>> Announcer: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officers Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, everybody. This is theCUBE, the leader in live tech coverage, and we're here covering exclusive coverage of IBM's Chief Data Officer Strategy Summit. This is the summit, as I said, they book in at each coast, San Francisco and Boston. Intimate, a lot of senior practitioners, chief data officers, data folks, people who love data. Caitlyn Halferty is back. She's the Client Engagement Executive and the Chief Data Officer office at IBM. Great. And, Allen Crane, Vice President at USAA. >> Thank you. >> Good to see you. Thanks for coming on. All right. >> Thanks for having us. >> You're welcome. Well, good day today, as I said, a very intimate crowd. You're here as a sort of defacto CDO, learning, sharing, connecting with peers. Set up your role, Allen. Tell us about that. >> At USA, we've got a distributed data and analytics organization where we have centralized functions in our hub, and then each of the lines of business have their own data offices. I happen to have responsibility for all the different ways that our members interact with us, so about 100 million phone calls a year, about a couple billion internet and digital sessions a year, most of that is on mobile, and always lookin' at the ways that we can give back time to our membership, as well as our customer service reps, who we call our member service reps, so that they can serve our members better. The faster and more predictive we can be with being able to understand our members better and prompt our MSRs with the right information to serve them, then the more they can get on to the actual value of that conversation. >> A lot of data. So, one of the things that Inderpal talked about the very first time I met him, in Boston, he talked about the Five Pillars, and the first one was you have to understand as a CDO, how your organization gets value out of data. You said that could be direct monetization or, I guess, increased revenue, cut costs. That's value. >> Right. >> That's right. >> That's the starting point. >> Right. >> So, how did you start? >> Well, actually, it was the internal monetization. So, first off, I want to say USA never sells any of our member data, so we don't think of monetization in that framework, but we do think of it terms of how do we give something that's even more precious than money back to our company and to our members and the MSRs? And, that is really that gift of time. By removing friction from the system, we've been able to reduce calls per member, through digitization activities, and reduced transfers and reduced misdirects by over 10% every year. We're doing work with AI and machine learning to be able to better anticipate what the member is calling about, so that we can get them to the right place at the right time to the right set member service representatives. And, so all these things have resulted in, not just time savings but, obviously, that translates directly to bottom line savings, but at the end of the day, it's about increasing that member service level, increasing your responsiveness, increasing the speed that you're answering the phone, and ultimately increasing that member satisfaction. >> Yeah, customer satisfaction, lowers churn rates, that's a form of monetization, >> Absolutely. >> so it's hard dollars to the CFO, right? >> Absolutely, yeah. >> All right, let's talk about the role of the CDO. This is something that we touched on earlier. >> Yes. >> We're bringing it home here. >> Yes. >> Last segment. Where are we at with the role of the CDO? It was sort of isolated for years in regulated industries, >> Correct. >> permeated to mainstream organizations. >> Correct. >> Many of those mainstream organizations can move faster, 'cause their not regulated, so have we sort of reached parody between the regulated and the unregulated, and what do you discern there in terms of patterns and states of innovation? >> Sure. I think when we kicked off these summits in 2014, many of our CDOs came from CIO type organizations, defensive posture, you know, king of the data warehouse that we joke about, and now annuls reports of that time were saying maybe 20% of large organizations were investing in the CDO or similar individual responsible for enterprise data, and now we see analysts reports coming out to say upwards of 85, even 90%, of organizations are investing in someone responsible for that role of the CDO type. In my opening remarks this morning, I polled the room to say who's here for the first time. It was interesting, 69, 70% of attendees were joining us for the first time, and I went back, okay, who's been here last year, year before, and I said who was here from the beginning, 2014 with us, and Allen is one of the individuals who's been with us. And, as much as the topics have changed and the role has grown and the purview and scope of responsibilities, some topics have remained, our attendees tell us, they're still important, top-of-mind, and data monetization is one of those. So, we always have a panel on data monetization, and we've had some good discussions recently, that the idea of it's just the external resell, or something to do with selling data externally is one view, but really driving that internal value, and the ways you drive out those efficiencies is another perspective on it. So, fortunate to have Allen here. >> Well, we've been able to, for that very reason, we've been able to grow our team from about six or seven people five years ago to well over a hundred people, that's focused on how we inefficiency out of the system. That mere 10%, when your call-per-member reduction, when you're taking 30 million calls in the bank, you know, that's real dollars, three million calls out of the system that you can monetize like that. So, it's real value that the company sees in us, and I think that, in a sense, is really how you want to be growing in a data organization, because people see value in you, are willing to give you more, and then you start getting into those interesting conversations, if I gave you more people, could you get me more results? >> Let's talk about digital transformation and how it relates to all this. Presumably, you've got a top down initiative, the CEO says, he or she says, okay, this is important. We got to do it. Boom, there's the North Star. Let's go. What's the right regime that you're seeing? Obviously, you've got to have the executive buy-in, you've got the Chief Data Officer, you have the Chief Digital Officer, the Chief Operating Officer, the CFO's always going to be there, making sure things are on track. How are