>> Announcer: Live from New York City, it's The Cube, covering IBM data science for all. Brought to you by IBM. >> Welcome back to data science for all. This is IBM's event here on the west side of Manhattan, here on The Cube. We're live, we'll be here all day, along with Dave Vallente, I'm John Walls Poor Dave had to put up with all that howling music at this hotel last night, kept him up 'til, all hours. >> Lots of fun here in the city. >> Yeah, yeah. >> All the crazies out last night. >> Yeah, but the headphones, they worked for ya. Glad to hear that. >> People are already dressed for Halloween, you know what I mean? >> John: Yes. >> In New York, you know what I mean? >> John: All year. >> All the time. >> John: All year. >> 365. >> Yeah. We have with us now the head of data science, and the VP at Galvanize, Nir Kaldero, and Nir, good to see you, sir. Thanks for being with us. We appreciate the time. >> Well of course, my pleasure. >> Tell us about Galvanize. I know you're heavily involved in education in terms of the tech community, but you've got corporate clients, you've got academic clients. You cover the waterfront, and I know data science is your baby. >> Nir: Right. >> But tell us a little bit about Galvanize and your mission there. >> Sure, so Galvanize is the learning community for technology. We provide the training in data science, data engineering, and also modern software engineering. We recently built a very large, fast growing enterprise corporate training department, where we basically help companies become digital, become nimble, and also very data driven, so they can actually go through this digital transformation, and survive in this fourth industrial revolution. We do it across all layers of the business, from the executives, to managers, to data scientists, and data analysts, and kind of transform and upscale all current skills to be modern, to be digital, so companies can actually go through this transformation. >> Hit on one of those items you talked about, data driven. >> Nir: Right. >> It seems like a no-brainer, right? That the more information you give me, the more analysis I can apply to it, the more I can put it in my business practice, the more money I make, the more my customers are happy. It's a lay up, right? >> Nir: It is. >> What is a data driven organization, then? Do you have to convince people that this is where they need to be today? >> Sometimes I need to convince them, but (laughs) anyway, so let's back up a little bit. We are in the midst of the fourth industrial revolution, and in order to survive in this fourth industrial revolution, companies need to become nimble, as I said, become agile, but most importantly become data driven, so the organization can actually best respond to all the predictions that are coming from this very sophisticated machine intelligence models. If the organization immediately can best respond to all of that, companies will be able to enhance the user experience, get insight about their customers, enhance performances, and et cetera, and we know that the winners in this revolution, in this era, will be companies who are very digital, that master the skills of becoming a data driven organization, and you know, we can talk more about the transformation, and what it consisted of. Do you want me to? >> John: Sure. >> Can I just ask you a question? This fourth wave, this is what, the cognitive machine wave? Or how would you describe it? >> Some people call it artificial intelligence. I think artificial intelligence is like big data, kind of like a buzz word. I think more appropriately, we should call it machine intelligence industrial revolution. >> Okay. I've got a lot of questions, but carry on. >> So hitting on that, so you see that as being a major era. >> Nir: It's a game changer. >> If you will, not just a chapter, but a major game changer. >> Nir: Yup. >> Why so? >> So, okay, I'll jump in again. Machines have always replaced man, people. >> John: The automation, right. >> Nir: To some extent. >> But certain machines have replaced certain human tasks, let's say that. >> Nir: Correct. >> But for the first time in history, this fourth era, machine's are replacing humans with cognitive tasks, and that scares a lot of people, because you look at the United States, the median income of the U.S. worker has dropped since 1999, from $55,000 to $52,000, and a lot of people believe it's sort of the hollowing out of that factor that we just mentioned. Education many believe is the answer. You know, Galvanize is an organization that plays a critical role in helping deal with that problem, does it not? >> So, as Mark Zuckerberg says, there is a lot of hate love relationship with A.I. People love it on one side, because they're excited about all the opportunities that can come from this utilization of machine intelligence, but many people actually are afraid from it. I read a survey a few weeks ago that says that 36% of the population thinks that A.I. will destroy humanity, and will conquer the world. That's a fact that's what people think. If I think it's going to happen? I don't think so. I highly believe that education is one of the pillars that can address this fear for machine intelligence, and you spoke a lot about jobs I talk about it forever, but just my belief is that machines can actually replace some of our responsibilities, right? Not necessarily take and replace the entire job. Let's talk about lawyers, right? Lawyers currently spend between 40% to 60% of the time writing contracts, or looking at previous cases. The machine can write a contract in two minutes, or look up millions of data points of previous cases in zero time. Why a lawyer today needs to spend 40% to 60% of the time on that? >> Billable hours, that's why. >> It is, so I don't think the machine will replace the job of the lawyer. I think in the future, the machine replaces some of the responsibilities, like auditing, or writing contracts, or looking at previous cases. >> Menial labor, if you will. >> Yes, but you know, for example, the machine is not that great right now with negotiations skills. So maybe in the future, the job of the lawyer will be mostly around negotiation skills, rather than writing contracts, et cetera, but yeah, you're absolutely right. There is a big fear in the market right now among executives, among people in the public. I think we should educate people about what is the true implications of machine intelligence in this fourth industrial revolution and era, and education is definitely one of those. >> Well, one of my favorite stories, when people bring up this topic, is when Gary Kasparov lost to the IBM super computer, Blue Jean, or whatever it's called. >> Nir: Yup. >> Instead of giving up, what he said is he started a competition, where he proved that humans and machines could beat the IBM super computer. So to this day has a competition where the best chess player in the world is a combination between humans and machines, and so it's that creativity. >> Nir: Imagination. >> Imagination, right, combinatorial effects of different technologies that education, hopefully, can help keep those either way. >> Look, I'm a big fan of neuroscience. I wish I did my PhD in neuroscience, but we are very, very far away from understanding how our brain works. Now to try to imitate the brain when we don't know how the brain works? We are very far away from being in a place where a machine can actually replicate, and really best respond like a human. We don't know how our brain works yet. So we need to do a lot of research on that before we actually really write a very strong, powerful machine intelligence model that can actually replace us as humans, and outbid us. We can speak about Jeopardy, and what's on, and we can speak about AlphaGo, it's a Google company that kind of outperformed the world champion. These are very specific tasks, right? Again, like the lawyer, the machines can write beautiful contracts with NLP, machines can look at millions and trillions of data and figure out what's the conclusion there, right? Or summarize text very fast, but not necessarily good in negotiation yet. >> So when you think about a digital business, to us a digital business is a business that uses data to differentiate, and serve customers, and maintain customers. So when you talk about data driven, it strikes me that when everybody's saying digital business, digital transformation, it's about a data transformation, how well they utilize data, and if you look at the bell curve of organizations, most are not. Everybody wants to be data driven, many say they are data driven. >> Right. >> Dave: Would you agree most are not? >> I will agree that most companies say that they are data driven, but actually they're not. I work with a lot of Fortune 500 companies on a daily basis. I meet their executives and functional leaders, and actually see their data, and business problems that they have. Most of them do tend to say that they are data driven, but truly just ask them if they put data and decisions in the same place, every time they have to make a decision, they don't do it. It's a habit that they don't yet have. Companies need to start investing in building what we say healthy data culture in order to enable and become data driven. Part of it is democratization of data, right? Currently what I see if lots of organizations actually open the data just for the analyst, or the marketers, people who kind of make decisions, that need to make decisions with data, but not throughout the entire organization. I know I always say that everyone in the organization makes decisions on a daily basis, from the barista, to the CEO, right? And the entirety of becoming data driven is that data can actually help us make better decisions on a daily basis, so how about democratizing the data to everyone? So everyone, from the barista, to the CEO, can actually make better decisions on a daily basis, and companies don't excel yet in doing it. Not every company is as digital as Amazon. Amazon, I think, is actually one of the most digital companies in the world, if you look at the digital index. Not everyone is Google or Facebook. Most companies want to be there, most companies understand that they will not be able to survive in this era if they will not become data driven, so it's a big problem. We try at Galvanize to address this problem from executive type of education, where we actually meet with the C-level executives in companies, and actually guide them through how to write their data strategy, how to think about prioritizing data investment, to actual implementation of that, and so far we are highly successful. We were able to make a big transformation in very large, important organizations. So I'm actually very proud of it. >> How long are these eras? Is it a century, or more? >> This fourth industrial? >> Yeah. >> Well it's hard to predict that, and I'm not a machine, or what's on it. (laughs) >> But certainly more than 50 years, would you say? Or maybe not, I don't know. >> I actually don't think so. I think it's going to be fast, and we're going to move to the next one pretty soon that will be even more, with more intelligence, with more data. >> So the reason I ask, is there was an article I saw and linked, and I haven't had time to read it, but it talked about the Four Horsemen, Amazon, Google, Facebook, and