barn. I mean, imagine if >> we were still buying complete albums. Of course, we're buying tracks when we just wanna track the idea that we have to buy an entire season. No foul, 2430 games in an MLB season. Why won't you let me buy just one game? I say MLB leaves a million dollars on the table every single time is no hit bid because there's tons of people who have cut the cord, don't want to run to the bar, but would happily pay 99 cents to stream the last inning of a game on their phone on their commute. So I think it is a combination of digital. What shoring in that We're able to do these three single track sort of purchases, but also its people continue to cut the cord and rethink about how they spend their media dollars. It makes sense really interesting. So we're here. It's sports Tech, World Demo Day. What do you hope to get out of today? Why are you here? Gosh, at least to pay homage to the reason why I went to Tokyo for the first time and had life changing Rama and I feel like I need to sort of complete >> the cycle. Uh, sports like >> Tokyo is an amazing program. There's lots of different events that have shaped different ways. But there's something really unique about this. And when we all lands in Tokyo, I think it was something like 80 different entrepreneurs that came into met to meet with all of the Japanese sponsors. Everyone had the same vibe of just really happy >> to be there. >> They didn't take a percentage of these startups coming in, so you really saw different sizes, not just early stage, but late stages well and everyone was there, too. Connects and innovate and do interesting things together. So many of us were there for the first time that there's just a vibe to this event that I haven't seen in my 10 plus years in sports. Tak interesting. Well, Mark, great to sit down with you. Really cool story. And, um, I guess I'll be watching for your watching for your app. Is the man behind the man coming through my phone? Real sand Sounds great. >> All right. He's >> Mark. I'm Jeff. You're watching the Cube World. World Tech demo today here at Oracle Park. Thanks for watching. We'll see you next time.

>> 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.

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

>>Okay. We're back here, live in New York city for big data week. This is siliconangle.tvs, exclusive coverage of Hadoop world strata plus Hadoop world big event, a big data week. And we just wrote a blog post on siliconangle.com calling this the south by Southwest for data geeks and, and, um, it's my prediction that this is going to turn into a, quite the geek Fest. Uh, obviously the crowd here is enormous packed and an amazing event. And, uh, we're excited. This is siliconangle.com. I'm the founder John ferry. I'm joined by cohost update >>Volante of Wiki bond.org, where people go for free research and peers collaborate to solve problems. And we're here with Jack Norris. Who's the vice president of market marketing at map are a company that we've been tracking for quite some time. Jack, welcome back to the cube. Thank you, Dave. I'm going to hand it to you. You know, we met quite a while ago now. It was well over a year ago and we were pushing at you guys and saying, well, you know, open source and nice look, we're solving problems for customers. We got the right model. We think, you know, this is, this is our strategy. We're sticking to it. Watch what happens. And like I said, I have to hand it to you. You guys are really have some great traction in the market and you're doing what you said. And so congratulations on that. I know you've got a lot more work to do, but >>Yeah, and actually the, the topic of openness is when it's, it's pretty interesting. Um, and, uh, you know, if you look at the different options out there, all of them are combining open source with some proprietary. Uh, now in the case of some distributions, it's very small, like an ODBC driver with a proprietary, um, driver. Um, but I think it represents that that any solution combining to make it more open is, is important. So what we've done is make innovations, but what we've made those innovations we've opened up and provided API. It's like NFS for standard access, like rest, like, uh, ODBC drivers, et cetera. >>So, so it's a spectrum. I mean, actually we were at Oracle open world a few weeks ago and you listen to Larry Ellison, talk about the Oracle public cloud mix of actually a very strong case that it's open. You can move data, it's all Java. So it's all about standards. Yeah. And, uh, yeah, it from an opposite, but it was really all about the business value. That's, that's what the bottom line is. So, uh, we had your CEO, John Schroeder on yesterday. Uh, John and I both were very impressed with, um, essentially what he described as your philosophy of we, we not as a product when we have, we have customers when we announce that product and, um, you know, that's impressive, >>Is that what he was also given some good feedback that startup entrepreneurs out there who are obviously a lot of action going on with the startup community. And he's basically said the same thing, get customers. Yeah. And that's it, that's all and use your tech, but don't be so locked into the tech, get the cutters, understand the needs and then deliver that. So you guys have done great. And, uh, I want to talk about the, the show here. Okay. Because, uh, you guys are, um, have a big booth and big presence here at the show. What, what did you guys are learning? I'll say how's the positioning, how's the new news hitting. Give us a quick update. So, >>Uh, a lot of news, uh, first started, uh, on Tuesday where we announced the M seven edition. And, uh, yeah, I brought a demo here for me, uh, for you all. Uh, because the, the big thing about M seven is what we don't have. So, uh, w we're not demoing Regents servers, we're not demoing compactions, uh, we're not demoing a lot of, uh, manual administration, uh, administrative tasks. So what that really means is that we took this stack. And