(upbeat music) >> Announcer: From the cube Studios in Palo Alto and Boston, it's the cube! Covering IBM Think. Brought to you by IBM. >> Hi everybody, this is Dave Vellante of theCUBE, and you're watching our coverage of the IBM digital event experience. A multi-day program, tons of content, and it's our pleasure to be able to bring in experts, practitioners, customers, and partners. Sriram Raghavan is here. He's the Vice President of IBM Research in AI. Sriram, thanks so much for coming on thecUBE. >> Thank you, pleasure to be here. >> I love this title, I love the role. It's great work if you're qualified for it.(laughs) So, tell us a little bit about your role and your background. You came out of Stanford, you had the pleasure, I'm sure, of hanging out in South San Jose at the Almaden labs. Beautiful place to create. But give us a little background. >> Absolutely, yeah. So, let me start, maybe go backwards in time. What do I do now? My role's responsible for AI strategy, planning, and execution in IBM Research across our global footprint, all our labs worldwide and their working area. I also work closely with the commercial parts. The parts of IBM, our Software and Services business that take the innovation, AI innovation, from IBM Research to market. That's the second part of what I do. And where did I begin life in IBM? As you said, I began life at our Almaden Research Center up in San Jose, up in the hills. Beautiful, I had in a view. I still think it's the best view I had. I spent many years there doing work at the intersection of AI and large-scale data management, NLP. Went back to India, I was running the India lab there for a few years, and now I'm back here in New York running AI strategy. >> That's awesome. Let's talk a little bit about AI, the landscape of AI. IBM has always made it clear that you're not doing consumer AI. You're really tying to help businesses. But how do you look at the landscape? >> So, it's a great question. It's one of those things that, you know, we constantly measure ourselves and our partners tell us. I think we, you've probably heard us talk about the cloud journey . But look barely 20% of the workloads are in the cloud, 80% still waiting. AI, at that number is even less. But, of course, it varies. Depending on who you ask, you would say AI adoption is anywhere from 4% to 30% depending on who you ask in this case. But I think it's more important to look at where is this, directionally? And it's very, very clear. Adoption is rising. The value is more, it's getting better appreciated. But I think more important, I think is, there is broader recognition, awareness and investment, knowing that to get value out of AI, you start with where AI begins, which is data. So, the story around having a solid enterprise information architecture as the base on which to drive AI, is starting to happen. So, as the investments in data platform, becoming making your data ready for AI, starts to come through. We're definitely seeing that adoption. And I think, you know, the second imperative that businesses look for obviously is the skills. The tools and the skills to scale AI. It can't take me months and months and hours to go build an AI model, I got to accelerate it, and then comes operationalizing. But this is happening, and the upward trajectory is very, very clear. >> We've been talking a lot on theCUBE over the last couple of years, it's not the innovation engine of our industry is no longer Moore's Law, it's a combination of data. You just talked about data. Applying machine technology to that data, being able to scale it, across clouds, on-prem, wherever the data lives. So. >> Right. >> Having said that, you know, you've had a journey. You know, you started out kind of playing "Jeopardy!", if you will. It was a very narrow use case, and you're expanding that use case. I wonder if you could talk about that journey, specifically in the context of your vision. >> Yeah. So, let me step back and say for IBM Research AI, when I think about how we, what's our strategy and vision, we think of it as in two parts. One part is the evolution of the science and techniques behind AI. And you said it, right? From narrow, bespoke AI that all it can do is this one thing that it's really trained for, it takes a large amount of data, a lot of computing power. Two, how do you have the techniques and the innovation for AI to learn from one use case to the other? Be less data hungry, less resource hungry. Be more trustworthy and explainable. So, we call that the journey from narrow to broad AI. And one part of our strategy, as scientists and technologists, is the innovation to make that happen. So that's sort of one part. But, as you said, as people involved in making AI work in the enterprise, and IBM Research AI vision would be incomplete without the second part, which is, what are the challenges in scaling and operationalizing AI? It isn't sufficient that I can tell you AI can do this, how do I make AI do this so that you get the right ROI, the investment relative to the return makes sense and you can scale and operationalize. So, we took both of these imperatives. The AI narrow-to-broad journey, and the need to scale and operationalize. And what of the things that are making it hard? The things that make scaling and operationalizing harder: data challenges, we talked about that, skills challenges, and the fact that in enterprises, you have to govern and manage AI. And we took that together and we think of our AI agenda in three pieces: Advancing, trusting, and scaling AI. Advancing is the piece of pushing the boundary, making AI narrow to broad. Trusting is building AI which is trustworthy, is explainable, you can control and understand its behavior, make sense of it and all of the technology that goes with it. And scaling AI is when we address the problem of, how do I, you know, reduce the time and cost for data prep? How do I reduce the time for model tweaking and engineering? How do I make sure that a model that you build today, when something changes in the data, I can quickly allow for you to close the loop and improve the model? All of the things, think of day-two operations of AI. All of that is part of our scaling AI strategy. So advancing, trusting, scaling is sort of the three big mantras around which the way we think about our AI. >> Yeah, so I've been doing a little work in this around this notion of DataOps. Essentially, you know, DevOps applied to the data and the data pipeline, and I had a great conversation recently with Inderpal Bhandari, IBM's Global Chief Data Officer, and he explained to me how, first of all, customers will tell you, it's very hard to operationalize AIs. He and his team took that challenge on themselves and have had some great success. And, you know, we all know the problem. It's that, you know AI has to wait for the data. It has to wait for the data to be cleansed and wrangled. Can AI actually help with that part of the problem, compressing that? >> 100%. In fact, the way we think of the automation and scaling story is what we call the "AI For AI" story. So, AI in service of helping you build the AI that helps you make this with speed, right? So, and I think of it really in three parts. It's AI for data automation, our DataOps. AI used in better discovery, better cleansing, better configuration, faster linking, quality assessment, all of that. Using AI to do all of those data problems that you had to do. And I called it AI for data automation. The second part is using AI to automatically figure out the best model. And that's AI for data science automation, which is, feature engineering, hyperparameter optimization, having them all do work, why should a data scientist take weeks and months experimenting? If the AI can accelerate that from weeks to a matter of hours? That's data science automation. And then comes the important part, also, which is operations automation. Okay, I've put a data model into an application. How do I monitor its behavior? If the data that it's seeing is different from the data it was trained on, how do I quickly detect it? And a lot of the work from Research that was part of that Watson OpenScale offering is really addressing the operational side. So AI for data, AI for data science automation, and AI to help automate production of AI, is the way we break that problem up. >> So, I always like to ask folks that are deep into R&D, how they are ultimately are translating into commercial products and offerings? Because ultimately, you got to make money to fund more R&D. So, can you talk a little bit about how you do that, what your focus is there? >> Yeah, so that's a great question, and I'm going to use a few examples as well. But let me say at the outset, this is a very, very closed partnership. So when we, the Research part of AI and our portfolio, it's a closed partnership where we're constantly both drawing problem as well as building technology that goes into the offering. So, a lot of our work, much of our work in AI automation that we were talking about, is part of our Watson Studio, Watson Machine Learning, Watson OpenScale. In fact, OpenScale came out of Research working Trusted AI, and is now a centerpiece of our Watson project. Let me give a very different example. We have a very, very strong portfolio and focus in NLP, Natural Language Processing. And this directly goes into capabilities out of Watson Assistant, which is our system for conversational support and customer support, and Watson Discovery, which is about making enterprise understand unstructurally. And a great example of that is the Working Project Debater that you might have heard, which is a grand challenge in Research about building a machine that can do debate. Now, look, we weren't looking to go sell you a debating machine. But what did we build as part of doing that, is advances in NLP that are all making their way into assistant and discovery. And we actually just talked about earlier this year, announced a set of capabilities around better clustering, advanced summarization, deeper sentiment analysis. These made their way into Assistant and Discovery but are born out of research innovation and solving a grand problem like building a debating machine. That's just an example of how that journey from research to product happens. >> Yeah, the Debater documentary, I've seen some of that. It's actually quite astounding. I don't know what you're doing there. It sounds like you're taking natural language and turning it into complex queries with data science and AI, but it's quite amazing. >> Yes, and I would encourage you, you will see that documentary, by the way, on Channel 7, in the Think Event. And I would encourage you, actually the documentary around how Debater happened, sort of featuring back of the you know, backdoor interviews with the scientist who created it was actually featured last minute at Copenhagen International Documentary Festival. I'll invite viewers to go to Channel 7 and Data and AI Tech On-Demand to go take a look at that documentary. >> Yeah, you should take a look at it. It's actually quite astounding and amazing. Sriram, what are you working on these days? What kind of exciting projects or what's your focus area today? >> Look, I think there are three imperatives that we're really focused on, and one is very, you know, just really the project you're talking about, NLP. NLP in the enterprise, look, text is a language of business, right? Text is the way business is communicated. Within each other, with their partners, with the entire world. So, helping machines understand language, but in an enterprise context, recognizing that data and the enterprises live in complex documents, unstructured documents, in e-mail, they live in conversations with the customers. So, really pushing the boundary on how all our customers and clients can make sense of this vast volume of unstructured data by pushing the advances of NLP, that's one focus area. Second focus area, we talked about trust and how important that is. And we've done amazing work in monitoring and explainability. And we're really focused now on this emerging area of causality. Using causality to explain, right? The model makes this because the model believes this is what it wants, it's a beautiful way. And the third big focus continues to be on automation. So, NLP, trust, automation. Those are, like, three big focus areas for us. >> sriram, how far do you think we can take AI? I know it's a topic of conversation, but from your perspective, deep into the research, how far can it go? And maybe how far should it go? >> Look, I think we are, let me answer it this way. I think the arc of the possible is enormous. But I think we are at this inflection point in which I think the next wave of AI, the AI that's going to help us this narrow-to-broad journey we talked about, look, the narrow-to-broad journey's not like a one-week, one-year. We're talking about a decade of innovation. But I think we are at a point where we're going to see a wave of AI that we like to call "neuro-symbolic AI," which is AI that brings together two sort of fundamentally different approaches to building intelligence systems. One approach of building intelligence system is what we call "knowledge driven." Understand data, understand concept, logically, reasonable. We human beings do that. That was really the way AI was born. The more recent last couple of decades of AI was data driven, Machine learning. Give me vast volumes of data, I'll use neural techniques, deep learning, to to get value. We're at a point where we're going to bring both of them together. Cause you can't build trustworthy, explainable systems using only one, you can't get away from not using all of the data that you have to make them. So, neuro-symbolic AI is, I think, going to be the linchpin of how we advance AI and make it more powerful and trustworthy. >> So, are you, like, living your childhood dream here or what? >> Look, for me I'm fascinated. I've always been fascinated. And any time you can't find a technology person who hasn't dreamt of building an intelligent machine. To have a job where I can work across our worldwide set of 3,000 plus researchers and think and brainstorm on strategy with AI. And then, most importantly, not to forget, right? That you talked about being able to move it into our portfolios so it actually makes a difference for our clients. I think it's a dream job and a whole lot of fun. >> Well, Sriram, it was great having you on theCUBE. A lot of fun, interviewing folks like you. I feel a little bit smarter just talking to you. So thanks so much for coming on. >> Fantastic. It's been a pleasure to be here. >> And thank you for watching, everybody. You're watching theCUBE's coverage of IBM Think 2020. This is Dave Vellante. We'll be right back right after this short break. (upbeat music)

>> Live, from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to theCUBE's coverage of IBM Think 2019. Here in Moscone, we're talking so much multi clouds. It's been raining all day, really windy. To help us wrap up our third day, what we call theCUBE Insights, I have our co-CEO, Dave Vellante. I'm Stu Miniman and happy to welcome back to the program. It's been at least 15 times on the program, I think our counter is breaking as to how many you've been on, Ray Wang, who is the founder, chairman and analyst with Constellation Research, also the host of dDsrupTV who was gracious enough to have me on the podcast earlier this year, Ray. >> Little reciprocity there, Stu. >> Hey, we got to get you back on, this is awesome! Day three is wrap-up and this is going to be fun. >> Ray, as we say, theCUBE is everywhere, except it's really a subset of what you and the Constellation Research team do, we see you all over the place so thanks for taking time to join us. Alright, so tell us what's going on in your world, Ray. >> So what we're seeing here is actually really interesting, we've got a set of data-driven business models that are being lit up, and you see IBM everywhere in that network. And it's not about Cloud, it's not about AI, it's not about security, it's not about Blockchain. It's really about companies are actually building these digital networks, these business models, and they're lighting them up. IBM-Maersk, we saw things with insurance companies, you see it with food trust, you see it with healthcare. It's happening, and it's the top customers that are doing this. And so it's like we see a flicker of hope here at IBM that they're turning around, they're not just selling services, they're not just selling software, they're actually delivering these business models to executives and companies, and the early adopters are getting it. >> Ray that was one of the questions we had, is what's the theme of the show and-- >> There is no theme! >> You're giving us the theme here of what it should be because we talk digital, we talk cognitive, we talk all these other big thought-y words because we need to think while we're here, right? >> We need to think, we need to think! No, but the thing is this is a theme-less show, people can't figure it out but the main thing is, look, I've got a problem, this digital disruption is happening, my business models are changing. Help me be part of that shift, or I may go away! And people realize that and that's what they're starting to get, and you see that in all the reference customers the people that were on stage. The science slams were also really great. I don't know if you had a chance to catch those but the science slams were kind of a flicker into research, IBM research which is the heart of IBM, is coming up. They're going from concept to commercialization so much faster than they used to be, used to be research would do a project people are like, that's kind of cool, maybe I'll adopt it. They're now saying hey, let's get this into the market, let's get into academia, let's get early adopters on board. >> So Ray, what do you make of the Red Hat deal? What does it say about IBM's strategy? Do you like the deal? What does it say about the industry at large? >> It's a great question. The Red Hat deal to me was overpaid, however, at 20x multiples, that's what PE firms are paying. So every vendor is now competing with PE firms for assets. Red Hat, at about 9x, 10x? Makes a lot of sense, at 20x? It's kind of like, okay, is this the Hail Mary or is this the future strategy or is this basically what the new company is? I would have rather taken that money and put it into venture funds to continue what they're doing with these network models. That would have been a better strategy to me but Red Hat's a great company, you get a great team, you get great COs you get great tooling. >> So you would've rather seen tuck-ins to actually build that network effect that you've been alluding to. Of course that would have taken longer you know, wouldn't have solidified Ginni's legacy. So, it's a big move, a big move on the chessboard. >> Well the legacy's interesting, last year the stock was down some 20-some percent, it's up 20% since January so we're going to see what happens, but it's a doubt component. >> Well I've always said she inherited a bag of rocks from Palmisano at the peak of 2012 and then it got hit hard and she had to architect the transformation. It took, I don't know, five years plus, so, you know, she was dealt a tough hand, in my opinion. >> She had a bad hand, but we've had seven years to play this. I think that's what the market's saying. >> So it's on her, is what you're saying. >> It's now on her. She's got to turn this around, finish the legacy, but you've got a great CEO in waiting with the Red Hat guy. >> Jim Whitehurst you're saying? >> Yeah, he's good >> So she's what, Ginni is 60, 61? Is that about right? >> She's past the retirement age. Normally IBM CEOs would have gone through. >> 61 to 63 I think, is that range maybe, hey, women live longer so maybe they live longer as the CEO of IBM, I don't know. >> She did get a bad hand, but I think when you execute the strategy that money, here's the tough part. Investors are saying, hey, we'd rather take your money, back away from you through stock buybacks, dividends and mergers and acquisitions, and we don't trust you to do the innovation. That's happening to every company, including all of IBM's customers. The problem is if you do that, they're hedging against those companies too. The same investors are taking 50, 100 million, giving it to three kids in a start-up anywhere in the world and saying, hey, go disrupt these guys, so they're betting against their own investments and hedging. So that's the challenge she's up against. >> We talked about in our open for the show here. It's developers, though, that's the business model. We saw IBM struggle for years to get any real traction there, there's little pockets there, they've got great legacy in open source, but Red Hat's got developers. Ray, you go and see a lot of shows, who's doing well with developers out there? >> Microsoft redid their developer network by going younger with GitHub, whole bunch of other acquisitions, this is a great developer buy in that percent. But the other piece that we noticed here was it's the partner developers that are coming in in force. It's not your average developer. I'm going to build a coding and do a mobile app, it's people that work for large system integrators, large networks, small midsize VARs, those are where the developers are coming from and now they have a reason, right? Now they have a reason to build and I think that's been a good turnaround. >> How about Salesforce with the developer angle, what's your radar say there? >> It's not about the developer angle on the Salesforce side, what's interesting about the Salesforce side is Trailhead. This is, like, learning management meets gamification meets a whole LinkedIn training program in the back end. This is the way to actually take out LinkedIn without going after LinkedIn, by giving everyone a badge. There's a couple of million people actually on this thing. Think about this, all getting badges, all training each other, all doing customer support and experience, that's amazing! They crowd-source customer experience and learning right there. And they're building a community and they're building a movement. That's the thing, Salesforce is about a movement. >> Couple of others, SAP and Oracle, give us your update there. >> I think SAP's in the middle of trying to figure out what they have to do to make those investments. We see a lot of partnerships with Microsoft and IBM as they're doing the Cloud upgrades, that's an area. The acquisition of Qualtrics is another great example, 20x. 20x is the number people are now paying for for acquisitions and for assets on that end. And Oracle's going to be interesting to watch, post-Kurian to see how they come at it. They have a lot of the assets, they've got to put them together to get there, and then we've got all these interesting things like ServiceNow and Adobe on the other end. Like, ServiceNow is like, great platform! Awesome, people are building and extending the Cloud in ServiceNow, but no leadership! Right? I mean, you've got a consumer CEO trying to figure out enterprise, a consumer CMO trying to figure out enterprise, and they don't know if am I a platform or am I an app? You've got to figure that out now! People want to work with you! >> Well it is a company in transition at the top, for sure. >> But they can do nothing and still make a ton of money on the way out. >> And they've kicked butt since Donahoe came on, I mean just from a performance standpoint, amazing. >> Oh yeah, performance? You can do nothing and I think it's still going to coast but the thing is at some point it's going to come bite you, you got to figure that out. >> How do you think that Kurian will fit at Google, what's your take there? >> You know, early reactions on Kurian at Google is good, right? The developers are embracing him, he understands what the problems are. Let's be honest, I've said this many times to you guys in private and also in public, you know. It was a mess, it was a cluster before. I mean, you had three years, and you lost traction in the market, right? And it's because you didn't get enterprise, you couldn't figure out partners and, I mean, you paid sales people on consumption! Who does that? You're a sales rep, you're like, I'm not going to do this on consumption! Makes no sense! >> Ray, Kurian had been quoted that no acquisition is off the table, you know, they didn't buy GitHub, they didn't buy Red Hat, do you see them making a 10, 20 million dollar acquisition to get them into the enterprise space? >> Billion. >> Yeah, sorry, 20 billion. >> I think there's a lot that they go after. I know there's rumors about ServiceNow, there's a couple of other things. I think the first acquisition, if I were to make it would be Looker. I mean I love that thing that's on there and buy Snowflake too while you're at it. But we'll see what they do. I think the strategy is they've got to win back the trust of enterprises. People need to know, I'm buying your relationship, I have a relationship, I can count on you to be successful as opposed to, hey, you know, you can get this feature for less and if you do this on a sustained unit or, I want to know I can trust you and build that relationship and I think that's what they're going to focus on. >> Well, come on, isn't Google's business still ads? I mean, that's still where all their revenue is. >> It is, but the other category is $10 billion. That other category of devices and Cloud and all that? That's still a big category and that's where all the growth is. I mean look at this, it's a full frontal assault between Amazon and Google, Amazon Alexa versus Google Home, right? Amazon in ads, $10 billion in ads, going after Google's ad business. Amazon doing an AWS versus Google Cloud. Google's under assault right now! >> Give us the update on Constellation, your conference is really taking off, you've got great buzz in the industry, and congratulations on getting that off the ground. >> And the Tech for Good stuff, loved it. >> Thank you. We had great event, December 10th, talking about the future of the Internet. What it means in terms of, you know, digital rights, human rights in a digital age, was really that conference. Our big flagship conference is November 4th through 7th, it's at Half Moon Bay. We get about 250 CXOs together, about 100 vendors and tech folks that are visionaries and bring them together, that's doing well, and we do our healthcare summits. We brought on a new analyst, David Chou. David Chou, and if you've seen him before, he's like one of the top analysts for CIOs and chief data officers in the healthcare space, he's at HIMSS right now. >> He's awesome, we know him from Twitter. He's been on, he's great. >> Yeah, so we do healthcare summits twice a year and that's been picking up, some of the top thinkers in healthcare. We bring them in to Las Vegas, we do a brainstorming session, we work with them. They think about ideas and then we meet again, so. >> Alright, Ray, we want to give you the final word. We're halfway through IBM Think, what have you been thinking about this and any final musings on the industry? >> So I was very upset last year at how it was run. And I think this has run much better than last year. I think they did a good job. February in San Francisco? Never again, don't do that. I know it's May next year, is when this event's going to be. But I think the main thing is IBM's got to do more events than once a year. If you get enterprise marketing you realize it's at the beginning of the year, it's still sales kick-off and partners. March? March is like closing the quarter, so you do an event in April or May, and you do it in April or May but you have multiple events that are more targeted. This theme-less approach is not working. Right, partners are a little confused but they're here because it's once a year. But more importantly, build that pipeline over the quarters, don't just stop at a certain set of events, and I think they'll get very successful if they do that. >> Alright well, Ray, next time you come on the program, can you please bring a little bit of energy? We'll try to get you on early in the show when you're not so worn down. >> I know. >> Thanks as always. >> Appreciate you coming back on, man. >> Hey thanks, man, it's theCUBE! I love being on this thing.. >> Always a pleasure. >> Alright and, yeah, we always love helping you extract the signal from the noise. We're Dave Vellante, John Furrier, Lisa Martin. I'm Stu Miniman. Thanks for watching day three of theCUBE at IBM Think. Join us tomorrow, thanks for watching. (light music)

