>> Voiceover: Live from San Francisco, it's The Cube. Covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Okay, welcome back, everyone. Live here in San Francisco, this is The Cube's exclusive coverage of Samsung Developer Conference, SDC 2017. I'm John Furrier, co-founder of SiliconANGLE Media, and co-host of The Cube. My next guest is Dominic Venuto, who is the General Manager of the consumer division of The Weather Channel, and Watson Advertising, which is part of The Weather Company. Welcome to The Cube. >> Thank you for having me. >> Finally, I got the consumer guy on. I've interviewed The Weather Company folks from the IBM side, two different brands. One's the data, big data science operation going on, the whole Weather Company. But Weather Channel, the consumer stuff, Weather Underground, that's your product. >> Yes, you saved the best for last. We touch the consumer. >> So, weather content is good. So obviously, the hurricanes have been in the news over the years. Out here in California, the fires. People are interested in whether the impact, it used to be a unique thing on cable, go to the Weather Channel, check the forecast, read the paper. Now with online apps, weather is constantly a utility for users. So it's not a long-tail editorial product. It's pretty fundamental. >> Yeah, we want to be where our consumers are. Fundamentally we want to help people make better decisions and propel the world. And since weather touches everything, we need to be where the consumers are. So now, with all the digital touchpoints, whether that's your phone, or its a watch, your television, desktop if you still have one and you're still using it, as some of us do. We want to be there, for that very reason. And in fact, what we're aiming for, is to move from a utility, because if we are going to help people make better decisions, a utility only goes so far, would be a platform to anticipate behavior and drive decisions. >> So tell me about the Weather Underground and the weather.com consumer product. They're all one in the same now? Obviously one was very successful, with user generated content. This is not going away. Explain the product side of The Weather Channel consumer division. >> Yeah, so we have two brands in our portfolio, Weather Underground, which is more of a challenger brand. It's very data rich, and visualizes data in a number of different ways, that a certain user group really loves. So if you're a weather geek, as we call them, an avid aficionado of weather, and you really want to really get in there and understand what's happening, and look at the data, then Weather Underground is a platform. >> So for users to tie into, to put up weather stations, and other things that might be relevant. >> Exactly so, we started out in 2001, originally the first IOT implementation at the consumer level, connected devices. Where you could connect a personal weather station, put one in your back yard, and connect it to our platform, and feed hyper-local data into our network. And then we feed that into our forecast, to improve that, and actually validate whether the forecast is right or not, based on what people have at home. And we've hit a recent milestone. We've got over 250,000 personal weather stations connected to the network, which we are super thrilled about. And now, what we are doing is, we are extending that network to other connected devices, and air quality is a big topic right now, in other parts of the world, especially in Asia, where air quality is not always where it should be, that's a big thing we think we can... >> That's a big innovation opportunity for you, I mean, you point out the underground product was part of maker-culture, people do-it-yourself weather stations, evolve now into really strong products. That same dynamic could be used for air control, not just micro-climates. >> Exactly, yeah. >> In California, we had a problem this week. >> Exactly, California is a good example, really topical, where cities may have had great air quality, and all of the sudden the environment changes, and you want to know, what is it like? What is the breathing quality like outside right now? And you can come to our network and see that. And we're growing the air quality sensors every month, it's only been up a few months right now, so that's expanding quite well. >> So for the folks that don't know, The Weather Channel back end, has a huge data-driven product. I don't want to get into that piece, because we've talked about it. Go to youtube.com/siliconangle, search Weather Company. You'll see all our great videos from the IBM events, that are out, if you want the detail. But I do want to ask you, what's really happening with you guys, there's two things. One is, it's an app and content for devices, like Samsung is using. And two, essentially you're an IOT network. Sensors are sensors, whether they're user-generated, or user-populated, you guys are deploying a serious IOT capability. >> Absolutely, it's one of the reasons that IBM acquired The Weather Company, which houses the brands of Weather Underground and The Weather Channel, is that we have this fantastic infrastructure, this IOT infrastructure, ingesting large amounts of data, processing it, and then serving it back out to consumers at scale globally. >> What are you guys doing there with Samsung? Anything just particular in the IOT side, or? >> We've got a couple of initiatives going on with Samsung, a few I can't mention right now, but stay tuned. Some really cool things in the connect-at-home, that we're excited about, that builds on some of the work... >> Nest competitor? >> Not exactly a Nest competitor. Think more kitchen. >> Kitchen, okay. >> Think more kitchen. >> We had the goods, cooking in the kitchen, from our previous guest. So the question is, IOT personal, I get that. What else is going on with IOT, with you guys, that you can share? Lifestyle, in the home is great, but... >> So again, going back to how do we help people make better decisions, now that we are collecting data from not just personal weather stations, but air quality monitors, we are collecting it from cars, we are collecting it from the cell phone. We are really able to ingest data at scale, and when you're doing that, we've got hundreds of thousands of data sets that we are feeding into our models, when you do that, we've solved the computing challenge, now we are applying machine-learning and artificial intelligence to process this and extract insights. To validate data sets, in our forecast, and then deliver that back to the end user. >> One of the tech geek themes we talk about all of the time is policy-based something. Programming, setting the policy. So, connecting the dots from what you're saying is, I'm driving my car, and I want to know if it's hot, or the road temperature. I might want to know if I'm running too fast, and my sensor device on me wants to impact the weather, for comfortable breathing for me, for instance. The lifestyle impacts, the content of data, is not just watching a video on The Weather Channel. >> No, it's not. >> So this is a new user experience. It's immersive, it's lifestyle-oriented, it's relevant. What are some of the products you're doing with Samsung, that can enable this new user expectation? >> One of the products that we have right now, we we're one of the initial partners for the Made for Samsung program, is, we've got calendar integration in our app. So now we know, if you've got a meeting coming up, and you need to travel to get there, maybe there's a car trip involved, we know, obviously, the forecast. We know what traffic might be, and we can give you heads up, an alert, that says, hey you might want to leave 15 minutes early for that meeting coming up. That's in the Samsung product right now, which is really, again, helping people make better decisions. So we've got a lot of examples like that. But again, the calendar integration in the Made for Samsung app is really exciting. We recently announced, in fact I think it was this morning, we announced integration with Trip Advisor. So similarly, if we see time on your calendar, and the weather is fine for the weekend, we might suggest outdoor activities for you to go and explore, using Trip Advisor's almost one-billion library of events that they have. >> What's the coolest thing you guys are working on right now? >> Oh, that's a very long list. I say that I'm probably the luckiest guy in IBM right now, because I get to work with millions of consumers, we reach 250 million consumers a month, and I'm also bringing Watson to consumers, and artificial intelligence, which is a unique challenge to solve. Introducing consumers to a new paradigm of user interaction and abilities. So, I think the most exciting thing is taking artificial intelligence and machine-learning, and bringing that to consumers at scale, and solving some of the challenges there. >> Well contratulations. I'm a big fan of IBM, what they're doing with weather data, The Weather Company, The Weather Channel. Bringing that data and immersing it into these new networks that are being created, new capabilities, really helps the consumer, so. Hope to see you at the Think conference coming up next year. >> Yes, we are excited about that, and stay tuned, we may have some more exciting stuff to unveil. >> Make sure our writers get ahold of it, break the stories. It's The Cube, bringing you the data. The weather's fine in San Francisco today. I'm John Farrier with The Cube. More live from San Francisco, from the SDC Samsung Developer Conference, after this short break. (electronic music)

from Las Vegas accepting the signal from the noise it's the kue coverage interconnect 2016 brought to you by IBM now your host John hurry and Dave vellante okay welcome back around we are here live in Las Vegas for IBM interconnect 2016 special presentation of the cube our flagship program would go out to the events and extract the signal from the noise I'm John forreal echoes gave a lot they are next guest pricing Kohler who's the chief information technology officer and I'm saying this for the first time on the cube the weather company and IBM business welcome back to the cube thank you very much glad to be back last time you weren't an IBM business we were just the weather company were just the weather company so congratulations on your success want to say we really big fans of it but what Papa Chiana the team have done is visionary bold and very relevant so congratulations hey how's it feel it is grateful din we are really excited the opportunity with the IBM platform and you know the reach and the capabilities I mean it it really helps accelerate what we were trying to get done as the weather company you know as our own standalone business um and you know 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 is company is all about finding that intersection of consumer behavior helping prepare and protect you as a 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 fit straight into the work the Bob picciano in team have been doing with the insights you know economy with Watson and 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 it's all 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 the scuttlebutt as we talk to our sources inside the company close to the company have telling us that the weather companies is infiltrating and affecting the DNA 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 digitize everything yes and so take us through that I mean one I think it's fair to say that you guys are kind of infecting I play in a positive way the mindset of being large-scale data yeah well why is that so compelling and how did you guys get here obviously whether the 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 are no teams DNA you know I'm a change agent you would not want to hire me to maintain something good if you want to hire me to you know to break something and rebuild it better that's I'm your guy so you know I think when you look at the movement from you know the 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 and so you know looking at whether whether it 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 Walt and Dave talks about it was saw earlier in the opening about digital assets and in this digital transformation companies have to create more digital assets that's just dating yeah in this new model so when you look at the data aspect you say whether also is a use case where people are familiar with we were talking before we went on camera that people can understand the geekiness of whether it's different they're familiar with it but also highlights a real-life use case and the IOT Internet of Things wearables we heard you have 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 gonna 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 the humidity level outside of your house and I might be able to actually tweak your HVAC and I can make that run efficiently and I can now you know cut thirty percent of your cooling costs and all of these you know examples they're integrated they become real yeah and 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 you know we get it right seventy-eight percent of the time we'd get it wrong sometimes we're constantly working to maintain our number-one position and data accuracy on weather forecasting and you know the more data we have the more accurate we can make it and so we've got any safer to you think just think about the use cases of people's lives slippery rose you know events correct I mean it's all tied in no 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 appt in their pocket I can alert them if the car is up ahead are having here are their abs activated and if all of the cars up ahead are having their abs activated I could 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 ten minutes people love those because it's right now and I can make a decision right now lightning strikes are always fascinating oh god because I gotta see crisis so last fall at IBM insight we interviewed David Kinney death your CEO and then right after I think 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 in accuracy of whether i'll try that whether some reporter asked him about you know you factor in the weather i don't even pay attention i look at the weather forecast they're always wrong as a wait a minute I just I just interviewed David Kennedy he was bragging on the weather is the accuracy and how much it's improved so helping you mentioned seventy-eight percent of the time it's it's gotten better over time it has it still got rooms we're not perfect so 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 at so color to that 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 that the weather company is we've taken our forecast from what was roughly 2 million locations where we would do a forecast two million locations around the globe and today we we create a forecast for 2.2 billion locations around the globe because the weather is different at Fenway then Boston Logan it's just different than the the start time of rain the start time of a thunderstorm you know that'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 ours are 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 we've increased the the you know all aspects of that and when you when you now think about getting your weather forecast you can no longer just type in BOS for your airport code and say i want to 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 every that was fine for everybody right 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 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 your you know you know it in Andover further outside where I am now where you gonna be my guess I gotta get so different you leverage the gps capabilities get that pinpoint location it will improve what the forecast is telling 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 is huh wow really interesting remember my line Dell's by an emc IBM is buying the weather company oh how intriguing it's a contrast it's all about the data the Dane is a service and then somebody whispered in my ear well you know there's like 800 Rockstar data scientists that come along with that act 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 we're take uh uh well i think IBM made a very smart move i'm slightly biased on that opinion but I think I be made a very smart move at 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 so what we had fundamentally built was the world's you know 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 than all of IBM's customers and clients to to become smarter with better applications better alerts better triggers and that alerts 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 you know warehouse club that they need a stock you know aisle three differently put a different in cap on because we now have a new insight we have a new insight for what demand is going to be tomorrow and how do we shift what's going on that alert going down to a handheld device on the guy driving the four club yeah it's no different skoda tato yeah the capability to ingest transform store do analytics lon provide alerting on and then distribute data at massive scale that's what we do we talk about is what happened when Home Depot gets a big truck comes in a bunch of fans and say we know where this know the weather company did for you yeah we don't understand you'll understand you'll fake it later they file a big on the top of it so I OT as well as markets where people don't can't understand that some people don't know it means being like what's IOT Internet of Things I don't get it explain to them some little use cases that you guys are involved in today and some of these new areas that you're highlighting with with learning somehow see real life examples for for businesses and users there is a smarter planet kind of you know safe society kind of angle to it but it's also there's a nuts-and-bolts kind of practical if business value saving money saving lives changing you know maintenance what are 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 you know and so there are sensors in everything today things have become digitized and so those sensors are there as next as those next evolutions have come online those those sensors got connected to the Internet why because it was easier than to manage and monitor you know you know here we are at the mandalay bay how many thermostat sensors do you think this hotel 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 in a 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 we have a very accurate understanding of the state of the Earth's atmosphere right atop this building having those throws is very good for the weather data but now how does the weather data impact a business that cares about the weather that has there we understand what the Sun load is on the top of this building and so we can go ahead and pre-heat your pre cool rooms get ahead of what's changing out sign 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 it 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 fall around his body it's bumpy to get these through there anywhere machines can can can do this in real time collected and synthesize it from hundreds of aircraft that have been flying in that same route now we can actually take that and produce a better you know in flight plan for those for those machines we do that with with advertising so you know when you think about advertising you be easy 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 you know 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 skew data and weather data that no matter what what we do weathers gonna 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 much business intelligence it's all the above its contextual data help people get insights in subjective and prescriptive analytics all rolled into one in a tool that alerts the actual person may explain to people out they were predictive versus prescriptive means a lot people get those confused what's your how would you prescriptive is you know where we want data that just tell us what to do based upon historic looking trends so i can take ten years of weather data and I can marry that up with ten years of some other data set and I can come up with you know a trend based upon the past and with that then I could prescribe what you should do in the future hey looks like general trend bring an umbrella tomorrow it's good 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 feet of our real-time data but also forward-looking data the predictive analytics is really around the insights that we want to guess I got to ask you one question about the IBM situation and I want you to kind of reflect get him get you know all right philosophical for a second what's the learning that you've had over the past few weeks months post-acquisition inside IBM is there a learning that you to kind of hit you that you didn't expect there's something you'd expect what sure what was your big takeaway from this experience personally and you had some great success in the business now integrated into IBM what's the learning that cuz that's comes out of this for you I am really proud of the team at the weather company you know I I think what we have been able to accomplish as a small company you know comparative to my four hundred and sixty-eight thousand colleagues at IBM yeah what we've been able to accomplish what we've been able to do is really you know it's impressive and I've been 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 you can now scale that to benefit such a large organization and 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 retrans form 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 inside engines and then armed a sea of developers to use our API we can't just take what we've done and go mate rest on your laurels you gotta go reinvent so I think my biggest you know real learning and take away from the kind of integration process is well 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 and continue to be us but do it in a way that works as an IBM ER and and that's that's there's there's going to be an art to this and we've got a ways to learn so I'm going in while eyes wide open around what I have to learn but I also am very reflective on on how proud I am as a leader of the team that you know has created you know such an amazing capability acquisition is done you savor it you come in you get blue washed and I hope I had a Saturday afternoon where I say okay got all like what is this gonna think so and then okay so you 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 you know 12 12 months so so you know 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 is very supportive of that you know Bob picciano his team have been awesome and you know 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 were very candid saying we're gonna transform ourselves and adapt absolutely and so and so so we've got that on wrestling on my mind how do we go find immediate wins there's there's a a million different ways for us to win there's thousands of IBM sales teams that are out in front of clients it's 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 you know 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 the 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 prices thank you so much taking the time out of your busy schedule to see with us in the queue good to see you again or any congratulations IOT everything's a sensor that we're a sense are here in the cube and we sense that it's time to go to SiliconANGLE DV and check out all the videos we have a purpose our sensor is to get the data to share that out with you thanks for the commentary and insight appreciate it whether company great success weather effects of song could affect stock prices all kinds of things in the real world so we had a lot of a lot of big data thank you very much look you here live in Las Vegas right back more coverage at this short break

