 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. Welcome back to IBM Insight everybody, this is theCUBE. Go to ibmgo.com for the full digital social experience, we got a crowd chat running. This is day two of theCUBE at IBM Insight, Alistair Rennie is here, he's the general manager of solutions for IBM Analytics. Alistair, welcome back to theCUBE, good to see you again. Hey, thank you for having me, it's been a fun couple of days. You're welcome, we last talked at your vision event, which was fantastic, down in Orlando. Yeah, in May. You know, very interesting audience, a lot of the sort of CFO types, but also you guys showcase Watson Analytics, so give us the update, what's new since vision? So, Watson Analytics is moving at an incredible pace, I think it's becoming one of the most, certainly the fastest growing and one of the most demanded self-service analytics platforms out there. So, we first put Watson Analytics into the marketplace in October, so about a year ago, and there's about 500,000 people, half a million people now, members of Watson Analytics doing amazing things. So, incredibly rapid growth. We actually showcased an event in New York last week where we had a whole number of clients join us, and what's been fascinating is, kind of like what we hoped would happen, but probably even beyond our wildest expectations, is finding people in every kind of different business, putting powerful, cognitive tools into the hands of their business users, and they're discovering insights in their business that they never knew they had. So, we had a client from Point Defiant Zoo talking about how to optimize their fan experience and their venue experience. We had Zion's Bank Corp up talking about how they were doing risk management between compensation and banking using Watson Analytics. We had a company that does service contracts around public housing in the UK that were just sort of every day digesting new information and gaining insights. So, it's just going so fast and really putting analytics into everybody's hands. I want to ask you about that because you came up with a freemium model for Watson Analytics, put it out there, let anybody play with it. That was quite a different strategy for IBM and obviously one that was aimed at the business user pull rather than the IT push. What's the thinking behind your audience for Watson Analytics? So, we have so many powerful tools for the IT audience, but our observation at the time was that there was an emerging class of people that needed access to analytics and we kind of called them the citizen analyst. So, if you think about you or anybody else in their everyday business role, how many times a day are you confronted with making a decision where you're kind of left with making a hunch? And our thesis was, our idea at the time was if we could put just an entirely different level of consumable tools, kind of powered by cognitive in their hands, we could make every business decision better. And we wanted to do something pretty bold. We thought that it was really important to make that consumable not only from a technology perspective, which I think Watson Analytics really is, but also just from an access perspective. We didn't want anybody to have a barrier bigger than going to a URL and creating an account and starting. And that has been so important to us, giving analytics to tools to people that never even knew they were available, not having to go through complicated process. And now what it's starting to do is give us incredible insight because we can sort of understand the interactions on the system itself. We're getting all sorts of interesting data about how people are using data that we're using to make the tool better every week. So it's really created this incredible snowball effect. It's one of the most exciting things we've ever done. One of the keys to your title is the word solutions. It's an overused word in our business. What does solutions mean to you, specifically in IBM generally? So we start with the idea that the value of analytics is not so much in the technology, but it's in the business insight. So people care about analytics because they want to learn something about their business that they can take an action on and improve something, customer service, profitability, whatever happens to be. So we kind of take a couple of different perspectives on solutions. So one is clearly how do we put just a new generation of tools into the hands of every business user, Watson Analytics. And people are discovering, when we talk to clients using Watson Analytics or just people using it, they don't come up and tell us that they think it's the coolest technology in the world. They come up and they're rabid. They talk about profitability they never knew they had, or safety and regulatory concerns that they can now solve in a different way. So they come and talk to us about business outcome. So that certainly I think fits solutions. The other part of solutions that we're doing, we made some announcements this week at VisionOn is thinking about insights very deeply in a vertical industry context. So what do analytics mean in retail banking and merchandising and retail and industrial? And starting not so much from the technology, but what does it mean for a retailer to understand their mix of products or to understand segmentation and building truly repeatable solutions that we can deliver in very consumable ways to industry roles, to CMOs, to CFOs, to CSOs and thinking about it that way. So consumability is this long tail of every business user, but also deep thinking about vertical insights. I want to traditionally, if I could follow up on that, traditionally IBM would approach that problem with the heavy services layer. Are you, I'm inferring from what you said that repeatability, you're able to extract that industry knowledge and put it into software so that it's going to scale. I can consume it as a customer much more rapidly and probably more cost-effectively and scale it across my organization. So you've said it very well. I mean, we have I think a tremendous advantage in that we've done more than 50, 55,000 consulting engagements. So we have a pretty good sense of where analytic value can be found by industry. And certainly we do tremendous amounts of transformational engagements with clients. But there's also I think a really untapped market for exactly what you just said where people want to take that expertise, they want to get it captured in software and in fact, mostly software as a service so they can consume it from