>> Announcer: Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer's Summit, brought to you by IBM. (upbeat music) >> 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 co-host, Dave Vellante. >> Hey, hey. We are joined by Gene Leganza, he is the vice president and research director at Forrester Research. Thanks so much for coming on theCUBE. >> Pleasure, thanks for having me. >> So, before the cameras were rolling, we were talking about this transformation, putting data at the front and center of an organization, and you were saying how technology is a piece of the puzzle, a very important piece of the puzzle, but so much of this transformation involves these cultural, social, organizational politics issues that can be just as big and as onerous as the technology, and maybe bigger. >> Bigger in a sense that there can be intractable without any clear path forward. I was just in a session, at a breakout session, at the conference, as I was saying before, we could have had the same discussion 15 or 20 years ago in terms of how do you get people on board for things like data governance, things that sound painful and onerous to business people, something that sound like IT should take care of that, this is not something that a business person should get involved in. But the whole notion of the value of data as an asset to drive an organization forward, to do things you couldn't do before, to be either driven by insights, and if you're even advanced, AI, and cognitive sort of things, really advancing your organization forward, data's obviously very critical. And the things that you can do should be getting business people excited, but they're still having the same complaints about 20 years ago about this is something somebody should do for me. So, clearly the message is not getting throughout the organization that data is a new and fascinating thing that they should care about. There's a disconnect for a lot of organizations, I think. >> So, from your perspective, what is the push back? I mean, as you said, the fact that data is this asset should be getting the business guys' eyes lighting up. What do you see as sort of biggest obstacle and stumbling block here? >> I think it's easy to characterize the people we talk about. I came from IT myself, so the business is always the guys that don't get it, and in this case, the people who are not on board are somehow out of it, they're really bad corporate citizens, they're just not on board in some way that characterizes them as missing something. But I think what no one ever does who's in the position of trying to sell the value of data and data processes and data capabilities, is the fact that these folks are all doing their best to do their job. I mean, nobody thinks about that, right? They just think they're intractable, they like doing things the way they've always done them, they don't like change, and they're going to resist everything I try to do. But the fact is, from their perspective, they know how to be successful, and they know when risk is going to introduce something that they don't want to go there. It's unjustifiable risk. So the missing link is that no one's made that light bulb go off, to say, there is actually a good reason to change the way you've done things, right? And it's like, maybe it's in your best interest to do things differently, and to care more about something that sounds like IT stuff, like data governance, and data quality. So, that's why I think the chief data officer role, whether it's that title or chief analytics officer, or there's actually a chief artificial intelligence officer at the conference this time around, someone has to be the evangelist who can tell really meaningful stories. I mean, you know, 20 years ago, when IT was trying to convince the business that they should care more about data, data architects and DBAs could talk till they're blue in the face about why data was important. No one wanted to hear it. People get turned off even faster now than they did before, because they have a shorter attention span now than they did before. The fact is that somebody with a lot of credibility on the business side, people who kind of really believe it's capable of driving the business forward, hasta have a very meaningful message, not a half-hour wrap on why data is good for you, but what, specifically, can change in your business that you should want to change. I mean, basically, if you can't put it in terms of what's in it for me, why should they listen to you, right? And so yeah, you know, we've got this thing goin' on, it's really important, and everybody's behind it, and I can give you a list of people whose job title begins with C who really thinks that this is a really important idea, get right down to it, if it's not going to make their area of the business work better, or more efficiently, or, especially with, you know, top line growth sort of issues, they're not going to be that interested. And so it's the job of the person who's trying to evangelize these things to put it in those terms. And it might take some research, it certainly would take some in-depth business knowledge about what happens in that area of the business, you can't give an example from another industry or even another company. You've got to go around and find out what's broken, and talk about what can be fixed, you have to have some really good ideas about what can be innovative in very material terms. One of the breakout sessions I had earlier today, well, they're all around how you define new data products, and get innovative, and very interesting to hear some of the techniques by the folks who'd been successful there, down to, you know, it was somebody's job to go around, and when I say somebody, I don't mean a flunky, I need a chief analytics officer sort of person, talking to people about, you know, what did they hate about their job. Finding, collecting all the things that are broken, and thinking about what could be my best path forward to fix something that's going to get a lot of attention, that I can actually build a marketing message here about why everybody should care about this. And so, the missing link is really not seeing the value in changing behaviors. >> So one of the things that I've always respected about George Colony is he brings people into Forrester that care about social, cultural, organizational issues, not just technology. One of your counterparts, Doug Laney, just wrote a book called Infonomics. You mighta seen it on Twitter, there's a little bit of noise going around it. Premise of the book is essentially that organization shouldn't wait for the accounting industry to tell them how to value data. They should take it upon themselves, and he went into a lot of very detailed, you know, kind of mind-numbing calculations and ways to apply it. But there's a real cultural issue there. First of all, do you buy the premise, and what are you seeing in your client base in terms of the culture of data first, data value, and understanding data value? >> Really good question, really good question. And I do follow what Doug Laney does. Actually, Peter Burris, who you folks know, a long time ago, when he was at Forrester, said, "You know what Doug Laney is doing? "We better be doing that sort of thing." So he brought my attention to it a long time ago. I'm really glad he's working on that area, and I've been in conversations with him at other conferences, where people get into those mind-numbing discussions about the details and how to measure the value of data and stuff, and it's a really good thing that that is going on, and those discussions have to happen. To link my answer to that to answer to your second part of your question about what am I seeing in our client base, is that I'm not seeing a technical answer about how to value data in the books, in a spreadsheet, in some counting rules, going to be the differentiator. The missing link has not been that we haven't had the right rules in place to take X terabytes of data and turn it into X dollars of assets on the books. To me, the problem with that point of view is just that there is data that will bring you gold, and there's data that'll sit there, and it's valuable, but it's not really all that valuable. You know, it's a matter of what do you do with it. You know, I can have a hunk of wood on this table, and it's a hunk of wood, and how much it is, you know, what kind of wood is it and how much does it cost. If I make something out of it that's really valuable to somebody else, it'll cost something completely different based on what its function is, or its value as an art piece or whatever it might be. So, it's so much the product end of it. It's like, what do you do with it, and whether there's an asset value in terms of how it supports the business, in terms of got some regular reporting, but where all the interest is at these days, and why there's a lot of interest in it is like, okay, what are we missing about our business model that can be different, because now that everything's digitized, there are products people aren't thinking of. There are, you know, things that we can sell that may be related to our business, and somehow it's not even related our business, it's just that we now have this data, and it's unique to us, and there's something we can do with it. So the value is very much in terms of who would care about this, and what can I do with it to make it into an analytics product, or, you know, at very least I've got valuable data, I think this is how people tend to think of monetizing data, I've got valuable data, maybe I can put it somewhere people will download it and pay me for it. It's more that I can take this, and then from there do something really interesting with it and create a product, or a service, it's really it's on an app, it's on a phone, or it's on a website, or it's something that you deliver in person, but is giving somebody something they didn't have before. >> So what would you say, from your perspective, what are the companies that are being the most innovative at creating new data products, monetizing, creating new analytics products? What are they doing? What are the best practices of those companies from your perspective? >> You know, I think the best practice of those companies are they've got people who are actively trying to answer the question of, what can I do with this that's new, and interesting, and innovative. I'd say, in the examples I've seen, there been more small to medium companies doing interesting things than really, really huge companies. Or if they're huge companies, they're pockets of huge companies. It's kind of very hard to kind of institutionalize at the enterprise level. It's when you have somebody who gets it about the value of data, working to understand the business at a detailed level enough to understand what might be valuable to somebody in that business if I have a product, is when the magic can potentially happen. And what I've heard people doing are things like that hackathons, where in order to kind of surface these ideas, you get a bunch of folks who kind of get technology and data together with folks who get the business. And they play around with stuff, and they're matching the data to the business problem, comin' up with really kind of cool ideas. Those kind of things tend to happen on a smaller scale. You don't have a hackathon, as far as I can tell, with a couple thousand people in a room. It's usually a smaller sort of operation, where people are digging this up. So, it's folks who kind of get it, because they've been kind of working to find the value in analytics, and it's where there's pockets of people who're kind of working together with the business to make it happen. The profile is such that it's organizations that tend to be more mature about data. They're not complaining that data is something IT should take care of for me. They've kind of been there 10 years ago, or five years ago even, and they've gotten at a point where they actually wanted to move forward from defense and do some offensive playing. They're looking for those kind of cool things to do. So, they're more mature, certainly, than folks who aren't doing it. They're more agile and nimble, I think, than your typical organization in the sense of they can build cross disciplinary teams to make this happen, and that's really where the magic happens. You don't get a genius in the room to come up with this, you get this combination of technical skills, and data knowledge, and data engineering skills, and business smarts all in the same room, and that might be four or five different people to kind of brainstorm until they kind of come up with this. And so the folks who recognize that problem, make that happen, regardless of the industry, regardless of the size of the company, are where it's actually happening. >> I know we have to go, but I wanted to ask you, what about the IBM scorecard in terms of how they're doing in that regard? >> You know, I want to talk to them more. From what they said, you know, in a day, you hear a lot of talk, it's been a long day of hearing people talk about this. It sounds pretty amazing, you know, and I think, actually, we had a half hour session with Inderpal after his keynote, I'm going to get together with him more, and hear more about what's going on under the covers, 'cause it sounds like they're being very effective in kind of making this happen at the enterprise level. And I think that's the unusual thing. I mean, IBM is a huge, huge place. So the notion that you can take these cool ideas and make them work in pockets is one thing. Trying to make it enterprise class, scalable, cognitive-driven organization, with all the right wheels in motion to the data, and analytics, and process, and business change, and operating model change, is kind of amazing. From what I've heard so far, they're actually making it happen. And if it's really, really true, it's really amazing. So it makes me want to hear more, certainly, I have no reason to doubt that what they're saying is happening is happening, I just would love to hear just some more of the story. >> Yeah, you're making us all want to hear more. Well, thanks so much, Gene. It's been a pleasure-- >> Not a problem. >> having you on the show. >> A pleasure. >> Thanks. >> Thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from the CDO Summit just after this. (upbeat music)

okay we're back here live ending up day one of IBM's information on demand exclusive coverage for SiliconANGLE and Wikibon and constellation research breaking down the day one analysis I'm John furrier and join my co-host E on the cube Dave vellante of course as usual and for this closing wrap up segment of day one we have analyst and founder of constellation research ray Wang former analyst big data guru software heading up the partner pavilion kicking off all the flying around the world your own event this month past month things going great how are you how are you doing we're going to great man there's a lot of energy in q3 q4 we've been watching people look at trying to spend down their budgets and I think people are just like worried that there's going to be nothing in 2014 right so they're just bending down we're seeing these big orders like tonight I've got to fly out to New York to close out a deal and help someone else that's basically it was a big day to deal that's going down this is how crazy it's going on and so it's been like this pretty much like for the last four or five weeks so flows budget flush I just wash this budget lunchtime what are you seeing for the deals out there give us some of the examples of some of the sizes and magnitude is it you know you know how are you up and run to get get some cash into secure what size scopes are you seeing up yeah i mean what we're seeing I mean it's anything from a quarter million into like five million dollar deals some of our platform we sing at all levels the one that's really hot we were talking about this that the tableau conference was the date of is right dative is is still really really hot but on the back end we're saying data quality pop-up we're seeing the integration piece play a role we also saw a little bit of content management but not the traditional content management that's coming in more about the text mining text analytics to kind of drive that I mean I'm not sure what are you guys seeing alone yeah so what we're seeing a lot of energy I've seen the budget flush we're not involved in the deals like you are Dave is but for me what I'm seeing is IT the cloud is being accepted I'll you know those has not talked about publicly is kind of a public secret is amazon is just destroying the value proposition of many folks out there with cloud they're just winning the developers hand over fist and you know i'm not sure pivotal with cloud family even catch up even OpenStack has really got some consume energy around we're following that so it opens stack yet amazon on the public cloud winning everything no money's pouring into the enterprise saying hey we got to build the infrastructure under the hood so you can't have the application edge if you don't have the engine so the 100 x price advantage and that's really a scary thing but I think softlayer gives IBM a shot here yeah we were talking about self leyva so you are seeing more I'm seeing it aight aight figure deals and big data right and it's starting to get up there so softly I'd love to get your take on soft layers we've been having a debate all day Oh softlayer jaws mckenna what do you what's your take you're saying it's a hosting I've been a look at first of all yeah I love putting a huge gap 9 million dollars per lock event data center hosting now if that's a footprint they can shave that and kind of give their customers some comfort I think that's the way i see it i mean just I haven't gone inside the numbers to see where it's going to be where this energy is but like we're software virtualization is going on where everyone's going on with virtualization the data center I'll give them a cloud play I just don't see ya didn't have one before I mean happy cloud I mean whistling private club Wow is their software involved I think it provides them with an option to actually deliver cloud services with a compression ratio on storage and a speed that they need to do to deliver mobile mobile data analytics right there's things that are there that are required so it gives them an option to be playing the cloud well I just saw I mean in the news coverage and the small inspection that we did I did was I just didn't reek of software innovation it's simply a data center large hosting big on you agree they didn't really have a northern wobblin driving him before this was brilliant on your Sun setting their previous all these chairs deal kind of musical chairs me for the music stops get something it was that kind of the deal no I think they are feel more like customers asking for something and they wanted IBM to have it yeah IBM works it's an irr play for IBM they're gonna make money on this team not a tuck under deal 900 million no I know but they'll make money on it that's IBM almost always does with it I'll leave it up to you guys to rip on I was your conference oh thanks hey constellation connecting enterprise was awesome we were at the half moon bay Ritz we had 220 folks that were there senior level individuals one of the shocking things for me was the fact that when we pulled the audience on day one two things happen that I would never imagine first thing as ninety percent of the folks downloaded our mobile app which was like awesome right so the network was with them the knowledge is with them when they leave the event and all the relationships the second thing that really shocked me we knew we had really good ratios but it was seventy-five percent of the audience that was line of business execs and twenty-five percent IT it was like we were we didn't have to preach to the choir it was amazing and the IT folks that were they were very very innovative on that end so it was awesome in that way so a lot like the mix the mix here is much more line of business execs the last week at hadoop world loose you know the t-shirt crowd right a lot of practitioners you know scoop I've flume hey we got the earth animals ever right oh but no this event is actually interesting IBM iod for me is like I didn't realize this when I didn't I looked at numbers when we're doing a partner event yesterday and there are thirteen thousand attendees here that actually makes that the biggest big data and analytics conference bigger than strata bigger than a whole bunch of other ones and so I mean this is pretty much the Nexus of what about open world big data over there but this is a big opera you see world any world cloud big data yeah hey the between no but so IBM's done a fantastic job of really transitioning this conference from sort of an eclectic swix db2 informix right I'm management routine fest right yeah and now it's like what are the business things I mean what are we trying to save around the world are they telling the story effectively it's a hard story to tell you got big data analytics cloud mobile in the middle and you got social business but then you got all this use case they have success stories if customers that creating business outcomes they telling the story effectively is it not enough speeds and fees is it too what's your take the stories are there we've seen like 122 case studies from the business partner side we just haven't seen them percolate out and I think they've got to do a better job evangelizing stories but what's interesting is like there's that remember we talked about this data to decision level there's that data level that was IBM right here's the database here's the structure here's the content management here's the unstructured stuff this is where it sets then there was that information management level which that they started to do which is really about cleaning the data connecting that data connecting to upstream and downstream systems getting into CRM and payroll and then they got to this level about insights which was all the Cognos stuff right so they've been building up the stat from data decisions so they got data information information to insight and then we're getting to this decision-making level which they haven't made a lot of the assets or acquisitions there but that's the predictive analytics that's the cognitive computing you can see how they're wrapping around there I mean there's a lot of vendors to buy there's a lot of opportunity out there's a lot to connect and they've been working on it for a while but I guess I got to ask you how they doing what's your report card from last year this year better better storytelling better messaging I think the stories are getting better but we're seeing them in more deals now right before we'd see a lot more SI p traditional SI p oracle you know kind of competes and a little bit of IBM Cognos now we're seeing them in a lot of end-to-end deals and what we're talking about it's not like I T deals these are line of business folks that say look I really need to change my shopping experience what do you guys have we see other things like you know the fraud examples that any was talking about those are hilarious I mean those are real I see em in every place right I mean even with Obamacare right there's gonna be massive amounts of fraud there any places that people going to want to go in and figure out how to connect or correct those kind of things yeah so so seeing the use cases emerge yeah and in particular me last week in a dupe world it was financial services you're talking risk you talk a marketing you're talking fraud protection to forecasting yep the big three and then underneath that is predicted predictive analytics so you know that's all sort of interesting what's your take on on Amazon these days you know they are crushing it on so many different unbelievable right on more billion this year maybe it's when you build a whole company which is basically on the premise of hey let's get people to offset our cost structure from November 15th to january first I mean it's pretty amazing what you can do it's like everyone's covering for it and even more funny it's like they're doing in the physical world with distribution centers I know if we talked about this before but what's really interesting is they've got last mile delivery UPS FedEx DHL can't cat can't handle their capacity so now the ability from digital to physical goods they've got that and beezus goes out and buys the post so he can make the post for example a national paper overnight again he can do home delivery things that they couldn't do before they can take digital ads bring that back in and so basically what they're doing on the cloud side they're also doing on the physical distribution side amazing isn't it they're almost the pushing towards sunday delivery right US Postal Service go into five day deliveries sort of the different directions amazon I'm Amazon's going to be the postal service by the time they're done we're all going to subsidize it so so I gotta get you take on