Apple, and it said they will all be out of business in 50 years. Now, I don't know, I think Apple probably has 50 years of cash flow in the bank, but then they said, the one, the author said, if I had to predict one that would survive, it would be Amazon, to your point, because they are so data driven. The premise, again I didn't read the whole thing, was that some new data driven, digital upstart will disrupt them. >> Yeah, and you know, companies like Amazon, and Alibaba lately, that try kind of like in a competition with Amazon about who is becoming more data driven, utilizing more machine intelligence, are the ones that invested in these capabilities many, many years ago. It's no that they started investing in it last year, or five years ago. We speak about 15 and 20 years ago. So companies who were really a pioneer, and invested very early on, will predict actually to survive in the future, and you know, very much align. >> Yeah, I'm going to touch on something. It might be a bridge too far, I don't know, but you talk about, Dave brought it up, about replacing human capital, right? Because of artificial intelligence. >> Nir: Yup. >> Is there a reluctance, perhaps, on behalf of executives to embrace that, because they are concerned about their own price? >> Nir: You should be in the room with me. (laughing) >> You provide data, but you also provide that capability to analyze, and make the best informed decision, and therefore, eliminate the human element of a C-suite executive that maybe they're not as necessary today, or tomorrow, as they were two years ago. >> So it is absolutely true, and there is a lot of fear in the room, especially when I show them robots, they freak out typically, (John and Dave laugh) but the fact is well known. Leaders who will not embrace these skills, and understanding, and will help the organization to become agile, nimble, and data driven, will not survive. They will be replaced. So on the one hand, they're afraid from it. On the other side, they see that if they will not actually do something, and take an action today, they might be replaced in the future. >> Where should organizations start? Hey, I want to be data driven. Where do I start? >> That's a good question. So data science, machine learning, is a top down initiative. It requires a lot of funding. It requires a change in culture and habits. So it has to start from the top. The journey has to start from executive, from educating and executive about what is data science, what is machine learning, how to prioritize investments in this field, how to build data driven culture, right? When we spoke about data driven, we mainly speaks about the culture aspect here, not specifically about the technical side of it. So it has to come from the top, leaders have to incorporate it in the organization, the have to give authority and power for people, they have to put the funding at first, and then, this is how it's beautiful, that you actually see it trickles down to the organization when they have a very powerful CEO that makes a decision, and moves the organization quickly to become data driven, make executives look at data every time they make a decision, get them into the habit. When people look up to executives, they try to do the same, and if my boss is an example for me, someone who is looking at data every time he is making a decision, ask the right questions, know how to prioritize, set the right goals for me, this helps me, and helps the organization better perform. >> Follow the leader, right? >> Yup. >> Follow the leader. >> Yup, follow the leader. >> Thanks for being with us. >> Nir: Of course, it's my pleasure. >> Pinned this interesting love hate thing that we have going on. >> We should address that. >> Right, right. That's the next segment, how about that? >> Nir Kaldero from Galvanize joining us here live on The Cube. Back with more from New York in just a bit.

>> 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.

>> Narrator: Live from Santa Clara in the heart of Silicon Valley. It's the Cube. Covering Cloud Foundry Summit 2017. Brought to you by the Cloud Foundry Foundation and Pivotal. >> Welcome back, I'm Stu Miniman joined by my cohost, John Troyer. There's nothing we love more when we're at the User Conference is to actually be able to dig in and talk with the users. I want to welcome to the program Opal Perry who is a divisional CIO at Allstate. Did the keynote this morning. A really good community here. I know they were excited to hear your story and thank you so much for joining us. >> Thanks, it's great to be here with you. >> So Opal, we hear this term the digital transformation. Some people think it's just a buzz word but you talked in your keynote about the transformation that's going on in your world. Why don't you give us a quick overview of your role and what this transformation has been. >> Sure, so I've been with Allstate almost six years and I'm one of the vice presidents on the technology leadership team so we both work together as a whole team on initiatives that affect the entire enterprise. And then my particular day-to-day focus is Divisional CIO of Claims. We're a large insurer. The number publicly held insurer in the U.S. We support claims for auto, property, Allstate business insurance. It's a outstanding time to be in the business because there's just so much going on in technology. There's so many immersion areas and particularly when we are able to knit them together to serve our customers from insurance protection, restoration standpoint. It's really powerful. We do say and hear transformation so much that it feels sometimes like an overused term but I haven't found a better word for it yet because I think things really are transformative. We've been used to, for many years in the industry, change. Right, continuous improvement. We're always trying to change and get better. But what's happening now with this conversions of forces is truly transformative. We're not just replacing one way of doing things with a slightly improved way. We're changing the way people interact and serve the customer. >> And Opal, what was the driver for the change? Was there a pain point or competitive pressure? What drove this change? >> At Allstate, it's all about the customer opportunity. As I mentioned this morning, we've got 16 million customer households and that's just a tremendous responsibility and also a tremendous opportunity. To us, it was thinking about how do we bring the forces of this great 86-year-old company to bear and use the digital and technology changes emerging and really do that in support of giving our customer a better and better experience. How do we protect them? How do we restore them? >> As you are making this transformation to... We're here at the Cloud Foundry Summit, so interested in the Cloud Foundry story, how some of that decision process, obviously the tech is really cool, A. So was this coming out of the developers first, the technologists first or was it more of a needs analysis from the top-down that like a platform instead of technologies like Cloud Foundry? It could be what we need. >> It really came from a number of quarters but the tipping force was from our infrastructure area. As we looked like a lot of large companies do at what's the future of infrastructure, both in the data center, themes that have been emerging for many years in Cloud. There were a number of us that are leaders at Allstate that came from a banking background so we had seen previous era changes. Prior to Cloud Foundry been instantiated, I'd worked more in home-grown paths and seen that opportunity both from the developer but also from the infrastructure and so when Andy Zitney had joined us, he's with McKesson now, but he had joined and was our CTO for a period of time and had background from Chase and PayPal and various areas. He came in and build our platform team and really looked through their selection process, determined Cloud Foundry was a great option for us and something that we could grow with over time to start meeting the needs. But it was really an interest of saying hey, let's let infrastructure get out of the way, provide the foundation for the developers, and let the developers innovate great software for the business. But let's let the platform take care of things. He brought early awareness to a lot of those factors. >> Yeah, I think the joke is that nobody should be righting their own cryptographic software anymore (Stu chuckles). Nobody should be writing a distributed key-value ParaStore anymore. The Cloud Foundry people will tell you nobody should be writing their own platform anymore. That's hard enough, let somebody else take care of it. >> Yeah, maybe if you're a PhD student (interviewers chuckling) or researching the next great idea but in terms of being within an enterprise, whose primary role is to serve customers in a different way. Again, it just takes care of a lot of the lifting. That took a while when we introduced it for some people to understand. People would say to me why are you adding another layer? Getting them to understand the power of the abstraction and that's what we're really doing. We're lifting up above so we don't have to be worried so much about the exact infrastructure we're sitting on. >> That upscaling process that you're talking about, that training process. Both from the developer side and the operational side, there's a learning curve. Some people embrace it and some maybe not so much. Can you talk a little about how people have gotten trained up on the new skills, how you're helping people do that? >> Yes, in our platform team, it really started with Matt Curry who joined us a few years ago. He's a awesome engineer but also a great leader. He really set the tone culturally for the platform team to be learning environment and for people to share a lot. So a lot's really happened where he's led the hiring and training and seating of the platform team. From a developer perspective, when we looked across the enterprise and realized we've got a couple thousand developers that have worked for us for decades across different areas, we needed to do something more to reach scale more quickly. Initially, we were pairing with Pivotal and that was effective in getting some good results but we thought in order to make that scale and scale more quickly, we wanted to take a different approach. We partnered with Galvanize and brought in-house a 12-week bootcamp-style approach. >> Opal, one of the things that really resonated in your keynote, you talked about painting a picture as to how this technology really impacted your customers. There was a tree, there was a sun, there was your lab's environment and roots. Maybe if you could tease that out a little bit for us and explain how this technology really impacts your users. >> Yes, well, one I think in using that metaphor, it kind of acknowledges the environment is somewhat organic, right? The platform is still growing a lot, the ecosystem we're in, we have the chance to both contribute to the community and to take from it as it develops. To me, that's a really strong notion. The notion that particularly in leadership, we're kind of we're gardeners in a way, right? We're fostering the growth and so I thought that it's a really good example of thinking about as a tree or any plant really