if you look at HBase HBase today has about half of dupe users, uh, adopting HBase. So it's a lot of momentum in the market, uh, and, you know, use for everything from real-time analytics to kind of lightweight LTP processing. But it's an infrastructure that sits on top of a JVM that stores it's data in the Hadoop distributed file system that sits on a JVM that stores its data in a Linux file system that writes to disk. >>And so a lot of the complexity is that stack. And so as an administrator, you have to worry about how data gets permit, uh, uh, you know, kind of basically written across that. And you've got region servers to keep up, uh, when you're doing kind of rights, you have things called compactions, which increased response time. So it's, uh, it's a complex environment and we've spent quite a bit of time in, in collapsing that infrastructure and with the M seven edition, you've got files and tables together in the same layer writing directly to disc. So there's no region servers, uh, there's no compactions to deal with. There's no pre splitting of tables and trying to do manual merges. It just makes it much, much simpler. >>Let's talk about some of your customers in terms of, um, the profile of these guys are, uh, I'm assuming and correct me if I'm wrong, that you're not selling to the tire kickers. You're selling to the guys who actually have some experience with, with a dupe and have run into some of the limitations and you come in and say, Hey, we can solve some of those problems. Is that, is that, is that right? Can you talk about that a little bit >>Characterization? I think part of it is when you're in the evaluation process and when you first hear about Hadoop, it's kind of like the Gartner hype curve, right. And, uh, you know, this stuff, it does everything. And of course you got data protection, cause you've got things replicated across the cluster. And, uh, of course you've got scalability because you can just add nodes and so forth. Well, once you start using it, you realize that yes, I've got data replicated across the cluster, but if I accidentally delete something or if I've got some corruption that's replicated across the cluster too. So things like snapshots are really important. So you can return to, you know, what was it, five minutes before, uh, you know, performance where you can get the most out of your hardware, um, you know, ease of administration where I can cut this up into, into logical volumes and, and have policies at that whole level instead of at an individual file. >>So there's a, there's a bunch of features that really resonate with users after they've had some experience. And those tend to be our, um, you know, our, our kind of key customers. There's a, there's another phase two, which is when you're testing Hadoop, you're looking at, what's possible with this platform. What, what type of analytics can I do when you go into production? Now, all of a sudden you're looking at how does this fit in with my SLS? How does this fit in with my data protection, uh, policies, you know, how do I integrate with my different data sources? And can I leverage existing code? You know, we had one customer, um, you know, a large kind of a systems integrator for the federal government. They have a million lines of code that they were told to rewrite, to run with other distributions that they could use just out of the box with Matt BARR. >>So, um, let's talk about some of those customers. Can you name some names and get >>Sure. So, um, actually I'll, I'll, I'll talk with, uh, we had a keynote today and, uh, we had this beautiful customer video. They've had to cut because of times it's running in our booth and it's screaming on our website. And I think we've got to, uh, actually some of the bumper here, we kind of inserted. So, um, but I want to shout out to those because they ended up in the cutting room floor running it here. Yeah. So one was Rubicon project and, um, they're, they're an interesting company. They're a real-time advertising platform at auction network. They recently passed a Google in terms of number one ad reach as mentioned by comScore, uh, and a lot of press on that. Um, I particularly liked the headline that mentioned those three companies because it was measured by comScore and comScore's customer to map our customer. And Google's a key partner. >>And, uh, yesterday we announced a world record for the Hadoop pterosaur running on, running on Google. So, um, M seven for Rubicon, it allows them to address and replace different point solutions that were running alongside of Hadoop. And, uh, you know, it simplifies their, their potentially simplifies their architecture because now they have more things done with a single platform, increases performance, simplifies administration. Um, another customer is ancestry.com who, uh, you know, maybe you've seen their ads or heard, uh, some of their radio shots. Um, they're they do a tremendous amount of, of data processing to help family services and genealogy and figure out, you know, family backgrounds. One of the things they do is, is DNA testing. Uh, so for an internet service to do that, advanced technology is pretty impressive. And, uh, you know, you send them it's $99, I believe, and they'll send you a DNA kit spit in the tube, you send it back and then they process that and match and give you insights into your family background. So for them simplifying HBase meant additional performance, so they could do matches faster and really simplified administration. Uh, so, you know, and, and Melinda Graham's words, uh, you know, it's simpler because they're just not there. Those, those components >>Jack, I want to ask you about enterprise grade had duped because, um, um, and then, uh, Ted Dunning, because he was, he was mentioned by Tim SDS on his keynote speech. So, so you have some rockstars stars in the company. I was in his management team. We had your CEO when we've interviewed MC Sri vis and Google IO, and we were on a panel together. So as to know your team solid