>>And welcome back to the cubes coverage of coop con cloud native con 2021. We're in person physical venom, John free hosted a Q a Dave Nicholson, my CO's and Emma Laney, who is our not so roving reporter unemployed, software engineer, unemployed comedian. Great to have you on the cube. >>Thank you for that list of credentials. >>You're doing great. I saw you're having some fun down there. We've got this new show or testing out called the grill. Here it is. Okay. Um, what's the focus, what's the story behind everything. >>Uh, the focus of the show is trying to have some fun with tech. You know, tech has a lot of self seriousness. Uh, there's a lot that's ripe to make fun of. We're also having fun. We're not trying to grill people in. We're not trying to roast them. Right? We're having people come through. They're sharing funny stories. We're having a contest to find the best man split nation of Kubernetes. Right now, I got to say, a woman is in the lead. Oh, she killed that contest, like called me, sweetie. And everything. It just proves that it's not about the man. You identify as it's about the condensation in your heart when it comes to mansplaining. >>Um, what is the best criteria that you, when you get a candidate for the mansplaining competition, what is the criteria? >>I mean, number one, we're looking for condensation. You get extra points for you, the phrase, well, actually we want a supercilious attitude. Uh, if you are partially into explaining it and then you stop yourself because you think you've used too technical of a term and then step it down, all of those gets you extra points in the mansplaining. >>Can I ask you, what's your biggest observation as you kind of look at this ecosystem? I mean, it's a big event, but it's, COVID postpone even in COVID people are wearing masks, not wearing masks. >>I mean, people are wearing masks for the most part. Uh, you know, I did love this, uh, red light, yellow light green light system. They came up with green, meaning please touch me. I've been inside for too long red meaning I still care about COVID yellow. You know, ask me, we'll figure it >>Out. All right. What's the funniest thing you've heard so far. >>The funniest thing I have to say, I asked someone what their favorite tech joke is. And he said it worked on my computer That really stirred up some memories. >>Oh man, we're in LA though. This is a great area. It's literally with the best comedians you could think of or work their way through the system. But with techno and everything is tech with gadgets and with like Kubernetes, I mean, it's, it's the material writes itself. I mean, >>Surely >>You must be having, >>Oh, I'm definitely having a ton of fun. Uh, I wouldn't say the material writes itself. I would say hire me to write material, but it is quite a fertile. >>Okay. What would you write for, uh, looking at the keynote today? Looking at the vibe here, obviously a lot of people show because they're remote, but visually it's a packed house here, but what's your first comedic view of the, as the fog lifts in this community? >>I have to say the thing that really stuck out to me from the keynote addresses was that people have not yet adjusted to being in person. There were some very, very delayed applause breaks where people realize they were not muted watching on a screen and you'd still go, oh, that's right. We should interact. Like God bless those speakers. It's uh, people have been inside for a long time. >>Um, part-time comedian too. I mean, co-hosting queue. Um, I don't, I, >>I don't find anything funny with technology. And I'm curious when you use the word supercilious, is that a, is that a comedic term? I, I, yes. >>I heard that before. It's the Latin form of super silly. Yeah. Which is my brand of comedy. >>So the mansplaining, I don't know if you need to like, woman's plane, some of this stuff to me, but I'll English >>Major Splain. Okay. Okay. Super silliest. >>It sounds super silly. So is it, is it, is it okay to have a ringer come in and attempt make an attempt at the mansplaining or >>Okay. A hundred >>Percent come in wearing it. >>I'm trying to make this a safe space for women at the conference. I'm the only woman you should be mansplaining to. I'm a martyr falling on the sword of mansplaining for all the great technical women at this conference. You slip that in >>And translate that. >>Of course, John, I don't know how to explain that to them more detailed. Um, what I love about the vibe is that this technical people they're snarky. If you get at their core, I mean, we were at the bar. Everyone was like totally leaning into like comedy and more fun because it's almost like they're bust out, come out of the closet and beat comedian. >>Oh, there is a broiling anger in the soul of every developer and every person who's worked on technology. And the question is going to be, can we get it on camera when they are not drunk, we're doing our >>Best to drink. These developers don't >>Think, oh, they do desperately. >>We saw a few partaking in the bar at the GTA merit and a lot going on. You had the, you know, they had warriors game going on. You have a lot of Dodgers were playing the giants. So pretty active bar scene for this crowd. >>Yeah, no, it was, uh, it was very fun. I personally was disappointed that the warriors are not actually staying in our hotel. You know, if this software thing doesn't work out, NBA wife is a possible second. >>And the Ritz Carlton was right behind us. You could be right there too. All right. So the grill is, uh, an experiment. We're having some fun with it, but the purpose is to just chill a bit. What's the, what would you say the goal of the show is for you? >>I'd say the goal is to get people to come out of their shells a little bit, to have some fun, to poke fun at some of the tendencies that we see in tech that we often don't bring up. You know, like I'm having so much fun with the man's pollination. Uh, I've lived it a bit. And my favorite is, uh, as I asked men to mansplain it to me, the panic in their eyes, that's my ultimate goal is just to make men afraid. >>And the panic is because they don't know if they're mansplaining all the time or actually purposely mansplaining is hard enough, but they do it naturally. Sorry. >>I have three daughters and I can't wait for them to see this stuff. I cannot >>Wait. That's going to be >>Great. Well, we have cooler gen Z. >>Well, we have t-shirts right. Let me see the t-shirts give everyone a quick, if you come on, this is day one of coupons. So if you do come on the show with the grill, I'm the t-shirt ferry. The grill is real. It's like the V the cubes version of the view, but >>Wow, just because I'm a woman, the, uh, the t-shirt is a big incentive. I'm sure a lot of people go to tech conferences don't get any free. T-shirts good. >>I got grilled by a net. Lilium, the cube at cube con con not cube >>Con. It's a medium rare grilling. >>I couldn't resist the view jokes. I know I'm in color. We'll keep our day jobs here in the comedian angle. We got to >>Believe that's true. Yes. When I look at the wavelengths of >>Light on that, I'm super stoked to have you try that. I think it's a great program, Greg. God. So you guys doing a great job, loved the vibe, love the energy, love the creativity, having some fun. See the poster one last time. And the idea is to have some fun, right? It's a tough time. We're all coming back from the pandemic, welcoming back from the pandemic. And this is just a fun way to kind of let the air out and have some fun. So thanks for everyone. Thank you so much for doing that. Thank you. All right. Cute coverage here. Coop gone. Cloud native con I'm John Perry, David Nicholson. Be back with more day, one coverage of three days after the short break.