the cloud. And they want to get to value in days and weeks as opposed to a longer term transformation project and get the insight, take the action and then repeat and do more. So for us, it's exactly that. How do we put together access to data, predictive models, the right way to communicate through visualizations with business users and really treat that like a software as a service product that people essentially subscribe to and that we keep improving every week. And that runs as a sort of option on software or is that right? It's sort of a service that I can acquire through software or do you also run another cloud? Well, certainly, software is a key piece of infrastructure. Many of the services we're using to compose these solutions are available as APIs through Bluemix. So we use MATE analytics services or data cleansing services. So Bluemix for composability and ultimately consumed by our customers as a integrated service that's really tailored to their business problem. However they want to deploy it. Yeah, I mean, I think we certainly do on-prem and cloud. I think over time it's going to mostly be cloud because people are looking for the fewest possible barriers to entry. Most of these folks don't have IT teams or don't have large IT teams don't build up data science. So they simply want to consume outcome. And when you put a vertical lens on that, you can really accelerate time to them having the insights they need. After seeing what Watson did on Jeopardy, I think people tend to anthropomorphize technology sometimes and they think that, well, this is a general purpose solution for anything. So to turn the question around a little bit, what is Watson not good at? Well, so I think the win on Jeopardy was I think pretty iconic, right? But at the time, Watson was four or five technologies and really one service, if you will. It was sort of open domain Q&A. And I think that really stunned people what was possible. What's happened over time is Watson is now a collection of about 30 different technologies and come out as 30 different sort of APIs or services that people can use. So what we've done with Watson is really decompose it into services. So some of them are around Q&A, but some of them you saw yesterday in the keynote are around processing image, dealing with unstructured language, different kind of machine learning algorithms. So Watson really isn't necessarily one thing. It's this now collection of APIs. And we're going to focus on that core technology base. We'll integrate them into our own vertical solutions, but there's plenty of things that Watson doesn't do yet. And that's why putting those APIs into the Watson developer cloud and making them accessible to hundreds and thousands of developer partners is sparking, I think, a new golden age in cognitive app development. We saw something yesterday with travel and with Wine For Me and some others. So there's a tremendous amount we'll do on the core cognitive technology around unstructured data, reasoning, learning, but making them available services so that this whole ecosystem of developers can flourish as part of how we get Watson into all sorts of different places. So now, Bob Pitching, I don't pretty much scotch the question about whether IBM would ever open source Watson, but what you're talking about is developing the core engine through communities of developers. Do you have anything in place to kind of crowdsource Watson, make Watson improve Watson's cognitive capabilities through the crowd? Well, what we're trying to do is, I think in a much more contemporary way, rather than sort of saying open source Watson, what we're trying to do is take the Watson services and we are doing it through the Watson Developer Cloud and make those services available to just a huge range of developers. And what we get out of that to your point, you know, when you start opening them up to a large set of developers, we learn new use cases, we learn how they want the services improved, we're learning how they're consuming them, people will create their own services out of services, so you get this big network effect around cognitive that I think is pretty exciting. So a question that Paul and I were talking about is, who do you sell to? I mean, you mentioned developers, obviously that's a lever. Who else? I mean, it's CIO, is it the CMO, is it, talk about the roles that are influencing us? When we think about sort of analytics right now, I mean, certainly we do all sorts of tremendous work with the IT organization and many of the very best ones in the world are here and they're talking, if you hear them talking, their agenda is how do they make data accessible, how do they build a culture of analytics, how do they help people really unlock all that data and move to more modern infrastructure things, things like Spark and other tools to get to all the dark data. So that's clearly somebody who we sell to. Some of the other things I mentioned though are really, I think, important new audiences. We sell to CMOs in terms of how they think about customer experience or fan experience. We sell to CFOs on how they think about a dynamic, analytic driven planning process or risk management process. We sell to retail organizations and they care certainly about analytics, but they care about it in terms of the things that change how they work in their stores. I mean, Urban Outfitters, for example, is doing a keynote here and they're using analytics to understand what are the products inside their stores that punch above their weight. And they talked about the fact that they carry vinyl records and while vinyl records only does a small amount of revenue, it's actually responsible for a whole set of other merchandise. They can now understand deeply and change the way they merchandise. So it's kind of every business. These are constituents that are not traditional IBM constituency. So how are you changing the organization or maybe it's through working with partners? How are you changing the organization for your sales force to better understand these people and their needs? Well, I mean, you're right. So part of it is offerings that are just entirely more consumable by a business person. So when we talk about these industry solutions, we tend to talk about them very much in the language and the value points of really specific industry roles. So some of the things we do, one of my favorite ones we do in industrial is predictive maintenance for automotive robotics. I mean, it's an incredibly specific discussion and you don't talk to people that run that part