 Live from Las Vegas, Nevada, it's theCUBE. Covering IBM World of Watson 2016. Brought to you by IBM. Now, here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We are here live at the Mandalay Bay Convention Center for IBM World of Watson. This is SiliconANGLE's CUBE, our flagship program. We go out to the events and they extract the signal from the noise. I'm John Furrier, my co-host Dave Vellante, head of research at Wikibon.com. Our next guest is Bob Pacheano, CUBE alumni, been on many times. Senior Vice President of Analytics at IBM. Big business you got going on, welcome back to theCUBE. Thank you, John. Yeah, great to be here. So, 10 years anniversary, we talked before we came on camera, 10 year anniversary from the first information on demand, the first data conference. Take us through the history. And now we're here in Watson, encapsulating it all. It was just great. Yeah, I mean, it's actually a great story. I mean, you think about it. Thomas Freeman did a fantastic job this morning, sort of setting up the significance of all these interesting things that happened in 2007. So, as it turns out, this is the 10th anniversary year of the information on demand gathering around our analytics business, our information management business, our content management business analytics. And we started to really bring together that community before there was any terminology around big data, before Hadoop actually hit the marketplace. That happened a year later. And we saw what our clients needed in the way of different smart approaches, not just in the technology, but in the way that they were approaching the problems. And we thought we could do more if we engage the community, if we listened to more deeply about their individual challenges and resolve ourselves to go off and innovate around that problem set. So, here we are now 10 years later. This morning, I recapped a bunch of the promises that we made at the start of that. And I had no idea all the progress we would make. I mean, we not only kept those promises, but along the way we introduced new capabilities like Watson, Watson analytics, and reinvigorated the whole market around artificial intelligence based on this great set of capabilities. Dave and I were talking before you came on about how many times you've been on theCUBE, et cetera. One thing we always say, we're not saying this because you're here, you guys have always had a good nose for where the action is, getting out in front of that next wave. Never make a nose joke to an Italian. And you're out in front, you're surfing on the waves. So I got to ask you the question, going back 10 years, what have you learned? What's the learnings? What have you learned? Because Dave was making a comment again before camera that back then data, how did that deal with more fear? Now it's so much more opportunistic. What's the big learnings that you could share? Look, I mean, this is the story of our company, right? It's not anything that we just started doing in the last few years or in the last decade. Our company was built on the premise that we needed to really talk and listen to our clients, listen deeply to the things that were getting in their way in growing their business and transforming their business and addressing new markets, and then dedicate ourselves and the research and development that we would do on solving those problems. So we're blessed with this incredible confluence of deep partner listening skills that are relevant in the industry that our clients work in. Not just technology discussions, but industry discussions. And then having the great backing of our shareholders and our CEO to go deep in the R&D that's necessary to solve those problems. And our ambition are to solve the meaningful problems. Of course, we have to build robust businesses all the way that return value to our shareholders every day of our progress. But we want to help our clients solve the meaningful problems because we're a company that's about delivering value. So we weren't able to see your talk as we were live on theCUBE. What were some of those promises? What were the general themes? Well, you know, I do think, you know, David, you heard me give my narrative before about what I think is an interesting inflection point in the way that we serve our market in the field of analytics. And more broadly against IT as well. There is a major inflection point that we're seeing now where the focus and value of IT is not just about helping processes scale. I call that process economy. It's about helping insight scale. And I call that the insight economy. And you know, we were fortunate enough to really understand this because of our deep seated investments in the database space early on to understand that this was always going to be something that was going to drive more value for our clients. That's why we did smarter planet. That's why the whole on demand era of being able to get things out to our clients in an actionable and timely way were preoccupation. So along the way, you know, we made promises about helping our clients really figure out how to secure data, how to govern it, how to embrace more content. One of the very important themes of our very first information on demand conference was around content management, around the unstructured data. And I didn't even imagine at that point because those researchers hadn't approached John Kelly yet at that point that the way we would make content more valuable is really by illuminating it with deep machine learning, artificial intelligence and actually being able to learn, understand, reason, learn and interact around that content. Well, at the time, the narrative of data value sort of took second backseat to what was going on with the federal rules of several procedure and the general council sort of driving to get rid of the data. And there's still a great business around that with, you know, records management and retention and information life cycle governance and all those things are still very relevant. And in fact, you know, we just launched a big business around being able to help our clients with that aspect of regulatory technology and other things. But, you know, so those were some of the promises. Others were around continuing to innovate around our core franchises like DB2. And I was thrilled to make new announcements here around DB2 introducing HTAP, hybrid transaction, analytical processing on DB2 on Z and DB2 on Linux Unix Windows. And also, of course, now introducing