team, uh, so let's talk about, uh, Ted in a minute, but I want to ask you about the enterprise grade Hadoop conversation. What does that mean now? I mean, obviously you guys were very successful at first. Again, we were skeptics at first, but now your traction and your performance has proven this is a market for that kind of platform. What does that mean now in this, uh, at this event today, as this is evolving as Hadoop ecosystem is not just Hadoop anymore. It's other things. Yeah, >>There's, there's, there's three dimensions to enterprise grade. Um, the first is, is ease of use and ease of use from an administrator standpoint, how easy does it integrate into an existing environment? How easy does it, does it fit into my, my it policies? You know, do you run in a lights out data center? Does the Hadoop distribution fit into that? So that's, that's one whole dimension. Um, a key to that is, is, you know, complete NFS support. So it functions like, uh, you know, like standard storage. Uh, a second dimension is undependability reliability. So it's not just, you know, do you have a checkbox ha feature it's do you have automated stateful fail over? Do you have self healing? Can you handle multiple, uh, failures and, and, you know, automated recovery. So, you know, in a lights out data center, can you actually go there once a week? Uh, and then just, you know, replace drives. And a great example of that is one of our customers had a test cluster with, with Matt BARR. It was a POC went on and did other things. They had a power field, they came back a week later and the cluster was up and running and they hadn't done any manual tasks there. And they were, they were just blown away to the recovery process for the other distributions, a long laundry list of, >>So I've got to ask you, I got to ask you this, the third >>One, what's the third one, third one is performance and performance is, is, you know, kind of Ross' speed. It's also, how do you leverage the infrastructure? Can you take advantage of, of the network infrastructure, multiple Knicks? Can you take advantage of heterogeneous hardware? Can you mix and match for different workloads? And it's really about sharing a cluster for different use cases and, and different users. And there's a lot of features there. It's not just raw >>The existing it infrastructure policies that whole, the whole, what happens when something goes wrong. Can you automate that? And then, >>And it's easy to be dependable, fast, and speed the same thing, making HBase, uh, easy, dependable, fast with themselves. >>So the talk of the show right now, he had the keynote this morning is that map. Our marketing has dropped the big data term and going with data Kozum. Is that true? Is that true? So, Joe, Hellerstein just had a tweet, Joe, um, famous, uh, Cal Berkeley professor, computer science professor now is CEO of a startup. Um, what's the industry trifecta they're doing, and he had a good couple of epic tweets this week. So shout out to Joe Hellerstein, but Joel Hellison's tweet that says map our marketing has decided to drop the term big data and go with data Kozum with a shout out to George Gilder. So I'm kind of like middle intellectual kind of humor. So w w w what's what's your response to that? Is it true? What's happening? What is your, the embargo, the VP of marketing? >>Well, if you look at the big data term, I think, you know, there's a lot of big data washing going on where, um, you know, architectures that have been out there for 30 years or, you know, all about big data. Uh, so I think there's a, uh, there's the need for a more descriptive term. Um, the, the purpose of data Kozum was not to try to coin something or try to, you know, change a big data label. It was just to get people to take a step back and think, and to realize that we are in a massive paradigm shift. And, you know, with a shout out to George Gilder, acknowledging, you know, he recognized what the impact of, of making available compute, uh, meant he recognized with Telekom what bandwidth would mean. And if you look at the combination of we've got all this, this, uh, compute efficiency and bandwidth, now data them is, is basically taking those resources and unleashing it and changing the way we do things. >>And, um, I think, I think one of the ways to look at that is the new things that will be possible. And there's been a lot of focus on, you know, SQL interfaces on top of, of Hadoop, which are important. But I think some of the more interesting use cases are taking this machine J generated data that's being produced very, very rapidly and having automated operational analytics that can respond in a very fast time to change how you do business, either, how you're communicating with customers, um, how you're responding to two different, uh, uh, risk factors in the environment for fraud, et cetera, or, uh, just increasing and improving, um, uh, your response time to kind of cost events. We met earlier called >>Actionable insight. Then he said, assigning intent, you be able to respond. It's interesting that you talk about that George Gilder, cause we like to kind of riff and get into the concept abstract concepts, but he also was very big in supply side economics. And so if you look at the business value conversation, one of things we pointed out, uh, yesterday and this morning, so opening, um, review was, you know, the, the top conversations, insight and analytics, you know, as a killer app right now, the app market has not developed. And that's why we like companies like continuity and what you guys are doing under the hood is being worked on right at many levels, performance units of those three things, but analytics is a no brainer insight, but the other one's business value. So when you look at that kind of data, Kozum, I can see where you're going with that. >>Um, and that's kind of what people want, because it's not so much like I'm Republican because he's Republican George Gilder and he bought American spectator. Everyone knows that. So, so obviously