 Live from Las Vegas, extracting the signal from the noise. It's theCUBE, covering IBM Insight 2015, brought to you by IBM. Now your host, Dave Vellante and Paul Gillin. We're back to Mandalay Bay. Welcome to IBM Insight 2015. This is theCUBE. Check out ibmgo.com. It's the digital experience around IBM Insight. Bob Picciano is here as the Senior Vice President of IBM Analytics. Bob, welcome back to theCUBE. Great, Dave, thanks for having me. I'm great to be back. You're welcome, but this is your show, and I know you're not going to agree with that, but really, this is the show around IBM Analytics. You did a great job yesterday on the keynote. Oh, thanks. Hosting, you were crisp. Your messages have been right on. I was commenting, Bob, that a lot of these, we go to a lot of events, as you know, a lot of product, product, product, product, products, too much sometimes. You guys are laying forth a vision of the future. You're showing some real examples with customers, not just potential examples, real hardcore use cases. We saw Whirlpool with an IoT example. We saw SETI today, how you're working with NASA and IBM. Many, many examples of how things today are going to drive the future. So, first of all, how do you feel? What are your thoughts on Insight? Oh, I feel great, and I appreciate you really putting the perspective on the fact that our story is very much about our client's success. It's not really about certainly me or even IBM. It's about helping our clients on this journey in the Insight economy. And as we've talked a couple of times, Dave and Paul, last time at MIT, our vision is what's unfolding in the world in front of us is massive digitalization and also the need for more insight in terms of all the available data, especially dark data, sources of information that have not been tapped into before. And the Insight economy is where the IT value proposition is really hitting a new inflection point. Previous generations of IT capability was more around the process economy and helping organizations really industrialize the way they worked. How do you open an account? How do you hire an employee? How do you process a loan, a claim? And the Insight economy is really how to derive valuable, actionable insights in a fast and timely way from all of that disparate data. And more and more not just the data that's inside of your company or your organization, more and more the data's coming from a combination of that with external sources of information, whether that's sentiment or climate atmosphere, whether other company data sources, syndicated information, open information. So you mentioned dark data in your keynote yesterday, you just mentioned it again. By dark data you're talking about unexploited data, data that's not seen by the user, not necessarily data that's in the noise or is that included in dark data? It is included, right? Because data that is difficult to work with creates such a high barrier to entry that people typically forego trying to get any information out of it. And so you could look at the data that's generated by most of the sensors and analog to digital conversions that are in the market. About 90% of it never gets utilized in any meaningful way. And about 60% of that 90% loses its true value within a few milliseconds of it being generated. So that's a tremendously wasted opportunity. And that's what I think people are very excited about with the advent of capabilities like Spark or IBM Streams, where we can extract insight from that information almost immediately and really provide the ability to take fast actionable insights and enable organizations to build data-rich products and services that utilize that information that's generated in a way that transforms the customer experience. RULEPU Corporation was a great example of that. Yeah, I mean, I wanted one of those. I mean, I can't wait to get one. You think about the way it changes the consumer experience. You know, many of their customers today to go to a big box store, and maybe they're buying that washing machine or that dryer or that dishwasher or maybe the builder put it in their home. And they might not have any relationship with that consumer until maybe something happens. Maybe it goes wrong. And that's the first call that they get from the consumer is, hey, this happened. You know, my floor got wet, you know, it didn't work and I needed to draw my clothes. That's not where you want to start your client journey. So the internet of things and the work we're doing with them allows them to create really a bond of value, a service to the client much earlier in that cycle, which, you know, I think they'll be able to monetize in ways that we have yet to imagine. Yeah, well, you know, you don't worry about these appliances until they break and then all of a sudden the laundry's backing up or you're walking into a, you know, three inches of water on your floor and you'll be right. The first impression is, you know, darn that company. So even though it served you for 10 years, right? Yes, that's right, you know, and you didn't have to even think about it. At the recent Strata and Hadoop World Show, we saw a couple of themes and you just touched on one of them. One of them was data in motion and really your observation that data loses value very, very quickly. And so there was a big emphasis, there's always been an emphasis in real time, but that data that's in motion that's moving, talk about the importance of that type of data and how IBM is approaching that, maybe bringing Spark into the discussion. Yeah, so I mean, I do think that that's been an important part of what we've brought to the table in building a balanced