>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. Welcome everybody. You watching the cube, the leader in live tech coverage. We're here at the IBM data and AI forum. Wow. What a day. 1700 customers. A lot of hands on labs sessions. What used to be the IBM analytics university is sort of morphed into this event. Now you see the buzz is going on. At least the Galean is here. She's the vice president of global sales for IBM data and AI. Welcome to the cube. Thank you for coming on. So this event is buzzing the double from last year almost. And uh, congratulations. >>Well, thank you very much. We have con, uh, lots of countries represented here. We have customers from small to large, every industry represented. And a, it's a, I can see a marked difference in the conversations in just a year around our, how customers want to figure out how to embark on this journey to AI. >>So yeah. So why are they come here? What's the, what's the primary motivation? >>Well, I think one IBM is recognized as the leader in AI and we just came out in the IDC survey as the three time w you know, leader, a recognized leader in AI. And when they come here they know they're going to hear from other clients who have embarked on similar journeys. They know they're going to have access to experts, hands on labs, and we bring our entire IBM team that's focused on data and AI to this event. So it's intimate, it's high skilled, it's high energy and they are learning a ton while they're. >>Yeah, a lot of content and you're educating but you're also trying to inspire people. I mean a raise. I was the hub this morning, he wrote this book, but he's this extreme, extreme, extreme like ultra marathoner. Uh, which I thought was a great talk this morning. And then you did a, I thought a good job of sort of connecting, you know, his talk of anything's possible to now bringing AI into the equation. What are you hearing from customers in terms of what they want to make possible and, and what's that conversation like in the field? >>Well, it's interesting because there is a huge recognition that every client that I talked to you, and they all want to understand this, that they have to be transforming their businesses on this journey to AI. So they all recognize that they need to start. Now. What I find when I talk to clients is that they're all coming in at different entry points. There's a maturity curve. So some are figuring out, you know, how do I move away from just Excel spreadsheets? I'm still running my business on Excel, right? And these are no banks in major that are operating on Excel spreadsheets and they're looking at niche competitors, you know, digital banks that are entering the scene. And if they don't change the way they operate, they're not going to survive. So a lot of companies are coming in knowing that they're low on the maturity curve and they better do something to move up that curve pretty fast. >>Some are in almost the second turn of the crank where they've invested in a lot of the AI technologies, they've built data science platforms, and now they're figuring out how do they get that next rev of productivity improvement? How do they come up with that next business idea that's going to give them that competitive advantage? So what I find is every client is embarking on this journey, which is a big difference where I think we were even a year, 18 months ago, where they were sort of just, okay, this is interesting. Now there I better do something. >>Okay, so you're a resource, you know, as the head of global sales for this group. So when you talk to customers that are immature, if I hear you right, they're saying, help us get started because we're going to fall behind. Uh, we're inefficient right now. We're drowning in spreadsheets, data. Our data quality is not where it needs to be. Help. Where do we start? What do you tell them? >>Well, one, we have a formula that we've proven works with clients. Um, we bring them into our garages where we will do design thinking, architectural workshops, and we figure out a use case because what we try not to do with our clients is boil the ocean. We want them to sh to have something that they can prove success around very quickly, create that minimal viable product, bring it back to the business so that the business can see, Oh, I understand. And then evolve that use case. So we will bring technical specialists, we will bring folks that are our own data scientists to these garage environments and we will work with them on building out this first use case. >>Explain the garage a little bit more. Is that those, those are sort of centers of excellence around the world or how do I tap them as a customer? Is it, is it a freebie? Is it for pay? Isn't it like the data science elite team? How does it all work? >>Well, it is. There are a number of physical locations and it's open to all clients. We have created these with co-leadership from across the entire IBM company. So our services organization, our cloud cognitive organization, all play a role in these garages. So we have a formal structure where a team can engage through a request process into the garages. We will help them define the use case they want to bring into the garage. We will bring them in for a period of time and provide the resources and capabilities and skills and that's not charged to the client. So we're trying to get them started now that they'll take that back to their company and then they will look at follow on opportunities and those may, you know, work out to be different services opportunities as they move forward. But we're on that get started phase. >>Yeah. Yeah. I mean you're a fraud for profit company, so it's great to have a loss leader, but the line outside the door at the garage must be huge for people that want to get in. Hi. How are you managing the dominion? >>Yes, well we're increasing obviously our capacity around the garages. Um, and we're still making customers aware of the garages. So there's still, because it's a commitment on their side, like they just can't come in and kick the tires. We ask them to bring their line of business along with their technical teams into the garages because that's where you get the best product coming out of it. When you know you've got something that's going to solve a business problem, but you have to have buy in from both sides. >>I want to ask you about the AI ladder. You know, Rob Thomas has been using this construct for awhile. It didn't just come out of thin air. I'm sure there was a lot of customer input and a lot of debate about what should be on the ladder. We went, when I first heard of the day AI ladder, it was, there was data in IAA analytics, ML and AI, sort of the building, the technical or technology building blocks. It's now become verbs, which I love, which is collect, organize, analyze and infuse, which is all about operationalizing and scaling. How is that resonating with customers and how do they fit into that methodology or framework? >>Well, I'll tell you, I use that framework with every single client and I described that there is a set of steps and you know, obviously to the ladder that every customer has to embark upon. And it starts with some very basic principles and as soon as you start with the very basic principles, every client is like, of course like it seems so obvious that first and foremost you have to date as the foundation, right? AI is not created out of, you know, someone in a back room. The foundation to AI is, is information and data. Yet every customer, every customer struggles with that data is coming from multiple systems, multiple sources that they can't get to the data fast enough. They're shipping data around an organization. It's not managed. And yet that they know that in five years, the data they think they need today is going to be completely different. >>It could be 12 months, but certainly in the future. So how do you build out that architecture that allows them to build that now, but have the agility to grow as the requirements change? You start with that basic discussion and they're like, well of course. So that's collect and then you bring it up and you talk about how do you govern that data? How do you know where that data originated? Who is the owner? How do you know what that data means? What system did it come from? What's the, you know, who has access to it? How do you create that set of govern data? And we'll of course every client recognizes they have that set of issues. So I could continue working my way up the ladder and every client realizes that, okay, I re I'm, here's where I am today. What you just painted for me is absolutely what I need to focus on and address. Now help me get from a to B. >>So I'm really interested in this discussion because it sounds like you're a very disciplined sales leaders and you said you use the ladder with virtually every client and I presume your sales teams use the ladder. So you train your salespeople how to converse the ladder. And then the other observation I'd love your thoughts on this is every step of the ladder has these questions. So you're asking customers questions and I'm sure it catalyzes conversation, the, the answers to which you have solutions presumably from any of them. But I wonder if you could talk about that. >>Well, let me tell about the ladder and how we're using it with our Salesforce because it was a unifying approach, not just within our own team, our data and AI team, but outside of data and AI. Because not only did we explain it to clients this way, but to the rest of IBM, our business partners, our whole ecosystem. So unifying in that we started every single conversation with our sales team on enabling them on how do they talk to their clients, our materials, our use cases, our references, our marketing campaigns. We tied everything to this unified approach and it's made a huge difference in how we communicate our value to clients and explain this journey to AI in in comprehensive steps that everyone could understand and relate to. >>Love it. How is the portfolio evolving to map into that framework? And what can we expect going forward? What can you share with us at least? >>Well, the other amazing feat I'll call it that we produced around this is I'll talk to a client and I'll describe these capabilities and then I will say to a customer, you don't have to do every one of these things that I've just described, but you can implement what you need when you need it. Because we have built all of this into a unified platform called cloud pack for data and it's a modern data platform. It's built on an open infrastructure built on red hat OpenShift so that you can run it on your own premises as a private cloud or on public clouds, whether that be IBM or Amazon or Zohre. It allows you to have a framework, a platform built on this open modern infrastructure with access to all these capabilities I've just described as services and you decide completely open what services you need to deploy when you grow the platform as you need it. And, Oh, by the way, if you don't have the red hat OpenShift environment set up, we'll package that in a system and I will roll in the system to you and allow you to have access to the capabilities in ours. >>How's the red hat conversation going? I would imagine a lot of the traditional IBM customers are stoked. He just picked up red hat, you know, a very innovative company, open source mindset. Um, at the same time I would imagine a lot of red hat customers saying, is IBM really gonna? Let them keep their culture. How's that conversation going in the field? >>Well, I will tell you we've been a hundred percent consistent in terms of everything that you've heard Jenny and Arvin Krishna talk about in the fact that we are going to maintain their culture, keep them as that separate entity inside of IBM. It's absolutely perpetrated throughout the entire IBM company. Um, we have a lot to learn from, from them as I'm sure they have to learn from us, but it truly is operating and I see it in the clients that I'm working with as a real win-win. >>If you had to take one thing away from this event that you want customers to, to remember, what would it be? >>Start now. Um, because if you don't begin on this journey to AI, you will find yourselves, you know, fighting against new competitors, uh, increasing costs, you know, you have to improve productivity. Every client is embarking on this journey to AI start now. >>And when you were talking about, uh, the maturity model and, and one of those levels was folks that had started already and they wanted to get to the next level, when you go into those clients, do you discern a different sort of attitude? We've started, we're down the path. Did they have more of a spring in their step? Are they like chomping at the bit to really go faster and extend their lead relative to the competition competition? What's the dynamic like in those accounts? >>That's a great question because I was with a client this afternoon, um, a large manufacturer of, uh, of goods and they are at this turning point where they did kind of phase one, they implemented cloud pack for data and they did it to just join some of their disparate systems. Now, I mean, I, I barely got a word in because he was so excited cause he's, now what I'm going to do is I'm going to figure out where my factories should go based on where my products are selling. So he's now looking at how he can change his whole distribution process as a result of getting access to this data and analytics that he never had before. Um, and I was like, okay, well just tell me how I can help you. And he was like, no way ahead. >>So this was the big kickoff day. I know yesterday there was sort of deep learning hands on stuff, the big keynotes. Today we're only here for one day. What are we going to miss? What's, what's happening tomorrow? >>Well, it's a bit of a repeat of today. So we'll have another keynote tomorrow from Beth Smith who runs our Watson, uh, business for IBM. We'll have more hands on labs. We have a lot of customer presentations where they're sharing their best practices. Um, lots of fun. >>Where do you want to see this event go? And what kind of, what's next in an IBM event land? >>Well, the feedback from last year this year says we have to do this again next year. It's, it's, it will be bigger because I think this year approves that it's already doubled and we'll probably see a similar dynamic. Um, so I fully expect us to be here. Well, maybe not here. We're sort of outgrowing this hotel. Um, but doing this event again next year, >>AI machine learning automation, uh, I'll throw in cloud. These are the hottest topics going. Elise, thanks very much for coming to the cube was great to have you. >>It's great. It's great meeting with you. >>It. Thank you for watching everybody. That's a wrap from Miami. Go to siliconangle.com check out all the news of the cube.net is where you'll find all these videos and follow the, uh, the Twitter handles at the cube at the cube three 65. I'm Dave Volante. We're out. We'll see you next time.