the the Oracle early statement Larry Ellison said were the iphone for the data center that's his metaphor a couple of couple or global enrolls ago now you got open stack and though we kind of laugh at that but but amazon is like the iPhone you know it's disruptive its new its emerging like Apple was reading out of the ashes with Steve Jobs Oracle I think trying to shoehorn in an iphone positioning but if OpenStack if everyone's open and you got amazon here there is a plausible strategy scenario that says hey these guys can continue to to put the naysayers at the side of the road as they march forward to the enterprise and be the iphone they've turned the data center into an API so so we got the date as their lock in right so this sim lock in Apple has lock in so is that lock in what's your take of that scenario you think it's video in the open ecosystem world they're all false open because a walk-in also applies but but you've been even to this for a long time right and probably one of the things that you're seeing is that it's not about open versus closed it's about ubiquity right Microsoft was a closed evil empire back ten years ago now it's like oh the standard right it's like ok they're harmless Google was like open and now they're the evil empire right it just depends on the perception and the really is ubiquity Amazon's got ubiquity on it so i did is pushing their winning the developers the winning the developers they got the ecosystem they got ubiquity they've got a cost structure I mean I don't know what else could go wrong I think they could get s la's maybe and once that had I don't know what is Amazon's blind spot I mean s la's I think well a lumpy performance no one wants lumpy right they want the big Dayton who's got ever who's got better public as public cloud SL is denied well I think about what he just said us everybody no but here's think that's a public road statement not an amazon said let's crunch big data computation December fifteenth you tell me what this is all I want to know well I think I think an easy move is I mean this day you've got to do that on premise I just I just don't I just don't think that people are forecasting amazon the enterprise properly and you just set out the Washington Post that is a left-field move we can now look back and say okay I said makes sense amazon can continue to commoditize and disrupt and be innovative then shift and having some sort of on prem playing oh then it's over right then and then gets the stir days surrounded the castle but they really don't have a great arm tremblay have no on print but they could they could get one good I think they want to see well think they want to but I think with them what they figured out was let's go build some cool public service get everyone else to subsidize our main offerings right it's basically ultimate shared service everyone's subsidizing Amazon's destruction of their business right so if you're Macy is why the heck are you on amazon right you know if you're competing with them why the heck are you on Amazon you're basically digging your own grave I'm paying them to do it it's amazing I mean that's that's the brilliance of this goes invade they brag about it yeah digging your own brave like it's a you know put the compute power is great okay great but you're subsidizing Amazon's for the you know compute power so r a great shot great to have you here congratulations on your event constellation research awesome successful venues ahead last month top folks in you're doing a great job with your company and the end the day out today in the last word tell the folks what's happening with IBM what do you expect to hear from them tomorrow I know you're going to be another thing you had to fly to but what does IBM what's a trajectory coming out of the show for IBM what's your analysis I think the executives have figured out that the important audience here is really the line of business leaders and to figure out how to do couple things one democratize decision-making the second thing figure out how they can actually make it easy to consume IBM at different entry points and I think the third thing is really how can we focus on improving data visualization graphics I think you'll see something about that ray Wang on the cube cube alumni tech athlete entrepreneur new for his new firm not new anymore it's a couple years on his belt doing a great job but three years old congratulations we'll be back day two tomorrow stay with us here exclusive coverage of IBM information I'm John prairie with Dave vellante this is the cube will see you tomorrow the queue

>>Hey guys. Welcome back to the Cubes coverage of Ansible Fast 2022. This is day two of our wall to wall coverage. Lisa Martin here with John Ferer. John, we're seeing this world where companies are saying if we can't automate it, we need to, The automation market is transforming. There's been a lot of buzz about that. A lot of technical chops here at Ansible Fest. >>Yeah, I mean, we've got a great guest here coming on Cuba alumni, Dean Newman, future room. He travels every event he's got. He's got his nose to the grindstone ear to the ground. Great analysis. I mean, we're gonna get into why it's important. How does Ansible fit into the big picture? It's really gonna be a great segment. The >>Board do it well, John just did my job for me about, I'll introduce him again. Daniel Newman, one of our alumni is Back Principal Analyst at Future and Research. Great to have you back on the cube. >>Yeah, it's good to join you. Excited to be back in Chicago. I don't know if you guys knew this, but for 40 years, this was my hometown. Now I don't necessarily brag about that anymore. I'm, I live in Austin now. I'm a proud Texan, but I did grow up here actually out in the west suburbs. I got off the plane, I felt the cold air, and I almost turned around and said, Does this thing go back? Yeah. Cause I'm, I've, I've grown thin skin. It did not take me long. I, I like the warm, Come on, >>I'm the saying, I'm from California and I got off the plane Monday. I went, Whoa, I need a coat. And I was in Miami a week ago and it was 85. >>Oh goodness. >>Crazy. So you just flew in. Talk about what's going on, your take on, on Ansible. We've talked a lot with the community, with partners, with customers, a lot of momentum. The flywheel of the community is going around and round and round. What are some of your perspectives that you see? >>Yeah, absolutely. Well, let's you know, I'm gonna take a quick step back. We're entering an era where companies are gonna have to figure out how to do more with less. Okay? We've got exponential data growth, we've got more architectural complexity than ever before. Companies are trying to discern how to deal with many different environments. And just at a macro level, Red Hat is one of the companies that is almost certainly gonna be part of this multi-cloud hybrid cloud era. So that should initially give a lot of confidence to the buying group that are looking at how to automate their environments. You're automating workflows, but really with, with Ansible, we're focused on automating it, automating the network. So as companies are kind of dig out, we're entering this recessionary period, Okay, we're gonna call it what it is. The first thing that they're gonna look at is how do we tech our way out of it? >>I had a wonderful one-on-one conversation with ServiceNow ceo, Bill McDermott, and we saw ServiceNow was in focus this morning in the initial opening session. This is the integration, right? Ansible integrating with ServiceNow. What we need to see is infrastructure automation, layers and applications working in concert to basically enable enterprises to be up and running all the time. Let's first fix the problems that are most common. Let's, let's automate 'em, let's script them. And then at some point, let's have them self resolving, which we saw at the end with Project Wisdom. So as I see it, automation is that layer that enterprises, boards, technologists, all can agree upon are basically here's something that can make our business more efficient, more profitable, and it's gonna deal with this short term downturn in a way that tech is actually gonna be the answer. Just like Bill and I said, let's tech our way out of it. >>If you look at the Red Hat being bought by ibm, you see Project Wisdom Project, not a product, it's a project. Project Wisdom is the confluence of research and practitioners kind of coming together with ai. So bringing AI power to the Ansible is interesting. Red Hat, Linux, Rel OpenShift, I mean, Red Hat's kind of position, isn't it? Kind of be in that right spot where a puck might be coming maybe. I mean, what do you think? >>Yeah, as analysts, we're really good at predicting the, the recent past. It's a joke I always like to make, but Red Hat's been building toward the future. I think for some time. Project Wisdom, first of all, I was very encouraged with it. One of the things that many people in the market probably have commented on is how close is IBM in Red Hat? Now, again, it's a $34 billion acquisition that was made, but boy, the cultures of these two companies couldn't be more different. And of course, Red Hat kind of carries this, this sort of middle ground layer where they provide a lot of value in services to companies that maybe don't use IBM at, at, for the public cloud especially. This was a great indication of how you can take the power of IBM's research, which of course has some of the world's most prolific data scientists, engineers, building things for the future. >>You know, you see things like yesterday they launched a, you know, an AI solution. You know, they're building chips, semiconductors, and technologies that are gonna power the future. They're building quantum. Long story short, they have these really brilliant technologists here that could be adding value to Red Hat. And I don't know that the, the world has fully been able to appreciate that. So when, when they got on stage and they kind of say, Here's how IBM is gonna help power the next generation, I was immediately very encouraged by the fact that the two companies are starting to show signs of how they can collaborate to offer value to their customers. Because of course, as John kind of started off with, his question is, they've kind of been where the puck is going. Open source, Linux hybrid cloud, This is the future. In the future. Every company's multi-cloud. And I said in a one-on-one meeting this morning, every company is going to probably have workloads on every cloud, especially large enterprises. >>Yeah. And I think that the secret's gonna be how do you make that evolve? And one of the things that's coming out of the industry over the years, and looking back as historians, we would say, gotta have standards. Well, with cloud, now people standards might slow things down. So you're gonna start to figure out how does the community and the developers are thinking it'll be the canary in the coal mine. And I'd love to get your reaction on that, because we got Cuban next week. You're seeing people kind of align and try to win the developers, which, you know, I always laugh cuz like, you don't wanna win, you want, you want them on your team, but you don't wanna win them. It's like a, it's like, so developers will decide, >>Well, I, I think what's happening is there are multiple forces that are driving product adoption. And John, getting the developers to support the utilization and adoption of any sort of stack goes a long way. We've seen how sticky it can be, how sticky it is with many of the public cloud pro providers, how sticky it is with certain applications. And it's gonna be sticky here in these interim layers like open source automation. And Red Hat does have a very compelling developer ecosystem. I mean, if you sat in the keynote this morning, I said, you know, if you're not a developer, some of this stuff would've been fairly difficult to understand. But as a developer you saw them laughing at jokes because, you know, what was it the whole part about, you know, it didn't actually, the ping wasn't a success, right? And everybody started laughing and you know, I, I was sitting next to someone who wasn't technical and, and you know, she kinda goes, What, what was so funny? >>I'm like, well, he said it worked. Do you see that? It said zero data trans or whatever that was. So, but if I may just really quickly, one, one other thing I did wanna say about Project Wisdom, John, that the low code and no code to the full stack developer is a continuum that every technology company is gonna have to think deeply about as we go to the future. Because the people that tend to know the process that needs to be automated tend to not be able to code it. And so we've seen every automation company on the planet sort of figuring out and how to address this low code, no code environment. I think the power of this partnership between IBM Research and Red Hat is that they have an incredibly deep bench of capabilities to do things like, like self-training. Okay, you've got so much data, such significant size models and accuracy is a problem, but we need systems that can self teach. They need to be able self-teach, self learn, self-heal so that we can actually get to the crux of what automation is supposed to do for us. And that's supposed to take the mundane out and enable those humans that know how to code to work on the really difficult and hard stuff because the automation's not gonna replace any of that stuff anytime soon. >>So where do you think looking at, at the partnership and the evolution of it between IBM research and Red Hat, and you're saying, you know, they're, they're, they're finally getting this synergy together. How is it gonna affect the future of automation and how is it poised to give them a competitive advantage in the market? >>Yeah, I think the future or the, the competitive space is that, that is, is ecosystems and integration. So yesterday you heard, you know, Red Hat Ansible focusing on a partnership with aws. You know, this week I was at Oracle Cloud world and they're talking about running their database in aws. And, and so I'm kind of going around to get to the answer to your question, but I think collaboration is sort of the future of growth and innovation. You need multiple companies working towards the same goal to put gobs of resources, that's the technical term, gobs of resources towards doing really hard things. And so Ansible has been very successful in automating and securing and focusing on very certain specific workloads that need to be automated, but we need more and there's gonna be more data created. The proliferation, especially the edge. So you saw all this stuff about Rockwell, How do you really automate the edge at scale? You need large models that are able to look and consume a ton of data that are gonna be continuously learning, and then eventually they're gonna be able to deliver value to these companies at scale. IBM plus Red Hat have really great resources to drive this kind of automation. Having said that, I see those partnerships with aws, with Microsoft, with ibm, with ServiceNow. It's not one player coming to the table. It's a lot of players. They >>Gotta be Switzerland. I mean they have the Switzerland. I mean, but the thing about the Amazon deal is like that marketplace integration essentially puts Ansible once a client's in on, on marketplace and you get the central on the same bill. I mean, that's gonna be a money maker for Ansible. I >>Couldn't agree more, John. I think being part of these public cloud marketplaces is gonna be so critical and having Ansible land and of course AWS largest public cloud by volume, largest marketplace today. And my opinion is that partnership will be extensible to the other public clouds over time. That just makes sense. And so you start, you know, I think we've learned this, John, you've done enough of these interviews that, you know, you start with the biggest, with the highest distribution and probability rates, which in this case right now is aws, but it'll land on in Azure, it'll land in Google and it'll continue to, to grow. And that kind of adoption, streamlining make it consumption more consumable. That's >>Always, I think, Red Hat and Ansible, you nailed it on that whole point about multicloud, because what happens then is why would I want to alienate a marketplace audience to use my product when it could span multiple environments, right? So you saw, you heard that Stephanie yesterday talk about they, they didn't say multiple clouds, multiple environments. And I think that is where I think I see this layer coming in because some companies just have to work on all clouds. That's the way it has to be. Why wouldn't you? >>Yeah. Well every, every company will probably end up with some workloads in every cloud. I just think that is the fate. Whether it's how we consume our SaaS, which a lot of people don't think about, but it always tends to be running on another hyperscale public cloud. Most companies tend to be consuming some workloads from every cloud. It's not always direct. So they might have a single control plane that they tend to lead the way with, but that is only gonna continue to change. And every public cloud company seems to be working on figuring out what their niche is. What is the one thing that sort of drives whether, you know, it is, you know, traditional, we know the commoditization of traditional storage network compute. So now you're seeing things like ai, things like automation, things like the edge collaboration tools, software being put into the, to the forefront because it's a different consumption model, it's a different margin and economic model. And then of course it gives competitive advantages. And we've seen that, you know, I came back from Google Cloud next and at Google Cloud next, you know, you can see they're leaning into the data AI cloud. I mean, that is their focus, like data ai. This is how we get people to come in and start using Google, who in most cases, they're probably using AWS or Microsoft today. >>It's a great specialty cloud right there. That's a big use case. I can run data on Google and run something on aws. >>And then of course you've got all kinds of, and this is a little off topic, but you got sovereignty, compliance, regulatory that tends to drive different clouds over, you know, global clouds like Tencent and Alibaba. You know, if your workloads are in China, >>Well, this comes back down at least to the whole complexity issue. I mean, it has to get complex before it gets easier. And I think that's what we're seeing companies opportunities like Ansible to be like, Okay, tame, tame the complexity. >>Yeah. Yeah, I totally agree with you. I mean, look, when I was watching the demonstrations today, my take is there's so many kind of simple, repeatable and mundane tasks in everyday life that enterprises need to, to automate. Do that first, you know? Then the second thing is working on how do you create self-healing, self-teaching, self-learning, You know, and, and I realize I'm a little broken of a broken record at this, but these are those first things to fix. You know, I know we want to jump to the future where we automate every task and we have multi-term conversational AI that is booking our calendars and driving our cars for us. But in the first place, we just need to say, Hey, the network's down. Like, let's make sure that we can quickly get access back to that network again. Let's make sure that we're able to reach our different zones and locations. Let's make sure that robotic arm is continually doing the thing it's supposed to be doing on the schedule that it's been committed to. That's first. And then we can get to some of these really intensive deep metaverse state of automation that we talk about. Self-learning, data replication, synthetic data. I'm just gonna throw terms around. So I sound super smart. >>In your customer conversations though, from an looking at the automation journey, are you finding most of them, or some percentage is, is wanting to go directly into those really complex projects rather than starting with the basics? >>I don't know that you're, you're finding that the customers want to do that? I think it's the architecture that often ends up being a problem is we as, as the vendor side, will tend to talk about the most complex problems that they're able to solve before companies have really started solving the, the immediate problems that are before them. You know, it's, we talk about, you know, the metaphor of the cloud is a great one, but we talk about the cloud, like it's ubiquitous. Yeah. But less than 30% of our workloads are in the public cloud. Automation is still in very early days and in many industries it's fairly nascent. And doing things like self-healing networks is still something that hasn't even been able to be deployed on an enterprise-wide basis, let alone at the industrial layer. Maybe at the company's on manufacturing PLAs or in oil fields. Like these are places that have difficult to reach infrastructure that needs to be running all the time. We need to build systems and leverage the power of automation to keep that stuff up and running. That's, that's just business value, which by the way is what makes the world go running. Yeah. Awesome. >>A lot of customers and users are struggling to find what's the value in automating certain process, What's the ROI in it? How do you help them get there so that they understand how to start, but truly to make it a journey that is a success. >>ROI tends to be a little bit nebulous. It's one of those things I think a lot of analysts do. Things like TCO analysis Yeah. Is an ROI analysis. I think the businesses actually tend to know what the ROI is gonna be because they can basically look at something like, you know, when you have an msa, here's the downtime, right? Business can typically tell you, you know, I guarantee you Amazon could say, Look for every second of downtime, this is how much commerce it costs us. Yeah. A company can generally say, if it was, you know, we had the energy, the windmills company, like they could say every minute that windmill isn't running, we're creating, you know, X amount less energy. So there's a, there's a time value proposition that companies can determine. Now the question is, is about the deployment. You know, we, I've seen it more nascent, like cybersecurity can tend to be nascent. >>Like what does a breach cost us? Well there's, you know, specific costs of actually getting the breach cured or paying for the cybersecurity services. And then there's the actual, you know, ephemeral costs of brand damage and of risks and customer, you know, negative customer sentiment that potentially comes out of it. With automation, I think it's actually pretty well understood. They can look at, hey, if we can do this many more cycles, if we can keep our uptime at this rate, if we can reduce specific workforce, and I'm always very careful about this because I don't believe automation is about replacement or displacement, but I do think it is about up-leveling and it is about helping people work on things that are complex problems that machines can't solve. I mean, said that if you don't need to put as many bodies on something that can be immediately returned to the organization's bottom line, or those resources can be used for something more innovative. So all those things are pretty well understood. Getting the automation to full deployment at scale, though, I think what often, it's not that roi, it's the timeline that gets misunderstood. Like all it projects, they tend to take longer. And even when things are made really easy, like with what Project Wisdom is trying to do, semantically enable through low code, no code and the ability to get more accuracy, it just never tends to happen quite as fast. So, but that's not an automation problem, That's just the crux of it. >>Okay. What are some of the, the next things on your plate? You're quite a, a busy guy. We, you, you were at Google, you were at Oracle, you're here today. What are some of the next things that we can expect from Daniel Newman? >>Oh boy, I moved Really, I do move really quickly and thank you for that. Well, I'm very excited. I'm taking a couple of work personal days. I don't know if you're a fan, but F1 is this weekend. I'm the US Grand Prix. Oh, you're gonna Austin. So I will be, I live in Austin. Oh. So I will be in Austin. I will be at the Grand Prix. It is work because it, you know, I'm going with a number of our clients that have, have sponsorships there. So I'll be spending time figuring out how the data that comes off of these really fun cars is meaningfully gonna change the world. I'll actually be talking to Splunk CEO at the, at the race on Saturday morning. But yeah, I got a lot of great things. I got a, a conversation coming up with the CEO of Twilio next week. We got a huge week of earnings ahead and so I do a lot of work on that. So I'll be on Bloomberg next week with Emily Chang talking about Microsoft and Google. Love talking to Emily, but just as much love being here on, on the queue with you >>Guys. Well we like to hear that. Who you're rooting for F one's your favorite driver. I, >>I, I like Lando. Do you? I'm Norris. I know it's not necessarily a fan favorite, but I'm a bit of a McLaren guy. I mean obviously I have clients with Oracle and Red Bull with Ball Common Ferrari. I've got Cly Splunk and so I have clients in all. So I'm cheering for all of 'em. And on Sunday I'm actually gonna be in the Williams Paddock. So I don't, I don't know if that's gonna gimme me a chance to really root for anything, but I'm always, always a big fan of the underdog. So maybe Latifi. >>There you go. And the data that comes off the how many central unbeliev, the car, it's crazy's. Such a scientific sport. Believable. >>We could have Christian, I was with Christian Horner yesterday, the team principal from Reside. Oh yeah, yeah. He was at the Oracle event and we did a q and a with him and with the CMO of, it's so much fun. F1 has been unbelievable to watch the momentum and what a great, you know, transitional conversation to to, to CX and automation of experiences for fans as the fan has grown by hundreds of percent. But just to circle back full way, I was very encouraged with what I saw today. Red Hat, Ansible, IBM Strong partnership. I like what they're doing in their expanded ecosystem. And automation, by the way, is gonna be one of the most robust investment areas over the next few years, even as other parts of tech continue to struggle that in cyber security. >>You heard it here. First guys, investment in automation and cyber security straight from two analysts. I got to sit between. For our guests and John Furrier, I'm Lisa Martin, you're watching The Cube Live from Chicago, Ansible Fest 22. John and I will be back after a short break. SO'S stick around.