grows. It needs a variety of factors so I said our customers are like the sun to us, they're the reason for existing, and that's what we're all orbiting around. But the air represents all the business opportunity. The winds of change have been blowing mightily for years. The soil in which the tree is planted is like all the great Cloud Foundry instances. It's the training, it's the new role definition, it's the holistic program that really defines how we work as a digital product team. We put all that together and we need constant leadership support on a number of grounds to really make sure we take and cement the change. >> What about the developers? Where do they fit in this natural, organic analogy. >> They're the growing, thriving, strong plant itself. I think both. We aim for each individual product team and each individual, whether it's developer, product manager or designer to be continuously growing and using their creativity, discipline, strength, to bring us great business results. And then when you kind of back out and look at our network or product teams, that's a really important thing to me. An enterprise of our scale is very few breakthroughs will occur, I believe, because of a single digital product innovation. It's really in the ability to knit together different products to provide an end-to-end service or experience to the customer. >> How do you look at the public cloud? You know, Cloud Foundry allows? We were talking about BOSH, a multi-cloud environment. Where does your applications and deployments live today and how do you look at the public cloud? >> You know, we're still exploring some of the possibilities. Matt and his team have been very active looking. We started with on-premise installation for Cloud Foundry. And for myself, leading a development team, it's great as the platform is a look to kind of burst out into a multi-cloud environment. It'll be transparent to my team as long as we're operating to run on our Cloud Foundry instance, they can take us wherever we need to go. They've been doing a lot of work with our security team and other areas of the company to determine what's the right way to forge the path forward. I had a meeting with them Friday and they've got some great design things in the works. I think the next six moths to a year, are going to be looking at some real strong expansion of our cloud strategy. >> How does security fit into this whole picture? Obviously, a major concern for every CIO these days. >> Yeah, absolutely. I mean, to us, we've taken a real security-first approach. We're been our CISO team has been working really closely with Matt and the Cloud engineers and they're just defining how do we want to segregate parts of our environment? How do we follow the principle of trust no one and build security in from the get-go? Again, it's a little bit like the platform itself. I'm confident when they get a solution in place, they'll minimize the burden on my developers and we can just have a security-first mindset but have a lot of the hygiene taken care of by the platform implementation. >> Again, something you don't want to differentiate on. You want to be built into the foundation, or the roots, maybe of our metaphor here. >> Opal: Yes. I heard ya. >> Opal, can you talk a little bit about the apps? Obviously, we've already used words like scale here today. Allstate's a big company. You've got lots of apps. Legacy apps, many different kinds of stacks, generations of technology. How are you choosing what ends up being is this greenfield or things that are being moved? How are you all looking at different applications inside the company? Where they live on which cloud and how they get modernized? >> We're lighting the business needs and strategy, really drive how we prioritize. It really is a matter of a lot, at this point, triage and prioritization. We've got a rich set of opportunities. When we're building new apps in-house, we're certainly looking to take a cloud-first approach. Again, a lot of that's within our own walls today but we know that with the Foundry, it offers us the option to burst out at a later date and leaves us some optionality. The Allstate Corporation, the Allstate brand of insurance is what's best known but in Claims, I also support we have a brand called Encompass Insurance so we're looking to provide support for multiple companies and build technology that can serve everyone. There are a lot of cases too, in an ecosystem like ours, where we're working with third party vendors and they're increasingly offering cloud-based solutions. Again, we do a lot of work with them from the security and compliance perspective to make sure that their strategy is consistent with ours. To make sure we take appropriate care of our customer data. And then I personally get really excited by the refactoring opportunities. I'm really fortunate in Claims that our core claims system was implemented just about 10 years ago. I call it legacy now, but it's not, (John chuckles) as far back to the dark ages as some of the other systems that you'll find within the walls of enterprises. It was build as our last big monolithic implementation and we've been doing decoupling there. So whenever we know we're going to do a decoupling, we look for what opportunity to implement new cloud native microservices and again just stand that up in our environment with the platform team. >> I wanted to ask also about culture and technology adoption. We're sitting here in the middle of Silicon Valley. This cloud phenomenon driven a lot from Silicon Valley. Sometimes people think this cloud native stuff, it's for startups, it's for the kids, it's for whatever. You're based in the Midwest and I also, I'm an Illinois boy myself. You get sometimes, kind of a inferiority complex about the coast, both coasts. But this does not seem to be a coastal phenomenon. This does not seem to be something that only a startup can learn. This is Allstate, a mature company and with a Midwestern base, can you kind of talk a little about was there anything about that in terms of people saying we can't do that here or that sort of thing? >> No, no, I mean, in fact, I think it's a global phenomenon. I was living for almost two years in Belfast, Northern Ireland. We have a division there, Allstate Northern Ireland and we saw a lot of Foundry activity among different companies there. Of course, there's a European summit every year, as well, so I think it's just good common sense. A lot of us, again, before Cloud Foundry came through were working with the different predecessor technologies and Spring and Vmware, you know various aspects and kind of knitting together which felt like reinventing the wheel. So it's just good business sense, good common sense when there's a solution that you can leverage. I think it's just like you were commenting earlier, right? If it's there and you can use it and you can allow the focus to be on what really differentiates you as a business to your customers. That's the way to go. >> Opal, the last question I have for you is there either commentary on any of the announcements that were made this week or are there any things that you're hoping really, for either Pivotal, the fFundation in general, your ecosystem that would make your life easier that's kind of on your to-do list from the vendor side? >> There's so much to take in. I think it's probably still going to take me a week to absorb all the implications. It's great to watch the dynamics going on. I think Microsoft joining the Foundation, that's a very good move 'cause we have so many different technologies within our enterprise so to understand how different vendors are working and playing together in some way is really good. I think Abbey and the Foundation, they've been fantastic about always soliciting input from members like us and members of the community about what we want to see. For me, it's always a big eye-to-word scale. Again, we're a huge enterprise. There are even larger enterprises here that have started running and when this really becomes the we all achieve the aspirational goals and it becomes the day-to-day backbone. It's just making sure this is really hardened to run at true enterprise weight. I think that the enterprise scale of the future is going to be even bigger than what it has been historically because with all these new products, we're driving an appetite towards greater and greater customer interaction. I saw that in banking ten years ago and I think we're going to see it in insurance more and more so we just want to know that we're all working together to get that strength and that power that the customer needs. >> Opal Perry, really appreciate you sharing Allstate's digital transformation with us and our audience, for John and myself. We'll be back with more coverage here from the Cloud Foundry Summit. Thanks for watching the Cube. >> Opal: Thank you. (gentle lively music)

>> Voiceover: Live from Las Vegas, it's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We are live in Las Vegas for IBM InterConnect 2017. This is IBM's Cloud show and, now, data show. This is theCUBE's coverage. I'm John Furrier with my cohost, Dave Vellante. Our next guest is Meg Swanson, VP of Marketing for Bluemix, the whole kit and caboodle, SoftLayer of Bluemix. Now you get to watch some data platform, IOT. The Cloud's growing up. How you doing? Good to see you again. >> It's good. Good to see you guys. Every time we get together, it's just huge growth. Every time, every month to month. Under Bluemix, we've pulled together infrastructure. The area that was called SoftLayer. And because we had developers that absolutely you need a provision down to bare metal servers, all the way up to applications. So we pulled the infrastructure together with the developer services, together with our VMware partnership, all in a single console. Continuing to work on, with clients, on just having a unified experience. That's why we have it under the Bluemix brand. >> You knew us when we were just getting theCUBE started. We knew you when you were kicking off the developer program, with Bluemix, was announced here in theCUBE. Seems like 10 dog years ago, which is about 50 years, no, that was, what, four years ago now? Are you four years in? >> I think so. Yeah, 'cause I remember running from the Hakkasan club, we had just ended a virtual reality session, and I had to run, and then I sat down, and we started immediately talking about Bluemix 'cause we just launched it. >> So here's the update. You guys have been making a lot of progress, and we've been watching you. It's been fantastic, 'cause you really had to run fast and get this stuff built out, 'cause Cloud Native, it wasn't called Cloud Native back then, it was just called Cloud. But, essentially, it was the Cloud Native vision. Services, microservices, APIs, things, we've talked about that. What's the progress? Give us the update and the status, and where are you? >> Yeah, obviously just massive growth in services and our partners. When you look at, we had Twitter up with us today, we've had continual growth in the technology partners that we bring to bear, and then also definitely Cloud Native. But then also helping clients that have existing workloads and how to migrate. So, massive partnerships with VMware. We also just announced partnership with Intel HyTrust on secure cloud