>> Narrator: Live from Miami, Florida. It's theCUBE. Covering IBM's data and AI forum. Brought to you by IBM. >> Welcome back to the port of Miami everybody. This is theCube, the leader in live tech coverage. We're here covering the IBM AI and data forum. Of course, the centerpiece of IBM's AI platform is Watson. Beth Smith is here, she's the GM of IBM Watson. Beth, good to see you again. >> You too. Always good to be with theCUBE. >> So, awesome. Love it. So give us the update on Watson. You know, it's beyond Jeopardy. >> Yeah, yeah. >> Oh, wow. >> That was a long time ago now. (laughs) >> Right, but that's what a lot of people think of, when they think of Watson. What, how should we think about Watson today? >> So first of all, focus Watson on being ready for business. And then, a lot of people ask me, "So what is it?" And I often describe it as a set of tools, to help you do your own AI and ML. A set of applications that are AI applications. Where we have prebuilt it for you, around a use case. And there is examples where it gets embedded in a different application or system that may have existed already. In all of those cases, Watson is here, tuned to business enterprise, how to help people operational-wise, AI. So they can get the full benefit, because at the end of the day it's about those business outcomes. >> Okay, so the tools are for the super geeks, (Beth laughs) who actually want to go in and build the real AI. >> (laughs) That's right, that's right. >> The APPS are, okay. It's prebuilt, right? Go ahead and apply it. >> That's right. >> And the embedded is, we don't even know we're using it, right? >> That's right, or you may. Like, QRadar with Watson has an example of using Watson inside of it. Or, OpenPages with Watson. So sometimes you know you're using it. Sometimes you don't. >> So, how's the mix? I mean, in terms of the adoption of Watson? Are there enough like, super techies out there, who are absorbing this stuff? Or is it mostly packaged APPS? Is it a mix? >> So it is a mix, but we know that data science skills are limited. I mean, they're coveted, right? And so those are the geeks, as you say, that are using the tool chain as a part of it. And we see that in a lot of customers and a lot of industries around the world. And then from a packaged APP standpoint, the biggest use case of adoption is really around customer care, customer service, customer engagement. That kind of thing. And we see that as well. All around the world, all different industries. Lots of great adoption. Watson Assistant is our flagship in that. >> So, in terms of, if you think about these digital initiatives, we talked about digital transformation, >> Yup. >> Last few years, we kind of started in 2016 in earnest, it's real when you talk to customers. And there was a ton of experimentation going on. It was almost like spaghetti. Throw against the wall and see what sticks. Are you seeing people starting to place their bets on AI, Narrowing their scope, and really driving you know, specific business value now? >> Beth: Yeah. >> Or is it still kind of all over the place? >> Well, there's a lot of studies that says about 51% or so still stuck in experimentation. But I would tell you in most of those cases even, they have a nice pilot that's in production, that's doing a part of the business. So, 'cause people understand while they may be interested in the sexiness of the technology, they really want to be able to get the business outcomes. So yes, I would tell 'ya that things have kind of been guided, focused towards the use cases and patterns that are the most common. You know, and we see that. Like I mentioned, customer care. We see it in, how do you help knowledge workers? So you think of all those business documents, and papers and everything that exists. How do you assist those knowledge workers? Whether or not it's an attorney or an engineer, or a mortgage loan advisor. So you see that kind of use case, and then you see customers that are building their own. Focused in on, you know, how do they optimize or automate, or predict something in a particular line of business? >> So you mentioned Watson Assistant. So tell us more about Watson Assistant, and how has that affected adoption? >> So Watson Assistant as I said, it is our flagship around customer care. And just to give you a little bit of a data point, Watson Assistant now, through our public cloud, SaaS version, converses with 82 million end users a month. So it's great adoption. And this is, this is enabling customers. Customers of our customers, to be able to get self-service help in what they're doing. And Watson Assistant, you know, a lot of people want to talk about it being a chat bot. And you can do simple chat bots with it. But it's to sophisticated assistance as well. 'Cause it shows up to do work. It's there to do a task. It's to help you deal with your bank account, or whatever it is you're trying to do, and whatever company you're interacting with. >> So chat bots is kind of a, (laughs) bit of a pejorative. But you're talking about digital systems, it's like a super chat bot, right? >> Beth: Yeah. I saw a stat the other day that there's going to be, by I don't know, 2025, whatever. There's going to be more money spent on chat bot development, or digital assistance, than there is on mobile development. And I don't know if that's true or not, >> Beth: Mhm, wow. But it's kind of an interesting thing. So what are you seeing there? I mean, again I think chat bots, people think, oh, I got to talk into a bot. But a lot of times you don't know you're, >> Beth: That's right. >> so they're getting, they're getting better. I liken it to fraud detection. You know, 10 years ago fraud detection was like, six months later you'll, >> Right. >> you'll get a call. >> Exactly. >> And so chat bots are just going to get better and better and better, and now there's this super category that maybe we can define here. >> That's right. >> What is that all about? >> That's right. And actually I would tell you, they kind of, they can become the brain behind something that's happening. So just earlier today I was, I was with a customer and talking about their email CRM system, and Watson Assistant is behind that. So chat bots aren't just about what you may see in a little window. They're really about understanding user intent, guiding the user through what they're trying to either find out or do, and taking the action as a part of it. And that's why we talk about it being more than chat bots. 'Cause it's more than a FAQ interchange. >> Yes, okay. So it's software, >> Beth: Yes. >> that actually does, performs tasks. >> Beth: Yes. >> Probably could call other software, >> Beth: Absolutely. >> to actually take action. >> That's right. >> I mean, I see. We think of this as systems of agency, actually. Making, sort of, >> That's right. >> decisions and then I guess, the third piece of that is, having some kind of human interaction, where appropriate, right? >> That's right. >> What do you see in terms of, you know, infusing humans into the equation? >> So, well a couple of things. So one of the things that Watson Assistant will do, is if it realizes that it's not the expert on whatever it is, then it will pass over to an expert. And think of that expert as a human agent. And while it's doing that, so you may be in the queue, because that human person is tied up, you can continue to do other things with it, while you're waiting to actually talk to the person. So that's a way that the human is in the loop. I would tell you there's also examples of how the agents are being assisted in the background. So they have the interaction directly with the user, but Watson Assistant is helping them, be able to get to more information quicker, and narrow in on what the topic is. >> So you guys talk about the AI ladder, >> Beth: Mhm. >> Sort of, Rob talked about that this morning. My first version of the AI ladder was building blocks. It was like data and AI analytics, ML, and then AI on top of that. >> Beth: Yup. >> I said AI. Data and IA. >> Beth: Yup. >> Information Architecture. Now you use verbs. Sort of, to describe it. >> Beth: Yup. Which is actually more powerful. Collect, organize, analyze and infuse. Now infuse is like the Holy Grail, right? 'Cause that's operationalizing and being able to scale AI. >> Beth: That's right. >> What can you tell us about how successful companies are infusing AI, and what is IBM doing to help them? >> So, I'm glad you picked up first of all, that these are verbs and it's about action. And action leads to outcome, which is, I think, critical. And I would also tell you yes, infuse is, you know, the Holy Grail of the whole thing. Because that's about injecting it into business processes, into workflows, into how things are done. So you can then see examples of how attorneys may be able to get through their legal prep process in just a few minutes, versus 10, 15 hours on certain things. You can see conversion rates of, from a sales standpoint, improve significantly. A number of different things. We've also got it as a part of supply chain optimization, understanding a little bit more about both inventory, but also where the goods are along the way. And particularly when you think about a very complicated thing, there could be a lot of different goods in various points of transit. >> You know, I was sort of joking. Not joking, but mentioning Jeopardy at first. 'Cause a lot of people associate Watson with Jeopardy. >> Beth: Right. >> I can't remember the first time I saw that. It had to be the mid part of the last decade. What was it? >> Beth: February of 2011. >> 2011, okay I thought I even saw demos before that. I'm actually sure I did. Like in, back in some lab in IBM. And of course, the potential like, blew your mind. >> Right. >> I suspect you guys didn't even know what you had at the time. You were like, "Okay, we're going to go change the world." And you know, when you drive up and down 101 in Silicone Valley, it's like, "Oh, Watson this, Watson that." You know, you get the consumer guys, doing facial recognition, ad serving. You know, serving up fake news, you know. All kinds of applications. But IBM started to do something different. You're trying to really change business. Did you have any clue as to what you had at the time? And then how much of a challenge you were taking on, and then bring us to where we are now, and what do you see as a potential for the next 10 years? >> So, of course we had a clue. So let me start there. (Dave laughs) But with that, I think the possibilities of it weren't completely understood. There's no question in my mind about that. And what the early days were, were understanding, okay, what is that business application? What's the pattern that's going to come about as a part of it? And I think we made tremendous progress on that along the way. I would tell you now, you mentioned operationalizing stuff, and you know, now it's about, how do we help companies have it more throughout their company? Through different lines of business, how does it tie to various things that are important to us? And so that brings in things like trust, explainablity, the ethics of what it's doing. Bias detection and mitigation. And I actually believe a lot of that, and the operationalizing it within the processes, is where we're going to head, going forward. Of course there'll continue to be advancements on the features and the capabilities, but it's going to be about that. >> Alright, I'm going to ask you the it's depends question. (Beth laughs) So I know that's your answer, but at the macro, can machines make better diagnosis than doctors today, and if not, when will they be able to, in your view? >> So I would actually tell you that today they cannot, but what they can do is help the doctor make a better diagnosis than she would have done by herself. And because it comes back to this point of, you know, how the machine can process so much information, and help the expert, in this case the doctor's the expert, it could be an attorney, it could be an engineer, whatever. Help that expert be able to augment the knowledge that he or she has as a part of it. So, and that's where I think it is. And I think that's where it will be for my lifetime. >> So, there's no question in your mind that machines today, AI today, is helping make better diagnosis, it's just within augmented or attended type of approach. >> Absolutely. >> And I want to talk about Watson Anywhere. >> Beth: Okay, great. >> So we saw some discussion in the key notes and some demos. My understanding is, you could bring Watson Anywhere, to the data. >> That's right. >> You don't have to move the data around. Why is that important? Give us the update on Watson Anywhere. >> So first of all, this is the biggest requirement I had since I joined the Watson team, three and a half years ago. Was please can I have Watson on-prem, can I have Watson in my company data center, etcetera. And you know, we needed to instead, really focus in on what these patterns and use cases were, and we needed some help in the platform. And so thanks to Cloud Pak for data, and the underlying Red Hat OpenShift and container platform, we now are enabled to truly take Watson anywhere. So you can have it on premise, you can have it on the other public clouds, and this is important, because like you said, it's important because of where your data is. But it's also important because the workloads of today and tomorrow are very complex. And what's on cloud today, may be on premise tomorrow, may be in a different cloud. And as that moves around, you also want to protect the investment of what you're doing, as you have Watson customize for what your business needs are. >> Do you think you timed it right? I mean, you kind of did. All this talk about multicloud now. You really didn't hear much about it four or five years ago. For awhile I thought you were trying to juice your cloud business. Saying, "You want, if you want Watson, you got to go to the IBM cloud." Was there some of that, or was it really just, "Hey, now the timing's right." Where clients are demanding it, and hybrid and multicloud and on-prem situations? >> Well look, we know that cloud and AI go hand in hand. So there was a lot of positive with that. But it really was this technology point, because had I taken it anywhere three and a half years ago, what would've happened is, every deployment would've been a unique environment, a unique stack. We needed to get to a point that was a modern day, you know, infrastructure, if you will. And that's what we get now, with a container based platform. >> So you're able to scale it, such that every instance isn't a snowflake, >> That's right. >> that requires customization. >> That's right. So then I can invest in the enhancements to the actual capabilities it is there to do, not supporting multiple platform instantiations, under the covers. >> Well, okay. So you guys are making that transparent to the customer. How much of an engineering challenge is that? Can you share that with us? You got to run on this cloud, on that cloud, or on forever? >> Well, now because of Cloud Pak for data, and then what we have with OpenShift and Kubernetes and containers, it becomes, well, you know, there's still some technical work, my engineering team would tell you it was a lie. But it's simple now, it's straightforward. It's a lot of portability and flexibility. In the past, it would've been every combination of whatever people were trying to do, and we would not have had the benefit of what that now gives you. >> And what's the technical enable there? Is it sort of open API's? Architecture that allows for the interconnectivity? >> So, but inside of Watson? Or the overall platform? >> The overall platform. >> So I would say, it's been, at it's, at it's core it's what containers bring. >> Okay, really. So it's that, it's that. It's the marriage of your tech, >> Yeah. >> with the container wave. >> That's right. That's right. Which is why the timing was critical now, right? So you go back, yes they existed, but it really hadn't matured to a point of broad adoption. And that's where we are now. >> Yeah, the adoption of containers, Kubernetes, you know, micro services. >> Right, exactly. Now it's on a very steep curve. >> Exactly. >> Alright, give your last word on, big take away, from this event. What do you hearing, you know, what are you, some of the things you're most excited about? >> So first of all, that we have all of these clients and partners here, and all the buzz that you see. And that we've gotten. And then the other thing that I would tell you is, the great client examples. And what they're bragging on, because they are getting business outcomes. And they're getting better outcomes than they thought they would achieve. >> IBM knows how to throw an event. (Beth laughs) Beth, thanks so much for coming to theCUBE. >> Thank you, good to >> Appreciate it. >> see you again. >> Alright, great to see you. Keep it right there everybody, we'll be back. This is theCUBE live, from the IBM Data Forum in Miami, we'll be right back. (upbeat instrumental music)