>>Good morning live from Chicago. It's the cube on the floor at Ansible Fast 2022. This is day two of our wall to wall coverage. Lisa Martin here with John Furrier. John, we're gonna be talking next in the segment with two alumni about what Red Hat and IBM are doing to give Ansible users AI superpowers. As one of our alumni guests said, just off the keynote stage, we're nearing an inflection point in ai. >>The power of AI with Ansible is really gonna be an innovative, I think an inflection point for a long time because Ansible does such great things. This segment's gonna explore that innovation, bringing AI and making people more productive and more importantly, you know, this whole low code, no code, kind of right in the sweet spot of the skills gap. So should be a great segment. >>Great segment. Please welcome back two of our alumni. Perry is here, the Chief scientist, IBM Research and IBM Fellow. And Tom Anderson joins us once again, VP and general manager at Red Hat. Gentlemen, great to have you on the program. We're gonna have you back. >>Thank you for having >>Us and thanks for joining us. Fresh off the keynote stage. Really enjoyed your keynote this morning. Very exciting news. You have a project called Project Wisdom. We're talking about this inflection point in ai. Tell the audience, the viewers, what is Project Wisdom And Wisdom differs from intelligence. How >>I think Project Wisdom is really about, as I said, sort of combining two major forces that are in many ways disrupting and, and really constructing many a aspects of our society, which are software and AI together. Yeah. And I truly believe it's gonna result in a se shift on how not just enterprises, but society carries forefront. And as I said, intelligence is, is, I would argue at least artificial intelligence is more, in some ways mechanical, if I may say it, it's about algorithms, it's about data, it's about compute. Wisdom is all about what is truly important to bring out. It's not just about when you bring out a, a insight, when you bring out a decision to be able to explain that decision as well. It's almost like humans have wisdom. Machines have intelligence and, and it's about project wisdom. That's why we called it wisdom. >>Because it is about being a, a assistant augmenting humans. Just like be there with the humans and, and almost think of it as behave and interact with them as another colleague will versus intelligence, which is, you know, as I said, more mechanical is about data. Computer algorithms crunch together and, and we wanna bring the power of project wisdom and artificial intelligence to developers to, as you said, close the skills gap to be able to really make them more productive and have wisdom for Ansible be their assistant. Yeah. To be able to get things for them that they would find many ways mundane, many ways hard to find and again, be an assistant and augmented, >>You know, you know what's interesting, I want to get into the origin, how it all happened, but interesting IBM research, well known for the deep tech, big engineering. And you guys have been doing this for a long time, so congratulations. But it's interesting here at this event, even on stage here event, you're starting to see the automation come in. So the question comes up, scale. So what happens, IBM buys Red Hat, you go raid the, the raid, the ip, Trevor Treasure trove of ai. I mean this cuz this is kind of like bringing two killer apps together. The Ansible configuration automation layer with ai just kind of a, >>Yeah, it's an amazing relationship. I was gonna say marriage, but I don't wanna say marriage cause I may be >>Last. I didn't mean say raid the Treasure Trobe, but the kind of >>Like, oh my God. An amazing relationship where we bring all this expertise around automation, obviously around IP and application infrastructure automation and IBM research, Richie and his team bring this amazing capacity and experience around ai. Bring those two things together and applying AI to automation for our teams is so incredibly fantastic. I just can't contain my enthusiasm about it. And you could feel it in the keynote this morning that Richie was doing the energy in the room and when folks saw that, it's just amazing. >>The geeks are gonna love it for sure. But here I wanna get into the whole evolution. Computers on computers, remember the old days thinking machines was a company generations ago that I think they've sold or went outta business, but self-learning, learning machines, computers, programming, computers was actually on your slide you kind of piece out this next wave of AI and machine learning, starting with expert systems really kind of, I'm almost say static, but like okay programs. Yeah, yeah. And then now with machine learning and that big debate was unsupervised, supervised, which is not really perfect. Deep learning, which now explores some things, but now we're at another wave. Take, take us through the thought there explaining what this transition looks like and why. >>I think we are, as I said, we are really at an inflection point in the journey of ai. And if ai, I think it's fair to say data is the pain of ai without data, AI doesn't exist. But if I were to train AI with what is known as supervised learning or or data that is labeled, you are almost sort of limited because there are only so many people who have that expertise. And interestingly, they all have day jobs. So they're not just gonna sit around and label this for you. Some people may be available, but you know, this is not, again, as I as Tom said, we are really trying to apply it to some very sort of key domains which require subject matter expertise. This is not like labeling cats and dogs that everybody else in the board knows there are, the community's very large, but still the skills to go around are not that many. >>And I truly believe to apply AI to the, to the word of, you know, enterprises information technology automation, you have to have unsupervised learning and that's the only way to skate. Yeah. And these two trends really about, you know, information technology percolating across every enterprise and unsupervised learning, which is learning on this very large amount of data with of course know very large compute with some very powerful algorithms like transformer architectures and others which have been disrupting the, the domain of natural language as well are coming together with what I described as foundation models. Yeah. Which anybody who plays with it, you'll be blown away. That's literally blown away. >>And you call that self supervision at scale, which is kind of the foundation. So I have to ask you, cuz this comes up a lot with cloud, cloud scale, everyone tells horizontally scalable cloud, but vertically specialized applications where domain expertise and data plays. So the better the data, the better the self supervision, better the learning. But if it's horizontally scalable is a lot to learn. So how do you create that data ops where it's where the machines are gonna be peaked to maximize what's addressable, but what's also in the domain too, you gotta have that kind of diversity. Can you share your thoughts on that? >>Absolutely. So in, in the domain of foundation models, there are two main stages I would say. One is what I'll describe as pre-training, which is think of it as the, the machine in this particular case is knowledgeable about the domain of code in general. It knows syntax of Python, Java script know, go see Java and so, so on actually, and, and also Yammel as well, which is obviously one would argue is the domain of information technology. And once you get to that level, it's a, it's almost like having a developer who knows all of this but may not be an expert at Ansible just yet. He or she can be an expert at Ansible but is not there yet. That's what I'll call background knowledge. And also in the, in the case of foundation models, they are very adept at natural language as well. So they can connect natural language to code, but they are not yet expert at the domain of Ansible. >>Now there's something called, the second stage of learning is called fine tuning, which is about this data ops where I take data, which is sort of the SME data in this particular case. And it's curated. So this is not just generic data, you pick off GitHub, you don't know what exists out there. This is the data which is governed, which we know is of high quality as well. And you think of it as you specialize the generic AI with pre-trained AI with that data. And those two stages, including the governance of that data that goes into it results in this sort of really breakthrough technology that we've been calling Project Wisdom for. Our first application is Ansible, but just watch out that area. There are many more to come and, and we are gonna really, I'm really excited about this partnership with Red Hat because across IBM and research, I think where wherever we, if there is one place where we can find excited, open source, open developer community, it is Right. That's, >>Yeah. >>Tom, talk about the, the role of open source and Project Wisdom, the involvement of the community and maybe Richard, any feedback that you've gotten since coming off stage? I'm sure you were mobbed. >>Yeah, so for us this is, it's called Project Wisdom, not Product Wisdom. Right? Sorry. Right. And so, no, you didn't say that but I wanna just emphasize that it is a project and for us that is a key word in the upstream community that this is where we're inviting the community to jump on board with us and bring their expertise. All these people that are here will start to participate. They're excited in it. They'll bring their expertise and experience and that fine tuning of the model will just get better and better. So we're really excited about introducing this now and involving the community because it's super nuts. Everything that Red Hat does is around the community and this is no different. And so we're really excited about Project Wisdom. >>That's interesting. The project piece because if you see in today's world the innovation strategy before where we are now, go back to say 15 years ago it was of standard, it's gotta have standard bodies. You can still innovate and differentiate, but yet with open source and community, it's a blending of research and practitioners. I think that to me is a big story here is that what you guys are demonstrating is the combination of research and practitioners in the project. Yes. So how does this play out? Cuz this is kind of like how things are gonna get done in the cloud cuz Amazon's not gonna just standardize their stack at at higher level services, nor is Azure and they might get some plumbing commonalities below, but for Project Project Wisdom to be successful, they can, it doesn't need to have standards. If I get this right, if I can my on point here, what do you guys think about that? React to that? Yeah, >>So I definitely, I think standardization in terms of what we will call ML ops pipeline for models to be deployed and managed and operated. It's like models, like any other code, there's standardization on DevOps ops pipeline, there's standardization on machine learning pipeline. And these models will be deployed in the cloud because they need to scale. The only way to scale to, you know, thousands of users is through cloud. And there is, there are standard pipelines that we are working and architecting together with the Red Hat community leveraging open source packages. Yeah. Is really to, to help scale out the AI models of wisdom together. And another point I wanted to pick up on just what Tom said, I've been sort of in the area of productizing AI for for long now having experience with Watson as well. The only scenario where I've seen AI being successful is in this scenario where, what I describe as it meets the criteria of flywheel of ai. >>What do I mean by flywheel of ai? It cannot be some research people build a model. It may be wowing, but you roll it out and there's no feedback. Yeah, exactly. Okay. We are duh. So what actually, the only way the more people use these models, the more they give you feedback, the better it gets because it knows what is right and what is not right. It will never be right the first time. Actually, you know, the data it is trained on is a depiction of reality. Yeah. It is not a reality in itself. Yeah. The reality is a constantly moving target and the only way to make AI successful is to close that loop with the community. And that's why I just wanted to reemphasize the point on why community is that important >>Actually. And what's interesting Tom is this is a difference between standards bodies, old school and communities. Because developers are very efficient in their feedback. Yes. They jump to patterns that serve their needs, whether it's self-service or whatever. You can kind of see what's going on. Yeah. It's either working or not. Yeah, yeah, >>Yeah. We get immediate feedback from the community and we know real fast when something isn't working, when something is working, there are no problems with the flow of data between the members of the community and, and the developers themselves. So yeah, it's, I'm it's great. It's gonna be fantastic. The energy around Project Wisdom already. I bet. We're gonna go down to the Project Wisdom session, the breakout session, and I bet you the room will be overflowed. >>How do people get involved real quick? Get, get a take a minute to explain how I would get involved. I'm a community member. Yep. I'm watching this video, I'm intrigued. This has got me enthusiastic. How do I get more confident with this opportunity? >>So you go to, first of all, you go to red hat.com/project Wisdom and you register your interests and you wanna participate. We're gonna start growing this process, bringing people in, getting ready to make the service available to people to start using and to experiment with. Start getting their feedback. So this is the beginning of, of a journey. This isn't the, you know, this isn't the midpoint of a journey, this is the begin. You know, even though the work has been going on for a year, this is the beginning of the community journey now. And so we're gonna start working together through channels like Discord and whatnot to be able to exchange information and bring people in. >>What are some of the key use cases, maybe Richie are starting with you that, that you think maybe dream use cases that you think the community will help to really uncover as we're looking at Project Wisdom really helping in this transformation of ai. >>So if I focus on let's say Ansible itself, there are much wider use cases, but Ansible itself and you know, I, I would say I had not realized, I've been working on AI for Good for long, but I had not realized the excitement and the power of Ansible community itself. It's very large, it's very bottom sum, which I love actually. But as I went to lot of like CTOs and CIOs of lot of our customers as well, it was becoming clear the use cases of, you know, I've got thousand Ansible developers or IT or automation experts. They write code all the time. I don't know what all of this code is about. So the, the system administrators, managers, they're trying to figure out sort of how to organize all of this together and think of it as Google for finding all of these automation code automation content. >>And I'm very excited about not just the use cases that we demonstrated today, that is beginning of the journey, but to be able to help enterprises in finding the right code through natural language interfaces, generating the code, helping Del us debug their code as well. Giving them predictive insights into this may happen. Just watch out for it when you deploy this. Something like that happened before, just watch out for it as well. So I'm, I'm excited about the entire life cycle of IT automation, Not just about at the build time, but also at the time of deployment. At the time of management. This is just a start of a journey, but there are many exciting use cases abound for Ansible and beyond. >>It's gonna be great to watch this as it unfolds. Obviously just announcing this today. We thank you both so much for joining us on the program, talking about Project wisdom and, and sharing how the community can get involved. So you're gonna have to come back next year. We're gonna have to talk about what's going on. Cause I imagine with the excitement of the community and the volume of the community, this is just the tip of the iceberg. Absolutely. >>This is absolutely exactly. You're excited about. >>Excellent. And you should be. Congratulations. Thank, thanks again for joining us. We really appreciate your insights. Thank you. Thank >>You for having >>Us. For our guests and John Furrier, I'm Lisa Barton and you're watching The Cube Lie from Chicago at Ansible Fest 22. This is day two of wall to wall coverage on the cube. Stick around. Our next guest joins us in just a minute.

>> (vocalizing) >> Narrator: From theCube studios in Palo Alto and Boston, it's theCube. Covering IBM Think, brought to you by IBM. >> Hi everybody, this is Dave Vallante and this is our continuing coverage of Think 2020, the digital event experience. This is the post-thing, the sort of halo effect, the afterthoughts, and joining me is Rob Thomas, he's back. The Senior Vice president of Cloud and Data Platform. Rob, thanks for taking some time to debrief on Think. >> Absolutely Dave, great to be here, good to see you again. >> Yeah, so you have a great event, you guys put it together in record time. I want to talk about sort of your innovation agenda. I mean, you are at the heart of innovation. You're talking cloud, data, AI, really the pillars of innovation, I could probably add in edge to extend the cloud. But I wonder if you could talk about your vision for the innovation agenda and how you're bringing that to customers. I mean, we heard from PayPal, you talked about Royal Bank of Scotland, Credit Mutual, a number of customer examples. How are you bringing innovation forward with the customer? >> I wouldn't describe innovation, maybe I'd give it two different categories. One is, I think the classic term would be consumerization, and you're innovating by making interiorized technology really easy to use. That's why we built out a huge design capability, it's why we've been able to get products like Watson Assistant to get companies live in 24 hours. That's the consumerization aspect, just making enterprise products really easy to use. The second aspect is even harder, which is, how do you tap into an institution like IBM Research, where we're doing fundamental invention. So, one of our now strengths in the last couple of months was around taking technology out of IBM Debater, project Debater, the AI system that could debate humans and then putting that into enterprised products. And, you saw companies like PayPal that are using Watson Assistant and now they have access to that kind of language capability. There's only two aspects here, there's the consumerization and then there's about fundamental technology that really changes how businesses can operate. >> I mean, the point you made about speed and implementation in your key note was critical, I mean really, within 24 hours, very important during this pandemic. Talk about automation, you know, you would think by now right, everything's automation. But, now you're seeing a real boom in automation and it really is driven by AI, all this data, so there's seems to be a next wave, almost a renaissance, if you will, in automation. >> There is and I think automation, when people hear first of the term, it's sometimes a scary term. Because people are like hey, is this going to take my job? Gain a lot of momentum for automation is a difficult, repetitive tasks that nobody really wanted to do in the first place. Whether it's things like data matching, containerizing an application. All these are really hard things and the output's great, but nobody really wants to do that work, they just want the outcome. And, as we've started to demonstrate different use cases for automation that are in that realm, a lot of momentum has taken off, that we're seeing. >> I want to come back to this idea of consumerization and simplification. I mean, when you think about what's been happening over the last several years. And, you and I have talked about this a lot, AI for consumer versus AI for business and enterprise. And really, one of the challenges for the encumbrance, if you will, is to really become data driven, put data at the core and apply machine intelligence to that, just to that data. Now the good news is, they don't have to invent all this stuff, because guys like you are doing that and talk about how you're making that simple. I mean, cloud packs is an example of that, simplification, but talk about how customers are going to be able to tap into AI without having to be AI inventors. >> Well, the classic AI problem actually is a data problem, and the classic data problem is data slide over, which is a company has got a lot of data but it's spread across a hundred or a thousand or tens of thousands different repositories or locations. Our strategy when we say a hybrid cloud is about how do we unify those data storage. So, it's called PaaS, on red hat open shift. We do a lot of things like data virtualization, really high performance. So, we take what is thousands of different data sources and we have that packed like a single fluid item. So then, when you're training models, you can train your models in one place and connect to all your data. That is the big change that's happening and that's how you take something like hybrid cloud, and it actually starts to impact your data architecture. And once you're doing that, then AI becomes a lot easier, because the biggest AI challenge that I described is, where's the data? Is the data in a usable form? >> A lot of times in this industry, you know, we go whale hunting, there are a lot of big companies out there, a lot of times they take priority. You know, at the same time though, a lot of the innovations are coming from companies, you know, we've never even heard of that could be multi-billion dollar companies by the end of the decade. So, how can, you know, small companies and mid-sized companies tap into this trend? Is it just for the big whales or could the small guys participate? >> The thing that's pretty amazing about modern cloud and data technology, I'll call it, is it's accessible to companies of any size. When we talked about, you know, the hundred or so clients that have adopted Watson Assistant since COVID-19 started, many of those are very small institutions with no IT staff or very limited IT staff. Though, we're making this technology very accessible. when you look at something like data, now a small company may not have a hundred different repositories, which is fine, but what they do have is they do want to make better predictions, they do want to automate, they do want to optimize the business processes that they're running in their business. And, the way that we've transformed our model consumption base starting small, it's really making technology available to, you know, from anywhere from the local deli to the Fortune 50 Company. >> So, last question is, What are your big takeaways from Think? I would ask that question normally when we're in a live event. It's a little different with the digital event, but there are still takeaways. What was your reaction and what do to leave people with? >> Even as we get back to doing physical events, which I'm positive will happen at some point. What we learned is there is something great about an immersive digital experience. So, I think the future of events is probably higher than this. Meaning, a big digital experience, to complement the physical experience. That's one big takeaway because the reaction was so positive to the content and how people could access it. Second one is the, all the labs that we did. So, for developers, builders, those were at capacity, meaning we didn't even take any more. So, there's definitively a thirst in the market for developing new applications, developing new data products, developing new security products. That's clear just by the attendance that we saw, that's exciting. Now, I'd say third, that is that AI is now moving into the mainstream, that was clear from the customer examples, whether it was with Tansa or UPS or PayPal that I mentioned before, that was talking with me. AI is becoming accessible to every company, that's pretty exciting. >> Well, the world is hybrid, oh you know the lab, the point you're making about labs is really important. I've talked to a number of individuals saying, "Hey I'm using this time to update my skills. I'm working longer hours, maybe different times of the day, but I'm going to skill up." And you know, the point about AI, 37 years ago, when I started in this business AI was all the buzz and it didn't happen. It's real this time and I'm really excited Rob, that you're at the heart of all this innovation, so really, I appreciate you taking the time. And, best of luck, stay safe, and hopefully we'll see you face to face. >> Offscreen Man: Sure. >> Thanks Dave, same to you and the whole team at theCube, take care. >> Thank you Rob, and thank you for watching everybody, this is Dave Vellante for theCube and our coverage of IBM Think 2020, the digital event experience and the post-event. We'll see you next time. (music)

