 Live from the Mandalay Convention Center in Las Vegas, Nevada, it's theCUBE at IBM Insight 2014. Here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We are here live in Las Vegas for IBM Insight. This is theCUBE. I'm John Furrier with Dave Vellante, and we'd love to talk to all the influencers, thought leaders, experts, but we'd love to bring in also the IBM tech athletes, Bob Pitchie, John Furrier, senior vice president of the analytics group here with IBM. I know he's got a short schedule, but it's great to have you in, swing by theCUBE and share your color and what's going on on the keynote. So give us some highlights. What was the key things, a couple of things that we would like to talk about, but give us the quick highlights of the keynote. Sure, look, John, I'm happy to be here. I'm happy you guys are here again. I think this is a great venue where people can actually hear a diverse set of opinions about what's going on here at the conference from wherever they are. You guys reach out very wide in the marketplace, so we're grateful that you came here so that the story about all the innovation that's happening here is going to get out into the marketplace. So the keynote, I thought it was great today. It allowed me to talk about something I'm very passionate about, which is I see that we're at a new inflection point on an IT value creation basis. And that inflection point is really an inflection point that moves from process-based value creation to insight-based value creation. And it is right in line with our view that we're moving out of the programmatic era of computing and into the cognitive era of computing. And when you think about the programmatic era, it was all about codification of business process and logic and driving that into applications and industrializing business process. But when you think about the insight-based economy, it really has to do with the deployment of rich analytics, analytics-empowered individuals that are really going to get the discipline of predictive analytics employed for them in the business process that they're trying to serve and optimize, and that will allow the organizations to scale. And that insight that they're going to derive from that data in context of the business process, in context to the person and systems of engagement, in context to the business areas, that is going to be where the value curve is going to hit. So process improvement, you know, go back to the Drucker books and all the management consulting, things we've read and we were getting our MBAs and trying to study up, that's old school, right? I mean, that's client-server, that's computing, it adds value there. Certainly process improvement is great, but now you mentioned this new insight. Just drill down on that a piece, be specific. I mean, there's still process improvement happening, there's some sort of transformation going on in parallel to this new insight. What is that dynamic? Can you just unpack that a little bit? I will, and I'm careful to call this an inflection point, not a shift, right? And there's a distinction between an inflection point and a shift. You know, when a shift, everybody moves into that new space. And an inflection point, in fact, both continue to be important, but that new value creation that gets additive on top of the improvements that we're continuing to make in a process-based economy continue to exist. And one of the things that fuels more efficiency in that process-based space is really cloud computing. And all of the economies that are available in cloud computing, and importantly, the integration of hybrid cloud or hybrid IT between using the efficiencies of systems of record that clients have already invested into today in collaboration with new services that they're going to bring online, either in dedicated private clouds or public clouds. And more importantly, I think really, as it relates to the data, I think it's about data and cloud and engagement. So I see more companies now concerned probably more so with the data that's available to them outside their firewall and how to use that in a collaborative manner with the data that's inside their firewall. So they need a fluid data layer that matches the fluidity of the cloud as it has done for business processes. So what you're saying is the process improvement external is a process that has no process. That's unstructured data. You don't know what's going to come at you, whether it's Internet of Things, consumer experience, live events. Well, I do think all of those are still in the context of a business problem in process. Even Internet of Things, right? Maybe we're looking at asset optimization or predictive maintenance and quality. So the business process there is about how I ensure that my capital investments are going to continue to stay online and introduce the least amount of disruption to the clients I'm serving and the clients they're serving. So I still think that's the business process. Unstructured data has an enormous role in the context of being able to do it. Dave wants to jump in, but I want to add one more thing. One problem that we see in programming right now, the old namespace console, here's my namespace, set it up and do all that stuff, is a data space. Observation space, as Jeff Jonas talks about it. The big problem we're seeing is customers set up their data space and they think we're