you seeing that whole thing shake out, at least in your organization? >> Well, one thing that we've been seeing is digital digitization or the digital transformation is not about just going only digital. It's how does all this work together. It can't just be an additive function, where you're still taking just as many calls and so forth, but it's got to be something that that experience online has got to do something that's transformative in your organization. So, we really look at the member all the way through that whole ecosystem, and not just through the digital lens. And, that's really where teams like ours have really been able to stitch together the member experience across all their channels that they're interacting with us, whether that's the marketing channels or the digital channels or the call channel, so that we can better understand that experience. But, it's certainly a complementary one. It can't just be an additive one. >> I wonder if we could talk about complacency, in terms of digital transformation. I talk to a lot of companies and there's discussion about digital, but you talk to a lot of people who say, well, we're doing fine. Maybe not in our industry. Insurance is one that hasn't been highly disruptive, financial services, things like aerospace. I'll be retired by the time this all, I mean, that's true, right? And, probably accurate. So, are you seeing a sense of complacency or are you seeing a sense of urgency, or a mix or both? What are you seeing, Caitlyn? >> Well, it's interesting, and people may not be aware, but I'm constantly polling our attendees to ask what are top-of-mind topics, what are you struggling with, where are you seeing successes, and digital was one that came up for this particular session, which is why tomorrow's keynote, we have our Chief Digital Officer giving the morning keynote, to show how our data office and digital office are partnering to drive transformation internally. So, at least for our perspective, in the internal side of it, we have a priority initiative, a cognitive sales advisor, and it's essentially intended to bring in disparate part of customer data, obtained through many different channels, all the ways that they engage with us, online and other, and then, deliver it through sales advisor app that empowers our digital sellers to better meet their revenue targets and impact, and develop more of a quality client relationship and improve that customer experience. So, internally, at least, it's been interesting to see one of our strongest partnerships, in terms of business unit, has been our data and digital office. They say, look, the quality of the data is at the core, you then enable our digital sellers, and our clients benefit, for a better client experience. >> Well, about a year ago, we absolutely changed the organization to align the data office with the digital office, so that reports to our executive counsel level, so their peers, that reporting to the same organization, to ensure that those strategies are connected. >> Yeah, so as Caitlyn was saying, this Chief Data Officer kind of emerged from a defensive posture of compliance, governance, data quality. The Chief Digital Officer, kind of new, oftentimes associated with marketing, more of an external, perhaps, facing role, not always. And then, the CIO, we'll say, well, wait a minute, data is the CIO's job, but, of course, the CIO, she's too busy trying to keep the lights on and make everything work. So, where does the technology organization fit? >> Well, all that's together, so when we brought all those things together at the organizational level, digital, data, and technology were all together, and even design. So, you guys are all peers, reporting into the executive committee, essentially, is that right? Yes, our data, technology, and design, and digital office are all peers reporting to the same executive level. And then, one of the other pillars that Inderpal talks about is the relationship with the line of business. So, how is that connective tissue created? Well, being on the side that is responsible for how all of our members interact, my organization touches every product, every line of business, every channel that our members are interacting with, so our data is actually shared across the organization, so right now, really my focus is to make sure that that data is as accessible as it can be across our enterprise partners, it's as democratized as it can be, it's as high as quality. And then, things that we're doing around machine learning and AI, can be enabled and plugged into from all those different lines of business. >> What does success look like in your organization? How do you know you're doing well? I mean, obviously, dropping money to the bottom line, but how are you guys measuring yourselves and setting objectives? What's your North Star? >> I think success, for me, is when you're doing a good job, to the point that people say that question, could you do more if I gave you more? That, to me, is the ultimate validation. It's how we grew as an organization. You know, we don't have to play that justification game When people are already coming to the table saying, You're doing great work. How can you do more great work? >> So, what's next for these summits? Are you doing Boston again in the fall? Is that right? Are you planning >> We are, we are, >> on doing that? >> and you know, fall of last year, we released the blueprint, and the intent was to say, hey, here's the reflection of our 18 months, internal journey, as well as all our client interactions and their feedback, and we said, we're coming back in the spring and we're showing you the detail of how we really built out these internal platforms. So, we released our hybrid on-prem Cloud showcase today, which was great, and to the level of specificity that shows that the product solutions, what we're using, the Flash Storage, some of the AI components of machine learning models. >> The cognitive systems component? >> Exactly. And then, our vision, to your question to the fall, is coming back with the public Cloud showcases. So, we're already internally doing work on our public Cloud, in particular respect to our backup, some of our very sensitive client data, as well as some initial deep learning models, so those are the three pieces we're doing in public Cloud internally, and just as we made the commitment to come back and unveil and show those detail, we want to come back in the fall and show a variety of public Cloud showcases where we're doing this work. And then, hopefully, we'll continue to partner and say, hey, here's how we're doing it. We'd love to see how you're doing it. Let's share