(upbeat music) >> In 1946, the acerbic manager of the Dodgers, Leo the Lip Durocher famously said of baseball, great Mel Ott who was player manager of the Giants at the time. You know what happens to nice guys. They finished in last place. The phrase nice guys finish last was born. It became popular outside of baseball. Well joining me today is someone who was a consummate gentlemen and a nice guy who proves that idiom absolutely isn't true at all. He's also written a new book "Play nice and Win" Michael Dell chairman and CEO of Dell technologies, welcome back to the CUBE. >> Thank you very much, Dave, always great to be with you. Wonderful to be on the CUBE and thanks for your great coverage of Dell technologies world. >> Yeah. We're very excited to be covering the virtual version this year, next year we're back face to face I'm Sure. And we're going to talk about your book but I want to start by asking you to comment on the past 12 months, how are you going to remember 2020? >> I'm going to remember it by the resiliency of the world and our team, the adaptability the acceleration of digital transformation which is pretty amazing around the world. The vital role that technology played in addressing some of the biggest challenges, whether it was the creation of vaccines or, you know, decoding the virus itself or just addressing all the challenges that the world had. You know, I think it's a game changer in terms of disease identification and how we prevent these kinds of things going forward. You know, there's still a long way to go in terms of how do we get 7.5 billion people vaccinated and safe. I also think it exposed, you know some of the fault lines in our society. And that's a great learning for all of us in terms of access to healthcare and education and, you know, the digital resources that power the world. And so, yeah, those are some of the things that really stand out for me. >> Well, I mean, I think leaders like yourself and position of influence, absolutely passionate about some of those changes that we see coming in society. So hopefully we'll have time to talk about that but I wanted to get into the business. I think a lot of people, myself included felt that 2020 was going to be a down year for big tech companies like yours and that relied heavily on selling products that data centers and central offices but the remote work trend and the laptop, boom offset, some of those on-prem softness and headwinds combined with VMware the financial performance of Dell technologies was actually quite amazing. Why were you able to do so well last year? >> Well, first of all, you're right. We did, we had record pretty much everything record revenues, record operating income, record cashflow and be also paid down a record amount of debt. And so I think the strength and resiliency of our supply chain, as well as the broad diversified nature of what we provide our customers continue to serve us very well as they moved to this sort of do anything from anywhere in the world. And it continues the first part of this year, business is very strong >> You know, a few weeks ago, of course you officially announced the spinoff of Dell technologies. Wasn't a huge surprise but the 81% equity ownership of VMware are you worried about untethering VMware from Dell or maybe you can share more on what this means for the future of, your two companies and your customers. >> Right? So, I think this will drive additional growth opportunities for both Dell Tech and VMware, while it unlocks a lot of value for our stakeholders. What we've done is to formalize the commercial relationship into a series of agreements and those are unique and differentiated and they provide lots of flexibility and we've driven a tremendous amount of innovation together and that's going to continue and it will, one of the things we said back in 2015 you'll remember is our commitment to keep the VMware ecosystem open and independent and working across the whole industry. We've done that. You'll continue to see us innovate together with Edge solutions, certainly all the great work we've done with VxRail SD LAN, you know Tanzu creates this platform to modernize applications and VMware Cloud and Dell technologies are the easy path to a multi-cloud architecture. And, that continues to work super well and is not going to be slowed down at all. So... and of course, I'll continue to be a chairman of both companies and we're not selling VMware we're distributing our ownership to our shareholders. >> Well, of course, Dell is the largest sort channel if you will, for VMware. So that's ... you guys got a tight relationship but I want to ask you about digital transformation and everybody talked about it pre COVID but nobody really knew exactly what it was but COVID sort of brought that into focus very quickly. If you weren't a digital business, you were out of business. So going forward, how do you see that whole digital transformation playing out? >> You know I think the plot of any company is to figure out how it can use its data and turn that into insights and outcomes and better results and ultimately competitive advantage faster. And as you said, you know, if it's not able to do that, it's probably going to go out of business. And that agenda just got massively accelerated because it was kind of digital was sort of the only thing that worked during this, this past period. So every organization has figured out that technology is not the IT department, it's actually the fulcrum of progress in the entire company. And so we're seeing sort of across the board a dramatic acceleration in the investment in digital technologies, you know, Edge is growing very fast. I think 5G just accelerates this and, you know you're seeing it in all the demand trends. It's quite positive and, you know, I think you'll see even a more rapid separation from those companies that are able to take advantage of this and quickly adjust their businesses their organizations, and those that are >> You better hop on board or get left behind, you know, the Edge. You mentioned the Edge it's a little bit like digital transformation, you know kind of pre COVID and even post COVID. It means a lot of things to a lot of different people but the telecoms transformation and 5G they have there certainly real. How do you see the Edge? >> You know, the Edges is ... think of it as actually the real world, right? It's, not a data center sitting in the center of the universe somewhere. And look today, you know only 10% of data is processed outside of the data center, but, you know, it's estimated by 2025 you got 75% of enterprise data will be processed outside of a traditional data center or a Cloud. And so as everything becomes intelligent connected 5G accelerates that it's going to be a huge acceleration of this whole process of digital transformation. And you know, again, think about this. I mean, the cost of making something intelligent used to be really expensive. Now it's asymptotically approaching zero. And of course all those things are connected. They're talking to each other and exactly what does this mean for every industry. Nobody's really quite sure and not everything is going to work, but, you know we're seeing it in manufacturing, in retail, in healthcare and the growth on the Edge is really accelerating in a meaningful way. And it's not so much about, you know people talking people with machines, we know how to do that. Now it's about the thing right And, you know you've got like 200 billion arm processors, you know out there in the last couple of years, all those things talking to the other things, generating data it happens in the real world. That's what the Edge is. >> Yeah as you know, we're a big fans of the arm model. And I think it just presents huge opportunities for companies like Dell. I want to ask you about Cloud. And I have to say, I think, you know companies like Dell have been maybe a little bit defensive over the last several years when it comes to Cloud but I think you starting to see the Cloud as a gift with all that CAPEX that's being built out by these hyperscalers. You know, thank you. It seems to me, you can build on top of that. How are you thinking about the Cloud as an opportunity for you and your customers especially as the definition of Cloud evolves? >> Well, first, you know, what we see is and the Edge is kind of the third place or the third premise, right? You got Clouds in the public form, you've got the Colo which is really growing fast and, you know the private hybrid Clouds, and now you've got the Edge. And so you've got infrastructure all over the place with Edge being the fastest growing. You know, one of the big things we see is that customers want a consistent way to operate and execute across that whole platform. And, you know, one of the other things that we've been focused on at Dell technologies is how can we move our business to more of a service and subscription on demand and provide customers that flexibility to to pay as they consume. And so, to some extent this is an evolution of, you know, products to services to managed services, to everything as a service. And so, you know, looking at our balance sheet you'll see over $40 billion in remaining performance obligations as we moved the business to that kind of model and it's been growing double digits for several quarters in a row. And so, you know, we're embracing Cloud and on-demand, and as a service, and obviously here at Dell technologies world we're talking a lot about Apex and our continuing initiatives to move our whole business in that direction. >> Yeah. Apex is a real accelerator for that model. I want to switch topics a little bit. I got a long list of things I want to talk about ESG, sustainability, inclusion, you know, is another topic that, that I'm interested in. I want it. And I said before, people like yourself in a position of influence to influence public policy and obviously the employees and your ecosystem why is it not just the right thing to do? Why is... why are those things good business, Michael? >> Well, it's good business because people want to be part of something that is important and purposeful. You know, it's not just make a profit and earn a living right? You know, people want to be inspired and feel that they're part of something special. And look, I think if you look at the positive changes that have occurred in the world certainly you could turn on the news and see the horrible things that happened in the last 24 hours or something like that. But if you step back and think about the amazing progress that's happened in the last several decades, you know a lot of it's been driven by technology and by businesses that have stepped up and made a difference and made commitments. And, you know, we're one of those companies that has made a series of commitments you know, 10 years ago, we set out with our 2020 goals. We accomplished significant majority of those retired those. Now we set out our progress made real 2030 goals all around the ESD themes. And it's not only the right thing to do but it is good for business. It inspires our team members, our customers and I think initiatives like progress made real at Dell and thousands of other companies. Ultimately, those are the things that are going to drive progress forward. I believe, you know, more so than government edicts