optimization. When we first met, we talked so much about you're going to win this with an ecosystem. And the coolest thing is seeing that pay off every day with the number of partners that we've been so blessed to have coming to us and working together with us to build out this ecosystem for our clients. >> And what's the differentiator, because what's happening now is you're starting to see the clear line of sight from the big cloud players. You have you guys, you have Oracle, you see Microsoft, you see SAP, you all got the version of the cloud. And it's not a winner-take-all market, it's a multi-cloud world, as we're seeing. Certainly open-source is driving that. How do you guys differentiate, and is it the same message? What's new in terms of IBM's differentiators? What's the key message? >> That we're absolutely staying core to the reason we went into this business. We are looking at, what are the challenges that our clients are looking to solve? How do we build out the right solutions for them? And look at the technologies they're using today, and not have them just forklift everything to a public cloud, but walk with them every step of the way. It's absolutely been about uncovering the partnerships between on-premises and the Cloud, how you make that seamless, how you make those migrations in minutes versus hours and days. The growth that we've seen is around helping clients get to that journey faster, or, if they're not meant to go fully public Cloud, that's okay, too. We've been absolutely expanding our data centers, making sure we have everything lined up from a compliance standpoint. Because country to country, we have so many regulations that we need to make sure we're protecting our clients in. >> I want to ask you, and David Kenny referenced it a little bit today, talked about we built this for the enterprise, it didn't stem out of a retailer or a search. I don't know who he was talking about, but Martin Schroeter, on the IBM earnings call, said something that I want to get your comment on, and if we can unpack a little bit. He said, "Importantly, we've designed Watson "on the IBM Cloud to allow our clients "to retain control of their data and their insights, "rather than using client data "to educate a central knowledge graph." That's a nuance, but it's a really big statement. And what's behind that, if I can infer, is use the data to inform the model, but we're not going to take your data IP and give it to your competitors. Can you explain that a little bit, and what the philosophy is there? >> Yeah, absolutely. That is a core tenet of what we do. It's all about clients will bring their data to us to learn, to go to school, but then it goes home. We don't keep client data, that's critical to us that everything is completely within the client's infrastructure, within their data privacy and protection. We are simply applying our cognitive, artificial intelligence machine learning to help them advance faster. It's not about taking their insights in learning and fueling them into our Cloud to then resell to other teams. That, absolutely, it's great that you bring up that very nuanced point, but that's really important. In today's day and age, your data is your lifeblood as a company, and you have to trust where it's going, you have to know where it's going, and you have to trust that those machine learnings aren't going to be helping other clients that are possibly on the same cloud. >> Is it your contention that others don't make that promise, or you don't know, or you're just making that promise? >> We're making that promise. It's our contention that the data is the client's data. You look at the partnerships that we've made throughout Cloud, throughout Watson, it's really companies that have come to us to solve problems. You look at the healthcare industry, you look at all these partnerships that we have. Everything that we've built out on the IBM Cloud and within Watson has been to help advance client cases. You rarely see us launching something that's completely unique to IBM that hasn't been built together with a client, with a partner. Versus, there are other companies out there in this market where they're constantly providing infrastructure to run their own business, maybe their own retail store, and their own search engine. And they will continue to do that, and they absolutely should, but at the end of the day, when you're a client, what do you want to do? Are you trying to build somebody else's business, or do you want someone who's going to be all in on your business and helping you advance everything that you need to do. >> Well, it seems like the market has glombed on to public data plus automation. But you're trying to solve a harder problem. Explain that. >> When you look at the clients that we're working with and the data that we're working with, it's not just information that's out there to work in a sandbox environment and it's available to anyone, baseball statistics or something that's just out there in the wild. Every client engagement we're in, this is their critical data. You look at financial services. We just launched the great financial services solutions for developers. You look at those areas, and, oh my word, you cannot share that data, yet those clients, you look at the work we're doing with H&R Block, you have to look at, that is absolutely proprietary data, but how do we send in cognitive to help us learn, to help teach it, help teach them alongside, for the H&R Block example, the tax advisor. So we're helping them make their business better. It's not as if we ingested all of the tax data to then run a tax solution service from IBM. It's a nuance, but it's an important nuance of how we run this company. >> So seven years ago, I met this guy, and he said, the 2010 John, you said, "Data is the new development kit." And I was like, "What are you talking about?" But now we see this persona of data scientist and data engineer and the developer persona evolving. How are you redefining the developer? >> Yeah, it's a great point, because we see cognitive artificial intelligence machine learning development in developers really emerging strong as a career path. We see data scientists, especially where as you're building out any application, any solution, data is at the core. So, you had it 10 years ago, right? (laughs) >> (mumbles) But I did pitch it to Dave when I first met him in 2010. No, but this is the premise, right? Back then, web infrastructure, web scale guys were doing their own stuff. The data needs to be programmable. We've been riffing on this concept, and I want to get your thoughts on this. What DevOps was for infrastructurous code, we see a vision in our research at Wikibon that data as code, meaning developers just want to program and get data. They don't want to deal with all the under-the-hood production, complicated stuff like datasets, the databases. Maybe the wrangling could be done by another process. There's all this production heavy lifting that goes on. And then there's the creativity and coolness of building apps. So now you have those worlds starting to stabilize a bit. Your thoughts and commentary on that vision? >> Yeah, that's absolutely where it has been heading and is continuing to head. And as you look at all the platforms that developers get to work in right now. So you have augmented reality, virtual reality are not just being segmented off into a gaming environment, but it's absolutely mainstream. So you see where developers absolutely are looking for. What is a low-code environment for? I'd say more the productivity. How do I make this app more productive? But when it comes to innovation, that's where you see, that's where the data scientist is emerging more and more every day in a role. You see those cognitive developers emerging more and more because that's where you want to spend all your time. My developers have spent the weekend, came back on Monday, and I said, "What'd you do?" "I wrote this whole Getting Started guide "for this Watson cognitive service." "That's not your job." "Yeah, but it's fun." >> Yeah, they're geeking out on the weekends, having some beer and doing some hackathons. >> It's so exciting to see. That's where, that innovation side, that's where we're seeing, absolutely, the growth. One of the partnerships that we announced earlier today is around our investment in just that training and learning. With Galvanize. >> What was the number? How much? >> 10 million dollars. >> Evangelizing and getting, soften the ground up, getting people trained on cognitive AI. >> Yeah, so it's really about making an impactful investment in the work that we started, actually a couple years ago when we were talking, we started building out these Garages. The concept was, we have startup companies, we starting partnering with Galvanize, who has an incredible footprint across the globe. And when you look at what they were building, we started embedding our developers in those offices, calling them Garages because that is your workshop. That's where you bring in companies that want to start building applications quickly. And you saw a number of the clients we had on stage today consistently, started in the Garage, started in the Garage, started in the Garage. >> Yeah, we had one just on theCUBE earlier. >> Yeah, exactly, so they start with us in the Garage. And then we wanted to make sure we're continuing to fuel that environment because it's been so successful for our clients. We're pouring into Galvanize and companies in training, and making sure these areas that are really in their pioneering stages, like artificial intelligence, cognitive, machine learning. >> On that point, you bring up startups and Garage, two-prong question. We're putting together, I'm putting together an enterprise-readiness matrix. So you have startups who are building on the Cloud, who want to sell to the enterprise. And then you have enterprises themselves who are adopting Hybrid Cloud or a combination of public, private. What does enterprise-readiness mean to you guys? 'Cause you guys have a lot of experience. Google next, they said, "We're enterprising." They're really not. They're not ready yet, but they're going that way. You guys are there. What is enterprise-readiness? >> Yeah, and I see a lot of companies have ambitions to do that, which is what we need them to do. 