some best practices, accelerate, build these capabilities. And, I'll say to your business benefit question, what we've found is once we've built that platform, we call it, internally, a one IBM architecture, out our platform, we can then drive critical initiatives for the enterprise. So, for us, GVPR, you know, we own delivery of GVPR readiness across the IBM corporation, working with senior executives in all of our lines of business, to make sure we get there. But, now we've got the responsibility to drive out initiatives like that cross business unit, to your question on the partnerships. >> The evolution of this event seems to be, well, it's got a lot of evangelism early on, and now it's really practical, sort of sharing, like you say, the blueprint, how to apply it, a lot of people asking questions, you know, there's different levels of maturity. Now, you guys back tomorrow? You got to panel, you guys are doing a panel on data monetization? >> We're doing a panel on data monetization tomorrow. >> Okay, and then, you've got Bob Lord and Inderpal talking about that, so perfect juxtaposition and teamwork of those two major roles. >> And, this is the first time we've really showcased the data/digital partnership and connection, so I'm excited, want to appeal to the developer viewpoint of this. So, I think it'll be a great conversation about data at the core, driving digital transformation. And then, as you said, our data monetization panel, both external efforts, as well as a lot of the internal value that we're all driving, so I think that'll be a great session tomorrow. >> Well, and it's important, 'cause there's a lot of confusing, and still is a lot of confusion about those roles, and you made the point early today, is look, there's a big organizational issue you have to deal with, particularly around data silos, MyData. I presume you guys are attacking that challenge? >> Absolutely. >> Still, it's still a-- >> It's an ongoing-- >> Oh, absolutely. >> I think we're getting a lot better at it, but you've got to lean in, because if it's not internal, it's some of the external challenges around. Now we're picking Cloud vendors and so forth. Ten years ago, we had our own silos and our own warehouses, if we had a warehouse, and then, we were kind of moving into our own silos in our own databases, and then as we democratized that, we solved the one problem, but now our data's so big and compute needs are so large that we have no choice but to get more external into Cloud. So, you have to lean in, because everything is changing at such a rapid rate. >> And, it requires leadership. >> Yep. >> Absolutely. >> The whole digital data really requires excellent leadership, vision. IBM's catalyzing a lot of that conversation, so congratulations on getting this going. Last thoughts. >> Oh, I would just say, we were joking that 2014, the first couple of summits, small group, maybe 20-30 participants figuring out how to best organize from a structural perspective, you set up the office, what sort of outcomes, metrics, are we going to measure against, and those things, I think, will continue to be topics of discussion, but now we see we've got about 500 data leaders that are tracking our journey and that are involved and engaged with us. We've done a lot in North America, we're starting to do more outside the geographies, as well, which is great to see. So, I just have to say I think it's interesting to see the topics that continue to be of interest, the governance, the data monetization, and then, the new areas around AI, machine learning, data science, >> data science >> the empowering developers, the DevOps delivery, how we're going to deliver that type of training. So, it's been really exciting to see the community grow and all the best practices leveraged, and look forward to continuing to do more of that this year as well. >> Well, you obviously get a lot of value out of these events. You were here at the first one, you're here today. So, 2018. Your thoughts? >> I think the first one, we were all trying to figure out who we are, what's our role, and it varied from I'm a individual contributor, data evangelist in the organization to I'm king of the warehouse thing. >> Right. >> And, largely, from that defensive standpoint. I think, today, you see a lot more people that are leaning in, leading data science teams, leading the future of where the organizations are going to be going. This is really where the center of a lot of organizations are starting to pivot and look, and see, where is the future, and how does data become the leading edge of where the organization is going, so it's pretty cool to be a part of a community like this that's evolving that way, but then also being able to have that at a local level within your own organization. >> Well, another big take-away for me is the USAA example shows that this can pay for itself when you grow your own organization from a handful of people to a hundred plus individuals, driving value, so it makes it easier to justify, when you can demonstrate a business case. Well, guys, thanks very much for helping me wrap here. >> Absolutely. >> I appreciate you having us here. >> Thank you. >> It's been a great event. Always a pleasure, hopefully, we'll see you in the fall. >> Sounds good. Thank you so much. >> All right, thanks, everybody, for watching. We're out. This is theCUBE from IBM CDO Summit. Check out theCUBE.net for all of the videos, siliconangle.com for all the news summaries of this event, and wikibon.com for all the research. We'll see you next time. (techy music)

(techno music) >> 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, this is theCUBE, the leader in live tech coverage. We're here at the Parc 55 in San Francisco covering the IBM CDO Strategy Summit. I'm here with Joel Horwitz who's the Vice President of Digital Partnerships & Offerings at IBM. Good to see you again Joel. >> Thanks, great to be here, thanks for having me. >> So I was just, you're very welcome- It was just, let's see, was it last month, at Think? >> Yeah, it's hard to keep track, right. >> And we were talking about your new role- >> It's been a busy year. >> the importance of partnerships. One of the things I want to, well let's talk about your role, but I really want to get into, it's innovation. And we talked about this at Think, because it's so critical, in my opinion anyway, that you can attract partnerships, innovation partnerships, startups, established companies, et cetera. >> Joel: Yeah. >> To really help drive that innovation, it takes a team of people, IBM can't do it on its own. >> Yeah, I mean look, IBM is the leader in innovation, as we all