 Live from Las Vegas, Nevada, it's The Cube at IBM Interconnect 2015. Brought to you by headline sponsor, IBM. Okay, welcome back everyone. You are watching The Cube live in Las Vegas at IBM Interconnect. It's a special presentation of our show, The Cube, which we go out to the events and extract the civil noise. I'm John Furrier, the founder of Silicon Angle, and I'm excited to have two awesome guests here. Nigel Hook, CEO of Silver Hook and Ian Taylor, CEO of Animation Research and Virtual Eye who are using data and technology in an application that is so awesome and cool boat racing on oceans and on lakes. So we're going to have a great conversation around how you guys are harnessing technology into the racing and the technology. And I'll say it's the objective and the outcome is very simple, win the race. There's no like anything else that matters, right? So welcome to The Cube. Thank you, thank you. And actually it's winning the race and having as many people as possible see it. And that's been the struggle of our sport, racing on the ocean. It's very difficult to see what's happening. And this technology is transforming that as Ian did with America's Cup to bring that experience into the home of the fan. So talk about Ian with the technology, we'll get into some of the conversation, but you know, set the context. It's been well documented that this is a competitive advantage. We've seen it on the car circuits where it's NASCAR or Formula One. It's no brainer. This is an input into design, execution and ultimately the outcome. So that's clear. It's hard to do. So I want you to illuminate some insights around what goes on, how hard it is and how does it render itself for the application of the users? The users aren't geeks, their application is to race, they're geeky, crew chief, they're using data, so explain all this. So I think the premise we work on is our job is to turn digital data into pictures that people can understand. You mentioned Formula One, we cover Formula One, we do all those sorts of things, but there's nothing quite as difficult as chasing what Nigel's doing. It's a boat that's done 150, 180 miles an hour out on the ocean in 10 foot waves. So we're actually, when we started the America's Cup, we got two bits of data from the boat. That was 1992. Now we're getting 2,000 bits of data a second from Nigel from, he's actually wearing this little bra and stuff on there with car brakes. So turning those into pictures is what the challenge is. So the old days was at a serial cable, monochrome monitor, back in the day? It was a pigeon, you need to tie the signal on and fly the pigeon out. So give us some of the insight, honestly the race has got high speed, 150 miles an hour on the ocean waves. You guys doing lake racing as well? Is there on lakes too? Is there an ocean? We know the big lakes like Lake Michigan can get just as rough as the ocean sometimes. So it's generally, we try to find race tracks where there's going to be some big water because that's what separates our boats from, say, the hydroplanes which people see. So I got to ask you in the big data world, since it came up yesterday, what's a better metaphor for the future of big data? Data lakes or data oceans? It's a progression, you know? Data lake is a current phase. Data ocean is really going to subscribe all the data that's out there, right? And being able to amass that into one meaningful set of relevant data. And I think, you know, the meaningful thing here is a tidal wave. You know, we are seeing a tidal wave of data and the great thing that's happening here with the Bluemix stuff that we're working with IBM, we're actually able to fire that tidal wave up into there. It sorts out the data and gives us only the bits we need to know and that's important. So the notion of a rogue wave is in the big data world is what? Well, you know, I think the really cool thing is when these rogue wave hits, we hand it over to IBM to sort the rogue wave out, calm it down, send it back so we can actually use it. Actually Watson, while I should tell you, a rogue wave is coming, navigate the other way. That's right. That's the big deal with this. So Nigel's sitting in his boat. There's just Nigel and his wheel man. Meanwhile, back on shore, we're taking all of the data from the boat and delivering it to a crew. So there's kind of like eight people in this boat, even though there's only nine, there's eight people and Watson up there helping. All right, so let's break this down. This is such a cool topic. So just take us through some of the mechanics. You're driving the boat 150 miles an hour. You got potentially 10-foot waves in an ocean. You got a data ocean coming in. All these data points coming into the command center. Your technology is gathering it. Watson's involved. But you're driving the boat. You don't have time to look at the screens. Like looking at your phone while driving. You can't text while driving. You can't navigate a boat doing 150 miles an hour. So who does it? You have a crew of people. Explain that whole process. Yeah, that's a great question actually because what all this data that comes out, there's only a certain amount which is relevant. And