 From Las Vegas, expecting the signal from the noise. It's theCUBE, covering Interconnect 2016. Brought to you by IBM. Now your host, John Furrier and Dave Vellante. Okay, welcome back everyone. We are here live in Las Vegas for IBM Interconnect 2016, special presentation of theCUBE. Our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier, my co-host Dave Vellante. Our next guest, Bryson Kohler, who's the Chief Information and Technology Officer. And I'm saying this for the first time on theCUBE, the weather company and IBM business. Welcome back to theCUBE. Thank you very much, glad to be back. Last time you weren't an IBM business. We were not. You were just the weather company. You were just the weather company. So congratulations on your success. I want to say we're really big fans of it. But what Bapachian and the team have done is visionary, bold, and very relevant. So congratulations. Thank you. How's it feel? It feels great. To be pulled in. We are really excited. The opportunity with the IBM platform and the reach and the capabilities. I mean, it really helps accelerate what we were trying to get done as the weather company is our own standalone business. And as you try to prepare and protect the entire planet, all of its people and all of its businesses, prepare and protect them for tomorrow, which is really what the weather company is all about. Finding that intersection of consumer behavior, helping prepare and protect you as in your personal life and your family, but also you as a business owner, how do we prepare and protect you to do better tomorrow because of the weather and the insights that we can provide? It fits straight into the work that Bob, Pitchiana and team have been doing with the insights, you know, economy, with Watson and with analytics, with insights as a service. All of that just kind of plugs together in it. It really is a natural fit. It's interesting to see IBM's move. We were asked to guess on from IBM earlier and Jamie Thomas said, you know, talk about open source. We want to get in early. So this is an early bet for IBM, certainly a bold move with the weather company. But it's interesting. Shuttlebutt, as we talk to our sources inside the company, close to the company, have telling us that the weather company is infiltrating and affecting the DNA of IBM in a good way. And you guys have always been a large scale data company. And that is what all businesses are striving to, to digitize everything. And so take us through that. I mean, one, I think I think it's fair to say that you guys are kind of infecting, I'll put it in a positive way, the mindset of being large scale data. Well. Why is that so compelling? And how did you guys get here? Obviously the weather is a big data problem. Share some commentary around where it all came from. Well, I think, you know, it's in my DNA, first of all. And it's in our company's DNA. It's in our team's DNA. You know, I'm a change agent. You would not want to hire me to maintain something. If you want to hire me to, you know, to break something and rebuild it better, that's under your guy. So, you know, I think when you look at the movement from, you know, kind of the movement over time of IBM and, you know, the constant evolution that IBM goes through, time is ripe. When you take the cloud capabilities and you take data and you take analytics and the whole concept and capabilities of Watson, Watson gets smarter as it learns more. Watson can only be as smart as the data you feed it. And so for Watson to continue to learn and continue to solve new problems and continue to expand its capability set, we do have to feed it more data. And so, you know, looking at weather, weather was the original big data problem. Ever since the first mainframe, the first, you know, application ever written on a mainframe was a weather forecast. And ever since then, everybody's been trying to figure out how to make the forecast more accurate. And a lot of that comes from more data. The more data you have, the more accurate your forecast is going to be. So we've been trying to solve this big data problem for a long time. And Dave talks about, and we saw him earlier in the opening about digital assets and in this digital transformation, companies have to create more digital assets. That's just data in this new model. So when you look at the data aspect, you say weather also is a use case where people are familiar with, we were talking about before we went on camera, that people can understand the geekiness of weather because they're familiar with it. But it also highlights a real life use case and the IOT, internet of things, wearables, we heard sports guys on here, tracking sensors. This brings up that digital digitizing is going to be everything, not just IT. Right? It makes it real, right? If I think about my parents, right? We've been talking about IOT. Hey dad, you're going to have a connected refrigerator. Why does he care? What do I need a connected refrigerator for? But as you start to bring these insights to life and you make them real, and you say, you know what? If I actually understand the humidity levels in your house and I can get that off the sensor on the air intake of your refrigerator, I can now correlate that to the humidity level outside of your house and I might be able to actually tweak your HVAC unit and I can make that run efficiently and I can now cut 30% of your cooling costs and all of these examples, they become real. And I think weather is great because everybody checks their weather app, the Weather Channel app or the Weather Underground app every day, they're always looking at it. And we get it right 78% of the time, we get it wrong sometimes. We're constantly