know. We're the market leader for patents, that we put out each year, and how you get that technology in the hands of the real innovators, the developers, the longtail ISVs, our partners out there, that's the challenging part at times, and so what we've been up to is really looking at how we make it easier for partners to partner with IBM. How we make it easier for developers to work with IBM. So we have a number of areas that we've been adding, so for example, we've added a whole IBM Code portal, so if you go to developer.ibm.com/code you can actually see hundreds of code patterns that we've created to help really any client, any partner, get started using IBM's technology, and to innovate. >> Yeah, and that's critical, I mean you're right, because to me innovation is a combination of invention, which is what you guys do really, and then it's adoption, which is what your customers are all about. You come from the data science world. We're here at the Chief Data Officer Summit, what's the intersection between data science and CDOs? What are you seeing there? >> Yeah, so when I was here last, it was about two years ago in 2015, actually, maybe three years ago, man, time flies when you're having fun. >> Dave: Yeah, the Spark Summit- >> Yeah Spark Technology Center and the Spark Summit, and we were here, I was here at the Chief Data Officer Summit. And it was great, and at that time, I think a lot of the conversation was really not that different than what I'm seeing today. Which is, how do you manage all of your data assets? I think a big part of doing good data science, which is my kind of background, is really having a good understanding of what your data governance is, what your data catalog is, so, you know we introduced the Watson Studio at Think, and actually, what's nice about that, is it brings a lot of this together. So if you look in the market, in the data market, today, you know we used to segment it by a few things, like data gravity, data movement, data science, and data governance. And those are kind of the four themes that I continue to see. And so outside of IBM, I would contend that those are relatively separate kind of tools that are disconnected, in fact Dinesh Nirmal, who's our engineer on the analytic side, Head of Development there, he wrote a great blog just recently, about how you can have some great machine learning, you have some great data, but if you can't operationalize that, then really you can't put it to use. And so it's funny to me because we've been focused on this challenge, and IBM is making the right steps, in my, I'm obviously biased, but we're making some great strides toward unifying the, this tool chain. Which is data management, to data science, to operationalizing, you know, machine learning. So that's what we're starting to see with Watson Studio. >> Well, I always push Dinesh on this and like okay, you've got a collection of tools, but are you bringing those together? And he flat-out says no, we developed this, a lot of this from scratch. Yes, we bring in the best of the knowledge that we have there, but we're not trying to just cobble together a bunch of disparate tools with a UI layer. >> Right, right. >> It's really a fundamental foundation that you're trying to build. >> Well, what's really interesting about that, that piece, is that yeah, I think a lot of folks have cobbled together a UI layer, so we formed a partnership, coming back to the partnership view, with a company called Lightbend, who's based here in San Francisco, as well as in Europe, and the reason why we did that, wasn't just because of the fact that Reactive development, if you're not familiar with Reactive, it's essentially Scala, Akka, Play, this whole framework, that basically allows developers to write once, and it kind of scales up with demand. In fact, Verizon actually used our platform with Lightbend to launch the iPhone 10. And they show dramatic improvements. Now what's exciting about Lightbend, is the fact that application developers are developing with Reactive, but if you turn around, you'll also now be able to operationalize models with Reactive as well. Because it's basically a single platform to move between these two worlds. So what we've continued to see is data science kind of separate from the application world. Really kind of, AI and cloud as different universes. The reality is that for any enterprise, or any company, to really innovate, you have to find a way to bring those two worlds together, to get the most use out of it. >> Fourier always says "Data is the new development kit". He said this I think five or six years ago, and it's barely becoming true. You guys have tried to make an attempt, and have done a pretty good job, of trying to bring those worlds together in a single platform, what do you call it? The Watson Data Platform? >> Yeah, Watson Data Platform, now Watson Studio, and I think the other, so one side of it is, us trying to, not really trying, but us actually bringing together these disparate systems. I mean we are kind of a systems company, we're IT. But not only that, but bringing our trained algorithms, and our trained models to the developers. So for example, we also did a partnership with Unity, at the end of last year, that's now just reaching some pretty good growth, in terms of bringing the Watson SDK to game developers on the Unity platform. So again, it's this idea of bringing the game developer, the application developer, in closer contact with these trained models, and these trained algorithms. And that's where you're seeing incredible things happen. So for example, Star Trek Bridge Crew, which I don't know how many Trekkies we have here at the CDO Summit. >> A few over here probably. >> Yeah, a couple? They're using our SDK in Unity, to basically allow a gamer to use voice commands through the headset, through a VR headset, to talk to other players in the virtual game. So we're going to see more, I can't really disclose too much what we're doing there, but there's some cool stuff coming out of that partnership. >> Real immersive experience driving a lot of data. Now you're part of the Digital Business Group. I like the term digital business, because we talk about it all the time. Digital business, what's the difference between a digital business and a business? What's the, how they use data. >> Joel: Yeah. >> You're a data person, what does that mean? That you're part of the Digital Business Group? Is that an internal facing thing? An external facing thing? Both? >> It's really both. So our Chief Digital Officer, Bob Lord, he has a presentation that he'll give, where he starts out, and he goes, when I tell people I'm the Chief