what the IBM SPSS Analytics does is sift through all of that data looking for patterns of what could predict an unwelcome outcome like an engine which is going out of tune or a propeller which is vibrating too much. So that information out of all that data will be sent to me. And if I can't see it, which is most likely the case because we're bouncing around, it's also going out to our team to our crew chief. And he sends me on the radio to tell me that, hey, you've got a battery that's losing power and you need to switch to the other charging systems. And that I could never think of it out during a race. Something like that actually happened in the World Championships in Key West and we were 30% of a race left to go and we would have definitely not finished. And being able to have that information to me in the cockpit enables us to finish a race and we finish strong. And you made a tactical adjustments on the fly, just another condition of the race based on the data that you would have been blindsided. Actually had to switch systems while we're running the boat. So which wasn't an easy thing. And I wouldn't have done that if I'd not gotten the certainty but absolutely the issue. Because I could have thought maybe it was, but with this data, they got a certainty that this is a problem and this is a solution. Yeah, that's phenomenal. It's just a real world application. It's in every vertical. I mean, oil rigs out in the ocean. Don't have T1, DS3s and all the high-piece connectivity use the cloud. So how's all this coming together? You're on a boat. It's not like you're an internet of thing in terms of the computer's concern. So how does all this come together? And what is IBM's role in all this? IBM's role is absolutely critical while we're collecting all the data, it's just too much for us to process. So it's going up, I like to say, it's going to Watson. He's sitting at his desk up there in that mysterious thing called the cloud. He's going, this is relevant. That's not, that's not. 2000 bits a second and he's going, this is important, send it back. Our job is then to turn it into a visualization system so his support crew can see it and warn him. You know, there is another thing I like to refer to this, Nigel's the CEO of his boat. He's sitting in his office and it's just like a business. There's this tidal wave of information coming at your business every day. You need somebody analyzing that so you only get the bits that actually you need to do something about, not this kind of. Okay, and how we got here was, I got two offices. One office is with a company called DataSkill which is a long-term IBM partner that focuses on intelligent systems and then I have the other office which is in the race boat. We're two converged and it was through a relationship of IBM's JSTART team that we started contemplating how to make use of all this data. And it was through the Bluemix platform we were able to very quickly with a cloud infrastructure be able to develop this, bring it in with virtualized to animate the data. And then with people like in Dunedin, the South Island of New Zealand, we're in California, the JSTART teams on the East Coast working together very quickly. In fact, how long did you think it took to end? Three weeks and I think that's the power of the internet of things. You know, that's what we're talking about but this is it happening practically. So talk about the instrumentation pieces. This is fascinating. So what's instrumented? What's not instrumented? Where's the data coming from? Obviously it's our external data. Obviously you have weather, you have data, seas. What's kind of data you collect and what's going on in real time and how do you instrument all this? I'll talk about the boat and Ian can talk about the environment because that's very sim applicable to America's Cupways, a lot of experience. But in the boat, we're now taking sensors of heat sensors of different types of mechanical functions of the boat like the gearboxes, like in the engine itself. We've got pressure sensors for fuel pressure for all the different types of pressure systems, water pressure. We have GPS signals, we have accelerometers, we have load sensors on the boat. And now what we've added this year, the difference between last year and 2015 is we've got the biometrics. It's actually two people in our cockpit. I'm the throttle man, I have a driver. And so we've both got a full-time job in the cockpit of a boat. We're both wearing these harnesses that's underneath the shirt here. It's an equivital piece which monitors all the different aspects of the human body. All of this has been transmitted to Ian. So you guys have to have cadence because I've worked together, it's total teamwork. One falls down, the other one's down too. So like, how do you guys throttle, how does the throttle man work with the driver? And what is the heart rate matters? Like, oh, he's got a heart attack, get a sub in, he's fatigued. I mean, is that basic thing? It is, it's a very unique sport actually because I don't think there's any other motor sport where one person's got the wheel, controls where you're going, and that's all they've got. You have a person that's