working to maintain our number one position in data accuracy on weather forecasting. And the more data we have, the more accurate we can make it and so we've got. And safer too. You think just think about the use cases of people's lives, slippery rows, events. Correct. I mean, it's all tied in. It goes back to another, you know, if I understand what's going on with the anti-lock braking system of a car and I already have a communication vehicle into everybody in that car, which is our app in their pocket, I can alert them if the cars up ahead are having, you know, they're ABS activated. And if all of the cars up ahead are having their ABS activated, I can alert them two miles back and say, hey, get ready, slow down. It's real. It's not forecasted. It's real data. I'm giving you a real alert. You should really take action. And, you know, as we move from, you know, weather alerts that we're looking out forward in time many hours, as we're now doing rain alerts where we tell you it's going to start raining in the next seven minutes, 10 minutes. People love those because it's right now and I can make a decision right now. Lightning strikes are always fascinating. So good. I got to ask you a question. So last fall at IBM Insight, we interviewed David Kenny. Yes. And then right after, I think it was the week after, I was watching some, you know, I was in Boston watching some sports program and there's Bill Belichick complaining about the inaccuracy of weather. Ah, I don't shred the weather. Some reporter asked him about, you know, do you factor in the weather? I don't even pay attention. I look at the weather forecast. They're always wrong. I said, wait a minute. I just interviewed David Kenny. He was bragging on the weather, the accuracy and how much it's improved. So help, you mentioned 78% of the time. It's gotten better over time. It has. We're not perfect. So talk about that progression. It is the data, but how much better are you over time? Where is that better? Is it just short term or is it longer term? Add some color to that. Yeah, it's a great question. And it's a fair point. I think one of the biggest changes we've made in the last three years at The Weather Company is we've taken our forecast from what was roughly two million locations where we would do a forecast. Two million locations around the globe. And today we create a forecast for 2.2 billion locations around the globe. Because the weather is different at Fenway than Boston Logan. It's just different. The start time of rain, the start time of a thunderstorm, you know, it's gonna be different. Now, maybe five minutes, but it's different. The temperature, the wind, it's different. And so as we've increased the accuracy and granularity of our locations, we've also done that from a time perspective as well. So we used to produce a forecast every four to six hours, depending upon how fast the models ran and did they run and complete successfully. We now update our forecast every 15 minutes. And so we've increased the, you know, all aspects of that. And when you now think about getting your weather forecast, you can no longer just type in BOS for your airport code and say I wanna know what the weather is at Boston Logan. If you're, you know, if you're in Cambridge, the Boston Logan forecast is not accurate for you. You know, five years ago, that was fine for everybody, right? And so we have to retrain people to think about and make sure that when they're looking for a forecast and they're using our apps, they can get a very specific forecast for where they are, whatever point on the globe they are. And don't have, you know, Boston, you know, Logan as your, you know, favorite for your city if you're sitting in Cambridge or you're, you know, you know, and over further outside. And where I am now, right? And where you wanna work. I got a GPS on your phone. Am I gonna get soaked? Different, you leverage the GPS capabilities, get that pinpoint location, it will improve what the forecast is telling you. So I feel like this is one of those omni-headed acquisition monsters for lack of a better term. Because when the acquisition was first announced, it says, oh, wow, really interesting. Remember my line, Dell's buying EMC, IBM's buying the weather company. Oh, how intriguing. What a contrast. It's all about the data, data as a service. And then somebody whispered in my ear, well, you know, there's like 800 rock star data scientists that come along with that acquisition. Like, wow, it's all about the data scientists. And then on IBM's earnings call, I hear the weather company will provide the basis for our IoT platform. Like, okay, there's another one. So what's your take? Well, I think IBM made a very smart move. I'm slightly biased on that opinion, but I think IBM made a very smart move, a very forward-looking move, and one built on a cloud foundation, not kind of a legacy foundation. And when you think about IoT data sets, we ingest 100 terabytes of data a day. I ingest 62 different types of data at the weather company. I ingest this data, and then I distributed it massive volumes. And so what we had fundamentally built was the world's largest cloud-based IoT data platform. And, you know, IBM has many capabilities of their own, and as we bring these things together and create a true next-gen cloud-based IoT data engine, the ability for IBM to become smarter, for Watson to become smarter, and all of IBM's customers and clients to become smarter with better applications, better alerts, better triggers. And that alert, if you think about alerting, my capability to alert hundreds of millions of people, weather alerts, whether that's a lightning alert, a rain alert, a tornado warning, whatever