 Live from the Hilton at Bonnet Creek, Orlando, Florida, extracting the signal from the noise, it's theCUBE, covering Vision 2015, brought to you by IBM. Welcome back to IBM Vision, everybody. This is Dave Vellante with Jeff Frick. Check out ibmvisiongo.com. It's the interactive digital experience for this site. Alistair Rene is here as the general manager of IBM Business Analytics. And it's really your show. Welcome to theCUBE. Thanks very much, thanks for being here. Yeah, so you were in the keynote this morning, the general session, kicking things off, helping us sort of understand this portfolio that you guys have. So, good session, packed house, how do you feel? It was a really good morning. I thought we had an awful lot of interesting content and feedback's been really good. Some interesting announcements, good perspective on how we're trying to take a lot of different capabilities that I think has been in, maybe in silos, as people look maybe at risk, maybe they looked at sort of what they're doing in enterprise planning, sales performance management. And I think we're trying to elevate that up to a way for people to think about that as an analytically driven platform to think more about how to connect the dots across their business. In your session this morning, you talked about the Wright brothers and skeptics that didn't believe it was actually possible to have a human flight. Yeah, amazing. And some little beekeeping journal was writing about it and the mainstream publications wouldn't. What's the analog today? Are we doing things that people just don't believe are possible? Well, I think the first thing, the reason I brought that story up and I just, I found it unbelievably, it was hard to believe when I read this thing. When we talk to our clients about their businesses, I think some really get it. Some are really trying to figure out how do they fundamentally change the way they work. And they've got an incredible urgency around that. And when they think about that, almost all the new business model innovation you see is being driven by analytics of some kind or engagement of some kind. So if you think about what Uber's doing to transportation or you think about how I mentioned Pratt and Whitney and how they're fundamentally rethinking power plants on planes, all driven out of analytic insight. And I think the people that truly take on this notion that there is a massive change underway and the speed of that change is unprecedented are doing amazing things. I think on the other hand though, there's probably too many people that are a little bit, like the press corps in Dayton, Ohio in 1905 that don't believe that this change is happening. And certainly I don't think understand the pace at which it's happening. So that was just a great example of, you've got to have a lot of peripheral vision and I think especially today, it's not just change, it's really fast change. And it seems like, I mean everybody's talking about the digital economy, trying to digitize their businesses and transform their businesses. And when you think about the role that data plays in that, it seems like data is this sort of fabric that cuts across everything these days. What's your take on that? And are companies moving fast enough? Are they doing the things necessary to take advantage of these changes? So I would agree that if you look at almost any major advance right now in how people are putting together the model for their business, how they're thinking about engaging with their clients, or how they're thinking about optimizing, whatever happens to be, almost 100% of that right now is driven by people taking data, refining it, gaining insight and changing the way they work. I think it's the platform for transformation for just about every industry you see. Automobiles, client engagement, mechanical stuff, assets and operations, how people think about security, how people think about counter fraud, how people think about compliance, any of this stuff. It's all driven by that. I don't know though that we're at the stage where sort of those benefits are evenly distributed. I think you've got a set of folks that really lead it, they understood it, they think about what is an analytic culture, they think about what are the tools, they think about the information in their organization as an asset to be curated and managed, they think about new skills, and they put it to work and they're willing to do disruptive things. They need to driven organizations. Absolutely, that whole sense of culture. But it's not evenly distributed. You have the long tail I think of enterprises that are still at the very early stages of maybe basic descriptive reporting. They're very much in the tell me what happened last week as opposed to a real predictive culture and how they could change. I think there's an awful lot of things we're doing, things like Watson analytics, things that we're doing to integrate that into some of the planning tools that are going to give that next wave of adopters a leg up, let them go even faster. So you have a vast portfolio. One of our guests, John Koltard, called it an embarrassment of riches. And so you've got this portfolio and it seems like now you've got this secret weapon called Watson analytics that overlays that portfolio and sort of brings it all together with cognitive computing, visualization. Are we thinking about that the right way? Is Watson the sort of umbrella that ties all these pieces together or is it different? So Watson analytics is