assuming how fast they go, and actually flying the boat. You've got the roll capability, you've got to control and the pitch of the boat. So the throttle man's controlling the boat and how fast it goes. The driver's just something where it's going to go. I mean, that's got to be harmonized at speeds in excess of 140 miles an hour in a rough sea. So that's tricky as it is right there. And what we're doing is we're taking the health measurements of both the pilot and the copilot, the throttle man and the driver, and then combining that during the stressful one hour race. And there's different conditions of stress like going into a corner. The driver may be more stressed on that because he's got the wheel than the throttle man. In the straightaways going through big waves, the throttle man maybe has more stress. But seeing how the two people respond to it with the respiratory rate, heart rate, body temperature, that I think has a lot of significance. And where we see that actually progressing to is like into healthcare. Because people are wearing wearables now, not just for athletes, but also in triage and transition of patients and hospitals. So there's a lot of commercial application that you don't want to get fatigued because if you're running the throttle and you're stressed, so you guys just pelting the data? I didn't get that. Are you using the data on the heart rate or not? We are, yes, yes. So because one of the challenges we face is heat exhaustion and performance degradation. You know, as your body gets hotter and you get at those sort of extreme conditions, your reflex is not quite as pristine, right? So that can be monitored on these systems. As you're in a boat, you don't know it. You're just racing to the checkerboard. So you have a sub, like a backup and understudy. It's like when you're fatigued and you might not even admit that you tap out. Well, I think the key is to, as a race driver, you're under a lot of stress. But if you've got some feedback that you're overstressed, I think you have the ability to take calm it down. So it's self-governing. So it's more about internal feedback. It's like a biofeedback, yes. That's really beneficial because throughout the whole race we want to see, we want to be at the lowest possible stress during that race. And this is kind of feedback which helps. But you're putting that in context. I mean, you asked about that feedback. This stuff's going again. It's going up to the cloud coming down and on shore, the medical team. So you've got an engineering team, but the medical team is sitting there getting this data live from the... It's a safety issue too. It's also a self-improvement... Yes, it is, yeah. Performance enhancement there. All right, so let's get back to the tech. Now, back to the instrumentation on the boat. Give us through the tech under the hood, what's happening. You got instrumentation on the boat. It's going into the cloud. What is IBM doing? What's real time? Where's the data sets come from? Can you share? Yeah, so everything is real time. And as I said before, around about 2,000 bits of data every second, which we just can't handle. So the great new development is Blue Mix in the cloud. So the data is going up there and it's sorting through all of this amount of data in real time and only sending us the bits we really need to know. So it's looking for patterns, it's looking for this, it's looking for that. So it's kind of predicting that you've got a problem with this engine starting now, do something about it, because in about 30 seconds time, it's going to blow up. Okay, so I got to ask you guys because you guys are real practitioners and it's a fun area people can relate to. We talked about data lake, data ocean, obviously they're not usually exclusive. One's batch slower, one's more dynamic, more stronger and different, unpredictable. But in the big data world, what do you guys see as the next step, the next wave of innovation that's coming, so to speak, pun intended for big data? I mean, is there an extensibility to it? Where do you guys see this going from your standpoint? Knowing what you're doing now, connect the dots forward. As I said, if I could answer this almost for you Ian, Ian's technology with virtualize is incredible because they take this data and they animate it and it looks just real. I mean, to see his animation compared to the real video, the TV, there's actually less and less difference. So the ultimate goal for this, I think, from the visualization point of view is to use like the virtual reality because with the data, Ian can take that into a virtual world. And so people sitting on there in their home can actually be in the boat, sitting with me as I turn my head, they will see what I see out the window, they'll see my gauges, they'll see all of that. Yeah, I think that bigger question is the internet of things is the word things and there are more and more things that are connecting to the internet. So your fridge, your watch, your heart rate, and I think what we're seeing here and it was a real eye opener for me because I didn't know about Bluemix. I mean, we come from a small agile company and the last company I ever believed we'd ever be