good. And then new data comes in, they've got to re-engineer their infrastructure. How are you guys? Is that software, the magic bullet there? What is the key aspect that's going to change that to? I set up some base infrastructure. I can stand up stuff, software. And then how can I be agile? Look, I think it is not just software. I think it's very specific discipline of software. And I think it's around context computing. And so, you know, data changes data. And sometimes that data is a temporal element that's going to allow me to go back and reassess other decisions that I've made up to this point. Because it could significantly change that next decision I make. So this aspect of context, especially as it relates to all the ambitions around omnichannel interactions, richer systems of engagement. When you go out into any of those spaces, the notion of a master data object, whether it's a person, whether it's a product, whether it's a company, it gets more sparse. It's less clear, there's less focus. So you have to use more of the context computing fabric to be able to bring that data alive, so to speak, and utilize more real-time influences to understand what's going on in the business moment and how I should assess the best decision I can make at that time. And many times, the thing that I learn has to go back and update the past to be able to give me the best insight into the future. I'm intrigued by this notion of a fluid data layer, Bob. And if you look at historically your clients within any industry, whether it's finance, manufacturing, healthcare, media, they've built their stacks. And those stacks are hardened. Production, manufacturing, partnerships, the ecosystem cement hardened stacks. And this fluid data layer seems to be changing that. We heard from Pratt and Whitney this morning earlier on how they're taking advantage of this. But he's stressed, look, it's early days. So can your clients, how can you help your clients take advantage of this fluid data layer? And not just the data layer, it's obviously cloud. It's systems of engagement. Fluid analytic layer. So you've got this digital matrix now emerging. And it seems to be news business models are riding on top of that. I wonder if you could talk about what you're seeing in that regard. Well, look, I think it's a good point that you raise about the rigidity of some of the client environments as it relates to data processing or analytics processing. And clients that have gone down a route of being proprietary locked in. And I even see some vendors make an announcement recently about, hey, we're in the cloud now, we're great in the cloud, and then you look at what they're doing. And they're just trying to create new contemporary interfaces into that same proprietary lock-in. So we step back from this and we said, look, there's a new reference architecture that really should be laid into the foundation of companies as they think about the role of the chief data officer or chief analytics officer or as they think about these problem-solving arenas. IBM does not approach this as a technology problem. It's a business outcome problem. And if you map that reference architecture to the capabilities you already have, it becomes clear what I can use, both in terms of the data that's available and the processing zones or analytics zones that are available and how that needs to be augmented with new capabilities like context computing or stream computing or new front-end capabilities like Watson analytics or information privacy, security and governance or unstructured data management. All those things sort of light up, if you will, against the contrast of a reference architecture to say, all right, if I want to solve that problem, I really know how these other zones of capability, these other analytic disciplines, and they don't have them in that existing rigid architecture. So since we're based on all open interfaces with a reference architecture, I think we can come in with agility and help our clients make that difficult traverse. So and not all customers are going to get this to the same degree of each other, right? So you have a spectrum. Sure. And so can you discern, and what are the characteristics of those organizations? Let's keep it a traditional organization, Fortune 1000, that are actually leveraging whether it's that data fabric or the cloud fabric, the engagement fabric. Can you discern the ones that sort of get it? I hate to use that term. Yes, the Kool-Aid. Because we all love to talk about the sort of mean, but there are 10% that are really, really good at it. Are you starting to see those guys emerge? We are definitely seeing the standard deviations emerge. And we've released this research paper around the generation D enterprises, and those generation D enterprises are really data savvy, analytics savvy, data centric in the way that they think about things. Now, I see great organizations. I mean, Larry is a good example of this, sort of understanding that in order to really get the value of this opportunity, it has to be a marriage of line of business outcomes with what he can do in an IT fabric. So, you know, a very interesting thing that Larry did when he engaged with us is he said, look, I have two years of data. I'm going to give you guys 18 months, and guess what? It's the first 18 months. If you guys are so good, I want you to use your predictive