 from Las Vegas, it's theCUBE. Covering IBM Think 2018, brought to you by IBM. Welcome back to Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, and this is day three of our wall-to-wall coverage of IBM's inaugural Think Conference. Mary Glackin is here. She's the vice president of Weather Business Solutions. Public and private partnerships, IBM Watson, and she's joined by Sherry Bakstein, is the global head of consumer business at the Weather Company and IBM Company. Ladies, welcome to theCUBE. Thanks so much for coming on. Thank you, and you're welcome, thanks. All right, Mary, I got to start with the Weather Company. When IBM acquired the Weather Company, a lot of people were like, what, okay, data science, I get that, and then there was an IoT spin on that. Obviously you have a lot of data, but I got to ask you, what business are you in? So what we like to say is we're in that, not in the weather business, we're in the decision business. We're really dedicated every day to help businesses make the best decisions possible, and Sherry works from the consumer end of the business to exactly the same thing. So talk about your respective roles, Sherry. You're in the consumer side, as Mary just said. What does that entail? So the consumer side is any touch point where we're bringing weather and weather insights to our consumers, whether it's on our Weather Channel app, whether it's on our web platform, mobile web, on wearables. And so it's anywhere where we're connecting with consumers. And as Mary said, it's really about helping consumers make decisions. In our field, the forecast, in some of the weather data has become a commodity almost. And we've actually shared our weather data with a lot of partners. And so now we're using machine learning and data science to really come up with weather insights to help consumers make decisions. And it could be something just as simple as what to wear today, what's going to happen for a big event, or it can be around how do I keep people safe during severe weather. Yeah, I mean, we all look at the weather. I mean, I look at it every day. Of course, when you travel, what do I bring? What do I wear? Living in the East Coast these days, a lot of storms that we've encountered in the East Coast. I wonder if you could talk about life at IBM. I mean, again, it was a curious acquisition to a lot of people. How has, have you guys assimilated? Well, how has it changed your business? I would say pretty dramatically. So, you know, coming back to IBM acquiring us, they acquired us really for two reasons. One is we had some underlying technology that was really of interest to them, that they're leveraging today. But the other part was because weather impacts so many businesses. So as we've come into IBM, we've had alliances with IBM Research. We're working on a pretty exciting project in bringing the next generation weather model to market, using high performance computing there. We've had alliances definitely through Watson in bringing AI into our products. And then our product lines marry up with a lot of IBM product lines. So we've rolled out a really exciting offering and closed captioning, and it really works well with some of the classical media business, weather media business that we have been providing. So how do you guys make money? Maybe we could talk about the consumer side and the business side. A lot of people must ask that question. Advertising, okay, fine. But that's not the core of what you guys do. Yeah, so on the consumer side, a big majority of our revenue is driven by advertising. But we have to look at that business as well, because as programmatic advertising is kind of taken of the landscape, how did we pivot to really generate more revenue? And so we've done that by creating Watson advertising. And that was one of the first implementations of Watson after the acquisition on the consumer side. And what we've done is we've created an open scalable environment that now we can not only sell meaningful insights on our platform, but we can now give that to our partners that they can go off our property and use the weather insights. We can use different data around location and media to help our partners really have a better experience, not only on our platform, but on any publisher's platform. So that's your customers using Watson for advertising to drive their business. It's not like IBM is doing, getting into the advertising business, per se. Directly, is that right? Right, well, we're leveraging the power of Watson to create these insights. One of the products we created is called Weather Effects. And really what it's doing is taking predictive analytics on the retail side, which is really an underused technology for retailers, but taking our historical weather data, mixing it with their retail data to come up with insights. So we can come up with interesting things that say in the Northeast, like right now during the winter, soda sells tremendously during very snowy or rainy winters. We can look at strawberry pop tarts sell very well right before a hurricane. And so these are insights that we can bring to retailers, but it helps them with their supply chain, helps them with their inventory, can actually even help them with pricing. And so this is one of the ways we're taking our weather technology and marrying it with the advertising world to help provide those insights. For real, with the strawberry pop tarts? For real, yeah, I guess, you know, you don't have to cook them or something, I don't know. Hey, simple if the lights go out. Okay, Mary, I want to ask you about the, in your title, public and private partnerships. That's interesting, what is that all about? So it's really about, you know, the fact that weather has really been something that's been shared globally around the world for, you know, for 100s of years at this point. And so the weather company and IBM take it very seriously that we be good partners in that community of weather providers. So one of the things that we feel passionately about is we have a shared safety mission with National Meteorological Services globally. So here in the U.S., we transmit, Sherry's team does, the warnings that come from the National Weather Service unaltered with attribution to the National Weather Service. We feel that it's really important that there's a sole authoritative voice when there's really danger. So we share that safety mission. And then we're trying to help in other parts of the world. We have, we've had some partnerships to try to increase the observing in Africa, which is really a part of the world that's under observed. So some of IBM's philanthropic efforts have been helping to fill in there and work with those National Meteorological Services. So it's really one of the really fun parts of my job. You know, we talk a lot about digital transformation and Ginny Rometti was talking about the incumbent disruptors. And we've been riffing on that all week. We've made the observation that companies that are digital have data at their core and they've organized sort of human expertise around that data. Most companies, Fortune 1000, are built around, you know, human expertise is built around other assets, the bottling plant or the factory, et cetera. Look, a weather company has a data company and I think that's probably fair. Did you evolve into that? Data is clearly at your core. Has it always been? And it's very interesting that IBM has acquired this company as it changes its DNA. I wonder if you could address that. Go ahead. So I think there's a couple aspects around our data. There's obviously the weather data, which is really powerful. But then there's also location data. We're one of the largest location data providers besides Google and some of the others because our weather accuracy starts with location, which is really important. We