working with was IBM. You know, there was this monumental shift as they start to take this stuff and understand that it's about analyzing data. It's now no longer about big machines. All this happens on my iPhone. So it's about this tidal wave of data and developing technologies that can sift through the stuff and give us the stuff that's important. You know, I love the new IBM because the new way that is their slogan but it's an old school company that's grounded in computing solutions. But they're modern now. They've got the Bluemix, they've got the big data, the Watsons, not only winning Jeopardy, now they're winning races. So they're a cool company. I think they're very cool. I've been watching them for years and decades, frankly, but like now, recently, they're on the right path. But the challenge outside the tech world is real world. And I think what you guys present is an example of social business, right? Because boat racing is a lot like in the moment, real time. I mean, it's a race. You've got hard race. If you make a bad turn, you're out of business. You could be, you could die. You could get hurt and not finish where you want to finish. So the outcome is very important. But that's like real time. In the moment, people talking on mobile phones. A CEO, you're the captain running the throttle. Someone's navigating with the wheel. That's an executive team. It is, it's a great metaphor because you're right with the internet of things now, there's so much instrumented data coming into people to make decisions. And there's just too much data. So this is where IBM has a technology with Watson to do the cognitive intelligence and SPSS for the analytics server. But what's really key is how do you get that development time to implement that down to a very compressed number, which is acceptable, right? And that's where BlueMix really comes in. By having a development environment where you can quickly develop these things, iterate them very fast, as we did here, that's really the, that accelerates as a differentiator. And that worked for you. The BlueMix was working fine, being in school. I mean, three years ago, this would have taken three years to do. This time it took three weeks. Yeah, that's awesome. I mean, I think the outcome based in the moment is the new application, whether it's people talking. See, most tech people think like, oh, systems of engagement. That's how to make someone, here's some persona data, maybe they can make a purchase or buy an ad or something. Your application is engaging the ocean, winning the race. Your outcome is very specific. That's like real world. It's like a business. It's a real world. A user out there has got an objective, a customer's customer, they object, their outcome is to win customers and keep their customers. And you know, while other forms of motor racing like Form 1 have been doing this before, the difference what we've proven with the IBM technology is to be able to do that in a very violent environment. When you're in the ocean, you've got rooster tails coming at you at a hundred and something miles an hour. You've got big waves, salt water, doesn't go with electronics, right? There's a lot of complications there. And to be able to maintain that stream as we're firing the data up there to make sure what's coming back to us is not only relevant but accurate, that requires an enterprise level type of software. It changes the game because now you have all the data, post race, post mortem, you know, from health to boats to add it. Okay, final question, we're getting the hook here. Nigel and Ian, share with us a quick sound bite on the most exciting thing that's happened to you guys with this new data solution relative to the racing. What's like the coolest thing that's happened? I'd say the coolest thing for me was in the final race of the World Championships. It was a rough race. So our boat is a rough water boat. It was coming into its own and we had three laps to go and we didn't know it, but we had a battery system going out, a charging system going out. And if we hadn't have gotten alert to the crew chief about that, we wouldn't have finished. And we would have had this great performance, but you know, we'd have come in, you know, wouldn't have finished the race. So having that alert, which got to me so I could switch those systems over and we could finish strong, that was like, it was like, could never have happened before. That's a good return on investment. That was like, well, the blue mix. Well, the blue mix. The most exciting thing for me was this group of people working just for it places around the world. We hadn't even met each other. And there we turned this thing on. The most exciting thing for me was looking at it and going, oh my God, it works. Collaboration, meeting people. Hey, you're, love you online. Not great to meet you in person. Yeah. That's why these events are good. Guys, thanks for coming on theCUBE. Really appreciate it. This is theCUBE, Extracting the Sea from the Noise. Data Ocean, that's the future of big data and nice guys are racing in the ocean and we'll be right back to share more data with you this theCUBE. We'll be right back after this short break.