(techno music) >> (Narrator) Live from Washington, DC. It's Cube conversations with John Furrier. (techno music) >> Hello and welcome to the special exclusive Cube Conversations here in Washington, DC. I'm John Furrier host of the Cube. Here at Amazon Web Services Headquarter World Headquarters for Public Sector Summit in Arlington, Virginia. Our special guest is Tricia Davis-Muffett, who is the Director of Marketing for Worldwide Amazon Web Services. Thanks for joining me. >> Yep. >> So we see each other and reinvent Public Sector Summit, but you're always running around. You got so many things going on. >> I am. >> Big responsibility here. (Tricia laughs) >> You guys are running hard and you have great culture, Teresa's team. Competitive, like to have fun. Don't like to lose. (Tricia laughs) >> What's it like being a marketer for the fastest growing hottest product in Washington, DC and around the world? >> Yeah. I mean it's really been amazing. When I came here, I kind of took a leap of faith on the company because it's four and a half years ago that I came. I literally accepted the job before we had even gotten our first fed ramp approval. So it wasn't entirely sure that this was going be the place to go to for technology for the government, but I really loved the way that we were helping the government innovate and save money of course. I think most of us who are in Public Sector have a passion for citizens, and for making government better and so that's really what I saw in Teresa and her team that they had such a passion to do that and that the technology was going to help the government really improve the lives of citizens. It's been great. One of the things that's been amazing is the passion that our customers have for our technology. I think they get a little taste of it and they go "Wow, I can't believe what I can do "that I thought was impossible before." And so I love seeing what our customers do with the technology. >> It's something people would think might be easy to be a marketer for Amazon, but if you think about it, you have so much speed in your business. You have a cult of personality in the Cloud addiction, or Cloud value. In addition to the outcomes that are happening. >> Uh huh. >> We're a customer and one kind of knows that's pretty biased on it. We've seen the success ourselves, but you guys have a community. Everywhere you go, you're seeing Amazon as they take more territory down. Public Cloud originally, and now Enterprise, and Public Cloud, Public Sector Enterprise, Public Cloud. Each kind of wave of territory that Amazon goes in to Amazon Web Services, is a huge community. >> Yeah. >> And so that's another element. I mean Public Sector Summit last year it felt like Reinvent. So this years going to be bigger. >> Yeah. We had 65 hundred plus people attend last year, just in the Washington DC area and we've also expanded that program now and we are taking our Public Sector Summit specifically for government education non-profit around the world. So this year we will be in Brussels, and Camber, Australia. We have great adoption in Australia as well with the government there. In Singapore, Ottawa. So we're really expanding quite a bit and helping governments around the world to adopt. >> So if that's a challenge, how are you going to handle that because you guys have always been kind of with Summits. Do you coattail Summits? Do you go separate? >> No. We go separate. We actually have the Public Sector Summits we take the experience of our technology to government towns that wouldn't typically get a Summit. So for instance here in the United States of course, San Francisco and New York there's a lot of commercial businesses. We have our big Summits there, but there's not as much commercial business here in Washington DC, so really Public Sector takes the lead here. And then we focus on some of the things that really are most important to our Public Sector customers. Things like, procurement and acquisition. Things like the security and compliance that's so critical in the government sector. And then also, we do a really careful job of curating our customers, because we know that our government customers want to hear from each other. They want to hear from people who are blazing a trail within the Public Sector. They don't necessarily want to hear about what we want to say. They want to hear what their peers are doing with the technology. So last year, we had over a hundred of our Public Sector customers speaking to each other about what they were doing with the Cloud. >> And I find that's impressive. I actually commented on the Cube that week that it's interesting you let the customers do the talking. I mean, that's the best ultimate sign of success and traction. >> Yeah. And the great thing is, you know I've worked in other places in the Public Sector and government customers can be kind of shy about talking about what they're doing. You know, there are very motivated to just keep things going calmly, quietly, you know get their jobs done. But I think... >> Well, it doesn't hurt when you have the top guy at the CIA say, "Best decision we've ever made." "It's the most innovative thing we've ever done." I mean talk about being shy. >> Yeah. >> That's the CIA, by the way. That's the CIA. And we've also had, people like NASA JPL who've been very outspoken. Tom Soderstrom said that it was conservatively 1/100th of the cost of what it would have been if he had built out the infrastructure himself to build the infrastructure for his Mars landing. I mean that kind of... >> It just keeps giving. You lower prices. Okay I got to change gears, because a couple things that I've observed to every Reinvent, as being a customer and I think I've used Amazon I first came out as an entrepreneur. (inaudible) had no URL support, but that's showing my age. (Tricia laughs) But, here's the thing, you guys have enabled customers to solve problems that they couldn't solve in the past. >> (Tricia) Right. >> You mentioned NASA and then a variety of other (inaudible). But you guys are also in Public Sectors specifically are doing new things. New problems that no ones ever seen before. And society, entrepreneurship, diversity inclusion, education, non-profits. You don't think of Gov Cloud and Public Sector; you think non-profits, education. So it's kind of these sectors that are coming together. This is a new phenomenon. Can you talk and explain the dynamic behind that and the opportunity? >> Sure. I love to hear the stories of what our customers are doing when they really are tackling a problem that no one had thought of before. So for instance, at Reinvent this year, one of our Public Sector customers who spoke was Thorne. And they are using AI to crawl the dark web and help find people who are trafficking children in human trafficking, and that's a great use of AI and that's the kind of thing. It also helps our public servants because it helps to make police officers' jobs more effective. So of course we know that police officers, there are never enough police officers to go around. There's never enough detectives to look into everything that they need to and this makes them so much more effective to make the world a safer, better place. I also love some of the things about educational outcomes. Ivy Tech Community College is one of our great community college customers. And their using big data analysis to put together all of the different data sets that they have about their students and identify who might be at risk of failing a class 10 days into the semester so that they can help intervene with those students. >> Where was that class when I needed it? >> I know. >> Popup and say, "Hey homework time." >> I mean it really is looking at what kind of issues that they're having very early on with attendance, with different behavioral things. >> A great example at Reinvent with the California Community College system. That was a very interesting way. He was up there bragging like it was nobody's business. >> Yeah, and I think the community colleges that really goes into this idea of we're trying to expand opportunity for a wide-range of people. You might think of computer scientists as that's going to be all the Carnegie Mellon and Stanford and MIT people. And of course those are great contributors to computer science, but the fact is that computer science is so critical in so many aspects of life and in so many different kinds of careers. We know that one of the limiters to our own growth is going to be the talent that we have available to take advantage of the technology. We've been really working hard to expand opportunity for a wide-range of people, so that any smart person with an idea, can be using our technology, that's part of what's behind building the AWS Educate Program, which is a program to offer free computer science training to any university student or college student anywhere in the world. >> So it's a program you guys are doing? >> (Tricia) This is a program we are doing, >> What's it called again? >> AWS Educate. And it's a program that offers free credits to use AWS to any student who is enrolled in any kind of university or college anywhere around the world. >> That's a gateway drug to Cloud computing. >> Absolutely. >> Free resources. >> Yeah, and we're giving them a training path so that they can... >> So they want to write some code, or whatever they want to do. >> Yeah, and they can take different paths and learn. Okay, I want to learn a data science pathway, so I'm going to go that way. I want to learn a websites pathway. And they can go through things and build a portfolio of projects that they've actually built. >> So can they tap into some of the AWS AI tools too? >> They can tap into a wide range of tools and they have different levels of tiers of credits that they get, so it's a really great program to really open up Cloud computing. >> Now is there any limitations on that? What grade levels, is it college and above? >> Actually at Reinvent we just opened it up to students 14 and above. >> (John) Beautiful. That's awesome. >> And we also have a program called... >> How do they prove they're a student? >> Having a school, an EDU email address, or their school being registered through the program. >> (John) Okay, that's awesome. >> And then we also have another program called We Power Tech, and that really is a program to help open up the talent pool again to women to underserved communities, to people of different ethnic backgrounds who might not see themselves in technology because they don't see themselves as computer programmers on TV or whatever. >> Or they don't see their peer group in there, or some sort of might be an inclusion issue. >> Right and we're looking at if you take educate and We Power Tech, we're looking at that full pipeline of talent all the way from kids who are deciding should I pursue computer science or not, all the way through to professionals and getting them to try to stay in technology. >> So you guys are legit on this. You're not going to just check the box and focus on narrow things. A lot of companies do that, where they go oh we're targeting young girls or women. You guys are looking at the spectrum broader. >> Yep. And we're really looking at different communities and helping people to find their community in technology so that they can find supportive networks and also find people to mentor them or find people to mentor who are elsewhere. >> How big of a problem is it right now in today's culture and in the online culture to find peers and friends to do work like this? Because it just doesn't seem to me like there's been any innovation in online message groups. Seems like so 30 years ago. (Tricia laughs) >> Yeah. I think it is tough and I think there are somethings that we're trying to break through. For instance, a lot of the role models out