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome back to downtown Miami. Everybody. We're here at the Intercontinental hotel covering the IBM data AI form hashtag data AI forum. My name is Dave Volante and you're watching the cube, the leader in live tech coverage. Ritika gunner is here. She's the vice president of data and AI expert labs and learning at IBM. Ritika, great to have you on. Again, always a pleasure to be here. Dave. I love interviewing you because you're a woman executive that said a lot of different roles at IBM. Um, you know, you've, we've talked about the AI ladder. You're climbing the IBM ladder and so it's, it's, it's, it's awesome to see and I love this topic. It's a topic that's near and dear to the cubes heart, not only women in tech, but women in AI. So great to have you. Thank you. So what's going on with the women in AI program? We're going to, we're going to cover that, but let me start with women in tech. It's an age old problem that we've talked about depending on, you know, what statistic you look at. 15% 17% of, uh, of, of, of the industry comprises women. We do a lot of events. You can see it. Um, let's start there. >>Well, obviously the diversity is not yet there, right? So we talk about women in technology, um, and we just don't have the representation that we need to be able to have. Now when it comes to like artificial intelligence, I think the statistic is 10 to 15% of the workforce today in AI is female. When you think about things like bias and ethicacy, having the diversity in terms of having male and female representation be equal is absolutely essential so that you're creating fair AI, unbiased AI, you're creating trust and transparency, set of capabilities that really have the diversity in backgrounds. >>Well, you work for a company that is as chairman and CEO, that's, that's a, that's a woman. I mean IBM generally, you know, we could see this stuff on the cube because IBM puts women on a, we get a lot of women customers that, that come on >>and not just because we're female, because we're capable. >>Yeah. Well of course. Right. It's just because you're in roles where you're spokespeople and it's natural for spokespeople to come on a forum like this. But, but I have to ask you, with somebody inside of IBM, a company that I could say the test to relative to most, that's pretty well. Do you feel that way or do you feel like even a company like IBM has a long way to go? >>Oh, um, I personally don't feel that way and I've never felt that to be an issue. And if you look at my peers, um, my um, lead for artificial intelligence, Beth Smith, who, you know, a female, a lot of my peers under Rob Thomas, all female. So I have not felt that way in terms of the leadership team that I have. Um, but there is a gap that exists, not necessarily within IBM, but in the community as a whole. And I think it goes back to you want to, you know, when you think about data science and artificial intelligence, you want to be able to see yourself in the community. And while there's only 10 to 15% of females in AI today, that's why IBM has created programs such as women AI that we started in June because we want strong female leaders to be able to see that there are, is great representation of very technical capable females in artificial intelligence that are doing amazing things to be able to transform their organizations and their business model. >>So tell me more about this program. I understand why you started it started in June. What does it entail and what's the evolution of this? >>So we started it in June and the idea was to be able to get some strong female leaders and multiple different organizations that are using AI to be able to change their companies and their business models and really highlight not just the journey that they took, but the types of transformations that they're doing and their organizations. We're going to have one of those events tonight as well, where we have leaders from Harley Davidson in Miami Dade County coming to really talk about not only what was their journey, but what actually brought them to artificial intelligence and what they're doing. And I think Dave, the reason that's so important is you want to be able to understand that those journeys are absolutely approachable. They're doable by any females that are out there. >>Talk about inherent bias. The humans are biased and if you're developing models that are using AI, there's going to be inherent bias in those models. So talk about how to address that and why is it important for more diversity to be injected into those models? >>Well, I think a great example is if you took the data sets that existed even a decade ago, um, for the past 50 years and you created a model that was to be able to predict whether to give loans to certain candidates or not, all things being equal, what would you find more males get these loans than females? The inherent data that exists has bias in it. Even from the history based on what we've had yet, that's not the way we want to be able to do things today. You want to be able to identify that bias and say all things being equal, it is absolutely important that regardless of whether you are a male or a female, you want to be able to give that loan to that person if they have all the other qualities that are there. And that's why being able to not only detect these things but have the diversity and the kinds of backgrounds of people who are building AI who are deploying this AI is absolutely critical. >>So for the past decade, and certainly in the past few years, there's been a light shined on this topic. I think, you know, we were at the Grace Hopper conference when Satya Nadella stuck his foot in his mouth and it said, Hey, it's bad karma for you know, if you feel like you're underpaid to go complain. And the women in the audience like, dude, no way. And he, he did the right thing. He goes, you know what, you're right. You know, any, any backtrack on that? And that was sort of another inflection point. But you talk about the women in, in AI program. I was at a CDO event one time. It was I and I, an IBM or had started the data divas breakfast and I asked, can I go? They go, yeah, you can be the day to dude. Um, which was, so you're seeing a lot of initiatives like this. My question is, are they having the impact that you would expect and that you want to have? >>I think they absolutely are. Again, I mean, I'll go back to, um, I'll give you a little bit of a story. Um, you know, people want to be able to relate and see that they can see themselves in these females leaders. And so we've seen cases now through our events, like at IBM we have a program called grow, which is really about helping our female lead female. Um, technical leaders really understand that they can grow, they can be nurtured, and they have development programs to help them accelerate where they need to be on their technical programs. We've absolutely seen a huge impact from that from a technology perspective. In terms of more females staying in technology wanting to go in the, in those career paths as another story. I'll, I'll give you kind of another kind of point of view. Um, Dave and that is like when you look at where it starts, it starts a lot earlier. >>So I have a young daughter who a year, year and a half ago when I was doing a lot of stuff with Watson, she would ask me, you know, not only what Watson's doing, but she would say, what does that mean for me mom? Like what's my job going to be? And if you think about the changes in technology and cultural shifts, technology and artificial intelligence is going to impact every job, every industry, every role that there is out there. So much so that I believe her job hasn't been invented yet. And so when you think about what's absolutely critical, not only today's youth, but every person out there needs to have a foundational understanding, not only in the three RS that you and I know from when we grew up have reading, writing and arithmetic, we need to have a foundational understanding of what it means to code. And you know, having people feel confident, having young females feel confident that they can not only do that, that they can be technical, that they can understand how artificial intelligence is really gonna impact society. And the world is absolutely critical. And so these types of programs that shed light on that, that help bridge that confidence is game changing. >>Well, you got kids, I >>got kids, I have daughters, you have daughter. Are they receptive to that? So, um, you know, I think they are, but they need to be able to see themselves. So the first time I sent my daughter to a coding camp, she came back and said, not for me mom. I said, why? Because she's like, all the boys, they're coding in their Minecraft area. Not something I can relate to. You need to be able to relate and see something, develop that passion, and then mix yourself in that diverse background where you can see the diversity of backgrounds. When you don't have that diversity and when you can't really see how to progress yourself, it becomes a blocker. So as she started going to grow star programs, which was something in Austin where young girls coded together, it became something that she's really passionate about and now she's Python programming. So that's just an example of yes, you need to be able to have these types of skills. It needs to start early and you need to have types of programs that help enhance that journey. >>Yeah, and I think you're right. I think that that is having an impact. My girls who code obviously as a some does some amazing work. My daughters aren't into it. I try to send them to coder camp too and they don't do it. But here's my theory on that is that coding is changing and, and especially with artificial intelligence and cognitive, we're a software replacing human skills. Creativity is going to become much, much more important. My daughters are way more creative than my sons. I shouldn't say that, but >>I think you just admitted that >>they, but, but in a way they are. I mean they've got amazing creativity, certainly more than I am. And so I see that as a key component of how coding gets done in the future, taking different perspectives and then actually codifying them. Your, your thoughts on that. >>Well there is an element of understanding like the outcomes that you want to generate and the outcomes really is all about technology. How can you imagine the art of the possible with technology? Because technology alone, we all know not useful enough. So understanding what you do with it, just as important. And this is why a lot of people who are really good in artificial intelligence actually come from backgrounds that are philosophy, sociology, economy. Because if you have the culture of curiosity and the ability to be able to learn, you can take the technology aspects, you can take those other aspects and blend them together. So understanding the problem to be solved and really marrying that with the technological aspects of what AI can do. That's how you get outcomes. >>And so we've, we've obviously talking in detail about women in AI and women in tech, but it's, there's data that shows that diversity drives value in so many different ways. And it's not just women, it's people of color, it's people of different economic backgrounds, >>underrepresented minorities. Absolutely. And I think the biggest thing that you can do in an organization is have teams that have that diverse background, whether it be from where they see the underrepresented, where they come from, because those differences in thought are the things that create new ideas that really innovate, that drive, those business transformations that drive the changes in the way that we do things. And so having that difference of opinion, having healthy ways to bring change and to have conflict, absolutely essential for progress to happen. >>So how did you get into the tech business? What was your background? >>So my background was actually, um, a lot in math and science. And both of my parents were engineers. And I have always had this unwavering, um, need to be able to marry business and the technology side and really figure out how you can create the art of the possible. So for me it was actually the creativity piece of it where you could create something from nothing that really drove me to computer science. >>Okay. So, so you're your math, uh, engineer and you ended up in CS, is that right? >>Science. Yeah. >>Okay. So you were coded. Did you ever work as a programmer? >>Absolutely. My, my first years at IBM were all about coding. Um, and so I've always had a career where I've coded and then I've gone to the field and done field work. I've come back and done development and development management, gone back to the field and kind of seen how that was actually working. So personally for me, being able to create and work with clients to understand how they drive value and having that back and forth has been a really delightful part. And the thing that drives me, >>you know, that's actually not an uncommon path for IBM. Ours, predominantly male IBM, or is in the 50 sixties and seventies and even eighties. Who took that path? They started out programming. Um, I just think, trying to think of some examples. I know Omar para, who was the CIO of Aetna international, he started out coding at IBM. Joe Tucci was a programmer at IBM. He became CEO of EMC. It was a very common path for people and you took the same path. That's kind of interesting. Why do you think, um, so many women who maybe maybe start in computer science and coding don't continue on that path? And what was it that sort of allowed you to break through that barrier? >>No, I'm not sure why most women don't stay with it. But for me, I think, um, you know, I, I think that every organization today is going to have to be technical in nature. I mean, just think about it for a moment. Technology impacts every part of every type of organization and the kinds of transformation that happens. So being more technical as leaders and really understanding the technology that allows the kinds of innovations and business for informations is absolutely essential to be able to see progress in a lot of what we're doing. So I think that even general CXOs that you see today have to be more technically acute to be able to do their jobs really well and marry those business outcomes with what it fundamentally means to have the right technology backbone. >>Do you think a woman in the white house would make a difference for young people? I mean, part of me says, yeah, of course it would. Then I say, okay, well some examples you can think about Margaret Thatcher in the UK, Angela Merkel, and in Germany it's still largely male dominated cultures, but I dunno, what do you think? Maybe maybe that in the United States would be sort of the, >>I'm not a political expert, so I wouldn't claim to answer that, but I do think more women in technology, leadership role, CXO leadership roles is absolutely what we need. So, you know, politics aside more women in leadership roles. Absolutely. >>Well, it's not politics is gender. I mean, I'm independent, Republican, Democrat, conservative, liberal, right? Absolutely. Oh yeah. Well, companies, politics. I mean you certainly see women leaders in a, in Congress and, and the like. Um, okay. Uh, last question. So you've got a program going on here. You have a, you have a panel that you're running. Tell us more about. >>Well this afternoon we'll be continuing that from women leaders in AI and we're going to do a panel with a few of our clients that really have transformed their organizations using data and artificial intelligence and they'll talk about like their backgrounds in history. So what does it actually mean to come from? One of, one of the panelists actually from Miami Dade has always come from a technical background and the other panelists really etched in from a non technical background because she had a passion for data and she had a passion for the technology systems. So we're going to go through, um, how these females actually came through to the journey, where they are right now, what they're actually doing with artificial intelligence in their organizations and what the future holds for them. >>I lied. I said, last question. What is, what is success for you? Cause I, I would love to help you achieve that. That objective isn't, is it some metric? Is it awareness? How do you know it when you see it? >>Well, I think it's a journey. Success is not an endpoint. And so for me, I think the biggest thing I've been able to do at IBM is really help organizations help businesses and people progress what they do with technology. There's nothing more gratifying than like when you can see other organizations and then what they can do, not just with your technology, but what you can bring in terms of expertise to make them successful, what you can do to help shape their culture and really transform. To me, that's probably the most gratifying thing. And as long as I can continue to do that and be able to get more acknowledgement of what it means to have the right diversity ingredients to do that, that success >>well Retika congratulations on your success. I mean, you've been an inspiration to a number of people. I remember when I first saw you, you were working in group and you're up on stage and say, wow, this person really knows her stuff. And then you've had a variety of different roles and I'm sure that success is going to continue. So thanks very much for coming on the cube. You're welcome. All right, keep it right there, buddy. We'll be back with our next guest right after this short break, we're here covering the IBM data in a AI form from Miami right back.