or regulation, those can play a role. But I think, companies voluntarily driving things like the circular economy and how we include everyone in our business and provide opportunities for everyone to succeed no matter where they come from. I think those are the things that are really going to drive the world forward. >> Well, I want to ask you about public policy because as you say, it's not just the government, but of course sometimes the government can get in the way. You're seeing a lot of vitriol around Val break up big tech but the same time, you're seeing the US government and the EU very willing to help out with the semiconductor competitiveness in the like I know you were tapped with the new administration President Biden, tapping, you know, the best minds in tech and you were asked to part sort of participate give feedback. What can you tell us about, you know your advice to the US government? >> Well, you know, lots of great discussion with the new administration and it's a delight to see that they're focused on semiconductors and sort of the industries of the future. This is a big deal. I mean, you know, we've got some big global competitors out there other nations that are with a deterministic strategy very focused on the industries of the future. But US, you know if you think about the atomic age and, you know the Apollo missions that created the whole semiconductor industry ARPANET and ultimately the Internet and that kind of stopped right there, you know, there wasn't as much government investment in some of those big R and D initiatives that really drove an enormous creation of industries and success for the United States and its citizens. And so I think focusing on semiconductors and how you build the infrastructure of the future really important for the United States to continue to be a leader in that you know, we were, you know, producing a one point about 37% of the world's semiconductors. It's now down to 12% and dropping and really important that more investments are made in that area. It's a combination of capital, talent, you know education knowledge, and also, you know, the policies that promote the development of these kinds of businesses. >> Yah well, Pat's got a very big challenge ahead of them. And so that's why but we've said Intel's too strategic to fail in our view but I wanted to plug your book a little bit. My former boss, you and I have talked about this. He was also a gentleman who proved Leo Durocher wrong. He was very nice guy, but also a winner, Play Nice But Win, why did you decide to write another book? >> Well, you know, Dave, a lot has happened in the last 20 years and especially the last nine or so years since we went private and, you know merged with EMC and VMware and went public again. And, you know, I'd say we... first of all, you know when I wrote the first book in 1998 I wasn't comfortable disclosing a lot. And, and I wasn't vulnerable enough and didn't feel, you know, able to do that. Now I do, you know, I'm older, you know hopefully a little wiser. And so I think everybody's going to like hearing some of the fun stories about not only my childhood but you know, the dorm room and beyond, and leading up to, you know the pivotal changes that have occurred the last decade my alligator wrestling with Carl Icahn and other, you know there's lots of fun stories in there. I got arrested one time. It was only for speeding tickets, don't worry but you know, lots of fun. I'm really looking forward to the book coming out and being able to talk about it. >> I can't wait. You know, I've said many times anybody who could beat the great icon is interesting to me. I wanted to ask you, I mentioned my old boss, Pat McGovern. I used to say to them all the time, "Pat how come you don't buy more companies?" And he'd say," Dave, you know the vast majority of acquisitions and mergers they failed to meet their objectives." Did you ever imagine, I mean... I did the EMC acquisition. Did... how could it not have exceeded your expectations? I wonder if you could give us your final thoughts on that. >> You know, and I talk about this a lot in the book. I mean, these are kind of the ultimate considered decisions. And in the case of the EMC combination it was something that we had thought about going back to 2008, 2009. And then, you know, started thinking about it in 2014 worked on it for a full year before it got announced in 2015 and finally closed in 2016. But yeah, I mean, you know, we thought it would be great. It turned out to be even better than We thought the revenue synergies were far greater. The teams were quite energized. Customers liked what we were providing and you know it's ... and, of course the markets were supportive Right? You know, we were paying close attention to interest rates and how we could structure the merger in a attractive way. And, you know, thank goodness, lots of hard work lots of determination, you know, it's worked out quite well. >> Yeah, great commitment from the Dell team as well. Congratulations on that. Go ahead, please. >> And any adventure continues right? It's...( both chuckles) >> I can't wait to see the next chapter and I can't wait to get the book, but congratulations on that, all your tremendous success you're you are a winner and a gentleman and a friend of the CUBE, Michael Dell. Thanks so much. >> Thank you so much Dave. >> And thank you for watching. And this is the CUBE continuous coverage of Dell tech world 2021, the virtual edition. Keep it right there, right back. (upbeat music)

>> From Sand Hill Road in the heart of Silicon Valley, it's theCUBE. Presenting, the People First Network, insights from entrepreneurs and tech leaders. >> Hello everyone I'm John Furrier with theCUBE, we are here in Sand Hill Road up at Mayfield Venture Capital Firm for their 50th anniversary, their People First Network series, produced with theCUBE and Mayfield, I'm John Furrier, with Bill Schlough, the Chief Information Officer of the San Francisco Giants, CUBE alumni, great to see you thanks for joining me today for this People First Series we're doing with Mayfield's 50th anniversary, thanks for coming in. >> Good to be here, John. >> So, been a while since we chatted, it's been a year, A lot's happening in tech, you can't go a year, that's like seven dog years in tech, lot happening, you're managing, as the CIO for the Giants, a lot of things going on in baseball, what's the priorities for you these days, obviously, you guys, great social, great fan experience, what's new for you, what's the priority? >> Man, there's always something new. It's what I love about it, this'll be my 20th season with the Giants comin' up. And, it never gets old, there's always new challenges. On the field, in the seats, off the field, you name it. As we look toward next year, really excited about bringin' in a new video board, which we haven't publicly announced, maybe I just did publicly announce, we're breaking news on theCUBE today. So we're puttin' in a new video board, it'll be over three times the size of the one we have today. That's big news, we're doing a lot of exciting things in the ticketing world. The ticketing world is really transforming right before our eyes in terms of the way fans buy tickets. It's changed a lot. Once up on a time you could call a game a sellout, and we sold out 530 straight games at AT&T Park, but really there's no such thing as a sellout anymore I mean, at any point you can get a great ticket, so we have to adapt to that and change the product that we're delivering to fans, so making some changes on the ticketing front, the fan experience, the ballpark with the video board, and another thing that's changing a lot is the way fans consume our game when they're not at the ballpark. It's rare that you're going to see somebody sit on a couch for three plus hours and watch a game continuously anymore. Fans are consuming through mobile devices, streaming, catching clips here and there, all different methods, and it's fun to be a part of that, because, fans still love the game, but they're just consuming it in different ways. >> Yeah, I love having chats with you on theCUBE because one of the things that have always been the same from nine years doing theCUBE is, the buzzword of consumerization of IT has been out there, overused, but you're living it, you have a consumer product, the ultimate consumer product, in Major League Baseball, and the Giants, great franchise, in a great city, in a great stadium, with a rabid fanbase, and they know tech, so you have all the elements of tech, but the expectation of consumers, and the experiences are changing all the time, you got to deliver on the expectations and introduce new experiences that become expectations, and this is the flywheel of innovation, and it's really hard, but I really respect what you guys are doing over there, and that's why I'm always curious, but, always, the question comes back to, is, can I get faster wifi in the stadium? (laughs) It's always the number one question >> It's funny that you ask that because it is AT&T Park, you know, so, honestly, we got to check that box, and we've had to for years, all the way back to when we first rolled it out, way back in 2004 when we first rolled out wifi in the park, people weren't asking for it then, people were coming to the ballpark with a laptop and plugging a card into it, and there were about a hundred of them that were accessing it, but today, what's interesting is, who knows what next, but we're not talkin' about wifi as much, wifi is just kind of, expected, you got to have it, like water. You're talkin' about 5G networks, and new ways to connect. Honestly, this past season, our wifi usage in terms of the number of fans that use wifi, what we call the take rate, the percentage of fans, was actually down 30% from the previous year. Not because we had less fans in the stadium, because this is the take rate, a percentage of fans in the stadium, went down, because AT&T made some massive investments in their cellular infrastructure at the ballpark, and if you're just connecting, and you got great bandwidth, you don't feel the need to switch over to wifi, so who knows what the future will hold? That's a great point, and you see the LTE networks have so much more power, it used to be you needed wifi to upload your photos, so you'd go in, log in, and if they auto login that's cool, but people don't need to. >> Not with photos, what they need it now for is when we see it really maxing out is events, like our Eagles concert, or Journey concert, or a really big game, like opening day, or honestly, Warriors playoffs game, 49ers football games, that's when folks are streamin' to video. For streamin' to video, they're still goin' to that wifi. Yeah, that's the proven method, plus they don't want to jack up their charges on the AT&T site, but I won't go there, Let's talk about innovat-- Most say unlimited, I will go there, most say unlimited these days. >> Really, I got to find that plan, my daughter's killin' me with her watchin' Netflix on LTE, I tell her. Innovation is changing, I want to get your thoughts on this, 'cause I know you're on the front end of a lot of innovations, you do a lot of advising here at Mayfield. The VC's always trying to read the tea leaves, you're living it, what's the innovation formula look like now for you 'cause