there are the same people over and over again. We're trying to find new role models. And we find that through our customers. We find customers who are doing interesting work and we're trying to cultivate their voice and help put them on stage. >> New voices because it's new things. Machine learning, these are new disciplines. Data science across the board. >> Yeah, and one of the things that I love about the technology is it really is has democratizing affect. If you have an idea, you can make that idea happen for very little money, with just your ingenuity and your ability to stick to it. >> I got to ask you the hard question. Shouldn't be hard for you, but Amazon is gritty. It's been called gritty by me, hustling, but they're very good with their money. They don't really waste a lot in marketing. >> Yeah we're frugal. >> Very frugal, but you're very efficient, so I got to ask your favorite gorilla marketing technique. Cause you guys do more with less. >> (Tricia) We do. >> Once been criticized in Wired magazine. I remember reading years ago about they were comparing the Schwag bag to Reinvent. (Tricia laughs) Google almost gave out phones. It's kind of like typical reporter, but my point is you guys spend your money on education to engineers. You don't skip on that, but you might not put the flair onto an event, but now you guys are doing it. >> I think there are two things. So one of them is the aesthetic of our events. We typically do have a very stripped down aesthetic and we've made frugal look cool. I think that's one of the things I learned when I came here was go ahead and have the concrete floor and put quotes from customers there instead of paying to carpet it. So don't waste money on things that don't add value that's one of the core tenants of what we do in marketing. >> Get a better band instead of the rug. You guys have always had great music. >> We do always have great music. >> Tricia, tell me about your favorite program or project you've done a lot over the years. Pick your favorite child. What's your favorite? You have a lot of great stuff going on. Do you have a favorite? >> I think that my favorite is probably the City on a Cloud Innovation Challenge which is something we've done every year for the last four years. And we really went and asked cities, "Tell us what you're doing with our technology." Because we weren't sure what they were doing cause it's not very expensive for cities to run on us. We found that they were doing incredible things. They were doing water monitoring in their cities to help improve the quality of life of their citizens. They were delivering education more effectively. They were helping their transportation run in a more effective way. New York City Department of Transportation was doing really cool citizen facing apps to help them manage their transportation challenges and also cities all around the world. We've had people put in things about garbage management in Jerusalem and about lighting management in a Japanese city. We've had all kinds of really interesting stories come out and I just love hearing what the customers are doing and this year we added a Dream Big category where we said, "If you had the money, what would "you do with technology in your city?" and we've been really thrilled to be able to offer grants and fund some of those things to help cities get started. >> That's awesome. Not only is it engaging for them to engage with you through the program, it's inspirational. The use cases are everything from IOT to every computer. >> Yeah and we've also had partners submit as well, and we've learned about things like parking applications that cities are putting in place to help their citizens find better parking or all kinds of really interesting. How to keep track of the tree and do a tree census in their cities. Things like that. >> Maybe I'll borrow that and give you credit for it as a Cube question. What would you do if you had unlimited money? >> Exactly. (John laughs) Well the great part is that most of the cities find out that they can do what they want to do with very little money. They think it's going to be millions of dollars and then they realize, "Oh my gosh, it's going to be hard "for me to spend this 50 thousand dollar grant "because it doesn't cost that much." >> That's awesome and you got a big event coming up in June. Public Sector Summit again. Any preview on that? Any thing you can share? I'm sure it's a lot of things up in the air. >> A lot of really cool things. We are very excited to have some of our great customers on stage again. We're also this year going to have a pre day where we're going to feature Air and Space workloads on AWS. So that's going to be really interesting. I think we're going to have Blue Origin there and we're going to talk about what it's going to take to get to the next planet. >> And certainly that's beautiful for Cloud and also a huge robotics trend. People love to geek out on space related stuff. >> Yep. >> Awesome. Well the Cube will be there. Any numbers? Is it going to be the same location? >> It's going to be the same location at the Convention Center June 20th and 21st. We're going to have boot camps and certification labs and all that kind of stuff. I expect we'll grow again, so definitely more than seven thousand people. >> How big was the first one? >> Oh my gosh, the first one was in a little hotel conference room. I think there were a hundred and 50 people there. (Tricia laughs) >> Sounds like Reinvent happening all over again. We've seen this movie before. >> (Tricia) Yep. >> Tricia, thanks so much for coming on the Cube here. In the headquarters of Amazon Web Services Public Sector Summit in Washington DC. We're in Arlington, Virginia, right next to the nation's capital. I'm John Furrier. Thanks for watching. (techno music)

>> Narrator: Live from New York City, it's theCUBE covering IBM Data Science For All brought to you by IBM. >> Welcome back here on theCUBE. We are live in New York continuing our coverage here for Data Science for All where all things happen. Big things are happening. In fact, there's a huge event tonight I'm going to tell you about a little bit later on, but Tricia Wang who is our next guest is a part of that panel discussion that you'll want to tune in for live on ibmgo.com. 6 o'clock, but more on that a little bit later on. Along with Dave Vellante, John Walls here, and Tricia Wang now joins us. A first ever for us. How are you doing? >> Good. >> A global tech ethnographer. >> You said it correctly, yay! >> I learned a long time ago when you're not sure slow down. >> A plus already. >> Slow down and breathe. >> Slow down. >> You did a good job. Want to do it one more time? >> A global tech ethnographer. >> Tricia: Good job. >> Studying ethnography and putting ethnography into practice. How about that? >> Really great. >> That's taking on the challenge stretch. >> Now say it 10 times faster in a row. >> How about when we're done? Also co-founder of Sudden Compass. So first off, let's tell our viewers a little bit about Sudden Compass. Then I want to get into the ethnography and how that relates to tech. So let's go first off about Sudden Compass and the origins there. >> So Sudden Compass, we're a consulting firm based in New York City, and we help our partners embrace and understand the complexity of their customers. So whenever there are, wherever there's data and wherever there's people, we are there to help them make sure that they can understand their customers at the end of the day. And customers are really the most unpredictable, the most unknown, and the most difficult to quantify thing for any business. We see a lot of our partners really investing in big data data science tools and they're hiring the most amazing data scientists, but we saw them still struggling to make the right decisions, they still weren't getting their ROI, and they certainly weren't growing their customer base. And what we are helping them do is to say, "Look, you can't just rely only on data science. "You can't put it all into only the tool. "You have to think about how to operationalize that "and build a culture around it "and get the right skillsets in place, "and incorporate what we call the thick data, "which is the stuff that's very difficult to quantify, "the unknown, "and then you can figure out "how to best mathematically scale your data models "when it's actually based on real human behavior, "which is what the practice of ethnography is there to help "is to help you understand what do humans actually do, "what is unquantifiable. "And then once you find out those unquantifiable bits "you then have the art and science of figuring out "how do you scale it into a data model." >> Yeah, see that's what I find fascinating about this is that you've got hard and fast, right, data, objective, black and white, very clear, and then you've got people, you know? We all react differently. We have different influences, and different biases, and prejudices, and all that stuff, aptitudes. So you are meshing this art and science. >> Tricia: Absolutely. >> And what is that telling you then about how best to your clients and how to use data (mumbles)? >> Well, we tell our clients that because people are, there are biases, and people are not objective and there's emotions, that all ends up in the data set. To think that your data set, your quantitative data set, is free of biases and has some kind of been scrubbed of emotion is a total fallacy and it's something that needs to be corrected, because that means decision makers are making decisions based off of numbers thinking that they're objective when in fact they contain all the biases of the very complexity of the humans that they're serving. So, there is an art and science of making sure that when you capture that complexity ... We're saying, "Don't scrub it away." Traditional marketing wants to say, "Put your customers in boxes. "Put them in segments. "Use demographic variables like education, income. "Then you can just put everyone in a box, "figure out where you want to target, "figure out the right channels, "and you buy against that and you reach them." That's not how it works anymore. Customers now are moving faster than corporations. The new net worth customer of today has multiple identities is better understood when in relationship to other people. And we're not saying get rid of the data science. We're saying absolutely have it. You need to have scale. What is thick data going to offer you? Not scale, but it will offer you depth. So, that's why you need to combine both to be able to make effective decisions. >> So, I presume you work with a lot of big consumer brands. Is that a safe assumption? >> Absolutely. >> Okay. So, we work with a lot of big tech brands, like IBM and others, and they tend to move at the speed of the CIO, which tends to be really slow and really risk averse, and they're afraid to over rotate and get ahead over their skis. What do you tell folks like that? Is that a mistake being so cautious in this digital age? >> Well, I think the new CIO is on the cutting edge. I was just at Constellation Research Annual Conference in Half Moon Bay at-- >> Our friend Ray Wang. >> Yeah, Ray Wang. And I just spoke about this at their Constellation Connected Enterprise where they had the most, I would have to say the most amazing forward thinking collection of CIOs, CTOs, CDOs all in one room. And the conversation there was like, "We cannot afford to be slow anymore. "We have to be on the edge "of helping our companies push the ground." So, investing in tools is not enough. It is no longer enough to be the buyer, and to just have a relationship with your vendor and assume that they will help you deliver all the understanding. So, CIOs and CTOs need to ensure that their teams are diverse, multi-functional, and that they're