>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Welcome back to IBM. Think 2020. The global experience. My name, Stupid man. And happy to welcome to the program. Aaron Bobick, who is the vice president and chief design officer for the IBM Cloud Data and AI portfolios. Thank you so much for joining us. >>Thank you, Steven. Great to >>be here. Alright. So I always love talking to design people. My background is engineering. I said on the Cube a couple of times I feel they didn't really teach us in school enough about design. We all know on the consumer side, when you have >>a >>phenomenal technology and beautiful designed together, it's an amazing experience. So you've got a brought purview. You've had a very diverse background. Help us understand. You know what a chief design office they're across, you know, cloud and Data and ai is responsible for >>so in a in a just my job is to really ensure that we design and develop usable and meaningful experiences for our users. Finds customers and partners in the little mawf cloud in the eye both evolving technologies. Um, adoption challenges here and there, and our job is to simplify >>the complex and the network. Okay, that's awesome. You know, I think back, you know, early web days, you know, we were happy if we just had a u I let alone Didn't think about the ux experience there. So you know, what are some of the important things? You know, what? What's IBM looking at? To make sure that that user interface is something that is Yeah. >>So I'll take a step back. And question is doing Say that, you know, in the sounding times while we're still figuring out new ways stood up So to get work done and really get the essence off being more productive design is there to help figure out a solution to these human, because at the end of it, design is really an expression of intent and intend to help solve the problem and overcome everyday challenges. So, you know, be at IBM is basically focusing on helping our users and partners and customers be more productive. And the feeling is that design has become really important to IBM, not just IBM does. Other landed companies are having great advantages. So if I just call it a few studies in a recent guard from the study found that 89% of companies that they would focus and you extend them apart. So this is about differentiation by design the second Forrester Little study, and they found that 70% of projects fail because of poor us, and that's a huge number. There's also city by the GM of the Design Management Institute that says that design that companies are poor home S and P 500 by 20. So all in all this is that design is now a very important aspect of how we go to market, and it's essential. The good news. IBM has always been part of Indiana money for ponderous Thomas. What Jr said, Good design is good business, though We're in it for the long run. >>Yeah, obviously a long history. There are over 100 years of focus on that. So one of the big themes we've heard the last couple of years, you know, see X. That's about that customer experience and not only the external customers but the internal customers we're talking about, you know, support agents and the like. So how is IBM making sure that it is on the leading edge for the >>great questions to over the last? I would say a good 10 years. We really work hard to develop a culture off designing, design, thinking and close by IBM. Whether it's product development, the services we offer support. We work with customers pretty much every touch point of the user has with us. Design has had an influence in it. To get to where we are today, we had to go hire a whole bunch of formally trained designers. We're working across more than 50 plus global design studio to bring in diversity and part of an idea. And at the end of the day, it's not about this confidence in craft. It's also what the baby work. So we had to hire designers, but we also changing the way IBM offers across organizations work. The level of the strain were called the Enterprise Design Thinking Framework, which is essentially our take a human centered design. Build a scale for the enterprise, so the enterprise is a key element here. The practices we've developed using those frameworks helps our team collaborate better keeping the users and their need at the center of everything we do. But it's not just for us. We also developed it for generally everyone. So if anyone wants to take it up, they could try IBM dot com slash design thinking and give it a shot. And through all of these, we have managed to see some incredible progress internally across organizations with alignment and go to market. But we've also seen some great progress that internally as well, case in point over 20 international designer words for design in the Enterprise. But with the last two years across the portfolio, So it's been a fun ride and our focus for customer experience because the endpoints, all the touchpoints has really given us >>a lot of minutes. Well, congratulations on the award is there. We know enterprises are particular and challenging there. They're not necessarily the first to deploy something new. But one of the big discussions we've had for years when you talk about Cloud and AI is a skill set and training. So what are some of the unique challenges that you have from a design stand point in the enterprise? >>I think the answer to your question is in your question, and it comes down to the enterprise. Enterprise is unique in many different ways, right? First of all, it's about mission critical needs, and second is about productivity. Our minds and the users are coming to us to help them solve these massive, complex challenges and problems, from data management to automation to modernization, to being on the cloud or adopting AI. They're really looking detained, the way they work and at scale. This means that we, as designers and at IBM, have to really take the time to understand the users, to see what their pain points are detected environments and the context of the working so that IBM can ultimately >>help solve the conflict. >>No, that's one part second because it's in the enterprise but also dealing with the fact that technology is evolving at a very rapid pace. Thinking about containers, ai Blockchain, you name it and we know that in order to meet the needs of this modern day age workers, we really need to think out of the box and be a little bit ahead of the curve designed for collaboration and the adoption of these emerging technologies without adding a huge learning curve, but that's a challenge as well. How do we adopt technologies without adding learning curves? So as a profession in design, we have to keep up with it, adopt and constantly lead with innovation. In essence, you know, designing for the enterprise brings interesting and unique challenges, and IBM is >>up for it. Well, you know, it sounds great to talk about just having a design that is super easy. And people get, um I'm wondering if you have any, any tips that you could have out there because, you know, I know myself. I'm always Frank, talk to other people, understand what they're doing. And sometimes it's like, Oh, well, today I learned this, and I wish I had learned this two years ago because, boy, you saved me, you know, an hour, a week of my time when I did this. And it's one of things I enjoy doing is trying to help people with short cuts or new ways of doing things. So we get set in our ways when we learn a new technology that tends to be where it fossilized in our brain, and it's upto look at something with fresh eyes and say, Oh, I got an update G. Maybe I should press that button and or float over and to understand what it does. Is there any any guidance that you can have? Is how do you make it simple and intuitive yet overcoming all of the legacy that we have when when we come into it with what interfaces were used? >>I do think that designers have this unique talent of being able to connect the dots, and that's our superpower. So in terms of tips I would take get to know your users get to know them really, really well, think about what exactly are their blockers and then think about technology and see how it can solve that over to connect the dots. So just to give an example. And I was talking about sort of design being broader than this interface design, you know in IBM started reacting to over 19. We need a lot of things. One of the things we did was we kinda defined solution to improve human computer interaction, very using sort of AI technologies like Watson Assistant and Children's Hospitals to help answer the huge number of questions coming in around 19. So from that standpoint, design is about beyond interfaces. And I feel if we take a step back and figure out, what problem are we trying to solve here? And how do we ensure that the users mental model off the things that they used to using in the everyday use, like 20 maps? How can you bring in those innovations back in the enterprise? That issue? >>Okay, you mentioned technologies are changing so fast, you know, AI containers loud. How's your team keeping up with all of this? You know, the pace of change and stop for a drop. You know, we're in S T I C D model these days. So what's the role of the designer in both? Keeping up with the new things and making sure that you know you're helping the user along the way. >>Fortunately, IBM we have a few advantages in having a broader organization called IBM Research. And IBM Research is a little bit forward facing, and they try to predict the uptake of technology that we have a little bit of a heads up on stage now that is a quantum computing, and such as Well, we got enough up there to as a designer. The inherent trade for designers to be curious and Barbara curiosity is to make sure that we learned, and we can combine them and instead of you bring in a sponge. And I think the fact that designers have this golden acid of empathy is very tender and used, and these superpowers to work with designers in other parts of the business, depending the doctor. But how can we not only solve? The problem is we see it but also solve the problems that are not visible. So the later needs of users. So I feel in a lot of different ways. Designers, you know, >>I >>have to be curious there to solve complex problems, and they have to keep up with technology. It's decimated. >>Yeah, I'm curious. It's exciting times. What excites you about the field of design these days? >>I had no Let me take a step back. Your question at the heart of it. I believe that I'm a designer because I believe we can design solutions that impacts people's lives. So in some ways we are adding to a value of human life, and that's what you mean to design and especially in enterprise design, is about that complexity if the messiness off, complex infrastructure and business use cases and localization and globalization is a really hairy problem. So I feel from an intellectual standpoint, this gives me a way to use my that are curious mind as well as my expertise to help solve this problem. So that's what drew me into >>delight. Excellent. Well, so much going on at IBM Think this week I want to give you the final word. What message do you want to share with IBM users, customers and business partners? >>Thank you. Stupid opportunity. Of course. I want to say thank you. Thank you for believing in us for being a North Star. You are The reason why we've invested so much in design and user experience really make our lives better and your willingness to sort of work alongside us every step of the way. It's really appreciate it. I mean, we tend to really feel that you see with us, so help us innovate, help us bring in great experiences that help you get your business are so on that note. If I could do a little shout out to want to be for our customers and prospects here who are listening in the joining on the user experience program. So we can co create experiences with you to solve your problems and hopefully build solutions that you love. Check out the link IBM that based on these experiences, the easy sign up and the second thing that popped a little bit of a user research like invite you to join in on the research about your journey here is that it's still involving field. I understand we're all going to challenges in adopting AI. Let's all learn, share and help each other and infusing AI in your enterprise. Thank you for being >>part of our innovation journey. Excellent. Well, thank you so much for sharing with our community. This update love the fusion of technology and design co creations. One of our favorite words when we talk about this part of the model that we do on the Cube. So thank you so much for joining us. Thank you. All right. Lots more coverage from IBM. Think 2020 The global experience. I'm stupid, man. And thank you for watching the Cube. >>Yeah, Yeah, yeah, yeah

>>live from Miami, Florida It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, everybody. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum, World 2019 brought to you by the M Wear and its ecosystem partners. >> Welcome back to San Francisco. Day three of our coverage here on the Cube Of'em world 2019. I'm John Wall's Glad to have you here aboard for our continuing coverage here Day Volonte is also joining me, as is the sartorially resplendent Eric Herzog, cm of and vice president. Global storage channels that IBM storage. Eric, good to see you and love the shirt. Very >> nice. Thank you. Well, always have a wine shirts when I'm on the Cube >> I love in a long time Cuba to we might say, I'm sure he's got the record. Yeah, might pay. Well, >> you and pattern, neck and neck. We'll go to >> the vault. And well, >> since Pat used to be my boss, you know, couch out a path. >> Well, okay. Let the little show what IBM think. Maybe. Well, that's OK. Let's just start off a big picture. We're in all this, you know. Hybrid. Multilingual. This discussion went on this week. Obviously, just your thoughts about general trends and where the business is going now supposed to wear? Maybe we're 23 years ago. Well, the >> good thing is for IBM storage, and we actually came to your partner and titty wiki Bond when our new general manager, Ed Walsh, joined. And we came and we saw Dave and John at the old office are at your offices, and we did a pitch about hybrid multi cloud. Remember that gave us some feedback of how to create a new slide. So we created a slide based on Dave's input, and we were doing that two and 1/2 years ago. So we're running around telling the storage analyst Storage Press about hybrid multi cloud based on IBM storage. How weaken transparently move data, things we do with backup, Of course. An archive. You've got about 450 small and medium cloud providers. Their backup is a service engine. Is our spectrum protect? And so we talked about that. So Dave helped us craft the slide to make it better, because he said, we left a couple things >> out that Eric >> owes you. There were a few other analysts I'm sure you talked to and got input, but but us really were the first toe to combine those things in your in your marketing presentations. But >> let's I'd love to get >> an update on the business. Yes, help people understand the IBM storage organization. You guys created the storage business, you know, years and years and years ago. It's a it's a you know you've got your core business, which is column arms dealers. But there's a lot of Regent IBM, the Cloud Division. You've got the service's division, but so help us understand this sort of organizational structure. So >> the IBM story division's part of IBM Systems, which includes both the mainframe products Z and the Power Server entities. So it's a server in storage division. Um, the Easy guys in particular, have a lots of software that they sell and not just mainframe. So they have a very, very large software business, as do we. As you know, from looking at people that do the numbers, We're the second largest storage software company in the world, and the bulk of that software's not running on IBM gear. So, for example, spectrum protect will back up anyone's array spectrum scale and our IBM Cloud Object storage are sold this software only software defined as the spectrum virtualized. You could basically create a J. Bader Jabo after your favorite distributor or reseller and create your honor. Rates are software, but the all of the infrastructure would actually not be ours, not branded by us. And you call us for tech support for the software side. But if you had a bad power supplier fan, you'd have to call, you know, the reseller distributor said this very robust storage software business. Obviously you make sure that was compatible with the other server elements of IBM systems. But the bulk of our storage is actually sitting connect to some server that doesn't have an IBM logo on it. So that's the bulk of our business connected to Intel servers of all types that used to include, of course, IBM Intel Server division, which was sold off to Lenovo. So we still have a very robust business in the array space that has nothing to do with working on a power machine are working on a Z machine, although we clearly worked very heavily with them and have a number of things going with him, including something that's coming very shortly in the middle of September on some new high end products that we're going to dio >> went 90 Sea Counts All this stuff. Do they >> count to give IBM credit for all the storage that lives inside of the IBM Cloud? Do you get you get credit for that or >> not get credit for that? So when they count our number, it's only the systems that we sell and the storage software that we sell. So if you look at if we were a standalone company, which would include support service made everything, some of which we don't get credit for, right, the support and service is a different entity at IBM that does that, UM, the service's group, the tech support that all goes to someone else. We don't have a new credit >> so hypothetical I don't I don't think this is the case, but let's say hypothetically, if pure storage sold an array into IBM Cloud, they would get credit for it. But if you're array and I'm sure this happens is inside of the IBM, you don't get credit for it. >> That's true interesting, so it's somewhat undercounts. Part of that is the >> way we internally count because we're selling it to ourselves. >> But that's it. >> It's not. It's more of an accounting thing, but it's different when we sell the anybody else. So, for example, we sell the hundreds of cloud providers who in theory compete with the IBM Cloud Division >> to you Get credit for that. You get credit for your own away. That's way work. But if we were standing >> on coming for, say, government, we were Zog in store and I bought the company away, we would be about a $6.3 billion standalone storage software company. That's what we would be if we were all in because support service manes. If we were our own company with our own right legal entity, just like net app or the other guys, we'd be Stanley would be in that, you know, low $6 billion range, counting everything all in. When we do report publicly, we only report our storage system because we don't report our storage software business. And as you notice a few times, our CFO has made comments. If we did count, the storage software visit would be ex, and he's publicly stated that price at least two times. Since I've been an idea when he talks about the software on, but legally we only talk about IBM storage systems. When he publicly state our numbers out onto Wall Street, that's all >> we publicly report. So, um, you're like, you're like a walking sheet of knowledge here, but I wonder if you could take the audience through the portfolio. Oh, it's vast. How should we think about it? And the names have changed. You talk about, you know, 250 a raise, whatever it is the old sand volume control. And now it's a spectrum virtualized, >> right? So take us to the portfolio. What's the current? It's free straight for. >> We have really three elements in the portfolio, all built around, if you will, solution plays. But the three real elements in the portfolio our storage arrays, storage systems, we have entry mid range and high end, just like our competitors do. We lead with all flash, but we still sell hybrid and obviously, for backup, an archive. We still sell all hard drive right for those workloads. So and we have filed blocking object just like most other guys do, Um, for an array, then we have a business built around software, and we have two key elements. Their software defined storage, and we saw that software completely stand alone. It happens, too, by the way, be embedded on the arrays. So, for example, Dave, you mentioned Spectrum virtualized that ship's on flash systems and store wise. But if you don't want our raise, we will sell you just spectrum virtualized alone for block spectrum scale for Big Big Data A. I file Workloads and IBM caught object storage, which could all of them could be bought on an array. But they also could be bought. Itjust Standalone component. Yes, there's a software so part of the advantage we feel that delivers. It's some of the people that have software defined storage, that air raid guys. It's not the same software, so for us, it's easier for us to support and service. It's easier for a stack developing have leading it. Features is not running two different pieces of software running, one that happens to have a software on Lee version or an array embedded version. So we've got that, and then the third is around modern data protection, and that's really it. So a modern data protection portfolio built around spectrum, protect and Protect Plus and some other elements. A software to find storage where we sell the software only, and then arrays. That's it. It's really three things and not show. Now they're all kinds components underneath the hood. But what we really do is we sell. We don't really run around and talk about off last race. We talk about hybrid multi cloud. Now all of our flash raise and a lot of our software defined storage will automatically tear data out, too. Hybrid multi cloud configurations. We just So we lead with that same thing. We have one around cyber resiliency. Now, the one thing that spans the whole portfolio of cyber resiliency way have cyber rebellion see and a raise. We have some softer on the mainframe called Safeguarded Copy that creates immutable copies and has extra extra security for the management rights. You've got management control, and if you have a malware ransomware attack, you couldn't recover to these known good copies. So that's a piece of software that we sell on the mainframe on >> how much growth have you seen in that in? Because he's never reveals if you've got it resonating pervasive, right, Pervasive. So >> we've got, for example, malware and ransomware detection. Also, Inspector protect. So it's taken example. So I'm going to steal from the Cube and I'm gonna ask Dave and for you, I want a billion dollars and Dave's gonna laugh at me because he used a spectrum protect. He's gonna start laughing. But if I'm the ransomware guy, what do I do? I go after your snapshots, your replicas and your backup data sets. First, I make sure I've got those under control. And then when I tell you I'm holding you for ransom, you can't go back to a known good copy. So Ransomware goes after backup snaps and replicas first. Then it goes half your primary storage. So what we do, inspector protect, for example, is we know that at Weeki Bond and the Cube, you back up every night from 11 32 1 30 takes two hours to back you up every night. It's noon. There's tons of activity in the backup data sets. What the heck is going on? We send it out to the admin, So the admin for the Cube wicky bond takes a look and says, No server failure. So you can't be doing a lot of recovery because of a bad server. No storage failures. What the heck is going on? It could be a possible mount where ransomware attack. So that type of technology, we encrypt it, rest on all of our store to raise. We have both tape and tape and cloud air gapping. I'm gonna ask you about that. We've got both types of air gapped >> used to hate tape. Now he loves my love, right? No, I used to hate it, But now I love it because it's like the last resort, just in case. And you do air gapping when you do a WR gapping with customers, Do you kind of rotate the You know, it's like, uh, you know, the Yasser Arafat used to move every night. You sleep in a different place, right? You gonna rotate the >> weird analogy? You do >> some stuff. There's a whole strategy >> of how we outlined how you would do a tape air gap, you a cloud air gap. Of course you're replicating or snapping out to the cloud anyway, so they can't get to that. So if you have a failure, we haven't known good copy, depending on what time that is, right. And then you just recover. Cover back to that and even something simple. We have data rest, encryption. Okay. A lot of people don't use it or won't use it on storage because it's often software based, and so is permanent. Well, in our D s platform on the mainframe, we can encrypt with no performance hit on our flash system products we can encrypt with no performance it on our high end store. Wise, we have four models on the two high end stores models we could encrypt with no performance penalty. So why would you not encrypt all your debt? When there's a performance penalty, you have to sort of pick and choose. My God, I got to encrypt this valuable financial data, but, boy, I really wish it wasn't so slow with us. There is no performance it when you encrypt. So we have encryption at rest, encryption at flight malware and ran somewhere detection. We've got worm, which is important, obviously, doesn't mean I can't steal from wicked Bond Cube, but I certainly can't go change all your account numbers for all your vendors. For sake. of argument, right? So and there's obviously heavily regulated industries that still require worm technology, right? Immutable on the fine, by the way, you could always if it's wormed, you could encrypt it if you want to write. Because Worm just means it's immutable. It doesn't. It's not a different data type. It's just a mutable version of that data. >> So the cyber resiliency is interesting, and it leads me to another question I have around just are, indeed so A lot of companies in this industry do a lot of D developing next generation products. I think, you know, look a t m c when you were there, you know, this >> was a lot of there. Wasn't a ton, >> of course, are a lot of patents and stuff like that. IBM does corps are a lot of research and research facilities, brainiac scientists, I want if you could talk about how the storage division takes advantage of that, either specifically, is it relates to cyber resiliency. But generally, >> yes, so as you know, IBM has got, I think it's like 12 12 or 15 research on Lee sites that that's all they do, and everyone there is, in fact, my office had to be. Akiyama didn't labs, and there's two labs actually hear. The AMA didn't research lab and the Silicon Valley lab, which is very close about five miles away. Beautiful. Almost everything. There is research. There's a few product management guys I happen, Navid desk there every once. Well, see a sales guy or two. But essentially, they're all Richard with PhDs from the leading inverse now at Al Madden and many sites, all the divisions have their own research teams there. There's a heavy storage contingent at Al Midan as an example. Same thing in Zurich. So, for example, we just announced last week, as you know, stuff that will work with Quantum on the tape side. So you don't have to worry about because one of things, obviously, that people complain about quantum computing, whether it's us or anyone else, the quantum computing you can crack basically any encryption. Well, guess what? IBM research has developed tape that can be encrypted. So if using quantum computer, whether it be IBM or someone else's when you go with quantum computing, you can have secured data because the quantum computer can't actually cracked the encryption that we just put into that new tape that was done at IBM Research. How >> far away are we from From Quantum, actually being ableto be deployed and even minor use cases. >> Well, we've got available right now in ibm dot com for Betas. So we've got several 1000 people who have been accidents in it. And entities, we've been talking publicly in the 3 to 7 year timeframe for quantum computer crap out. Should it? Well, no, because if you do the right sort of security, you don't but the power. So if you're envisioning one of my favorite movies, I robot, right where she's doing her talking and that's that would really be quantum in all honesty. But at the same time, you know, the key thing IBM is all about ethics and all about how we do things, whether it be what we do with our diversity programs and hiring. And IBM is always, you know, at the forefront of doing and promoting ethical this and ethical. Then >> you do a customer data is huge. >> Yeah, and what we do with the customer data sets right, we do. GDP are, for example, all over the world were not required by law to do it really Only in Europe we do it everywhere. And so if you're not, if you're in California, if you happen to be in Zimbabwe or you're in Brazil, you get the same protection of GDP are even though we're not legally required to do it. And why are we doing that? Because they're always concerned about customers data, and we know they're paranoid about it. We want to make sure people feel comfortable with IBM. We do. Quantum computing will end up in that same vein. >> But you know, I don't worry about you guys. I were about the guys on the other side of the fence, the ones that I worry about, the same thing Capabilities knew that was >> on, of course. And you know, he talked about it in his speech, and he talked about action on the Cube yesterday about some of his comments on the point, and he mentioned that was based on Blockchain. What he said was Blockchain is a great technology. They've got Blockchain is no. IBM is a big believer in Blockchain. We promoted all over the place and in fact we've done all kinds of different Blockchain things we just did. One announced it last week with Australia with the Australian. I think it is with their equivalent of Wall Street. We've done some stuff with Merrick, the big shipping container thing, and it's a big consortium. That's all legal stuff that was really talking about someone using it the wrong way. And he's very specific point out that Blockchain is a great technology if used ethically, and IBM is all about how we do it. So we make sure whether be quantum computing, Blockchain, et cetera, that everything we do at IBM is about helping the end users, making sure that we're making, for example, open source. As you know. Well, the number one provider of open source technology pre read had acquisition is IBM. We submit Maur into the open community. Renounce Now are we able to make some money off of that? Sure we are, but we do it for a reason, because IBM believes as day point out in this core research. Open computing is court research, and we just join the Open Foundation last week as well. So we're really big on making sure that what we do ourselves is Ethel now We try to make sure that what happens in the hands of people who buy our technology, which we can always track, is also done ethically. And we go out of our way to join the right industry. Associations work with governments, work with whatever we need to do to help make sure that technology could really be iRobot. Anyone who thinks that's not true. If you talk to your grandparent's goto, go to the moon. What are you talking about? >> What Star Trek. It's always >> come to me. Oh, yeah, >> I mean, if you're your iPhone is basically the old community. Transport is the only thing I wish I could have the transfer. Aziz. You know, >> David has the same frame us up. I'm afraid of flying, and I I felt like two million miles on United and David. He's laughs about flowers, so I'm waiting for the transport. I know that's why anymore there's a cone over here. Go stand. Or maybe maybe with a little bit of like, I'm selling my Bitcoin. No, hang on, just hold on. There's always a comeback. Not always. There could be a comeback because Derek always enjoy it as always. Thanks for the good seeing you. All right, Back with more Veum. World 2019 The Cube live in San Francisco.

