 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 in Las Vegas for IBM's World of Watson Conference. Gene Romney, CEO, just gave an epic keynote and we're here. Next guest, get back on the coverage. Jeff Spicer, who's the CMO of IBM Analytics. Welcome to theCUBE. Thank you, thanks for having me. It's great to be here. So you're new to IBM, so you're seeing a new blood. So when did you join recently? How long, a month ago? Or five months ago? Five months ago, okay. So you're getting your sea legs. Yes, I am. So my first question is, you've been in the industry. You worked at Oracle, you worked at VMware. IBM's bringing in a lot of new blood. What's your take right now? What's your view? I mean, you're a new perspective, you're fresh eyes. You just saw a pretty inspiring keynote. I saw it was amazing and then she gave an amazing keynote at Grace Hopper last week. And she's, I haven't really seen her in public. She doesn't come to these events, but it's inspiring, but you're on the inside. What's it like? Tell us. Yeah, there are a group of us, like you said, that have joined from the outside at the executive level. And each of us has a different reason for joining, but I think there's a common thread and that's around the word of transformation. I know that word is overused a lot, right? Some people roll their eyes when you use the word transformation. But it's true and it's happening at multiple levels right now at IBM. It's happening at the executive level, at the genie level. She is driving transformation across the entire company through product strategy, through business process. And then at the BU level, people like my boss, Bob Pitchiano, are very interested in how his business operates. So from my perspective, he was interested in marketing. How do we actually go to market? How do we market our products? How do we create relationships with business partners? And he wanted somebody from the outside to come in and redefine that part of his business. So that's why I came on board. I wanted to be a part of that transformation. And the thing that you said about genie is really worth talking about. Genie is a very inspirational and emotional leader. You saw her today on stage, connecting with the audience, connecting with her guests. She really has that- Clever snark too, it was clever comments, tongue in cheek. Kind of New Yorkish, but she's from the Midwest originally, so it's kind of like you love her for it. She was cool. Yeah, yeah, she's got a really interesting hip vibe when she's on stage. But I think it's leaders like Genie and the commitment that they have to transformation is what brought people like me on board from the outside. That's awesome. And when you look at sort of come in and you're now in the sort of analytics, you look at the history of the data management business, how that's gone from, we've talked about it all week. Used to be data is a liability, you're going to get rid of it. And now it's this asset, you can make money with it. What's your perspective coming in and when you did that little external scan? Yeah, really interesting question because again that's one of the things that attracted me to IBM right now. IBM in our division has a philosophy around data first and I know you've probably heard that from other IBMers but it's something that we really believe in. That data is something that a company can use to differentiate itself and the insights that it gets from that data are what it can use to compete in the inside economy which is how we refer to it. So coming in from the outside, I was particularly intrigued by IBM's philosophy around data first and the wood that it's putting behind the arrow. It's not just about technology but it's about the ecosystem and about the method to help our customers understand how they can put data first in their business. Talk about the data platform because Bob Puccino announced that yesterday. Just go a little bit deeper on that because they didn't have a lot of time, we could have done three hours on the impact of that and I love this inside economy. I love that angle even though it's got the legacy word insight from last year's IBM insight which was a great name too. Watson has obviously a star power but inside economy is all about the data platform. Spend a minute to go a little bit deeper. What does it mean? How are you guys going to be talking about that? What are some of the key features? Well just to take a step back and talk about data and why the data platform is important. So data as you know has exploded over the past half decade to decade. The number of data sources that we have, the complexity of the data, the complexity of ingesting the data, governing, normalizing, managing that data and all of that has ended up creating data silos within the enterprise. So companies have real trouble extracting insights when their data is spread across different silos and sharing those insights across different audiences. So what we at IBM thought would be the next logical step is to extract some of that complexity. To create a platform across the data silos, across the data sources and normalize the access to that data, to give access to everyone or to democratize the access to that data. So it was a logical step then to create a platform approach into which we could ingest data from all different sources, govern that data, manage