totally integrated embedded into the business. And I don't mean just involve a business analyst as if that's cutting edge. I'm saying, "No, you need to make sure that every team "has qualitative people, "and that they're embedded and working closely together." The problem is we don't teach these skills. We're not graduating data scientists or ethnographers who even want to talk to each other. In fact, each side thinks the other side is useless. We're saying, "No, "we need to be able to have these skills "being taught within companies." And you don't need to hire a PhD data scientist or a PhD ethnographer. What we're saying is that these skills can be taught. We need to teach people to be data literate. You've hired the right experts, you have bought the right tools, but we now need to make sure that we're creating data literacy among decision makers so that we can turn these data into insights and then into action. >> Let's peel that a little bit. Data literate, you're talking about creativity, visualization, combining different perspectives? Where should the educational focus be? >> The educational focus should be on one storytelling. Right now, you cannot just be assuming that you can have a decision maker make a decision based on a number or some long PowerPoint report. We have to teach people how to tell compelling stories with data. And when I say data I'm talking about it needs the human component and it needs the numbers. And so one of the things that I saw, this is really close to my heart, was when I was at Nokia, and I remember I spent a decade understanding China. I really understood China. And when I finally had the insight where I was like, "Look, after spending 10 years there, "following 100 to 200 families around, "I had the insight back in 2009 that look, "your company is about to go out of business because "people don't want to buy your feature phones anymore. "They're going to want to buy smartphones." But, I only had qualitative data, and I needed to work alongside the business analysts and the data scientists. I needed access to their data sets, but I needed us to play together and to be on a team together so that I could scale my insights into quantitative models. And the problem was that, your question is, "What does that look like?" That looks like sitting on a team, having a mandate to say, "You have to play together, "and be able to tell an effective story "to the management and to leadership." But back then they were saying, "No, "we don't even consider your data set "to be worthwhile to even look at." >> We love our candy bar phone, right? It's a killer. >> Tricia: And we love our numbers. We love our surveys that tell us-- >> Market share was great. >> Market share is great. We've done all of the analysis. >> Forget the razor. >> Exactly. I'm like, "Look, of course your market share was great, "because your surveys were optimized "for your existing business model." So, big data is great if you want to optimize your supply chain or in systems that are very contained and quantifiable that's more or less fine. You can get optimization. You can get that one to two to five percent. But if you really want to grow your company and you want to ensure its longevity, you cannot just rely on your quantitative data to tell you how to do that. You actually need thick data for discovery, because you need to find the unknown. >> One of the things you talk about your passion is to understand how human perspectives shape the technology we build and how we use it. >> Tricia: Yes, you're speaking my language. >> Okay, so when you think about the development of the iPhone, it wasn't a bunch of surveys that led Steve Jobs to develop the iPhone. I guess the question is does technology lead and shape human perspectives or do human perspectives shape technology? >> Well, it's a dialectical relationship. It's like does a hamburger ... Does a bun shape the burger or does the bun shape the burger? You would never think of asking someone who loves a hamburger that question, because they both shape each other. >> Okay. (laughing) >> So, it's symbiote here, totally symbiotic. >> Surprise answer. You weren't expecting that. >> No, but it is kind of ... Okay, so you're saying it's not a chicken and egg, it's both. >> Absolutely. And the best companies are attuned to both. The best companies know that. The most powerful companies of the 21st century are obsessed with their customers and they're going to do a great job at leveraging human models to be scaled into data models, and that gap is going to be very, very narrow. You get big data. We're going to see more AI or ML disasters when their data models are really far from their actual human models. That's how we get disasters like Tesco or Target, or even when Google misidentified black people as gorillas. It's because their model of their data was so far from the understanding of humans. And the best companies of the future are going to know how to close that gap, and that means they will have the thick data and big data closely integrated. >> Who's doing that today? It seems like there are no ethics in AI. People are aggressively AI for profit and not really thinking about the human impacts and the societal impacts. >> Let's look at IBM. They're doing it. I would say that some of the most innovative projects that are happening at IBM with Watson, where people are using AI to solve meaningful social problems. I don't think that has to be-- >> Like IBM For Social Good. >> Exactly, but it's also, it's not just experimental. I think IBM is doing really great stuff using Watson to understand, identify skin cancer, or looking at the ways that people are using AI to understand eye diseases, things that you can do at scale. But also businesses are also figuring out how to use AI for actually doing better things. I think some of the most interesting ... We're going to see more examples of people using AI for solving meaningful social problems and making a profit at the same time. I think one really great example is WorkIt is they're using AI. They're actually working with Watson. Watson is who they hired to create their engine where union workers can ask questions of Watson that they may not want to ask or may be too costly to ask. So you can be like, "If I want to take one day off, "will this affect my contract or my job?" That's a very meaningful social problem that unions are now working with, and I think that's a really great example of how Watson is really pushing the edge to solve meaningful social problems at the same time. >> I worry sometimes that that's like the little device that you put in your car for the insurance company to see how you drive. >> How do you brake? How do you drive? >> Do people trust feeding that data to Watson because they're afraid Big Brother is watching? >> That's why we always have to have human intelligence working with machine intelligence. This idea of AI versus humans is a false binary, and I don't even know why we're engaging in those kinds of questions. We're not clearly, but there are people who are talking about it as if it's one or the other, and I find it to be a total waste of time. It's like clearly the best AI systems will be integrated with human intelligence, and we need the human training the data with machine learning systems. >> Alright, I'll play the yeah but. >> You're going to play the what? >> Yeah but! >> Yeah but! (crosstalk) >> That machines are replacing humans in cognitive functions. You walk into an airport and there are kiosks. People are losing jobs. >> Right, no that's real. >> So okay, so that's real. >> That is real. >> You agree with that. >> Job loss is real and job replacement is real. >> And I presume you agree that education is at least a part the answer, and training people differently than-- >> Tricia: Absolutely. >> Just straight reading, writing, and arithmetic, but thoughts on that. >> Well what I mean is that, yes, AI is replacing jobs, but the fact that we're treating AI as some kind of rogue machine that is operating on its own without human guidance, that's not happening, and that's not happening right now, and that's not happening in application. And what is more meaningful to talk about is how do we make sure that humans are more involved with the machines, that we always have a human in the loop, and that they're always making sure that they're training in a way where it's bringing up these ethical questions that are very important that you just raised. >> Right, well, and of course a lot of AI people would say is about prediction and then automation. So think about some of the brands that you serve, consult with, don't they want the machines to make certain decisions for them so that they can affect an outcome? >> I think that people want machines to surface things that is very difficult for humans to do. So if a machine can efficiently surface here is a pattern that's going on then that is very helpful. I think we have companies that are saying, "We can automate your decisions," but when you actually look at what they can automate it's in very contained, quantifiable systems. It's around systems around their supply chain or logistics. But, you really do not want your machine automating any decision when it really affects people, in particular your customers. >> Okay, so maybe changing the air pressure somewhere on a widget that's fine, but not-- >> Right, but you still need someone checking that, because will that air pressure create some unintended consequences later on? There's always some kind of human oversight. >> So I was looking at your website, and I always look for, I'm intrigued by interesting, curious thoughts. >> Tricia: Okay, I have a crazy website. >> No, it's very good, but back in your favorite quotes, "Rather have a question I can't answer "than an answer I can't question." So, how do you bring that kind of there's no fear of failure to the boardroom, to people who have to make big leaps and big decisions and enter this digital transformative world? >> I think that a lot of companies are so fearful of what's going to happen next, and that fear can oftentimes corner them into asking small questions and acting small where they're just asking how do we optimize something? That's really essentially what they're asking. "How do we optimize X? "How do we optimize this business?" What they're not really asking are the hard questions, the right questions, the discovery level questions that are very difficult to answer that no big data set can answer. And those are questions ... The questions about the unknown are the most difficult, but that's where you're going to get growth, because when something is unknown that means you have not either quantified it yet or you haven't found the relationship yet in your data set, and that's your competitive advantage. And that's where the boardroom really needs to set the mandate to say, "Look, I don't want you guys only answering "downstream, company-centric questions like, "'How do we optimize XYZ?"'" which is still important to answer. We're saying you absolutely need to pay attention to that, but you also need to ask upstream very customer-centric questions. And that's very difficult, because all day you're operating inside a company . You have to then step outside of your shoes and leave the building and see the world from a customer's perspective or from even a non existing customer's perspective, which is even more difficult. >> The whole know your customer meme has taken off in a big way right now, but I do feel like the pendulum is swinging. Well, I'm sanguined toward AI. It seems to me that ... It used to be that brands had all the power. They had all the knowledge, they knew the pricing, and the consumers knew nothing. The Internet changed all that. I feel like digital transformation and all this AI is an attempt to