analytics to tell me what happened in that last six months. And we were able to nail, with a 98% accuracy, what happened in that last six months. Okay, who's going to win the World Series? I'm in Vegas. I'm going to put some bets down. So, you know, we track a lot of data points. You're holding that secret weapon in the table. It's like, you've got nukes, and you're not bringing them out yet. There's some things I just can't talk about. So, we've got the World Series. I've got to ask you. I know we've got a couple of minutes left. I asked it earlier. Kansas City Royals obviously got dominant pitching, and the Giants are scrappy, and they always find a way to win. And we were talking just earlier about some of the sports radio commentary, and they say the Giants win because they have a mindset. Sometimes they go up against good pitching and can hit, but they come in, and how they approach the game is what makes them unique. So, I've got to ask you. The practitioners that are really pushing this scrappy trying to get the inflection point rolling has got that serious heat coming at them, pitching from, no, stop. We shouldn't be changing. It's too expensive. How do you approach that? What's your advice to customers saying, here's an approach to take? Well, look, it's actually a great set-up question because it relates to your question as well. It's not about going in and trying to hit the Grand Slam to win the game in the ninth inning. It's about production. I mean production, I mean, the way the Giants won Game 4 here was really about everybody produced, everybody had a hit. Everybody took the opportunity at the plate to contribute something in the form of a single to get the runners around the bases. Great approach. And that's a great approach. So, when you think about the quick wins that we engage in with our clients, it's about the line of business and IT agreeing there's a quick win here. Let's not try to boil the ocean. Let's not try to deploy a full reference architecture or try to drive forklifts around in the data center. Let's identify that single, the one that's going to move the ducks around the pond and let's start there and we'll get the buy-in. Small ball. Small IT. Small IT. It's because it gets infectious, right? Culturally it becomes infectious and you get this domino effect. So, I think it's hard to bet against the Giants right now with the way they're producing. Let's see, Kansas City has got tough pitching. The fans are crazy and Kansas City are very loyal and I wouldn't be surprised if they could run the table. I'm a Red Sox fan in general. Well, thanks a lot. I really appreciate you coming on. I'll give you the final word here. What's the vibe? And I'll see the insights now. The new show was called IOD, which is information on demand. I'll see the insight. You nailed the inflection point. Love that concept. What's this going to morph into? What's your vision for the show? It is really bad insight and it really is about client outcomes. One of the things I'm very excited about is the speed and the completeness that we're introducing our portfolio as composable services in Blue Mix and the cloud and also as software services when we think we have a differentiated analytics platform. So when you really look around, we've gone and produced, I think, like nobody else has with the range of data services and the range of analytically powered software services that our clients could take advantage. And when you talk about agility and doing those quick wins, sometimes that's the best place to start. Developers are the new king makers, so we want to put the tools and the jewels in their hands to take this thing to the next level. So comment on the economics of the cloud as a final word. Obviously, there's always the calm before the storm in terms of leverage. The cloud is the ultimate leverage. You look at the economics of what the cloud is in here. He was talking about some of the new services data works. It's all self-service. I mean, when the flywheel gets going, the economics are pretty significant. You guys have great software presence in there. Just share with the folks some of the executive conversation around the economic leverage that's going to come out of this engine of innovation. Yeah, and see, I thought you said I had the last word. No, I want you to get that out there one more time. Well, I do think it's undeniable. It's about data and cloud and engagement. So I even think, John, if you stop at just data and cloud, it's not enough. I do think you have to think about what system of engagement, what new analytically powered outcome are you going to drive? How is that going to change the course of me seizing that opportunity or creating a new business model? So I think it really has to be all three, and that cloud is an ether, not just for the productivity of the developer and what they can do in terms of composable services, but also the fluidity of the data access that it gives you, both things that are inside the enterprise and the things that are outside the enterprise. Generation D is here. Bapachiano, Senior Vice President here at IBM inside theCUBE, sharing his insight and a drill down on some of these important topics. Data changes, data, cognitive computing all happening. This is theCUBE here, live in Las Vegas for IBM Insight in the social lounge. We'll be right back after this short break.