part of the Watson portfolio that we actually previewed it first at this conference last year and made it available in December. And what it intends to do is to really put these powerful analytic tools into the hands of just about every business user. So I mean you could think about what the spreadsheet did for empowering business people. I mean, I think the number of business processes that even today sit in spreadsheets. Still the number one BI tool, right? Exactly, because people could all of a sudden put directly the tools in the hands of the business person. And Watson analytics does that, but it does it an entirely different level. I think it's created just a new category of tool around analytic discovery. So what it does is make simple and easy to use really complicated things. Put data in, start to explore that data through natural language, sort of the cognitive piece of this. So you can ask questions rather than writing queries and we can suggest things and get visualizations. And we allow people to do incredible things like multivariable predictive analytics, but without having to have them be a data scientist. And it's set up so that you can wind deeper and deeper into it to get comfortable with the work we're doing. But to do that, I mean this embarrassment of riches things to put this together isn't something you can wake up in the morning and say we're going to be experts at this. It's the life's work of three or four different groups to make these tools consumable. So I think that's the next, the next boundary is how do you take the powerful and make it consumable for every business person. That's why it's so important. So the interesting thing to, go ahead. I'm just going to say, so as we pulled back the Excel analogy, it's almost like Watson, potentially Watson is the big data in analytics as Excel was back in the day. We're now, you're giving that power to the person at the desktop, as opposed to waiting for the guys running big reports. I mean, that's exactly, I mean, so think about every time you get into a meeting with a report, usually it's okay. So this KPI is up or down. This isn't, you know, whatever kind of insight you get. But what's always the next question? Oh, why is that? What's influencing that? And then, you know, it gets into people postulating and hypothesis, you know, sort of just kind of guessing. What Watson analytics does is let you go to that next level of why. What's the probability of why? What's the influencer of that? But importantly, it's not, let's go off and have a multi-month data scientist project. It's a, let's do that in the next few minutes. You know, that's a huge change in how people can consume data. It's your number one thing in your keynote this morning. You talked about empowering every person and locking the enterprise advantage and accelerating business innovation. But number one was empowering every person. Which even if you're in a digital business, like finance, they've been digital for a long, long time. This is a transformation, not that they are digital business now that they weren't before, but really moving that power down into a much broader group of users. How do you spread out this insight? So we talk about an analytic culture. And, you know, I think part of that is going to be just to make those tools available for, you know, all the right people in that organization, customer-facing, or, you know, maybe back office. To actually have the tools to be able to make these assumptions and be empowered in them. And I mentioned this on stage. What's really, really surprised us is we expected a lot of the use cases would cluster. You know, maybe segmentation or something like that. And we're discovering, much like I think we did in the world of the spreadsheet, we're discovering this long tail where everybody's got, you know, the five or the 10 questions a day. They're just trying to get some insight into and it's about the speed to do it. So I really do think it's got the power to change how people think about consuming information. Yeah, you had the theme of analytics for everyone this morning. We've been talking about citizens, data, citizen data scientist. What are the keys to putting, and that's been a criticism of the, just generically the BI business for a long time. Very few users, very powerful leverage, but not permeating the organization. What are the changes in technology that are allowing that concept of analytics for everyone to begin to take shape? Well, I think it's a number of things. I mean, so with something like Watson Analytics, it's new ways of interacting with data. So, you know, to be able to actually type questions in English and be able to get answers and suggested visualizations and, you know, bootstrap people through that first part of data discovery, it is absolutely huge. I mean, because the first five minutes is the most important part. So helping people through that is incredibly important. Taking, you know, taking people through predictive analytics in a way that we don't have to go back and teach them data scientist stuff and making it consumable is a big piece. I also think a big part of what's, you know, coming into Watson Analytics is giving people, you know, the tools to do, you know, data cleanup and data shaping. Because, you know, almost nobody's got a perfect set of data to start with or, you know, all the tables joined or the labels, you know, the way you want them. So, you know, tools to help them sort of in a very simple way and in many ways