create that asymmetry again back in favor of the brand. I see people getting very aggressive toward, certainly you see this with Amazon, Amazon I think knows more about me than I know about myself. Should we be concerned about that and who protects the consumer, or is just maybe the benefits outweigh the risks there? >> I think that's such an important question you're asking and it's totally important. A really great TED talk just went up by Zeynep Tufekci where she talks about the most brilliant data scientists, the most brilliant minds of our day, are working on ad tech platforms that are now being created to essentially do what Kenyatta Jeez calls advertising terrorism, which is that all of this data is being collected so that advertisers have this information about us that could be used to create the future forms of surveillance. And that's why we need organizations to ask the kind of questions that you did. So two organizations that I think are doing a really great job to look at are Data & Society. Founder is Danah Boyd. Based in New York City. This is where I'm an affiliate. And they have all these programs that really look at digital privacy, identity, ramifications of all these things we're looking at with AI systems. Really great set of researchers. And then Vint Cerf (mumbles) co-founded People-Centered Internet. And I think this is another organization that we really should be looking at, it's based on the West Coast, where they're also asking similar questions of like instead of just looking at the Internet as a one-to-one model, what is the Internet doing for communities, and how do we make sure we leverage the role of communities to protect what the original founders of the Internet created? >> Right, Danah Boyd, CUBE alum. Shout out to Jeff Hammerbacher, founder of Cloudera, the originator of the greatest minds of my generation are trying to get people to click on ads. Quit Cloudera and now is working at Mount Sinai as an MD, amazing, trying to solve cancer. >> John: A lot of CUBE alums out there. >> Yeah. >> And now we have another one. >> Woo-hoo! >> Tricia, thank you for being with us. >> You're welcome. >> Fascinating stuff. >> Thanks for being on. >> It really is. >> Great questions. >> Nice to really just change the lens a little bit, look through it a different way. Tricia, by the way, part of a panel tonight with Michael Li and Nir Kaldero who we had earlier on theCUBE, 6 o'clock to 7:15 live on ibmgo.com. Nate Silver also joining the conversation, so be sure to tune in for that live tonight 6 o'clock. Back with more of theCUBE though right after this. (techno music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Everyone welcome back to the cubes. Coverage of AWS Reinvent Amazon Web services Annual conference theme. Cuba's normally there in person. This year we can't be. It's a virtual event. This is the Cube virtual. I'm your host for the Cube. John Ferrier Tresh Damn Kroger, VP of G M and G m of the high performance computing team at Intel is here in the Cube until a big part of the cube every year. Trish, thank you for coming on Were remote. We can't be in person. Um, good to see you. >>Good to see you. >>I'm really impressed with Reinvent Has grown from kind of small show eight years ago to now kind of a bellwether. And and every year it's the same story. More scale, more performance, lower prices. This is kind of the intel cadence that we've seen of Intel over the years. But high performance computing, which >>has been >>around for a while, has gotten much more mainstream thinking because it's applying now to scale. So I want to get your thoughts and and just set the table real quick. What is high performance computing mean these days from Intel? And has that relate to what people are experiencing >>e high performance computing? Um, yes, it's been traditionally known as something that's, you know, in the in the labs and the government, you know, not used widely. But high performance computing is truly just changing the world is what you can dio Cove. It is a great example of where they've taken high performance computing to speed up the discovery of drugs and vaccines for or cova 19. They use it every day. You know, whether it's making Pampers or Clorox boxes. So they are those bottles so that they, when you drop them, they don't break, um, to designing airplanes and designing, um, Caterpillar tractors. So it is pervasive throughout. And, um, sometimes people don't realize that high performance computing infrastructure is kind of that basics that you use whenever you need to do something with dense compute. >>So what some examples of workloads can you just share? I mean, obviously Xeon processor. We've covered that many times, but I mean from a workload standpoint, what kind of workloads are high performance computing kind of related or unable or ideal for that's out there, >>right? Z on scalable processors are the foundation for high performance computing. If you look at what most people run high performance computing on its see on, and I think that it's so broad. So if you look at seismic processing or molecular dynamics for the drug discovery type work or if you think about, um, open foam for fluid dynamics or, um, you know, different financial trade service, you know, frequency, fats, frequency trading or low. I can't even think of that word. But anyway, trading is very common using high performance computing. I mean, it's just used pervasively throughout. >>Yeah, and you're seeing you're seeing the cloud of clarification of that. I want to get your thoughts. The next question is, you know it's not just Intel hardware. You mentioned Zeon, but HBC in AWS were here. It reinvent. Can you share how that plays out? What's your what's your What's your take on that? Because it's not just hard work and you just take them into explain relationship, >>right? So we definitely have seen the growth of high performance computing in the cloud over the last couple of years. We've talked about this for, you know, probably a decade, and we've definitely seen that shift. And with AWS, we have this wonderful partnership where Intel is not only bringing the hardware like you say, the Z on scalable processors, but we're also having accelerators and then on that whole software ecosystem where we work closely with our I s V and O S v partners. And when we bring, um, not only compilers but also analyzers in our full to tool suite so people can move between an on Prem situation Thio Public cloud like aws. Um, seamlessly. >>So talk about the developer impact. As I say, it's that learning show reinvent. There's a lot of developers here. I'll see mainstream you're seeing, you know, obviously the born in the cloud. But now you're seeing large scale enterprises and big businesses. You mentioned financial services from high frequency trading to oil and gas. Every vertical has a need for cloud and and what, you should be traditionally on premises compute. So you have. You're kind of connecting those dots here with AWS. Um, what is some of the developer angle here? Because they're in the cloud to they want to develop. How does how does the developer, um, engage with you guys on HPC in Amazon, >>Right? Well, there's there's a couple ways. I mean, so we do work with some of our partners eso that they could help move those workloads to the cloud. So an example is 69 which recently helped a customer successfully port a customized version of the in car models for prediction across scales. So they chose the C 59 18 x large instance type because this is what really deliver the highest performance and the lowest price for compute ratio. Another great example is P. K. I, which is a partner out of the UK, has worked with our customers to implement AI in retail and other segments running on Intel Instances of the EEC too. So I think these air just so you could have people help you migrate your workloads into the cloud. But then also, one of the great things I would like to talk about is, um a ws has come out with the parallel cluster, which is an Intel select solution, which really helps, um, ease that transition from on Prem to cloud. >>That's awesome. Um, let's get into that parallel cluster and you mentioned Intel Select Solution program. There's been some buzz on that. Can you take a minute to explain what that is? I >>mean, the HBC has, AH reputation of being hard, and the whole philosophy between behind the Intel Select solution is to make it easier for our customers to run HBC workloads in the cloud or on Prem and with E Intel Select Solution. It's also about scaling your job across a large number of notes, so we've made it a significant investment into the full stack. So this is from the silicon level all the way up to the application level so that we ensure that your application runs best on Intel and we bring together all the everything that you need into. Basically, it's a reference design. So it's a recipe where we jointly created it with our I, C, P and O S V partners and our open source environment for all the different relevant workloads. And so Amazon Web Services is the first cloud service provider to actually verify a service such as Intel Select Solution and this is this is amazing because this truly means that somebody can say it works today on Prem, and I know it will work exactly the same in AWS Cloud. >>That's huge. And I wanna just call that out because I think it's worth noting. You guys just don't throw this around like in the industry like doing these kind of partnerships. Intel's been pretty hard core on the quality, and so having a cloud service provider kind of go through the thing, it's really notable you mentioned parallel cluster um, deal. What is Can you just tie that together? Because if I get this right, the Intel, uh, select solution with the cloud service provider Amazon is a reference designed for how to go on premise or edge or revenue. It is to cloud in and out of cloud. How does this parallel cluster project fit into all this? Can you just unpack that a little bit? >>Right. So the parallel cluster basically, um, it's a parallel cluster until select solution. And there's three instances that we're featuring with the Intel Xeon Scalable processor, which gives you a variety of compute characteristics. So the select solution gives you the compute, the storage, the memory the networking that you need. You know, it says the specifications for what you need to run a non optimal way. And then a WS has allowed us to take some of the C five or some of the instances, and we are on. Three different instances were on the C five, in instance. But that's for your compute optimize work clothes. We're on the in five instance and that's really for a balanced between higher memory per core ratio. And then you have your are five and instance at a W s that's really targeted for that memory intensive workloads. And so all of these are accessible within the single A. W s parallel cholesterol environment on bits at scale. And it's really you're choosing of what you want to take and do. And then on top of that, the they're enabled with the next generation AWS Nitro system, which delivers 100 gigabits of networking for the HBC workloads. So that is huge for HPC. >>I was gonna get to the Nitro is my one of my top questions. Thanks >>for >>thanks for clarifying that. You know, I'm old enough to remember the old days when you have the intel inside the PC a shell of, ah box and create all that great productivity value. But with cloud, it's almost like we're seeing that again. You just hit on some key points you have. Yeah, this is HPC is like memory storage. You've got networking a compute. All these things kind of all kind of working together. If I get that right, you just kind of laid that out there. And it's not an intel Has to be intel. Everything. Your intel inside the cloud now and on premise, which