Digital Officer they usually think I just manage the website. You know, if I tell people I'm a Chief Data Officer, it means I manage our data, in governance over here. The reality is that I think these Chief Digital Officer, Chief Data Officer, they're really responsible for business transformation. And so, if you actually look at what we're doing, I think on both sides is we're using data, we're using marketing technology, martech, like Optimizely, like Segment, like some of these great partners of ours, to really look at how we can quickly A/B test, get user feedback, to look at how we actually test different offerings and market. And so really what we're doing is we're setting up a testing platform, to bring not only our traditional offers to market, like DB2, Mainframe, et cetera, but also bring new offers to market, like blockchain, and quantum, and others, and actually figure out how we get better product-market fit. What actually, one thing, one story that comes to mind, is if you've seen the movie Hidden Figures- >> Oh yeah. >> There's this scene where Kevin Costner, I know this is going to look not great for IBM, but I'm going to say it anyways, which is Kevin Costner has like a sledgehammer, and he's like trying to break down the wall to get the mainframe in the room. That's what it feels like sometimes, 'cause we create the best technology, but we forget sometimes about the last mile. You know like, we got to break down the wall. >> Where am I going to put it? >> You know, to get it in the room! So, honestly I think that's a lot of what we're doing. We're bridging that last mile, between these different audiences. So between developers, between ISVs, between commercial buyers. Like how do we actually make this technology, not just accessible to large enterprise, which are our main clients, but also to the other ecosystems, and other audiences out there. >> Well so that's interesting Joel, because as a potential partner of IBM, they want, obviously your go-to-market, your massive company, and great distribution channel. But at the same time, you want more than that. You know you want to have a closer, IBM always focuses on partnerships that have intrinsic value. So you talked about offerings, you talked about quantum, blockchain, off-camera talking about cloud containers. >> Joel: Yeah. >> I'd say cloud and containers may be a little closer than those others, but those others are going to take a lot of market development. So what are the offerings that you guys are bringing? How do they get into the hands of your partners? >> I mean, the commonality with all of these, all the emerging offerings, if you ask me, is the distributed nature of the offering. So if you look at blockchain, it's a distributed ledger. It's a distributed transaction chain that's secure. If you look at data, really and we can hark back to say, Hadoop, right before object storage, it's distributed storage, so it's not just storing on your hard drive locally, it's storing on a distributed network of servers that are all over the world and data centers. If you look at cloud, and containers, what you're really doing is not running your application on an individual server that can go down. You're using containers because you want to distribute that application over a large network of servers, so that if one server goes down, you're not going to be hosed. And so I think the fundamental shift that you're seeing is this distributed nature, which in essence is cloud. So I think cloud is just kind of a synonym, in my opinion, for distributed nature of our business. >> That's interesting and that brings up, you're right, cloud and Big Data/Hadoop, we don't talk about Hadoop much anymore, but it kind of got it all started, with that notion of leave the data where it is. And it's the same thing with cloud. You can't just stuff your business into the public cloud. You got to bring the cloud to your data. >> Joel: That's right. >> But that brings up a whole new set of challenges, which obviously, you're in a position just to help solve. Performance, latency, physics come into play. >> Physics is a rough one. It's kind of hard to avoid that one. >> I hear your best people are working on it though. Some other partnerships that you want to sort of, elucidate. >> Yeah, no, I mean we have some really great, so I think the key kind of partnership, I would say area, that I would allude to is, one of the things, and you kind of referenced this, is a lot of our partners, big or small, want to work with our top clients. So they want to work with our top banking clients. They want, 'cause these are, if you look at for example, MaRisk and what we're doing with them around blockchain, and frankly, talk about innovation, they're innovating containers for real, not virtual containers- >> And that's a joint venture right? >> Yeah, it is, and so it's exciting because, what we're bringing to market is, I also lead our startup programs, called the Global Entrepreneurship Program, and so what I'm focused on doing, and you'll probably see more to come this quarter, is how do we actually bridge that end-to-end? How do you, if you're startup or a small business, ultimately reach that kind of global business partner level? And so kind of bridging that, that end-to-end. So we're starting to bring out a number of different incentives for partners, like co-marketing, so I'll help startups when they're early, figure out product-market fit. We'll give you free credits to use our innovative technology, and we'll also bring you into a number of clients, to basically help you not burn all of your cash on creating your own marketing channel. God knows I did that when I was at a start-up. So I think we're doing a lot to kind of bridge that end-to-end, and help any partner kind of come in, and then grow with IBM. I think that's where we're headed. >> I think that's a critical part of your job. Because I mean, obviously IBM is known for its Global 2000, big enterprise presence, but startups, again, fuel that innovation fire. So being able to attract them, which you're proving you can, providing whatever it is, access, early access to cloud services, or like you say, these other offerings that you're producing, in addition to that go-to-market, 'cause it's funny, we always talk about how efficient, capital efficient, software is, but then you have these companies raising hundreds of millions of dollars, why? Because they got to do promotion, marketing, sales, you know, go-to-market. >> Yeah, it's really expensive. I mean, you look at most startups, like their biggest ticket item is usually marketing and sales. And building channels, and so yeah, if