automatically start to clean up that data and make it accessible is another big part of it. I think the other thing that's been interesting is the way we're taking this to market. So, you know, a lot of BI tools have gone to market through the IT organization. You know, where you have to do an deployment, do an integration, take your requests to the IT team. This is all about empowering individuals. You know, this is a tool that's available on the cloud, freemium version, individual users can get there, can put their data in, can get started. Because it's in the cloud, you know, the tool gets better every week. So, you're not on any sort of a long IT-dependent cycle. It's very much empowering individual people and I think that's pretty cool. Someone talked about that a little bit more because we do a lot of events and a lot of the upstarts love to take potshots at the established players, of course, right? That's what they do. They get some pieces of money. Good way to get noticed. They make a lot of noise, they grow, they add some value and then you buy them. That's a good, you know, circle of life. But it seems like you talked about something this morning talking about the enterprise advantage. I want to explore that a little bit because you were talking earlier about Watson Analytics just sort of laying over, my words, overlaying your portfolio, but you have a data quality and a richness that a startup's not going to have. You've got this portfolio. What did you mean by the enterprise advantage? Can you talk about that? So, for a lot of our clients, you know, if you especially, so let's take predictive analytics or even basic descriptive stuff. Getting an insight is an interesting thing. But that insight, you know, that insight about, you know, a predictive maintenance event or a customer action or a segmentation insight, whatever it happens to be, that insight is of no value to them until it's put into action in some way. So let me use predictive analytics around maintenance as an example. So, you know, we can look across a class of physical assets. Say, you know, I'll pick on submersible pumps, you know, for the oil industry. So, you know, expensive to get at. So, you know, we can be incredibly accurate on a class of assets, physical assets like that, about when they're going to need maintenance, when they might fail, when they're in an unusual condition. But just knowing that in and of itself is of no use unless you can then connect that to, you know, a set of things to take action. So, connect it to how you do scheduling and planning. Connect it to how you manage inventory. Connect it to how you schedule repair people. So the enterprise advantage is not just, you know, having some interesting insight. It's actually connecting that insight all the way through action, which I think is a unique thing we can do. Yeah, and having a data quality ethos that allows you to take that action with confidence. Yeah, exactly. And it seems like a big emphasis is on speed, of simplifying the complexity, reducing the elapsed time. Where are you in that vision? So I think, you know, we've done, as IBM, over the course of, you know, the last several years, we've done more than 50,000 engagements with clients around applied analytics. And, you know, the value people get out of those is tremendous. But one of the things we've gotten out of it is, you do 50,000 of these engagements and you start to see the patterns. And I think what we're going to start to see with the next generation of analytic solutions is, how do you move from things being maybe more bespoke or custom, to how do you take those patterns and then start to distill them into analytic solutions that have a very specific problem, come with, you know, a set of all the ingredients included. Here's a analytic model, here's the right data models, here's the right connectors to the other systems you need to influence. Here's a user experience that a business person can understand and start to deliver these analytic solutions much more out of the box. And I think, you know, that's an evolution we've seen before in technology. And I think we're at the brink of that, which is a huge scaling factor for how people take advantage. Allison, you were on stage this morning with Deloitte, gold standard in that financial compliance area. Announcing a relationship or partnership, talk about what that partnership is all about, and then I want to talk about other possible opportunities. As much as people are moving quickly to put analytics to work in all sorts of customer engagement or back office or if they're doing machinery, for many industries where regulation applies, it is just a whole new world in terms of regulatory compliance and expectations from the market. Financial services, pharma, I mean, pick them all. And in many cases, the ability for people to actually transform is gated by their ability to comply as they transform. So in the same way we think about changes in data and analytics being applied to customer-facing things, IBM and certainly our partner in this Deloitte think that there's a next generation of how to look at compliance. So what we're trying to do is say, let's take advantage of, for example, being able to look at most compliance historically or regulatory stuff is backwards looking on essentially databases of historical things, looking for some kind of past trend. What if you could start to look at these masses of data in real time to catch events as they're happening and take action? Can do