is the There is no on premise anymore. It's cloud operations. If I get this right because you're essentially bridging the two worlds with the chips, you bring on premise which could be edge a big edge or small legend in cloud. Is that right? I mean, this is kind of where this is >>going. Yeah, so I mean, what I think about so a lot of them. The usages for HBC in the cloud is burst capacity. Most HBC centers are 100% not 100% because they have to do maintenance, but 95% utilized, so there is no more space. And so when you have a need to do a larger run or you need thio, you know, have something done quickly you burst to the cloud. That's just what you need to do now. I mean, or you want to try out different instances. So you want to see whether maybe that memory intensive workload would work better? Maybe in kind of that are five in instance, and that gives you that opportunity to see and also, you know, maybe what you want to purchase. So truly, we're entering this hybrid cloud bottle where you can't, um the demand for high performance computing is so large that you've got to be able to burst to the cloud. >>I think you guys got it right. I'm really impressed. And I like what I'm seeing. And I think you talked about earlier the top of the interview, government labs and whatnot. I think those are the early adopters because when they need more power and they usually don't have a lot of big budgets, a little max out and then go to the cloud Whether it's, you know, computing, you know what's going on in the ocean and climate change are all these things that they work on that need massive compute and power. That's a a pretext to enterprise. So if you can't connect the dots, you're kind of right in line with what we're seeing. So super impressive. Thanks for sharing that. Final thoughts on this is that performance. So Okay, the next question is, OK, all great. You're looking good off the tee or looking down the road. Clear path to success in the future. How does the performance compare in the cloud versus on premise? >>It could be well, and that's one of the great things about the Intel select solution because we have optimized that reference designed so that you can get the performance you're used to on Prem in the AWS Cloud. And so that is what's so cool honestly, about this opportunity So we can help you know, that small and medium business that doesn't maybe have this resource is or even those industries that do. And they know they're already a reference using that modeling SIM reference design, and they can now just burst to the cloud and it will work. But the performance they expect >>Trish, great to have you on great insight. Thanks for sharing all the great goodness from Intel and the A W s final thoughts on the on the partnership. We're not in person. And by the way, Intel usually has a huge presence. The booth is usually right behind the cube stage, which you guys sponsor. Thank you very much greater. Always partner with you. Great party. You sponsor the replay, which is always great, and it's always great party and great partnership. Good content. We're not there this year. What's the relationship like? And you take a minute to explain your final thoughts on a Amazon Web services and intel. >>Yeah, I know we have, Ah, Long term partnership 14 plus year partnership with AWS. And I mean, I think it's with the your, um taking Intel Select solution. It's going to be even a richer partnership we're gonna have in the future. So I'm thrilled that I have the opportunity to talk about it and really talk about how excited I am to be able Thio bring Mawr HBC into the world. It's all about the democratization of HBC because HBC changes the world >>well. Tricia, congratulations on the select program with AWS and the first cloud service provider really is a nice directional indicator of what's gonna happen. Futures laid out. Of course. Intel's in front. Thank you for coming. I appreciate it. >>Oh, thank you, John. >>Okay, that's the cubes. Virtual coverage Cube. Virtual. We're not in person. Aws reinvent 2020 is virtual. Three weeks were over the next three weeks, we're gonna bring you coverage. Of course. Cube Live in studio in Palo Alto will be covering a lot of the news. Stay with us from or coverage after this short break. Thank you.

>>thank you. You know, haven't been in technology my entire career. Uh, technology and data has really evolved from being the province of a few and an organization to frankly being critical to everyone's business outcomes. Now, every business leader really needs to embrace data analytics and technology. We've been talking about digital transformation. Probably the last 57 years we've all talked about disruptor be disrupted. At the core of that digital transformation is the use of data, data and analytics that we derive insights from and actually improve our decision making by driving a differentiated experience and capability into market. So Data has involved as being, I would say, almost tactical in some sense over my technology career to really being a strategic asset of what we leverage personally in our own careers, but also what we must leverage as companies to drive a differentiated capability to experience and remain relative in the market today. Mhm. >>No. Yeah, I I agree with Lisa. It has definitely become a the lifeblood of every business, right? It used to be that there were a few companies in the business of technology. Every business is now a technology business. Every business is a data business. It is the way that they go to market, shaped the market and serve their clients. Whether you're in construction, whether you're in retail, whether you're in health care doesn't matter. Right? Data is necessary for every business to survive and thrive. And I remember at the beginning my career It was you know, data was always important, but it waas about storing data. It was about giving people individual reports. It was about supplying that data toe one person or one business unit in silos, and it then evolved right over the course of time into integrating data and to saying, Alright, how does one piece of data correlate to the other? And how can I get insights out of that data now? It's gone to the point of how do I use that data to predict the future? How do I use that data toe automate the future? How do I use that data? Not just for humans to make decisions, but for other machines to make decisions right, which is a big leap, Onda big change and how we use data, how we analyze data and how we use it for insights and evolving our businesses. Yeah. >>Yeah. Well, since I'm on the snowflake board, I'll talk a little bit about the snowflake data cloud. You know, we're getting your company's data out of the silos that exist all over your organization. We're bringing third party data into combined with your own data, and we're wrapping a governance structure around it and feeding it out to your employees so that they can get their jobs done. And it's a simple is that, uh I think we've all seen the pandemic accelerate the digitization of our work. And if you ever doubted that the future of work is here, it is here. And companies are scrambling to catch up by providing the right amount of data, uh, collaboration tools, workflow tools for their workers to get their jobs done. You know, it used to be as prior, people have mentioned that in order thio work with data. You have to be a data scientist. But, you know, I was an auditor back in the day, and we used to work on 16 columns, spreadsheets. And now, if you're an accounting major coming out of college, joining an auditing firm, you have to be checked and data savvy because you're going to be extracting, manipulating, analyzing and auditing data that massive amounts of data that sit in your client's I T systems. I'm on the board of Warby Parker, and you might think that their most valuable asset is their amazing frame collection. But it's actually their data. There are 360 degree view of the customer, and so, if you're a merchant or urine strategy or marketing or talent or the co CEO, you're using data every day in your work. And so I think it's going to become a ubiquitous skill that any anyone who's a knowledge worker has to be able to work with. Data. >>Yeah, absolutely. You know, most enterprises today are, I would say, hybrid multi cloud enterprises. What does that mean? That means that we have data sitting on Prem. We have data sitting in public clouds through software. As a service applications. We have a data everywhere. Most enterprises have data everywhere. Certainly those that have owned infrastructure or weren't born on the web. One of the areas that I love that data cloud is addressing is the area around data portability and mobility because I have data sitting in various locations through my enterprise. How doe I aggregate that data to really drive meaningful insights out of that data to drive better business outcomes and a blue shield of California. One of our key initiatives is what we call an experience cube. What does that mean? It means how doe I drive transparency of data between providers, members and payers so that not only do I reduce overhead on providers and provide them a better experience our hospital systems or doctors, But ultimately, how do we have the member have at their power of their fingertips the value of their data holistically so that we're making better decisions about their health care? You know, one of the things Teresa was talking about was the use of this data, and I would drive to data democratization. We got to put the power of data into the hands of everyone, not just data scientists. Yes, we need those data scientists to help us build a I models to really drive and tackle these tough hold, tougher challenges, business problems that we may have in our environments. But everybody in the company, both on the I T side both on the business side really need to understand. Of how do we become a data insights driven enterprise? Put the power of the data into everyone's hands so that we can accelerate capabilities right and leverage that data toe ultimately drive better business results. So as a leader, as a technology leader, part of our responsibility, our leadership, is to help our companies do that. And that's really one of the exciting things that I'm doing in my role now at Blue Shield of California took, >>oh, great question. And I am so passionate about this for ah, lot of reasons, not the least of which is I have two daughters of my own. Andi, I know how important it is for women and young girls. Toe actually start early in their love for technology and data and all things digital, right? So I think it's one very important to start early start in early education, building confidence of young girls that they can do this showing them role models. You know, we have Deloitte just partnered with L. A B engineer toe actually make comic books centered around young girls and boys in the early elementary age to talk about how heroes and text solve everyday problems on DSO, really helping to get people's minds around Tech is not just in the back office coating on a computer. Tech is about solving problems together. That helped us a citizens as customers, right and as humanity s. So I think that's important. I also think we have to expand that definition of tech. As we just said. It's not just about right database design. It's not just about, you know, Java and python coding. It's about design. It's about the human machine interfaces. It's about how do you use it to solve real problems and getting people to think in that kind of mindset makes it more attractive and exciting. And lastly, I'd say, Look, we have a absolute imperative to get a diverse population of people, not just women but minorities. You know, those with other types of backgrounds, disabilities, etcetera involved because