'Cause as you mentioned, it's a multi-cloud environment for clients, and so we need clouds to be enterprise-ready. And that really comes down to security, compliance, scalability, multiple zones. It comes down to making sure you don't have just five developers that can work on something, but how do you scale that to 500? How do you scale that to 500,000? You've got these companies that you have to be able to ensure that developers can immediately interact with each other. You need to make sure that you've got the right compliance by that country, the data leaving that country. And it's why you see such a focus from us on industry. Because enterprise-grade is one thing. Understanding an industry top to bottom, when it comes to cloud compliance is a whole other level. And that's where we're at. >> It's really hard. Most people oversimplify Cloud, but it's extremely difficult. >> It is, 'cause it's not just announcing a healthcare practice for Cloud doesn't mean you just put everybody in lab coats and send out new digital material. It is you have to make sure you've got partnerships with the right companies, you understand all the compliance regulations, and you've built everything and designed it for them. And then you've brought in all the partner services that they need, and you've built that in a private and a public cloud environment. And that's what we've done in healthcare, that's what we're doing in finance, you see all the work we're doing with Blockchain. We are just going industry by industry and making sure that when a company comes to us in an industry like retail, or you saw American Airlines on stage with us today. We're so proud to be working with them. And looking at everything that they need to cover, from regulation, uptime, maintenance, and ensuring that we know and understand that industry and can help, guide, and work alongside of them. >> In healthcare and financial services, the number of permutations are mind-boggling. So, what are you doing? You're pointing Watson to help solve those problems, and you're codifying that and automating that and running that on the Cloud? >> That's a part of it. A part of it is absolutely learning. The whole data comes to school with us to learn, and then it goes back home. That's absolutely part of it, is the cognitive learning. The other part of it is ensuring you understand the infrastructure. What are the on-premises, servers that that industry has? How many transactions per second, per nanosecond, are happening? What's the uptime around that? How do you make sure that what points you're exposing? What's the security baked into all of that? So, it's absolutely, cognitive is a massive part of it, but it is walking all the way through every part of their IT environment. >> Well, Meg, thanks for spending the time and coming on theCUBE and giving us the update. We'll certainly see you out in the field as we cover more and more developer events. We're going to be doing most, if not all, of the Linux foundation stuff. Working a lot with Intel and a bunch of other folks that you're partnering with. So, we'll see you guys out at all the events. DockerCon, you name it, they're all there. >> We'll be there, too, right with them. >> Microservices, we didn't even get to Kubernetes, we could have another session on containers and microservices. Meg Swanson, here inside theCUBE, Vice President of Bluemix Marketing. It's theCUBE, with more coverage after this short break. Stay with us, more coverage from Las Vegas. (techno music)

>> Narrator: Live from Las Vegas, it's the CUBE, covering Interconnect 2017, brought to you by IBM. >> Welcome back, everyone. We're live in Las Vegas for the CUBE's coverage of Interconnect 2017. This is three days of wall-to-wall coverage. Stay with us for the entire event. This is day two. I'm John Furrier with my co-host Dave Vellante and Esques' Willie Tejada, who's the IBM chief developer advocate at IBM. Welcome to the CUBE. >> Thank you, guys. I'm really pleased to be here. >> So, love to have you on because all we do is talk about developers and what's in it for them, who's doing what, who's got the better cloud, who's enterprise ready, all that good stuff, commentating. But I love Ginny Rometty's conversation today because we just had Google Next, covered Amazon events, all the cloud events, and the thing that's been on our agenda, we've been really looking at this, is cloud readiness in the enterprise. And this is really kind of fundamental, what she was talking about, enterprise strong, data first, cognitive to the core, which kind of is their three pillars, but this is the, where the action is right now. >> Yeah you know for developers that's exactly true. You know, what you outlined is really this idea of basically there's three kind of core architectures, right? It's cloud, number one, followed by data layered on top of that, and essentially AIR cognitive on top. And what that means actually for the developer communities is that there's a new set of skill sets that are probably moving faster than we have ever seen before, right? And a lot of it's actually driven by this explosion of data, and so um, one of the things that we think that there's going to be a huge shortage of and there is a huge shortage of, is data scientists and cognitive developers. Because in those layers, what we've seen is that more and more, you operate on a data first model and by that, by just that definition, what you need to know about data is pushing towards a practitioner level of data scientists and the reality is that we think that type of core skill sets going to be needed across all of the developer communities. >> So take a minute to describe what will define a cognitive developer >> Tajeda: Sure, >> And what that, and the nuance behind it, because obviously the developers are doing really cool creative things, and then you've got the heart under the hood, production work loads and IT so where is the cognitive developer fit in those spectrums and what is the core definition from your standpoint? >> Yeah you know, the cognitive developer really is a person who's actually participating in actually the generation of a system that's fully cognitive, so you know, adding a cognitive feature is one thing, but actually building a full cognitive system is something different. You know if I use a comparison, think about how some of these roles in big data came about big data came, but we didn't have things like a data scientist, we didn't have a data engineer, and it kind of came after the fact the roles that were actually defined. Now we're onto these new cognitive systems where everything from, you have to train the system you have to have explicit knowledge of what the APIs actually do and you have to have infrastructure that actually curates data that continues this training along those lines. So you know the cognitive developers, really one that's participating in that particular ecosystem now what's really important though about that is they are usually programming in the language that their usually programming in. Whether it be Java, data scientists are using r or they're using Python, but the reality is that a cognitive developer's is that one that's applying those cognitive properties to their system that they're developing. >> So this is interesting, you mentioned the cognitive develop new tools and stuff, but there's some really good trends out there that are, that's the wind at the back of the developer right now. Cloud native is a booming trend that's actually phenomenal, you're seeing container madness continue, you've got micro services, all with kubernetes under the hood so there's some cool exciting things in the trend lines, can you unpack that for us and what this means to the developers, how does it impact their world? I mean we hear composability, lego blocks most developers know API economy is here, but now you've got these new tail winds, these new trends, >> Tajeda: Yep >> What's the, what are they, add to at, what's the impact to the developer? >> Well we talked about the new container service based on kubernetes that's allowing us to actually build to tremendous scale, and really simplify that type of development actually when you're doing native cloud development. You know, probably the most important things for developers is just accessibility of all these pieces, of course it's driven by open source, but you know if you want to learn these technologies if you want to participate and experiment with these technologies, they've never been more available than they actually are today. >> Vellante: So if I may, so Tanmay is a good example of a cognitive developer >> Absolutely. >> He's all cloud native, he's all cognitive, >> Nice shout out from the CEO today >> Yeah. >> He's also an algoithmist, you know self declared algorithmist, >> I can't even say that, >> Okay so here's Tanmay, he's never going to know anything else, right? But now, of you're a sort of mainstream developer, what do you do, you know? Where do you get the skills, what do you recommend that that individual does, and how do they get up the ramp? >> So you know, lots of times as you know the developer's learnings is not like kind of a linear pattern, right? They go to blogs basically they go and pull basic a library for them to >> Vellante: They figure it out. >> Along those lines, they go to a meet up or a hack from that stand point that's based on cognitive development and you know, so they should just go about what they normally do kind of along those lines, and then you know I think basically there's am advancement because ultimately we're publishing these things called journeys, which are really kind of use cases in the cognitive based environment so as an example, we might publish a journey on a cognitive retail chat bot, and it will combine a variety of these micro services that Watson's actually built on but give them exploration as to how they use the chat bot, how they use a service called discovery, and how they use persistence basically so that essentially they can learn from the data that they actually have and then ultimately if what they want to do is get deeper into it, there's organizations that we partner with where we give them cognitive curricullum that allows them to experience these pieces like top coder you can go on and do a cognitive challenge, right on top coder or you can go to a a cognitive course designed by galvanized one of our partners in relation to skills development. >> So that's interesting about that journey, so when you think about big data we talked about big data before, the sort of point at which at a company like IBM would engage in that journey is somebody who's exploring and maybe kicking the tires a little bit or somebody at a data warehouse that was like killing them, right? Where is, obviously there's a part of that in the cognitive world which is experimental >> Tajeda: Yep >> Is there a sort of analog to the data warehouse sort of disaffection if you will. >> Yeah, you know one of the things that we spend a lot of time on is that every organization that's going to build a cognitive system is looking for cognitive developers and data scientists, you know so essentially, >> Furrier: It's across all industries by the way, >> Absolutely >> Cyber securities to, >> Absolutely so you know, one of the key pieces is what kind of tools do you actually give that data scientist, to mess around with that data set, we provide something called a data science experience, and the idea there is essentially how do you give them an environment that allows them essentially to look into the data very quickly actually have these sets, and really kind of explore the data in a way that they never were capable of actually doing that, you know, those are the types of things that we're actually trying to that a data scientist, so that you can bridge over if you were a data engineer, or you're a business analyst, and you're looking to actually get into data science, you can actually play with some of these big data sets and actually explore what things you can do. >> Willie, I couldn't agree with you more on the whole, how developers learn it's really not a course ware online and the fiscal classroom, maybe they're offering it in college but, it's the practitional world of non linear learning through experience and these journeys are super valuable, and