>> Announcer: From theCUBE studios in Palo Alto and Boston. It's theCUBE. Covering IBM Think. Brought to you by IBM. >> Hi everybody, we're back this is Dave Vellante from theCUBE and you're watching our wall-to-wall coverage of IBM's Digital Think 2020 event and we are really pleased to have Guillermo Miranda here. He's the Vice President of Corporate and Social Responsibility. Guillermo thanks for coming on theCUBE. >> Absolutely, good afternoon to you. Good evening, wherever you are. >> So, you know this notion of corporate responsibility, it really has gained steam lately and of course with COVID-19, companies like IBM really have to take the lead on this. The tech industry actually has been one of those industries that has been less hard hit and IBM as a leader along with some other companies are really being looked at to step up. So talk a little bit about social responsibility in the context of the current COVID climate. >> Absolutely. Now thank you for the question. Look, first our responsibility is with the safety of our employees and the continuity of business for our clients. In this frame what we have done is see what is the most adequate areas to respond to the emergency of the pandemic and using what we know in terms of expertise and the talent that we have is why we decided to work first with high performance computing. IBM design and produce the fastest computers in the world. So Summit and a consortium of providers of high performance computing is helping on the discovery of vaccinations and drugs for the pandemic. The second thing that we are doing is related with data and insights. We own The Weather Company which is at 80 million people connected to check the weather every morning, every afternoon. So through The Weather Company, we are providing insights and data about county level information on COVID-19. Another thing that we are doing is we are offering some of our products for free. Watson, it is a chatbot to inform about what is adequate, what is needed in the middle of a pandemic if you are a consumer. We are also helping with our volunteers. IBM volunteers are helping teachers and school districts to rapidly flip into remote learning and get used to the tools of working on a remote environment. And finally we have a micro volunteering opportunity for anybody that has a computer or an android phone. So with the world community grid, you can help with the discovery also of drugs and vaccinations for COVID-19. >> Wow that's great, those are four awesome initiatives. They can't get the vaccine fast enough. Getting good quality information in the hands of people in this era of fake news also very very important. Students missing out on some of the key parts of their learning so remote learning is key. I love this idea of kind of micro crowd sourcing solutions. Really kind of opening that up and hopefully we'll have some big wins there Guillermo. Thank you for that. I want to ask you people talk about blue collar jobs, they talk about white collar jobs, you guys talk about new collar jobs. You and others. What are new collar jobs and why are they important? >> Look, in this data, digital, artificial intelligence driven economy, it's important not to have a digital divide between the haves and the have nots on the foundational skills to be operational in a digital economy. So new collar jobs are precisely the intersection of the skills that you need to operate in this digital driven economy with the basic knowledge to be a user of technology. So think about a cyber security analyst. You don't need a masters degree in industrial engineering to be a cyber security analyst. You just need the basic things about operating an environment on a security control center for instance. Or talk about blockchain or talk about software engineering, full stack developer. There are many roles that you can do in this economy where you don't need to have a full four-year degree in a university to have a decent paying job for the digital economy. These are the new collar jobs and what we are attempting to do with the new collar job definition is to get rid of the paradigm that the university degree is the only passport to a successful career in the marketplace. You can start in different, having the opportunity to have a job in a high tech area. Not necessarily with a PhD in engineering as I said, it's something important for us, for our clients and for the community. >> Yeah, so that's a very interesting concept that a lot of us can relate to. To go back to our university days, many of the courses that we took, we shook our heads and said, "okay, why do I have to take this?" Okay, I get it, well rounded liberal arts experience, that's all good but it's almost like you're implying that the notion of specialization that we've known for years like for instance, in vocations, auto-mechanic, woodworking, etc. Planning that have really critical aspect of the economy. Applying that to the technology business. It's genius and very simple. >> Absolutely. Look, this is the reinvention of vocational education for the 21st century where you continue to need the plumber, you continue to need the hairdresser but also you need people that operate the digital platforms and are comfortable with this environment and they don't need to pass at the beginning through full university. And it's also the concept that we have divided the secondary education, high school from college, university etc., like a Chinese wall. Here is high school, here is college. No! There can be a clear integration because you can start to get ready without finishing high school yet. So there are several paradigms that we have evolved in the previous century that now we need to change and be ready for this 21st century digital driven economy. >> Yeah, very refreshing. Really about time that this thinking came into practice. Talk about P-Tech. How does P-Tech fit into acquiring these skills? And maybe you could give us a sense as to the sort of profile of the folks and there backgrounds and give us a sense as to and add some color to how that's all working. >> Absolutely, so look, the P-Tech model started 10 years ago in a high school in New York City, in Brooklyn. And the whole idea is to go to an under-served area and create a ramp onto success that will help you to first finish high school. Finishing high school is very important and has a lot connotations for your future. And then at the same time, they start getting an associate degree in an area of high growth. The third component is the industry partner. An industry partner that works with the school district and the community college in order to bring the knowledge of what is needed in that community in order to create real job opportunities and we will send you the people and then you will use it. No! We need to work together in order to train the talent for the future. And you just go to the middle age and the guilds were the ones that were preparing the workers. So the industry was preparing the workforce. Why in the 20th century we renounced to that? Having real, relevant skills starting in high school, helping the kids to graduate with a dual diploma. High school, college and practice in real life what it is to be in a workplace environment. So we have more than 220 schools. In this school year, we have more than 150,000 kids in 24 countries already working through the P-Tech model. >> Love it and really scaling that up. So let's say I'm an individual. I'm a young person, I'm from a diverse background, maybe my parents came to this country and I'm a first generation American. Of course, it's not just the United States, it's global but let's say I'm from a background that's less advantaged, how do I take advantage? How hard is it for me to tap in to something like P-Tech and get these skills? >> Well, first one of the characteristics of the model is this is free admission. So there is not a barrier fence. If your school district offers P-Tech, you can apply to P-Tech and get into the P-Tech model education without any barrier without any account. And the second thing that you need to have is curiosity. Because it's not going to be the typical high school where you have math, science, gym, whatever. This is more of an integration of how the look of a career will be in the future and how you have to start understanding that there are drivers into the economy that are fast tracks into well paid jobs. So curiosity on top of being ready to join a P-Tech school in the school district where you live in. >> That's great Guillermo, thank you for sharing that. Now of course corporate responsibility, that's a wide net. This is one of your passions. I'll give you the last word to kind of, where do you see this whole corporate responsibility movement going generally and specifically within IBM? >> I think that this whole pandemic will just accelerate some of the clear trends in the marketplace. Corporate responsibility cannot be an afterthought as before in the '80s or '90s. I will put a foundation. I have a little of profits that are left and then I will distribute grants and that's my whole corporate responsibility approach. Corporate responsibility needs to be within the fabric of how do you do business. It has to be embedded into the values of your company and your value proposition and you have to serve those projects with the same kind of skills and technology, in the case of IBM, that you do for your commercial engagements. And this is what we do in IBM. We help IBMers to be helpful to their communities with the same kind of quality and platforms that we offer to our clients. And we help to solve one of the most complicated problems in society through technology, innovation, time. >> Love it. Guillermo thanks so much, you're doing great work. Really appreciate you coming on theCUBE and sharing with our audience. Congratulations. >> Absolutely. Thank you for very much for having me. >> You're very welcome and thank you for watching everybody. This is Dave Vellante from theCUBE. You're watching our continuous coverage of IBM Think 2020, the digital version. Keep it right there, we'll be right back after this short break. (bright music)

>> Live from San Francisco it's theCUBE. Covering IBM Think 2019, brought to you by IBM. (electronic beat music) >> Welcome back to theCUBE. We are live at IBM Think 2019 in soggy San Francisco. I'm Lisa Martin, with Dave Vellante. Dave, I hope you brought a big umbrella today. >> Well luckily the Marriott lent me one, so-- >> I got one from my hotel, too. And what a perfect day to day have the hybrid, multi-cloud open upon us, shower San Francisco with rain, and talk about weather with an IBM expert. Mark Gildersleeve, welcome to the Cube. You are Vice President, Head of Business Solutions, and Watson Media, The Weather Company. >> Thank you for having me. >> Our pleasure, so, we think IBM, this is the second annual IBM Think. There's about what, 30,000 people here, 2,000 plus business and technical sessions. There is a lot, a broad spectrum, no pun intended, of topics to cover, but excited to talk with you today about what IBM is doing in the agriculture industry. Let's talk about it from the growers perspective first, and we'll cover some other, other outlets. But, what are some of the challenges that growers are facing in 2019? >> So, first of all, if you think about it, this is a really sporty industry for growers to be in. They've got to worry about things that they can't have any control over: the weather, pest and disease, government regulation, trade, commodity pricing, there's a lot that they can't control. To make matters worse, they have very slim margins, okay, and they had to learn all these various aspects of technology to try to become better. And so, they're almost drowning in data, trying to figure out what do I do about it to get more yield, to get more profitability, to get better quality? There's a lot of challenges that they're wrestling with today. (people chattering) >> Well this is a huge problem, because the, the amount of farmable land isn't growing. It's essentially flat. >> It's flat. >> Maybe it's even shrinking. >> It's flat. >> They're talking with a multi-decade, 20, 30-year time frame. Population growth, we're talking about another two, two and a half billion people over the next three decades. So, something's got to give. What does the data say? >> So you're exactly correct, the estimates of population growth are 2.3 billion between now and 2050. That's 30% population growth. With zero incremental air-able lands, so, huge yeah. So we have to get yields, at least 30% higher. Okay, so if you think about that problem we're not going to get that yield increase status quo. We're not going to get that yield increase without having a much more data and an AI driven approach to agriculture, and that's exactly what we're doing. Our solution right now has 14 different AI and analytic capabilities inserted into it. Just to try to help growers, for one, make sense of their data and make better decisions to try and get their yield up, their profit up, their quality up. >> And is there enough in your estimation markers, is there enough head room actually to accommodate that population growth, given the constraints? >> Absolutely, taking a simple example of being a corn grower in the U.S. The average corn grower gets 175 bushels per acre, but the 70th percentile gets like 250, okay? So, if we got in, in the example of corn, every person that's at the 50th percentile, up to the 70th percentile, which is extremely doable. You can, you are, by definition, increasing the yield 30% in that case. So, it's doable, and we can see examples of growers doing it today. But what you have to understand is that 70% of the differences in performance between growers are just their farming practices. So, we have to get a handle on what farming practices drive better yield. We have to get those people at 50% to 70%. The people at 30% up to 50. We just have to get them about 20 points better in the benchmarking, and we will actually solve this problem from a U.S. perspective, then we have to do different things for other parts of the world. >> Now there's a multi-variable problem here as well though, because you got consumer patterns changing, people want, you know, more sustainable. You go into the grocery store now, you see all grass-fed, or free-range, and, so that takes up more land. Do consumer, how do consumer preferences, and the shifting consumer preferences factor in? >> It's the biggest change I think that's happened in this industry in the last 20 years. If you look at 20 years ago, 30 years ago, the tech chains were being driven kind of more from the ag-input side, and that's kind of the people that are selling to the growers. Now, we have the food companies hearing from consumers that they want sustainable, they want better quality, they want more nutrition, they want to understand how to have less chemicals going into their food. Okay, now we have the buyers of the growers, pushing on those growers to say you need to give me a better product. This change of consumers, and this ripple through the food eco-system is the big change. And the food companies are at the center of this revolution. And it's actually really interesting, and I think it actually will knit together this whole ag-eco-system, so that you now have to worry about the ag input people, the growers, the food companies, and the retailers, the bankers and the insurers, all kind of understanding, and coming together to figure out how to get better product to the consumers, and also, by the way, increase the yield so they can solve the food production problem. >> So, where do you start? Are you talking, what's the lowest hanging fruit? Is it going to the large-scale growers that have more resources, potentially resources that understand technology enough to start at that source? What about the smaller scale farmer growers? >> So, I think that, we have IBM clients that are interested in solving every aspect of the kind of size of foreign problem. So, I met with one organization from Africa today. In Africa, it's all a small farmer problem, right? And, and the vast bulk of growers in the world are small farmers, okay? But when we're looking at kind of solving the problem overall, we want to start with the food companies, and the people in finance. Because, right now, food companies, when they're trying to deal with their growers, they're trying to manage these growers with spreadsheets. Even though these are very sophisticated companies, very sophisticated. We need to help those food companies better understand what's going on the field. What chemicals that are going onto the land? When was the crop planted? When is it going to be harvested? When can I expect it in my storage facility? And they really want to understand, what are the farmers doing that are giving them the best quality crop? And how can they learn from the data, to get best practices for all the rest of their growers? If we start with the food companies, and have them work with their growers and the agronomists, that's going to be the best way to introduce change into this sector, I believe. >> And they're kind of the the pivot point between the consumer, they understand the consumer demand, they can feed that back to the farmers. Of course, they're ultimate goal is to make a profit. But look at it, if you give the people what they want, there's going to be a way to make money here. It's just, it's not going to be the same way that they've made money for the past 50 years. >> Exact, exactly right. But you know, take an example, in my house, we buy organic milk, okay? We're paying a premium for organic milk. We're willing to pay a premium. >> Happy to do so, yup. >> Happy to do it. We feel like it tastes better. We feel good about also the quality of it. So, I think in many cases, food companies are willing to pay a premium to growers to deliver a very specific crop to them. And so, this issue of food companies having more growers under contract, and working with those growers to deliver a better product, is of high interest to virtually every food company, every beer company that we've talked to. Every retailer that's worrying about the supermarket shelves. They're all worried about trying to get better product to the shelf, 'cause that's what the consumers are asking for. There is