cognitive into more problem solving around the information management professions of data science, data engineering and application development. I'm very excited about that. You know, if you dial back 10 years, the guys who got their governance act together had a data quality, they have a data quality leg up now. You buy that? Is that what you're seeing in the client? Oh, no question about it. It's been something because, again, we listen to our clients very closely about all the aspects of the challenges they have in managing information and gaining value out of it. So, one of those core elements is you can't have good insights without good data. And you can't have good data unless you have really the ability to share it and protect the customer interest in it. Otherwise, people just put up silos of having their data participate and looking at the problem from different dimensions. So, the better we can govern that information, the better data quality capabilities we can put into that mix, it becomes less frictionless. And that is exactly what we're going for with the big announcement we made here around the Watson data platform. That data platform is a first of its kind in multiple ways. First off, it is a single platform where all of the professions that have to interact with data information and want to gain insights can collaborate, meaning the citizen analyst, the data scientist, the data engineer, and the application developer. Not just on common data, but on common data governance, on common metadata. Think about taking the friction out of that process today. Today, each of those personas, if you will, operate in silos. And when they finish their tasks, then they think about getting the data to the next step in the process. Slows down innovation. Let's talk about the modern environment. We always joke, you know, where in the stack is data supposed to fit? And we even saw in he last night, briefly, and she kind of, you know, we told her, hey, you know, Wikibon research, we're moving up the stack because data is not a low level stacking, although there's data in the data centers. The SaaS business, which is every company will be a SaaS business at some point, if you believe the cloud vision, which we do. So where does that, the data piece fit? Because the digital transformation at the app level, because the apps are driving innovation. Workloads are the apps they run, they dictate to the policy of the infrastructure. That's a real driver right now, the app-centric thinking. Sure. And how does your cloud or your data platform fit into that narrative? I think we've been clear that it's not just about digitalization or making those apps digital and making it engaging. It's about the combination of digital and digital intelligence. And I think, you know, as every company looks to make that exact transformation that you have just laid out, unless they're thinking provocatively now about how they're introducing that digital intelligence along the way in a native, intimate, engaging format, all they're going to have is a digital set of processes. They'll still be back in the process economy. If they think about how to add the digital intelligence, then they're preparing to use natural language processing, deep reasoning, deep learning, so that the engagement that they're trying to foster is more meaningful for the client they're trying to serve or the service they're trying to create. What's the biggest thing when you talk to customers? Because there's so much transformation going on. We just had one of the guests talk about how things are being flipped out, it's upside its head, 3D sphere of change, if you will. Whatever the buzzword is, there's a lot of action happening. When you listen to customers, what's the one thing that they want in their environment that's going to be an impact from the intelligence view soon? Data provides capabilities, there's management of the data, there's intelligence of the data. What impact are they looking for? Well, look, I do think there's this combination of the digital strategy and the digital intelligence strategy. The companies that I work with that understand that are also preoccupied with design. Look, Watson is the supercharger for the inside economy. There's no question in my mind that that's the case. And we can accelerate faster in delivering our clients that intimacy of digital intelligence by having them exploit Watson, which is why we've made it available broadly to developers and why we've engaged deeply in industry that we can transform things. But those clients who really understand that aspect of design, one of the things we've done, and maybe quietly, is we've built the largest and most interesting digital agency inside of our consulting organization. So companies don't just engage us to come in and say, hey, how is Watson going to help transform this aspect of what I do? It's how do I really transform the way I'm serving the client, thinking about all the design elements? Like the inside economy, can you give an example? I mean, the sharing economy was a buzzword that has come and gone, but they use Airbnb and Uber as an example of the whole sharing economy. Can you give some specific examples of the inside economy? What's the poster child for the inside economy? Is it self-driving cars? Is it, I mean, what is, is there a specific example? Yeah, look, I mean, I think there are, I'll give you a very good example. One that's like a personal one, right? We, as a part of our digital journey, our mobile journey, all see advertisements every day. I mean, you know this example as well, and what I'm going to talk about. So we see advertisements every day, and it's something that's served contextually to the individual, right? And so, the more that people think about intelligently how to serve that ad, they're thinking about where that person might be, what the weather might be outside, what are the pollen conditions, is there influenza in that particular area? So they can think about how to serve the particular ad, that will only go so far. That creates the context for an action. And what we're doing with the things like the inside economy, and in particular with cognitive transformation, is now we've opened up those ads to have a natural language dialogue between the brand and the person. So we announced here, we announced last week, Watson ads. So a brand like Campbell's Soup, they know- Watson ads. Watson ads, Watson ads. So it's Watson-powered advertisements that