this data is being used to drive decision making, and if we're all involved right and how that data makes decisions, it can lead to unnatural biases that no one intended but can happen just because we haven't involved a diverse enough group of people around it. Absolutely. Lisa. Curious about your thoughts on this? >>I agree with everything that she has said. I've been passionate about this area. I think it starts with First. We need more role models way. Need more role models as women, uh, in these leadership roles throughout various sectors, and it really is. It starts with us and helping to pull other women forward. So I think certainly it's part of my responsibility. I think all of us as female executives that if you have exceeded the table toe leverage that seat at the table to drive change, to bring more women forward, more diversity forward into the board room and into our executive suites. I also want to touch on a point that she had made about women were the largest consumer group in the company. Um, yet we're consumers, but we're not builders. This is why it's so important that we start changing that perception of what tech is, and I agree that it starts with their young girls. We know the data shows that we lose our young girls by middle school. Very heavy peer pressure. It's not so cool to be smart to do robotics or be good in math and science. We start losing our girls in middle school, so they're not prepared when they go to high school. And they're not taking those classes in order to major in these stem fields in college. So we have to start the pipeline early, Um, with our girls. And then I also think it's a measure of what your boards air doing. What is the executive leadership in your goals around diversity and inclusion? How do we invite more diverse population to the decision making table? So it's really a combination of effort. One of the things that certainly is concerning to me is during this pandemic. I think we're losing one in four women in the workforce now because of all the demands that our families are having to navigate through through this pandemic. The last statistic I saw in the last four months is we've lost 850,000 women in the workforce. This pipeline is critical to making that change in these leadership positions. What? Yeah, >>um, I'd encourage you to view to become an active sponsor. Research shows that women and minorities are less likely to be sponsored than white men. Sponsorship is a much more active form than mentorship. Sponsorship involves helping someone identify career opportunities and actively advocating for them in those roles. Opening your network, giving very candid feedback. And we need men to participate to. There are not enough women in tech to pull forward and sponsor the high potential women that are in our pipelines. And so we need you to be part of the solution. >>Let's say Look around your teams, see who's on them and make deliberate decisions about diversifying those teams as positions. Open up. Make sure that you have a diverse set of candidates. Make sure that there are women that are part of that team on DNA. Make sure that you are actually hiring and putting people into positions based on potential, not just experience. >>Wow, it's hard Thio with Nishida and with Tricia shared, I think we're very powerful actions. I think it starts with us, uh, taken action at our own table, making sure you're driving diverse panels and hiring um, setting goals for the company. Having your board engaged in holding us accountable and driving to those goals will help us all see a better outcome with more women at the executive table and diverse populations. >>Yeah, but

>> Hi and welcome to Data Cloud catalyst Women in Tech Round Table Panel discussion. I am so excited to have three fantastic female executives with me today, who have been driving transformations through data throughout their entire career. With me today is Lisa Davis, SVP and CIO OF Blue shield of California. We also have Nishita Henry who is the Chief Innovation Officer at Deloitte and Teresa Briggs who is on a variety of board of directors including our very own Snowflake. Welcome ladies. >> Thank you. >> So I am just going to dive right in, you all have really amazing careers and resumes behind you, am really curious throughout your career, how have you seen the use of data evolve throughout your career and Lisa am going to start with you. >> Thank you, having been in technology my entire career, technology and data has really evolved from being the province of a few in an organization to frankly being critical to everyone's business outcomes. Now every business leader really needs to embrace data analytics and technology. We've been talking about digital transformation, probably the last five, seven years, we've all talked about, disrupt or be disrupted, At the core of that digital transformation is the use of data. Data and analytics that we derive insights from and actually improve our decision making by driving a differentiated experience and capability into market. So data has involved as being I would say almost tactical, in some sense over my technology career to really being a strategic asset of what we leverage personally in our own careers, but also what we must leverage as companies to drive a differentiated capability to experience and remain relative in the market today. >> Nishita curious your take on, how you have seen data evolve? >> Yeah, I agree with Lisa, it has definitely become a the lifeblood of every business, right? It used to be that there were a few companies in the business of technology, every business is now a technology business. Every business is a data business, it is the way that they go to market, shape the market and serve their clients. Whether you're in construction, whether you're in retail, whether you're in healthcare doesn't matter, right? Data is necessary for every business to survive and thrive. And I remember at the beginning of my career, data was always important, but it was about storing data, it was about giving people individual reports, it was about supplying that data to one person or one business unit in silos. And it then evolved right over the course of time into integrating data into saying, alright, how does one piece of data correlate to the other and how can I get insights out of that data? Now, its gone to the point of how do I use that data to predict the future? How do I use that data to automate the future? How do I use that data not just for humans to make decisions, but for other machines to make decisions, right? Which is a big leap and a big change in how we use data, how we analyze data and how we use it for insights and involving our businesses. >> Yeah its really changed so tremendously just in the past five years, its amazing. So Teresa we've talked a lot about the Data Cloud, where do you think we are heading with that and also how can future leaders really guide their careers in data especially in those jobs where we don't traditionally think of them in the data science space? Teresa your thoughts on that. >> Yeah, well since I'm on the Snowflake Board, I'll talk a little bit about the Snowflake Data Cloud, we're getting your company's data out of the silos that exist all over your organization. We're bringing third party data in to combine with your own data and we're wrapping a governance structure around it and feeding it out to your employees so they can get their jobs done, as simple as that. I think we've all seen the pandemic accelerate the digitization of our work. And if you ever doubted that the future of work is here, it is here and companies are scrambling to catch up by providing the right amount of data, collaboration tools, workflow tools for their workers to get their jobs done. Now, it used to be as prior people have mentioned that in order to work with data you had to be a data scientist, but I was an auditor back in the day we used to work on 16 column spreadsheets. And now if you're an accounting major coming out of college joining an auditing firm, you have to be tech and data savvy because you're going to be extracting, manipulating, analyzing and auditing data, that massive amounts of data that sit in your clients IT systems. I'm on the board of Warby Parker, and you might think that their most valuable asset is their amazing frame collection, but it's actually their data, their 360 degree view of the customer. And so if you're a merchant, or you're in strategy, or marketing or talent or the Co-CEO, you're using data every day in your work. And so I think it's going to become a ubiquitous skill that any anyone who's a knowledge worker has to be able to work with data. >> Yeah I think its just going to be organic to every role going forward in the industry. So, Lisa curious about your thoughts about Data Cloud, the future of it and how people can really leverage it in their jobs for future leaders. >> Yeah, absolutely most enterprises today are, I would say, hybrid multicloud enterprises. What does that mean? That means that we have data sitting on-prem, we have data sitting in public clouds through software as a service applications. We have a data everywhere. Most enterprises have data everywhere, certainly those that have owned infrastructure or weren't born on the web. One of the areas that I love that Data Cloud is addressing is area around data portability and mobility. Because I have data sitting in various locations through my enterprise, how do I aggregate that data to really drive meaningful insights out of that data to drive better business outcomes? And at Blue Shield of California, one of our key initiatives is what we call an Experienced Cube. What does that mean? That means how do I drive transparency of data between providers, members and payers? So that not only do I reduce overhead on providers and provide them a better experience, our hospital systems are doctors, but ultimately, how do we have the member have it their power of their fingertips the value of their data holistically, so that we're making better decisions about their health care. One of the things Teresa was talking about, was the use of this data and I would drive to data democratization. We got to put the power of data into the hands of everyone, not just data scientists, yes we need those data scientists to help us build AI models to really drive and tackle these tough old, tougher challenges and business problems that we may have in our environments. But everybody in the company both on the IT side, both on the business side, really need to understand of how do we become a data insights driven enterprise, put the power of the data into everyone's hands so that we can accelerate capabilities, right? And leverage that data to ultimately drive better business results. So as a leader, as a technology leader, part of our responsibility, our leadership is to help our companies do that. And that's really one of the exciting things that I'm doing in my role now at Blue Shield of California. >> Yeah its really, really exciting time. I want to shift gears a little bit and focus on women in Tech. So I think in the past five to ten years there has been a lot of headway in this space but the truth is women are still under represented in the tech space. So what can we do to attract more women into technology quite honestly. So Nishita curious what your thoughts are on that? >> Great question and I am so passionate about this for a lot of reasons, not the least of which is I have two daughters of my own and I know how important it is for women and young girls to actually start early in their love for technology and data and all things digital, right? So I think it's one very important to start early started early education, building confidence of young girls that they can do this, showing them role models. We at Deloitte