as you're sittin' in your staff meetings, as you look at the team of people around you, you guys want to foster, you do foster, innovation culture. What's the formula, what do you guys do when you have those meetings, when everyone's sitting around the table sayin', what do we do next? "How do we create a better experience? "How can we get better fans, and better product "in their hands as fast as possible?" What's your strategy? >> You know, it's funny, people talk about the secret sauce for innovation, what's the formula? I would say, for us, it's really a symbiotic relationship with a lot of things, first of all, where we are, geographically, we've got folks like Mayfield, down the street, and many others, that we can talk to, that are, when innovation is happening, when the startups are incubating, they're being funded by these guys, a lot of times they are here, and our phones are ringing off the hook with a lot of folks so my formula for innovation is answer the phone and take the meetings, but, to be honest, that creates its own problems, because there's so many great ideas out there, if you try to do all of them, you're going to fail at all of them. You got to pick a very small few to try to experiment with, give it a shot, we just don't have the bandwidth, we only have 250 full-time staff on the business side. For us, geographically, you have to really be laser-focused and say okay, there are so many great ideas out here, which are the three or four that we're going to focus on this year, and really give it a try, that's really going to drive, propel our business forward, enhance our product on the field, whatever it might be, but I'll tell you where it really truly starts. It's from the top with our CEO. And, I've had a few different bosses over the years, but with the Giants, our CEO is singularly focused on all of us doing things folks have never done before regardless of what business unit you're in. Whether you're in ticketing, finance, marketing, sales, what drives him, and drives all of us, is innovation. And his eyes glaze over when I talk to him about cost-cutting, and his eyes can glaze over really fast. But when I talk to him about doing something no one's ever done before, that's when he sits forward in his chair, he gets engaged, and I just have a great boss, Larry Baer, he's been with us for 25 years wit the Giants, and he is the driver for it, he creates the culture from the top, where all of us, we want to impress him, and to impress him, you got to do sometin' nobody's ever done before, and what's even more interesting is there are some challenges and some changes talking place across our industry, as I said before, ticketing and other areas, and I've sat in meetings with him where somebody might raise their hand and say, "But this is happening across the industry, "so it's just a macro trend," and he'll get upset, be like, "I don't care about macro trends. "We are here in the Bay Area, "we're the San Francisco Giants, "we're going to do it our way." >> And so when you do it your way, he promotes risk-taking, so that's a great culture. What are some of the things you have tried that were risky, and/or risque, or maybe an experiment, that went well, and maybe ones that didn't go well, can you share some color commentary around that? >> Sure, over 20 years we've had some of all of those. I would say, I've had some real scary moments, our culture is collaborative, but I wouldn't call it combative, but we all have strong opinions, a lot of us have been there a long time, and we have strong opinions and so we'll battle, internally, a lot, but then once the battle is over, we'll all align behind the victory. Thinking back, one of the most stressful times for me at the ballpark was related to wifi, when we decided to take our antennas and put 'em under people's seats. No one had ever done that before, and there were two major concerns with that. One is, honestly are people going to get cancer from these antennas under their seats, it's never been done before, what's going to happen, and whether it's going to happen or not, what's the perception of our fans going to be, because, these are, the bread and butter is, the golden goose here, all the fans, so, yeah it's great that they're going to be, have faster connection here at AT&T Park, but if they think they're going to get cancer, they're going to cancel their season ticket plans, we got to problem. Number two is, we're taking away a little storage space also, under the seats, so it was very controversial internally, we did all of our research, we proved that having a wifi antennae under your seat is the equivalent to having a cell phone in your pocket, most people do that, so we're pretty safe there, and from the storage space perspective, honestly, it actually elevates your stuff, if somebody spills a Coke behind ya, it'll fall all around your purse, which is sitting on top of that wifi antenna so we came up with a good solution, but that was an example of something that was really controversial >> So beer goes on the antennae not your bag. (laughs) >> Exactly, your bag stays dry, we found a way to spin that but, there have been so many, I can go way back in time, back to the days when it was the PalmPilot that ruled the day instead of the apple >> Well you guys also did a good job on social media, I got to give you guys props, because, you're one of the first early adopters on making the fan experience very interactive. That was, at that time, not viewed as standard. Yeah, built the @Cafe at our ballpark, which is still there really to try to bring social media to the fans. >> I think you're the first ballpark to have a kale garden, too, I think. >> That's a little off topic, but yes, driven by one of our players, who's a big kale fan, yeah, the garden out in center field. >> So sustainibility's certainly important, okay, I got to ask the question around your role in the industry, because one of the things that's happening more and more in Major League Baseball and certainly as it crosses over to tech her at Mayfield Venture Capital, there's a lot of collaboration going on, and it's a very people-centric culture where, it used to be people would meet at conferences, or you'd do conference calls, now people are in touch in real time, so these networks are forming. It takes a village to create innovative products, whether you're inside the Giants, or outside in the ecosystem, how have you personally navigated that, and can you share some experiences to the folks watching, how you became successful working in an environment where it's collaborative inside the walls of the San Francisco Giants, but also outside? >> %100, the topic is near and dear to my heart, and from when I started with the Giants, that's what I love about our industry We compete on the field, and only on the field. When you look at who the Giants competitors are, from a business perspective, honestly the Dodgers are not a competitor from a business perspective. The A's are barely a competitor from a business perspective. We got a lot of competitors and very few of them are in our actual industry, so we collaborate all day, and it's been amazing, I can count on one hand, across all of sports, folks who have not been collaborative. There's a very small group of teams, your favorite team, the Boston Red Sox, are not on that list, they are very collaborative, but their arch rival, well there's a few others out there that may be less collaborative, but most of them are highly collaborative, from top down, and so, what I did from when I first started the first trip I made, was to Cleveland. And this was many years ago, Cleveland Indians had a reputation of being very progressive so I called up my counterpart there, I said, "I'm new to the industry, can I come out, "can I learn from you?" And that's where it started, and ever since, every year, we travel to two cities, I take at least four of my staff, to two cities each year and we meet with all the sports teams in those cities. This year, we went to Milwaukee and we met with the Brewers, and we did the Packers as well. Every year, over the 20 years we've visited pretty much every professional sports city, and we just go through it again, and always, red carpet, open door, and you build those face-to-face relationships, that you can pick up the phone and make the call, in a few weeks we're all going to get together in Denver at our MLB IT Summit, my job at the IT Summit every year is I host the golf classic, so I bring all the golfers, the hackers, the duffers out, and we have a great time on the golf course and build those relationships and again, the only thing that we don't really talk about that much is the technology we use to enhance the product on the field. Everything else is fair game. >> So share the business side, but the competitive advantage, where the battle's really having Dodger and Giants obviously on the field, highly competitive-- >> But what's cool about that is then I can meet with the other sports teams to talk about that, so I'll leave the teams nameless, but we've had some awesome collaborative discussions with NBA teams especially to talk about what they're doing to assess talent, and there's no competition there. >> So there's kind of rules of the road, kind of like baseball, unwritten rules. >> Right. >> So talk about the coolest thing that you guys have done this year, share something that you personally feel proud of, or fans love, what were some of the cool things this year that pops out for you? >> Sure, the technology that we invested in this year that I thought was a game-changer, we saw, we experimented with last season, but this year, we've been experimenting with VR and AR a little bit. But, a technology that we thought was really cool is called 4DReplay, it's a company out of Korea. And we saw them, we did an experiment with them, and then we implemented them for the full season this year and we've seen them at some other venues as well, the Warriors tried them at the Playoffs, but we had 'em full year and what we did was they put in about 120 cameras, spaced approximately five feet apart, between the bases. 