(electronica) >> Live, from San Francisco, California it's theCube. Covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at Fisherman's Wharf covering the IBM Chief Data Officer event, the 10th anniversary. You're watching theCube, the leader in live tech coverage. Just off the keynotes, Martin Schroeter is here as the Senior Vice President of IBM Global Markets responsible for revenue, profit, IBM's brand, just a few important things. Martin, welcome to theCube. >> They're important, they're important. >> Inderpal Bhandari, Cube alum, Global Chief Data Officer at IBM. Good to see you again. >> Good to see you Dave, >> So you guys, just off the keynotes, Martin, you talked a lot about disruption, things like digital trade that we're going to get into, digital transformation. What are you hearing when you talk to clients? You spent a lot of time as the CFO. >> I did. >> Now you're spending a lot of time with clients. What are they telling you about disruption and digital transformation? >> Yeah, you know the interesting thing Dave, is the first thing every CEO starts with now is that "I run a technology company." And it doesn't matter if they're writing code or manufacturing corrugated cardboard boxes, every CEO believes they are running a technology company. Now interestingly, maybe we could've predicted this already five or six years ago because we run a CEO survey, we run a CFO, we run surveys of the C-suite. And already about five years ago, technology was number one on the CEO's list of what's going to change their company in the next 3-5 years. It led. The CFO lagged, the CMO lagged, everyone else. Like, CEO saw it first. So CEOs now believe they are running technology businesses, and when you run a technology business, that means you have to fundamentally change the way you work, how you work, who does the work, and how you're finding and reaching and engaging with your clients. So when we talk, we shorthand of digitizing the enterprise. Or, what does it mean to become a digitally enable enterprise? It really is about how to use today's technology embedded into your workflows to make sure you don't get disintermediated from your clients? And you're bringing them value at every step, every touchpoint of their journey. >> So that brings up a point. Every CEO I talk to is trying to get "digital right." And that comes back to the data. Now you're of course, biased on that. But what are your thoughts on a digital business? Is digital businesses all about how they use data and leverage data? What does it mean to get "digital right" in your view? >> So data has to be the starting point. You actually do see examples of companies that'll start out on a digital transformation, or a technology transformation, and then eventually back into the data transformation. So in a sense, you've got to have the digital piece of it, which is really the experience that users have of the products of the company, as well as the technology, which is kind of the backend engines that are running. But also the workflow, and being able to infuse AI into workflows. And then data, because everything really rides on the data being in good enough shape to be able to pull all this off. So eventually people realize that really it's not just a digital transformation or technology transformation, but it is a data transformation to begin with. >> And you guys have talked a lot at this event, at least this pre-event, I've talked to people about operationalizing AI, that's a big part of your responsibilities. How do you feel about where you're at? I mean, it's a journey I know. You're never done. But feel like you're making some good progress there? Internally at IBM specifically. >> Yes, internally at IBM. Very good progress. Because our whole goal is to infuse AI into every major business process, and touch every IBM. So that's the whole goal of what we've been doing for the last few years. And we're already at the stage where our central AI and data platform for this year, over 100,000 active users will be making use of it on a regular basis. So we think we're pretty far along in terms of our transformation. And the whole goal behind this summit and the previous summits as you know, Dave, has been to use that as a showcase for our clients and customers so that they can replicate that journey as well. >> So we heard Ginni Rometty two IBM thinks ago talk about incumbent disruptors, which resonates, 'cause IBM's an incumbent disruptor. You talked about Chapter One being random acts of digital. and then Chapter Two is sort of how to take that mainstream. So what do you see as the next wave, Martin? >> Well as Inderpal said, and if I use us as an example. Now, we are using AI heavily. We have an advantage, right? We have this thing called IBM Research, one of the most prolific Inventors of Things still leads the world. You know we still lead the world in patents so have the benefit. For our our clients, however, we have to help them down that journey. And the clients today are on a journey of finding the right hybrid cloud solution that gives them bridges sort of "I have this data. "The incumbency advantage of having data," along with "Where are the tools and "where is the compute power that I need to take advantage of the data." So they're on that journey at the same time they're on the journey as Inderpal said, of embedding it into their workflows. So for IBM, the company that's always lived sort of at the intersection of technology and business, that's what we're helping our clients to do today. Helping them take their incumbent advantage of data, having data, helping them co-create. We're working with them to co-create solutions that they can deploy and then helping them to put that into work, into production, if you will, in their environments and in their workflows. >> So one of the things you stressed today, two of the things. You've talked about transparency, and open digital trade. I want to get into the latter, but talk about what's important in Chapter Two. Just, what are those ingredients of success? You've talked about things like free flow of data, prevent data localization, mandates, and protect algorithms and source codes. You also made another statement which is very powerful "IBM is never giving up its source code to our government, and we'd leave the country first." >> We wouldn't give up our source code. >> So what are some of those success factors that we need to be thinking about in that context? >> If we look at IBM. IBM today runs, you know 87% of the world's credit card transactions, right? IBM today runs the world's banking systems, we run the airline reservation systems, we run the supply chains of the world. Hearts and lungs, right? If I just shorthand all of that, hearts and lungs. The reason our clients allow us to do that is because they trust us at the very core. If they didn't trust us with our data they wouldn't give it to us. If they didn't trust us to run the process correctly, they wouldn't give it to us. So when we say trust, it happens at a very base level of "who do you really trust to run you're data?" And importantly, who is someone else going to trust with your data, with your systems? Any bank can maybe figure out, you know, how to run a little bit of a process. But you need scale, that's where we come in. So big banks need us. And secondly, you need someone you can trust that can get into the global banking system, because the system has to trust you as well. So they trust us at a very base level. That's why we still run the hearts and lungs of the enterprise world. >> Yeah, and you also made the point, you're not talking about necessarily personal data, that's not your business. But when you talked about the free flow of data, there are governments of many, western governments who are sort of putting in this mandate of not being able to persist data out of the country. But then you gave an example of "If you're trying to track a bag at baggage claim, you actually want that free flow of data." So what are those conversations like? >> So first I do think we have to distinguish between the kinds of data that should frow freely and the kinds of data that should absolutely, personal information is not what we're talking about, right? But the supply chains of the world work on data, the banking system works on data, right? So when we talk about the data that has to flow freely, it's all the data that doesn't have a good reason for it to stay local. Citizen's data, healthcare data, might have to stay, because they're protecting their citizen's privacy. That's the issue I think, that most governments are on. So we have disaggregate the data discussion, the free flow of data from the privacy issues, which are very important. >> Is there a gray area there between the personal information and the type of data that Martin's talking about? Or is it pretty clear cut in your view? >> No, I think this is obviously got to play itself out. But I'll give you one example. So, the whole use of a blockchain potentially helps you address and find the right balance between privacy of sensitive data, versus actually the free flow of data. >> Right. >> Right? So for instance, you could have an encryption or a hashtag. Or hash, sorry. Not a hashtag. A hash, say, off the person's name whose luggage is lost. And you could pass that information through, and then on the other side, it's decrypted, and then you're able to make sure that, you know, essentially you're able to satisfy the client, the customer. And so there's flow of data, there's no issue with regard to exposure. Because only the rightful parties are able to use it. So these things are, in a sense, the technologies that we're talking about, that Martin talked about with the blockchain, and so forth. They are in place to be able to really revolutionize and transform digital trade. But there are other factors as well. Martin touched on a bunch of those in the keynote with regard to, you know, the imbalances, some of the protectionism that comes in, and so on and so forth. Which all that stuff has to be played through. >> So much to talk about, so little time. So digital trade, let's get into that a little bit. What is that and why is it so important? >> So if you look at the economic throughput in the digital economy, the size of the GDP if you will, of what travels around the world in the way data flows, it's greater than the traded goods flow. So this is a very important discussion. Over the last 10 years, you know, out of the 100% of jobs that were created, 80% or so had a digital component to it. Which means that the next set of jobs that we're creating, they require digital skills. So we need a set of skills that will enable a workforce. And we need a regulatory environment that's cooperative, that's supportive. So in the regulatory environment, as we said before, we think data should flow freely unless there's a reason for it not to flow. And I think there will be some really good reasons why certain data should not flow.. But data should flow freely, except for certain reasons that are important. We need to make sure we don't create a series of mandates that force someone to store data here. If you want to be in business in a country, the country shouldn't say "Well if you want to business here "you have to store all your data here." It tends to be done on the auspice of a security concern, but we know enough about security that doesn't help. It's a false sense of security. So data has to flow freely. Don't make someone store it there just because it may be moving through or it's being processed in your country. And then thirdly, we have to protect the source code that companies are using. We cannot force, no country should force, a company to give up their source code. People will leave, they just won't do business there. >> That's just not about intellectual property issue there, right? >> It's huge intellectual property issue, that's exactly right. >> So the public policy framework then, is really free flow of data where it makes sense. No mandates unless it makes sense, and- >> And protection of IP. >> Protection of IP. >> That's right. >> Okay, good. >> It's a pretty simple structure. And based on my discussions I think most sort of aligned with that. And we're encouraged. I'm encouraged by what I see in TPP, it has that. What I see in Europe, it has that. What I see in USMCA it has that. So all three of those very good, but they're three separate things. We need to bring it all together to have one. >> So it was a good example. GDDPR maybe as a framework that seems to be seeping its way into other areas. >> So GDPR is an important discussion, but that's the privacy discussion wrapped around a broader trade issue. But privacy is important. GDPR does a good job on it, but we have a broader trade issue of data. >> Inderpal give me the final word, it's kind of your show. >> Well, you know. So I was just going to say Dave, I think one way to think about it is you have to have the free flow of data. And maybe the way to think about it is certain data you do need controls on. And it's more of the form in which the data flows that you restrict. As opposed to letting the data flow at all. >> What do you mean? >> So the hash example that I gave you. It's okay for the hash to go across, that way you're not exposing the data itself. So those technologies are all there. It's much more the regulatory frameworks that Martin's talking about, that they've got to be there in place so that we are not impeding the progress. That's going to be inevitable when you do have the free flow of data. >> So in that instance, the hash example that you gave. It's the parties that are adjudicating, the machines are adjudicating. Unless the parties want to expose that data it won't be exposed. >> It won't happen, they won't be exposed. >> All right. Inderpal, Martin, I know you got to run. Thanks so much for coming out. >> Thank you. Thanks for the talk. >> Thank you >> You're welcome. All right. Keep it right there everybody, we'll be back with our next guest from IBMCDO Summit in San Francisco. You're watching theCube. (electronica)

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officers Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Inderpal Bhandari, he is the Global Chief Data Officer at IBM. Thank you so much for coming back on theCUBE, Inderpal. >> It's my pleasure. >> It's great to have you. >> Thank you for having me. >> So I want to talk, I want to start by talking a little bit about your own career journey. Your first CDO job was in the early 2000s. You were one of the first CDOs, ever. In the history of Chief Data Officers. Talk a little bit about the evolution of the role and sort of set the scene for our viewers in terms of what you've seen, in your own career. >> Yes, no thank you, December 2006, I became a Chief Data Officer of a major healthcare company. And you know, it turned out at that time there were only four of us. Two in banking, one in the internet, I was the only one in healthcare. And now of course there are well over 1,999 of us and the professions taken off. And I've had the fortune of actually doing this four times now. So leading a legacy in four different organizations in terms of building that organizational capability. I think initially, when I became Chief Data Officer, the culture was one of viewing data's exhaust. Something that we had to discard, that came out of the transactions that you were, that your business was doing. And then after that you would discard this data, or you didn't really care about it. And over the course of time, people had begun to realize that data is actually a strategic asset and you can really use it to drive not just the data strategy, but the actual business strategy, and enable the business to go to the next level. And that transitions been tremendous to watch and to see. I've just been fortunate that I've been there for the full journey. >> Are you seeing any consensus developing around what background makes for a good CDO? What are the skills that a CDO needs? >> Yeah, no that's a very, very good question. My view has been evolving on that one too, over the last few years, right, as I've had these experiences. So, I'll jump to the conclusion, so that you kind of, to answer your question as opposed to what I started out with. The CDO, has to be the change agent in chief, for the organization. That's really the role of the CDO. So yes, there's the technical sharps that you have to have and you have to be able to deal with people who have advanced technical degrees and to get them to move forward. But you do have to change the entire organization and you have to be adept at going after the culture, changing it. You can't get frustrated with all the push back, that's inevitable. You have to almost develop it as an art, as you move forward. And address it, not just bottom up and lateral, but also top down. And I think that's probably where the art gets the most interesting. Because you've got to push a for change even at the top. But you can push just so far without really derailing everything that you are trying to do. And so, I think if I have to pick one attribute, it would be that the CDO has to be the change agent in chief and they have to be adept at addressing the culture of the organization, and moving it forward. >> You're laying out all of these sort of character traits that someone has to be indefatigable, inspirational, visionary. You also said during the keynote you have six months to really make your first push, the first six months are so important. When we talk about presidents, it's the first 100 days. Describe what you mean by that, you have six months? >> So if a new, and I'm talking here mainly about a large organization like an IBM, a large enterprise. When you go in, the key observation is it's a functioning organization. It's a growing concern. It's already making money, it's doing stuff like that. >> We hope. >> And the people who are running that organization, they have their own needs and demands. So very quickly, you can just become somebody who ends up servicing multiple demands that come from different business units, different people. And so that's kind of one aspect of it. The way the organization takes over if you don't really come in with an overarching strategy. The other way the organizations take over is typically large organizations are very siloed. And even at the lower levels you who have people who developed little fiefdoms, where they control that data, and they say this is mine, I'm not going to let anybody else have it. They're the only one's who really understand that curve. And so, pretty much unless you're able to get them to align to a much larger cause, you'll never be able to break down those silos, culturally. Just because of the way it's set up. So its a pervasive problem, goes across the board and I think, when you walk in you've got that, you call it honeymoon period, or whatever. My estimate is based on my experience, six months. If you don't have it down in six months, in terms of that larger cause that your going to push forward, that you can use to at least align everybody with the vision, or you're not going to really succeed. You'll succeed tactically, but not in a strategic sense. >> You're about to undertake the largest acquisition in IBM's history. And as the Chief Data Officer, you must be thinking right now about what that's going to mean for data governance and data integration. How are you preparing for an acquisition that large? >> Yeah so, the acquisition is still got to work through all the regulations, and so forth. So there's just so much we can do. It's much more from a planning stand point that we can do things. I'll give you a sense of how I've been thinking about it. Now we've been doing acquisitions before. So in that since we do have a set process for how we go about it, in terms of evaluating the data, how we're going to manage the data and so forth. The interesting aspect that was different for me on this one is I also talked back on our data strategy itself. And tried to understand now that there's going to be this big acquisition of move forward, from a planning standpoint how should I be prepared to change? With regard to that acquisition. And because we were so aligned with the overall IBM business strategy, to pursue cognition. I think you could see that in my remarks that when you push forward AI in a large enterprise, you very quickly run into this multi-cloud issue. Where you've got, not just different clouds but also unprime and private clouds, and you have to manage across all that and that becomes the pin point that you have to scale. To scale you have to get past that pin point. And so we were already thinking about that. Actually, I just did a check after the acquisition was announced, asking my team to figure out well how standardized are we with Red Hat Linux? And I find that we're actually completely standardized across with Red Hat Linux. We pretty much will have use cases ready to go, and I think that's the facet of the goal, because we were so aligned with the business strategy to begin with. So we were discovering that pinpoint, just as all our customers were. And so when the cooperation acted as it did, in some extent we're already ready to go with used cases that we can take directly to our clients and customers. I think it also has to do with the fact that we've had a partnership with Red Hat for some time, we've been pretty strategic. >> Do you think people understand AI in a business context? >> I actually think that that's, people don't really understand that. That's was the biggest, in my mind anyway, was the biggest barrier to the business strategy that we had embarked on several years ago. To take AI or cognition to the enterprise. People never really understood it. And so our own data strategy became one of enabling IBM itself to become an AI enterprise. And use that as a showcase for our clients and customers, and over the journey in the last two, three years that I've been with IBM. We've become more, we've been putting forward more and more collateral, but also technology, but also business process change ideas, organizational change ideas. So that our clients and customers can see exactly how it's done. Not that i'ts perfect yet, but that too they benefit from, right? They don't make the same mistakes that we do. And so we've become, your colleagues have been covering this conference so they will know that it's become more and more clear, exactly what we're doing. >> You made an interesting comment, in the keynote this morning you said nobody understands AI in a business context. What did you mean by that? >> So in a business context, what does it look like? What does AI look like from an AI enterprise standpoint? From a business context. So excuse me I just trouble them for a tissue, I don't know why. >> Okay, alright, well we can talk about this a little bit too while he-- >> Yeah, well I think we understand AI as an Amazon Echo. We understand it as interface medium but I think what he was getting at is that impacting business processes is a lot more complicated. >> Right. >> And so we tend to think of AI in terms of how we relate to technology rather than how technology changes the rules. >> Right and clearly its such, on the consumers side, we've all grasped this and we all are excited by its possibilities but in terms of the business context. >> I'm back! >> It's the season, yes. >> Yeah, it is the season, don't want to get in closer. So to your question with regard to how-- >> AI in a business context. >> AI in a business context. Consumer context everybody understands, but in a business context what does it really mean? That's difficult for people to understand. But eventually it's all around making decisions. But in my mind its not the big decisions, it's not the decisions we going to acquire Red Hat. It's not those decisions. It's the thousands and thousands of little decisions that are made day in and night out by people who are working the rank and file who are actually working the different processes. That's what we really need to go after. And if you're able to do that, it completely changes the process and you're going to get just such a lot more out of it, not just terms of productivity but also in terms of new ideas that lead to revenue enhancement, new products, et cetera, et cetera. That's what a business AI enterprise looks like. And that's what we've been bringing forward and show casing. In today's keynote I actually had Sonya, who is one of our data governance people, SMEs, who works on metadata generation. Really a very difficult manual problem. Data about data, specifically labeling data so that a business person could understand it. Its all been done manually but now it's done automatically using AI and its completely changed the process. But Sonya is the person who's at the forefront of that and I don't think people really understand that. They think in terms of AI and business and they think this is going to be somebody who's a data scientist, a technologist, somebody who's a very talented technical engineer, but it's not that. It's actually the rank and file people, who've been working these business processes, now working with an intelligent system, to take it to the next level. >> And that's why as you've said it's so important that the CDO is a change agent in chief. Because it is, it does require so much buy-in from, as you say, the rank and file, its not just the top decision makers that you're trying to persuade. >> Yes, you are affecting change at all levels. Top down, bottom up, laterally. >> Exactly. >> You have to go after it across the board. >> And in terms of talking about the data, it's not just data for data's sake. You need to talk about it in terms that a business person can understand. During the keynote, you described an earlier work that you were doing with the NBA. Can you tell our viewers a little bit about that? And sort of how the data had to tell a story? >> Yes, so that was in my first go 'round with IBM, from 1990 through '97. I was with IBM Research, at the Watson Research Lab, as a research staff member. And I created this program called Advanced Scout for the National Basketball Association. Ended up being used by every team on the NBA. And it would essentially suggest who to put in the line up, when you're matching lines up and so forth. By looking at a lot of game data and it was particularly useful during the Playoff games. The major lesson that came out of that experience for me, at that time, alright, this was before Moneyball, and before all this stuff. I think it was like '90, '93, '92. I think if you Google it you will still see articles about this. But the main lesson that came out for me was the first time when the program identified a pattern and suggested that to a coach during a playoff game where they were down two, zero, it suggested they start two backup players. And the coach was just completely flabbergasted, and said there's no way I'm going to do this. This is the kind of thing that would not only get me fired, but make me look really silly. And it hit me then that there was context that was missing, that the coach could not really make a decision. And the way we solved it then was we tied it to the snippets of video when those two players were on call. And then they made the decision that went on and won that game, and so forth. Today's AI systems can actually fathom all that automatically from the video itself. And I think that's what's really advanced the technology and the approaches that we've got today to move forward as quickly as they have. And they've taken hold across the board, right? In the sense of a consumer setting but now also in the sense of a business setting. Where we're applying it pretty much to every business process that we have. >> Exciting. Well Inderpal, thank you so much for coming back on theCUBE, it was always a pleasure talking to you. >> It's my pleasure, thank you. >> I'm Rebecca Knight for Paul Gillin, we will have more from theCUBE's live coverage of IBM CDO coming up in just a little bit. (upbeat music)