just for a tactical question, where do they find the journeys, or URL? >> What you'll find basically, come April first, we're going to launch a number of them, on developer.ibm.com/accelerate so they'll be focused on several different categories, number one will be just developing in the cloud cloud native, what's a journeys basically that they're kind of like common set ups that you actually need, we'll do, next one's on cognitive analytics where you pull together a set of services along those lines, and as you heard Ginny talk about, you know it's really important that a cloud have knowledge about a domain or an industry and so we'll create some journeys that are actually very industry specific, you know we announced, >> Furrier: Like they're like templates bascially, >> They are, >> People jump start it, not so much a reference implementation, >> Exactly, >> You know what I'm saying, the old days >> But you know, what it's all about is you mentioned this non linear journey that developers don't actually learn fundamentally they have a core thing that they're trying to get actually get done which is, get you help me get my stuff done faster, right? And fundamentally, when you talk about cognitive or data science, we're trying to actually deliver them tool sets or examples that do that. >> So I now got to go to the next level with that question, because it's first of all it's awesome, now how do you intersect that with community? Because now, that's super important because and you might want to take a minute to just do a plug for IBM in terms of the open source goodness you guys are doing because you guys do a great job with open source. >> Tajeda: You know we just hosted a very large, what we believe is, one of the largest open tech meet ups, right before basically InterConnect started, and we had one of the ballrooms actually full, and we talked about our new service we had Jim Basic from the Linux Foundation actually come, he stated a stat which was really interesting in open source which IBM is a large contributor to, that I think the stat that he said was Linux basically has a project now, there's 10,800 new lines of code and 1,800 lines of code that are modified every day, right? >> Furrier: Yeah, >> And that's the community. >> And that's only going to get faster, if you think about like just, the physical media like ssds, in memory, which spark the kernal, >> Vellante: The quantum, >> Linux is going evolve in a radical and killer way I mean, this is just the beginning. >> And to your point about the community, when you think about that advancement at the pace by which basically that software's actually going to move, there's not one organization that can outpace that type of community in the way they actually do it, it doesn't matter what the services actually are so, >> Well the other interesting thing is the impact on human kind, you heard Benny Hoff and Ginny talking about this morning and they were both really emphasizing machine augmented, right? But, it's like a Pac Man device, I mean there's so much human interaction that's being automated today, >> Tajeda: Yeah, abslutely, >> So, and I know IBM obviously big believer in augmentation, but it's hard to predict what things human's are going to be do, be able to do that machine's can't do, any insight on that? >> Yeah you know, I think, we like to use the word cognitive assisted, So when you think about it, I'll give one example, let's say for example in the medical profession, so, if you look at it, in the healthcare industry, about 90 percent of the data in there is not structured data, right? It's all unstructured data, a lot of it is images, so if you take a look at someone basically that's in oncology work taking a look at things like melanoma, the amount of time I think the data set said the amount of time he needed to watch or get trained on to look at all the new papers that were ever published, was probably three weeks basically, if he's thinking about that in a month. The amount of time that that person allocates to actually keeping up with all these particular trade journals is a few hours a week, and so he's constantly behind, this where something like a watson enabled, or a cognitive enabled type of application can help him actually keep up to date with all the new findings and research papers in his particular field, and do something like ingest millions of documents and understand them but actually apply that to his work, so you know what you find is doctors actually utilizing a cognitive assistant powered by Watson to help him do a better diagnosis. >> Will you're an advocate for the chief developer advocate for IBM, talk about for the last couple minutes we have, what's on your plan, we just saw the news yesterday, the 10 million dollar investment to get education out there and bring this cognitive developer category, kind of lift that up and, with Galvanize which we've supported some of those signature moment events with the Cube, where are you going to be out in the field, what's some of your go to market activities how you going to do this, and then talk about the patterns you've seen in the developer make up. >> Yeah, >> Just over the past year, what's changed, what's notable? >> Yeah, so you know what, you know some of the things that we're actually doing is number one, we're we're taking up very large presence in probably nine cities around the world with a very big emphasis on building on data science and cognitive developers, so you know, there's kind of the usual suspects, the San Franciscos, the New Yorks, the Tokyos, the Londons, some presence in Sao Paulo, we're doing Beijing, we recently basically announced a partnership of how we can actually get presence actually there and through that, we're looking actually to bring, basically this presence into those communities, so this idea that we help, actually put forth these journeys but in many cases actually be right in the presence of things, we have, in some cases we have some programs that we're actually spinning up that are all about essentially how we actually do things like IOT Thursdays, or Cognitive Tuesdays where they can actually see actual experts in those particular areas, and just come do office assignments, >> Furrier: Do Throwback Thursday, you hack on a mainframe >> Tajeda: That's it! (laughter) >> That's what they're actually looking at from that standpoint so, so yeah a lot of this stuff basically is just actually getting to some of those folks in a very very intimate way, and like you said actually kind of populating these folks where kind of where they are, and really what that's all about is actually getting the tools and tool sets in the communities that they find and the peer learning that they do, which is real, >> Furrier: Well we'll see you at some of the Galvanize events you guys got goin on we'll certainly see you at Dockercon we got a lot of Cube line ups, for this Spring tour, and the Fall ton of developer activity, the Cloud Native stuff is really an intersection point with big data colliding with cloud IOT and AI and this cognitive is just an accelerant, >> Tajeda: Absolutely, absolutely >> For the cloud, the perfect storm is a good opportunity. >> There's never been more available time in terms of technology, and the technology never moved as fast, >> I was just saying to Tanmay when he was on yesterday, "I wish I could be 13 again", coding is so much more fun now than it was when we were doing it. Well great to have you on Willie, >> Hey thanks very much, it was actually very good visiting with you guys. >> Great insight, insight from the chief developer advocate here at IBM, I'm John Furrier, Steve Vellante stay with us for more coverage, great interviews all day today, and tomorrow, here live in Las Vegas, we'll be right back.

on the ground from galvanized San Francisco its excused covering amplified woman's bitch night here's Jeff hey welcome back here ready Jeff Rick here with a cute we're at galvanized a downtown San Francisco at the girls in tech amplify event which is is women's pitch night so I think they have ten entrepreneurs here pitching their company pitching their ideas somebody's gonna walk away with 10,000 bucks some computers and other fun stuff so we're happy to be here share the stories with you because this is where innovation happens and our next guest is Ellen health can hard do I get that right from Stila welcome thank you first I've had your pitch go pretty good yes okay so your project is called still emotion so what is still emotion still emotion is that pocket-sized secure system it's had a little sample here okay so this is what it looked like so it's a smaller device that you put on anything that you wants to be still like a bag or computer or a stroller or on a window and and if something moves that shouldn't you get an instant notification to your phone or to your SmartWatch okay so it's got accelerometers and those types of things in there that it's really all about motions yeah little app onto your phone or whatever exactly okay so so there's a lot of action right now with tile and those types of things how is it different kind of what's the different value propositions well it's as I said it's kind of the opposite instead of like finding your thinks it's not losing them in the first place so it's it's a preventative device rather than it's proactive instead of reactive okay but it does have similar functionality as well so if you forget your bag at the cafe you will you will get a notification when the distance is Williams contact but yeah if you're sitting here with me and your bag walks away the tiles the trackers won't help versus the Stila would tell you directly someone is touching your your bag right so how did you come up with the idea I was actually learning how to program Arduino boards I I was running an hour working at a hardware accelerator and my background is more sorry ated but I'm I'm a geek at heart so I had like this little Arduino board that I that I learned how to code with so I I just I literally I'm so I'm so geeky I have Arduino boards in my purse but yes I do so this is a little computer kind of so we were sitting in like learning this at the cafe and I dropped it on my bike and when I lifted it up it started blinking and I'll say you know what that would be great if it did something else to plate and that's why I came up with the idea and how long ago is that so give a little just about kind of history the company how many people are you kind of where you on yeah and the development phase still prototype well I also had idea because I've done a hundred startup before but it took like four years and I'm kind of I wasn't willing to give it four years so it's like is it possible to I like the idea but I didn't know if I want to commit so much time and effort and money so I was like can I make it in a hundred days it's it possible to start a Harvard company in a hundred days so I kind of challenged myself to do it did you have a regular job while you're doing this a normal job i I worked this Australia consulting because I had to fund myself so I had no money no money no funding no team no nothing yeah and I was like yeah I'm gonna make this little button so I did this and then I literally make prototypes in clay and paint and like I talk to people I I got a team together and I did