money, in this system, if you get the quality up. So that's really what we're focusing on with the food companies. >> People happy to pay for that and this eco-system is actually quite interesting. You talk a bit about, you talked about the banks. They're, even health care is part of the eco-system. >> It's the other constituent. >> They've said that people start making better food choices. It could ripple through to health effects. So, maybe you're paying more, as a consumer, for an individual product, but you could be living longer, having better health, maybe having lower health care costs. >> One analogy that I think you might find interesting, is that, just as all of us have an electronic medical record, that has all the images that would have been taken of our body, like an MRI, or our health history, our hospitalizations, what surgeries we've had. We're now, as IBM, bringing the electronic field record, which is an exact analogy to the electronic medical record, but it's about the field. What's been grown there? What have been the yields? What are the chemicals? When was the crop planted? What kind of tillage practices are being used? And we're trying to, essentially build that database of the electronic field record as the cornerstone for all the analytics for the AI that we're building, and running against, to help figure out benchmarks for all the corn growers in U.S.A., or the potato growers in the Netherlands. And beyond the benchmarks, best practices, so that we can say, what are the people that are 70th percentile doing, that the people that are 30th percentile aren't doing? We can bring all those people up. It's very cool. >> So we're talking about IBM, the computer company, right? So, what's the big picture of IBM's role? Obviously, there's a data angle. But what's the IBM story here? The holistic story. >> So, first pillar is data. Every piece of data coming off of a combine or a sprayer, so the equipment data, the machine data. All the environmental data, remotely-sensed data, soil-sensed data, stuff that's going on to the field, as well as the farm practices. So, there's a whole data story that, who better than IBM to handle massive amounts of data? Secondly, AI and analytics, right? So, we've got 13 or 14 different analytics and AI products embedded in our decision platform. All intending to give that grower a better first guess, a better recommendation of, here's what the data tells us about your field. It's still up to the grower and the agronomist to make the final call, but we can give them a much better guess than they have just based on their own personal fields experience. Then lastly, it's decisions that we can help that grower make. So, an example would be: we can help a banker understand exactly what crop is being grown on a piece of land without having the banker have to send somebody out and look at it. So, they can understand compliance-wise, Was a loan that I wrote being used in the purpose that was intended? But there are many enterprise examples of that. So it's data, AI, decisions. And that's then connected across the eco-system. It's a great IBM story 'cause we've been in business, we've been serving the USDA for 91 years. We've been in agriculture a long time. Lots of people in IBM don't know it, but we've been at this a long time. >> And if we look at the growers for a second, this is really kind of where it all starts, right? I understand this triangulation, and the constituents that are involved from the food companies, to the retailers, to the bankers. But, if we look at the growers, what are some of the benefits? Do you have a favorite success story where, whether it's a large-scale grower or something smaller, where their, maybe their loan terms are better? Or they have lower costs? Or they're actually making a better impact on the environment? What's your favorite grower impact story? >> There are lots actually, but let's pick a few. The first is, we have a lot of aspects of crop protection, where we can use satellite imagery to figure out where a crop is under stress. Where, what part of the field is under stress. Help them go out and scout that field. Take a picture with their smart phone and have Watson tell you what the disease is that's infecting that crop. And, essentially, be able to take faster action. When you're faster with crop protection, you are saving a lot of your crop. You get better yield, that's money in the bank. So crop protection is one. A second example is, with best practices, showing some of these growers what the 70th percentile growers are doing, that the 50th percentile guys are not doing. You can say, here are the four things that these 70th percentile guys are doing. You should try those four things. Or you might want to try two of them this year, two of them next year. But best practice is a huge impact. The last impact is, we help people with yield. So, we can now say okay, this is the projected yield that you're going to have at the end of the season. Here's what you can sell at the middle of the season. Here's what you're going to be able to sell at the end of the season. And we help them with market timing. Trading profitability can be easily 20, 30 bucks of incremental profit per acre. So, there's kind of a financial angle, there's a best practices angle, and there's a protecting your field angle, as the three examples I give you. >> Well, and that's huge from the standpoint of the debt loads that farmers face around the world. Over a trillion dollars in debt, in just, you know, a few countries. What does the future hold from that standpoint? What are the implications of that debt load? Obviously there's an imperative to improve yields and improve profitability, but your thoughts? >> So, first of all, you're correct that debt is a really enormous issue. So, for example, there's an article in the Wall Street Journal last week. Bankruptcies are at the highest level in the U.S. since the crash of 2008. So, this debt load, and the debt service is a really large problem. Here's how I'd like to try to focus it. Many growers have been taught to worry about better yield. When we should have been focusing more on better profit per acre. There are two ways you can get out that profit per acre. One is, you can do things with new chance fertilization, seed type, plant date, that can drive your yield better. But the other aspect is, there are parts of your land that are going to be lower productivity potential. Your smartest move is to put less inputs on those portions of the land and double down on the inputs on the highest productivity areas of the land. Because most farmers don't understand that there's 25% of their land, where they're actually losing money, and they'd be better to actually not be planting. But instead the idea is, plant at a lower population rate, put less input costs in, and then you can even make that area of less productive land profitable. If we improve the profitability of these growers, they can afford the debt service, and that's kind of the way to do it. The other aspect is that, everybody that's doing contract growing for a given food company is getting a premium on their crop. Oftentimes, 10%, or even 15% premium. That 10%, or 15%, solves the problem of the debt service for almost every grower, in the U.S. that's doing zero crops. >> That focus on profitability versus pure yield per acre. That's potentially involves a a different crop? And a shifting strategy? >> Usually it's a different farming practice. So, it's applying variable rate technology. It's essentially understanding how to treat each aspect of your field differently so that you're not treating it homogeneously. But you're actually saying, I'm going to do this practice, and with this level of input costs down over here, in this section of the land. And do a different practice over here. Because, every piece of land has low productivity areas, high productivity areas, and areas that are either high or low, depending on the weather. Understanding how the land varies is a huge data insight that we give growers with our data insights using AI. >> And that can drop right to the bottom line, obviously. >> It's all bottom line, baby. >> Last question before we have to wrap, this is, I feel like we're scratching just the surface here, of such an interesting topic of, and the massive global implications of IBM and agriculture can have on all of us. Where can people go on the IBM website for example, to learn more about this? >> You can go to the, well, so at the Think, there are a number of sections actually that we have right now. Talks that we're giving later on Friday morning. All related to the Watson Decision Platform for Agriculture. And there's material at the Think exhibit stuff that you can go to. We're also exhibiting in the Watson Media and Weather section downstairs. We'd ask everybody to come there. >> Excellent, well Mark, thanks so much for joining Dave and me on the program today, really interesting conversation. >> Great story. >> Thank you for having me. >> Our pleasure. We want to thank you for watching the Cube, I'm Lisa Martin, with Dave Vellante. Live, from IBM Think 2019. Stick around, we'll be right back shortly with our next guest. (electronic music beat)

>> It's theCUBE, covering AWS Summit, New York, 2018, brought to you by Amazon Web Services and its ecosystem partners. >> Here in New York City, we're live at Amazon Web Services AWS Summit. This is their big show that they take on the road. It kind of originates at Amazon re:Invent in Las Vegas, their big kickoff show for the year, and then goes out to the different geographies and goes out and talks to the customers, and actually rolls out all the greatest of the cloud from Amazon's perspective. Of course theCUBE covering it, wall-to-wall cloud coverage, I'm John Furrier, co-host with Jeff Frick here today in New York City for special coverage. Jeff, Amazon obviously continue to dominate, but competition is heating up, Google Nexus next week, we'll be there live. Microsoft's got a big show, Azure's gaining market share, Amazon's still racing ahead. They got a book they're giving out here called Ahead in the Cloud, Best Practices for Enterprise IT. Amazon, clearly we talk about this all the time, they've cleared the runway from winning the startups, small, medium-sized growing business in the cloud native, to actually putting big dent in the market share for acquiring large enterprise customers. That has been their mission, that's Andy Jackson's mission, that's the team. Their head count is growing, Jeff Bezos is the richest man in history of the world. Pretty impressive story, we've been covering it since 2012, >> What's crazy is it's barely got started, John. I mean, just looking up some numbers before we came on, Gardener has a bunch of projected public cloud cans, anywhere from 180 billion to 260 billion. So even with Amazon at the head of the pace, I can't remember their last statement, I think it was 18 billion run rate, and everybody's saying Microsoft's brewing so fast. They barely still scratch the surface, and that I think is what's really scary. There'll be 50,000 people probably at re:Invent, there's 10,000 here in New York, they have these summits all over the country, all over the world, and so as impressive as the story is, what I think is even crazier is we've barely just begun. You were just at Public Sector, that's a whole 'nother giant tranche that's growing. >> Well you started to see the ecosystem develop nicely, and cloud native certainly is a tailwind for overall Amazon. Obviously the have the winning cloud formula, they've been ahead for many, many years. But again, competition's keeping up. But if you look behind us, you probably can't see in the cameras, it doesn't give justice, but this show, it's in New York City, it's a regional kind of like event. Its now looking the size of what re:Invent was just a few years ago. Public Sector Summit, which is the global public sector that Teresa Carlson runs, in really its third year roughly since it got big, started out a couple years ago. That's now morphing into the size of re:Invent, so pretty massive. >> And they said there's 10,000 people here. I don't know how many were at Public Sector. 138 sponsors, just some of the numbers that Werner shared in the keynote. 80 sessions, really an education session, it's a one-day event. We're excited to be here, but what's amazing is even though pretty much every enterprise has something going on in the public cloud, in terms of the vast majority of the workload, still most of 'em are not, and you know, really an interesting play. We were there when the AWS VMware announcement was made a couple of years back in San Francisco, as kind of this migration path, that's both been really good for VMware, and also really good for Amazon, 'cause now they have an answer to the, kind of the enterprise legacy question. >> I mean Jeff, did you look at the big picture? If you want to squint through the noise of cloud, what's really going on is, one, the analysts that are looking at market share, I think are looking at old data. It's hard to know who's really winning when you look about revenue, 'cause everyone can bundle in, Microsoft bundles Office revenue in. So it's actually, that's hard to understand, but if you look at the overall big picture, the landscape that's happening is that the enterprise and IT market has moved from being consumerization of IT to digital transformation. Those are the two buzzwords. But really what's happening is the operational model of cloud has created two real personas in the enterprise from a technical perspective. The developers who are building apps, and operators who are running the infrastructure, running the software, running the dashboards, running the operations. And so you start to see that interplay between operators and developers working together but yet decoupled, different personas. These are the ones that are changing how work gets done. So the future of how work and computing is going to be applied for end user benefits, user benefits, consumers, whether it's B2B or B2C companies, the cloud is the power engine of innovation, and new apps are coming on faster, and the roles are changing, and this is causing a shift of value. This is what the analysts at Wikibon, theCUBE, insights team has been looking at is that this is really the big thing. And machine learning, and AI, really take advantage of that, and you're going to start to see IoT, security, AI, start to be the critical apps to take advantage of this power of the cloud, and as enterprises transform their operations and their development frameworks, then I think you're going to see a whole new level of innovation. >> Right. They just had Epic Games on, the company that makes Fortnite which is a huge global phenomenon. If you don't know anything about it, ask somebody who's under the age of 15, they'll tell you all about it. >> 135 million gamers. >> The core value proposition of cloud is still the same, its flexibility, its global reach, its ability to scale up and scale down, and we've asked this question before and we're getting closer and closer with each passing day, is if we live in a world, John, with infinite compute, infinite bandwidth and infinite store, basically priced at zero, asymptotically approaching zero. What could you build? And if you could get that to the entire world instantly, what could you build, and we're really getting closer and closer to that and it's a very different way to think about the world than where you have to provision at 50% overhead, and you got to buy it and plug it in and turn it on. You know, that world is over. We're not going back, I don't think. >> If you look at the cloud players you've got Microsoft, Amazon, Google, and then we throw Alibaba, that's more of a China thing. Those are the main ones, you got Oracle for Oracle and IBM in there. You look at the companies, and look at the ones that have consumer experience, and look at the ones that don't. Microsoft has failed on the consumer business, although they have some consumer stuff, really not really been successful. Oracle and Microsoft, IBM have been business to business companies. Google and Amazon have been consumer companies that have bolted on a cloud just to run their operations. So to me what's interesting is, which one of those sides of the street, which one will emerge as the victorious cloud platform. I think I would bet on the consumer side. I like Google, I like Amazon better than Azure and Oracle and IBM, mainly because they have consumer experience, they understand the ultimate end user, and built clouds for that, and now are rolling that business. So the question is will that be the better model than having Azure or Oracle or IBM, who know the business model-- >> Right. >> But yet, will the devices matter? So this is going to be a big thing that we're going to watch on theCUBE is, which cloud play will win, or does it matter? Is it winner take all, winner take most? >> Yeah. >> This is the questions. >> Pretty interesting. You know you interviewed Mark Hurd many moons ago, for a long time, and he talked about cloudifying all the Oracle applications. The problem is, Clayton Christensen's book, Innovator's Dilemma, is still the best business book ever written. It's really hard to knock off your own core business, especially when it's profitable. That I think is Oracle's biggest problem. The other thing I think they have is, they're a sales culture, it's built around a sales culture. People are going out and it's a hard sell. That's not what the cloud is all about. It's really the commercialization of shadow IT. I need it, I turn it on, I activate it, I don't need it anymore, I turn it down, I turn it off, I turn it over. So I think Oracle's in a tough position to eat their own business. IBM is you know, continues to try different things and you know, with The Weather Company and Ustream, and they're doing a lot of things. But the core three have such momentum, Google we'll see, we're excited to be there next week and kind of get an update on what their story is, but still in the enterprise they barely scratch the surface of the available workload. >> I think that's the main story, the surface is just being scratched. If this is like the first or second inning of this game, or the second game of a double header, as Matt Dew has said on theCUBE many times, he'll come on today, it's interesting because if you think about the clouds that are best position to take advantage of new technologies, like AI, like blockchain, like token economics, those are the ones that have to be adaptable and flexible enough to take on new things, because if we're just scratching the surface, the new things that are going to come out have to scale, have to be data driven, have to be mobile, have to use AI, have to have the compute power. If you're kind of stuck in the old model and you have a ME2 cloud, it's going to be always hard to ratchet up and kind of always rearchitect and change, you need an architecture that will essentially be flexible and be adaptive. To me I think that's what we're going to look for here in the interviews today, and of course, security, Jeff, continues to be the number one conversation, at AWS re:Invent, and AWS Public Sector Summit. Security is getting better and better in the cloud and some say it's better than on-premises security. >> I think the resources that can be applied at a company like AWS, the security teams, the technology, the hardening, the private fiber connections, I mean so many things that they can apply because they have such scale, that you just can't do as a private enterprise. The other thing, right, is that people usually take better care of their customers than their own, and we know a lot of security breaches and data breaches are just from employees or somebody lost a laptop. They're these types of things where if you're an actual vendor for someone else and you're responsible for their security, you're going to be a little bit different, a little bit more diligent than kind of protecting once you're already inside the wall. >> And it changes the infrastructure, I mean just in the news this week, obviously Trump was in Helsinki, all I can see in my mind is, the servers, where are the servers, where are the servers? With the cloud you don't need servers. The whole paradigm is shifting. If you use cloud you can get encryption, you can get security. These are things that are going to start that I think be the table stakes for security, the idea of having a server, provisioning a server, managing servers per se, unless you're a cloud service provider, at some level, you're tier two or tier one, you don't need servers. This is the serverless trend. Again, Lambda functions, AI, application developers, all driving change. Again, two personas, operators and developers. This is what the swim lanes are starting to look at, we're starting to get the visibility. And of course we'll get all the data here in theCUBE, and share that with you this week. Today in New York City, live theCUBE, I'm John Furrier with Jeff Frick. Stay with us for coverage here for AWS Summit 2018. We'll be right back.