>> Narrator: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, we're here at the Parc 55 at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante and IBM's Chief Data Officer Strategy Summit, they hold them on both coasts, one in Boston and one in San Francisco. A couple times each year, about 150 chief data officers coming in to learn how to apply their craft, learn what IBM is doing, share ideas. Great peer networking, really senior audience. John Thomas is here, he's a distinguished engineer and director at IBM, good to see you again John. >> Same to you. >> Thanks for coming back in theCUBE. So let's start with your role, distinguished engineer, we've had this conversation before but it just doesn't happen overnight, you've got to be accomplished, so congratulations on achieving that milestone, but what is your role? >> The road to distinguished engineer is long but today, these days I spend a lot of my time working on data science and in fact am part of what is called a data science elite team. We work with clients on data science engagements, so this is not consulting, this is not services, this is where a team of data scientists work collaboratively with a client on a specific use case and we build it out together. We bring data science expertise, machine learning, deep learning expertise. We work with the business and build out a set of tangible assets that are relevant to that particular client. >> So this is not a for-pay service, this is hey you're a great customer, a great client of ours, we're going to bring together some resources, you'll learn, we'll learn, we'll grow together, right? >> This is an investment IBM is making. It's a major investment for our top clients working with them on their use cases. >> This is a global initiative? >> This is global, yes. >> We're talking about, what, hundreds of clients, thousands of clients? >> Well eventually thousands but we're starting small. We are trying to scale now so obviously once you get into these engagements, you find out that it's not just about building some models. There are a lot of challenges that you've got to deal with in an enterprise setting. >> Dave: What are some of the challenges? >> Well in any data science engagement the first thing is to have clarity on the use case that you're engaging in. You don't want to build models for models' sake. Just because Tensorflow or scikit-learn is great and build models, that doesn't serve a purpose. That's the first thing, do you have clarity of the business use case itself? Then comes data, now I cannot stress this enough, Dave, there is no data science without data, and you might think this is the most obvious thing, of course there has to be data, but when I say data I'm talking about access to the right data. Do we have governance over the data? Do we know who touched the data? Do we have lineage on that data? Because garbage in, garbage out, you know this. Do we have access to the right data in the right control setting for my machine learning models we built. These are challenges and then there's another challenge around, okay, I built my models but how do I operationalize them? How do I weave those models into the fabric of my business? So these are all challenges that we have to deal with. >> That's interesting what you're saying about the data, it does sound obvious but having the right data model as well. I think about when I interact with Netflix, I don't talk to their customer service department or their marketing department or their sales department or their billing department, it's one experience. >> You just have an experience, exactly. >> This notion of incumbent disruptors, is that a logical starting point for these guys to get to that point where they have a data model that is a single data model? >> Single data model. (laughs) >> Dave: What does that mean, right? At least from an experienced standpoint. >> Once we know this is the kind of experience we want to target, what are the relevant data sets and data pieces that are necessary to make their experience happen or come together. Sometimes there's core enterprise data that you have in many cases, it has been augmented with external data. Do you have a strategy around handling your internal, external data, your structured transactional data, your semi-structured data, your newsfeeds. All of these need to come together in a consistent fashion for that experience to be true. It is not just about I've got my credit card transaction data but what else is augmenting that data? You need a model, you need a strategy around that. >> I talk to a lot of organizations and they say we have a good back-end reporting system, we have Cognos we can build cubes and all kinds of financial data that we have, but then it doesn't get down to the front line. We have an instrument at the front line, we talk about IOT and that portends change there but there's a lot of data that either isn't persisted or not stored or doesn't even exist, so is that one of the challenges that you see enterprises dealing with? >> It is a challenge. Do I have access to the right data, whether that is data at rest or in motion? Am I persisting it the way I can consume it later? Or am I just moving big volumes of data around because analytics is there, or machine learning is there and I have to move data out of my core systems into that area. That is just a waste of time, complexity, cost, hidden costs often, 'cause people don't usually think about the hidden costs of moving large volumes of data around. But instead of that can I bring analytics and machine learning and data science itself to where my data is. Not necessarily to move it around all the time. Whether you're dealing with streaming data or large volumes of data in your Hadoop environment or mainframes or whatever. Can I do ML in place and have the most value out of the data that is there? >> What's happening with all that Hadoop? Nobody talks about Hadoop anymore. Hadoop largely became a way to store data for less, but there's all this data now and a data lake. How are customers dealing with that? >> This is such an interesting thing. People used to talk about the big data, you're right. We jumped from there to the cognitive It's not like that right? No, without the data then there is no cognition there is no AI, there is no ML. In terms of existing investments in Hadoop for example, you have to absolutely be able to tap in and leverage those investments. For example, many large clients have investments in large Cloudera or Hortonworks environment, or Hadoop environments so if you're doing data science, how do you push down, how do you leverage that for scale, for example? How do you access the data using the same access control mechanisms that are already in place? Maybe you have Carbros as your mechanism how do you work with that? How do you avoid moving data off of that environment? How do you push down data prep into the spar cluster? How do you do model training in that spar cluster? All of these become important in terms of leveraging your existing investments. It is not just about accessing data where it is, it's also about leveraging the scale that the company has already invested in. You have hundred, 500 node Hadoop clusters well make the most of them in terms of scaling your data science operations. So push down and access data as much as possible in those environments. >> So Beth talked today, Beth Smith, about Watson's law, and she made a little joke about that, but to me its poignant because we are entering a new era. For decades this industry marched to the cadence of Moore's law, then of course Metcalfe's law in the internet era. I want to make an observation and see if it resonates. It seems like innovation is no longer going to come from doubling microprocessor speed and the network is there, it's built out, the internet is built. It seems like innovation comes from applying AI to data together to get insights and then being able to scale, so it's cloud economics. Marginal costs go to zero and massive network effects, and scale, ability to track innovation. That seems to be the innovation equation, but how do you operationalize that? >> To your point, Dave, when we say cloud scale, we want the flexibility to do that in an off RAM public cloud or in a private cloud or in between, in a hybrid cloud environment. When you talk about operationalizing, there's a couple different things. People think that, say I've got a super Python programmer and he's great with Tensorflow or scikit-learn or whatever and he builds these models, great, but what happens next, how do you actually operationalize those models? You need to be able to deploy those models easily. You need to be able to consume those models easily. For example you have a chatbot, a chatbot is dumb until it actually calls these machine learning models, real time to make decisions on which way the conversation should go. So how do you make that chatbot intelligent? It's when it consumes the ML models that have been built. So deploying models, consuming models, you create a model, you deploy it, you've got to push it through the development test staging production phases. Just the same rigor that you would have for any applications that are deployed. Then another thing is, a model is great on day one. Let's say I built a fraud detection model, it works great on day one. A week later, a month later it's useless because the data that it trained on is not what the fraudsters are using now. So patterns have changed, the model needs to be retrained How do I understand the performance of the model stays good over time? How do I do monitoring? How do I retrain the models? How do I do the life cycle management of the models and then scale? Which is okay I deployed this model out and its great, every application is calling it, maybe I have partners calling these models. How do I automatically scale? Whether what you are using behind the scenes or if you are going to use external clusters for scale? Technology is like spectrum connector from our HPC background are very interesting counterparts to this. How do I scale? How do I burst? How do I go from an on-frame to an off-frame environment? How do I build something behind the firewall but deploy it into the cloud? We have a chatbot or some other cloud-native application, all of these things become interesting in the operationalizing. >> So how do all these conversations that you're having with these global elite clients and the challenges that you're unpacking, how do they get back into innovation for IBM, what's that process like? >> It's an interesting place to be in because I am hearing and experiencing first hand real enterprise challenges and there we see our product doesn't handle this particular thing now? That is an immediate circling back with offering management and development. Hey guys we need this particular function because I'm seeing this happening again and again in customer engagements. So that helps us shape our products, shape our data science offerings, and sort of running with the flow of what everyone is doing, we'll look at that. What do our clients want? Where are they headed? And shape the products that way. >> Excellent, well John thanks very much for coming back in theCUBE and it's a pleasure to see you again. I appreciate your time. >> Thank you Dave. >> All right good to see you. Keep it right there everybody we'll be back with our next guest. We're live from the IBM CDO strategy summit in San Francisco, you're watching theCUBE.