it is, that's not really any different than me being able to alert a store clerk, a night stock clerk at the local warehouse club that they need to stock aisle three differently, put a different end cap on, because we now have a new insight. We have a new insight for what demand is gonna be tomorrow, and how do we shift what's going on? That alert, going down to a handheld device on the guy driving the forklift, it's no different. And so the capability to ingest, transform, store, do analytics on, provide alerting on, and then distribute data at massive scale, that's what we do. That's what we're talking about, is what happens when digitized. So when home people gets a big truck, comes in and watch their fans and say, we didn't order this, no, the weather company did it for you. Yeah. And in two days, you'll understand all of it. You'll understand, you'll think he later, they finally are big on the top of it. So IoT is one of those markets where people don't can't understand this, and people don't understand the mainstream like, what's IoT Internet of Things? I don't get it. Explain to them some of the use cases that you guys are involved in today, and some of these new areas that you're highlighting with learning, some obviously real life examples for businesses and users. There's a smarter planet kind of, safe society kind of angle to it, but there's also there's a nuts and bolts kind of practical business value, cash, saving money, saving lives, changing maintenance. What do some of the things share? The IoT. So there's there's only two things there. So one is what is IoT? And IoT really is, is sensor data. At the end of the day, computers, sensors, electronic equipment has a sensor in it. Usually that sensor is there to do its job. It's there to make a decision for what if it's a thermostat, it has a sensor in it, what's the temperature? And so there's sensors in everything today. Things have become digitized. And so those sensors are there. As those next evolutions have come online, those sensors got connected to the internet. Why? Because it was easier than to manage and monitor. Here we are at the Mandalay Bay. How many thermostat sensors do you think this hotel and casino complex has? Thousands. And so you can't walk around and look at each one to understand, well, how's the temperature doing? They all needed to be shipped back to a central room so that the, you know, building manager could actually do his job more efficiently. Those things then got connected so you could look at it on a smartphone. Those things, they continued to get connected to make those jobs easier. That first version of all of those things, it was siloed. That data sat within just this hotel. But now as we move forward, we have the ability to take that data and merge it with other data sets. There's actually a personal, a weather underground, personal weather station on the roof of the Mandalay Bay. And it's actually collecting weather data every three seconds, sending it back to us. And we have a very accurate understanding of the state of the Earth's atmosphere right atop this building. Having those pros is very good for the weather data, but now how does the weather data impact a business that cares about the weather, that has their pros? Now we understand what the sun load is on the top of this building. And so we can go ahead and pre-heat or pre-cool rooms, get ahead of what's changing outside that will have an impact here inside. We have sensors on aircraft today that are collecting telemetry from aircraft, turbulence data, that helps us understand exactly what's going on with that airplane. And as that's fed in real time back down to the Earth, we process that and then send it back to the plane behind it and let that plane behind it know that it needs to alter its course, change its flight plan automatically and update the pilots that they need to change course to a smoother altitude. So gone are the days of the pilot having to radio down and call around to his buddies. Anybody got any screw there anywhere? Now the machines can do this in real time, collect it and synthesize it from hundreds of aircraft that have been flying in that same route. And now we can actually take that and produce a better in-flight plan for those machines. We do that with advertising. So when you think about advertising, the easy example is, hey, we know that you're going to sell more of X product when Y weather condition happens. That's easy. But what if I also help you know when not to run an ad? How do I help save you money? If I know that there's no way for me to actually impact demand of your product up or down because we know over the course of time looking at your SKU data and weather data that no matter what we do, weather is going to have this impact on your product. Save your money, don't run an ad tomorrow because it doesn't matter what you do, you're not going to actually move your product more. That's great. And it's business intelligence, it's all the above, it's contextual data, help people get insights into situations. It's both predictive and prescriptive analytics, all rolled into one in a tool that alerts the actual person making decisions. Explain to people out there what predictive versus prescriptive means. A lot of people get those confused. What's your, how would you? Prescriptive is where we want data to just tell us what to do based upon historic looking trends. So I can take 10 years of weather data and I can marry that up with 10 years of some other data set and I can come up with a trend based upon the past. And with that then, I can prescribe what you should do in the future. Hey, looks like general trend. Bring an umbrella tomorrow, it