just partnered with LV Engineer to actually make comic books centered around young girls and boys in the early elementary age to talk about how heroes in tech solve everyday problems. And so really helping to get people's minds around tech is not just in the back office coding on a computer, tech is about solving problems together that help us as citizens, as customers, right? And as humanity, so I think that's important. I also think we have to expand that definition of tech, as we just said it's not just about right, database design, It's not just about Java and Python coding, it's about design, it's about the human machine interfaces, it's about how do you use it to solve real problems and getting people to think in that kind of mindset makes it more attractive and exciting. And lastly, I'd say look we have a absolute imperative to get a diverse population of people, not just women, but minorities, those with other types of backgrounds, disabilities, et cetera involved because this data is being used to drive decision making in all involved, right, and how that data makes decisions, it can lead to unnatural biases that no one intended but can happen just 'cause we haven't involved a diverse enough group of people around it. >> Absolutely, lisa curious about your thoughts on this. >> I agree with everything Nishita said, I've been passionate about this area, I think it starts with first we need more role models, we need more role models as women in these leadership roles throughout various sectors. And it really is it starts with us and helping to pull other women forward. So I think certainly it's part of my responsibility, I think all of us as female executives that if you have a seat at the table to leverage that seat at the table to drive change, to bring more women forward more diversity forward into the boardroom and into our executive suites. I also want to touch on a point Nishita made about women we're the largest consumer group in the company yet we're consumers but we're not builders. This is why it's so important that we start changing that perception of what tech is and I agree that it starts with our young girls, we know the data shows that we lose our like young girls by middle school, very heavy peer pressure, it's not so cool to be smart, or do robotics, or be good at math and science, we start losing our girls in middle school. So they're not prepared when they go to high school, and they're not taking those classes in order to major in these STEM fields in college. So we have to start the pipeline early with our girls. And then I also think it's a measure of what your boards are doing, what is the executive leadership in your goals around diversity and inclusion? How do we invite more diverse population to the decision making table? So it's really a combination of efforts. One of the things that certainly is concerning to me is during this pandemic, I think we're losing one in four women in the workforce now because of all the demands that our families are having to navigate through this pandemic. The last statistic I saw in the last four months is we've lost 850,000 women in the workforce. This pipeline is critical to making that change in these leadership positions. >> Yeah its really a critical time and now we are coming to the end of this conversation I want to ask you Teresa what would be a call to action to everyone listening both men and women since its to be solved by everyone to address the gender gap in the industry? >> I'd encourage each of you to become an active sponsor. Research shows that women and minorities are less likely to be sponsored than white men. Sponsorship is a much more active form than mentorship. Sponsorship involves helping someone identify career opportunities and actively advocating for them and those roles opening your network, giving very candid feedback. And we need men to participate too, there are not enough women in tech to pull forward and sponsor the high potential women that are in our pipelines. And so we need you to be part of the solution. >> Nishita real quickly what would be your call to action to everyone? >> I'd say look around your teams, see who's on them and make deliberate decisions about diversifying those teams, as positions open up, make sure that you have a diverse set of candidates, make sure that there are women that are part to that team and make sure that you are actually hiring and putting people into positions based on potential not just experience. >> And real quickly Lisa, we'll close it out with you what would your call to action be? >> Wow, it's hard to what Nishita and what Tricia shared I think we're very powerful actions. I think it starts with us. Taking action at our own table, making sure you're driving diverse panels and hiring setting goals for the company, having your board engaged and holding us accountable and driving to those goals will help us all see a better outcome with more women at the executive table and diverse populations. >> Great advice and great action for all of us to take. Thank you all so much for spending time with me today and talking about this really important issue, I really appreciate it. Stay with us.

>>Yeah, it's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hi, everybody. And welcome to this cube special presentation of the vertical virtual Big Data conference. The Cube is running in parallel with Day One and day two of the vertical of Big Data event. By the way, the Cube has been every single big data event in It's our pleasure to be here in the virtual slash digital event as well. Gabriel Chapman is here. He's the director of Flash Blade Products Solutions Marketing at Pure Storage. Great to see you. Thanks for coming on. >>Great to see you too. How's it going? >>It's going very well. I mean, I wish we were meeting in Boston at the Encore Hotel, but, uh, you know, and hopefully we'll be able to meet it, accelerate at some point, future or one of the sub shows that you guys are doing the regional shows, but because we've been covering that show as well. But I really want to get into it. And the last accelerate September 2019 pure and vertical announced. Ah, partnership. I remember a joint being ran up to me and said, Hey, you got to check this out. The separation of compute and storage by EON mode now available on Flash Blade. So, uh and and I believe still the only company that can support that separation and independent scaling both on Prem and in the cloud. So I want to ask, what were the trends and analytical database and cloud led to this partnership? You know, >>realistically, I think what we're seeing is that there's been a kind of a larger shift when it comes to modern analytics platforms towards moving away from the traditional, you know, Hadoop type architecture where we were doing on and leveraging a lot of directors that storage primarily because of the limitations of how that solution was architected. When we start to look at the larger trends towards you know how organizations want to do this type of work on premises, they're looking at solutions that allow them to scale the compute storage pieces independently and therefore, you know, the flash blade platform ended up being a great solution to support America in their transition Tian mode. Leveraging essentially is an S three object store. >>Okay, so let's let's circle back on that you guys in your in your announcement of the flash blade, you make the claim that Flash Blade is the industry's most advanced file and object storage platform ever. That's a bold statement. So defend that What? >>I would like to go beyond that and just say, you know, So we've really kind of looked at this from a standpoint of, you know, as as we've developed Flash Blade as a platform and keep in mind, it's been a product that's been around for over three years now and has been very successful for pure storage. The reality is, is that fast file and fast object as a combined storage platform is a direction that many organizations are looking to go, and we believe that we're a leader in that fast object best file storage place in realistically, which we start to see more organizations start to look at building solutions that leverage cloud storage characteristics. But doing so on Prem for a multitude of different reasons. We've built a platform that really addresses a lot of those needs around simplicity around, you know, making things this year that you know, fast matters for us. Ah, simple is smart. Um we can provide, you know, cloud integrations across the spectrum. And, you know, there's a subscription model that fits into that as well. We fall that that falls into our umbrella of what we consider the modern day takes variance. And it's something that we've built into the entire pure portfolio. >>Okay, so I want to get into the architecture a little bit of flash blade and then understand the fit for, uh, analytic databases generally, but specifically for vertical. So it is a blade, so you got compute and network included. It's a key value store based system. So you're talking about scale out. Unlike, unlike, uh, pure is sort of, you know, initial products which were scale up, Um, and so I want on It is a fabric based system. I want to understand what that all means to take us through the architecture. You know, some of the quote unquote firsts that you guys talk about. So let's start with sort of the blade >>aspect. Yeah, the blade aspect of what we call the flash blade. Because if you look at the actual platform, you have, ah, primarily a chassis with built in networking components, right? So there's ah, fabric interconnect with inside the platform that connects to each one of the individual blades. Individual blades have their own compute that drives basically a pure storage flash components inside. It's not like we're just taking SSD is and plugging them into a system and like you would with the traditional commodity off the shelf hardware design. This is very much an engineered solution that is built towards the characteristics that we believe were important with fast filing past object scalability, massive parallel ization. When it comes to performance and the ability to really kind of grow and scale from essentially seven blades right now to 150 that's that's the kind of scale that customers are looking for, especially as we start to address these larger analytics pools. They are multi petabytes data sets, you know that single addressable object space and, you know, file performance that is beyond what most of your traditional scale up storage platforms are able to deliver. >>Yes, I interviewed cause last September and accelerate, and Christie Pure has been attacked by some of the competitors. There's not having scale out. I asked him his thoughts on that, he said Well, first of all, our flash blade is scale out. He said, Look, anything that adds complexity, you know we avoid. But for the workloads that are associated with flash blade scale out is the right sort of approach. Maybe you could talk about why that is. Well, >>realistically, I think you know that that approach is better when we're starting to work with large, unstructured data sets. I mean, flash blade is unique. The architected to allow customers to achieve superior resource utilization for compute and storage, while at the same time, you know, reducing significantly the complexity that has arisen around this kind of bespoke or siloed nature of big data and analytics solutions. I mean, we're really kind of look at this from a standpoint of you have built and delivered are created applications in the public cloud space of dress, you know, object storage and an unstructured data. And for some organizations, the importance is bringing that on Prem. I mean, we do see about repatriation coming on a lot of organizations as these data egress, charges continue to