120 of 'em, and they focus on the pitcher and the batter, so when you have a play, you can 3D, or 4D, 4D rotate around that play and watch the ball as it's moving off the bat, and get it from that full perspective, it's awesome for the fan experience, it gives them a perspective they never have, I love watching the picture, because you can see that hand, in full 4D glory pronating as it comes through on every pitch, if you can watch that hand carefully you can predict what kind of pitch it is, it's something that a fan has never had access to before, we did that for the first time this year. >> I had a new experience, obviously you see Statcast on TV now, a lot of this overlayed stuff happening, kind of creates like an esports vibe to the table. Esports is just coming. >> And it's just the beginning >> Your thoughts on esports, competitor, natural evolution, baseball's going to be involved in it, obviously, thing in the emerging technology's looking interesting, and the younger generation wants the hot, young... Sure, we feel like our game has been around a long time, and it still is, the rules haven't changed that much, but fans still enjoy it, but they just consume it differently and our game can be incredibly exciting in moments, but, there's also some gaps in there when you can build relationships. Some of the younger generation may fill those gaps with watching somethin' else, or two other things on their devices, but that's okay, we embrace that at the ballpark, but in terms of the emergence of esports, and the changing demographic of our fanbase, what we're trying to do is just package our game differently. One thing I'm really excited about, and startin' to see, we're in the early days, I consider with virtual reality, we experiment with it, maybe two or three years ago we've been doing some stuff with it, but I'd say it feels like we're in the second or third inning with virtual reality, where we're really going, and I've seen Intel doin' some of this stuff, I was out working with Intel in Pyeongchang, at the Olympics this past year, working with their PR team, and where it's going I can already visualize what this is going to be like, this concept of volumetric video. Where, it's not about having that courtside seat, in basketball, or that seat right behind home plate, it's about being wherever you want to be, anywhere in the action. And to me it's not about doin' it live, because in baseball, you don't know where the ball's going to go, it's about doin' it, replay, right after, okay, that ball was shot to Brandon Crawford, he made the most amazing diving play, picked it up, gunned it to first, where do you want to watch that from? Everybody's different, some people might want to watch it from right behind first base, some people might want to watch it right Brandon Crawford, behind the batter, with volumetric video and the future of VR, you'll be able to do that, and this esports generation, this fan's instant gratification want, unique experiences, that's what's going to deliver it. >> This is such an immersive environment, we're looking at this kind of volumetric things from Intel, and you got VR and AR, immersion, is a new definition, and it's not, I won't say putting pressure, it's evolving the business model, who would've thought that DraftKings and these companies would be around and be successful, that's gambling, okay, you now you got that, your VR so the business model's changing, I've been hearing even token and cryptocurrency, maybe baseball cards will be tokenized. So these are kind of new, crazy ideas that might be new fan experience and a business model for you guys. Your thoughts on those kind of wacky trends. >> That's why I love working with companies like Mayfield 'cause they're seeing the future before we see it, and I love being where we are, so we can talk to them, and learn about these companies. Another example, along those lines is, how are fans going to get to the ballpark five years from now, and how do we adapt to that because we're doing a major development right adjacent to the ballpark, we've got 4,000 parking spaces. Are we going to need those five years from now? Well we're going to build out that whole parking lot, we're going to put a structure in there. But five, ten years from now, we're building that structure so it can be adaptable, because, is anyone going to need to park? Is parking going to be like typing, you know on a typewriter, 10, 15 years now because everybody is in either self-driving cars, or ride shares, and the cars just, poof, go away, and they come back when you need 'em. >> Like I said, everything that's been invented's been on Star Trek except for the transporter room, but maybe they could transport to the game. >> We could use that in San Francisco. >> Bill, got to ask you about your role with Mayfield, because one of the things I've always been impressed with you is that you always have a taste for innovation, you're not afraid to put the toe in the water or jump in the deep end where the technology is, these guys are lookin' for some trends, too. How do you advise some of these guys, how do you work with Mayfield, what's the relationship, how are they to work with, what's the intersection between Mayfield and you? >> Well the one thing that Mayfield does is they put together a conference, each Summer, that I love comin' down to, and I get to meet a lot of my counterparts and we talked about meeting with my counterparts in sports, but I love meetin' with my counterparts across all industries, and Mayfield makes that possible, they bring us all together with some really interesting speakers on a variety of topics not all directly tech related, so it's a great opportunity for me to just get outside of the daily routine, get outside the box, open my mind, and I just have to drop down the road to do it. So that's an example, another thing is, Mayfield, and other firms will come to me, and just say, "Hey, here's a technology we're evaluating, "they think it would be a great fit in sports, "what do you think?" And so, I can give them some valuable feedback, on company's they're evaluating, companies will come to us, and I might throw them their way, so it's really a two way street >> Great relationship, so you're a sounding board for some ideas, you get to peek into the future, I mean, we've interviewed entrepreneurs, successful entrepreneurs here, it's a seven, eight year build out, so it's almost like an eight year peek into the future. >> Yeah, and it's super valuable, especially given where we are geographically and our inclination toward being on the leading edge. >> I want to just end the segment by sayin', thanks for comin' in, and I want you to show the ring there, 'cause I always, can't stop starin' at the hardware, you got the ring there, the world champion. >> It's a few years old at the moment, we're going to have to get a new one sometime soon. >> We got to work on that, so is there any cutting edge technology to help you evaluate the best player, who you lookin' at next year, what's goin' on? What's the trades goin' on, share us-- >> Are we off the record now, 'cause I have a feeling you're asking this for personal reasons, for your squad, so. >> I'm a Red Sox fan of the AL, obviously, moved here 20 years ago, big fan of the Giants, I love comin' to the games, you guys do a great job, fan experience is great, you guys do great job and I'm looking forward to seeing a great season. >> Thanks, yeah, hope springs eternal this time of year, we always block off October and expect to be busy, but when we have it back, it just gives us an opportunity to get a head start on everybody. >> Well Bill, thanks for coming in, Bill Schlough, CIO for the San Francisco Giants, here on Sand Hill Road talkin' about the 50th anniversary of Mayfield, and this is the People First Network, getting ideas from entrepreneurs, industry executives, and leaders. I'm John Furrier with theCUBE, thanks for watching. (electronic music)

>> Live, from Las Vegas! It's theCube! Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back everyone, it's theCube's live coverage in Las Vegas for VMworld 2018, it's theCube. We got two sets, 24 interviews per day, 94 interviews total. Next three days, we're in day two of three days coverage. It's our ninth year of covering VMworld. It's been great. I'm John Furrier with Dave Vellante, next guest, Cube alumni, number one in the leading boards right now, Sanjay Poonen did a great job today on stage, keynote COO for VMware. Great to have you back. Thanks for coming on. >> John and Dave, you're always so kind to me, but I didn't realize you've been doing this nine years. >> This is our ninth year. >> That's half the life of VMware, awesome. Unreal. Congratulations. >> We know all the stories, all the hidden, nevermind, let's talk about your special day today. You had a really, so far, an amazing day, you were headlining the key note with a very special guest, and you did a great job. I want you to tell the story, who was on, what was the story about, how did this come about? Tech for good, a big theme in this conference has really been getting a lot of praise and a lot of great feedback. Take us through what happened today. >> Well listen, I think what we've been trying to do at VMware is really elevate our story and our vision. Elevate our partnerships, you've covered a lot of the narrative of what we've done with Andy Jessie. We felt this year, we usually have two 90 minute sessions, Day One, Day Two, and it's filled with content. We're technical company, product. We figured why don't we take 45 minutes out of the 180 minutes total and inspire people. With somebody who's had an impact on the world. And when we brainstormed, we had a lot of names suggested, I think there was a list of 10 or 15 and Malala stood out, she never spoke at a tech conference before. I loved her story, and we're all about education. The roots of VMware were at Stamford Campus. Diane Greene, and all of that story. You think about 130 million girls who don't go to school. We want to see more diversity in inclusion, and she'd never spoken so I was like, you know what, usually you go to these tech conferences and you've heard somebody who's spoken before. I'm like, lets invite her and see if she would come for the first time, and we didn't think she would. And we were able to score that, and I was still a little skeptical 'cause you never know is it going to work out or not. So thank you for saying it worked, I think we got a lot of good feedback. >> Well, in your first line, she was so endearing. You asked her what you thought a tech conference, you said too many acronyms. She just cracked the place up immediately. >> And then you heard my response, right? If somebody tells me like that, you tell VMotion wrong she looked at me what? >> Tell them about our story, real quick, our story I want to ask you a point in question. Her story, why her, and what motivated you to get her? >> Those stories, for any of you viewers, you should read the book "I'm Malala" but I'll give you the short version of the story. She was a nine year old in the Pashtun Area of the Swat Valley in Pakistan, and the Taliban setted a edict that girls could not go to school. Your rightful place was whatever, stay at home and become a mom with babies or whatever have you. You cannot go to school. And her father ran a school, Moster Yousafzai, wonderful man himself, an educator, a grandfather, and says know what, we're going to send you to school. Violating this order, and they gave a warning after warning and finally someone shot her in 2012, almost killed her. The bullet kind of came to her head, went down, and miraculously she escaped. Got on a sort of a hospital on a plane, was flown to London, and the world if you remember 2012, the world was following the story. She comes out of this and she's unscathed. She looks normal, she has a little bit of a thing on the right side of her face but her brains normal, everything's normal. Two years later she wins the Nobel Peace Prize. Has started the Malala Fund, and she is a force of nature, an amazing person. Tim Cook has been doing a lot with her in the Malala Fund. I think that actually caught my attention when Tim Cook was working with her, and you know whatever Apple does often gets a little bit of attention. >> Well great job selecting her. How's that relevant to what you guys are doing now, because you guys had a main theme Tech for Good? Why now, why VMware? A lot of people are looking at this, inspired by it. >> There are milestones in companies histories. We're at our 20 year birthday, and I'm sure at people's birthday they want to do big things, right? 