might rain. But if I get into predictive analytics, now I can start to understand by looking at forward looking data, things that haven't happened yet or new data sets that I'm merging in in real time. Oh, wait a minute. We thought that every time it rained, more people went to this gas station to fill up. But wait a minute, today there's an accident on the road and people, no matter what we do, they're not going to go to that gas station because they're not even going to drive by it. So being able to predict based upon real time data, but also forward looking data, the predictive analytics is really around the insights that we want to give. So I've got to ask you one question about the IBM situation and I want you to kind of reflect, get philosophical for a second. What's the learning that you've had of the past few weeks, months, post acquisition inside IBM? Is there a learning that kind of hit you that you didn't expect? There's something that you'd expect? What was your big takeaway from this experience personally? And you had a great success in the business now integrated into IBM. What's the learning that comes out of this for you? You know, I am really proud of the team at the Weather Company. You know, I think what we have been able to accomplish as a small company, you know, comparative to my 468,000 colleagues at IBM, you know, what we've been able to accomplish, what we've been able to do is really, you know, it's impressive. And I'm proud of my team. I'm proud of our company. I'm proud of what we were able to get done as a company. And you know, the reflection really is, as you bring that into IBM, how do you make sure that you can now scale that to benefit such a large organization? And so while we were great at doing it for ourselves and we built an amazing business with amazing growth, you know, attracted lots of people that looked at buying us and obviously IBM executing on that, I think that's amazing. And I'm proud of that. But I think my biggest reflection is that doesn't necessarily equate to success at IBM. And we now have to retool and retransform ourselves again to be able to take what we know how to do really well, which is build great capabilities, build big data platforms, build analytics engines and insight engines, and then arm a sea of developers to use our API. We can't just take what we've done and go make that work. You can't rest on your laurels. You gotta go reinvent. So I think my biggest, you know, real learning and takeaway from the kind of integration process is, wow, we have a lot to learn and we have a lot of change we need to do so that we can actually now adapt and continue to be us, but do it in a way that works as an IBMer. And there's gonna be an art to this and we've got a ways to learn. So I'm going in eyes wide open around what I have to learn, but I also am very reflective on how proud I am as a leader of the team that has created such an amazing capability. So the acquisition is done. You savor it. You come in, you get blue washed. Now, ho. I had a Saturday afternoon where I savor it. You savor it. I was about all I needed. You savor it. So and then, okay, so you wake up in the morning and you sort of described at a high level, you know, what you're doing, but top three things that you're focused on the next 12 months. So the biggest thing that I'm focused on, number one is making sure that we protect the weather company culture and how we know how to do and build great things. And so I've got to lead us through obviously becoming integrated with IBM, but not losing who we are. And IBM's very supportive of that. Bob Pitchiano and his team have been awesome. And John Kelly and team have been awesome. Everybody that we have worked with has been so supportive of, Bryson, please make sure you find the right way through this. We don't want to break you. And I think that's natural for any acquisition for any. Yeah, but you guys aren't dogmatic. You are very candid saying, we're going to transform ourselves and adapt. Absolutely. And so we've got that on wrestling on my mind. How do we go find immediate wins? There's a million different ways for us to win. There's thousands of IBM sales teams that are out in front of clients just today with new problems. How do we quickly adapt what we've been good at doing and help solve new problems very quickly? So that's on my mind. And then wrapping that in a way that becomes self-service. We can't, I don't want to scale my team through people to solve all these problems. I want to find a way to make sure that all these capabilities, new data sets, new insights, new capabilities that we bring to life, I want to do that in a self-service way. I want to make sure that our technology, the way we interact with developers, the developer community that we bring in to kind of work on our behalf to make this happen. I don't want to solve all these problems. I want to enable others to solve the problems. And so we're very focused on the self-service aspect, which I think is very new. Right, so thank you so much for taking the time out of your busy schedule to sit with us on theCUBE. Good to see you again, and congratulations. IoT, everything's a sensor. We're a sensor here on theCUBE and we sense that it's time to go to siliconangle.tv and check out all the videos. We have a purpose. Our sensor is to get the data and share that out with you. Thanks for the commentary and insight. Appreciate it. Weather company, great success. Weather affects us all and can affect stock prices. All kinds of things in the real world. So we just had a lot of big data. Thank you very much. I'm theCUBE here live in Las Vegas. We'll be right back with more coverage after this short break.