are placed inside of our applications like the Weather Company app, the Weather Underground app. In those advertisements, we're opening up a conversation between the brand and the individual. So it's not just about contextually serving the ad, but what if that person, soup sounds good, but I really want to make something that's like beef stew. So they can say, do you have a recipe where I can make beef stew? All I have is tomato soup. And Campbell's will engage Chef Watson. Chef Watson will answer the question to the individual. Or in the case of, say, KlaxoSmithKline, perhaps they're introducing its flu season. And they want to get TheraFlu out into the space or its allergy season. And people are thinking about flonase, but people have questions about that. Does it work for these allergens? Is it safe for my son? Is it safe for my daughter? And Watson now can provide that dialogue, that conversation, so that people can gain the confidence not just to look at the ad, but really to take it. It's not an impression, it's an interaction, it's a relationship. That's right. It comes between the process space-based, which is just producing that digital advertisement to the inside economy, which is introducing the intimacy between the brand and the customer. So we last had you on theCUBE at the Data Science Summit in Boston. You were just about to make a big announcement in New York City, which you made data works. We couldn't talk about it on theCUBE, but it was a huge announcement. It was killing me. You guys got it, it was killing me too. I'm like, come on, Bob. But we attended the event. It was a great event. Thank you for being there. It was a great data, a Chief Data Officer community. But that was really an end-to-end data platform, kind of unique in the industry. Maybe talk about that a little bit. We can unpack it. Right, that is now the Watson data platform. And as I talked about that, that provides that one platform where there are persona-specific services for the citizen analyst, Watson analytics, for the data scientists, the data science experience, including also the new Watson machine learning service. All of these personas, the application developer through Bluemix, the data engineer through our Bluemix services around data lift, around transformation, forge, and keystone are served those individuals personas. But now, they're all powered by Watson and artificial intelligence. So we're using machine learning on that journey to help those professions use cognitive capabilities. So in the case of a data scientist, which models match best with this data? What additional data would be interesting in solving that problem? How do I best automate which models to rerun based on the volatility of the data set? I talk to data scientists all the time and they're sort of hunting and pecking, running hundreds and hundreds and hundreds of models, trying to figure out the right combination before they then move it downstream to be deployed. We're going to take the burden out of that process with the cognitive assistant for data science and the data science experience. So was data works like a placeholder name or is that still in place? It was a project name. Okay, so it's a rebranding. Double secret code name. So I looked at my notes from that night. I had ingest, shape, classify, model, apply machine learning, analyze, visualize, operationalize, all these components that used to be bespoke capabilities. You brought together, I presume a combination of things that, capabilities you've purchased in, organic development, but it's a single experience now. Is that correct? Yeah, it's actually, I would say, it is a single platform with a set of purpose-built experiences for the professional. So if you're the data scientist, you feel like that data science experience was built for you. And it works in the native open source methods that you want to work. It works with Python, it works with PySpark. It embraces RStudio, Jupyter Notebooks. That's what you want to be able to do. And we've introduced the machine learning service, the Watson Machine Learning Service into that format as well, the same kind of support. It's a service that supports multiple machine learning libraries in the runtime environment and can be callable as a set of APIs as well as runnable through the service. So as an individual, if you're using Watson Analytics, I feel like that is built personally for me. But underneath the covers is this Watson data platform doing all the work. And then it's building out the value proposition because then when now, maybe I do want a data scientist to think more deeply about a problem. I don't have to think about what data I need to curate for them. They're just going to move into that with their service experience. So the entry point is pretty straightforward. They can come in to the pre-existing platform. That's right. They can select which services they want. They can license it by the month. They can license it by the enterprise. They can license it across all experiences. And it starts at like $50 a month. So we're talking about something that's not just unbelievably powerful. It's very pragmatic in terms of pricing so that we can really help, you know, really power these professionals. The reason why I think this is so important is because when we first started at Wikibon sizing the big data market and you and I have talked about this before, we saw the predominance of revenue going to professional services. We've talked about this business is not going to scale unless you can codify those services and put them into software. This is an example of that. And you know, very importantly, this thing was designed on and built for open source. So we were very excited that a bunch of great partners right from the onset are joining us on this journey and also Galvanize, Jim Dieter's. You know, really we collaborated to build a methodology called the data-first methodology. So you talked about the importance of professional services. We want to teach organizations to do this themselves, to be sufficient. So whether they're transforming a data warehouse, building a new data lake, or thinking about how to exploit machine learning services, the data-first methodology will get them the on-ramp that they need. I want to ask you a question because we were talking before we came on camera about our development backgrounds and our computer science degrees kind of figure out who graduated first before the week. But there's an interesting