>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. (upbeat music) >> Welcome back to San Francisco, everybody. You're watching theCUBE, the leader in live tech coverage. We're covering the IBM Chief Data Officer Strategy Summit #ibmcdo. Ed Walsh is here. He's the General Manager of IBM Storage, and Steven Eliuk who's the Vice President of Deep Learning in the Global Chief Data Office at IBM, Steven. >> Yes, sir. >> Good to see you again. Welcome to The CUBE. >> Pleasure to be here. So there's a great story. We heard Inderpal Bhandari this morning talk about the enterprise data blueprint and laying out to the practitioners how to get started, how to implement, and we're going to have a little case study as to actually how you're doing this. But Ed, set it up for us. >> Okay, so we're at this Chief Data Officer Summit in the Spring, we do it twice a year and really get just Chief Data Officers together to think through their different challenges and actually share. So that's where we're at the Summit. And what we've, as IBM, as kind of try to be a foot forward, be that cognitive enterprise and showing very transparently what we're doing at our organization be more data-driven. And we've talked a bunch of different times. Everyone needs to be data-driven. Everyone wants to be data-driven, but it's really challenging for organizations. So what we're doing is with this blueprint which we're showing as a showcase, in fact you can actually physically come in and see our environment. But more importantly we're being very transparent on all the different components, high-level processes, what we did in governance, but also down to the Lilly Technology level and sharing that with our... Not because they want to do all of it, but maybe they want to do some of it or half of it, but it would be a blueprint that's worked. And then we're being transparent about what we're getting internally for our own transformation as IBM. Because really if we looked at this as a platform, it's really an enterprise cognitive data platform that all of IBM uses on all our transformation work. So our client, in fact, is Steven, and I think you can give what are we doing. By the way, it also, same type of infrastructure allows you to do what we did in the national labs, the largest supercomputers in the world, same infrastructure and the same thing we're trying to do, is make it easier for people to get insights from the data at scale in the enterprise. So that's why I want to bring Steven on. >> I joked with Inderpal. I said, "Well, if you can do it at IBM, "if you can do it there you can do it anywhere," (Ed laughing) because he's point oh. We're at a highly complex organization. So Steven, take us through how you got started and what you're doing. >> For sure, so I'm what's referred to probably as a difficult customer. So because we're so multifaceted we have so many different use cases internally in the orders of hundreds, it doesn't mean that I can just say, "Hey, this is a specific pattern that I need, Ed. "You need to make sure your hardware is sufficient in this area," because the next day I'm going to be hitting him and say, "Hey Ed, I need you to make sure "that it's also efficient in terms of bandwidth as well." And that's the beauty of working in this domain, is that I have those hundreds of use cases and it means that I'm hitting low latency requirements, bandwidth requirements, extensibility requirements because I have a huge number of headcount that I'm bringing on as well. And if I'm good now I don't have to worry about in six months to be stating, "Hey, I need to roll out new infrastructure "so I can support these new data scientists "and effectively so that they can get outcomes quicker." And I'd need to make sure that all the infrastructure behind the scenes is extensible and supports my users. And what I don't want them to have to worry about specifically is how that infrastructure works. I want them to focus on those use cases, those enterprise use cases, and I want them to touch as many of those use cases as possible. >> So Inderpal laid out sort of his five things that a CDO should do. He starts with develop a clear data strategy. So as the doer in the organization, how'd you go about doing that? Presumably you participated in that data strategy, but you're representing the lines of business presumably to make sure that it's of value to them. You can accelerate business value, but how did you start? I mean that's a big challenge, chewy. >> For sure, yeah, it's a huge challenge. And I think effectively curating, locating, governing, and quality aspects of that data is one of the first aspects. And where does that data reside, though, and how do we access it quickly? How does it support structured and unstructured data effectively? Those are all really important questions that had to come to light. And that's some of the approaches that we took. We look at the various business units and we look at are they curating the data correctly? Is it the data that we need? Maybe we have to augment that curation process before we actually are able to kind of apply new techniques, new machine-learning techniques, to that use case. There's a number of different aspects that kind of get rolled into that, and bringing effective storage and effective compute to the table really accelerates us in that journey. >> So Ed, what are the fundamental aspects of the infrastructure that supports this sort of emerging workload? >> Yeah, no, good question. And some of it is what we're going to talk about, what's a storage layer and what's a compute layer, but also what are the tools we're putting in place to use a lot of these open-source toolsets and make it easier for people to use but also use that underlying infrastructure better. So if you look at the high level, we use a storage infrastructure that is built for these AI workloads which is closer to an HPC workload. So the same infrastructure we use, we use the term ESS or elastic storage server. It's a combination. It's a turnkey solution, half rack, full rack. But it can start very small and grow to the biggest supercomputers in the world like what we're doing in the national labs, like the largest top five supercomputers in the world. But what that is is a file system called Spectrum Scale. Allows you to scale up at the performance but also low latency, gets added to the metadata but also high throughput. So we can do layers on that either on flash being all the hot tiers'll be on flash because it's not just the throughput you need which is high. So our lowest end box's close to like what, 26 gigabytes a second. Our highest one like national labs is 4.9 terabytes a second throughput. But it's also the low latency quick access. So we have a storage infrastructure but then we also have high-performance compute. So what we have is our Power Systems, our POWER9 Systems with GPUs, and the idea is how do you, we use the term feed the beast? How do you have the right throughput or IOPS to get the data close to that CPU or the GPU? The Power Systems have a unique bandwidth, so it's not like what you just find from a Comodo, the Intel servers. It's a much faster throughput, so it allows us to actually get data between the GPU CPU in storage or memory very fast. So you can get these deep learning times, and maybe you can share some of that. The learning times go up dramatically, so you get the insight. And then we're also putting layers on top which are IBM Cloud Private, is basically how do you have a hybrid cloud container-based service that allows you to move things seamlessly across and not have to wrestle with how to put all these things together either so it works seamlessly between a public cloud and private cloud? Then we have these toolsets, and I talked about this last time. It might not seem like storage or what you have in APU but we use the term PowerAI, is taking all these machine-learning tools because everyone always used open source. But we make them one more scale but also to ease your use. So how do you use a bunch of great GPUs and CPUs, great throughput, and how do you scale that? A lot of these tools were basically to be run on one CPU. So to be distributed, key research from IBM allows you to actually with PowerAI take the same TensorFlow workflows or dot dot dot and run it across a grid dramatically changing what you're doing from learning times. But anyway you can probably give more, I think, but it's a multiple layer. It's not one thing but it's not what you use for digital storage infrastructure, compute infrastructure for normal workloads. It is custom so you can't... A lot of people try to deploy maybe their NAS storage box and maybe it's flash and try to deploy it. And you can get going that way but then you hit a wall real quick. This is purposely built for AI. >> So Beth Smith was on earlier. She threw out a stat. She said that 85% of their, based on some research, I'm not sure if it was IBM or Forrest or Gartner, said 85% of customers they talked to said AI will be a competitive advantage but only 20% can use it today at scale. So obviously scale is a big challenge, and I want to ask you to comment on another potential challenge. We always talk about elastic infrastructure. You scale up, scale down, or end of month, okay. We sometimes use this concept of plastic infrastructure. Basically plastic maintains its shape because these workloads are so diverse. I don't want to have to rip down my infrastructure and bring in a new one every time my workload changes. So I wonder if you can talk about the sort of requirements from your perspective both in terms of scale and in terms of adaptability to changing workloads. >> Well, I think one of the things that Ed brought up that's really, really important is these open-source frameworks assume that it's running on a single system. They assume that storage is actually local, and that's really the only way that you get really effective throughput from it, is if it's local. So extending it via PowerAI, via these appliances and so forth means that you can use petabytes of storage at a distance and still have good throughput and not have those GP utilization coming down because these are very expensive devices. So if the storage is the blocker, is their controller and he's limiting that flow of data then ultimately you're not making the most effective use of those very expensive computational mediums. But more importantly it means that your time from ideation to product is slowed down, so you're not able to get those business outcomes. That means your competitor could get those business outcomes if they don't have it. And for me what's really important is I mentioned this briefly earlier, is that I need those specialists to touch as much of the data or as much as those enterprise use cases as possible. At the end of the year it's not about touching three use cases. It's the touching three this year, five, ten, more and more and more. And with the infrastructure being storage and computation, all of that is key attributes to kind of seeing that goal. >> Without having to rip that down and then repurpose building it every time. >> Steven: Yeah. >> And just being able to deal with the grid as a grid and you can place workloads across a grid. >> 100%. >> That's our Spectrum compute products that we've been doing for all the major banks in the world to do that and take these workloads and place them across a grid is also a key piece of this. So we always talk about the infrastructures being hey, Ed, that's not storage or infrastructure. No, you need that. And that's why it's part of my portfolio to actually build out the overall infrastructure for people to build on prim but also talk about everything we did with you on prim is hybrid. It's goes to the Cloud natively because some workloads we believe will be on the Cloud for good reasons, and you need to have that part of it. So everything we're going with you is hybrid cloud today, not in the future, today. >> No, 100%, and that's one of the requirements in our organization that we call A-1 architecture. If we write it for our own prim we have to be able to run it on the Cloud and it has to have the same look and feel and painted glass and things like that as well. So it means we only have to write it once, so we're incredibly efficient because we don't have to write it multiple times for different types of infrastructure. Likewise we have expectations from the data scientists that the performance all still have to be up to par as well. We want to really be moving the computation directly to where the data resides and we know that it's not just on prim, it's not in the Cloud, it's a hybrid scenario. >> So don't hate me for asking you this, Ed, but you've only been here for a couple years. Did you just stumble into this? You got this vast portfolio, you got this tooling, you got cloud. You got a part of your organization saying we got to do on prim. The other part's saying we got to do public. Or was this designed to the workload? Was kind of a little bit of both? >> Well, I think luck is good, but it's a embarrassment of riches inside IBM between our primary research, some of the things we were just talking about. How do you run these frameworks in a distributed fashion and not designed that way and do it performing at scale? That's our primary, that's research. That's not even in my group. What we're doing is for workload management. That's in storage, but we have these toolsets. The key thing is work with the clients to figure out what they're trying to do. Everyone's trying to be data-driven, so as we looked at what you need to do to be truly data-driven, it's not just having faster storage although that's important. It's not about the throughput or having to scale up. It's not about having just the CPUs. It's not just about having the open frameworks, but it's how to put that all together that we're invisible. In fact you said it earlier. He doesn't want his users to know at all what's underneath. He just wants to run their workload. You have people from my organization because I'm one of your customers. You're my customer but we go to you and say, "We're trying to use your platform "for a 360 view of the client," and our not data scientists, not data engineers, but ops team can use his platform. So anyway, so I actually think it's because IBM has its broad portfolio that we can bring together. And when IBM shows up which we're showing up in AI together in the Cloud, that's when you see something that we can truly do that you can't get from other organizations. And it's because of the technology differentiation we have from the different groups, but also the industry contacts that we bring. >> 100%. >> And also when you're dealing with data it is the trust. We can engage the clients at a high level and help them because we're not a single-product company. We might be more complex, but when we show up and bring the solution set we can really differentiate. And I think that's when IBM shows up. It's pretty powerful. >> And I think it's moved from "trust me" as well to "show me," and we're able to show it now because we're eating what we're producing. So we're showing. They called it a blueprint. We're using that effectively inside the organization. >> So now that you've sort of built this out internally you spend a lot of time with clients kind of showing them or...? >> Probably 15% of my time. >> So not that much. >> No, no, because I'm in charge of internal transformation operations. They're expecting outcomes from us. But at the same time there's clients that are in the exact same boat. The realization that this is really interesting. There's a lot of noise, a lot of interesting stuff in AI out there from Google, from Facebook, from Amazon, from all, Microsoft, but image recognition isn't important to me. How do I do it for my own organization? I have legacy data from 50 years. This is totally different, and there's no Git repo that I can go to and download them all and use it. It's totally custom, and how do I handle that? So it's different for these guys. >> What's on your wishlist? What's on Ed's to do list? >> Oh geez, uh... I want it so simple for my data scientists that they don't have to worry about where the data's coming from. Whether it be a traditional relational database or an object store, I want it to feed that data effectively and I don't want to have to have them looking into where the data is to make sure the computation's there. I want it just to flow effortlessly. That's really the wishlist. Likewise, I think if we had new accelerators in general outside the box, not something from the traditional GPU viewpoint, maybe data flow or something in new avant-garde-type stuff, that would be interesting because I think it might open up a new train of thought in the area just like GPUs did for us. >> Great story. >> Yeah I know, I think it's... So we're talking about AI for business, and I think what you're seeing is we're trying to showcase what IBM's doing to be really an AI business. And what we've done in this platform is really a showcase. So we're trying to be as transparent as possible not because it's the only way to do it but it's a good example of how a very complex business is using AI to get dramatically better and everyone's using the same kind of platform. >> Well, we learned, we effectively learned being open is much better than being closed. Look at the AI community. Because of its openness that's where we're at right now. And following the same lead we're doing the same thing, and that's why we're making everything available. You can see it and we're doing it, and we're happy to talk to you about it. >> Awesome, all right, so Steven, you stay here. >> Yeah. >> We're going to bring Sumit on and we're going to drill down into the cognitive platform. >> That's good. This guy, thanks for setting it up. I really, really appreciate it. >> Thank you very much. >> All right, good having you guys. All right, keep it right there, everybody. We'll be back at the IBM CDO Strategy Summit. You're watching theCUBE. 