>> Narrator: From Las Vegas, it's the Cube, covering IBM Think 2018, brought to you by IBM. >> Welcome back to Las Vegas, everybody. You're watching the Cube, 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's here, she's the vice president of weather business solutions, public, private partnerships, IBM Watson, and she's joined by Sheri Bachstein as the global head of consumer business at the Weather Company, an IBM company. Ladies, welcome to the Cube, thanks so much for coming on. >> Thank you, you're welcome. >> Thanks. >> Alright, Mary, going to start with the Weather Company. When IBM acquired the Weather Company, a lot of people were like, "What?", and they said, "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, not in the weather business, we're in the decision business. We're really dedicated, everyday, to help businesses, make the best decisions possible, and Sheri works on the consumer end of the business to do exactly the same thing. >> So, talk about your respective roles. Sheri, you're on the consumer side, as Mary just said, what does that entail? >> So, the consumer side is any touchpoint 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, 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 and 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 everyday. >> Yeah. >> Of course, when you travel, like, what do I bring, what do I wear? Living in the East Coast these days, a lot of storms that we've >> That's right. >> 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. Have you guys assimilated, how has it changed your business? >> I would say pretty dramatically. So, 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 in 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. >> Yeah. >> They're advertising, okay, fine, >> Yeah. >> but that's not the core of what you guys do. >> Yeah, so, on the consumer side, a big majority of our revenue is drive by advertising, but we had to look at that business as well, 'cause as programmatic advertising has kind of taken up 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. >> That's right. >> It's not like IBM is 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 FX, and, really, what it's doing, it's 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, you know, strawberry Pop-Tarts sell fairly well right before a hurricane, and, so, these are insights that we can bring to retailers, but it helps them with their supply chain, it helps them with their inventory, it 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 'em or something. I don't know, so, yeah. >> Right, yeah, it's simple if the lights go out, okay. I mean, we want to ask you about your title, public and private partnerships. It's interesting, what is that all about? >> So, it's really about the fact that weather has really been something that's been shared globally around the world for hundreds 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 US, we transmit, Sheri'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'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 met services. So, it's really one of the really fun parts of my job. >> You know, we talk a lot about digital transformation, and Ginni Rometty 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 human expertise and built around other assets, the bottling plant or the factory, et cetera. I look at the Weather Company as a data company, 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 (laughs). >> 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 in to 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 for better weather insights for, not only our consumers, but for our businesses. >> Yeah, yeah. >> Do you use, I mean, how do you use social? I mean, you know how 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? >> 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 going to 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 can reroute fleets around it and avoid weather like that and keep people safe. >> Talk about, oh, go ahead, please. >> One of the brands within our portfolio is Weather Underground, and what they brought to the table for us is a personal weather station that works. 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 in helping looking at the forecast. So, that's one of the ways that we're using kind of a social network in sensoring to influence some of the work that we're doing. >> I mean, the weather forecast, for years, have been the butt of many jokes. You guys are data science oriented, data scientists, the data doesn't lie. We just keep iterating >> Yeah. >> and make it better and better and better. What could you tell us about the improvements of the forecast over the last decade? Maybe Bill Belichick makes jokes about the weather and you hear it, you say, "You know, actually "the weather's predictions have gotten much better." You guys measure it, what can you share with us? >> Oh, it's gotten so much better over 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, >> Dave: Right. >> and, so, we are, today, pulling off of cellphones atmospheric pressure data and that's going into our next generation model. So, this'll 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 cellphones, 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 anti-lock brakes things lock, that roads are icy, all of that. So, all of that will come in to improve forecasting. >> So, this requires partnerships with all that and amazing supply chain. >> Absolutely. >> 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 empowered by IBM. >> Yep, absolutely. >> Yeah. >> There's no question about that, and it's about finding the win-win. 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, is that correct, or does 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 Prim-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, you mentioned retail before. Obviously, government's very interested in this. I would imagine investors are interested in the weather in a big way. >> Yeah. >> Maybe you could talk about some of the more interesting industries, use cases, business models. >> Yeah, 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, if they know certain stores are seeing more footfall traffic, that will give them some indication, a little edge up in the marketplace for that. So, we see those kind of things, and other traditional areas as well, agriculture, what you would expect there. >> So people, you know, you hear a lot of talk in the 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 could we take 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 will say, and, really, 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 or 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. 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 bum knee I have, you know, you might want to get ahead of the pain. >> That's right, with the arthritis, yes, yes, so, definitely. >> Alright, 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 Ginni says it really well. It's about AI and the person as well. You know, AI doesn't take over. It's really finding the way to AI to really assist decision makers and that's 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, Sheri and Mary, thanks very much for coming on the Cube >> Thank you. >> and sharing your experiences. >> Thanks so much. >> Great, thank you. >> You're welcome, alright, keep it right there, everybody, you're watching the Cube. We're live from Think 2018 and we'll be right back. (techno beat)

>> Announcer: Live from Las Vegas, (electronic music) it's theCUBE. Covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, and this is day two of our wall-to-wall coverage of IBM Think. We've been doing IBM shows for years. This is the big, consolidated show, 30 to 40 thousand people, too many people to count. Cameron Clayton is here. He is a GM of Watson Content and IoT Platform at IBM. Thanks for coming on. >> Thanks very much for having me. >> So quite a show, right? Standing room only! >> A large, large show. >> Standing room only and also great announcements. >> So tell us about your announcements. >> Yeah, so we got to couple of things we're really, really excited about. The team's been working really hard on for the last few months. One is a way to train Watson to make Watson even smarter than it already is out of the box. And so, we've been building data kits by vertical industry. So for financial services, for travel and transportation, for the hospitality industry, for health care and for government, on how do you give Watson a high machine IQ right out of the gate as opposed to having to train it in your area of industry. And so, once again, we're really focused on making Watson the AI system for Enterprise, and this is another step on that journey to make Watson really, really smart. >> It's really prioritizing it in a way that's much easier to consume. >> Much easier to consume, and if you think about it, there's a lot of jargon in each industry, right? To be an expert in industry, you got to know a lot of jargon, understand the context of that. An AI system doesn't know that unless it's taught that. And so we are teaching Watson that. And then how to apply it successfully in each of those industries. So it's a pretty material leap forward in how we're training Watson. >> So it hits the content component >> Cameron: Hits the content. >> And then industries you're knocking down? Where are you starting? >> Yeah, so we're starting with financial services. We're launching in travel and transportation and in hospitality. So we're basically, this is a pretty fun one, I love food. But basically Watson went out and scanned the entire internet and collected all the recipes that it could find on the internet and trained itself on food. And so, you can ask it now questions about food, what restaurants, about really specific things. If you're a vegan you can find out what's available near you. If you're gluten intolerant, you can find out things on the menu like that. But then there's other things, like in the travel and transportation industry. Virtual agents for travel agents, they can ask questions of Watson, and it can ask very specific, very deep things, very much like a human would. And so you can say a simple thing like, "Where should I stay in New York?" And a human would respond, "Well, are you a member of any hotel rewards program?" Normal AI chatbot wouldn't. It would just say, "These are the lists of the 4,000 hotels in New York." Watson will actually ask human-like questions to give you the best answer possible. But all that requires training, and that's what were built in with these Watson content data kits, and we're really excited about 'em. >> So I'll come back to that. But so if I take that example of Watson Chef, there's this discussion on AI for the enterprise versus AI for consumers. >> Right. Are you crossing over? That was kind of a consumer-y application. >> Cameron: Yeah. >> Is that just an example? >> It's just an example. No, it's very much about AI for the enterprise, right? And so the four priority industries that we're focused on, first is financial services, sort of the sweet spot for IBM. The second is supporting our government clients to make sure that Watson is trained in the language and nuisances the of government. The third is Watson health, so the health care industry, both the regulation and the language itself. So everything from pharmacology, et cetera. And then the fourth is travel and transportation. So it's very much about making Watson the smartest AI system for enterprise. That's absolutely its focus. >> What's the IoT angle in your title? >> Yeah, so-- >> What's going on there? >> I run the IoT platform for IBM, and so The Weather Company, which is how I joined IBM, which I also run, really is one of the largest IoT platforms in the world, which was actually a big part of the acquisition case for acquiring The Weather Company. We're now bringing the ability to ingest 35 to 40 billion data requests every day with The Weather Company platform to the IoT platform. We've combined those things together. So we can ingest data and content at a scale unlike pretty much anyone else in the world, sort of second only to Google in terms of the scale of data and content we can ingest. And we use that data to help train Watson on one hand, and on the other hand, to support our clients in multiple industries around the world. >> Yeah, I remember when IBM did that acquisition, Bob Picciano told me, "Well, you got to understand. "This is an IoT play as much as it is a data science play." So how has that evolved, come together, with IBM's core? >> Yeah, so I think in a couple of ways. One is, it's taken the way the company was mostly a domestic US business. IBM, in the last couple of years, has globalized that business in a very material way. A great example is in aviation, where we have the top 30 US operators. Now we have hundreds of operators all around the world helping them make decisions every day. At its core, this IoT platform that started with the way the company is now much larger than that, has grown into a decision platform, right? We make recommendations for people to make decisions. Mostly that's with Watson and AI, but sometimes it's just with machine learning and more traditional methods. >> So you got some other stuff going on. >> We were talking off camera >> We do. >> about this real-time closed captioning. I was showing you our video clipper tool. You said, "Hey-- >> Yeah! >> "We have something very similar." We're going to maybe talk and see if we can't-- >> Yeah, that'll be great. >> collaborate. I can't wait to try that out. So talk more about what you're doing with real-time closed captioning. It's a mandate, >> That's right. >> for broadcasters and other folks like YouTube. >> That's right. . How are you helping them? >> Yeah, so, as you mention, closed captioning is a regulated space for broadcasters, both local and national. It's a cost center for them, right? They have to do it, and it takes time, people, effort, and energy. We're automating that and we're doing it in a real-time way, so in true real time. So as we're speaking, Watson is listening. It's recording and it's annotating everything that goes on in the video clip. And then it's also breaking it up into essentially a highlight reel, right? And so you can ask questions. Hey, show me the highlights of the US Open or the Masters Golf Tournament. And it'll automatically select the very best clips that came from that tournament based on sentiment analysis, tone of voice, trending key words that were showing in social media, and surface those clips up, typically to a human editor who will then process them. It basically automates a system that today requires human intervention to deliver and makes it completely seamless by being in real-time. >> So Watson will analyze social data, Twitter data, take the fire hose and say, "OK, based on the Olympics," or whatever it was, "this is what was hot." >> Cameron: That's right. >> Curling was off the charts hot. >> (laughs) Curling is always hot in Olympics. >> Hashtag curling. >> Right. >> OK, cool. >> That's right. >> And this is a product that's out on the market today? >> It's a product that's launching here at Think and is being tested by multiple clients right now and is a really great accuracy, quality scores, 95% plus accuracy. But most importantly, it's no human intervention. So no person has to do anything, and it meets all of the regulatory requirements. For digital content creators, which are the fastest growing part of the video ecosystem, people like yourself and others, are also using it to automatically meta tag all their clips. So not only does it do sentiment analysis of the clips and the content itself using the closed captioning, but it's also going out and measuring social media key words and hashtags that are trending and looking for those key words in the closed captioning and clipping that out and surfacing it to make it easier. >> And I consume that as a monthly service kind of thing? >> Exactly, exactly, yep. >> How 'about GDPR? That's hot topic these days. Can you help me with my GDPR problem? 'Cause the clocks ticking on my defines, kicking in. >> Clocks ticking on GDPR. If you haven't started on GDPR yet, you're in some trouble. >> You're way late. >> You're way late, but you better call IBM pretty quickly, and we'll parachute in and try and help. >> How can you help? >> So I think we can help in multiple ways. So one is, obviously, our services group with GBS. We're doing thousands of engagements trying to help people with GDPR. I think, secondly, is we've got a big effort with our consumer weather business to be ready for GDPR. We have 250 million users of our weather app around the world, and they'll have to be compliant here pretty quickly. And so, we've got that all set up, ready to go. And then, these data kits also learn the regulations, right? So you can ask questions of Watson about GDPR and your specific use cases as a customer, and we'll show you how to apply the regulations of GDPR to your business. >> So earlier on, you talked about these data kits. I mean, in my head I was thinking SDK. >> Cameron: Right. So how does that all work? >> Yeah, so you can, you basically on a SAS basis, you essentially rent these data kits, everything from a general knowledge kit to a industry specific kit for financial services, to a sub-industry like wealth management within financial services. And you basically can rent each of those pieces. Within the government category, we have a GDPR capability, along with other regulatory capabilities within the data kits. >> OK, so how does that work? I sort of train my internal system? >> It's super easy. You, basically, go to Bluemix, and you can just use it as a subscription out of Bluemix is the fastest, easiest way to do it. Secondly, you can talk to any of your IBM associates about how you use data kits with Watson. It's always used in conjunction with Watson services themselves, is how you basically deploy our products. >> Let's say I got data all over the place in my organization, it's siloed out, and I'm freaking out because I've got personal data on an individual here and one over her and one over here. What do I do? I point my corpus of data at Watson, and it helps me extract from itities, dedupe, surface? >> The first step in all of our engagements is to listen and understand exactly where all the data is, and everyone's on a journey, right? From on prem to hybrid to some public cloud and everything in between. >> Dave: And they don't know where it all is. >> And they don't know where it all is. And so, step one is for us to go in and listen. We have a rule in our group, two ears and one mouth, use them proportionally. And so we go in and we try to listen, find out, map out sort of a architecture of where our client's data is. And then understand what problem they're really trying to solve because, often times, there's lots of good ideas, but there's only a couple of problems that really matter to that client to solve. Right now, GDPR is certainly one of those problems. But whether it's revenue or efficiency, we can help, but we really need to understand what the problem set is first. And so we have an engineering team that goes in and does sort of architectural work and listens upfront. And then we go into a sort of solutioning mode to solve problems. >> One of the question's we often ask on theCUBE is, how far can we take machine intelligence? How far should we take machine intelligence? What are the things that machines can do that humans can't? How is that changing? How will they complement each other? How will they compete? You must think about that a lot in your role. You're augmenting, sometimes replacing a lot of human tasks. But what are your thoughts on those big picture questions? >> Yes, I think we've, as a company, work really, really hard to make sure that we are always augmenting people wherever possible. We fundamentally believe that every job is going to be changed by AI, but we believe that humans are really good at creativity, at curiosity, and at risk management. We don't really think about us being good at risk management, but from when we're born, just learning to walk is a risk management exercise, right? Look at any toddler wobbling, learning to walk, you sort of realize it's a risk management exercise. AI systems have to learn all these things. And so surfacing and recommending decisions is what we believe Watson and AI is best equipped to do, and then have a person actually make the final call. >> Great. All right, Cameron, hey, thanks very much for coming on theCUBE. >> You're welcome. >> It was really a pleasure meeting you. >> Absolutely, likewise. >> And look forward to the follow up. >> Absolutely, we'll follow up. >> Excited to see that. All right, keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the show theCUBE live from IBM Think 2018. We'll be right back. (electronic music)

>> Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We are here with Joseph Selle, he is the Cognitive Transformation Lead at IBM. Thanks so much for joining us, Joe. >> Hi, Rebecca, thank you. Hi, Dave. >> Good to see you, Joe. >> You, too. >> So, your job is to help drive the internal transformation of IBM. Tell our viewers what that means and then talk about your approach. >> Right, it a very exciting, frankly, it's one of the best jobs I've ever had personally. It's wonderful. We're transforming the company from the inside out. We're engaging with all of the functional areas within IBM's operations, and we're challenging those functional teams to breakdown their business process and reinvent it using some new tooling. And in this case, it's cognitive approaches to data analysis, and to crowd sourcing information, and systems that learn. We've talked a lot about at this conference, machine learning and deep learning. We're providing all of these tools to these functional teams so they can go reinvent HR and procurement, and even our M&A process, everything is fair game. So, it's very exciting and it really allows us to reinvent IBM. >> So, reinventing all of these individual functions, I mean, where to do you start? How do you begin to build the blueprint? >> Well, in our case, where we started was we had to get the whole company thinking about a large-scale enterprise, cultural transformation. We have a company of 300-some odd thousand people, employees, speaking all languages, all over the globe. So, how do you move that mass? So, we had cognitive jam, that's basically a technology enabled brainstorm session that spreads across the entire globe. And, by engaging about 300,000 IBM'ers, we were able to call and bring together all kinds of very disruptive, interesting ideas to remake all these business processes. We culled those ideas, and through some prioritization, almost a shark tank-like process, we ended up with a few that were really worthy, we felt, of investment. We've put money in, and our cognitive reinvention was born. Just like that. >> That's a lot of brain power. (laughs) >> Well, that's why it's wonderful to be at IBM, 'cause we have hundreds of thousands of brainy people working for us. >> You have talked about, when he was a controller during the Gerstner transformation, I don't know were you there back then? >> Yes, I was. >> Okay, so you guys were young pups back then, still young pups, I guess. But, he talked about, as the controller, he was an unhappy customer because he didn't have the data. So, can you talk about, sort of, what's different today? I mean, it's a lot different, obviously, the state of the industry, the technology, the amount of the data, et cetera. But, maybe talk about data as the starting point and how that was different from, maybe, the Gerstner transformation. >> The early days. >> Which was epic, by the way. You know, took IBM to new levels and be part of what the company is today. >> And this story that I'm going to tell you, is generally applicable to most any company that's global in nature. The data are not visible and they're not easy to see and discern any value from in the early stages of your transformation. So, when Jim was controller, he had data that was one, hard to get, and two, he had no tools to organize it except for, maybe, some smart people with Excel and, whatever it was back then, LotusPro, or something, I can't remember the name of that. (laughter) >> Something that ran on OS/2. >> There was no tooling, no approach. And, the whole idea of big data was not even around at that point. Because the data was organized and disorganized in little towers and databases all around, but there wasn't a flood of data. So, what's different between those days and this time period that we're in is, you can see data now and data are everywhere. And they're coming at us in high, high volumes and at high speeds. If you think about The Weather Company, one of the acquisitions we made two years ago, that is a stream of huge, big data, coming at us very fast. You can think about The Weather Company as a giant internet of things, device, which is pulling data from the sky and from people interacting with the environment, and bringing that all together. And now, what can we do with that data? Well, we can use it to help predict when we're going to have a supply chain disruption, or, I mean in an almost obvious sense, or we can use it when we're trying to respond to some sort of operational disturbance. If we're looking at where we can reroute things, or if we're trying to anticipate some sort of blockage on our supply chain, incoming supply chain, or outgoing supply chain of products. Very important, and we just see much more now then Jim ever could when he was a controller. >> In the scope of your data initiative, is everything, I mean, he's mentioned supply chain, you got customer data? >> It is, it is. But, I'll say that, you know, if a company's going to embark down this path, you don't want to try to boil the ocean at the start. You want to try to go after some selective business challenges, that are persistent challenges that you wish you had a way to solve because a lot of value's at play. So, you go in there and you solve a few problems. You deal with a data integrity and access problem, on a, sort of a, confined basis. And you do this, maybe, several times across different parts of your company. Then, once you've done that four or five times, or some small number of times, you begin to learn how to handle the problem more generally, and you can distill approaches and tools that can then be applied broadly. And where we are in our evolution, is that Inderpal and Jim, and the internal workings of IBM, were building a cognitive enterprise data platform. So, we're taking all of these point solutions that I just referred to, bringing them together onto a platform, and applying some common tooling to all of these common types of problems around data organization, and governance, and meta-data tagging, and all this geeky stuff that you have to be able to do if you're going to make any value. You know, if you're going to make an important, valuable business decision, based on a stream of data. >> So, where has it had tangible, measurable, business impact, this sort of cognitive initiative? >> Well, a couple of the areas where we're most mature, one would be in supply chain and procurement. We've been able to take jobs that, frankly, involve a lot of churning analysis, and be able to say to a procurement specialist, okay, what used to take you six hours, or an hour, or what ever the task was, we can shrink that down using a cognitive tool, down to just a few minutes. So, procurement, we've been able to get staffing efficiencies, and we've been able, even more importantly, to make sure that we're buying things at the best possible price. Because those same analysts want to know what's happening in the market, where's the market sentiment going? Is this market tightening or loosening? Is it a buyer or a seller market? If we're trolling the web, bringing back information on the micro-movements of all the regional markets in various electronics commodities, we know an aggregate, whether we should be hard bargainers or easy bargainers, essentially. So, that's procurement. But, you could talk about human resources, where the Watson tool can recommend a game plan for how you would manage the career of a person. You don't want to lose your star people. And it's wonderful that deep, subject matter experts in HR know how to anticipate what you're thinking, and those are the people you want in charge of HR. But, there's a lot of other people who aren't, maybe, as good as that one person at HR, now the system can help you by giving you a playbook, making you a better HR manager. So, that's HR, but I got one more that's really exciting that I'm working on right now in the area of M&A. So, IBM and any large company that has multiple offerings and geographies is involved in M&A. We're using cognition and big data to speed up our M&A process. Now, we have a small team of M&A, so we're not going to make millions of dollars of staffing efficiencies, but, if we can capture a company, if we can be the first one to make an offer on a company, rather than the third one, then we're going to get the best company. And if you can bring the best company in, like The Weather Company as an example in that space, or like any other type of data-mining company or something, you want the best company. And if you can use cognition to enhance your process to move very quickly, that's going to really help you. >> So, this is a huge transformation of the business model, but then you've also talked about the cultural transformation of IBM. How would you describe this new IBM, going through this transformation? How would you describe the culture and collaboration? >> So, luckily, we're pretty far along in the transformation and we're at a stage where we actually have a data platform that's been deployed internally. And, people know about the potential of cognition to redefine and remake their business processing, create all this value. So, now we're getting people to come on to the platform as citizen analysts, if you want to call them that, they're not operations PhD's, they're not necessarily data scientists, they're regular business analysts. They're coming onto the platform and they're finding data and they're finding tools to manipulate that data. They're coming in on a self-service model and being able to gain insights to bring back into their business decisions without the CIO office being involved. >> So that's a workbench on the Cloud, essentially, is that right? >> Yes, that it a good way to put it, yep. >> Workbench, we out of trademark that. (laughs) >> Let's do that. >> Good descriptor, I think. >> Well, Joe, thanks so much for joining us, it's been a pleasure talking to you. >> My pleasure, thank you. >> Thanks, thanks a lot. >> I'm Rebecca Knight, for Dave Vellante, we will have more from IBM CDO Summit just after this.

>> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. (upbeat electronic music) >> Welcome back to theCUBE's coverage of the IBM Chief Data Officer Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Dave Vellante. We are joined by Jim Kavanaugh. He is the Senior Vice President transformation and operations at IBM. And Inderpal Bhandari he is the chief, the global chief data officer at IBM. Thanks so much for joining us. >> Thanks for having us. >> Happy to be here. >> So, you both spoke in the key note today and Jim, you were talking about how we're in a real seminal moment for businesses with this digital, this explosion in digital and data. CEOs get this obviously, but how do you think, do companies in general get it? What's the buy-in, in terms of understanding just how big a moment we're in? >> Well, as I said in the key note, to your point, I truly believe that all businesses in every industry are in a true, seminal moment. Why? Because this phenomenon, the digital disruption, is impacting everything, changing the nature of competition, altering industry structures, and forcing companies to really rethink to design a business at its core. And that's what we've been doin' here at IBM, trying to understand how we transition from an old world of going after pure efficiency just by gettin' after economies of scale, process standardization, to really know, how do you drive efficiency to enable you to get competitive advantage? And that has been the essence of what we've been trying to do at IBM to really reinvent our company from the core. >> So most people today have multiple jobs. You guys, of course, have multiple jobs. You've got an internal facing and an external facing so you come to events like this and you share knowledge. Inderpal, when we first met last year, you had a lot of knowledge up here, but you didn't have the cognitive blueprint, ya know, so you were sharing your experiences, but, year plus in now, you've developed this cognitive blueprint that you're sharing customers. So talk about that a little bit. >> Yeah so, we are internally transforming IBM to become a cognitive enterprise. And that just makes for a tremendous showcase for our enterprise customers like the large enterprises that are like IBM. They look at what we're doing internally and then they're able to understand what it means to create a cognitive enterprise. So we've now created a blueprint, a cognitive enterprise blueprint. Which really has four pillars, which we understand by now, given our own experience, that that's going to be relevant as you try to move forward and create a cognitive enterprise. They're around technology, organization considerations, and cultural considerations, data, and also business process. So we're not just documenting that. We're actually sharing not just those documents, but the architecture, the strategies, pretty much all our failures as we're learning going forward with this, in terms of, developing our own recipes as we eat our own cooking. We're sharing that with our clients and customers as a starting point. So you can imagine the acceleration that that's affording them to be able to get to process transformation which, as Jim mentioned, that's eventually where there's value to be created. >> And you talked about transparency being an important part of that. So Jim, you talked about three fundamentals shifts going on that are relevant, obviously, for IBM and your clients, data, cloud, and engagement, but you're really talking about consumerization. And then you shared with us the results of a 4,000 CXO survey where they said technology was the key to sustainable business over the next four or five years. What I want to ask you, square the circle for me, data warehouse used to be the king. I remember those days, (laughing) it was tough, and technology was very difficult, but now you're saying process is the king, but the technology is largely plentiful and not mysterious as it is anymore. The process is kind of the unknown. What do you take away from that survey? Is it the application of technology, the people and process? How does that fit into that transformation that you talked about? >> Well, the survey that you talked about came from our global businesses services organization that we went out and we interviewed 4,000 CXOs around the world and we asked one fundamental question which is, what is number one factor concerning your long term sustainability of your business? And for the first time ever, technology factors came out as the number one risk to identify. And it goes back to, what we see, as those three fundamental shifts all converging and occurring at the same time. Data, cloud, engagement. Each of those impacting how you have to rethink your design of business and drive competitive advantage going forward. So underneath that, the data architecture, we always start, as you stated, prior, this was around data warehouse technology, et cetera. You applied technology to drive efficiency and productivity back into your business. I think it's fundamentally changed now. When we look at IBM internally, I always build the blueprint that Inderpal has talked about, which everything starts with a foundation of your data architecture, strategy governance, and then business process optimization, and then determining your system's architecture. So as we're looking inside of IBM and redesigning IBM around enabling end-to-end process optimization, quote-to-cash, source to pay, hire to exit. Many different horizontal process orientation. We are first gettin' after, with Inderpal, with the cognitive enterprise data platform what is that standard data architecture, so then we can transform the business process. And just to tie this all together to your question earlier, we have not only the responsibility of transforming IBM, to improve our competitiveness and deliver value, we actually are becoming the showcase for our commercialized entities of software solutions, hardware, and services. To go sell that value back to clients over all. >> And part of that is responsibility for data ownership. Who owns the data. You talked about the West Coast, the unnamed West Coast companies which I of course tweeted out to talk about Google and Amazon. And, but I want to press on that a little bit because data scientists, you guys know a lot of them especially acquiring The Weather Company They will use data to train models. Those models, IP data seeps into those models. How do you protect your clients from that IP, ya know, seepage? Maybe you could talk about that. >> Talk about trust as a service and what it means. >> Yeah, ya know, I mentioned that in my talk at the key note, this is a critical, critical point with regard to these intelligent systems, AI systems, cognitive systems, in that, they end up capturing a lot of the intellectual capital that the company has that goes to the core of the value that the company brings to it's clients and customers. So, in our mind, we're very clear, that the client's data is their data. But not only that, but if there's insights drawn from that data, that insight too belongs to them. And so, we are very clear about that. It's architected into our setup, you know, our cloud is architected from the ground up to be able to support that. And we've thought that through very deeply. To some extent, you know, one would argue that that's taken us some time to do that, but these are very deep and fundamental issues and we had to get them right. And now, of course, we feel very confident that that's something that we are able to actually protect on the behalf of our clients, and to move forward and enable them to truly become cognitive enterprises, taking that concern off the table. >> And that is what it's all about, is helping other companies move to become cognitive enterprises as you say. >> Based on trust, at the end of the day, at the heart of our data responsibility at IBM, it's around a trusted partner, right, to protect their data, to protect their insights. And we firmly believe, companies like IBM that capture data, store data, process data, have an obligation to responsibly handle that data, and that's what Jenny Rometty has just published around data responsibility at IBM. >> Great, well thank you so much Inderpal, Jim. We really appreciate you coming on theCUBE. >> [Jim and Inderpal] Thank you. >> We will have more from the IBM Chief Data Officer Strategy Summit, just after this. (upbeat music)

(upbeat music) >> Covering InterConnect 2017, brought to you by IBM. >> Welcome back. We're here live in Las Vegas from Mandalay Bay for the IBM InterConnect 2017, this is Cube's exclusive coverage with SiliconANGLE media. I'm John Furrier, my co-host Dave Vellante here all week. We missed our kickoff this morning on day two and, because the keynotes went long with Ginni Rometty. Great star line up, you had Marc Benioff, the CEO of AT&T, and CEO of H&R Block, which I love their ad with Mad Men's guy in there. Dave let's wrap up day two. Big day, I mean traffic on the digital site, ibmgo.com was off the charts and the site just performed extremely well, excited about that. Also the keynote from the CEO of IBM, Ginni, really kind of brings us themes we've been talking about on theCUBE. I want to get your reaction to that, which is social good is now a purpose that's now becoming a generational theme, and it's not just social good in terms of equality of pay for women, which is great and of course more STEM, it's everything, it's society's global impact but also the tagline is very tight. Enterprise strong, has a Boston strong feeling to it. Enterprise strong, data first, cognitive to the core, pretty much hits their sweet spot. What did you think of her keynote presentation? >> I thought Ginni Rometty nailed it. I've always been a huge fan of hers, I first met her when she was running strategy, and you know the question you used to always get because IBM 19 quarters of straight declining revenue, how long is Ginni going to get? How long is Ginni going to get? You know when is her tenure going to be up? My answer's always been the same. (laughs) Long enough to prove that she was right. And I think, I just love her presentation today, I thought she was on, she was engaging, she's a real pro and she stressed the innovation that IBM is going through. And this was the strategy that she laid out, you know, five, six years ago and it's really coming to fruition and it was always interesting to me that she never spoke at these conferences and she didn't speak at these conferences 'cause the story was not great you know, it was coming together the big data piece or the analyst piece was not formed yet. >> So you think she didn't come to these events because the story wasn't done? >> Yeah, I think she was not-- >> That is not a fact, you believe that. >> No, this is my belief. She was not ready to showcase you know, the greatness of IBM and I said about a year ago, I said you watch this whole strategy is coming together. You are going to see a lot more of Ginni Rometty than you've seen in the past. You started to see her on CNBC much more, we saw her at the Women in Tech Conference, at the Grace Hopper Conference, we saw her at World of Watson and now we see her here at InterConnect and she's very good on stage. She's extremely engaging, I thought she was good at World of Watson, I thought she was even better today. And a couple of notable things, took a swipe at both AWS and maybe a little bit at HPE, I'm not so sure that they worry about HPE. Sam Palmisano, before he left on a Wall Street Journal interview, said "I don't worry about HPE, they don't invest in RND. "I worry about Oracle." But nonetheless, she said, it's not just a new way, cloud is not just a new way to deliver IT. Right that's the Amazon you know. >> HP. >> And certainly new way of you style by IT. >> You style by IT. >> Is Meg's line. She also took a swipe at Google basically saying, look we're not taking your data to inform some knowledge draft that we're going to take your IP and give it to the rest of the world. We're going to protect your data, we're going to protect your models. They're really making a strong statement in that regard which I think is really important for CIOs and CDOs and CEOs today. Thoughts? >> I agree. I first of all am a big fan of Ginni, I always kind of question whether she came in, I never put it together like you intuitively around her not seeing the story but you go to all the analyists thing, so I think that's legit I would say that I would buy that argument. Here's what I like. Her soundbite is enterprise strong, data first, cognitive to the core. It's kind of gimmicky, but it hits all their points. Enterprise strong is core in the conversations with customers right now. We see it in theCUBE all the time. Certainly Google Nexus was one event we saw this clearly. Having enterprise readiness is not easy and so that's a really tough code to crack. Oracle and Microsoft have cracked that code. So has IBM of the history. Amazon is getting faster to the Enterprise, some of the things they are doing. Google has no clue on the Enterprise, they're trying to do it their way. So you have kind of different dimensions. So that's the Enterprise, very hard to do, table stakes are different than having pure cloud native all the time 100%, lift and shift, rip and replace, whatever you want to call it. Data First is compelling because they have a core data strategy analytics but I thought it was interesting that they had this notion of you own your own data, which implies you're renting everything else, so if you're renting everything else, infrastructure (laughs) and facilities and reducing the cost of doing business, the only thing you really got is data, highlighted by Blockchain. So Blockchain becomes a critical announcement there. Again, that was the key announcement here at the show is Blockchain. IOT kind of a sub-text to the whole show but it's supported through the Data First. And finally Cognitive to the Core is where the AI is going to kind of be the shiny, silly marketing piece with I am Watson, I'm going to solve all your health problems. Kind of showing the futuristic aspect of that but under the hood there is machine learning, under that is a real analytics algorithms that they're going to integrate across their business whether it's a line of business in verticals, and they're going to cross pollinate data. So I think those three pillars, she is a genius (laughs) in strategy 'cause she can hit all three. What I just said is a chockfull of strategy and a chockfull execution. If they can do that then they will have a great run. >> So I go back to Palmisano's statement before Ginni took over and it was a very candid interview that he gave. And as they say, you look at when he left IBM, it was this next wave was coming like a freight train that was going to completely disrupt IBM's business, so it was, it's been a long turn around and they've done it with sort of tax rates, (laughs) stock buybacks, and all kinds of financial engineering that have held the company's stock price up, (laughs) and cash flow has been very strong and so now I really believe they're in a good position. You know to get critical for just a second, yes there's no growth but look who else isn't growing. HPE's not growing, Oracle's not growing, Tennsco's not growing, Cisco's not growing, Microsoft's not growing. The only two companies really in the cartel that are growing showing any growth really are Intel a little bit and SAP. The rest of the cartel is flat (laughs) to down. >> Well they got to get on new markets and I mean the thing is new market penetration is interesting so Blockchain could be an enabler. I think it's going to be some resistance to Blockchain, my gut tells me that but the innovative entrepreneur side of me says I love Blockchain. I would be all over Blockchain if I was an entrepreneur because that really would change the game on identity and value and all that great stuff. That's a good opportunity to take the data in. >> Well the thing I like is IBM's making bets, big bets, Blockchain, quantum computing, we'll see where that goes, cloud, clearly we could talk about, you know you said it (laughs) InterConnect two or three years ago you know SoftLayer's kind of hosting. True, but Blu makes the investments hoping-- >> SoftLayer's is not all Blu makes. >> That's right, well yeah so but any rate, the two billion dollar bet that they made on SoftLayer has allowed them to go to clients and say we have cloud. Watson, NAI, Analytics, IOT these are big bets which I think are going to pay off. You know, we'll see if quantum pays off in the year term, we'll see about Blockchain, I think a lot of the bets they've been making are going to pay off, Stark, et cetera. >> So let's talk about theCUBE interviews Dave, what got your attention? I'll start while you dig up something good from your notes. I loved Willie Tejada talked about this, they're putting in these clouds journey pieces which is not a best practice it's not a reference architecture but it's actually showing the use cases of people who are taking a cross functional journey of architecture and cloud solutions. I love the quantum computing conversation we had with believe it or not the tape person. And so from the tape whatever it was, GS. >> GS8000. >> GS8000. >> It's a storage engineering team. >> But in terms of key points, modernizing IOT relevance was a theme that popped out at me. It didn't come out directly. You start to see IOT be a proof point of operationalizing data. Let me explain, IOT right now is out there. People are focused on it because it's got real business impact, because it's either facilities, it's industrial or customer connected in some sort. That puts the pressure to operationalize that data, and I think that flushes out all the cloud washing and all the data washing, people who don't have any solutions there. So I think the operationalizing of the data with IOT is going to force people to come out with real solutions. And if you don't, you're gone, so that's, you're dead. The cultural issue is interesting. Trust as now table stakes in the equation of whether it's product trusts, operational trusts, and process trusts. That's something I saw very clearly. And of course I always get excited about DevOps and cloud native, as you know. And some of the stuff we did with data as an asset from the chief data architect. >> A couple I would add from yesterday, Indiegogo who I thought had a great case study, and then Mohammed Farooq, talking about cloud brokering. 