you're, you know we're talking to a number of partners who want to work with us because of the fact that, it's not just like, the direct kind of channel, it's also, as you kind of mentioned, there's other challenges that you have to overcome when you're working with a larger company. for example, security is a big one, GDPR compliance now, is a big one, and just making sure that things don't fall over, is a big one. And so a lot of partners work with us because ultimately, a number of the decision makers in these larger enterprises are going, well, I trust IBM, and if IBM says you're good, then I believe you. And so that's where we're kind of starting to pull partners in, and pull an ecosystem towards us. Because of the fact that we can take them through that level of certification. So we have a number of free online courses. So if you go to partners, excuse me, ibm.com/partners/learn there's a number of blockchain courses that you can learn today, and will actually give you a digital certificate, that's actually certified on our own blockchain, which we're actually a first of a kind to do that, which I think is pretty slick, and it's accredited at some of the universities. So I think that's where people are looking to IBM, and other leaders in this industry, is to help them become experts in their, in this technology, and especially in this emerging technology. >> I love that blockchain actually, because it's such a growing, and interesting, and innovative field. But it needs players like IBM, that can bring credibility, enterprise-grade, whether it's security, or just, as I say, credibility. 'Cause you know, this is, so much of negative connotations associated with blockchain and crypto, but companies like IBM coming to the table, enterprise companies, and building that ecosystem out is in my view, crucial. >> Yeah, no, it takes a village. I mean, there's a lot of folks, I mean that's a big reason why I came to IBM, three, four years ago, was because when I was in start-up land, I used to work for H20, I worked for Alpine Data Labs, Datameer, back in the Hadoop days, and what I realized was that, it's an opportunity cost. So you can't really drive true global innovation, transformation, in some of these bigger companies because there's only so much that you can really kind of bite off. And so you know at IBM it's been a really rewarding experience because we have done things like for example, we partnered with Girls Who Code, Treehouse, Udacity. So there's a number of early educators that we've partnered with, to bring code to, to bring technology to, that frankly, would never have access to some of this stuff. Some of this technology, if we didn't form these alliances, and if we didn't join these partnerships. So I'm very excited about the future of IBM, and I'm very excited about the future of what our partners are doing with IBM, because, geez, you know the cloud, and everything that we're doing to make this accessible, is bar none, I mean, it's great. >> I can tell you're excited. You know, spring in your step. Always a lot of energy Joel, really appreciate you coming onto theCUBE. >> Joel: My pleasure. >> Great to see you again. >> Yeah, thanks Dave. >> You're welcome. Alright keep it right there, everybody. We'll be back. We're at the IBM CDO Strategy Summit in San Francisco. You're watching theCUBE. (techno music) (touch-tone phone beeps)

>> Announcer: Live from downtown San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody. We're at the Parc 55 in Union Square and this is the Cube, the leader in live tech coverage and we're covering exclusive coverage of the IBM CDO strategy summit. IBM has these things, they book in on both coasts, one in San Francisco one in Boston, spring and fall. Great event, intimate event. 130, 150 chief data officers, learning, transferring knowledge, sharing ideas. Cayn Woodruff is here as the principle data scientist at IBM and she's joined by Ritesh Ororo, who is the director of digital analytics at HCL Technologies. Folks welcome to the Cube, thanks for coming on. >> Thank you >> Thanks for having us. >> You're welcome. So we're going to talk about data management, data engineering, we're going to talk about digital, as I said Ritesh because digital is in your title. It's a hot topic today. But Caryn let's start off with you. Principle Data Scientist, so you're the one that is in short supply. So a lot of demand, you're getting pulled in a lot of different directions. But talk about your role and how you manage all those demands on your time. >> Well, you know a lot of, a lot of our work is driven by business needs, so it's really understanding what is critical to the business, what's going to support our businesses strategy and you know, picking the projects that we work on based on those items. So it's you really do have to cultivate the things that you spend your time on and make sure you're spending your time on the things that matter and as Ritesh and I were talking about earlier, you know, a lot of that means building good relationships with the people who manage the systems and the people who manage the data so that you can get access to what you need to get the critical insights that the business needs, >> So Ritesh, data management I mean this means a lot of things to a lot of people. It's evolved over the years. Help us frame what data management is in this day and age. >> Sure, so there are two aspects of data in my opinion. One is the data management, another the data engineering, right? And over the period as the data has grown significantly. Whether it's unstructured data, whether it's structured data, or the transactional data. We need to have some kind of governance in the policies to secure data to make data as an asset for a company so the business can rely on your data. What you are delivering to them. Now, the another part comes is the data engineering. Data engineering is more about an IT function, which is data acquisition, data preparation and delivering the data to the end-user, right? It can be business, it can be third-party but it all comes under the governance, under the policies, which are designed to secure the data, how the data should be accessed to different parts of the company or the external parties. >> And how those two worlds come together? The business piece and the IT piece, is that where you come in? >> That is where data science definitely comes into the picture. So if you go online, you can find Venn diagrams that describe data science as a combination of computer science math and statistics and business acumen. And so where it comes in the middle is data science. So it's really being able to put