 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. And now your hosts, Dave Vellante and Jeff Frick. Welcome back to IBM Vision, everybody, I'm Dave Vellante and I'm with my co-host, Jeff Frick. This is IBM Vision 2015. We're here in Orlando at the Hilton. This is theCUBE, we go out to the events, we extract the signal from the noise, we've been here for a day and a half, this is our day two. Doug Barton is here, he's the Director of Product Marketing for IBM Analytics. Doug, great to see you, welcome to theCUBE. Good to see you, Dave. So we were talking off camera about the changes and the ebbs and flow that have been going on in this business, but let's start with sort of your purview, what's your scope and response to it? Yeah, yeah, so I'm responsible for a part of the IBM Analytics Solution portfolio that focuses on the functions of finance, sales, and GRC professionals. And this is a set of actors within the enterprise that have responsibilities well beyond their functional excellence, really to inflect, improve enterprise performance. And we're just thrilled to have a vision conference, really, that can help serve them. Help them get the thinking right before they get started on their initiatives. Yeah, it was interesting, the conversation we were having off camera, how the promise of this business for years has been a 360 degree view of your business, predictive nature, looking forward, not looking back. And then we discussed it, sort of, Sarbanes-Oxley just threw a wrench in that whole thing. And the whole industry had to go and solve that problem for CFOs and help them with compliance. And it became a distraction. Are we back on track now? Well, I think we are, you know, the, there is another shoe to drop in regulation. There will always be a need for increased transparency. There will always be new regulation that helps balance the interest of, if you will, the citizenry from, you know, profit seeking companies and the like. But I'll tell you, here's the thing, is our clients need to find the silver lining in those investments so that they can worry about the most pressing challenges. And that's delivering, right, on the productivity of their assets, driving growth and profit, which ultimately, right, is kind of the wellspring to grow our businesses, grow our economy, make sure we have jobs, you know, for our economy. So I'll tell you, Dave, I would completely agree that there was a moment in time where I thought we were on our way to creating a real time fabric for better decision making, aligning resources with opportunity to drive better outcomes. And the need to focus on compliance in some way swamped an effort. But this, I think we can go ahead with a more balanced approach now and really kind of get those benefits that we had forecasted, you know, some decade ago. Well, I think there's some other things going on. Let's talk about that. I mean, technology is evolving. I want to talk about that. And also the mindset shift. I think the whole Hadoop thing flipped, you know, data as a liability to data as an asset. How do I turn that into an opportunity? Or at least data that was incidental to our business. We accumulated because maybe we had to run our network better, but now it's really a source of location services. So I know where, you know, my entire, my customers are located on a network, you know, thinking of mobile operators. But I think you're absolutely right. There's a wealth of data that's collected that might have been just incidental to the business that can now give us new insight, insight into the behavior of our suppliers or customers, even our employees. And I think the real challenge as some of our speakers touched upon today is to put it to use, to structure it, not as content, but as the context of a decision in the business process. And I think that's the most exciting about, you know, about the topic of today's keynote as an example. I've been fortunate, just real quick, I've been really fortunate to be, to play a part in the organization of this conference and to work thematically on the things I think, we think that the finance, sales, and GRC professionals should keep in mind as they move forward. So we constructed the new way to work concept into two parts, a new way to adapt and a new way to know. And you certainly saw today the value of external data, big data, in the context of decision making within the enterprise. Yeah, so that's interesting. And when you look at that, I like to look at it as a balance sheet, you know, information asset and liability management. And as we were saying, it sort of, it swung to one side of the pendulum. And you remember the general counsel for a while was sort of, you know, running the ship in terms of, you know, reducing risk, the federal rules of civil procedure, you had that with Sarbanes-Oxley and it really has shifted back into more of a balanced approach now. So talk about the technology piece, because that's changed quite dramatically and particularly Watson analytics. Yeah, I think Watson analytics, as you probably had many speakers over the last couple of days share with you, we think that brings, it makes advanced analytics and external data much more accessible than it's ever been to more decision makers within the enterprise. And that's one of its benefits, right? More accessible. So the hundreds of what if questions that can be asked, that we're dying to have answers to are more easily answered in a self-service way, right? By those asking the questions. And of course it brings feature like natural language, so