60% of IBM's business is still services. Services is very very important. And I think that when I look at IBM's big challenge, to me, John, it's when you take that deep industry expertise that they have that competes with Accenture and ENY and Deloitte and PWC. Can you take that deep industry expertise and codify it in software and transform into a more software-oriented company? That's what IBM's doing, trying to do anyway, and challenging. To me it's all about differentiation. IBM has a substantially differentiated cloud strategy that allows them not to have to go head to head with Amazon, even though Amazon is a huge factor. And the last thing I want to say is, it's what IBM calls the clients. It's the customers. They have a logo slide, they bring up the CEOs of these companies, and it's very very impressive, almost in the same way that Amazon does at its conferences. They bring up great customers. IBM brings in the C-Suite. They're hugging Ginni. You know, it was a hug fest today. Betty up on stage. It was a pretty impressive lineup of partners and customers. >> I didn't know AT&T and IBM were that close. That was a surprise for me. And seeing the CEO of AT&T up there really tees it out. And I think AT&T's interesting, and Mobile World Congress, one of the things that we covered at that event was the over the top Telco guys got to get their act together, and that's clear that 5G and wireless over the top is going to power the sensors everywhere. So the IOT on cars, for instance, and life, is going to be a great opportunity for, but Telco has to finally get a business model. So it's interesting to see his view of digital services from a Telco standpoint. The question I have for AT&T is, are they going to be dumped pipes or are they actually going to move up the stand and add value? Interesting to see who's the master in that relationship. IBM with cognitive, or AT&T with the pipes. >> And, you know, you're in Silicon Valley so you hear all the talk from the Silicon Valley elites. "Oh well, Apple and Amazon "and Google and Facebook, "much better AI than Watson." I don't know, maybe. But IBM's messaging-- >> Yes. >> Okay, so yes, fine. But IBM's messaging and positioning in the enterprise to apply their deep industry knowledge and bring services to bear and solve real problems, and protect the data and protect the models. That is so differentiable, and that is a winning strategy. >> Yeah but Dave, everyone who's doing-- >> Despite the technical. >> Anyone who's doing serious AI attempts, first of all, this whole bastardized definition. It's really machine learning that's driving it and data. Anyone who's doing any serious direction to AI is using machine learning and writing their own code. They're doing it on their own before they go to Watson. So Watson is not super baked when it comes to AI. So what I would say is, Watson has libraries and things that could augment traditional custom-built AI as a kernel. Our 13-year-old guest Tanmay was on. He's doing his own customizing, then bring it to Watson. So I don't see Watson being a mutually exclusive, Watson or nothing else. Watson right now has a lot of things that adds to the value but it's not the Holy Grail for all things AI, in my opinion. The innovation's going to come from the outside and meet up with Watson. That to me is the formula. >> Going back to Mohammed Farooq yesterday, he made the statement, roughly, don't quote me on these numbers, I'll quote myself, for every dollar spent on technology, 10 dollars are going to be spent on services. That's a huge opportunity for IBM, and that's where they're going to make Watson work. >> If I'm IBM and Watson team, and I'm an executive there and engineering lead, I'm like, look it, what I would do is target the fusion aspect of connecting with their customers data. And I think that's what they're kind of teasing out. I don't know if they're completely saying that, but I want to bring my own machine learning to the table, or my own custom stuff, 'cause it's my solution. If Watson can connect with that and handshake with the data, then you got the governance problem solved. So I think Seth, the CDO, is kind of connecting the dots there, and I think that's still unknown, but that's the direction that I see. >> And services, it remains critical because of the complexity of IBM's portfolio, but complexity has always been the friend of services. But at the same time, IBM's going to transform its services business and become more software-like, and that is the winning formula. At the end of the day, from a financial perspective, to me it's cash flow, cash flow, cash flow. And this company is still a cash flow cow. >> So the other thing that surprised me, and this is something we can kind of end the segment on is, IBM just reorganized. So that's been reported. The games, people shift it a little bit, but it's still the same game. They kind of consolidated the messaging a little bit, but I think the proof point is that the traffic for on the digital side, for this show, is 2X World of Watson. The lines to get into keynotes yesterday and today were massive. So there's more interest in InterConnect than World of Watson. >> Well we just did. >> Amazing, isn't it? >> Well then that was a huge show, so what that means is, this is hitting an interest point. Cloud and data coming together. And again, I said it on the intro yesterday. IOT is the forcing function. That to me is bringing the big data world. We just had Strata Hadoop and R event at BigDataSV. That's not Hadoop anymore, it's data and cloud coming together. And that's going to be hitting IOT and this cognitive piece. So I think certainly it's going to accelerate at IBM. >> And IBM's bringing some outside talent. Look at Harry Green who came from Thomas Cook, Michelle Peluso. Marketing chops. They sort of shuffled the deck with some of their larger businesses. Put Arvind Krishna in charge. Brought in David Kenny from the Weather Company. Moved Bob Picciano to the cognitive systems business. So as you say, shuffle things around. Still a lot of the same players, but sometimes the organization-- >> By the way, we forgot to talk about Don Tapscott who came on, my favorite of the day. >> Another highlight. >> Blockchain Revolution, but we interviewed him. Check out his book, Blockchain can be great. Tomorrow we got a big lineup as well. We're going to have some great interviews all day, going right up to 5:30 tomorrow for day three coverage. This is theCUBE, here at the Mandalay Bay for IBM InterConnect 2017. I'm John Furrier and Dave Vellante. Stay with us, join us tomorrow, Wednesday, for our third day of exclusive coverage of IBM InterConnect 2017, thanks for watching.

>> Announcer: Live from Boston Massachusetts this is theCube. Covering Spark Summit East 2017, brought to you by Databricks. Now here are your hosts Dave Vellante and George Gilbert. >> Welcome back to theCube, everybody, we're here in Boston. The Cube is the worldwide leader in live tech coverage. This is Spark Summit, hashtag #SparkSummit. And Robbie Strickland is here. He's the Vice President of Engines & Pipelines, I love that title, for the Watson Data Platform at IBM Analytics, formerly with The Weather Company that was acquired by IBM. Welcome to you theCube, good to see you. >> Thank you, good to be here. >> So, it's my standing tongue-in-cheek line is the industry's changing, Dell buys EMC, IBM buys The Weather Company. [Robbie] That's right. >> Wow! That sort of says it all, right? But it was kind of a really interesting blockbuster acquisition. Great for the folks at The Weather Company, great for IBM, so give us the update. Where are we at today? >> So, it's been an interesting first year. Actually, we just hit our first anniversary of the acquisition and a lot has changed. Part of my role, new role at IBM, having come from The Weather Company, is a byproduct of the two companies bringing our best analytics work and kind of pulling those together. I don't know if we have some water but that would be great. So, (coughs) excuse me. >> Dave: So, let me chat for a bit. >> Thanks. >> Feel free to clear your throat. So, you were at IBM, the conference at the time was called IBM Insight. It was the day before the acquisition was announced and we had David Kenny on. David Kenny was the CEO of The Weather Company. And I remember we were talking, and I was like, wow, you have such an interesting business model. Off camera, I was like, what do you want to do with this company, you guys are like prime. Are you going public, you going to sell this thing, I know you have an MBA background. And he goes, "Oh, yeah, we're having fun." Next day was the announcement that IBM bought The Weather Company. I saw him later and I was like, "Aha!" >> And now he's the leader of the Watson Group. >> That's right. >> Which is part of our, The Weather Company joined The Watson Group. >> And The Cloud and analytics groups have come together in recognition that analytics and The Cloud are peanut butter and jelly. >> Robbie: That's absolutely right. >> And David's running that organization, right? >> That is absolutely right. So, it's been an exciting year, it's been an interesting year, a lot of challenges. But I think where we are now with the Watson Data Platform is a real recognition that the use dase where we want to try to make data and analytics and machine learning and operationalizing all of those, that that's not easy for people. And we need to make that easy. And our experience doing that at The Weather Company and all the challenges we ran into have informed the organization, have informed the road map and the technologies that we're using to kind of move forward on that path. >> And The Watson Data Platform was announced in, I believe, October. >> Robbie: That's right. >> You guys had a big announcement in New York City. And you took many sort of components that were viewed as individual discreet functions-- >> Robbie: That's right. >> And brought them together in a single data pipeline. Is that right? >> Robbie: That's right. >> So, maybe describe that a little bit for our audience. >> So, the vision is, you know, one of the things that's missing in the market today is the ability to easily grab data from some source, whether it's a database or a Kafka stream, or some sort of streaming data feed, which is actually something that's often overlooked. Usually you have platforms that are oriented around streaming data, data feeds, or oriented around data at rest, batch data. One of the things that we really wanted to do was sort of combine those two together because we think that's really important. So, to be able to easily acquire data at scale, bring it into a platform, orchestrate complex workflows around that, with the objective, of course, of data enrichment. Ultimately, what you want to be able to do is take those raw signals, whatever they are, and turn that into some sort of enriched data for your organization. And so, for example, we may take signals in from a mobile app, things like beacons, usage beacons on a mobile app, and turn that into a recommendation engine so we can feed real time content decisions back into a mobile platform. Well, that's really hard right now. It requires lots of custom development. It requires you to essentially stitch together your pipeline end to end. It might involve a machine learning pipeline that runs a training pipeline. It might involve, it's all batch oriented, so you land your data somewhere, you run this machine learning pipeline maybe in Spark or ADO or whatever you've got. And then the results of that get fed back into some data store that gets merged with your online application. And then you need to have a restful API or something for your application to consume that and make decisions. So, our objective was to take all of the manual work of standing up those individual pieces and build a platform where that is just, that's what it's designed to do. It's designed to orchestrate those multiple combinations of real time and batch flows. And then with a click of a button and a few configuration options, stand up a restful service on top of whatever the results are. You know, either at an interim stage or at the end of the line. >> And you guys gave an example. You actually showed a demo at the announcement. And I think it was a retail example, and you showed a lot of what would traditionally be batch processes, and then real time, a recommendation came up and completed the purchase. The inference was this is an out of the box software solution. >> Robbie: That's right. >> And that's really what you're saying you've developed. A lot of people would say, oh, it's IBM, they've cobbled together a bunch of their old products, stuck them together, put an abstraction layer on, and wrapped a bunch of services around it. I'm hearing from you-- >> That's exactly, that's just WebSphere. It's WebSphere repackaged. >> (laughing) Yeah, yeah, yeah. >> No, it's not that. So, one of the things that we're trying to do is, if you look at our cloud strategy, I mean, this is really part and parcel, I mean, the nexus of the cloud strategy is the Watson Data Platform. What we could have done is we could have said let's build a fantastic cloud and compete with Amazon or Google or Microsoft. But what we realized is that there is a certain niche there of people who want to take individual services and compose them together and build an application. Mostly on top of just raw VMs with some additional, you know, let's stitch together something with Lambda or stitch together something with SQS, or whatever it may be. Our objective was to sort of elevate that a bit, not try to compete on that level. And say, how do we bring Enterprise grade capabilities to that space. Enterprise grade data management capabilities end-to-end application development, machine learning as a first class citizen, in a cohesive experience. So that, you know, the collaboration is key. We want to be able to collaborate with business users, data scientists, data engineers, developers, API developers, the consumers of the end results of that, whether they be mobile developers or whatever. One of the things that is sort of key, I think, to the vision is that these roles that we've traditionally looked at. If you look at the way that tool sets are built, they're very targeted to specific roles. The data engineer has a tool, the data scientist has a tool. And what's been the difficult part is the boundaries between those have been very firm and the collaboration has been difficult. And so, we draw the personas as a Venn diagram. Because it's very difficult, especially if you look at a smaller company, and even sometimes larger companies, the data engineer is the data scientist. The developer who builds the mobile application is the data scientist. And then in some larger organizations, you have very large teams of data scientists that have these artificial barriers between the data scientist and the data engineer. So, how do we solve both cases? And I think the answer was for us a platform that allows for seamless collaboration where there is not these clean lines between the personas, that the tool sets easily move from one to the other. And if you're one of those hybrid people that works across lines, that the tool feels like it's one tool for you. But if you're two different teams working together, that you can easily hand off. So, that was one of the key objectives we're trying to answer. >> Definitely an innovative component of the announcement, for sure. Go ahead, George. >> So, help us sort of bracket how mature this end-to-end tool suite is in terms of how much of the pipeline it addresses. You know, from the data origin all the way to a trained model and deploying that model. Sort of what's there now, what's left to do. >> So, there are a few things we've brought to market. Probably the most significant is the data science experience. The data science experience is oriented around data science and has, as its sort of central interface, Jupyter Notebooks. Now, as well as, we brought in our studio, and those sorts of things. The idea there being that we'll start with the collaboration around data scientists. So, data scientists can use their language of choice, collaborate around data sets, save out the results of their work and have it consumed either publicly by some other group of data scientists. But the collaboration among data scientists, that was sort of step one. There's a lot of work going on that's sort of ongoing, not ready to bring to market, around how do we simplify machine learning pipelines specifically, how do we bring governance and lineage, and catalog services and those sorts of things. And then the ingest, one of the things we're working on that we have brought to market is our product called Lift which connects, as well. And that's bringing large amounts of data easily into the platform. There are a few components that have sort of been brought to market. dashDB, of course, is a key source of data clouded. So, one of the things that we're working on is some of these existing technologies that actually really play well into the eco system, trying to tie them well together. And then add the additional glue pieces. >> And some of your information management and governance components, as well. Now, maybe that is a little bit more legacy but they're proven. And I don't know if the exits and entries into those systems are as open, I don't know, but there's some capabilities there. >> Speaking of openness, that's actually a great point. If you look at the IIG suite, it's a great On-Premise suite. And one of the challenges that we've had in sort of past IBM cloud offerings is a lot of what has been the M.O. in the past is take a great On-Prem solution and just try to stand it up as a service in the cloud. Which in some cases has been successful, in other cases, less so. One of the things we're trying to look at with this platform is how do we leverage (a) open source. So that whatever you may already be running open source on, Prem or in some other provider, that it's very easy to move your workloads. So, we want to be able to say if you've got 10,000 lines of fraud detection code to map produce. You don't need to rewrite that in anything. You can just move it. And the other thing is where our existing legacy tech doesn't necessarily translate well to the cloud, our first strategy is see if there's any traction around an existing open source project that satisfies that need, and try to see if we can build on that. Where there's not, we go cloud first and we build something that's tailor made to come out. >> So, who's the first one or two customers for this platform? Is it like IBM Global Business Services where they're building the semi-custom industry apps? Or is it the very, very big and sophisticated, like banks and Telcos who are doing the same? Or have you gotten to the point where you can push it out to a much wider audience? >> That's a great question, and it's actually one that is a source of lots of conversation internally for us. If you look at where the data science experience is right now, it's a lot of individual data scientists, you know, small companies, those sorts of things coming together. And a lot of that is because some of the sophistication that we expect for Enterprise customers is not quite there yet. So, we wouldn't expect Enterprise customers to necessarily be onboarded as quickly at the moment. But if we look at sort of the, so I guess there's maybe a medium term answer and a long term answer. I think the long term answer is definitely the Enterprise customers, you know, leveraging IBM's huge entry point into all of those customers today, there's definitely a play to be made there. And one of the things that we're differentiating, we think, over an AWS or Google, is that we're trying to answer that use case in a way that they really aren't even trying to answer it right now. And so, that's one thing. The other is, you know, going beta with a launch customer that's a healthcare provider or a bank where they have all sorts of regulatory requirements, that's more complicated. And so, we are looking at, in some cases, we're looking at those banks or healthcare providers and trying to carve off a small niche use case that doesn't actually fall into the category of all those regulatory requirements. So that we can get our feet wet, get the tires kicked, those sorts of things. And in some cases we're looking for less traditional Enterprise customers to try to launch with. So, that's an active area of discussion. And one of the other key ones is The Weather Company. Trying to take The Weather Company workloads and move The Weather Company workloads. >> I want to come back to The Weather Company. When you did that deal, I was talking to one of your executives and he said, "Why do you think we did the deal?" I said, "Well, you've got 1500 data scientists, "you've got all this data, you know, it's the future." He goes, "Yeah, it's also going to be a platform "for IOT for IBM." >> Robbie: That's right. >> And I was like, "Hmmm." I get the IOT piece, how does it become a platform for IBM's IOT strategy? Is that really the case? Is that transpiring and how so? >> It's interesting because that was definitely one of the key tenets behind the acquisition. And what we've been working on so hard over the last year, as I'm sure you know, sometimes boxes and arrows on an architecture diagram and reality are more challenging. >> Dave: (laughing) Don't do that. >> And so, what we've had to do is reconcile a lot of what we built at The Weather Company, existing IBM tech, and the new things that were in flight, and try to figure out how can we fit all those pieces together. And so, it's been complicated but also good. In some cases, it's just people and expertise. And bringing those people and expertise and leaving some of the software behind. And other cases, it's actually bringing software. So, the story is, obviously, where the rubber meets the road, more complicated than what it sounds like in the press release. But the reality is we've combined those teams and they are all moving in the same direction together with various bits and pieces from the different teams. >> Okay, so, there's vision and then the road map to execute on that, and it's going to unfold over several years. >> Robbie: That's right. >> Okay, good. Stuff at the event here, I mean, what are you seeing, what's hot, what's going on with Spark? >> I think one of the interesting things with what's going on with Spark right now is a lot of the optimizations, especially things around GPUs and that. And we're pretty excited about that, being a hardware manufacturer, that's something that is interesting to us. We run our own cloud. Where some people may not be able to immediately leverage those capabilities, we're pretty excited about that. And also, we're looking at some of those, you know, taking Spark and running it on Power and those sorts of things to try to leverage the hardware improvements. So, that's one of the things we're doing. >> Alright, we have to leave it there, Robbie. Thanks very much for coming on theCube, really appreciate it. >> Thank you. >> You're welcome. Alright, keep it right there, everybody. We'll be right back with our next guest. This is theCube. We're live from Spark Summit East, hashtag #SparkSummit. Be right back. >> Narrator: Since the dawn of The Cloud, theCube.