>> Live from Boston, it's theCUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight*, and I'm joined by cohost, Paul Gillan*. We have a guest today, John Thomas. He is the Distinguished Engineer and Director* at IBM. Thank you so much for coming, returning to theCUBE. You're a CUBE veteran, CUBE alum. >> Oh thank you Rebecca, thank you for having me on this. >> So tell our viewers a little bit about, you're a distinguished engineer. There are only 672 in all of IBM. What do you do? What is your role? >> Well that's a good question. Distinguished Engineer is kind of a technical executive role, which is a combination of applying the technology skills, as well as helping shape IBM strategy in a technical way, working with clients, et cetera. So it is a bit of a jack of all trades, but also deep skills in some specific areas, and I love what I do (laughs lightly). So, I get to work with some very talented people, brilliant people, in terms of shaping IBM technology and strategy. Product strategy, that is part of it. We also work very closely with clients, in terms of how to apply that technology in the context of the client's use status. >> We've heard a lot today about soft skills, the importance of organizational people skills to being a successful Chief Data Officer, but there's still a technical component. How important is the technical side? What is, what are the technical skills that the CDOs need? >> Well, this is a very good question Paul. So, absolutely, so, navigating the organizational structure is important. It's a soft skill. You are absolutely right. And being able to understand the business strategy for the company, and then aligning your data strategy to the business strategy is important, right? But the underlying technical pieces need to be solid. So for example, how do you deal with large volumes of different types of data spread across a company? How do you manage that data? How do you understand the data? How do you govern that data? How do you then master leveraging the value of that data in the context of your business, right? So an understanding, a deep understanding of the technology of collecting, organizing, and analyzing that data is needed for you to be a successful CDO. >> So in terms of, in terms of those skillsets that you're looking for, and one of the things that Inderpal said earlier in his keynote, is that, there are just, it's a rare individual who truly understands the idea of how to collect, store, analyze, curatize, monetize the data, and then also have the soft skills of being able to navigate the organization, being able to be a change agent who is inspiring, inspiring the rank and file. How do you recruit and retain talent? I mean, this seems to be a major challenge. >> Expertise is, and getting the right expertise in place, and Inderpal talked about it in his keynote, which was the very first thing he did was bring in talent. Sometimes it is from outside of your company. Maybe you have a kind of talent that has grown up in your company. Maybe you have to go outside, but you've got to bring in the right skills together. Form the team that understands the technology, and the business side of things, and build this team, and that is essential for you to be a successful CDO. And to some extent, that's what Inderpal has done. That's what the analytic CDO's office has done. Seth Dobrin, my boss, is the analytics CDO , and he and the analytics CDO team actually hired people with different skills. Data engineering skills, data science skills, visualization skills, and then put this team together which understands the, how to collect, govern, curate, and analyze the data, and then apply them in specific situations. >> There's been a lot of talk about AI, at this conference, which seems to be finally happening. What do you see in the field, or perhaps projects that you've worked on, of examples of AI that are really having a meaningful business impact? >> Yeah Paul, that is a very good question because, you know, the term AI is overused a lot as you can imagine, a lot of hype around it. But I think we are past that hype cycle, and people are looking at, how do I implement successful use cases? And I stress the word use case, right? In my experience these, how I'm going to transform my business in one big boil the ocean exercise, does not work. But if you have a very specific bounded use case that you can identify, the business tells you this is relevant. The business tells you what the metrics for success are. And then you focus your attention, your efforts on that specific use case with the skills needed for that use case, then it's successful. So, you know, examples of use cases from across the industries, right? I mean everything that you can think of. Customer-facing examples, like, how do I read the customer's mind? So when, if I'm a business and I interact with my customers, can I anticipate what the customer is looking for, maybe for a cross-sell opportunity, or maybe to reduce the call handing time when a customer calls into my call center. Or trying to segment my customers so I can do a proper promotion, or a campaign for that customer. All of these are specific customer phasing examples. There also are examples of applying this internally to improve precesses, capacity planning for your infrastructure, can I predict when a system is likely to have an outage, or can I predict the traffic coming into my systems, into my infrastructure and provision capacity for that on demand, So all of these are interesting applications of AI in the enterprise. >> So when your trying, what are the things we keep hearing, is that we need to data to tell a story To, the data needs to be compelling enough so that the people, the data scientist get it but then also the other kinds of business decision makers get it to. >> Yep >> So, what are sort of, the best practices that have emerged from your experience? In terms of, being able to, for your data to tell a story that you want it to tell. >> Yeah, well I mean if the pattern doesn't exist in the data then no amount of fancy algorithms can help, you know? and sometimes its like searching for a needle in a haystack but assuming, I guess the first step is, like I said, What is the use case? Once you have a clear understanding of your use case and such metrics for your use case, do you have the data to support that use case? So for example if it's fraud detection, do you actually have the historical data to support the fraud use case? Sometimes you may have transactional data from your, transocular from your core enterprise systems but that may not be enough. You may need to alt mend it with external data, third party data, maybe unstructured data, that goes along with your transaction data. So the question is, can you identify the data that is needed to support the use case and if so can I, is that data clean, is that data, do you understand the lineage of the data, who has touched and modified the data, who owns the data. So then I can start building predictive models and machine learning, deep learning models with that data. So use case, do you have the data to support the use case? Do you understand how that sata reached you? Then comes the process of applying machine learning algorithms and deep learning algorithms against that data. >> What are the risks of machine learning and particularly deep learning, I think because it becomes kind of a black box and people can fall into the trap of just believing what comes back, regardless of whether the algorithms are really sound or the data is. What is the responsibility of data scientist to sort of show their work? >> Yeah, Paul this is fascinating and not completely solid area, right? So, bias detection, can I explain how my model behaved, can I ensure that the models are fair in their predictions. So there is a lot of research, a lot of innovation happening in the space. IBM is investing a lot into space. We call trust and transparency, being able to explain a model, it's got multiple levels to it. You need some level of AI governments itself, just like we talked about data governments that is the notion of AI governments. Which is what motion of the model was used to make a prediction? What were the imports that went into that model? What were the decisions that were, that were the features that were used to make a sudden prediction? What was the prediction? And how did that match up with ground truth. You need to be able to capture all that information but beyond that, we have got actual mechanisms in place that IBM Research is developing to look at bias detection. So pre processing during execution post processing, can I look for bias in how my models behave and do I have mechanisms to mitigate that? So one example is the open source Python library, called AIF360 that comes from IBM Research and has contributed to the open source community. You can look at, there are mechanisms to look at bias and provide some level of bias mitigation as part of your model building exercises. >> And the bias mitigation, does it have to do with, and I'm going to use an IMB term of art here, the human in the loop, is it how much are you actually looking at the humans that are part of this process >> Yeah, humans are at least at this point in time, humans are very much in the loop. This notion of Peoria high where humans are completely outside the loop is, we're not there yet so very much something that the system can for awhile set off recommendations, can provide a set of explanations and can someone who understands the business look at it and make a corrective, take corrective actions. >> There has been, however to Rebecca's point, some prominent people including Bill Gates, who have speculated that the AI could ultimately be a negative for humans. What is the responsibility of company's like IBM to ensure that humans are kept in the loop? >> I think at least at this point IBM's view is humans are an essential part of AI. In fact, we don't even use artificial intelligence that much we call it augmented intelligence. Where the system is pro sending a set of recommendations, expert advise to the human who can then make a decision. For example, you know my team worked with a prominent health care provider on you know, models for predicting patient death in the case of sepsis, sepsis-onset. This is, we are talking literally life and death decisions being made and this is not something you can just automate and throw into a magic black box, and have a decision be made. So this is absolutely a place where people with deep, domain knowledge are supported, are opt mended with, with AI to make better decisions, that's where I think we are today. As to what will happen five years from now, I can't predict that yet. >> Well I actually want to- >> But the question >> bring this up to both of you, the role, so you are helping doctor's make these decisions, not just this is what the computer program says about this patient's symptoms here but this is really, so you are helping the doctor make better decisions. What about the doctors gut, in the, his or her intuition to. I mean, what is the role of that, in the future? >> I think it goes away, I mean I think, the intuition really will be trumped by data in the long term because you can't argue with the facts. Some people do these days. (soft laughter) But I don't remember (everyone laughing) >> We have take break there for some laughter >> Intrested in your perspective onthat is there, will there, should there always be a human on the front line, who is being supported by the back end or would you see a scenario were an AI is making decisions, customer facing decisions that are, really are life and death decisions? >> So I think in the consumer invest way, I can definitely see AI making decisions on it's own. So you know if lets say a recommender system would say as you know I think, you know John Thomas, bought these last five things online. He's likely to buy this other thing, let's make an offer to him. You know, I don't need another human in the loop for that >> No harm right? >> Right. >> It's pretty straight forward, it's already happening, in a big way but when it comes to some of these >> Prepoping a mortgage, how about that one? >> Yeah >> Where bias creeps in a lot. >> But that's one big decision. >> Even that I think can be automated, can be automated if the threshold is set to be what the business is comfortable with, were it says okay, above this probity level, I don't really need a human to look at this. But, and if it is below this level, I do want someone to look at this. That's you know, that is relatively straight forward, right? But if it is a decision about you know life or death situation or something that effects the very fabric of the business that you are in, then you probably want a domain explore to look at it. In most enterprises, enterprises cases will fall, lean toward that category. >> These are big questions. These are hard questions. >> These are hard questions, yes. >> Well John, thank you so much for doing >> Oh absolutely, thank you >> On theCUBE, we really had a great time with you. >> No thank you for having me. >> I'm Rebecca Knight for Paul Gillan, we will have more from theCUBE's live coverage of IBM CDO, here in Boston, just after this. (Upbeat Music)

live from Boston it's the cube covering IBM chief data officer summit brought to you by IBM welcome back everyone to the cubes live coverage of the IBM CDO summit here in Boston Massachusetts I'm your host Rebecca Knight and I'm joined by co-host Paul Gillan we have a guest today John Thomas he is the distinguished engineer and director at IBM thank you so much for coming returning to the cube you're a cube veteran so tell our viewers a little bit about your distinguished engineer there are only 672 in all of IBM what do you do what is your role that's a good question distinguished engineer is kind of a technical execute a role which is a combination of applying the technology skills as well as helping shape by the inscriber gene in a technical way working with clients etcetera right so it is it is a bit of a jack-of-all-trades but also deep skills in some specific areas and I love what I do so you get to work with some very talented people brilliant people in terms of shaping IBM technology and strategy products for energy that is part of it we also work very closely with clients in terms of how do you apply that technology in the context of the clients use cases we've heard a lot today about soft skills the importance of organizational people skills to being a successful chief data officer but there's still a technical component how important is the technical side what is what are the technical skills that the cdos need oh this is a very good question Paul so absolutely so navigating the organizational structure is important it's a soft skill you're absolutely right and being able to understand the business strategy for the company and then aligning your data strategy to the business strategy is important right but the underlying technical pieces need to be solid so for example how do you deal with large volumes of different types of data spread across the company how do you manage the data how do you understand the data how do you govern that data how do you then mast are leveraging the value of the data in the context of your business right so and understand deep understanding of the technology of collecting organizing and analyzing that data is needed for you to be a success for CBL so in terms of in terms of those skill sets that you're looking for and one of the things that Interpol said earlier in his keynote is that they're just it's a rare individual who truly understands the idea of how to collect store analyze curate eyes monetize the data and then also has the the soft skills of being able to navigate the organization being able to be a change agent is inspiring yeah inspiring the rank-and-file yeah how do you recruit and retain talent it seems to be a major tech expertise is not getting the right expertise in place and Interpol talked about it in his keynote which was the very first thing he did was bring in Terrence sometimes it is from outside of your company maybe you have a kind of talent that has grown up in your company maybe you have to go outside buddy God bring in the right skills together form the team that understands the technology and the business side of things and build esteem and that is essential for you to be a successful CTO and to some extent that's what Interpol has done that's what the analytic CEOs office has done a set up in my boss is the analytics EDF and he and the analytic CDO team actually engineering skills data science skills visualization skills and then put this team together which understands the how to collect govern curate and analyze the data and then apply them in specific situations a lot of talk about AI at this conference what seems to be finally happening what do you see in the field or perhaps projects that you've worked on examples of AI that are really having a meaningful business impact yeah Paul it's a very good question because you know the term AI is overused a lot as you can imagine a lot of hype around it but I think we are past that hype cycle and people are looking at how do i implement successful use cases and I stressed the word use case right in my experience these how I'm going to transform my business in one big boil the ocean exercise does not work but if you have a very specific bounded use case that you can identify the business tells you this is relevant the business tells you what the metrics for success are and then you focus your your attention your your efforts on that specific use case with the skills need for that use case then it's successful so you know examples of use cases from across the industries right I mean everything that you can think of customer-facing examples like how do I read the customers mind so when when if I'm a business and I interact with my customers can I anticipate what the customer is looking for maybe for a cross-sell opportunity or maybe to reduce the call handling time and a customer calls in to my call center or trying to segment my customer so I can do a proper promotion or a campaign for that customer all of these are specific customer facing examples there are also examples of applying this internally to improve processes capacity planning for your infrastructure can I predict when a system is likely to have an outage and or can I predict the traffic coming into my systems into my infrastructure and provision capacity that on-demand so all these are interesting applications of AI in the enterprise so when you're trying I mean one of the things we keep hearing is that we need data to tell a story the data needs to the data needs to be compelling enough so that the people the data scientists get it but then also that the other kinds of business decision makers get it - so what are sort of the best practices that have emerged from your experience in terms of being able to for your data to tell the story that you wanted to tell yeah well I mean if the pattern doesn't exist in the data then no amount of fancy algorithms can help you know so and sometimes it's like searching for a needle in a haystack but assuming I guess the first step is like I said what is the a use case once you have a clear understanding of your use case and success metrics for the use case do you have the data to support that use case so for example if it's fraud detection do you actually have the historical data to support the fraud use case sometimes you may have transactional data from your your transaction data from your current or PI systems but that may not be enough you may need to augment it with external data third party data may be unstructured data that goes along with the transaction data so question is can you identify the data that is needed to support the use case and if so can I do is that data clean is that is that data do you understand the lineage of the data who has touched and modified the data who owns the data so that I can then start building predictive models and machine learning be planning models with that data so use case do you have the data to support the use case do you understand how the data reached you then comes the process of applying machine learning algorithms and deep learning algorithms against that data one of the risks of machine learning and particularly deep learning I think is it becomes kind of a black box and people can fall into the trap of just believing what comes back regardless of whether the algorithms are really sound or the data is somewhat what is the responsibility of data scientists to sort of show their work yeah Paul this is a fascinating and not completely solved area right so bias detection can I explain how my model behaved can I ensure that the models are fair in their predictions so there's a lot of research lot of innovation happening in the space iBM is investing a lot in the space we call trust and transparency being able to explain a model it's got multiple levels to it you need some level of AI governments itself so just like we talked about data governance there is the notion of AI governance which is what version of the model was used to make a prediction what were the inputs that went into that model what were the decisions that are that what were the features that were used to make a certain prediction what was the prediction and how did that match up with ground truth you need to be able to capture all that information but beyond that we have got actual mechanisms in place that IBM Research is developing to look at bias detection so pre-processing during execution post-processing can I look for bias in how my models behave and do I have mechanisms to mitigate that so one example is the open source Python library called AI F 360 that comes from IBM's research on its contributor to the open source community you can look at there are mechanisms to look at bias and and and provide some level of bias mitigation as part of your model building exercises and is the bias mitigation does it have to do with and I'm gonna use an IBM term of art here at the human in the loop I mean is how much are you actually looking at the humans that are part of this process humans are at least at this point in time humans are very much in the loop this this notion of P or AI where humans are completely outside the loop is we're not there yet so very much something that the system can it provide a set of recommendations can it provide a set of explanations in can someone who understands the business look at it and make corrective take corrective action as needed there has been however to Rebecca's point some prominent people including Bill Gates who have have speculated that AI could ultimately be a negative for humans are what is the responsibility of companies like IBM to ensure that humans are kept in the loop I think at least at this point IBM's V was humans are an essential part of AI in fact we don't even use the term artificial intelligence that much we call it augmented intelligence where the system is presenting a set of recommendations expert advice to the human who can then make a decision so for example you know my team worked with a prominent healthcare provider on you know models for predicting patient death death in in the case of sepsis sepsis onset this is we're talking literally life and death decisions being made and this is not something that you can just automate and throw it into a magic black box and have a decision be made right so this is absolutely a place where people with deep domain knowledge are supported are augmented with with AI to make better decisions that's where that's where I think we are today as to what will happen five years from now I can't predict that yet the role so you are helping doctors make these decisions not just this is what the computer program says about this patients symptoms here but this is really you're helping the doctor make better decisions what about the doctors gut and the ease into his or her intuition too I mean what is what is the role of that in the future I think it goes away I mean I think the intuition really will be trumped by data in the long term because you can't argue with the facts much as some some people do these days the perspective on that is there will there all should there always be a human on the front lines who is being supported by the backend or would would you see a scenario where an AI is making decisions customer-facing decisions that are really are life and death so I think in the consumer industry I can definitely see AI making decisions on its own right so you know if let's say a recommender system which says you know I think you know John Thomas bought these last five things online he's likely to buy this other thing let's make an offer team you know I don't even in the loop for no harm it's it's it's it's pretty straightforward it's already happening in a big way but when it comes to some of these mortgage yeah about that one even that I think can be can be automated can be automated if the thresholds are said to be what the business is comfortable with where it says okay about this probability level I don't really need a human to look at this but and if it is below this level I do want someone to look at this that's you know that is relatively straightforward right but if it is a decision about you know life-or-death situations or something that affects the the very fabric of the business that you are in then you probably want to domain expert to look at it and most enterprises enterprise use cases will for lean towards that category these are big questions they're hard questions are questions yes well John thank you so much oh absolutely thank you we've really had a great time with you yeah thank you for having me I'm Rebecca night for Paul Gillen we will have more from the cubes live coverage of IBM CDO here in Boston just after this

>> [Announcer] 18, brought to you by Red Hat >> Well, welcome back everyone. This is theCUBE's exclusive coverage here in San Francisco, California, for Red Hat Summit 2018. I am John Furrier, co-host of theCUBE with my analyst co-host this week, John Troyer, co-founder of the TechReckoning advisory services. And our next guest is Arvind Krishna, who is the Senior Vice President of Hybrid Cloud at IBM and Director of IBM Research. Welcome back to theCUBE, good to see you. >> Thanks John and John great to meet you guys here. >> You can't get confused here you've got two John's here. Great to have you on because, you guys have been doing some deals with Red Hat, obviously the leader at open storage. You guys are one of them as well contributing to Linuxes well documented in the IBM history books on your role and relationship to Linux so check, check. But you guys are doing a lot of work with cloud, in a way that, frankly, is very specific to IBM but also has a large industry impact, not like the classic cloud. So I want to tie the knot here and put that together. So first I got to ask you, take a minute to talk about why you're here with Red Hat, what's the update with IBM with Red Hat? >> Great John, thanks for giving me the time. I'm going to talk about it in two steps: One, I'm going to talk about a few common tenets between IBM and Red Hat. Then I'll go from there to the specific news. So for the context, we both believe in Linux, I think that easy to state. We both believe in containers, I think that is the next thing to state. We'll come back talk about containers because this is a world, containers are linked to Linux containers are linked to these technologies called Kubernetes. Containers are linked to how you make workloads portable across many different environments, both private and public. Then I go on from there to say, that we both believe in hybrid. Hybrid meaning that people want the ability to run their workload, where ever they want. Be it on a private cloud, be it on a public cloud. And do it without having to rewrite everything as you go across. Okay, so let's establish, those are the market needs. So then you come back and say. And IBM has a great portfolio of Middleware, names like WebSphere and DB2 and I can go on and on. And Red Hat has a great footprint of Linux, in the Enterprise. So now you say, we've got the market need of hybrid. We've got these two thing, which between them are tens of millions, maybe hundreds of millions of end points. How do you make that need get fulfilled by this? And that's what we just announced here. So we announced that IBM Middleware will run containerized on Red Hat containers, on Red Hat Enterprise Linux. In addition, we said IBM Cloud Private, which is the ability to bring all of the IBM Middleware in a sort of a cloud-friendly form. Right you click and you install it, it keep it self up, it doesn't go down, it's elastic in a set of technologies we call IBM Cloud Private, running in turn on Red Hat OpenShift Container service on Red Hat Linux. So now for the first time, if you say I want private, I want public, I want to go here, I want to go there. You have a complete certified stack, that is complete. I think I can say, we're a unique in the industry, in giving you this. >> And this is where, kind of where, the fruit comes off the tree, for you guys. Because, we've been following you guys for years, and everyone's: Where's the cloud strategy? And first of all, it's not, you don't have a cloud strategy you have cloud products. Right, so you have delivered the goods. You got the, so just to replay. The market need we all know is the hybrid cloud, multi-cloud, choice et cetera, et cetera. >> You take Red Hat's footprint, your capabilities, your combined install base, is foundational. >> [Arvind] Right >> So, nothing needs to change. There's no lift and shift, there's no rip and replace, >> you can, it's out there it's foundational. Now on top of it, is where the action is. That where you're kind of getting at, right? >> That's correct, so we can go into somebody running, let's say, a massive online banking application or they're running a reservation system. It's using technologies from us, it's using Linux underneath and today it's all a bunch of piece pods, you have a huge complex stuff it's all hard-wired and rigidly nailed down to the floor in a few places and now you can say: Hey, I'll take the application. I don't have to rewrite the application. I can containerize it, I can put it here. And that same app now begins to work but in a way that's a lot more fluid and elastic. Or my other way: I want to do a bit more work. I want to expose a bit of it up as microservices. I want to insert some IA. You can go do that. You want to fully make it microservices enabled to be able to make it into little components >> and ultimately you can do that. >> So you can take it in sort of bite size chunks and go from one to other, at the pace that you want. >> [John F.] Now that's game changing. >> Yeah, that's what I really like about this announcement. It really brings best of breed together. You know, there is a lot of talk about containers. Legacy and we've been talking about what goes where? And do you have to break everything up? Like you were just saying. But the announcement today, WebSphere, the battle tested huge enterprise scale component, DB2, those things containerized and also in a frame work like with IBM, either with IBM microservices and application development things or others right, that's a huge endorsement for OpenShift as a platform. >> Absolutely, it is and look, we would be remiss if we didn't talk a little bit. I mean we use the word containers and containerized a lot. Yes, you're right. Containers are a really, really important technology but what containers enable is much more than prior attempts such as VM's and all have done. Containers really allow you to say: Hey, I solved the security problem, I solved the patching problem, the restart problem, all those problems that lie around the operations of a typical enterprise, can get solved with containers. VM's solved a lot about isolating the infrastructure but it didn't solve, as John was saying, the top half of the stack. And that's I think the huge power here. >> Yeah, I want to just double click on that because I think the containers thing is instrumental. Because it, first of all, being in the media and loving what we do. We're kind of a new kind of media company but traditional media is been throwing IBM under the bus since saying: Wow old guard and all these things. Here's the thing, you don't have to change anything. You got containers you can essentially wrap it up and then bring a microservice architecture into it. So you can actually leverage at cloud scale. So what interests me is that you can move instantly, >> value proposition wise, pre-existing market, cloudify it, if you will, with operational capabilities. >> Right. >> This is where I like the Cloud Private. So I want to kind of go there for a second. If I have a need to take what I have at IBM, whether it is WebSphere. Now I got developers, I got installed base. I don't have to put a migration plan away. I containerize it. Thank you very much. I do some cloud native stuff but I want to make it private. My use case is very specific, maybe it's confidential, maybe it's like a government region, Whatever. I can create a cloud operations, is that right? I can cloudify it, and run it? >> Absolutely correct, so when you look at Cloud Private, to go down that path, we said Cloud Private allows you to run on your private infrastructure but I want all these abilities you just described John. I want to be able to do microservices. I want to be able to scale up and down. I want to be able to say operations happen automatically. But it gives you all that but in the private without it having to go all the way to the public. If you cared a lot about, your in a regulated industry, you went down government or confidential data. Or you say this data is so sensitive, I don't really, I am not going to take the risk of it being anywhere else. It absolutely gives you that ability to go do that and that is what brought Cloud Private to the market for and then you combine that with OpenShift and now you get the powers of both together. >> See you guys essentially have brought to the table the years of effort with Bluemix, all that good stuff going on, you can bring it in and actually run this in any industry vertical. Pretty much, right? >> Absolutely, so if you look at part what the past has been for the entire industry. It has been a lot about constructing a public cloud. Not just us, but us and our competition. And a public cloud has certain capabilities and it has certain elasticity, it has a global footprint. But it doesn't have a footprint that is in every zip code or in every town or in every city. That's not what happens to a public cloud. So we say. It's a hybrid world meaning that you're going to run some workloads on a public cloud, I'd like to run some workloads on a private and I'd like to have the ability that I don't have to pre decide which is where. And that is what the containers and microservices, the OpenShift that combination all give you to say you don't need to pre decide. You rewrite the workload onto this and then you can decide where it runs. >> Well I was having this conversation with some folks at a recent Amazon Web services conference. Well, if you go to cloud operations, then the on premise is essentially the edge. It's not necessarily. Then the definition of on premise, really doesn't even exist. >> So if you have cloud operations, in a way, what is the data center then? It's just a connected issue. >> That's right, it's the infrastructure which is set up and then, at that point, the Software Manger, at the data center, as