architecture for next generation solutions that are analytically powered. Is that the ability for things to be extracted quickly? I said, I think on theCUBE a couple of years ago, that, you know, I foresee a time that big data is usurped by the term fast data or fast actionable insights. And that's the more meaningful thing in terms of delivering client value. Jake Poraway on stage said, big data isn't just about getting more data in a bigger bucket, it's about really being able to get insights out of a larger collection of more varied information. So we've always focused on the ability to get that fast data, whether it's structured information that might come off of a sensor, weather sensors, those sorts of things, or whether that's audio and video, which in many cases also isn't being analyzed to its fullest extent. So it's an important part of illuminating the value in that data, whether it's something that's going to be serving an enterprise or a profession, like a doctor, a public safety officer, a lawyer, those sorts of professions, which aren't served by rich analytics today in the way that they can be. Another big theme that came out of Hadoop World was that complexity is limiting customer's ability to realize what you call outcomes. Does Spark address that, or is it just sort of less complex than Hadoop? And how is IBM addressing the analytics complexity problem? Well, I mean, I do think as it relates to utilizing more common skills to be able to work with data at scale, it helps with that element of the problem. But an important component of the bigger problem is that you must combine information from a variety of different places. And as you know, that data doesn't necessarily integrate well. So in things like Watson Analytics, we're applying machine learning to the data preparation and the data integration to semantically improve the integrity of the information to help reduce the sparsity, which improves the insight that you can derive from those data sources. And we can do that using the models that we've generated over a long amount of time in partnership with industry experts. And one of the things that you saw us roll out is these expert storybooks, which is really, I think, a killer app for user self-discovery of information, serving somebody I call the citizen analyst. They need aspects of business intelligence and reporting, but those are for the questions they already know how to ask and get answered. What about the questions that they don't even know how to ask? That's been an unserved market for many of the analysts that exist in companies. Watson Analytics allows someone to bring together those disparate data sources and actually start to glean insights from that information because we use machine learning to build those combinations and really ignite the deep insights. We're even more excited because now experts like Ogilvy One, Nucleus Research, The Weather Company, Twitter, Ari Ball, EdgeUp Sports, I mean, the list goes on and on, bringing together their insights about their data sets, their models, and using the palette of Watson Analytics as the place to bring that together so people can combine their enterprise information and their data. You talked about learning and the word cognitive keeps turning up at this event. Is this an area that IBM thinks it can stake out uniquely, this idea of maybe using Watson as the camel's nose in the tent? The idea that organizations can learn from analytics and can grow and get better from analytics, something that your competitors, I don't think have staked out. Well, I think we have a tremendously important technology lead in the space of cognitive business. And it was a space that you saw first unfold in the public eye in 2011 when we famously played Jeopardy with one set of capabilities, open domain question and answer. Now, since that time, we've been fervently working in a very determined way on the sorts of things that make cognitive business a reality for more businesses. And just recently, Ginny at Gartner, Ginny Rometti, our CEO at Gartner, unveiled our vision of helping businesses really transform to be cognitive businesses. And an important aspect of the cognitive business journey is that every business is really working in a very, very rapid way to digitize more of its client experience. But when everybody's done with that, it's all going to look the same. What's going to be your differentiator? So what we're saying is when that digital enablement of the business is matched by digital intelligence, that's really a cognitive business. And digital intelligence means how do you learn and ignite and illuminate that dark data in a way that really transforms the knowledge value to more professionals in the business experience? So we hear a lot about digital business these days. Are you saying that you're trying to move that conversation sort of up further? Even further, oh yeah. Further towards toward cognitive? Yeah, because in many companies' endeavors, the digital business strategy is around the interactive digital experience and it's about omnichannel experiences. But those will be differentiated unless they're fueled by really differentiated analytics, whether that's advanced predictive analytics, prescriptive analytics about what to do with that information, or in the most profound ways, the insights that a cognitive capability can offer to the information that's there, many of which will be unstructured. It'll be very, very noisy and fast moving. And it will be also things that have to understand the behavior and the longitudinal view where that individual is being served. Bob, I wonder if we could talk about your business