of the world or retail lift. So consumability offerings, I think, is an area we've just, I think, set a new bar. Our sales teams are more and more aligned to have a sort of a consultative discussion at the business level. They have expertise by industry. They have expertise in certain analytic domains. Maybe it's customer analytics or risk analytics, and they can help the customer through application of the tools to their business problem, not just the IT infrastructure itself. And then lastly, many of them are here. We have a tremendous set of partners that have very deep vertical expertise and we're giving them just a whole new set of tools to go work with. Talk about your specific business. Help us frame that. What does it comprise? So, as part of the whole analytics group, I think my team's got a pretty fun job. So my colleagues are doing amazing work around open data platforms, cloud data services, and to my team, that's all real capability to consume. I've got the solutions unit and we've taken a pretty strong point of view that we're really about making analytics consumable in the context of the business roles that people have. So when we think about how to progress what we're doing, certainly we all like technology, but we try to think really hard about how can we put that together into a insight that matters to a business person in their role and then put the solutions around that. So we have all the platform tools to pick from, but we've built out pretty deep industry expertise and we also then can bring in kind of the secret weapons. How do we start to add to those solutions data that our clients don't have with some of the cloud insight services we announced this week to bring in, it's like Twitter, weather and other data, curate that and put it into an industry context. So we're really all about, how do we help people apply all this technology to rapid business outcome? So you're the master chefs? Absolutely, it's top chef every day. You've got a lot of products. You've got a lot of point products that you've acquired that you've built over the years and 25 acquisitions I believe I've read. Is that a problem at some point that there's so many different brands you have in the analytics market are you trying to harmonize those in some way or are you comfortable having point solutions? I think all of those point solutions come with a client value. So from that perspective, every one of those companies that we brought into IBM had a really specific reason to be here and a very powerful kind of role to play. So I don't think it's a particular problem. I think what we are seeing though, especially as we start to decompose things into APIs and we start to make things more consumable, what we have now is a whole set of organic innovation we can do around those assets to deliver some of these solutions. So when we think about, I'll pick on one, we're doing fan insight. How do we help a sports franchise get better insight into what's going on in their stadium? So the fact that we can understand how to work with commerce systems, how to work with financial planning systems, it just gives us I think a much richer set of ingredients to work with as we distill business value. Your sales force has to be a challenge though, right? Because they have to know where each of these pieces fits and when you have a solution like that that may involve several different products. Well, what we do around that though is we take a customer point of view. So back to this whole point is solution. We don't try to expose all the parts of the car. Our job is to put that solution together and show people what they can do from a driving perspective. I won't take that analogy too far, but we pre-integrate that. So more and more what we're doing is taking a vertical lens, a specific business problem, and then building solutions for that. So the conversation with the client isn't about all the pieces of technology that come to bear, it's about what problem we're solving for them and that simplifies everything. So what's with the red sweater that you brought here? So I was pretty proud of this. I was up at the Hockey Hall of Fame earlier this year. So there's nothing better. So we had our keynote this morning and I mentioned doing fan insight. So we worked with the Ottawa Senators, pre-fabulous hockey team, and we helped them with a solution around fan insight. So they were nice enough to kind of give me this jersey and that's a pretty special number for hockey fans. Can we see that? That's pretty cool. That's pretty cool. Hold it right up in front of you. There you go. Sure, that's a great place. So that's pretty fun for the Ottawa Senators. That's pretty cool. But what was really cool was Peter O'Leary, their CMO, their marketing lead, was talking about, if you think about a sports franchise now, the way they apply analytics is around who is in their audience? Who do they know, who don't they know, how do they get to know them? He used a word I'd never heard called fanavidity, which is fanavidity, right? So I was like, how avid are they? How avid are they? And of course, that goes, the Senators had a tremendous finish last year so they had this incredible energy in their fan base which presents very specific opportunities for marketing and promotion. But through the course of a long season like hockey or MLB or something like that, fanavidity can change. And the marketing organizations need to be able to pretty dynamically change their tax and how they speak to their audience and adjust merchandise and all these things that become part of it. So the Ottawa Senators have become a great partner in becoming an insight-driven business. Great city of Ottawa. Can Watson help us figure out what's wrong with my Bruins? Is that, can you help us solve that? We can try, but I'm working on the Blue Jays first. I think it's because the Cognos business is based in Ottawa, right? Well, we have a tremendous team in Ottawa that's working on Cognos. They're also a pretty important part of what we're doing with Watson analytics. So yeah, there's maybe a little hometown bias, but I think, no discounts. But I think we won that business on our merit. I mean, they looked at a number of people and I think we won because we were more consumable. Excellent. Alston, thanks very much for coming back in the queue. Hey, anytime, it's a real pleasure. All right. Keep right there, everybody. We'll be back with our next guest right after this. Check out ibmgo.com for the full digital social experience at IBM Insight. It's a cube, right? Be right back.