20, 30, 40, 50, these decades are big ones and we thought, lets make this year a year to remember in various things we do. We had a 20 year anniversary celebration on campus, we invited Diane Greene back. It was a beautiful moment internally at Vmware during one of our employee meetings. It was a private moment, but just with her to thank her. And man, there were people emotional almost in tears saying thank you for starting this company. A way to give back to us, same way here. What better way to talk about the impact we're having in the community than have someone who is of this reputation. >> Well we're behind your mission 100%, anything you need. We loved the message, Tech for Good, people want to work for a mission driven company. People want to buy >> We hope so. >> from mission driven companies, that stated clear and the leadership you guys are providing is phenomenal. >> We had some rankings that came out around the same time. Fortune ranked companies who are changing the world, and VMware was ranked 17th overall, of all companies in the world and number one in the software category. So when you're trying to change the world, hopefully as you pointed out it's also an attractor of talent. You want to come here, and maybe even attractor of customers and partners. >> You know the other take-away was from the key note was how many Cricket fans there are in the VMworld Community. Of course we have a lot of folks from India, in our world but who's your favorite Cricketer? Was it Sachin Tendulkar? (laughs) >> Clearly you're reading off your notes Dave! >> Our Sonya's like our, >> Dead giveaway! >> Our Sonya's like our Cricket Geek and she's like, ask him about Sachin, no who's your favorite Cricketer, she wants to know. >> Sachin Tendulkar's way up there, Shayuda Free, the person she likes from Pakistan. I grew up playing cricket, listen I love all sports now that I'm here in this country I love football, I love basketball, I like baseball. So I'll watch all of them, but you know you kind of have those childhood memories. >> Sure >> And the childhood memories were like she talk about, India, Pakistan games. I mean this was like, L.A. Dodgers playing Giants or Red Socks, Yankee's, or Dallas Cowboys and the 49ers, or in Germany playing England or Brazil in the World Cup. Whatever your favorite country or team rivalry is, India Pakistan was all there more, but imagine like a billion people watching it. >> Yeah, well it was a nice touch on stage, and I'd say Ted Williams is my favorite cricketer, oh he plays baseball, he's a Red Sock's Player. Alright Sanjay, just cause your in the hot seat, lets get down to business here. Great moment on stage, congratulation. Okay Pat Gelsinger yesterday on the key note talked about the bridges, VMware bridging, connecting computers. One of the highlights is kind of in your wheelhouse, it's in your wheelhouse, the BYOD, Bring Your Own Device bridge. You're a big part of that. Making that work on on the mobile side. Now with Cloud this new bridge, how is that go forward because you still got to have all those table stakes, so with this new bridge of VMware's in this modern era, cloud and multicloud. Cluely validated, Andy Jassy, on stage. Doing something that Amazon's never done before, doing something on premise with VMware, is a huge deal. I mean we think it's a massive deal, we think it's super important, you guys are super committed to the relationship on premises hybrid cloud, multicloud, is validated as far as we're concerned. It's a done deal. Now ball's in your court, how are you going to bring all that mobile together, security, work space one, what's your plan? >> I would say that, listen on as I described in my story today there's two parts to the VMware story. There's a cloud foundation part which is the move the data center to the cloud in that bridge, and then there's the desk job move it to the mobile. Very briefly, yes three years of my five years were in that business, I'm deeply passionate about it. Much of my team now that I put in place there, Noah and Shankar are doing incredible jobs. We're very excited, and the opportunity's huge. I said at my key note of the seven billion people that live in the world, a billion I estimate, work for some company small or big and all of them have a phone. Likely many of those billion have a phone and a laptop, like you guys have here, right? That real estate of a billion in a half, maybe two billion devices, laptops and phones, maybe in some cases laptop, phone, and tablets. Someone's going to manage and secure, and their diverse across Apple, Google, big option for us. We're just getting started, and we're already the leader. In the data center, the cloud world, Pat, myself, Raghu, really as we sat three years ago felt like we shouldn't be a public cloud ourselves. We divested vCloud Air, as I've talked to you on your show before, Andy Jassy is a friend, dear friend and a classmate of mine from Harvard Business School. We began those discussions the three of us. Pat, Raghu, and myself with Andy and his team and as every quarter and year has gone on they become deeper and deep partnerships. Andy has told other companies that VMware Amazon is the model partnership Amazon has, as they describe who they would like to do business more with. So we're proud when they do that, when we see that happen. And we want to continue that. So when Amazon came to us and said listen I think there's an opportunity to take some of our stack and put it on premise. We kept that confidential cause we didn't want it to leak out to the world, and we said we're going to try'n annouce it at either VMworld or re:Invent. And we were successful. A part with these projects is they inevitably leak. We're really glad no press person sniffed it out. There was a lot of speculation. >> Couldn't get confirmation. >> There was a lot of speculation but no one sniffed it out and wrote a story about it, we were able to have that iPhone moment today, I'm sorry, yesterday when we unveiled it. And it's a big deal because RDS is a fast growing business for them. RDS landing on premise, they could try to do on their own but what better infrastructure to land it on than VMware. In some cases would be VMware running on VxRail which benefits Dell, our hardware partners. And we'll continue doing more, and more, and more as customers desire, so I'm excited about it. >> Andy doesn't do deals, as you know Andy well as we do. He's customer driven. Tell me about the customer demand on this because it's something we're trying to get reporting on. Obviously it makes sense, technically the way it's working. You guys and Andy, they just don't do deals out of the blue. There's customer drivers here, what are those drivers? >> Yeah, we're both listening to our customers and perhaps three, four, five years ago they were very focused on student body left, everybody goes public cloud. Like forget your on premise, evaporate, obliterate your data centers and just go completely public. That was their message. >> True, sweep the floor. >> Right, if you went to first re:Invent I was there on stage with them as an SAP employee, that's what I heard. I think you fast forward to 2014, 2015 they're beginning to realize, hey listen it's not as easy. Refactoring your apps, migrating those apps, what if we could bring the best of private cloud and public cloud together enter VMware and Amazon. He may have felt it was harder to have those cultivations of VMware or for all kinds of reasons, like we had vCloud Air and so on and so forth but once we divested that decision culminations had matured between us that door opened. And as that door opened, more culminations began. Jointly between us and with customers. We feel that there are customers who want many of those past type of services of premise. Cause you're building great things, relational database technology, AI, VI maybe. IoT type of technologies if they are landing on premise in an edge-computing kind of world, why not land on VMware because we're the king of the private cloud. We're very happy to those, we progress those discussion. I think in infrastructure software VMware and Amazon have some of the best engineers on the planet. Sometimes we've engineers who've gone between both companies. So we were able to put our engineering team's together. This is a joint engineering effort. Andy and us often talk about the fact that great innovation's built when it's not just Barny go to Marketing and Marketing press releases this. The true joint engineering at a deep level. That's what happened the last several months. >> Well I can tell you right now the commitment I've seen from an executive level and deep technology, both sides are deep and committed to this. It's go big or go home, at least from our perspective. Question I want to ask you Sanjay is you're close to the customer's of VMware. What's the growth strategy? If you zoom out, look down on stage and you got vSAN, NSX at the core, >> vSANjay (laughs) >> How can you not like a product that has my name on it? >> So you got all these things, where's the growth going to come from, the merging side, is the v going to be the stable crown jewels at NSX? How do you guys see the growth, where's it going to come from? >> Just kind of look at our last quarter. I mean if you peel back the narrative, John and Dave, two years ago we were growing single digits. Like low single digits. Two, three percent. That was, maybe the legacy loser description of VMware was the narrative everyone was talking about >> License revenue was flattish right? >> And then now all of sudden we're double digits. 