patent applications and got apps together and did yeah so I went from this to functioning Harvard product in 100 days and then I was like I actually want to do this so I kind of quit my job and and made sure that I went all in and got a team together and some of the basic investors to help me out and then yeah we got going and it took a hundred days to make the first product and then it had taken almost a year to build the company right right this is just the start like this is the first product and they will have a whole line of things that we're doing so we're you know what are you gonna do with the money kind of is it for scaling is it for more licensing what are you gonna yeah we just closed our campaign okay on Friday last week and we are now in the last batch of prototyping so I'm going down to our hardware engineers in Orange County next week to finalize the production before we kind of go into tooling and then after that we're doing final stages of the app and then we're going to send Jen in China where produce and started tooling and manufacturing process so we're shipping and March next year more chips here awesome so great story and you said you've got team members from all over the world five of seven continents you're leveraging a lot of others technology like accelerometers to pull this thing together in such a short period of time yeah well very exciting well good luck to you and I hope get some of that money tonight thank you all right we'll keep an eye and worship people go to get more information about the company yet my still of calm my still at ICON spelled STI ll a all right my STI ll a with my in the front awesome all right Alan well thank you very much thank you all right I'm Jeff Rick we are at the girls of tech amplify pitch night here at galvanize in San Francisco we'll be right back after this short break

>> Narrator: Live from New York, it's the Cube. Covering Big Data New York City 2016. Brought to you by headline sponsors: Cisco, IBM, Nvidia, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and Jeff Frick. >> Welcome back to New York City, everybody. This is the Cube, the worldwide leader in live tech coverage. Rob Thomas is here, he's the GM of products for IBM Analytics. Rob, always good to see you, man. >> Yeah, Dave, great to see you. Jeff, great to see you as well. >> You too, Rob. World traveller. >> Been all over the place, but good to be here, back in New York, close to home for one day. (laughs) >> Yeah, at least a day. So the whole community is abuzz with this article that hit. You wrote it last week. It hit NewCo Shift, I guess just today or yesterday: The End of Tech Companies. >> Rob: Yes. >> Alright, and you've got some really interesting charts in there, you've got some ugly charts. You've got HDP, you've got, let's see... >> Rob: You've got Imperva. >> TerraData, Imperva. >> Rob: Yes. >> Not looking pretty. We talked about this last year, just about a year ago. We said, the nose of the plane is up. >> Yep. >> Dave: But the planes are losing altitude. >> Yep. >> Dave: And when the funding dries up, look out. Interesting, some companies still are getting funding, so this makes rip currents. But in general, it's not pretty for pure play, dupe companies. >> Right. >> Dave: Something that you guys predicted, a long time ago, I guess. >> So I think there's a macro trend here, and this is really, I did a couple months of research, and this is what went into that end of tech companies post. And it's interesting, so you look at it in the stock market today: the five highest valued companies are all tech companies, what we would call. And that's not a coincidence. The reality is, I think we're getting past the phase of there being tech companies, and tech is becoming the default, and either you're going to be a tech company, or you're going to be extinct. I think that's the MO that every company has to operate with, whether you're a retailer, or in healthcare, or insurance, in banking, it doesn't matter. If you don't become a tech company, you're not going to be a company. That's what I was getting at. And so some of the pressures I was highlighting was, I think what's played out in enterprise software is what will start to play out in other traditional industries over the next five years. >> Well, you know, it's interesting, we talk about these things years and years and years in advance and people just kind of ignore it. Like Benioff even said, more SaaS companies are going to come out of non-tech companies than tech companies, OK. We've been talking for years about how the practitioners of big data are actually going to make more money than the big data vendors. Peter Goldmacher was actually the first, that was one of his predictions that hit true. Many of them didn't. (laughs) You know, Peter's a good friend-- >> Rob: Peter's a good friend of mine as well, so I always like pointing out what he says that's wrong. >> But, but-- >> Thinking of you, Peter. >> But we sort of ignored that, and now it's all coming to fruition, right? >> Right. >> Your article talks about, and it's a long read, but it's not too long to read, so please read it. But it talks about how basically every industry is, of course, getting disrupted, we know that, but every company is a tech company. >> Right. >> Or else. >> Right. And, you know, what I was, so John Battelle called me last week, he said hey, I want to run this, he said, because I think it's going to hit a nerve with people, and we were talking about why is that? Is it because of the election season, or whatever. People are concerned about the macro view of what's happening in the economy. And I think this kind of strikes at the nerve that says, one is you have to make this transition, and then I go into the article with some specific things that I think every company has to be doing to make this transition. It starts with, you've got to rethink your capital structure because the investments you made, the distribution model that you had that got you here, is not going to be sufficient for the future. You have to rethink the tools that you're utilitizing and the workforce, because you're going to have to adopt a new way to work. And that starts at the top, by the way. And so I go through a couple different suggestions of what I think companies should look at to make this transition, and I guess what scares me is, I visit companies all over the world, I see very few companies making these kind of moves. 'Cause it's a major shake-up to culture, it's a major shake-up to how they run their business, and, you know, I use the Warren Buffett quote, "When the tide goes out, you can see who's been swimming naked." The tide may go out pretty soon here, you know, it'll be in the next five years, and I think you're going to see a lot of companies that thought they could never be threatened by tech, if you will, go the wrong way because they're not making those moves now. >> Well, let's stay cognitive, now that we're on this subject, because you know, you're having a pretty frank conversation here. A lot of times when you talk to people inside of IBM about cognitive and the impact it's going to have, they don't want to talk about that. But it's real. Machines have always replaced humans, and now we're seeing that replacement of cognitive functions, so that doesn't mean value can't get created. In fact, way more value is going to be created than we can even imagine, but you have to change the way in which you do things in order to take advantage of that. >> Right, right. One thing I say in the article is I think we're on the cusp of the great reskilling, which is, you take all the traditional IT jobs, I think over the next decade half those jobs probably go away, but they're replaced by a new set of capabilities around data science and machine learning, and advanced analytics, things that are leveraging cognitive capabilities, but doing it with human focus as well. And so, you're going to see a big shift in skills. This is why we're partnering with companies like Galvanize, I saw Jim Deters when I was walking in. Galvanize is at the forefront of helping companies do that reskilling. We want to help them do that reskilling as well, and we're going to provide them a platform that automates the process of doing a lot of these analytics. That's what the new project Dataworks, the new Watson project is all about, is how we begin to automate what have traditionally been very cumbersome and difficult problems to solve in an organization, but we're helping clients that haven't done that reskilling yet, we're helping them go ahead and get an advantage through technology. >> Rob, I want to follow up too on that concept on the capital markets and how this stuff is measured, because as you pointed out in your article, valuations of the top companies are huge. That's not a multiple of data right now. We haven't really figured that out, and it's something that we're looking at, the Wikibon team is how do you value the data from what used to be liability 'cause you had to put it on machines and pay for it. Now it's really the driver, there's some multiple of data value that's driving those top-line valuations that you point out in that article. >> You know it's interesting, and nobody has really figured that out, 'cause you don't see it showing up, at least I don't think, in any stock prices, maybe CoStar would be one example where it probably has, they've got a lot of data around commercial real estate, that one sticks out to me, but I think about in the current era that we're in there's three ways to drive competitive advantage: one is economies of scale, low-cost manufacturing; another is through network effects, you know, a number of social media companies have done that well; but third is, machine learning on a large corpus of data is a competitive advantage. If you have the right data assets and you can get better answers, your models will get smarter over time, how's anybody going to catch up with you? They're not going to. So I think we're probably not too far from what you say, Jeff, which is companies starting to be looked at as a value of their data assets, and maybe data should be on the balance sheet. >> Well that's what I'm saying, eventually does it move to the balance sheet as something that you need to account for? Because clearly there's something in the Apple number, in the Alphabet number, in the Microsoft number, that's more than regular. >> Exactly, it's not just about, it's not just about the distribution model, you know, large companies for a long time, certainly in tech, we had a huge advantage because of distribution, our ability to get to other countries face to face, but as the world has moved to the Internet and digital sales and try/buy, it's changed that. Distribution can still be an advantage, but is no longer the advantage, and so companies are trying to figure out what are the next set of assets? It used to be my distribution model, now maybe it's my data, or perhaps it's the insight that I develop from the data. That's really changed. >> Then, in the early days of the sort of big data meme taking off, people would ask, OK, how can I monetize the data? As opposed to what I think they're really asking is, how could I use data to support making money? >> Rob: Right. Right. >> And that's something a lot of people I don't think really understood, and