phenomenon going on in the industry when you get your take on it and your observations. And it's kind of a new emerging psychographic personas that are developing in the cloud business. You're seeing the emerging week and we're seeing it on our media business, a business-minded developer. And then on the IoT side, we're seeing an engineering-oriented developer, meaning they're either double E's who have self-taught computer science and on the digital side it's a business savvy. Neither want to get an MBA but they're being forced into business dialogue because they're going to the front lines. So these are emerging. One, do you agree that that's kind of a trend? And two, what does it mean for the business and how do you get more, how do you address this phenomenon? Any observations? Look, I think your general observation is correct. And at IBM, we've always believed that deep industry, domain of discourse expertise really matters. The application of technology is one thing, but it is an applied technology in the craft of an industry. So when we brought Watson to the market, we didn't just think, okay, we'll do deep learning generally in the market. We said, no, we can transform industries. We can really build a cognitive assistant for oncologists, for pharmacologists, for clinicians, for practitioners, and really help them address this issue of information overload, whether they're thinking about how to make it a more personalized experience by including genomic information, thinking about the breadth of clinical trials they should be considering. All those things, as you know, are a tidal wave of information that really renders them much less effective. And now Watson can help them really be that supercharger so that they can take advantage of Watson's ability to understand all that instruction information. But again, that doesn't happen without that deep industry knowledge and about the ability to engage in that industry and build that partnership. And I think one of the sets of announcements here that we've made, and we're making many, is this notion of Watson professions and thinking about the industry and the professions on these industries that are going to be transformed in this inside economy, that are going to be transformed in the cognitive era and how all aspects of analytics and the cognitive capabilities will help that. Building on what you're saying then, then the phenomenon might not be a new persona, it might be just an expansion of developing. Yeah. I mean, because if you go into verticals, the role of who is the developer is a whole other equation, right? And I think again, you know- Unboarding more developers. Right, right. I'm going to look at the technology aspect of this. It's really the consumerization of business. And that consumerization is happening because of technology, because of technology's ability to personalize. I'm going to ask a tough question. So the tough question you mentioned earlier, you know, for $50,000, they can get in, $50,000, or whatever the price point is, IBM has always sold big ticket items to customers. You mentioned the relationship, so land and expand has been- That's not true, by the way. You know. I'll take you on that right now. May phase are pretty big. Sure, they're pretty big, but, you know, and they are big, but if you look at the economic unit of work that they provide and the marginal value to the business, they're very cheap. And when you think about what they do in order to open up that company's ability to address the needs of a mobile market, we look at what we've done with our banking clients and how it allowed them to scale with the tidal wave of mobile transactions that they opened up with the new systems of engagement. That would have never been possible without the mainframe and the backend securing all those transactions. And for us changing the economic model, so it was very easy for them just to open the door to that. And if you look at our Pego capabilities on the cloud, I challenge you, you're competitive, I will rephrase the question. That wasn't so tough. Bring them a tough question. Okay, I'll rephrase the question. Sales is a land and expand, right? So land and expand is the business model of the cloud. So sales guys like to expand that net contract value. So on the field force, very service-oriented, a lot of joint development with solutions. Is it changing the sales mix and the behavior and culturally with the company? And look, in that context, I think we're on our own digital transformation. And you're going to talk to Bob Lord, I think, who's been absolutely wonderful. He's been such a great addition to the firm. And he's going to tell you about our digital strategy to make sure that we're addressing those kind of requirements and needs for our clients as well. And just in general health on the business, we're getting the sign here to get the hook here. I can see that. I'm not even looking at it. Two minutes ago. Two minutes ago. We're going to go a little over with all these good questions. Or good answers, I should say. Good guess. What's the outlook of the business? What's the health? Give us a taste of what's happening. What are some general trends that you've talked publicly about? You'd like to share? Look, I mean, as you see, we continue to accelerate and grow in our strategic comparatives businesses. And that's really our primary focus because that's where we're delivering our new value to our clients. And we've seen lots of spaces in our, what we consider our core businesses. We know we've seen great growth in our relational database business over the last couple of quarters because we built a continuum of value for our clients who are exploring it now in the cloud. And we're also seeing those innovations come back into their on-prem, typical client server environment. So if you think about how to look at IBM, look at the strategic comparatives, look at the innovation that we're delivering in the market. And I think that really tells the important story. Well, congratulations on the portfolio. Congratulations on the 10 years now, the world of Watson, kind of the new rebrand of the show. But again, the inside economy has been great, been watching the trajectory. Thanks for joining theCUBE. I'm John Furrier, Dean of Vice President Analytics at IBM, Mr. Cube. I'm John Furrier, Dave Vellante. We'll be right back with more live coverage from the Mandalay Bay here in Las Vegas at the World of Watson conference. We'll be right back.