he's a Republican, but politics aside, the business side of what big data is implementing is massive. Now that I guess that's a Republican concept. Um, but not really. I mean, businesses is, is, uh, all parties. So relative to data caused them. I mean, no one talks about e-business anymore. We talking to IBM at the IBM conference and they were saying, Hey, that was a great marketing campaign, but no one says, Hey, uh, you and eat business today. So we think that big data is going to have the same effect, which is, Hey, are you, do you have big data? No, it's just assumed. Yeah. So that's what you're basically trying to establish that it's not just about big. >>Yeah. Let me give you one small example, um, from a business value standpoint and, uh, Ted Dunning, you mentioned Ted earlier, chief application architect, um, and one of the coauthors of, of, uh, the book hoot, which deals with machine learning, uh, he dealt with one of our large financial services, uh, companies, and, uh, you know, one of the techniques on Hadoop is, is clustering, uh, you know, K nearest neighbors, uh, you know, different algorithms. And they looked at a particular process and they sped up that process by 30,000 times. So there's a blog post, uh, that's on our website. You can find out additional information on that. And I, >>There's one >>Point on this one point, but I think, you know, to your point about business value and you know, what does data Kozum really mean? That's an incredible speed up, uh, in terms of, of performance and it changes how companies can react in real time. It changes how they can do pattern recognition. And Google did a really interesting paper called the unreasonable effectiveness of data. And in there they say simple algorithms on big data, on massive amounts of data, beat a complex model every time. And so I think what we'll see is a movement away from data sampling and trying to do an 80 20 to looking at all your data and identifying where are the exceptions that we want to increase because there, you know, revenue exceptions or that we want to address because it's a cost or a fraud. >>Well, that's what I, I would give a shout out to, uh, to the guys that digital reasoning Tim asked he's plugged, uh, Ted. It was idolized him in terms of his work. Obviously his work is awesome, but two, he brought up this concept of understanding gap and he showed an interesting chart in his keynote, which was the date explosion, you know, it's up and, you know, straight up, right. It's massive amount of data, 64% unstructured by his calculation. Then he showed out a flat line called attention. So as data's been exploding over time, going up attention mean user attention is flat with some uptick maybe, but so users and humans, they can't expand their mind fast enough. So machine learning technologies have to bridge that gap. That's analytics, that's insight. >>Yeah. There's a big conversation now going on about more data, better models, people trying to squint through some of the comments that Google made and say, all right, does that mean we just throw out >>The models and data trumps algorithms, data >>Trumps algorithms, but the question I have is do you think, and your customer is talking about, okay, well now they have more data. Can I actually develop better algorithms that are simpler? And is it a virtuous cycle? >>Yeah, it's I, I think, I mean, uh, there are there's, there are a lot of debate here, a lot of information, but I think one of the, one of the interesting things is given that compute cycles, given the, you know, kind of that compute efficiency that we have and given the bandwidth, you can take a model and then iterate very quickly on it and kind of arrive at, at insight. And in the past, it was just that amount of data in that amount of time to process. Okay. That could take you 40 days to get to the point where you can do now in hours. Right. >>Right. So, I mean, the great example is fraud detection, right? So we used the sample six months later, Hey, your credit card might've been hacked. And now it's, you know, you got a phone call, you know, or you can't use your credit card or whatever it is. And so, uh, but there's still a lot of use cases where, you know, whether is an example where modeling and better modeling would be very helpful. Uh, excellent. So, um, so Dana custom, are you planning other marketing initiatives around that? Or is this sort of tongue in cheek fun? Throw it out there. A little red meat into the chum in the waters is, >>You know, what really motivated us was, um, you know, the cubes here talking, you know, for the whole day, what could we possibly do to help give them a topic of conversation? >>Okay. Data cosmos. Now of course, we found that on our proprietary HBase tools, Jack Norris, thanks for coming in. We appreciate your support. You guys have been great. We've been following you and continue to follow. You've been a great support of the cube. Want to thank you personally, while we're here. Uh, Matt BARR has been generous underwriter supportive of our great independent editorial. We want to recognize you guys, thanks for your support. And we continue to look forward to watching you guys grow and kick ass. So thanks for all your support. And we'll be right back with our next guest after this short break. >>Thank you. >>10 years ago, the video news business believed the internet was a fat. The science is settled. We all know the internet is here to stay bubbles and busts come and go. But the industry deserves a news team that goes the distance coming up on social angle are some interesting new metrics for measuring the worth of a customer on the web. What zinc every morning, we're on the air to bring you the most up-to-date information on the tech industry with scrutiny on releases of the day and news of industry-wide trends. We're here daily with breaking analysis, from the best minds in the business. Join me, Kristin Filetti daily at the news desk on Silicon angle TV, your reference point for tech innovation 18 months.