it's starting to come into focus now. And then, once you figure that out, you can figure out what data sources, and how to get quality in that data and enrich that data and trust that data, right? Is that sort of a logical sequence that companies are now going through? >> It's an interesting observation, because you think about it, the companies that were early on in purely monetizing data, companies like Dun & Bradstreet come to mind, Nielsen come to mind, they're not the super-fast-growing companies today. So it's kind of like, there was an era where data monetization was a viable strategy, and there's still some of that now, but now it's more about, how do you turn your data assets into a new business model? There was actually a great, new Clay Christensen article, it was published I think last week, talking about companies need to develop new business models. We're at the time, everybody's kind of developed in, we sell hardware, we sell software, we sell services, or whatever we sell, and his point was now is the time to develop a new business model, and those will, now my view, those will largely be formed on the basis of data, so not necessarily just monetizing the data, to your point, Dave, but on the basis of that data. >> I love the music industry, because they're always kind of out at the front of this evolving business model for digital assets in this new world, and it keeps jumping, right? It jumped, it was free, then people went ahead and bought stuff on iTunes, now Spotify has flipped it over to a subscription model, and the innovation of change in the business model, not necessarily the products that much, it's very different. The other thing that's interesting is just that digital assets don't have scarcity, right? >> Rob: Right. >> There's scarcity around the data, but not around the assets, per se. So it's a very different way of thinking about distribution and kind of holding back, how do you integrate with other people's data? It's not, not the same. >> So think about, that's an interesting example, because think about the music, there's a great documentary on Netflix about Tower Records, and how Tower Records went through the big spike and now is kind of, obviously no longer really around. Same thing goes for the Blockbusters of the world. So they got disrupted by digital, because their advantage was a distribution channel that was in the physical world, and that's kind of my assertion in that post about the end of tech companies is that every company is facing that. They may not know it yet, but if you're in agriculture, and your traditional dealer network is how you got to market, whether you know it or not, that is about to be disrupted. I don't know exactly what form that will take, but it's going to be different. And so I think every company to your point on, you know, you look at the music industry, kind of use it as a map, that's an interesting way to look at a lot of industries in terms of what could play out in the next five years. >> It's interesting that you say though in all your travels that people aren't, I would think they would be clamoring, oh my gosh, I know it's coming, what do I do, 'cause I know it's coming from an angle that I'm not aware of as opposed to, like you say a lot of people don't see it coming. You know, it's not my industry. Not going to happen to me. >> You know it's funny, I think, I hear two, one perception I hear is, well, we're not a tech company so we don't have to worry about that, which is totally flawed. Two is, I hear companies that, I'd say they use the right platitudes: "We need to be digital." OK, that's great to say, but are you actually changing your business model to get there? Maybe not. So I think people are starting to wake up to this, but it's still very much in its infancy, and some people are going to be left behind. >> So the tooling and the new way to work are sort of intuitive. What about capital structure? What's the implication to capital structures, how do you see that changing? So it's a few things. One is, you have to relook at where you're investing capital today. The majority of companies are still investing in what got them to where they are versus where they need to be. So you need to make a very conscious shift, and I use the old McKinsey model of horizon one, two and three, but I insert the idea that there should be a horizon zero, where you really think about what are you really going to start to just outsource, or just altogether stop doing, because you have to aggressively shift your investments to horizon two, horizon three, you've really got to start making bets on the future, so that's one is basically a capital shift. Two is, to attract this new workforce. When I talked about the great reskilling, people want to come to work for different reasons now. They want to come to work, you know, to work in the right kind of office in the right location, that's going to require investment. They want a new comp structure, they're no longer just excited by a high base salary like, you know, they want participation in upside, even if you're a mature company that's been around for 50 years, are you providing your employees meaningful upside in terms of bonus or stock? Most companies say, you know, we've always reserved that stuff for executives. That's not, there's too many other companies that are providing that as an alternative today, so you have to rethink your capital structure in that way. So it's how you spend your money, but also, you know, as you look at the balance sheet, how you actually are, you know, I'd say spreading money around the company, and I think that changes as well. >> So how does this all translate into how IBM behaves, from a product standpoint? >> We have changed a lot of things in IBM. Obviously we've made a huge move towards what we think is the future, around artificial intelligence and machine learning with everything that we've done around the Watson platform. We've made huge capital investments in our cloud capability all over the world, because that is an arms race right now. We've made a huge change in how we're hiring, we're rebuilding offices, so we put an office in Cambridge, downtown Boston. Put an office here in New York downtown. We're opening the office in San Francisco very soon. >> Jeff: The Sparks Center downtown. >> Yeah. So we've kind of come to urban areas to attract this new type of skill 'cause it's really important to us. So we've done it in a lot of different ways. >> Excellent. And then tonight we're going to hear more about that, right? >> Rob: Yes. >> You guys have a big announcement tonight? >> Rob: Big announcement tonight. >> Ritica was on, she showed us a little bit about what's coming, but what can you tell us about what we can expect tonight? >> Our focus is on building the first enterprise platform for data, which is steeped in artificial intelligence. First time you've seen anything like it. You think about it, the platform business model has taken off in some sectors. You can see it in social media, Facebook is very much a platform. You can see it in entertainment, Netflix is very much a platform. There hasn't really been a platform for enterprise data and IP. That's what we're going to be delivering as part of this new Watson project, which is Dataworks, and we think it'll be very interesting. Got a great ecosystem of partners that will be with us at the event tonight, that're bringing their IP and their data to be part of the platform. It will be a unique experience. >> What do you, I know you can't talk specifics on M&A, but just in general, in concept, in terms of all the funding, we talked last year at this event how the whole space was sort of overfunded, overcrowded, you know, and something's got to give. Do you feel like there's been, given the money that went in, is there enough innovation coming out of the Hadoop big data ecosystem? Or is a lot of that money just going to go poof? >> Well, you know, we're in an interesting time in capital markets, right? When you loan money and get back less than you loan, because interest rates are negative, it's almost, there's no bad place to put money. (laughing) Like you can't do worse than that. But I think, you know the Hadoop ecosystem, I think it's played out about like we envisioned, which is it's becoming cheap storage. And I do see a lot of innovation happening around that, that's why we put so much into Spark. We're now the number one contributor around machine learning in the Spark project, which we're really proud of. >> Number one. >> Yes, in terms of contributions over the last year. Which has been tremendous. And in terms of companies in the ecos-- look, there's been a lot of money raised, which means people have runway. I think what you'll see is a lot of people that try stuff, it doesn't work out, they'll try something else. Look, there's still a lot of great innovation happening, and as much as it's the easiest time to start a company in terms of the cost of starting a company, I think it's probably one of the hardest times in terms of getting time and attention and scale, and so you've got to be patient and give these bets some time to play out. >> So you're still sanguine on the future of big data? Good. When Rob turns negative, then I'm concerned. >> It's definitely, we know the endpoint is going to be massive data environments in the cloud, instrumented, with automated analytics and machine learning. That's the future, Watson's got a great headstart, so we're proud of that. >> Well, you've made bets there. You've also, I mean, IBM, obviously great services company, for years services led. You're beginning to automate a lot of those services, package a lot of those services into industry-specific software and other SaaS products. Is that the future for IBM? >> It is. I mean, I think you need it two ways. One is, you need domain solutions, verticalized, that are solving a specific problem. But underneath that you need a general-purpose platform, which is what we're really focused on around Dataworks, is providing that. But when it comes to engaging a user, if you're not engaging what I would call a horizontal user, a data scientist or a data engineer or developer, then you're engaging a line-of-business person who's going to want something in their lingua franca, whether that's wealth management and banking, or payer underwriting or claims processing in healthcare, they're going to want it in that language. That's why we've had the solutions focus that we have. >> And they're going to want that data science expertise to be operationalized into the products. >> Rob: Yes. >> It was interesting, we had Jim on and Galvanize and what they're doing. Sharp partnership, Rob, you guys have, I think made the right bets here, and instead of chasing a lot of the shiny new toys, you've sort of thought ahead, so congratulations on that. >> Well, thanks, it's still early days, we're still playing out all the bets, but yeah, we've had a good run here, and look forward to the next phase here with Dataworks. >> Alright, Rob Thomas, thanks very much for coming on the Cube. >> Thanks guys, nice to see you. >> Jeff: Appreciate your time today, Rob. >> Alright, keep it right there, everybody. We'll be back with our next guest right after this. This is the Cube, we're live from New York City, right back. (electronic music)