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>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit, 2018 brought to you by IBM. >> Welcome back to San Francisco everybody. We're here covering the IBM CDO strategy summit. You're watching theCUBE, the leader and live tech coverage hashtag IBM CDO. Beth Smith is here, she's the general manager at Watson data and AI at IBM and of course Inderpal Bandari who's the global chief data officer at IBM. Folks, welcome back to theCUBE. It's great to see you both again. >> Good to be back. >> So I love these shows, they're intimate, practitioner really they're absorbing everything. You're getting some good questions, some good back and forth but Beth share with us what you're hearing from customers. What matters for enterprises right now in the context of the cognitive enterprise, the AI enterprise. >> So you know customers are looking at how did they get the benefit? They recognize that they've got a lot of valuable data, data that we haven't always called data. Sometimes it's documents and journals and other kinds of really unstructured things and they want to determine how can they get value from that and they look out and compare it to maybe consumer things and recognize they don't have the same volume of that. So it's important for customers, how do they get started and I would tell you that most of them start with a small project, they see value with that quickly they then say, okay how do we increment and grow from that. >> So Inderpal you had said I think I got this right, this is your fourth CDO gig. You're not new to this rodeo. Were you the first healthcare CDO is that right? >> I was. >> Dave: Okay you got it all started. >> There were four of us at that time. >> Okay so forth and four okay I did get that right. So you obviously bring a lot of experience here and one of the things you stressed today in your talk is you basically want to showcase IBM so you're applying sort of data enterprise data strategies to IBM and then you showcase that to your clients. Talk about that a little bit. >> Yeah I mean if you think about it, we are the quintessential complex enterprise. We're global, we're far-flung, we have literally thousands of products. We acquire companies, we move forward at a global scale and also we are always competing at a global scale. So there literally is that complexity that enterprises face which all our customers who are the large enterprises have to also deal with. So given all that we felt that the best way to talk about an AI enterprise is to use ourselves as a showcase. >> Okay Beth, I got to ask you about Watson's law. Okay so we had Moore's law we all know what that is. Metcalfe's law the network effect, Watson's law and I have a noodling on this a little bit. We're entering a new era which I think is underscored by... and names matter. We use a parlance in our industry whether it's cloud or a big data or internet or whatever it is and so we're trying to sort of figure out what this new era is like. What do you envision as Watson's law. I'd love to have a little riff on that. >> So first of all as we look at all those things and compare them back, they're all about opportunities to scale and how things changed because of a new scaling effect. So I would argue that the one we're in now, which we like to call Watson's law the future will determine what it's actually called is about scaling knowledge and applying knowledge so it's about how to use AI machine learning applied to data, all forms of data and turn that into knowledge and that's what's going to separate people and I would tell you that's also going to come back and give the incumbents an opportunity because the incumbents are strong in their industries, in their domains, they can leverage the data that they have, the knowledge and experience they have and then use that for how do they disrupt and really become the disruptors of the future. >> So okay what about the math behind this? I'm kind of writing down some notes as you were talking so my version of Watson's law and love your comment is innovation in the future and the current is going to be a function of the data, your ability to apply AI or cognitive to that data and then your ability to your point scale, the cloud economics. Does that make sense to you guys, is that where innovation is going to come? >> It's true but I have to go back at this point Dave of knowledge so I think you take data and you take AI or machine learning and those are sort of your ingredients. The scaling factor is going to be on knowledge and how does that ultimately get applied. Cloud again gives us an ingredient if you will that can be applied to it but the thing that'll make the difference on it, just like networking was in the past and opened up opportunities around the internet is that in the other will be how folks use knowledge. It's almost like you could think of it as a learning era and how that's not just going to be about individuals but about companies and business models etc. >> So the knowledge comes from applying cognitive to the data and then being able to scale it. Okay and then Inderpal, how do I address the access issue? I've got many if not most incumbents data are in silos. The marketing department has data, the sales department has data, the customer service department has data. How do you as a CDO address that challenge? >> Well what you've got to do is use the technology to actually help you address that challenge. So building data lakes is a good start for both structured and unstructured data where you bring data that's traditionally been siloed together but that's not always possible. Sometimes you have to let the data be where they are but you at least have to have a catalog that tells you where all the data is so that an intelligent system can then reason about that when working with somebody on a particular use case actually help them find that data and help them apply it and use it. >> So that's a metadata challenge correct? >> It's a metadata challenge in the AI world because the metadata challenge has always been there but now you have the potential to apply AI to not just create metadata but then also to apply it effectively to help business users and subject matter experts who are not data experts find the right data and work it. >> You guys make a big deal about automating some of this stuff up front as the point of creation or use automating. Classification is a good example. How are you solving that problem from a technology perspective? >> Well some of our core Watson capabilities are all about classification and then customers use that. It can be what I will call a simple use case of email classification and routing. We have a client in France that has 350,000 emails a week and they use Watson for that level of classification. You look at all sorts of different kinds of ticketing things you look at AI assistants and it comes down to how do you really understand what the intent is here and I believe classification is one of the fundamental capabilities in the whole thing. >> Yeah it's been a problem that we've been trying to solve in this industry for a while kind of pre AI and you really there's not a lot you can do if you don't have good classification if you can't automate it then you can't scale. >> That's right. >> So from a classification standpoint, I mean it's a fundamental always been fundamental problem. Machines have gotten much better at it with the AI systems and so forth but there's also one aspect that's quite interesting which is now you have open loop systems so you're not just classifying based on data that was historically present and so in that sense you're confined to always repeat your mistakes and so forth. You hear about hedge funds that implode because their models are no longer applicable because there's a Black Swan event. Now with the AI systems you have the opportunity to tap the realtime events as they're going and actually apply that to the classification as well. So when Beth talks about the different APIs that we have available to do classification, we also have NLP APIs that allow you to bring to bare this additional stuff that's going on and go from a closed-loop classification to an open-loop one. >> So I want to ask you about the black box problem. If you think about AI, I was saying this in my intro, I know when I see a dog but if I have to describe how I actually came to that conclusion, it's actually quite difficult to do and computers can show me here's a dog or I joked in Silicon Valley. I don't know if you guys watch that show Silicon Valley. Hot dog or not so your prescription at IBM is to make a white box, open that up, explain to people which I think is vitally important because when line of business people get in the room. like how'd you get to that answer and then it's going to be it's going to actually slow you down if you have arguments but how do you actually solve that black box problem? >> It's a much harder problem obviously but there are a whole host of reasons as to why you should look at it that way. One is we think it's just good business practice because when people are making business decisions they're not going to comply unless they really understand it. From my previous stint at IBM when I was working with the coaches of the NBA, they would not believe what patterns were being put forward to them until such time as we tied it to the video that showed what was actually going on. So it's that same aspect in terms of being able to explain it but there's also fundamentally more important reasons as well. You mentioned the example of looking at a dog and saying that's a dog but not being able to describe it. AI systems have that same issue. Not only that what we're finding is that you can take an AI system and you can just tweak a little bit of the data so that instead of recognizing it as a dog now it's completely fooled and it will recognize it as a rifle or something like that. Those are adversarial examples. So we think that taking this white box approach sets us up not just tactically and from a business standpoint but also strategically from a technical standpoint because if a system is able to explain it, describe it and really present its reasoning, it's not going to be fooled that easily either. >> Some of the themes that we hear from IBM, you own your own data, the Facebook blowback has actually been an unbelievable tailwind for that meme and most of the data in the world is not publicly searchable. So build on those themes and talk about how IBM is helping its customers take advantage of those two dynamics. >> So they kind of go hand-in-hand in the sense that because customers have most of the data behind their firewall if you will, within their business walls it means it's unlikely that it's annotated and labeled and used for a lot of these systems so we're focusing on how do we put together techniques to allow systems to learn more with less data. So that goes hand-in-hand with that. That's also back to the point of protecting your data because as we protect your data, you and your competitor we cannot mix that data together to improve the base models that are a part of it so therefore we have to do techniques that allow you to learn more with less data. One of the simplest thing is around the customization. I mean we're coming up on two years since we provided the capability to do custom models on top of visual recognition, Watson visual recognition. The other guys have been bragging about it in the last four to five months. We've been doing it in production with clients, will be two years in July so you'd say okay, well what's that about? We can end up training a base model that understands some of the basics around visual type things like your dog example and some other things but then give you the tools to very quickly and easily create your custom one that now allows you to better understand equipment that may be natural to you or how it's all installed or agricultural things or rust on a cell phone tower or whatever it may be. I think that's another example of how this all comes about to say that's the part that's important to you as a company, that's part that has to be protected that also has to be able to learn with you only spending a few days and a few examples to train it, not millions and billions. >> And that base layer is IBM, but the top layer is client IP and you're guaranteeing the clients that my IP won't seep into my competitors. >> So our architecture is one that separates that. We have hybrid models as a part of it and that piece that as you said is the client piece is separate from the rest of it. We also do it in such a way that you could see there could be a middle layer in there as well. Let's call it industry or licensed data so maybe it comes from a third party, it's not owned by the client but it's something that's again licensed not public as a part of it. That's a part of what our architecture is-- >> And you guys, we saw the block diagrams in there. You're putting together solutions for clients and it's a combination of your enterprise data architecture and you actually have hardware and software components that you can bring to bear here. Can you describe that a little bit? >> Go ahead, it's your implementation. >> Yeah so we've got again the perfect example of a large enterprise. There's significant on-prem implementations, there's private cloud implementations, there's public cloud implementations. You've got to bridge all that and do it in a way that makes it seamless and easy for an enterprise to adopt so we've worked through all that stuff. So we've learned things the hard way about well if you're truly going to do an AI data lake, you better have it on flash. For that reason we have it on flash on-prem but also on the cloud, our storage is on flash and so we were able to make those types of decisions so that we've learned through this, we want to share that with our clients. How do you involve deep learning in this space, well it's going to be proximate to your data lake so that the servers can get to all that data and run literally thousands and thousands of experiments in time that it's going to be useful for the decision. So all those hard learnings we are packaging that in the form of these showcases. We're bringing that forward but right now it's around hybrid cloud and the bridge so that because we want to talk about everything and then going forward it's all public cloud as we leverage that for the elasticity of the computer. >> Well IBM if you can do it there you can do it anywhere. It's a highly complex organization. So it's been what two years in for you now two? >> Little over two years. >> So you're making a lot of progress and I could see the practitioners eating this stuff up and that's got to feel good. I mean you have an impact obviously. >> It absolutely feels very good and I'm always in fact I kind of believe this coming into IBM that this is a company that has not only a number of products that are pertinent to this space but actually the framework to create an AI enterprise. These are not like disparate products. These are all going towards creating an AI enterprise and I think if you look across our portfolio of products and then you kind of map that back to our showcases, you'll see that come to life but in a very tangible way that yes if you truly want to create an AI enterprise, IBM is the place to be because they've got the answers across all the dimensions of the problem as opposed to just one specific dimension like let's say a data mining algorithm or something machine learning and that's basically it. When we cover the full gamut and you have to otherwise you can't really create an AI enterprise. >> In the portfolio obviously coming together IBM huge ambitions with with Watson and everybody's familiar with the ads and so you've done a great job of getting that you know top of mind but you're really starting to work with clients to implement this stuff. I know we got to end here but you had thrown out of stat 85% of executive CAI as a competitive advantage but only 20% can use it at scale so there's still that big gap, you're obviously trying to close that gap. >> Yeah so in a way I would correct it only 20% of them are using it at scale. I think Dave it's a part of how do they get started and I think that comes back to the fact that it shouldn't be a large transformational, scary multi-year project. It is about taking small things, starting with two or three or five use cases and growing and scaling that way and that's what our successful customers are doing. We give it to them in a way that they can use it directly or we leverage it within a number of solutions, like cyber security, like risk and compliance for financial services like health care that they can use it in those solution areas as well. >> Guys thanks so much for coming to theCUBE and sharing your story. We love coming to these events you see guys I used to say you see the practitioners, it's a board level discussion and these guys are right in it so good to see you guys, thank you. >> You too, thank you. >> You're welcome, all right keep it right to everybody, we'll be back with our next guest you're watching theCUBE live from the IBM Chief Data Officer Strategy Summit in San Francisco, we'll be right back.