I don't have to think of structuring the question or that I want to ask in a query, right? Or a visualization and actually makes those choices for me. But I want to say that that's just kind of the tip of the iceberg. You may remember Alistair's themes about empower every person, create an enterprise advantage, right? Business innovation. Empowering every person is vital, but making them work together in purposeful coordination, right? Sales and marketing and operations and customer service and finance and HR, that is where the magic happens. Businesses, enterprises, it's a team sport. So we have business processes that weave through every one of those functional areas and we think we can create smarter participants in the process but also get them to work in a much more agile and responsive way than ever before. Now Doug, you've been quarterbacking the internet of Dave initiative. Sure. Dave called it this morning, the ultra-cyclist. Talk about how that came about. Yeah, well, it's a great story. I'm a bit of a fitness geek myself. I've completed five Ironman distance triathlons. So I spent a fair amount of time. Look out, Jeff Jonas. Yeah, I got nothing on Jeff. For look, Jeff, if you're looking, if you're watching, you're good. For those that don't know, he completed every Ironman, every location around the world and he continues on that. One of only two people to do that. That's right, yeah. Luis Alvarez from Mexico is the other. Remarkable stories, both of them, obviously, but proud to be on the same IBM team as Jeff. But so I'm a bit of a data geek, right? And I've used analytics to better monitor my training as I say in a rather simple way, build the best engine, the fitness, that I need for race day, to stand up to the demands of a long swim, a long bike ride and a long run. And through social media, I actually got reconnected with Dave. It turns out it's a reconnection. Dave and I are from the same hometown, right? But when I learned about his attempts at racing across America, his previous success as three-time top American finisher, I was really inspired about what a difference we could make. It turns out the problems he's going to face, right? Under those conditions, that we can really bring a bit of foresight to the decisions he makes, might be able to pick up the half a day he needs, right? To get himself to the podium. Do you have any data on the Delta that you can provide just with your trials and training? And I'm sure you guys have been doing some things along the path on whether it's windy up ahead or now's a good time to push or now's a good time to brake. I mean, do you have any sense of what that Delta's going to be? We're calibrating the model right now, but I'll tell you, it's all gonna depend on the conditions. I mean, we can all know that, look, if conditions were the same throughout the race, then the decisions are made a lot easier. It's really about Dave, his recovery when he sleeps, you know, what we forecast for his recovery and his ability to output and effort and translate power into speed. But it's really the fact that conditions are gonna change. Sometimes it make a lot of sense for him to take the benefit of recovery because the benefit of efforting right now is worth a lot less to him. I mean, we all know this if we're ever gonna bike ride or stick your hand out of a car window, you know, efforting into a headwind is not as rewarding as catching a tailwind. So it's because we face forward conditions that can change that analytics really becomes useful. And in particular, the type of analytics we use to come up with a recommendation for his crew is what we call decision optimization. I know you've talked to product experts about what that is. It's a really exciting part of our portfolio that literally makes recommendations based on available predictions, historical data. And ultimately we'll make a recommendation to Dave and his crew about what he should do next. Yeah, and it reminds you of when you hear people learning to fly, right? Which I think came up earlier as well. Instrument training, no matter what you feel, you know, you must trust your instruments. Right. And for him to be able to trust the data that's coming out of your process, not necessarily how he feels, and I think it was interesting in his keynote saying there are times where I feel really good and the data's not supporting it, there's times where I feel like I'm not really doing well and the data doesn't support it. So we talked about kind of having this trust in the data to be predictive and prescriptive versus kind of looking back, okay, that's what happened. That's right, and you said him, so him being Dave on this. I think at some point in that race, we probably can leave Dave out of it. Dave's not gonna be conscious, it'll be his crew chief making most of the decisions, but you know, I've just seen some footage of how brutal that race is, and his ability to reason about halfway across America is probably diminished a little bit. He's a good friend after all, but hey, let's be honest. He's gonna be in just robot mode, I would think, about halfway. So for those of you who don't know, we're talking about a 3,000 mile race from East Coast to West Coast, and it's done in eight, nine days. Correct. And we're talking sleep two hours. Sleep two hours a day. A day, maybe, you know, 30 hours straight and then sleep for a couple hours. So what does kind of highlights, and let's just think about that, so the decision is go most of the time. The decision to rest is made six, seven times, right, at the most