opposed to anything else. And that's kind of been the goal that we're all been wanting. >> Sounds like this is visibly at IBM's essentially execution plan from day one. We've been seeing it and connecting the dots. Having the ability to take either pre-existing resources, foundational things like Red Hat or what not in the enterprise. Not throwing it away. Building on top of it and having a new operating model, with software, with elastic scale, horizontally scalable, Synchronous, all these good things. Enabling microservices, with Kubernetes and containers. Now for the first time, >> I can roll out new software development life cycles in a cloud native environment without forgoing legacy infrastructure and investment. >> Absolutely, and one more element. And if you want to insert some cloud service into the environment, be it in private or in public, you can go do that. For example, you want to insert a couple of AI services >> into the middle of your application you could go do that. So the environment allows you to, do what you described and these additionals. >> I want to talk about people for a second. The titles that we haven't mentioned CIO, Business Leader, Business Unit Leaders, how are they looking at >> digital transformation and business transformation in your client bases you go out and talk to them. >> Let's take a hypothetical bank. And every bank today is looking about simple questions. How do I improve my customer experience? And everybody want, when they say customer experience, really do mean digital customer experience to make it very tangible. And what they mean by that is how do I get my end customer engaged with me through an app. The app is probably in a device like this. Some smart phone, we won't say what it is, and so how do you do that? And so they say: Well, all obviously to check your balance. You obviously want to check your credit card. You want to do all those things. The same things we do today. So that application exists, there is not much point in rewriting it. You might do the UI up but it's an app that exists. Then you say but I also want to give you information that's useful to you in the context to what you're doing. I want say, you can get a 10 second loan, not a 30 day loan, but a 10 second loan. I want to make a offer to you in the middle of you browsing credit card. All those are new customergistics, where do you construct those apps? How do you mix and match it? How do you use all the capabilities along with the data you've got to go do that? And what we're trying to now say, here is a platform that you can go, do all that on. Right, that complete lifecycle you mentioned, the development lifecycle but I got to add to it >> the data lifecycle, as well as, here is the versioning, here are my AI models, all those things, built in, into one platform. >> And scales are huge, the new competitive advantage. You guys are enabling that. So I got to ask you a question on multi cloud. Obviously, as people start building out the cloud on PRIM and with Public Cloud and the things you're laying out. I can see that going on for a while, a lot of work being done there. We're seeing that Wikibon had a true Private Cloud report what I thought was truly telling. A lot of growth there, still not going away. Public Cloud's certainly grown in numbers are clear. However, the word multicloud's being kicked around I think it's more of a future stay obviously but people have multiple clouds Will have relationships with multiple clouds. No one's going to have one cloud. It's not a winner take all game. Winner take most but you know you're have multiple clouds. What does multi cloud mean to you guys in your architecture? Is that moving workloads in real time based upon spot pricing indexes or is that just co-locating on clouds and saying I got this app on this cloud, that app on that cloud, control plane it. These are architectural questions. What the hell is multicloud? >> So there's a today, then there is a tomorrow, then there is a long future state, right? So let's take today, let's take IBM. We're on Salesforce, we're on ServiceNow, we're on Workday, we're on SuccessFactors, well all of there are different clouds. We run our own public cloud, we run our own private cloud and we have Judicial Data Center. And we might have some of the other clouds also through apps that we barter we don't even know. Okay, so that's just us. I think everyone of our clients are like this. The multicloud is here today. I begin with that first, simple statement. And I need to connect the data and can connect when thing go where. The next step, I think people, nobody's going to have even one public cloud. Even amongst the big public clouds, most people are going to have two if not more That's today and tomorrow. >> Your channel partners have clouds, by the way, your Global SI's all have clouds, theCUBE is a cloud for crying out load. >> Right, so then you go into the aspirational state and that may be the one you said, where people just spot pricing. But even if I stay back from spot pricing and completely (mumbles) I make. And I'm worrying about network and I'm worrying about radio reach. If I just backup around to but I may decide I have this app, I run it on private, well, but I don't have all the infrastructures I want to burst it today and I, where do I burst it? I got to decide which public and how do I go there? >> And that's a problem of today and we're doing that and that is why I think multicloud is here now. >> Not some point in the future. >> The prime statement there is latency, managing, service level agreements between clouds and so on and so forth. >> Access control on governance, Where does my data go? Because there may be regulatory reasons to decide where the data can flow and all those things. >> Great point about the cloud. I never thought about it that way. It is a good illustration. I would also say that, I see the same arguments in the data base world. Not everyone has DB2, not everyone has Oracle, not everyone has, databases are everywhere, you have databases part of IoT devices now. So like no one makes a decision on the database. Similar with clouds, you see a similar dynamic. It's the glue layer that, interest me. As you, how do you bring them together? So holistically looking at the 20 miles stare in the future, what is the integration strategy long-term? If you look at distributed system or an operating system there has to be an architectural guiding principle for integration, your thoughts. >> This has been a world 30 years in the making. We can say networking, everyone had their own networking standard and the, let's say the '80s probably goes back to the '70s right? You had SNA, you had TCP/IP, you had NetBIO's-- >> DECnet. DECnet. You can on and on and in the end it's TCP/IP that won out as the glue. Others by the way, survived but in packets and then TCP/IP was the glue. Then you can fast forward 15 years beyond that and HTTP became the glue, we call that the internet. Then you can fast forward and you can say, now how do I make applications portable? And I will turn round and tell you that containers on Linux with Kubernetes as orchestration is that glue layer. Now in order to make it so, just like TCP/IP, it wasn't enough to say TCP/IP you needed routing tables, you needed DNS, you needed name repository, you needed all those things. Similarly, you need all those here are called the scatlog and automation, so that's the glue layer that makes all of this work >> This is important, I love this conversation because I have been ranting on theCUBE for years. You nailed it. A new stack is developing and DNS's are old and internet infrastructure, cloud infrastructure at the global scale is seeing things like network effect, okay we see blockchain in token economics, databases, multiple databases, on structure day >> a new plethora of new things are happening that are building on top of say HTTP >> [Arvind] Correct! >> And this is the new opportunity. >> This is the new platform which is emerging and it is going to enable business to operate, as you said, >> at scale, to be very digital, to be very nimble. Application life cycles aren't always going to be months, they're going to come down to days and this is what gets enabled >> So I what you to give your opinion, personal or IBM or whatever perspective because I think you nailed the glue layer on Kubernetes, Docker, this new glue layer that and you made references to, things like HTTP and TCP, which changed the industry landscape, wealth creation, new brands emerged, companies we never heard of emerged out of this and we're all using them today. We expect a new set of brands are going to emerge, new technologies are going to emerge. In your expert opinion, how gigantic is this swarm of new innovation going to be? Just, 'cause you've seen many ways before. In you view, your minds eye, what are you expecting? >> Share your insight into how big of a shift and wave is this going to be and add some color to that. >> I think that if I take a shorter and then a longer term view. in the short term, I think that we said, that this is in the order of $100 billion, that's not just our estimate, I think even Gartner has estimated about the same number. That will be the amount of opportunity for new technologies in what we've been describing. And that is I think short term. If I go longer term, I think as much as a half but at least a fourth of the complete IT market is going to shift round to these technologies. So then the winners of those that make the shift and then by conclusion, the losers are those who don't make the shift fast enough. If half the market moves, that's huge. >> It's interesting we used to look at certain segments going back years just company, oh this company's replatformizing, >> replatforming their op lift and shift and all this stuff. What you're talking about here is so game changing because the industry is replatforming >> That's correct. It's not a company. >>It's an industry! That's right. And I think the internet era of 1995, to put that point, is perhaps the easiest analogy to what is happening. >> Not the emergence of cloud, not the emergence of all that I think that was small steps. >> What we are talking about now is back to the 1995 statement >> [John] Every vertical is upgrading their stack across what from e-commerce to whatever. >> That's right. >> It's completely modernizing. >> Correct. Around cloud. >> What we call digital transformation in a sense, yes >> I'm not a big fan of the word but I understand what you mean. Great insight Arvind, thanks for coming on theCUBE and sharing. We didn't even get to some of the other good stuff. But IBM and Red Hat doing some great stuff obviously foundational, I mean, Red Hat, Tier one, first class citizen in every single enterprise and software environment you know, now OpenSource runs the world. You guys are no stranger to Linux being the first billion dollar investment going back >> so you guys have a heritage there so congratulations on the relationship. >> I mean 18 years ago, if I remember 1999. >> I love the strategy, hybrid cloud here at IBM and Red Hat. This is theCUBE, bringing all the action here in San Francisco. I am John Furrier, John Troyer. More live coverage. Stay with us, here in theCUBE. We'll be right back. (upbeat music)

brought to you by Red Hat well welcome back everyone this two cubes exclusive coverage here in San Francisco California for Red Hat summit 20:18 I'm John Ferreira co-host of the cube with my analyst co-host this week John Troy year co-founder of The Reckoning advisory services and our next guest is Arvind Krishna who's the senior vice president of hybrid cloud at IBM Reese and director of IBM Research welcome back to the cube good to see you hey John and John Wade you guys just kick it confuse get to John's here great to have you on because you guys are doing some deals with Red Hat obviously the leader at open source you guys are one of them as well contributing to Linux it's well documented the IBM has three books on your role relationship to Linux so yeah check check but you guys are doing a lot of work with cloud in a way that you know frankly is very specific to IBM but also has a large industry impact not like the classic cloud so I want to get who tie the knot here and put that together so first I got to ask you take a minute to talk about why you're here with red hat what's the update with IBM with Red Hat yeah great John thanks and thanks for giving me the time I'm going to talk about it in two steps one I'm going to talk about a few common Tenace between IBM and Red Hat and then I'll go from there to the specific news so for the context we both believe in Linux I think that's easy to state we both believe in containers I think that's the next thing to state and we'll come back and talk about containers because this is a world containers are linked to Linux containers are linked to these technologies called kubernetes containers are linked to how you make workloads portable across many different environments both private and public then I go on from there to say and we both believe in hybrid hybrid meaning that people want the ability to run their workload wherever they want beat on a private cloud beat on a public cloud and do it without having to rewrite everything as you go across okay so let's just average those are the market needs so then you come back and say an IBM as a great portfolio of middleware names like WebSphere and db2 and I can go on and on and rather has a great footprint of Linux in the enterprise so now you say we got the market need of hybrid we got these two things which between them of tens of millions maybe hundreds of millions of endpoints how do you make that need get fulfilled by this and that's what we just announced here so we announced that IBM middleware will run containerized on RedHat containers on Red Hat Enterprise Linux in addition we said IBM cloud private which is the ability to bring all of the IBM middleware in a sort of a cloud friendly form right you click and you install it keeps itself up it doesn't go down it's elastic in a set of technologies we call IBM cloud private running in turn on Red Hat open shift container service on Red Hat Linux so now for the first time if you say I want private I want public I want to go here I want to go there you have a complete certified stack that is complete I think I can say we are unique in the industry and giving you this this and this is where this is kind of where the fruit comes on the tree off the tree for you guys you know we've been good following you guys for years you know every where's the cloud strategy and first well it's not like you don't have a cloud strategy you have cloud products right so you have to deliver the goods you've got the system replays the market need we all knows the hybrid cloud multi-cloud choice cetera et cetera right you take Red Hat's footprint your capabilities your combined install base is foundational right so and nothing needs to change there's no lifting shift there's no rip and replace you can it's out there it's foundational now on top of it is where the action is that's what we're that's what were you kind of getting at right that's correct so so we can go into somebody there running let's say a massive online banking application or the running a reservation system is using technologies from Asus using Linux underneath and today it's all a bunch of piece parts you have a huge complex stuff it's all hard wired and rigidly nailed down to the floor in a few places and I can say hey I'll take the application I don't have to rewrite the application I can containerize it I can put it here and that same app now begins to work but in a way that's a lot more fluid in elastic well by the way I want to do a bit more work I want to expose a bit of it up as micro-services I want search Samia you can go do that you want to fully make it microservices enable to be able to make it as little components and digestible you can do that so you can take it in sort of bite-sized chunks and go from one to the other at the pace that you want and that's game-changing yeah that's what I really like about this announcement it really brings the best of breed together right you did you know there's a lot of talk about containers and legacy and we you know we've been talking about what goes where and do you have to break everything up like you were just saying but the the announcement today you know WebSphere the this the you know a battle-tested huge enterprise scale component db2 those things containerized and also in a framework like with IBM we either with IBM Microsoft things or others right that's um that's a huge endorsement for open shipped as a platform absolutely it is and look we would be remiss if we didn't talk a little bit I mean we use the word containers and containers a lot yes you're right containers is a really really important technology but what containers enable is much more than prior attempts such as vm's and all have done containers really allow you to say hey I saw the security problem I solved the patching problem the restart problem all those problems that lie around the operations of a typical enterprise can get solved with containers VM sold a lot about isolating the infrastructure but they didn't solve as John was saying the top half of the stack and that's I think the huge power here yeah I want to just double click on that because I think the containers thing is instrument because you know first of all being in the media and loving what we do we're kind of a new kind of media company but traditional media has been throwing IBM under the bus and saying oh you know old guard and all these things but here's the thing you don't have to change anything you could containers you can essentially wrap it up and then bring a micro-services architecture into it so you can actually leverage at cloud scale so what interests me is is that you can move instantly value proposition wise pre-existing market cloud if I if you will with operational capabilities and this is where I like the cloud private so I want to kind of go with the ever second if I have a need to take what I have an IBM when it's WebSphere now I got developers I got installed base I'd have to put a migration plan away I containerize it thank you very much I do some cloud native stuff but I want to make it private my use case is very specific maybe it's confidential maybe it's like a government region whatever I can create a cloud operations is that right I can cloud apply it and run it absolutely correct so when you look at about private to go down that path we said well private allows you to run on your private infrastructure but I want all these abilities you just described John I want to be able to do micro services I want to be able to scale up and down I want to be able to say operations happen automatically so it gives you all that but in the private without having to go all the way to the public so if you cared a lot about you're in a regulated industry because you went down government or confidential data or you say this data is so sensitive I don't really I'm not going to take the risk of it being anywhere else it absolutely gives you that ability to go do that and and that is what we brought to our private to the market for and then you combine it with open shift and now you get the powers of both together so you guys essentially have brought to the table the years of effort with bluemix all that good stuff going on you can bring any he'd actually run this in any industry vertical pretty much right absolutely so if you look at what what the past has been for the entire industry it has been a lot about constructing a public cloud not just to us but us and our competition and a public cloud has certain capabilities and it has certain elasticity it has a global footprint but it does not have a footprint that's in every zip code or in every town or in every city that song ought to happen to the public cloud so we say it's a hybrid world meaning that you're going to run some bulk loads on a public cloud and like to run some bulk loads on a private and I'd like to have the ability that I don't have to pre decide which is where and that is what the containers the micro services the open ship that combination all gives you to say you don't need to pre decide you fucker you rewrite the workload on to this and then you can decide where it runs well I was having this conversation with some folks at and recent Amazon Web Services conference to say well if you go to cloud operations then the on-prem is essentially the edge it's not necessary then the definition of on-premise really doesn't even exist so if you have cloud operations in a way what is the data center then it's just a connected tissue that's right it's the infrastructure which you set up and then at that point the software manages the data center as opposed to anything else and that's kind of being the goal that we are all being wanted it sounds like this is visibility into IBM's essentially execution plan from day one we've been seeing in connecting the dots having the ability to take either pre-existing resources foundational things like red hat or whatnot in the enterprise not throwing it away building on top of it and having a new operating model with software with elastic scale horizontally scalable synchronous all those good things enabling micro search with kubernetes and containers now for the first time I could roll out new software development life cycles in a cloud native environment without foregoing legacy infrastructure and investment absolutely and one more element and if you want to insert some public cloud services into the environment beat in private or in public you can go do that for example you want to insert a couple of AI services into your middle of your application you can go do that so the environment allows you to do what he described and these additions we're talking about people for a second though the the titles that we haven't mentioned CIO you know business leader business unit leaders how are they looking at the digital transformation and business transformation in your client base as you go out and talk to us so let's take a hypothetical back and every bank today is looking about at simple questions how do i improve my customer experience and everyone in this a customer experience really do mean digital customer experience to make it very tangible and what they mean by that is how I get my end customer engaged with me through an app the apps probably on a device like this some smartphone we won't say what it is and and so how do you do that and so they say well well you were to check your balance you obviously want to maybe look at your credit card you want to do all those things the same things we do today so that application exists there is not much point in rewriting it you might do the UI up but it's an app that exists then you say but I also want to give you information that's useful to you in the context of what you're doing I want to say you can get a 10 second not a not a 30-day load but a ten-second law I want to make it offer to you in the middle of you browsing credit cards all those are new customer this thinks are hot where do you construct those apps how do you mix and match it how do you use all the capabilities along with the data you got to go do that and what we are trying to now say here is a platform that you can go all that do all that on right to that complete lifecycle you mentioned the development lifecycle but I got to add to the the data lifecycle as well as here is the versioning here are my area models all those things built in into one platform and scales are huge the new competitive advantage you guys are enabling that so I got to ask you on the question on on multi cloud I'll see as people start building out the cloud on pram and with public cloud the things you're laying out I can see that going on for a while a lot of work being done there we seeing that wiki bond had a true private cloud before I thought was truly telling a lot of growth they're still not going away public cloud certainly has grown the numbers are clear however the word multi clouds being kicked around I think it's more of a future state obviously but people have multiple clouds will have relationships with multiple clouds no one's gonna have one Klaus not a winner-take-all game winner take most but you're gonna have multiple clouds what does multi-cloud mean to you guys in your architecture because is that moving workloads in real time based upon spot pricing indexes or is that just co-locating on clouds and saying I got this SAP on that cloud that app on that cloud control plane did these are architectural questions it's the thing hell is multi cloud so these are today and then there is a tomorrow and then there is a long future state right so let's take today let's check IBM we're on Salesforce we're on service now we're on workday we're on SuccessFactors well all these are different clouds we run our own public cloud we run our own private cloud and we have traditional data center and we might have some of the other clouds also through apps that we bought that we don't even know okay so let's just toss I think every one of our clients is like this so multi cloud is here today I begin with that first simple statement and I need to connect the data and it comes connect when things go away the next step I think people nobody's gonna have only one even public cloud I think the big public clouds most people are gonna have to if not more that's today and tomorrow your channel partners have clouds by the way your global s lies all have clouds there's a cloud for crying out loud right so then you go into the aspirational state and that may be the one he said where people do spot pricing but even if I stay back from spot pricing and completely dynamic and of worrying about network and I'm worrying about video reach I just back up on to but I may decide it I have this app I run it on private well but I don't have all the infrastructures I want to bust it today and I've very robust it to I got to decide which public and how do I go there and that's a problem of today and we're doing that and that is why I think multi-cloud is here now not some pointed problem the problem statement there is latency managing you know service level agreements between clouds and so on and so forth governance where does my data go because there may be regulate regulate through reasons to decide where the data can flow and all the great point about the cloud I never thought about that way it's a good good illustration I would also say that I see the same argument of database world not everyone has db2 that everyone has Oracle number one has databases are everywhere you have databases part of IOT devices now so like no one makes a decision on the database similar was proud you're seeing a similar dynamic it's the glue layer that to me interest me as you how do you bring them together so holistically looking at the 20 mile stare in the future what is the integration strategy long term if you look at a distributed system or an operating system there has to be an architectural guiding principle for absolute integration you know well that's 30 years now in the making so we can say networking everybody had their own networking standards and the let's say the 80s though it probably goes back to the 70s right yeah an SN a tcp/ip you had NetBIOS TechNet deck that go on and on and in the end is tcp/ip that one out as the glue others by the way survived but in pockets and then tcp/ip was the glue then you can fast forward 15 years beyond that an HTTP became the glue we call that the internet then you can fast forward you can say now how to make applications portable and I would turn around and tell you that containers on linux with kubernetes as orchestration is that glue layer now in order to make it so just like in tcp/ip it wasn't enough to say tcp/ip you needed routing tables you needed DNS you needed name repositories you needed all those things similarly you need all those here I've called those catalogs and automation so that's the glue layer that makes all of this work this is important I love this conversation because I've been ranting on this in the queue for years you're nailed it a new stack is development DNS this is olden Internet infrastructure cloud infrastructure at the global scale is seeing things like Network effect okay we see blockchain in token economics like databases multiple database on structured data a new plethora of new things are happening that are building on top of say HTTP correct and this is the new opportunity this is the new the new platform which is emerging and it's going to enable businesses to operate you said at scale to be very digital to be very nimble application life cycles are not always going to be months they're gonna come down to days and this is what gets enabled so I want you to give your opinion personal or IBM or whatever perspective because I think you nailed the glue layer on cue and a stalker and these this new glue layer that and you made reference system things like HTTP and TCP which changed the industry landscape wealth creation new up new new brands emerged companies we've never heard of emerged out of this and we're all using them today we expect a new set of brands are gonna emerge new technologies and emerge in your expert opinion how gigantic is this swarm of new innovation gonna be just because you've seen many ways before in your view your mind's eye what are you expecting wouldn't share your your insight into how big of a shift and wave is this is going to be and add some color to that I think that if I take a take a shorter and then a longer term view in the short term I think that we said that this is on the order of 100 billion dollars that's not just our estimate I think even Gartner estimated about the same number that'll be the amount of opportunity for new technologies in what we've been describing and that is I think short term if I go longer term I think as much as 1/2 but at least 1/4 of the complete ID market is going to shift onto these technologies so then the winners are those that make the shift and then bye-bye clusion the losers of those who don't make this shift faster Afghan and stop the market moves that's that's he was interesting we used to like look at certain segments going back years oh this companies reap platform Ising we platforming they're their operative lift and shift and all this stuff what you're talking about here is so game-changing because the industries Reap lat forming that's a company that's it's an industry that's right any and I think the the the Internet era of 1995 to put that point it's perhaps the easiest analogy to what is happening not the not the emergence of cloud not the emergence of all that I think that was small steps what we're talking about now is back to the 1995 statement every vertical is upgrading their stack across the board from e-commerce to whatever that's right it's completely modernizing correct around cloud what we call digital transformation in a sense yes what not a big fan of the word but I lied I understand what you mean great insight our thanks for coming on the Kuban Sharon because we even get to some of the other good stuff but IBM and Red Hat doing some great stuff obviously foundational I mean Red Hat Tier one first-class citizen in every single enterprise and software environment you know now saw open source runs the world you guys you guys are no stranger to Linux being the first billion dollar investment going back so you guys have a heritage there so congratulations on the relationships that go around about ninety nine nine yeah and and I love the strategy hybrid cloud here at IBM and right at this the cube bring you all the action here in San Francisco I'm John for John Troy you're more live covers stay with us here in the cube Willie right back