have 250 million users that use our application and we want to give them the most accurate forecast and that starts with location. Because we add value, users will opt in to give us that data, which is really important to us that we do keep their data private and opt into that to get that location data. So that's really powerful because now we can deliver products based on time and location and weather. And it just makes a really better weather insights for not only our consumers, but for our businesses. So do you use, I mean, how do you use social? I mean, Waze tells you where the traffic is and you report back. Do you guys rely heavily on that or do you more rely on machines to help you with your forecast? Is it a combination or? So I could talk a little bit. One of our new market areas we've been going into is ground transportation. So we do have a partner that's providing us some transportation traffic information, but what we bring to it is being able to do the predictive thing is to take the weather piece and how that's gonna influence that traffic. So as the storm comes through, we know by looking at past events, what that will mean and we bring that piece to the table. So it's an example of how we go, not just giving you a weather forecast, but really forecasting the impacts and giving you insights so that if you're running a large trucking operation, you know, you can reroute fleets around it and avoid weather like that and keep people safe. What about? One of the brands within our portfolio is Weather Underground and what they bought to the table for us is a personal weather station network. So we have about 270,000 around the world and these are people that just really love the weather. They have a personal weather station in their backyard and they provide that data that then goes into Mary's team and helping looking at the forecast. So that's one of the ways that we're using kind of a social network and censoring to influence some of the work that we're doing. I mean weather forecasts for years have been the butt of many jokes. You guys are data science oriented. Data scientists, the data doesn't lie. You just keep iterating and make it better and better and better. What can you tell us about the improvements of the forecast over the last decade? I mean, Bill Belichick makes jokes about the weather and you hear it and you say, you know, actually the weather's predictions have gotten much better. You guys measure it. What can you share with us? It's gotten so much better over, you know, the course of my career, it's pretty dramatic and it's getting better still. You're going to see some real breakthroughs coming up. So one of the things that we've really put a lot of bets on in IBM is the internet of things. And so we are today pulling off of cell phones and atmospheric pressure data and that's going into our next generation model. So this will be more data than anybody has powering that model. So you're able to augment traditional data sources like you may or may not know, we still launch weather balloons twice a day to measure through the atmosphere. But in our technology, we take data off of airplanes. We take data off of cell phones. We'll soon be taking data off of cars which will tell us when the windshield wipers are moving is it raining or not? When the interlock brakes things lock, the roads are icy, all of that. So all of that will come in to improve forecasting. So this requires partnerships with all that amazing supply chain. How do you guys, I presume IBM helps there as well, but did you have a lot of that in motion prior to the acquisition? How does that all work? I think we've really been powered by IBM. There's no question about that. And it's about finding the win-win. You know, when we work with car manufacturers, they're looking to have safe experiences for their drivers and we can help in that regard. And as we move into autonomous vehicles, there's just going to be even more demand for very high-resolution accurate weather information. Am I correct at all? The weather data from all these devices actually goes back to the IBM cloud, is that right? And that's where the models are iterated and developed and is that correct? Or is it some of it stay out in the network? It's all a cloud-based operation that's here. We do do some, I mentioned before, that we're working with IBM Research on next-generation high-performance computing, which is actually, it can be cloud-based, but it's also on-prem-based because of the very large cores we need for computing these models. We're going to run a very high-resolution model, globally, at a very high frequency. So, thinking about some of the industries that you're helping, I mean, mentioned retail before. Obviously, government's very interested in this. I would imagine investors are interested in the weather in a big way. Maybe you could talk about some of the more interesting industries, use cases, business models. There's a lot out there. There's traditional ones we've served for years, like energy traders that are very interested in, you know, because they're trying to make decisions about that. The financial services sector is also very interested when they can get some additional insights through footfall traffic, you know. If they know certain stores are seeing more footfall traffic, that will give them some indication, a little, you know, a little edge up in the marketplace for that. So we see those kind of things. And, you know, other traditional areas as well, agriculture, what you would expect there. People, you know, you hear a lot talking to press about artificial intelligence and Elon Musk predictions and the like, but here's an example where machine intelligence, everybody welcomes, keeps getting better and better and better. How far can we take, you know, AI and weather? Where do you see this going in the next 10 years? So on the consumer side, I think it's really about transforming the way that we're delivering weather on the digital platform. The new age of the weather app, we'll say. And really, you know, users want a personalized experience. They want to know how the weather's going to impact me, but they don't want to personalize, right? So that's where machine learning is coming in, that we can be able to provide those insights. We'll know that maybe you're an allergy sufferer, migraine sufferer, and we're going to tell you that the conditions are right for that you might have symptoms related to that around health. And so there's a lot of ways on the consumer side, more personalized experience, giving you more assurance that you don't have to necessarily go to the app to find information. We're going to send it to you more proactively. And so machine learning is helping us do that, cognitive science as well. So it's a pretty exciting time to be part of the weather. Yeah, that's fun to me, I have. You know, you might want to get ahead of the pain. It's our time, yes, yes, definitely. All right, Mary, we'll give you last word on IBM Think and, you know, the whole trend of AI and weather. So I think it's really exciting. I think Ginny says it really well. It's about AI and the person as well. You know, we're not taking, AI doesn't take over. It's really finding the way to AI to really assist decision makers. And that's where we're going on the business end of things is really sorting through tons and tons of data to really provide the insights that people can make, businesses can make really great decisions. Well, it's always been a really fascinating acquisition to me, and now just to see how it's evolving is really amazing. So Sherry and Mary, thanks very much for coming on theCUBE and sharing your experiences. Thanks so much. You're welcome. All right, keep it right there, everybody. You're watching theCUBE. We're live from Think 2018, and we'll be right back.