if we're on schedule. And I think that highlights that there are decisions, moments of truth that matter more than others. And there's gonna be a time when it's absolutely the right thing to do to stand down, to rest now, turn off that engine, get the benefit of recovery, because again, I said this a little bit earlier, but it's worth repeating. Efforting right now is not rewarding. By the way, this happens in our businesses all the time. Discriminating between when it's a good time to effort because the reward's there, versus stand down, keep the powder dry, use it in a different area. This is what analytics does for our businesses. In fact, it's set in an interesting way. I was inspired by a speech Ginny Rometti gave at one point in history and she called it the death of average. We don't make decisions on average anymore because that averages height, if you will, the distribution, the tails, the probabilities. We don't have to make decisions on average. Come up with a plan on average, but you know what, adjust based on conditions. Yeah, and get to that right side of the bell curve where excellence is achieved, right? That's very well said. In fact, if I can, one other speaker we've had at our vision conference in the past, one of my favorite writers, Michael Mobison, wrote a book recently about the analytics of untangling skill and luck. And he made an observation about outliers in sports, right, in businesses. They tend to be very skillful. So they're a draw from the right hand distribution of you kind of picture a bell curve, you know, Warren Buffett, smarter than Doug Barton, okay? But they also tend to get a right hand draw from the luck curve as well. And that's really kind of together creates the outlier. So what's kind of interesting, and I think Dave even referred to it perhaps in his interview here, is he needs to write his perfect race and his perfect race is going to depend on the conditions. And we might have described it as being his fitness, a function of his fitness plus good luck. Well, now it's fitness plus foresight, right? Plus some good luck. So our foresight is again, interpolating the forward conditions, making the right decision in the moment that ultimately gives him the best shot at getting across America. And it's a great example of humans and machines interacting to make better decisions. You know, I love the, I learned just about a month ago that the greatest chess player in the world is not a computer. It's a computer plus a team of humans, you know? And so we're always looking for these areas. We talk to, you know, your kids and young people. What things can humans do well that computers can't do well and how in combination can we improve society? Didn't John Gordon underscore that point so well this morning, right? John from our Watson group. I'm just fascinated by the whole area of cognitive computing. And of course it's really augmenting what an expert does, right? In many ways. And so I can have the benefit instead of a Google search to find out, you know, the treatment for my medical condition. It would be great to have something that I have a lot more, you know, intelligent that can replicate the intelligence, right? The observations, evaluate the alternatives, give me a confidence, give a recommendation and the reasoning behind it so that my next action is the right action. And it's informed by really the body of work, body of research of experts. So I've been saying this week that in even previous events that Watson and Watson Analytics is not only a shiny new toy, it's like a secret weapon that you guys have and nobody else does at this point in time. So I wonder if you could talk about when you talk to customers, it's a competitive marketplace. You've been in the industry for a while. You know there's alternatives out there. What do you tell customers? Why IBM? Yeah, well I think it's two things. And we have to recognize where our solution buyers are. And I say solution buyers, you know, sometimes we refer to them in the line of business but that's really not fair. There's no line of business department, you know, you don't knock on the door and deliver mail to the line of business. You deliver it to a sales, a marketer, operations, a finance, a risk. So these people all have their current concerns and there's some remarkable findings about how often spreadsheets in many ways is still the productivity tool and too often maybe the tool that automates the process. Well, there's a couple things about this. So we can be of service to our clients to help structure and automate, make fast, repeatable, efficient these processes. And the next thing you do, make them smart, right? It's really great if we're connected, if I'm in sales, you're in operations and he's in finance, that when we get around the table and come up with a demand forecast, you're ready to make the products that I'm ready to sell, right? And he knows about it so he can calibrate the investment in raw materials or the employees, the customer service. We can literally be on the same page and you need to process there because as soon as we start the clock, things are going to change, the plan is going to change based on current conditions. So we need a process that sort of structures and automates the interactions. But then, now what's possible if every actor in that process now has access to advanced analytics, right? To predictions, to external data that can inform our judgments. We had Erin Houseworth from Jable on stage this morning, first session. You know, she talked a little bit about how Jable's