>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit, 2018, brought to you by IBM. (techno music) >> Welcome to San Francisco everybody. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage, and we're at the IBM CDO Strategy Summit, #IBMCDO. The chief data officer role emerged about a decade ago, and it was typically focused in regulated industries, health care, financial services, and government. And it sort of emerged from a dark, back office role of governance and compliance and data quality. But increasingly as the big data wave came to the market, people realized there was an opportunity to take that sort of wonky back office governance, compliance, discipline, and really point it toward generating value, whether that was with direct monetization of data or contributing to an organization's data strategy. And, over the next five to seven years, that chief data officer role... Couple things happen, one is got much much deeper into those regulated industries, but also permeated other non-regulated industries beyond those three that I mentioned. IBM is an organization that has targeted the chief data officer role as a key constituency as part of what IBM calls the cognitive enterprise. And IBM hosts shows in Boston and San Francisco each year, gathering chief data officers, about 100 to 150 chief data officers, in each city. These are very focused and targeted events that comprise of chief data officers, data analytics officers, and the like, people focused sometimes on compliance and governance. They're very intimate events and today, we heard from a number of IBM experts, Inderpal Bhandari, who's been on theCUBE a number of times, who is IBM's global chief data officer, laying out, sort of a blueprint, an enterprise blueprint, for data strategy. So the audience is filled with practitioners who are really sort of lapping up sort of the how to implement some of these techniques, and ultimately platforms. IBM has put together solutions, that not only involve, of course, Watson, but also some of the other components, whether its cognitive systems, governance systems, compliance systems, to create a solution that chief data officers and their colleagues can implement. So, this morning we heard about the cognitive enterprise blueprint, what IBM calls the AI enterprise, or the cognitive enterprise, talking about organizational issues. How do you break down silos of data? If you think about most incumbent organizations, the data lives in silos. It may be data in the marketing department, data in the sales department, data in the customer service department, data in the maintenance department. So these are sort of separate silos of data. How do you break those down? How do you bring those together so you can compete with some of these born digital AI-oriented companies, the likes of, just the perfect example is Facebook, Google, LinkedIn, et cetera, who have these sort of centralized data models. How do you take an existing organization, break down those silos, and deal with a data model that is accessible by everyone who needs to access that data, and as well, very importantly, make it secure, make it enterprise-ready. The other thing that IBM talked about was process. We always talk about on theCUBE, people, process, and technology. Technology is the easiest piece of that. It's the people and process components of that matrix that you need to really focus on before you even bring in the technology, and then, of course, there is the technology component. IBM is a technology company. We've heard about Watson. IBM has a number of hardware and software components that it brings to bear to try to help organizations affect their data strategy, and be more effective in the marketplace. So, as I say this is about 130, 150 chief data officers. We heard from Kaitlin Lafferty, who's going to come on a little later. She's going to be my quasi-co-host, which will be interesting. Beth Smith, who is the GM of Watson Data. She talked a lot about use cases. She gave an example of Orange Bank, a totally digital bank, using Watson to service customers. You can't call this bank. And they've got some interesting measurements that they'll share with us in terms of customer satisfaction and born-digital or all-digital bank. She also talked about partnerships that they're doing, not directly, sort of indirectly I inferred, she talked about IT service management embedding Watson into the IT service management from an HR perspective. I believe that she was referring to, even though she didn't mention it, a deal that IBM struck with ServiceNow. IBM's got similar deals with Watson with Salesforce. Salesforce Einstein is based on Watson. So what you're seeing is embedding AI into different applications, and we've talked about this a lot at siliconANGLE and theCUBE and at Wikibon. It's really those embedded use cases for AI that are going to drive adoption, as opposed to generalized horizontal AI. That seems to be not the recipe for adoption success, really more so specific use cases. I mean the obvious ones are some consumer ones, and even in the enterprise as well: security, facial recognition, natural language processing, for example. Very specific use cases for AI. We also heard from Inderpal Bhandari, the global chief data officer of IBM, talking about the AI enterprise, really showcasing IBM as a company that is bringing this AI enterprise to itself, and then teaching, sharing that knowledge with its clients and with its customers. I really like talking to Inderpal Bhandari. I learn a lot from him. This is his fourth CDO gig, okay. He was the very first CDO ever in health care when there, I mean I think he was the first of four or one of four, first CDOs in health care. Now there are thousands. So this is his fourth gig as a CDO. He talks about what a CDO has to do to get started, starting with a clear data strategy. When I've talked to him before, he said, he mentioned, how does data contribute to the monetization of your organization? Now it's not always monetization. If it's a non-public company or a health care company, for example, that's not-for-profit, it's not necessarily a monetization component, it's more of a how does it effect your strategy. But that's number one is sort of, how does data drive value for you organization? The second is, how do you implement the system that's based on governance and security? What's the management system look like? Who has data and who has access to that data? How do you affect privacy? And then, how do you become a central source for that AI-framework, being a service organization essentially to the entire organization? And then, developing deep analytics partnerships with lines of business. That's critical, because the domain expertise for the business is obviously going to live in the line of business, not in some centralized data organization. And, then, finally, very importantly, skills. What skills do you need, identify those skills, and then how do you get those people? How do you both train internally and find those people externally? Very hard to find those skills. He talked about AI systems having four attributes. Number one is expertise, domain knowledge. AI systems have to be smart about the problem that they're trying to solve. Natural human interaction, IBM talks about natural language processing, a lot of companies do. Everybody's familiar with the likes of Alexa, Google Home, and Siri. Well IBM Watson also has an NLP capability that's quite powerful. So that's very important. And interestingly he talked about, I'll ask him about this, the black box phenomenon. Most AI is a black box. If you think about it, AI can tell you if you're looking at a dog, but think about your own human frame. How do you know when you're actually seeing a dog? Try to explain to somebody someday how you go about recognizing that animal. It's sort of hard to do. Systems today can tell you that if it's a dog or for you Silicon Valley watchers, hot dog. But, it's a black box. What IBM is saying is no, we can't live with a black box in the enterprise. We have to open up that black box, make it a white box, and share with our customers exactly how that decision is being made. That's an interesting problem that I want to talk to him about. And then, next, the third piece is learning through education. How do you learn at scale? And then the fourth piece was, how do you evolve, how do you iterate, how do you become auto-didactic or self-learning with regard to the system and getting better and better and better over time. And that sets a foundation for this AI enterprise or cognitive enterprise blueprints, where the subject matter expert can actually interact with the system. We had some questions from the audience. One came up on cloud and security concerns, not surprising. Data exposure, how do you automate a lot of this stuff and provide access, at the same time ensuring privacy and security. So IBM's going to be addressing that today. So, we're here all day, wall-to-wall coverage of the IBM CDO Strategy Summit, #IBMCDO. Of course, we're running multiple live programs today. I'm covering this show in San Francisco. John Furrier is in Copenhagen at KubeCon with The Linux Foundation. Stu Miniman is holding down the fort with a very large crew at Dell Technology's World. So keep it right there everybody. This is theCUBE at IBM's CDO Strategy Summit in San Francisco. We'll be right back after this short break. (techno music) (dial tones)

live from the Fairmont Hotel in San Jose California it's the queue at big data sv 2015 hello everyone welcome back this is the cube our flagship program we go out to the events they strike this evil noise i'm john furrier we're here with IBM to talk about big data big data analytics and we're doing a first-ever crowd chat simulcast of live feed with IBM so guys we're going to try this out it's like go to crouch at dan / Hadoop next and join the conversation and our guests here Rob Thomas vice president product development big data analyst at IBM and beth smith general manager of IBM analytics platform guys welcome to welcome to the cube thank you welcome back and so IBM mostly we're super excited to next week as I was the interconnect you're bigger than you guys mashed up three shows for the mega shows and and Aerosmith's playing so it's going to say I'm from the Boston air so I'm really excited about you know Aerosmith and all the activities of social lounge and and whatnot but we've been following you guys the transformation of IBM is really impressive you guys certainly think a lot of heat in the press in terms of some of the performance size in the business but it's pumping right now you guys seem to have great positioning the stories are hanging together a huge customer base huge services so we're at the Big Data world which is tends to be startup driven from the past few years over the past phase one the big cuppies came in and started saying hey you know there's a big market our customers see demand and that so I got your take on on as we're coming in to interconnect next next week what is the perspective of big data asli Watson has garnered headlines from powering toys to jeopardy to solving huge world problems that's a big data problem you guys are not new to Big Data so when you look at this big data week here and Silicon Valley what's the take sure so I'll start often embedded Bethke night in so our big focus is how we start to bring data to the masses and we start to think in terms of personas data science and plays an increasingly important role around big data how people are accessing that the developer community and then obviously the line of business community which is the client set that I've been serving four years but the announcements that we've made this week around Hadoop are really focused on the first two personas in terms of data scientists how they start to get better value out of Hadoop leveraging different tools we'll talk about what some of those are and so we're really starting to change it about Hadoop results me about insight it's not about infrastructure infrastructure is interesting but it's really about what you're getting out of it so that's why we're approaching it that way it's how well it has naturally the IBM strategy around data cloud and engagement and data is really about using the insights which like Rob said it's about the value can get from the data and how that can be used in to transform professions and industries and I think when we bring it back to Big Data and the topic of a doob I think frankly it has gotten to a point that clients are really beginning to say it's time to scale they're seeing the value in the technology what it can bring how it gives them some diversity in their data and analytics platform and they're ready to announce scale on their workloads as a part of it so the theme is Hadoop next okay so that takes us right to the next point which is okay what's next is a phase one okay we got some base position validation okay this new environments customers don't want that so what so what is next i mean we're earring things like in memories hot aussie spark has proven that there's an action in member that that kind of says okay analytics at the speed of business is something that's important you guys are all over that and we've heard some things from you guys so so what's how do we get to the next part where we take Hadoop as an infrastructure opportunity and put it into practice for solutions at what what are the key things that you guys see happening that must happen for the large customers to be successful so I think that actually ties into the announcements we made this week around the open data platform because that's about getting that core platform to ensure that their standardization around it there's interoperability around it and then that's the base and that vendors and clients are coming together do that and to really enable and facilitate the community to be able to standardize around that then it's about the value on top of that around it etc it's about the workloads and what could be brought to bear to extend up that how do you apply it to real time streaming how do you add things like machine learning how do you deal with things like text analytics I mean we have a we have a client situation where the client took 4 billion tweets and were able to analyze that to identify over a hundred and ten million profiles of individuals and then by integrating and analyzing that data with the internal data sources of about seven or eight different data sources they were able to narrow into 1.7 million profiles that matched at at least ninety percent precision you know now they've got data that they can apply on buying patterns and stuff it's about that it's about going up the stack we're going to talk for hours my mind's exploding privacy creepy I mean a personas is relevant now you talk about personalization I mean collective intelligence has been an AI concepts we try not to be creepy okay cool but now so that brings us to the next level I mean you guys were talk about cognitives on that is a word you guys kick around also systems of engagement systems of records an old term that's been around in the old data warehousing dates fenced-off resources of disk and data but now with systems of engagement real-time in the moment immersive experience which is essentially the social and/or kind of mobile experience what does that mean how do you guys get there how do you make it so it's better for the users more secure or I mean these are hot button issues that kind of lead us right to that point so I'll take you to that a couple ways so so first of all your first question round head tube next so Hadoop was no longer just an IT discussion that's what I've seen changed dramatically in the last six months I was with the CEO of one of the world's largest banks just three days ago and the CEO is asking about Hadoop so there's a great interest in this topic and so so why so why would a CEO even care I think one is people are starting to understand the use cases of the place so that talks about entity extraction so how you start to look at customer records that you have internally in your systems are record to your point John and then you you know how do you match that against what's happening in the social world which is more or the engagement piece so there's a clear use case around that that changes how clients you know work with their with their customers so so that's one reason second is huge momentum in this idea of a logical data warehouse we no longer think of the data infrastructure as oh it's a warehouse or it's a database physically tied to something not tied to just what relational store so you can have a warehouse but you can scale in Hadoop you can provision data back and forth you can write queries from either side that's what we're doing is we're enabling clients to modernize their infrastructure with this type of a logit logical data warehouse approach when you take those kinds of use cases and then you put the data science tools on top of it suddenly our customers can develop a different relationship with their customers and they can really start to change the way that they're doing business Beth I want to get your comments we have the Crouch at crowd chat / a dupe next some commentary coming in ousley transforming industries billion tweets killer for customer experience so customer experience and then also the link about the data science into high gear so let's bring that now into the data science so the logical you know stores okay Nick sands with virtualization things are moving around you have some sort of cognitive engines out there that can overlay on top of that customer experience and data science how are they inter playing because this came out on some of the retail event at New York City that happened last week good point of purchase personalization customer experience hated science it's all rolling together and what does that mean unpack that for us and simplify it if you can oh wows complexing is a big topic you know it's a big topic so a couple of different points so first of all I think it is about enabling the data scientists to be able to do what they their specialty is and the technologies have advanced to allow them to do that and then it's about them having the the data and the different forms of data and the analytics at their fingertips to be able to apply that I the other point in it though is that the lines are blurring between the person that is the data scientist and the business user that needs to worry about how do they attract new customers or how do they you know create new business models and what do they use as a part of do you think we're also seeing that line blurring one of the things that we're trying to do is is help the industry around growing skills so we actually have big data University we have what two hundred and thirty thousand participants and this online free education and we're expanding that topic now to again go up the stack to go into the things that data scientists want to deal with like machine learning to go into things that the business user really wants to now be able to capture it's a part of it trying to ask you guys kind of more could be a product question and/or kind of a