a little bit. So, I mean, every tech company, large tech companies facing headwinds and currency, they're also facing a dynamic where the traditional businesses that are managed decline and are not being offset fast enough by the growing businesses. What a lot of companies are doing is they're saying, okay, let's pick a cloud. Our cloud business is growing very, very rapidly, but it's very, very small. Your analytics business is quite large. It's the largest. It's huge, it's, well, let's see. Publicly, IBM said it's a $17 billion business last year. It's been growing in a 20% clip, so one could infer that it's going to be over 20 billion this year. Talk about your business because it's not just a new, little, tiny business that's growing very fast. It's a big, giant business that's growing very fast. Look, and I appreciate you pointing out the facts in our journey, right? We are the world's largest analytics business. We are the only business that really can offer our clients mature choices around the leadership in predictive analytics with our capabilities and the leadership of cognitive analytics, which is really defining the new frontier. It's fueled by 15,000 deep experts in consulting and data science that's really there at the disposal of our clients to help them on their journey with tens of thousands, over 40,000 engagements that we've done with clients over many, many years to really define those patterns of value that we can help them with, focused by industry. And one of the things that I think that I'm very proud about is the work that the teams have done to create industry solutions that are powered by analytics that are pre-integrated in a way that allow them to be very consumable and that provide much greater insights into the markets or the clients or the services that they're trying to provide to their end clients. So number one in the world in big data and analytics with, I think, very differentiated technology portfolio and growing faster than the market to the first three quarters, as you said, about 40%. And there's another nuance here which traditionally I've had a big services business and we produced a study earlier this year that said the predominance of services in big data analytics has to flip, has to go towards software and the interview with Joe Colley really underscored what you're doing is you're extracting industry knowledge from your services business and putting it into software at scale. So what's the potential for that in the next five to 10 years? Well, I think there's great potential. As you know, some of that journey started just last year here. When I was on Camel with you on that Monday, I sort of, you know, said, there could be some big news this week and we were excited that at the end of the week we were able to, on the Wednesday, announce our relationship with Twitter. And so that allowed us to really apply new analytics and new integration capabilities to make those insights actionable around sentiment information, which is very, very important on demand signaling, brand sentiment, brand loyalty, customer behavior. We all understand the value of that business, that capability brings to business. But many organizations were not using that in business decision making. So we really helped illuminate that. Then with the weather and climate and atmospheric information from the weather company, more unstructured information with relationships like box. But putting that together in cloud insight services, right, or inside cloud services so that we're packaging up things that are available for developers and APIs, but also the data packages so it makes it much more consumable to power new solutions that our partners will develop. So I know you got to run your customers asking for your time and I really appreciate you stopping by, but so let's close on differentiation. Your partnerships are not Barney deals. Look at Twitter, the weather channel, et cetera. So talk about differentiation, talk about your strategy with regard to differentiation. Well, let me just take one example, right? And I think we have talked quite a lot about what we're doing with Twitter around sentiment and also with the weather company. And I think you've had some of our guests talk about that. But if you take one good example of that, it would be the work that is happening between IBM and box. And in that example, we're actually code developing a whole new platform, if you will, of how to take content and collaboration and a new way to work to deliver more value to clients by industry. So it's not just a function of, hey, IBM is making a connection to be able to use box as a platform. No, no, no, that's not where it is. We're developing new analytics jointly around our capabilities and stored IQ. We're developing a new set of federation services around our content navigator. We're developing a new set of industry workflows around case management to make it easier for people to define the workflow at a human level of what has to happen to serve a client or a customer. And we're defining new ways to capture, classify, and actually build knowledge about instruction information as we add it to the platform. So these are really partnerships that go very deep in terms of developing the new sets of capabilities that allow them to scale to even greater sizes, but also greater value to the clients we serve. Clarity and vision and strategy backed up by execution, Bob Picciano. Thanks very much for coming on theCUBE. Thank you, Dave. Thank you, Paul. All right, keep it right there, we'll be back with our next guest right after this. This is theCUBE, we're live from IBM Insight 2015. Right back.