those things together. But, you know, what's what's so critical is you know, Interpol, actually, shared at the beginning here and I think a few years ago here, talked about the five pillars to building a data strategy. And, you know, one of those things is use cases, like getting out, picking a need, solving it and then going from there and along the way you realize what systems are critical, what data you need, who the business users are. You know, what would it take to scale that? So these, like, Proof-point projects that, you know, eventually turn into these bigger things, and for them to turn into bigger things you've got to have that partnership. You've got to know where your trusted data is, you've got to know that, how it got there, who can touch it, how frequently it is updated. Just being able to really understand that and work with partners that manage the infrastructure so that you can leverage it and make it available to other people and transparent. >> I remember when I first interviewed Hilary Mason way back when and I was asking her about that Venn diagram and she threw in another one, which was data hacking. >> Caryn: Uh-huh, yeah. >> Well, talk about that. You've got to be curious about data. You need to, you know, take a bath in data. >> (laughs) Yes, yes. I mean yeah, you really.. Sometimes you have to be a detective and you have to really want to know more. And, I mean, understanding the data is like the majority of the battle. >> So Ritesh, we were talking off-camera about it's not how titles change, things evolve, data, digital. They're kind of interchangeable these days. I mean we always say the difference between a business and a digital business is how they have used data. And so digital being part of your role, everybody's trying to get digital transformation, right? As an SI, you guys are at the heart of it. Certainly, IBM as well. What kinds of questions are our clients asking you about digital? >> So I ultimately see data, whatever we drive from data, it is used by the business side. So we are trying to always solve a business problem, which is to optimize the issues the company is facing, or try to generate more revenues, right? Now, the digital as well as the data has been married together, right? Earlier there are, you can say we are trying to analyze the data to get more insights, what is happening in that company. And then we came up with a predictive modeling that based on the data that will statically collect, how can we predict different scenarios, right? Now digital, we, over the period of the last 10 20 years, as the data has grown, there are different sources of data has come in picture, we are talking about social media and so on, right? And nobody is looking for just reports out of the Excel, right? It is more about how you are presenting the data to the senior management, to the entire world and how easily they can understand it. That's where the digital from the data digitization, as well as the application digitization comes in picture. So the tools are developed over the period to have a better visualization, better understanding. How can we integrate annotation within the data? So these are all different aspects of digitization on the data and we try to integrate the digital concepts within our data and analytics, right? So I used to be more, I mean, I grew up as a data engineer, analytics engineer but now I'm looking more beyond just the data or the data preparation. It's more about presenting the data to the end-user and the business. How it is easy for them to understand it. >> Okay I got to ask you, so you guys are data wonks. I am too, kind of, but I'm not as skilled as you are, but, and I say that with all due respect. I mean you love data. >> Caryn: Yes. >> As data science becomes a more critical skill within organizations, we always talk about the amount of data, data growth, the stats are mind-boggling. But as a data scientist, do you feel like you have access to the right data and how much of a challenge is that with clients? >> So we do have access to the data but the challenge is, the company has so many systems, right? It's not just one or two applications. There are companies we have 50 or 60 or even hundreds of application built over last 20 years. And there are some applications, which are basically duplicate, which replicates the data. Now, the challenge is to integrate the data from different systems because they maintain different metadata. They have the quality of data is a concern. And sometimes with the international companies, the rules, for example, might be in US or India or China, the data acquisitions are different, right? And you are, as you become more global, you try to integrate the data beyond boundaries, which becomes a more compliance issue sometimes, also, beyond the technical issues of data integration. >> Any thoughts on that? >> Yeah, I think, you know one of the other issues too, you have, as you've heard of shadow IT, where people have, like, servers squirreled away under their desks. There's your shadow data, where people have spreadsheets and databases that, you know, they're storing on, like a small server or that they share within their department. And so you know, you were discussing, we were talking earlier about the different systems. And you might have a name in one system that's one way and a name in another system that's slightly different, and then a third system, where it's it's different and there's extra granularity to it or some extra twist. And so you really have to work with all of the people that own these processes and figure out what's the trusted source? What can we all agree on? So there's a lot of... It's funny, a lot of the data problems are people problems. So it's getting people to talk and getting people to agree on, well this is why I need it this way, and this is why I need it this way, and figuring out how you come to a common solution so you can even create those single trusted sources that then everybody can go to and everybody knows that they're working with the the right thing and the same thing that they all agree on. >> The politics of it and, I mean, politics is kind of a pejorative word but let's say dissonance, where you have maybe of a back-end syst6em, financial system and the CFO, he or she is looking at the data saying oh, this is what the data says and then... I remember I was talking to a, recently, a chef in a restaurant said that the CFO saw this but I know that's not the case, I don't have the data to prove it. So I'm going to go get the data. And so, and then as they collect that data they bring together. So I guess in some ways you guys are mediators. >> [Caryn