able to use external data, social media data, and other data useful for prediction to get a better handle on demand and ultimately reduce the total cost of supply chain but deliver goods on time. That's an exciting proposition. So the customer panel was very good this morning, a lot of good through points. So talk more about the differentiation from you and the competition. Obviously Watson Analytics is part of that portfolio. What do you tell customers? Yeah, I think it's a combination of things. Too often, I think if you talk to vendors, they try to make it an or proposition, right? And we have an opportunity to bring the best of advanced analytics to the processes that we structure and automate. So it's really both of those value propositions, right? Make sure that in the incentive compensation, territory and quota management process, which that group is here today, we're bringing analytics to, how do you set a quota? So one sales rep doesn't have a cupcake goal and you've just given away incentive and haven't necessarily gotten your money's worth. And make sure you're kind of leveling quota based on the difficulty rather than on the absolute numbers. Every one of these processes can be better informed by analytics. So I think what's different about us versus the alternatives that might be evaluated is that we're going to bring the best of data science of predictive analytics, of external data and big data to the table. So that as these processes get more well structured, more agile, now they'll be smart as well. And I think that stands alone, I really do. So one of the criticisms that people will have sometimes of IBM is, okay, what does that mean? You have to bring in a big army of services people to make this happen. Do I, and is that sort of perception unfair? Is it changing? Well, let's be clear. We've been in this business and a lot of business for a long time. So we've seen kind of generations of technology and IBM's been added a while. So if you're born on the cloud, new cloud vendor and you build a more parameter driven configuration, sure you might be able to handle the simple problems more simply, but the clients will quickly reach the end of that reward and too quickly for many of our clients. And so when we compete against them, we often talk about the benefit of having technology that can scale to the complexity of your challenges, even though you may not be experiencing them today, but also you can start on cloud with us. You can start in a way that keeps your investment to the bare minimum. And I think that's what's changing is we've taken 100% of the portfolio that we have on display here and it's available in cloud. So people can get started in a small way, add in a rather kind of reasonable, rateable way, their costs basically go up. You pay for what you use, you get value out of what you're using and that's kind of the commercial benefit also of cloud. So SoftLayer and Bluemix have been a game changer for your business, haven't they? Talk about that a little bit. Well, yeah, so SoftLayer gives us that global network upon which we provide our cloud-based applications, secure as it is, performant as it is. So that's a huge benefit. Bluemix is yet another way that these analytic capabilities can be better infused. I don't know if it was mentioned yet so it's worth repeating that the internet of Dave benefits from Bluemix, right? The data we're pulling in from his sensor data sending over the cell network goes through the cloud and it's really Bluemix that brings the decision optimization to that data and then puts a recommendation back to the dashboard, right? That's a Bluemix dashboard. So we're very excited. It just demonstrates the, I guess, the vitality, the agility of that platform. We've been working with Dave for about 45 days and the team quickly brought those services together in order for us to get to our first POC. So, yeah. Vision. The trucks are packing up, they're packing up. People pulling away from vision on their way to insight this fall. What's the bumper sticker out of vision and what's the message coming into insight? Yeah, yeah, I'll tell you, thought a lot about this. It is a new way to work. It's time for a new way to work, right? When we plan for this event, we consciously broke apart the notion of a new way to work into a new way to adapt and a new way to know. And I think if you watch today's program, you saw terrific examples of how we can know more about the world around us. We've just been a little bit blind to it, right? Whether it's the internet of things, whether it's weather data, whether it's sensor data on an internet of Dave and a biker crossing the country or social media. And that data in the context of a decision can really provide a new way to know. And that's for the finance, sales, and GRC professionals in the audience. And a new way to adapt is so important. It's, again, about structuring and automating so we can be more agile. It's one thing that you break about the company that can turn on a dime, but it's another thing to know where the dime is. These aren't obvious things today. So yeah, we're very excited about the work we did here. I hope you enjoyed it as well. Awesome, it was great, really great event for us and for our audience. Doug Barton, follow him at Barton D. Guy knows a lot about this business and can help you with yours. Doug, thanks again for coming on theCUBE. Yeah, thank you so much. Thanks. All right, keep it right there, buddy. Jeff Frick and I will be back to wrap up IBM Vision. This is theCUBE, we'll be right back.