that sort of thing now. What if you move from describing compliance state to being able to predict a compliance state? What if you went from only looking at structured data coming out of maybe a financial system to being able to look at a massive unstructured data in terms of how your employees are conducting themselves or how communications is flowing, looking for that unknown pattern. All of that now becomes possible. So I think the combination of what we're delivering from an analytics perspective combined with, Deloitte is really the gold standard in risk consulting. What they know from a industry model, from a industry solution perspective, from what's going on deeply with the regulations. The ability to kind of rethink analytically powered intelligent risk solutions I think is a pretty big opportunity. Well, for the skeptics in the audience, I'm reminded of your Wright Brothers story and you're seeing things like Watson beat a Jeopardy champion. Anybody who can relate to this in a fraud detection. You get your credit card, somebody grabs your credit card in real time. It used to take six months to discover something like that. So these capabilities are here today. Alistair, are there other industries that you guys see going after for potentially out of the box solutions? Can you talk about that a little bit? From a risk perspective or more generally? Generally. So it's hard to, almost every industry we see has an opportunity to advance themselves around analytics. So we're going to focus around, we're actually going to announce some solutions in this space next week. And we'll share the URL this month. Yep, 28th of May. We're thinking in areas, obviously, every part of retail and the distribution industry has a keen desire to understand not only their client, but how to look at supply chain optimization, merchandising optimization, financial services, certainly about risk, financial services around behavioral segmentation, around approaches to wealth management. We look at telecommunications. I mean, there's analytics at play in terms of customer understanding. And obviously, a big part of what they're doing is network optimization, understanding what they're, you know, we've got a great set of capability with a tool that we call the now factory to understand in real time what's going on in a mobile network for a client experience perspective. Asset heavy industries, industrial aerospace. So it's really hard to imagine a space that isn't fundamentally being reinvented around analytics. Every single one of those industries, any, there's not an industry you can mention. Financial services, retail, telco. Automotive. Have you heard of it? Yes, aerospace. Every one of them is getting disrupted. Healthcare. Every one of them is getting disrupted. And there's this data layer that's emerging on which organizations are building new business models on top of that, or they're getting disrupted by new business models. Or looking to optimize across the whole business. I mean, look at healthcare. It's not so much about, certainly we're doing amazing things with Watson around diagnosis and care. But, you know, part of the new IBM healthcare organization that got announced a couple of weeks ago is how do you look at optimizing all these things into higher quality care at a better economic price point? It's interesting how the bit has flipped in your world, going from one that was almost purely risk-focused, risk-oriented, 10, five even years ago, to now one that's opportunity. Question is, is it harder for a company to understand who's trying to disrupt them? Everybody in retail, for instance, has an Amazon war room, right? Or is it harder to figure out where the opportunities are with data? It's risk and reward, isn't it? Well, we see both. And that's part of the reasons, I think, we're trying to package some of this capability into something that's more identifiable to a business person. I think it's increasingly now, the line between the technology side of the organization, the business side of the organization around analytics is totally blurred. We don't talk to anybody now, and it's in marketing. I mean, I know doctors at some of the hospitals that work at that are data scientists, as much as they are physicians. So one of our big focuses here is, how do we put the value of analytics into a much more focused business context? And I think that ability to accelerate the dialogue with business people with problems to help them connect to the technology is a big part of kind of expanding that adoption. So we're out of time, but Vision 2015, maybe you could summarize it, give us the bumper sticker, as the trucks are pulling away from Orlando. What's the big theme here? What should people take away from Vision 15? Well, I really think it's the three things we talked about today. It's there's a new generation of ways to empower everyone to be analytics at their fingertips, better decision making. I think that insight when translated across the entire enterprise delivers advantage. And I think as people look at their businesses, there are an incredible number of ways to fundamentally innovate what they do around data and analytics. Excellent, now it's the ready. Thanks very much for coming on theCUBE. And good luck with the rest of the event. And thanks for having us here. Thank you, real pleasure. All right, keep right there, buddy. Jeff Frick and I will be back with our next guest. This is theCUBE, we're live from IBM Vision 2015 in Orlando, we'll be right back.