expand and grow, um, and then organizations that want even higher performance and what we're able to get into the public cloud space. They are bringing that data back on Prem They are looking at from a stamp. We still want to be able to scale the way we scale in the cloud. We still want to operate the same way we operate in the cloud, but we want to do it within control of our own, our own borders. And so that's, you know, that's one of the bigger pieces to that. And we start to look at how do we address cloud characteristics and dynamics and consumption metrics or models? A zealous the benefits and efficiencies of scale that they're able to afford but allowing customers to do that with inside their own data center. >>So you're talking about the trends earlier. You have these cloud native databases that allowed of the scaling of compute and storage independently. Vertical comes in with eon of a lot of times we talk about these these partnerships as Barney deals of you know I love you, You love me. Here's a press release and then we go on or they're just straight, you know, go to market. Are there other aspects of this partnership that they're non Barney deal like, in other words, any specific engineering. Um, you know other go to market programs? Could you talk about that a little bit? Yeah, >>it's it's It's more than just that what we consider a channel meet in the middle or, you know, that Barney type of deal. It's realistically, you know, we've done some first with Veronica that I think, really Courtney, if they think you look at the architecture and how we did, we've brought to market together. Ah, we have solutions. Teams in the back end who are, you know, subject matter experts. In this space, if you talk to joy and the people from vertical, they're very high on our very excited about the partnership because it often it opens up a new set of opportunities for their customers to leverage on mode and get into some of the the nuance task specs of how they leverage the depot depot with inside each individual. Compute node in adjustments with inside their reach. Additional performance gains for customers on Prem and at the same time, for them, that's still tough. The ability to go into that cloud model if they wish to. And so I think a lot of it is around. How do we partner is to companies? How do we do a joint selling motions? How do we show up in and do white papers and all of the traditional marketing aspects that we bring to the market? And then, you know, joint selling opportunities exist where they are, and so that's realistically. I think, like any other organization that's going to market with a partner on MSP that they have, ah, strong partnership with. You'll continue to see us, you know, talking about are those mutually beneficial relationships and the solutions that we're bringing to the market. >>Okay, you know, of course, he used to be a Gartner analyst, and you go to the vendor side now, but it's but it's, but it's a Gartner analyst. You're obviously objective. You see it on, you know well, there's a lot of ways to skin the cat There, there their strengths, weaknesses, opportunities, threats, etcetera for every vendor. So you have you have vertical who's got a very mature stack and talking to a number of the customers out there who are using EON mode. You know there's certain workloads where these cloud native databases makes sense. It's not just the economics of scaling and storage independently. I want to talk more about that. There's flexibility aspect as well. But Vertical really has to play its its trump card, which is Look, we've got a big on premise state, and we're gonna bring that eon capability both on Prem and we're embracing the cloud now. There obviously have been there to play catch up in the cloud, but at the same time, they've got a much more mature stack than a lot of these other cloud native databases that might have just started a couple of years ago. So you know, so there's trade offs that customers have to make. How do you sort through that? Where do you see the interest in this? And and what's the sweet spot for this partnership? You know, we've >>been really excited to build the partnership with vertical A and provide, you know, we're really proud to provide pretty much the only on Prem storage platform that's validated with the yang mode to deliver a modern data experience for our customers together. You know, it's ah, it's that partnership that allows us to go into customers that on Prem space, where I think that there's still not to say that not everybody wants to go there, but I think there's aspects and solutions that worked very well there. But for the vast majority, I still think that there's, you know, the your data center is not going away. And you do want to have control over some of the many of the assets with inside of the operational confines. So therefore, we start to look at how do we can do the best of what cloud offers but on prim. And that's realistically, where we start to see the stronger push for those customers. You still want to manage their data locally. A swell as maybe even worked around some of the restrictions that they might have around cost and complexity hiring. You know, the different types of skills skill sets that are required to bring applications purely cloud native. It's still that larger part of that digital transformation that many organizations are going for going forward with. And realistically, I think they're taking a look at the pros and cons, and we've been doing cloud long enough where people recognize that you know it's not perfect for everything and that there's certain things that we still want to keep inside our own data center. So I mean, realistically, as we move forward, that's, Ah, that better option when it comes to a modern architecture that can do, you know, we can deliver an address, a diverse set of performance requirements and allow the organization to continue to grow the model to the data, you know, based on the data that they're actually trying to leverage. And that's really what Flash was built for. It was built for a platform that could address small files or large files or high throughput, high throughput, low latency scale of petabytes in a single name. Space in a single rack is we like to put it in there. I mean, we see customers that have put 150 flash blades into production as a single name space. It's significant for organizations that are making that drive towards modern data experience with modern analytics platforms. Pure and Veronica have delivered an experience that can address that to a wide range of customers that are implementing uh, you know, particularly on technology. >>I'm interested in exploring the use case. A little bit further. You just sort of gave some parameters and some examples and some of the flexibility that you have, um, and take us through kind of what the customer discussions are like. Obviously you've got a big customer base, you and vertical that that's on Prem. That's the the unique advantage of this. But there are others. It's not just the economics of the granular scaling of compute and storage independently. There are other aspects of take us through that sort of a primary use case or use cases. Yeah, you >>know, I mean, I could give you a couple customer examples, and we have a large SAS analyst company which uses vertical on last way to authenticate the quality of digital media in real time, You know, then for them it makes a big difference is they're doing their streaming and whatnot that they can. They can fine tune the grand we control that. So that's one aspect that that we address. We have a multinational car car company, which uses vertical on flash blade to make thousands of decisions per second for autonomous vehicle decision making trees. You know, that's what really these new modern analytics platforms were built for, um, there's another healthcare organization that uses vertical on flash blade to enable healthcare providers to make decisions in real time. The impact lives, especially when we start to look at and, you know, the current state of affairs with code in the Corona virus. You know, those types of technologies, we're really going to help us kind of get of and help lower invent, bend that curve downward. So, you know, there's all these different areas where we can address that the goals and the achievements that we're trying to look bored with with real time analytics decision making tools like and you know, realistically is we have these conversations with customers they're looking to get beyond the ability of just, you know, a data scientist or a data architect looking to just kind of driving information >>that we're talking about Hadoop earlier. We're kind of going well beyond that now. And I guess what I'm saying is that in the first phase of cloud, it was all about infrastructure. It was about, you know, uh, spin it up. You know, compute and storage is a little bit of networking in there. >>It >>seems like the next new workload that's clearly emerging is you've got. And it started with the cloud native databases. But then bringing in, you know, AI and machine learning tooling on top of that Ah, and then being able to really drive these new types of insights and it's really about taking data these bog this bog of data that we've collected over the last 10 years. A lot of that is driven by a dupe bringing machine intelligence into the equation, scaling it with either cloud public cloud or bringing that cloud experience on Prem scale. You know, across organizations and across your partner network, that really is a new emerging workloads. You see that? And maybe talk a little bit about what you're seeing with customers. >>Yeah. I mean, it really is. We see several trends. You know, one of those is the ability to take a take this approach to move it out of the lab, but into production. Um, you know, especially when it comes to data science projects, machine learning projects that traditionally start out as kind of small proofs of concept, easy to spin up in the cloud. But when a customer wants to scale and move towards a riel you know, derived a significant value from that. They do want to be able to control more characteristic site, and we know machine learning, you know, needs toe needs to learn from a massive amounts of data to provide accuracy. There's just too much data retrieving the cloud for every training job. Same time Predictive analytics without accuracy is not going to deliver the business advantage of what everyone is seeking. You know, we see this. Ah, the visualization of Data Analytics is Tricia deployed is being on a continuum with, you know, the things that we've been doing in the long in the past with data warehousing, data Lakes, ai on the other end. But this way, we're starting to manifest it and organizations that are looking towards getting more utility and better elasticity out of the data that they are working for. So they're not looking to just build apps, silos of bespoke ai environments. They're looking to leverage. Ah, you know, ah, platform that can allow them to, you know, do ai, for one thing, machine learning for another leverage multiple protocols to access that data because the tools are so much Jeff um, you know, it is a growing diversity of of use cases that you can put on a single platform I think organizations are looking for as they try to scale these environment. >>I think it's gonna be a big growth area in the coming years. Gable. I wish we were in Boston together. You would have painted your little corner of Boston orange. I know that you guys have but really appreciate you coming on the cube wall to wall coverage. Two days of the vertical vertical virtual big data conference. Keep it right there. Right back. Right after this short break, Yeah.

>> 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.