>> Narrator: Live from Boston, Massachusetts, this is theCUBE! Covering Spark Summit East 2017. Brought to you by, Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, Seth Dobrin is here, he's the vice president and chief data officer of the IBM Analytics Organization. Great to see you, Seth, thanks for coming on. >> Great to be back, thanks for having me again. >> You're welcome, so chief data officer is the hot title. It was predicted to be the hot title and now it really is. Many more of you around the world and IBM's got an interesting sort of structure of chief data officers, can you explain that? >> Yeah, so there's a global chief data officer, that's Inderpal Bhandari and he's been on this podcast or videocast a view times. Then he's set up structures within each of the business units in IBM. Where each of the major business units have a chief data officer, also. And so I'm the chief data officer for the analytics business unit. >> So one of Interpol's things when I've interviewed them is culture. The data culture, you've got to drive that in. And he talks about the five things that chief data officers really need to do to be successful. Maybe you could give us your perspective on how that flows down through the organization and what are the key critical success factors for you and how are you implementing them? >> I agree, there's five key things and maybe I frame a little differently than Interpol does. There's this whole cloud migration, so every chief data officer needs to understand what their cloud migration strategy is. Every chief data officer needs to have a good understanding of what their data science strategy is. So how are they going to build the posable data science assets. So not data science assets that are delivered through spreadsheets. Every chief data officer needs to understand what their approach to unified governance is. So how do I govern all of my platforms in a way that enables that last point about data science. And then there's a piece around people. How do I build a pipeline for me today and the future? >> So the people piece is both the skills, and it's presumably a relationship with the line of business, as well. There's sort of two vectors there, right? >> Yeah the people piece when I think of it, is really about skills. There's a whole cultural component that goes across all of those five pieces that I laid out. Finding the right people, with the right skillset, where you need them, is hard. >> Can you talk about cloud migration, why that's so critical and so hard? >> If you look at kind of where the industry's been, the IT industry, it's been this race to the public cloud. I think it's a little misguided, all along. If you look at how business is run, right? Today, enterprises that are not internet born, make their money from what's running their businesses today. So this business critical assets. And just thinking that you can pick those up and move them to the cloud and take advantage of cloud, is not realistic. So the race really, is to a hybrid cloud. Our future's really lie in how do I connect these business critical assets to the cloud? And how do I migrate those things to the cloud? >> So Seth, the CIO might say to you, "Okay, let's go there for a minute, I kind of agree with what you're saying, I can't just shift everything in to the cloud. But what I can do in a hybrid cloud that I can't do in a public cloud?" >> Well, there's some drivers for that. I think one driver for hybrid cloud is what I just said. You can't just pick everything up and move it overnight, it's a journey. And it's not a six month journey, it's probably not a year journey, it's probably a multi year journey. >> Dave: So you can actually keep running your business? >> So you can actually keep running your business. And then other piece is there's new regulations that are coming up. And these regulations, EUGDPR is the biggest example of them right now. There are very stiff fines, for violations of those policies. And the party that's responsible for paying those fines, is the party that who the consumer engaged with. It's you, it's whoever owns the business. And as a business leader, I don't know that I would be, very willingly give up, trust a third party to manage that, just any any third party to manage that for me. And so there's certain types of data that some enterprises may never want to move to the cloud, because they're not going to trust a third party to manage that risk for them. >> So it's more transparent from a government standpoint. It's not opaque. >> Seth: Yup. >> You feel like you're in control? >> Yeah, you feel like you're in control and if something goes wrong, it's my fault. It's not something that I got penalized for because someone else did something wrong. >> So at the data layer, help us sort of abstract one layer up and the applications. How would you partition the applications. The ones that are managing that critical data that has to stay on premises. What would you build up potentially to compliment it in the public cloud? >> I don't think you need to partition applications. The way you build modern applications today, it's all API driven. You can reduce some of the costs of latency, through design. So you don't really need to partition the applications, per say. >> I'm thinking more along the lines of that the systems of record are not going to be torn out and those are probably the last ones if ever to go to the public cloud. But other applications leverage them. If that's not the right way of looking at it, where do you add value in the public cloud versus what stays on premise? >> So some of the system of record data, there's no reason you can't replicate some of it to the cloud. So if it's not this personal information, or highly regulated information, there's no reason that you can't replicate some of that to the cloud. And I think we get caught up in, we can't replicate data, we can't replicate data. I don't think that's the right answer, I think the right answer is to replicate the data if you need to, or if the data and system of record is not in the right structure, for what I need to do, then let's put the data in the right structure. Let's not have the conversation about how I can't replicate data. Let's have the conversation about where's the right place for the data, where does it make most sense and what's the right structure for it? And if that means you've got 10 copies of a certain type of data then you've got 10 copies of a certain type of data. >> Would you be, on that data, would it typically be, other parts of the systems of record that you might have in the public cloud, or would they be new apps, sort of green field apps? >> Seth: Yes. >> George: Okay. >> Seth: I think both. And that's part of, i think in my mind, that's kind of how you build, that question you just asked right there. Is one of the things that guide how you build your cloud migration strategy. So we said you can't just pick everything up and move it. So how do you prioritize? You look at what you need to build to run your business differently. And you start there and you start thinking about how do I migrate information to support those to the cloud? And maybe you start by building a local private cloud. So that everything's close together until you kind of master it. And then once you get enough, critical mass of data and applications around it, then you start moving stuff to the cloud. >> We talked earlier off camera about reframing governance steps. I used to head a CIO consultancy and we worked with a number of CIOs that were within legal IT, for example. And were worried about compliance and governance and things of that nature. And their ROI was always scare the board. But the holy grail, was can we turn governance into something of value? For the organization? Can we? >> I think in the world we live in today, with ever increasing regulations. And with a need to be agile and with everyone needing to and wanting to apply data science at scale. You need to reframe governance, right? Governance needs to be reframed from something that is seen as a roadblock. To something that is truly an enabler. And not just giving it lip service. And what do I mean by that? For governance to be an enabler, you really got to think about, how do I upfront, classify my data so that all data in my organization is bucketed in to some version of public, propietary and confidential. Different enterprises may have 30 scales and some may only have two. Or some may have one. and so you do that up front and so you know what can be done with data, when it can be done and who it can by done with. You need to capture intent. So what are allowed intended uses of data? And as a data scientist, what am I intending to do with this data? So that you can then mesh those two things together? Cause that's important in these new regulations I talked about, is people give you access to data, their personal data for an intended purpose. And then you need to be able to apply these governance, policies, actively. So it's not a passive, after the fact. Or you got to stop and you got to wait, it's leveraging services. Leveraging APIs. And building a composable system of polices that are delivered through APIs. So if I want to create a sandbox. To run some analytics on. I'm going to call an API. To get that data. That API is going to call a policy API that's going to say, "Okay, does Seth have permission to see this data? Can Seth use this data for this intended purpose?" if yes, the sandbox is created. If not, there's a conversation about really why does Seth need access to this data? It's really moving governance to be actively to enable me to do things. And it changes the conversation from, hey it's your data, can I have it? To there's really solid reasons as to why I can and can't have data. >> And then some potential automation around a sandbox that creates value. >> Seth: Absolutely. >> But it's still, the example you gave, public prop6ietary or confidential. Is still very governance like, where I was hoping you were going with the data classification and I think you referenced this. Can I extend that, that schema, that nomenclature to include other attributes of value? And can i do it, automate it, at the point of creation or use and scale it? >> Absolutely, that is exactly what I mean. I just used those three cause it was the three that are easy to understand. >> So I can give you as a business owner some areas that I would like to see, a classification schema and then you could automate that for me at scale? In theory? >> In theory, that's where we're hoping to go. To be able to automate. And it's going to be different based on what industry vertical you're in. What risk profile your business is willing to take. So that classification scheme is going to look very different for a bank, than it will for a pharmaceutical company. Or for a research organization. >> Dave: Well, if I can then defensively delete data. That's of real value to an organization. >> With new regulations, you need to be able to delete data. And you need to be able to know where all of your data is. So that you can delete it. Today, most organizations don't know where all their data is. >> And that problem is solved with math and data science, or? >> I think that problem is solved with a combination of governance. >> Dave: Sure. >> And technology. Right? >> Yeah, technology kind of got us into this problem. We'll say technology can get us out. >> On the technology subject, it seems like, with the explosion of data, whether it's not just volume, but also, many copies of the truth. You would need some sort of curation and catalog system that goes beyond what you had in a data warehouse. How do you address that challenge? >> Seth: Yeah and that gets into what I said when you guys asked me about CDOs, what do they care about? One of the things is unified governance. And so part of unified governance, the first piece of unified governance is having a catalog of your data. That is all of your data. And it's a single catalog for your data whether it's one of your business critical systems that's running your business today. Whether it's a public cloud, or it's a private cloud. Or some combination of both. You need to know where all your data is. You also need to have a policy catalog that's single for both of those. Catalogs like this fall apart by entropy. And the more you have, the more likely they are to fall apart. And so if you have one. And you have a lot of automation around it to do a lot of these things, so you have automation that allows you to go through your data and discover what data is where. And keep track of lineage in an automated fashion. Keep track of provenance in an automated fashion. Then we start getting into a system of truly unified governance that's active like I said before. >> There's a lot of talk about digital transformations. Of course, digital equals data. If it ain't data, it ain't digital. So one of the things that in the early days of the whole big data theme. You'd hear people say, "You have to figure out how to monetize the data." And that seems to have changed and morphed into you have to understand how your organization gets value from data. If you're a for profit company, it's monetizing. Something and feeding how data contributes to that monetization if you're a health care organization, maybe it's different. I wonder if you could talk about that in terms of the importance of understanding how an organization makes money to the CDO specifically. >> I think you bring up a good point. Monetization of data and analytics, is often interpreted differently. If you're a CFO you're going to say, "You're going to create new value for me, I'm going to start getting new revenue streams." And that may or may not be what you mean. >> Dave: Sell the data, it's not always so easy. >> It's not always so easy and it's hard to demonstrate value for data. To sell it. There's certain types, like IBM owns a weather company. Clearly, people want to buy weather data, it's important. But if you're talking about how do you transform a business unit it's not necessarily about creating new revenue streams, it's how do I leverage data and analytics to run my business differently. And maybe even what are new business models that I could never do before I had data and data science. >> Would it be fair to say that, as Dave was saying, there's the data side and people were talking about monetizing that. But when you talk about analytics increasingly, machine learning specifically, it's a fusion of the data and the model. And a feedback loop. Is that something where, that becomes a critical asset? >> I would actually say that you really can't generate a tremendous amount of value from just data. You need to apply something like machine learning to it. And machine learning has no value without good data. You need to be able to apply machine learning at scale. You need to build the deployable data science assets that run your business differently. So for example, I could run a report that shows me how my business did last quarter. How my sales team did last quarter. Or how my marketing team did last quarter. That's not really creating value. That's giving me a retrospective look on how I did. Where you can create value is how do I run my marketing team differently. So what data do I have and what types of learning can I get from that data that will tell my marketing team what they should be doing? >> George: And the ongoing process. >> And the ongoing process. And part of actually discovering, doing this catalog your data and understanding data you find data quality issues. And data quality issues are not necessarily an issue with the data itself or the people, they're usually process issues. And by discovering those data quality issues you may discover processes that need to be changed and in changing those processes you can create efficiencies. >> So it sounds like you guys got a pretty good framework. Having talked to Interpol a couple times and what you're saying makes sense. Do you have nightmares about IOT? (laughing) >> Do I have nightmares about IOT? I don't think I have nightmares about IOT. IOT is really just a series of connected devices. Is really what it is. On my talk tomorrow, I'm going to talk about hybrid cloud and connect a car is actually one of the things I'm going to talk about. And really a connected car you're just have a bunch of connected devices to a private cloud that's on wheels. I'm less concerned about IOT than I am, people manually changing data. IOT you get data, you can track it, if something goes wrong, you know what happened. I would say no, I don't have nightmares about IOT. If you do security wrong, that's a whole nother conversation. >> But it sounds like you're doing security right, sounds like you got a good handle on governance. Obviously scale is a key part of that. Could break the whole thing if you can't scale. And you're comfortable with the state of technology being able to support that? At least with IBM. >> I think at least with an IBM I think I am. Like I said, a connected car which is basically a bunch of IOT devices, a private cloud. How do we connect that private cloud to other private clouds or to a public cloud? There's tons of technologies out there to do that. Spark, Kafka. Those two things together allow you to do things that we could never do before. >> Can you elaborate? Like in a connected car environment or some other scenario where, other people called it a data center on wheels. Think of it as a private cloud, that's a wonderful analogy. How does Spark and Kafka on that very, very, smart device, cooperate with something like on the edge. Like the cities, buildings, versus in the clouds? >> If you're a connected car and you're this private cloud on wheels. You can't drive the car just on that information. You can't drive it just on the LIDAR knowing how well the wheels are in contact, you need weather information. You need information about other cars around you. You need information about pedestrians. You need information about traffic. All of this information you get from that connection. And the way you do that is leveraging Spark and Kafka. Kafka's a messaging system, you could leverage Kafka to send the car messages. Or send pedestrian messages. "This car is coming, you shouldn't cross." Or vice versa. Get a car to stop because there's a pedestrian in the way before even the systems on the car can see it. So if you can get that kind of messaging system in near real time. If I'm the pedestrian I'm 300 feet away. A half a second that it would take for that to go through, isn't that big of a deal because you'll be stopped before you get there. >> What about the again, intelligence between not just the data, but the advanced analytics. Where some of that would live in the car and some in the cloud. Is it just you're making realtime decisions in the car and you're retraining the models in the cloud, or how does that work? >> No I think some of those decisions would be done through Spark. In transit. And so one of the nice things about something about Spark is, we can do machine learning transformations on data. Think ETL. But think ETL where you can apply machine learning as part of that ETL. So I'm transferring all this weather data, positioning data and I'm applying a machine learning algorithm for a given purpose in that car. So the purpose is navigation. Or making sure I'm not running into a building. So that's happening in real time as it's streaming to the car. >> That's the prediction aspect that's happening in real time. >> Seth: Yes. >> But at the same time, you want to be learning from all the cars in your fleet. >> That would happen up in the cloud. I don't think that needs to happen on the edge. Maybe it does, but I don't think it needs to happen on the edge. And today, while I said a car is a data center, a private cloud on wheels, there's cost to the computation you can have on that car. And I don't think the cost is quite low enough yet where you could do all that where it makes sense to do all that computation on the edge. So some of it you would want to do in the cloud. Plus you would want to have all the information from as many cars in the area as possible. >> Dave: We're out of time, but some closing thoughts. They say may you live in interesting times. Well you can sum up the sum of the changes that are going on the business. Dell buys EMC, IBM buys The Weather Company. And that gave you a huge injection of data scientists. Which, talk about data culture. Just last thoughts on that in terms of the acquisition and how that's affected your role. >> I've only been at IBM since November. So all that happened before my role. >> Dave: So you inherited? >> So from my perspective it's a great thing. Before I got there, the culture was starting to change. Like we talked about before we went on air, that's the hardest part about any kind of data science transformation is the cultural aspects. >> Seth, thanks very much for coming back in theCUBE. Good to have you. >> Yeah, thanks for having me again. >> You're welcome, all right, keep it right there everybody, we'll be back with our next guest. This is theCUBE, we're live from Spark Summit in Boston. Right back. (soft rock music)