market question at IBM's Ted at IBM event in he talked about a big medical example in one of her favorite use cases but she made a comment in their active data active date is not a new term for the data geeks out there but we look at data science lag is really important Realty near real time is not going to make it for airplanes and people crossing the street with mobile devices so real real time means like that second latency is really important speed so active date is a big part of that so can you guys talk about passive active data and how that relates to computing and because it's all kind of coming to get it's not an obvious thing but she highlighted that in her presentation because I see with medical medical care is obviously urgent you know in the moment kind of thing so if you would what does that all mean I mean is that something custom Street paying attention to is it viable is it doable so certainly a viable I mean it's a huge opportunity and i'd say probably most famous story we have around that is the work that we did at the university of toronto at the Hospital for Sick Children where we were using real-time streaming algorithms and a real-time streaming engine to monitor instance in the neonatal care facility and this was a million data points coming off of a human body monitoring in real time and so why is that relevant I mean it's pretty pretty basic actually if you extract the data you eat yell it somewhere you load in a warehouse then you start to say well what's going on it's way too late you know we're talking about you know at the moment you need to know what's happening and so it started as a lot was in the medical field would you notice there's some examples that you mentioned but real time is now going well beyond the medical field you know places from retail at the point of sale and how things are happening to even things like farming so real time is here to stay we don't really view that as different from what I would describe as Hadoop next because streaming to me as part of what we're doing with a dupe and with spark which we'll talk about in a bit so it's certainly it is it is the new paradigm for many clients but it's going to be much more common actually if i can add there's a client North Carolina State University it's where I went to school so it's a if it's a client that I talk about a lot but they in addition to what they do with their students they also work with a lot of businesses own different opportunities that may that they may have and they have a big data and analytics sort of extended education business education project as a part of that they are now prepared to be able to analyze one petabyte in near real time so the examples that you and Rob talked about of the real world workloads that are going to exist where real time matters are there there's no doubt about it they're not going away and the technology is prepared to be able to handle the massive amount of data and analytics that needs to happen right there in real time you know that's a great exact point I mean these flagship examples are kind of like lighthouses for people to look at and kind of the ships that kind of come into the harbor if you will for other customers as you always have the early adopters can you guys talk about where the mainstream market is right now I'll see from a services standpoint you guys have great presence and a lot of accounts where are these ships coming into which Harper where the lighthouse is actually medical you mentioned some of those examples are bringing in the main customers is it the new apps that are driving it what innovations and what are the forces and what are the customers doing in the main stream right now where are they in the evolution of moving to these kind of higher-end examples so I mean so Hadoop I'd say this is the year Hadoop where clients have become serious about Hadoop like I said it's now become a board-level topic so it's it's at the forefront right now I see clients being very aggressive about trying out new use cases everybody really across every interest industry is looking for one thing which is growth and the way that you get growth if you're a bank is you're not really going to change your asset structure what you're going to change is how you engage with clients and how you personalized offers if your retailer you're not going to grow by simply adding more stores it might be a short term growth impact but you're going to change how you're engaging with clients and so these use cases are very real and they're happening now Hadoop is a bore group discussion or big day I just didn't see you formula we should have more Hadoop or is it you know I see I've seen it over and over again I'll tell you where you see a lot from his companies that are private equity-owned the private equity guys have figured out that there's savings and there's innovation here every company i worked with that has private equity ownership Hadoop is a boardroom discussion and the idea is how do we modernize the infrastructure because it's it's because of other forces though it's because of mobile it's because of cloud that comes to the forefront so absolutely so let's take Hadoop so I do bits great bad just great a lot of innovations going on there boardroom in these private equity because one they're cutting edge probably they're like an investment they want to see I realized pretty quickly now speed is critical right I would infer that was coming from the private equity side speed is critical right so speed to value what does that mean for ibn and your customers how do you guys deliver the speed to value is that's one of the things that comes out on all the premises of all the conversations is hey you can do things faster now so value on the business side what do you guys see that sure so a a lot of different ways to approach that so we believe that as I said when I said before it's not just about the infrastructure it's about the insight we've built a lot of analytic capabilities into what we're doing around a dupe and spark so that clients can get the answers faster so one thing that we're going to be we have a session here at strata this week talking about our new innovation big R which is our our algorithms which are the only our algorithms that you can run natively on Hadoop where your statistical programmers can suddenly start to you know analyze data and you know drive that to decision make it as an example so we believe that by providing the analytics on top of the infrastructure you can you can change how clients are getting value out of that so how do we do it quickly we've got IBM SoftLayer so we've got our Hadoop infrastructure up on the cloud so anybody can go provision something and get started and ours which is not something that was the case even a couple years ago and so speed is important but the tools and how you get the insight is equally important how about speed 22 value from a customer deployment standpoint is it the apps or is it innovating on existing what do you sing well I think it's both actually um and and so you talked earlier about system of engagement vs system of record you know and I think at the end of the day for clients is really about systems of insight which is some combination of that right we tend to thank the systems of engagement or the newer things and the newer applications and we tend to thank the systems of record are the older ones but I think it's a combination of it and we see it show up in different ways so I'll take an example of telco and we have a solution on the now factory and this is now about applying analytics in real time about the network and the dynamics so that for example the operator has a better view of what's happening for their customers they're in users and they can tell that an application has gone down and that customers have now switched all of a sudden using a competitive application on their mobile devices you know that's different and that is that new applications or old or is it the combination and I think at the end of the day it really comes to a combination I love these systems of insight i'm just going to write that down here inside the inside the crowd chat so i got to talk about the the holy grail for big data analytics and big data from your perspective ideas perspective and to where you guys are partnering I'll see here there's a show of rich targets of a queue hires acquisitions partnerships I mean it's really a frill ground certainly Silicon Valley and and in the growth of a big data cloud mobile and social kind of these infrared photography biz is a message we've heard so what is the holy grail and then what are you guys looking for in partnerships and within the community of startups and or other alliances sure you want to start with the Holy Grail me yeah so so you know I think at the end of the day it is about using technology for business value and business outcome I you know I really think that's what said the spirit of it and so if I tell you why we have for example increased our attention and investment around this topic it's because of that it's because of what Rob said earlier when he said the state that clients are now in um so that's what I think is really important there and I think it's only going to be successful if it's done based own standards and something that is in support of you know heterogeneous environments I mean that's the world of technology that we live in and that's a critical element of it which leads to why we are a part of the Open Data Platform initiative so on the on the the piece of analytics I was just cus our comment about our for example I was just mentioning the crowd chat I had Microsoft just revolution analytics which is not our which is different community is there a land-grab going on between the big guys of you know IBM's a big company what do you guys see in that kind of area terms acquisition targets yeah man I think the numbers would say there's not a land-grab I don't think the MMA numbers have changed at a macro level at all in the last couple years I mean we're very opportunistic in our strategy right we look for things that augment what we do I think you know it's related to partner on your comment your question on partnering but we do acquisitions is not only about what that company does but it's about how does it fit within what IBM already does because we're trying to you know we're going after a rising tide in terms of how we deliver what clients need I think some companies make that mistake they think that if they have a great product that's relevant to us maybe maybe not but it's about how it fits in what we're doing and that's how we look at all of our partnerships really and you know we partner with global systems integrators even though we have one with an IBM we partner with ISVs application developers the big push this week as I described before is around data scientists so we're rolling out data science education on Big Data university because we think that data scientists will quickly find that the best place to do that is on an IBM platform because it's the best tools and if they can provide better insight to their companies or to their clients they're going to be better off so I was so yes that was the commenting on and certainly the end of last week and earlier this week about that Twitter and it's a lot of common in Twitter's figured out and people are confused by Twitter versus facebook and I know IBM has a relation but we're so just that's why pops in my head and I was are saying HP Buddha's got a great value and so I was on the side of Twitter's a winner i love twitter i love the company misunderstood certainly i think in this market where there's waves coming in more and more there's a lot of misunderstanding and i think i want to get your perspective you can share with the folks out there what is that next way because it's confusing out there you guys are insiders IBM i would say like twitter is winning doing very well certainly we're close to you guys we are we're deeply reporting on IBM so we can see the momentum and the positioning it's all in line what we see is that is where the outcomes will end up being for customers but there's still a lot of naysayers out there certainly you guys had your share as as to where's as an example so what is the big misunderstanding that you think is out there around the market we're in and what's the next wave as always waves coming in if you're not out in front that next wave usually driftwood as the old expression goes so what is that big misunderstanding and this kind of converged from a hyper targeted with analytics this is all new stuff huge opportunities huge shifts and inflection point as Bob picciano said on the cube is its kind of both going on the same time shift and it point so what's misunderstood and what's that next big waves so let me start with the next big way is that I'll back into the misunderstanding so the next big wave to me is machine learning and how do you start to take the data assets that you have and through machine learning and the application of those type of algorithms you start to generate better insights or outcomes and the reason i think is the next big wave is it's it may be one of the last competitive motes out there if you think about it if you have a a corpus of data that's unique to you and you can practice machine learning on that and have that you know either data that you can sell or to feed into your core business that's something that nobody else can replicate so it becomes incredibly powerful so one example I'll share with you and I want to bring you my book but it's actually not getting published next week since so maybe next week but so Wiley's publishing a book I wrote and one of the examples I give is a company by the name of co-star which I think very few people have heard of co-star is in the commercial real estate business they weren't even around a decade ago they have skyrocketed you know from zero to five hundred million dollars in revenue and it's because they have data on four million commercial properties out there who else has that absolutely nobody has that kind of reach and so they've got a unique data asset they can apply things like machine learning and statistics to that and therefore anybody who wants to do anything commercial real estate has to start with them so I pointed you're starting to get the point where you have some businesses where data is the product it's not an enabler it's the actual product I think that's probably one of the big misunderstandings out there is that you know data is just something that serves our existing products or existing services we're moving to a world where data is the product and that's the moat I wrote a post in 2008 called data is the new development kit and what you're basically saying is that's the competitive advantage a business user can make any innovation observation about data and not be a scientist and change the game that's what you were saying earlier similar right that's right okay so next big wave misunderstanding what do you wait bet what's your take on what are people not getting what is Wall Street what is potential the VCG really on the front end of some of the innovation but what is the general public not getting I mean we are in shift and an inflection what's it what's the big shift and misunderstanding going on so so I I would tend to you know actually agree with with Rob that I think folks aren't yet really appreciating and I guess I would twist it a little bit and say the insight instead of just the data but but they're not realizing what that is and what it's going to give us the opportunity for you know I would retire early if I actually could predict everything that was going to happen but but you know yeah but if you think about it you know if you think about you know mid to late 90s and what we would have all fault that the internet was going to allow us to do compared to what it actually allowed us to do is probably like night and day and I think the the time we're in now when you take data and you take mobility and you take cloud and you take these systems of engagement and the fact the way people individuals actually want to do things is is similar but almost like on steroids to what we were dealing with in the mid-90s or so and so you know the possibilities are frankly endless and and I think that's part of what people aren't necessarily realizing is that they have to think about that insight that data that actually has some value to it in very different ways there's a lot of disruptive enablers out Dunham's there's a lot to look at but finding which ones will be the biggest right it's hard I mean you get paid a lot of money to do that is if you can figure it out and keep it a secret um but you didn't you machine learning is now out there you just shared with us out competitive advantage so everyone knows know everyone kind of new kind of in the inside but but not everybody's using it right i mean i think another example a company like into it has done a great job of they started off as a software company they've become a data company i think what you what i've observed in all these companies is you can build a business model that's effectively recession proof because data becomes the IP in the organization and so I don't I actually you know I think for us those are the live in the world we this is well understood I don't think it's that well understood yet yeah insiders mic right and you know when we first started doing big data research and working with thousands of clients around the world there were there were six basic use cases it started of course with the customer the the end customer and the customer 360 and that sort of thing and went through a number of different things around optimization etc but the additional one is about those new business models and you know that is clearly in the last 12 to 18 months has become a lot more of what the topic is when I'm talking to clients and I think we will see that expand even more as we go in the future we've a lot of activity on the crowd chatter crowd chatter net / Hadoop necks and I'll mentioned we can probably extend time on that if you guys want to keep it keep it going conversation is awesome and we did getting the hook here so we'll remove the conversation to crouch at totnes Esther Dube next great thought leadership and I can go on this stuff for an hour you guys are awesome great to have you on the cube and so much to talk about a lot of ground will certainly see it in to connect go final question for you guys is what do you guys see for this week real quick summarize what do you expect to see it unfold for a big data week here at Silicon Valley Big Data asked me so I think you know a lot of the what we talked about machine learning is going to be a big topic I think there'll be a lot of discussion around the open data platform that Beth mentioned before it's a big move that we made along with another group supporting the apache software foundation I think that that's a big thing for this week but it should be exciting alright guys thanks for coming out to be IBM here inside the cube we're live in Silicon Valley would be right back with our next guest after the strip break I'm Jennifer this is the cube we write back