it and then to provide access to data scientists, to data engineers and to what we call citizen analysts. So a very logical approach to dealing with the complexity that multiple data sources have by their very nature. You came in, you said five months ago? Yes. So the planning had already started for the big launch in New York City. Yep. I mean, imagine what, you had a six month runway and a big launch like that. You know, sometimes maybe a little bit more. But you had enough time to shape some of that messaging and a big part of that messaging is what you just mentioned, the different roles. So, how new is that? Is that something that is going to continue to evolve and shape and touch other parts of the business? Yeah, that's a really great question. So when I came on board, a lot of the foundation had already been set. The philosophy, the strategy around reaching out to different roles, different data professionals and bringing them together across the common platform. That work was already underway. Some of the work that we did over the course of the summer and into the fall was to really understand these different roles like the data scientist, the citizen analyst and what their specific data needs were and how we could create messaging and functionality, capability that was going to be most meaningful for them. And to the second part of your question, you'll see this continue to evolve as we get feedback from customers on use cases and how they see the platform really providing value to them in their business. And the data sources are evolving. Let's talk about that a little bit. I mean, talk about IOT. Most of the IOT data is analog and you're trying to digitize it. So that's going to open up new roles, the engineers, the operations technology as opposed to the information technology people. What are you seeing there in the early innings? Exactly, there are other roles. Right now we're looking at what we call data professionals. So citizen analyst, data engineers, data scientists. But Dave, to your point, there are other roles in the business that are going to come forward and say, hey, I have a data need too. So a triple E engineer, for example, who has a certain type of data need that isn't represented by the professions that we're going after. Or a certain line of businesses, certain analysts in different areas that step forward and say, I have a business need too. So what we're trying to bring to this platform is flexibility and extensibility. And that's part of the reason, by the way, that one of the pillars of the data platform is the ecosystem. So we believe in having a very open ecosystem and open approach. So open APIs, open source, and open ecosystem so that as those additional use cases pop up, as new audiences come forward and say, I have a data need, if IBM doesn't have an immediate response for that, we can look to our ecosystem and it's very likely that one of those partners will step forward and say, hey, that use case, I've already got an answer. It's got a big portfolio that you have to look after. It ranges from sort of what traditionally this conference used to be, IOD, the whole governance piece, the Cognos, the traditional Cognos business, both the reporting side and the refreshed BI piece, a lot of the modern Watson analytics. So there's a lot of different pieces that are generally related but serve different purposes. So have you had time to think about your overall messaging architecture, the umbrella messaging and how that drills down into the product, you know, connective tissue? Yes and no. Yes, you had time to think or no time to think. It was a big announcement. Yes, I've... Yeah, yeah, well with a big announcement coming up, you probably had to spend a lot of time on that. We have, yeah, you know, seriousness. You're bringing up kind of an interesting question for us which is, and it's part of the reason we're starting to talk about a data platform instead of a series of point solutions for data management and analytics. So if you, you mentioned the explosion of data before. Each type of data that comes forward has its own data management solution. So back in the day we talked about relational databases and we formatted the data on read, or I'm sorry, on write. As we were putting the data into the database, we determined the structure of that data. Then we moved forward to analytic processing and we had different data structures. Then we moved forward to IoT data and so on and so forth. And every time we created new data, a new structure for data, we had a new data management platform or type of database that would help us create relationships and extract meaning from that data. And that's gotten really complex. So first, we have to rationalize all those different databases and data processes and create a very cogent structure to those, which we're in the process of doing. But this is why you see us doing a platform. Because the platform extracts or abstracts all that complexity, right? So we don't have to think about the complexity of the underlying data stores. We don't. But point solutions also have complexity in the sense that they're not tied together. Correct. And so a platform allows us to do that, right? It ties it together, it helps us bring together all of our integration and governance and cleansing applications, all the data stores and then all