>> Narrator: From San Jose, in the heart of Silicon Valley, it's The Cube, covering Big Data SV 2016. Now your hosts, John Furrier and Peter Burris. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data Week, Big Data SV Strata Hadoop. This is The Cube, SiliconANGLE's flagship program. We go out to the events and extract the signals from the noise. I'm John Furrier, my co-host is Peter Burris. Our next guest is Ritika Gunnar, VP of Data and Analytics at IBM and David Richards is the CEO of WANdisco. Welcome to The Cube, welcome back. >> Thank you. >> It's a pleasure to be here. >> So, okay, IBM and WANdisco, why are you guys here? What are you guys talking about? Obviously, partnership. What's the story? >> So, you know what WANdisco does, right? Data replication, active-active replication of data. For the past twelve months, we've been realigning our products to a market that we could see rapidly evolving. So if you had asked me twelve months ago what we did, we were talking about replicating just Hadoop, but we think the market is going to be a lot more than that. I think Mike Olson famously said that this Hadoop was going to disappear and he was kind of right because the ecosystem is evolving to be a much greater stack that involves applications, cloud, completely heterogeneous storage environment, and as that happens the partnerships that we would need have to move on from just being, you know, the sort of Hadoop-specific distribution vendors to actually something that can deliver a complete solution to the marketplace. And very clearly, IBM has a massive advantage in the number of people, the services, ecosystem, infrastructure, in order to deliver a complete solution to customers, so that's really why we're here. >> If you could talk about the stack comment, because this is something that we're seeing. Mike Olson's kind of being political when he says make it invisible, but the reality is there is more to big data than Hadoop. There's a lot of other stuff going on. Call it stack, call it ecosystem. A lot of great things are growing, we just had Gaurav on from SnapLogic said, "everyone's winning." I mean, I just love that's totally true, but it's not just Hadoop. >> It's about Alldata and it's about all insight on that data. So when you think about Alldata, Alldata is a very powerful thing. If you look at what clients have been trying to do thus far, they've actually been confined to the data that may be in their operational systems. With the advent of Hadoop, they're starting to bring in some structured and unstructured data, but with the advent of IOT systems, systems of engagement, systems of records and trying to make sense of all of that, Alldata is a pretty powerful thing. When I think of Alldata, I think of three things. I think of data that is not only on premises, which is where a lot of data resides today, but data that's in the cloud, where data is being generated today and where a majority of the growth is. When I think of Alldata, I think of structured data, that is in your traditional operational systems, unstructured and semi-structured data from IOT systems et cetera, and when I think of Alldata, I think of not just data that's on premises for a lot of our clients, but actually external data. Data where we can correlate data with, for example, an acquisition that we just did within IBM with The Weather Company or augmenting with partnerships like Twitter, et cetera, to be able to extract insight from not just the data that resides within the walls of your organization, but external data as well. >> The old expression is if you want to go fast, do it alone, if you want to go deeper and broader and more comprehensive, do it as a team. >> That's right. >> That expression can be applied to data. And you look at The Weather data, you think, hmmm, that's an outlier type acquisition, but when you think about the diversity of data, that becomes a really big deal. And the question I want to ask you guys is, and Ritika, we'll start with you, there's always a few pressure points we've seen in big data. When that pressure is relieved, you've seen growth, and one was big data analytics kind of stalled a little bit, the winds kind of shifted, eye of the storm, whatever you want to call it, then cloud comes in. Cloud is kind of enabling that to go faster. Now, a new pressure point that we're seeing is go faster with digital transformation. So Alldata kind of brings us to all digital. And I know IBM is all about digitizing everything and that's kind of the vision. So you now have the pressure of I want all digital, I need data driven at the center of it, and I've got the cloud resource, so kind of the perfect storm. What's your thoughts on that? Do you see that similar picture? And then does that put the pressure on, say, WANdisco, say hey, I need replication, so now you're under the hood? Is that kind of where this is coming together? >> Absolutely. When I think about it, it's about giving trusted data and insights to everyone within the organization, at the speed in which they need it. So when you think about that last comment of, "At the speed in which they need it," that is the pressure point of what it means to have a digitally transformed business. That means being able to make insights and decisions immediately and when we look at what our objective is from an IBM perspective, it's to be able to enable our clients to be able to generate those immediate insights, to be able to transform their business models and to be able to provide the tooling and the skills necessary, whether we have it organically, inorganically, or through partnerships, like with WANdisco to be able to do that. And so with WANdisco, we believe we really wanted to be able to activate where that data resides. When I talk about Alldata and activation of that data, WANdisco provided to us complementary capabilities to be able to activate that data where it resides with a lot of the capabilities that they're providing through their fusion. So, being able to have and enable our end-users to have that digitally infused set of reactive type of applications is absolutely something... >> It's like David, we talk about, and maybe I'm oversimplifying your value proposition, but I always look at WANdisco as kind of the five nines of data, right? You guys make stuff work, and that's the theme here this year, people just want it to work, right? They don't want to have it down, right? >> Yeah, we're seeing, certainly, an uptick in understanding about what high availability, what continuous availability means in the context of Hadoop, and I'm sure we'll be announcing some pretty big deals moving forward. But we've only just got going with IBM. I would, the market should expect a number of announcements moving forward as we get going with this, but here's the very interesting question associated with cloud. And just to give you a couple of quick examples, we are seeing an increasing number of Global 1,000 companies, Fortune 100 companies move to cloud. And that's really important. If you would have asked me 12 months ago, how is the market going to shape up, I'd have said, well, most CIO's want to move to cloud. It's already happening. So, FINRA, the major financial regulator in the United States is moving to cloud, publicly announced it. The FCA in the UK publicly announced they are moving 100% to cloud. So this creates kind of a microcosm of a problem that we solve, which is how do you move transactional data from on-premise to cloud and create a sort of hybrid environment. Because with the migration, you have to build a hybrid cloud in order to do that anyway. So, if it's just archive systems, you can package it on a disk drive and post it, right? If we're talking about transactional data, i.e, stuff that you want to use, so for example, a big travel company can't stop booking flights while they move their data into the cloud, right? They would take six months to move petabyte scale data into cloud. We solve that problem. We enable companies to move transactional data from on-premise into cloud, without any interruption to services. >> So not six months? >> No, not six months. >> Six hours? >> And you can keep on using the data while it is in transit. So we've been looking for a really simplistic problem, right, to explain this really complex algorithm that we've got that you know does this active-active replication stuff. That's it, right? It's so simple, and nobody else can do it. >> So no downtime, no disruption to their business? >> No, and you can use the cloud or you can use the on-prem applications while the data is in transit. >> So when you say all cloud, now we're on a theme, Alldata, all digital, all cloud, there's a nuance there because most, and we had Gaurav from SnapLogic talk about it, there's always going to be an on-prem component. I mean, probably not going to see 100% everyone move to the cloud, public cloud, but cloud, you mean hybrid cloud essentially, with some on-prem component. I'm sure you guys see that with Bluemix as well, that you've got some dabbling in the public cloud, but ultimately, it's one resource pool. That's essentially what you're saying. >> Yeah, exactly. >> And I think it's really important. One of the things that's very attractive e about the WANdisco solution is that it does provide that hybridness from the on-premises to cloud and that being able to activate that data where it resides, but being able to do that in a heterogeneous fashion. Architectures are very different in the cloud than they are on premises. When you look at it, your data like may be as simple as Swift object store or as S3, and you may be using elements of Hadoop in there, but the architectures are changing. So the notion of being able to handle hybrid solutions both on-premises and cloud with the heterogeneous capability in a non-invasive way that provides continuous data is something that is not easily achieved, but it's something that every enterprise needs to take into account. >> So Ritika, talk about the why the WANdisco partnership, and specifically, what are some of the conversations you have with customers? Because, obviously there's, it sounds like, the need to go faster and have some of this replication active-active and kind of, five nines if you will, of making stuff not go down or non-disruptive operations or whatever the buzzword is, but you know, what's the motivation from your standpoint? Because IBM is very customer-centric. What are some of the conversations and then how does WANdisco fit into those conversations? >> So when you look at the top three use cases that most clients use for even Hadoop environments or just what's going on in the market today, the top three use cases are you know, can I build a logical data warehouse? Can I build areas for discovery or analytical discovery? Can I build areas to be able to have data archiving? And those top three solutions in a hybrid heterogeneous environment, you need to be able to have active-active access to the data where that data resides. And therefore, we believe, from an IBM perspective, that we want to be able to provide the best of breed regardless of where that resides. And so we believe from a WANdisco perspective, that WANdisco has those capabilities that are very complementary to what we need for that broader skills and tooling ecosystem and hence why we have formed this partnership. >> Unbelievably, in the market, we're also seeing and it feels like the Hadoop market's just got going, but we're seeing migrations from distributions like Cloudera into cloud. So you know, those sort of lab environments, the small clusters that were being set up. I know this is slightly controversial, and I'll probably get darts thrown at me by Mike Olson, but we are seeing pretty large-scale migration from those sort of labs that were set up initially. And as they progress, and as it becomes mission-critical, they're going to go to companies like IBM, really, aren't they, in order to scale up their infrastructure? They're going to move the data into cloud to get hyperscale. For some of these cases that Ritika was just talking about so we are seeing a lot of those migrations. >> So basically, Hadoop, there's some silo deployments of POC's that need to be integrated in. Is that what you're referring to? I mean, why would someone do that? They would say okay, probably integration costs, probably other solutions, data. >> If you do a roll-your-own approach, where you go and get some open-source software, you've got to go and buy servers, you've got to go and train staff. We've just seen one of our customers, a big bank, two years later get servers. Two years to get servers, to get server infrastructure. That's a pretty big barrier, a practical barrier to entry. Versus, you know, I can throw something up in Bluemix in 30 minutes. >> David, you bring up a good point, and I want to just expand on that because you have a unique history. We know each other, we go way back. You were on The Cube when, I think we first started seven years ago at Hadoop World. You've seen the evolution and heck, you had your own distribution at one point. So you know, you've successfully navigated the waters of this ecosystem and you had gray IP and then you kind of found your swim lanes and you guys are doing great, but I want to get your perspective on this because you mentioned Cloudera. You've seen how it's evolving as it goes mainstream, as you know, Peter says, "The big guys are coming in and with power." I mean, IBM's got a huge spark investment and it's not just you know, lip service, they're actually donating a ton of code and actually building stuff so, you've got an evolutionary change happening within the industry. What's your take on the upstarts like Cloudera and Hortonworks and the Dishrow game? Because that now becomes an interesting dynamic because it has to integrate well. >> I think there will always be a market for the distribution of opensource software. As that sort of, that layer in the stack, you know, certainly Cloudera, Hortonworks, et cetera, are doing a pretty decent job of providing a distribution. The Hadoop marketplace, and Ritika laid this on pretty thick as well, is not Hadoop. Hadoop is a component of it, but in cloud we talk about object store technology, we talk about Swift, we talk about S3. We talk about Spark, which can be run stand-alone, you don't necessarily need Hadoop underneath it. So the marketplace is being stretched to such a point that if you were to look at the percentage of the revenue that's generated from Hadoop, it's probably less than one percent. I talked 12 months ago with you about the whale season, the whales are coming. >> Yeah, they're here. >> And they're here right now, I mean... >> (laughs) They're mating out in the water, deals are getting done. >> I'm not going to deal with that visual right now, but you're quite right. And I love the Peter Drucker quote which is, "Strategy is a commodity, execution is an art." We're now moving into the execution phase. You need a big company in order to do that. You can't be a five hundred or a thousand person... >> Is Cloudera holding onto dogma with Hadoop or do they realize that the ecosystem is building around them? >> I think they do because they're focused on the application layer, but there's a lot of competition in the application layer. There's a little company called IBM, there's a little company called Microsoft and the little company called Amazon that are kind of focused on that as well, so that's a pretty competitive environment and your ability to execute is really determined by the size of the organization to be quite frank. >> Awesome, well, so we have Hadoop Summit coming up in Dublin. We're going to be in Ireland next month for Hadoop Summit with more and more coverage there. Guys, thanks for the insight. Congratulations on the relationship and again, WANdisco, we know you guys and know what you guys have done. This seems like a prime time for you right now. And IBM, we just covered you guys at InterConnect. Great event. Love The Weather Company data, as a weather geek, but also the Apple announcement was really significant. Having Apple up on stage with IBM, I think that is really, really compelling. And that was just not a Barney deal, that was real. And the fact that Apple was on stage was a real testament to the direction you guys are going, so congratulations. This is The Cube, bringing you all the action, here live in Silicon Valley here for Big Data Week, BigData SV, and Strata Hadoop. We'll be right back with more after this short break.