12, 15 sort of in that range for both product revenue. It's harder to grow faster when you're bigger, and what's happened is that we stabilize compute with vSphere in that part and it's actually been growing a little bit because I think people in the VMware cloud provider part of our business, and the halo effect of the cloud meant that as they refresh the servers they were buying more research. That's good. The management business has started to grow again. Some cases double digits, but at least sort of single digits. NSX, the last few order grew like 30, 40%. vSAN last year was growing 100% off a smaller base, this year going 60, 70%. EUC has been growing double digits, taking a lot of share from company's like Citrix and MobileIron and others. And now, also still growing double digits at much bigger paces, and some of those businesses are well over a billion. Compute, management, end-user computing. We talked about NSX on our queue forming called being a 1.4 billion. So when you get businesses to scale, about a billion dollar type businesses and their sort of four, training five that are in that area, and they all get to grow faster than the market. That's the key, you got to get them going fast. That's how you get growth. So we focus on those on those top five businesses and then add a few more. Like VMware Cloud on AWS, right now our goal is customer logo count. Revenue will come but we talked on our earnings call about a few hundred customers of VMware Cloud and AWS. As that gets into the thousands, and there's absolutely that option, why? Because there's 500,000 customers of VMware and two million customers of Amazon, so there's got to be a lot of commonality between those two to get a few thousand. Then we'll start caring about revenue there too, but once you have logos, you have references. Containers, I'd like to see PKS have a few hundred customers and then, we put one on stage today. National Commercial Bank of Jamaica. Fantastic story of PKS. I even got my PKS socks for this interview. (John laughs) >> So that give you a sense as to how we think, there will be four, five that our businesses had scale and then a few are starting to get there, and they become business to scale. The nature of software is we'll always be doing this show because there will be new businesses to talk about. >> Yeah, hardware is easy. Software is hard, as Andy Patchenstien said on theCUBE yesterday. Congratulations Sanjay and all the success, you guys are doing great financially. Products looking really good coming out, the bloom is rising from the fruit you guys have harvested, coming together. >> John if I can say one last thing, I shared a picture of a plane today and I put two engines behind it. There's something I've learned over the last years about focus of a company, and I joked about different ways that my name's are pronounced but at the core of me there's a DNA. I said on stage I'd rather not be known as smart or stupid but having a big heart. VMware, I hope is known by our customers as having these two engines. An engine of innovation, innovating product and a variety of other things. And focused on customer obsession. We do those, the plane will go a long way. >> And it's looking good you guys, we can say we've been to Radio Event, we've been doing a lot of great stuff. Congratulations on the initiative, and a great interview with you today on doing Tech for Good and sharing your story. Getting more exposure to the kind of narratives people want to hear. More women in tech, more girls in tech, more democratization. Congratulations and thanks so much for sharing. >> Thank you John and Dave. >> Appreciate you being here. >> Sanjay Poonen, COO of VMware. Friend of theCUBE, Cube Alumni, overall great guy. Big heart and competitive too, we know that from his Twitter stream. Follow Sanjay on Twitter. You'll have a great time. I'm John Furrier with Dave Vellante, stay with us for more coverage from day two live, here in Las Vegas for VMware 2018. Stay with us. (tech music)

(upbeat music) >> Hey, welcome back everybody. Jeff Frick here with the CUBE's 2018, a new year. I think this is actually my first interview of the year. I'm pretty excited. I have a CUBE conversation here in the Palo Alto studios to talk about a pretty interesting topic. It's been growing over time but it's getting more and more sophisticated and a much bigger reach. And that's dynamic pricing. It's not just stick the sticker on the item like it used to be back in the day. And that's the price and it's much more complicated. Much more sophisticated. And we're excited to have Alex Shartsis. He's the CEO of Perfect Price. Alex, good to see ya. >> Thanks for having me. >> So, dynamic pricing, right. We've saw it. I guess probably the airlines are maybe the first ones to do it. Or you know, Priceline.com was kind of the first one to talk about. You know, hotels have rooms they can't get rid of. But it's moved a lot further down the path than that. I mean now the Giants I think have have flex pricing. Whether it's the Dodgers on a Friday night or it's Toronto on a Tuesday. >> Yeah, I think it's kind of just a really interesting subject, cause everybody's experienced it, right? I mean, you may not know you've experienced it. But everybody. Whether you've taken an Uber, taken a flight, stayed in a hotel. Even at this point going to an A's game or a Giants game. You've been dynamically priced. And I think what people don't realize is a lot of times they benefit from it. You're able to get that flight for a little bit less. You're able to get the Uber for a little bit less, especially than a taxi. And yeah sometimes there's surge pricing. There's last minute fares. There's things that are more expensive but it's something that every consumer has dealt with. And I think a lot of us think about pricing from a consumer standpoint cause we're all consumers. But from a business standpoint there's nothing more impactful than dynamic pricing. >> Yea, and pricing in and of itself is such a complicated issue. You go through some of the stuff on your website. You know are you coming at it from a cost point of view? Is it a cost plus kind of a model? Or is it a value model? So there's a lot of factors, right? There is no kind of perfect price. You don't want a price at the top of the market. You know, then you're giving up some volume. So what are some of the factors when you talk to people as the pricing evolution is happening from kind of what they used to do to what they're trying to do now with dynamic pricing and how you can help them? >> Yeah, so I think if you think about sort of pricing evolving from. Cost plus was kind of the beginning. Like I bought the potato from the farmer for five bucks a pound. And I'm going to sell it for 10 bucks a pound. That covers my cost of shipping it. Having a stall at the Bazaar, whatever. I think, you know today, a lot of companies still do that. Which still shocks me. But there's you know, there became this sort of in the middle of the last century. Which is kind of weird to say. Value based pricing became a thing. So it wasn't that I would sell them for 10 bucks a pound cause it was just double what I paid for them. It's people are willing to pay 10 bucks a pound and then if I try and sell them for 12 nobody buys them. Or a lot fewer people buy them and if I sell them for seven I run out. And I could have made a lot more money. So what value based pricing was is really like what is my customer willing to pay? And the Bazaar was a great place. You have a conversation. You know, Alex, how much do you really need this potato? How much do you really want this thing? Oh, you're like wearing a nice suit. I think I'm going to charge you more for this. And that obviously went away when the department store was invented. And people would walk around and see a tag on the item. And so what we do and I think what our customers are really benefiting from is this notion of really accurately figuring out what that. Not only the value the customer's getting but also factoring in all the other business related costs and fixed costs and things like that. That should or should not be part of that equation. So that the company can sometimes sell maybe at a loss on that one unit. But you know, in the case of a travel business like an airline or hotel. Loss is a very subjective thing. And you're able to make money by lowering the price for a certain segment. Or for a certain time or for a certain origin, destination. Whatever that combination is. And increase your overall profitability by doing so. Plus bring in some customers that wouldn't have been able to buy from you before. >> So, that's an interesting point of view right. Cause always what are you optimizing for? Are you optimizing for the single transaction? Or are you optimizing for the bucket of transactions? And then that can get you to very different places. So as you seen it kind of evolve what are some of the key factors that tell one of your customers you've got a great opportunity to increase profitability. Increase revenue, increase client satisfaction. Again, what are you measuring? What are you optimizing for by incorporating a dynamic pricing and how did it get started? >> Right, those are great questions. So we went into this thinking there are a lot of businesses that are stuck in cost plus pricing. And they would benefit the most from dynamic pricing. Or from using AI to price things because they're doing such a bad job of it today. And it turns out they liked doing a bad job of it for whatever reason. And we have now been successful at convincing them that maybe there's a better way to do it. But the companies that already have a lot of people and a lot invested in pricing in some fashion. Some companies call it revenue management. Those companies are the ones that really benefit and the reason is they've already seen an impact. So one of the key things for us as you. One of the first questions we ask people is why are we talking about pricing? Did you do something? Did something change in your business? Did you notice it had an impact? And everyone of our customers has been able to say yes to that. Somebody made a mistake and they changed the price and they saw a huge swing in their business. And they realize maybe we should think about it this time. >> It's usually some kind of mistake that undercuts. >> Not usually but more than once it has happened. And sometimes it's like we should do software here or not. And not let people fat finger things in. But for the more sophisticated companies. They've already seen. Some of the companies we've worked with have had pricing teams since the 70's. And so they are constantly improving and they see using AI to do dynamic pricing is the next evolution. And they don't want to get left behind. They know know it's a core of their business. And just as Enterprise Software is moving to the Cloud. Machine learning people are starting to use or have been using the graphics core for a while. You can't ignore that trend if it's a core to their business. >> So that's interesting so and we didn't really kind of talk about the impact of AI. And just really AI. Or intelligence to do a better job of optimization because as you said if you've already invested in pricing it's a complicated thing. There's so many factors and another thing about. Kind of Amazon and the Amazon pricing strategy. Or the vendors within Amazon even. And then how do you factor in convenience? How do you factor in prime? I mean there's these other things that have absolutely nothing to do with the physical price that can enable you. You know as you said, get more revenue. Get more profitability in these factors. So now we have AI. We have these crazy big machines. We have Cloud computing and big data. Huge disrupter to this marketplace and then really new opportunities to bring a lot more power to bare I would imagine. >> So I think Amazon is a great example. Cause people have really experienced dynamic pricing with Amazon. Just cause you put something in your cart the next day it changes by five cents. And Amazon's January pricing is really interesting because Bezos is being very vocal about being consumer centric. And so they're looking at what the market is doing and what things are priced elsewhere. And they're always trying to be competitive and give you value because they recognize. You said earlier. What are you trying to optimize for? Is it revenue, is is profit? There are other things you can optimize for that actually improve both of those numbers. Like how frequently you come back to that as a customer. Do I go to Amazon or do I look at Target or Walmart first. That is a huge impact in Amazon's profitability. And you may do that because of price that one time or over your experience with Amazon as a retailer. So I think what's interesting about AI is that it enables us to go. Just like the ad industry did. Went from having a