the analytics tools themselves. So one of the challenges with what you're talking about is there's no schema on right, there's no schema. No schema, right. When we talked to chief data officers like Interpol the other day, he talked about, okay, first step is how do you use data to make money? Not selling the data, but how does it support your data strategy? And then, okay, what are your data sources? And then how do I get quality out of my data? How do I trust that data? And if I'm hearing you right, you're saying the platform is sort of designed at least to focus on two and three. Yeah, actually the platform is designed to focus on all of it. Okay. How so on number one? So if you compare the platform to the life cycle of data, at every stage along the way in the data cycle, there's a set of tools or applications or processes that you would apply to that data in terms of ingesting the data, governing it, cleansing it, managing it, using it, making it available, and then gathering insights from it. All of that now can be managed at the platform level. So all of the tools, all of the capabilities that we have are surfaced in the platform as services, as composable services, that different audiences we are interested in reaching can then access. So again, it takes some of that complexity, Dave, and in a way shields the user from it. Now the data engineer is, of course, still very involved in managing that data, but the other roles that we're interested in reaching, the data scientists, the citizen analysts, this removes that complexity from their day-to-day life. You know what's interesting is that we're seeing the trend, and this is right up your wheelhouse being the CMO of analytics, it's a lot of pressure on your part, is that IBM is targeting the digital marketer in the organizations of your customers. So you guys are going big time digital from what we can see, and we've been watching IBM get digital big time every year, more and more digital mojo, big time. You have to then also use it, so you got to walk the talk internally amongst IBMers, because you're also selling a digital solution to customers. So as you go do that, what is the key thing that you focus on? Because you have to transform and be a digital company yourself, because analytics is a sweet spot for marketers, but it's not a standalone marketing solution. Absolutely, a couple of things there. First, the digital transformation that most companies are going through is not just about technology, it's not just about business process, it's about both of those plus data. And I think that's something that a lot of companies forget as they go through their digital transformation, that the data is absolutely critical, a critical thing to think about as you go through this sort of transformation. Now, the second part of your question, IBM does have a complete suite of marketing applications for CMOs, so we've made some acquisitions over the years, we have put together a marketing cloud. And a big part of that marketing cloud is the analytics capabilities. Those analytics capabilities come from RBU, but they work seamlessly with the marketing cloud. So if there is a- Well, they plug into this, your products and your business unit are used by the other business unit, so it's integration internally too. That's correct, it's integration, not just at the product level, but at the use case level. So marketers are in fact one of the best use cases for Watson analytics. Watson analytics is a tool that allows a marketer to go in and do discovery that's very self-directed. So instead of working with a data scientist and waiting for complex modeling to happen and for insights to come from the science side, the marketer, the layman or the business analyst can use Watson analytics to go in and discover insights or relationships that they might have not, otherwise not have none existed. And they can get those insights really quickly. Jeff, really appreciate you taking the time to come on theCUBE. I asked you one final question. What's your goals for the year? You're coming off this show, you're going to have a big bounce of momentum. The Watson brand obviously is getting a lot of attention. Great conversations and awareness on social club buzz, traffic's up on the keynotes, everything. What's your to-do plan as you go out and try to market the portfolio? Yeah, there are a couple of things that we're going to be doing as we go into the new year. One of them has to do with what you just said. It's really capitalizing on how we're shifting the conversation from data products and analytics tools to putting data first, to making data the foundation of your cognitive business and investing in not just a tool set, but a partner ecosystem and a method for getting the most out of your data. Awesome, Jeff Spicer, CMO of the IBM Analytics Business Unit. Of course, the Bapachiana was on yesterday talking about the Cloud, the Insight economy. And again, this is going to change the world kind of stuff. It's just the beginning. And the hero on this journey is the little Watson child, still growing up, it's still young. So that was a great quote, I love that keynote. Thanks for coming on theCUBE, appreciate it. Jeff Spicer here on theCUBE, I'm John Furrier, Dave Vellante. We'll be right back with more live coverage in Las Vegas at World of Watson after this short break.