lot of humans. Trying to solve a problem that really wasn't solvable by humans. So taking a lot of shortcuts. Doing what they could. It actually solves a problem. So if you think about the ad industry. If you're spending 10 million dollars on ads which I'm sure some of your listeners would be. And you're running a campaign. You probably have an agency. They probably have 10 people managing your campaign. They're looking at the 30 or 40 creatives. They have a 1,000 publishers it's running on. But pretty soon the numbers get big. I'm not going to do it right now on camera. But you multiply it out. You're talking about billions. >> And they're all multi varied right. So there's all the different. >> Right, well is the purple creative doing well on the female focus websites for 20 to 30. But not for 40 to 50 and at some point you can't keep track of all the permutation. And one of the weird twists I learned working in that industry is that. When you get down to people who actually click and convert. That's a very small number. So you might have millions or tens of millions of impressions. But you might only have a thousand or two thousand customers that ended up out of it. So you're trying to back out. Okay, that was a customer. Where did they start? And that becomes a very, very thin line to draw. And 10 years ago that was all people. You know, you had your agency. You had literally thousands of people that we traffic those campaigns. And today 78% of those ads are served by AI. Those decisions aren't made by humans anymore. And I think if you think about dynamic pricing for businesses that are very large and have really complex businesses. Like rental car companies, hotels, airlines. Transportation trucking where you're dealing with thousands of different factors. Why would you trust that to people if you don't have to? >> Yeah, as long as you have the data right. And the sophistication gets pretty interesting. You guys have a better appeal to people that already understand the value of dynamic pricing. Which you're really offering them is a new way to do it. An AI based way to do it. A Cloud based way to do it. >> The one place where we found a lot of interest that haven't had sophisticated solutions in the past. The companies that don't have a lot of direct competition. Cause a lot of, at least in travel, a huge part of the revenue manager function is what are the Jones' doing? Right, find the Hilton. What's the Marriot around the corner selling their rooms for? And for better or for worse I think there's a place for it. But it don't think it's quite the same place it's just easy for a human to go to your boss and say well boss. The Marriot around the corner is at 250 a night so we're at 260 cause I think our rooms are nicer. And yet in your data is actually the optimal price. If you look at your data. You can actually get to that price. Maybe you set some rules or you put some limits on the AI. So if the Marriot is at 300 you're not at a 1,000. Maybe you should be, right. You should maybe think about that a little bit if that's what the AI is thinking. But if you don't have that crutch. If you don't have a direct competitor around the corner from you. Then it becomes really hard. And that's why Uber started doing this in the first place. Because they knew taxi pricing was wrong. But to Travis and Ryan and the people who started Uber. The key part of it. The value proposition was always being able to get a car. And so the only way you could do that is basically by pressing people out of the market when you don't have enough cars. And then that one person who really needs to go to the hospital. Or is in DC and needs to go to a New Year's party. Whatever it is. They can pay the $200 to get to that thing they really need to cause there still is a car as opposed to not having a car. >> So you bring up a whole other kind of layer of complexity and that's the third party provider. And it just fascinates me that everyday it seems like there's a new Trivago or Kayak. Or God knows how many other kind of secondary marketplaces there are. So how does that factor in when you not only are worrying about your own pricing? Vis-a-vis your competition around the street or kind of your classic set of competitors. But now you've got this whole other layer of distribution that's kind of outside of your direct control with a whole different type of a pricing structure I would imagine. In terms of supporting. Are you seeing that expand to other places or is travel such a unique thing because of the perishability of the assets? >> So I think it will expand to other places. We think transportation in general, also trucking. I mean everything that has these sort of high operating leverage models. Where you have a lot of vehicles or distribution centers or things. The more accurately you fit your pricing to your demand the more money you'll make. The better run your business will be. The more time you save. It has a lot of implications. One of the things that's really interesting about the different channels is traditionally they have played a roll. You know you think about Nordstrom Rack or TJ Max or Priceline. Hotwire, right. You as the Hilton don't want to ruin your brand by renting your rooms for 50 bucks a night even though you know they're going to be empty. So you give them to Hotwire or you give them to Priceline. That always going to play a roll. A lot of these other places are drawing from the same inventory. So it's just yet another front door for you as a hotel or airline or a rental car company to get business from. What's interesting is because of software. Because of legal agreements and also because of software. There isn't a lot of variation in price. Even though every travel site says cheapest prices or best price guaranteed or whatever. They're all getting their pricing data from the same place. It is the same price. And so it's sort of. Unless it is run in inventory. Unless it is Hotwire where it's opaque. Where you don't know what you're getting. If you're getting a room at a Hilton. You could pretty positive that where ever you book that room Hilton's going to be the same price. >> So it's just pure marketing when they're trying to compete. Because ultimately the system kicks out what that third party available price is or is that even dynamically? >> Well, if you think about. I worked in the travel industry for a while so I don't want to share things that I shouldn't share but if you just think about. If you were the company that powered all these different sites. And had your own big consumer facing website. Would you be okay if Hilton rented its rooms for 50 or a 100 bucks less on its website? Then it lets you rent them for it. >> Probably not. >> Alex: Probably not. (laughing) >> So before we run out of time. So what are the key kind of attributes to the business that really lend itself to having an opportunity to increase profitability and revenue with dynamic pricing? >> So the biggest one is that you've seen. You've had some experience. It could be how ever trivial. And you've seen an impact. Pricing did impact your business. The second one is having a significant number of things that you sell. So if your ring and you sell doorbells and you have one product. Dynamically pricing the product is going to cause a lot more problems than it solves. But if you're a rental car company with thousands of cars. An hotel company with thousands of rooms. Anything where's there's either a lot of variation over a small number of products or a large number of products with a lot of variation. And finally to us it seems like there's this. That you're already a data focused company. Other people have written about this but you know that there's value in their data. You haven't figured out how to get it out of there yet. Or maybe you're doing some things with it. But you are committed to running your business more efficiently. I guess the marketers would call it a psycho graphic profile but that kind of attitude. You know not being content with. Hey, we've done this for four years this way and its worked great. But really wanted to leverage your data and knowing that there is enough data there. Those are the three things that really give us. >> And we don't really worry about price protection I guess. Nobody goes back once they buy their item their like. This is what I wanted. This is perfect. So and I just wondered too. What industries are people not thinking about maybe that you're starting to see get more involved in dynamic pricing. I mean obviously we know travel and those types. You've mentioned cars a number of times. Talked about kind of some of the crazy stuff that goes on Amazon. But is there other kind of ones that people might never think about? >> I mean I think the two big ones are the transportation trucking industry. There a ton of permutation there and they kind of got left out and went web 1.0. And so I think there's a lot to be done there. The other one is event ticketing. You mentioned the A's and the Giants but they're kind of the exceptions. I think there's a lot of ink that's been spilled over price gouging and scalpers and things like that. And I think that if that is you take a hard look at pricing their products more effectively. Everybody would be better off. Consumers and the promoters and the venues themselves. >> Yes, in the Boss' Letter he likes to talk a lot about the concert industry. Alright well Alex Shartsis. CEO of Perfect Price. Thanks for taking a few minutes our of your day and sharing the story. >> Thank you. >> Alrighty, he's Alex. I'm Jeff you're watching the CUBE from our Palo Alto studios. Happy New Year everybody. See you next time. (upbeat music) Welcome back everybody Jeff Frick here with the CUBE. It's 2018, a new year. I think this is actually my first interview of the year. I'm pretty excited to have a CUBE conversation here in the Palo Alto studios to talk about a pretty interesting topic. It's been growing over time but it's getting more and more Sophisticated in a much bigger region. That's dynamic pricing. It's not just stick the sticker on the item like it used to be back in the day. And that's the price and it's much more complicated. Much more sophisticated and we're excited to have Alex Shartsis. He is the CEO of Perfect Price. Alex, good to see you. >> Thanks for having me. So dynamic pricing, right. We've saw it I guess probably the airlines maybe the first ones to do it. Or Priceline.com was kind of the first one to talk about. Hotels have rooms they can't get rid of. But it's moved a lot further down the path in that. I mean now even the Giants I think have flex pricing whether its the Dodgers on a Friday night. Or it's Toronto on a Tuesday. >> Yeah, I think it's king of a really interesting subject cause everybody has experienced it, right. I mean you may not know you've experienced it but everybody whether you've taken an Uber or taken a flight. Stayed in a hotel. Even at this point gone to an A's game or Giant's game. You've been dynamically priced. And what I think that people don't realize is a lot of times they benefit from it. They're able to get that flight for a little bit less. You're able to get the Uber for a little bit less especially than a taxi. And yeah, sometimes there's surge pricing.

>> 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.