And Ritesh] Yes, yes. Absolutely. >> 'Cause the data doesn't lie you just got to understand it. >> You have to ask the right question. Yes. And yeah. >> And sometimes when you see the data, you start, that you don't even know what questions you want to ask until you see the data. Is that is that a challenge for your clients? >> Caryn: Yes, all the time. Yeah >> So okay, what else do we want to we want to talk about? The state of collaboration, let's say, between the data scientists, the data engineer, the quality engineer, maybe even the application developers. Somebody, John Fourier often says, my co-host and business partner, data is the new development kit. Give me the data and I'll, you know, write some code and create an application. So how about collaboration amongst those roles, is that something... I know IBM's gone on about some products there but your point Caryn, it's a lot of times it's the people. >> It is. >> And the culture. What are you seeing in terms of evolution and maturity of that challenge? >> You know I have a very good friend who likes to say that data science is a team sport and so, you know, these should not be, like, solo projects where just one person is wading up to their elbows in data. This should be something where you've got engineers and scientists and business, people coming together to really work through it as a team because everybody brings really different strengths to the table and it takes a lot of smart brains to figure out some of these really complicated things. >> I completely agree. Because we see the challenges, we always are trying to solve a business problem. It's important to marry IT as well as the business side. We have the technical expert but we don't have domain experts, subject matter experts who knows the business in IT, right? So it's very very important to collaborate closely with the business, right? And data scientist a intermediate layer between the IT as well as business I will say, right? Because a data scientist as they, over the years, as they try to analyze the information, they understand business better, right? And they need to collaborate with IT to either improve the quality, right? That kind of challenges they are facing and I need you to, the data engineer has to work very hard to make sure the data delivered to the data scientist or the business is accurate as much as possible because wrong data will lead to wrong predictions, right? And ultimately we need to make sure that we integrate the data in the right way. >> What's a different cultural dynamic that was, say ten years ago, where you'd go to a statistician, she'd fire up the SPSS.. >> Caryn: We still use that. >> I'm sure you still do but run some kind of squares give me some, you know, probabilities and you know maybe run some Monte Carlo simulation. But one person kind of doing all that it's your point, Caryn. >> Well you know, it's it's interesting. There are there are some students I mentor at a local university and you know we've been talking about the projects that they get and that you know, more often than not they get a nice clean dataset to go practice learning their modeling on, you know? And they don't have to get in there and clean it all up and normalize the fields and look for some crazy skew or no values or, you know, where you've just got so much noise that needs to be reduced into something more manageable. And so it's, you know, you made the point earlier about understanding the data. It's just, it really is important to be very curious and ask those tough questions and understand what you're dealing with. Before you really start jumping in and building a bunch of models. >> Let me add another point. That the way we have changed over the last ten years, especially from the technical point of view. Ten years back nobody talks about the real-time data analysis. There was no streaming application as such. Now nobody talks about the batch analysis, right? Everybody wants data on real-time basis. But not if not real-time might be near real-time basis. That has become a challenge. And it's not just that prediction, which are happening in their ERP environment or on the cloud, they want the real-time integration with the social media for the marketing and the sales and how they can immediately do the campaign, right? So, for example, if I go to Google and I search for for any product, right, for example, a pressure cooker, right? And I go to Facebook, immediately I see the ad within two minutes. >> Yeah, they're retargeting. >> So that's a real-time analytics is happening under different application, including the third-party data, which is coming from social media. So that has become a good source of data but it has become a challenge for the data analyst and the data scientist. How quickly we can turn around is called data analysis. >> Because it used to be you would get ads for a pressure cooker for months, even after you bought the pressure cooker and now it's only a few days, right? >> Ritesh: It's a minute. You close this application, you log into Facebook... >> Oh, no doubt. >> Ritesh: An ad is there. >> Caryn: There it is. >> Ritesh: Because everything is linked either your phone number or email ID you're done. >> It's interesting. We talked about disruption a lot. I wonder if that whole model is going to get disrupted in a new way because everybody started using the same ad. >> So that's a big change of our last 10 years. >> Do you think..oh go ahead. >> oh no, I was just going to say, you know, another thing is just there's so much that is available to everybody now, you know. There's not this small little set of tools that's restricted to people that are in these very specific jobs. But with open source and with so many software-as-a-service products that are out there, anybody can go out and get an account and just start, you know, practicing or playing or joining a cackle competition or, you know, start getting their hands on.. There's data sets that are out there that you can just download to practice and learn on and use. So, you know, it's much more open, I think, than it used to be. >> Yeah, community additions of software, open data. The number of open day sources just keeps growing. Do you think that machine intelligence can, or how can machine intelligence help with this data quality challenge? >> I think that it's it's always going to require people, you know? There's always going to be a need for people to train the machines on how to interpret the data. How to classify it, how to tag it. There's actually a really good article in Popular Science this month about a woman who was training a machine on fake news and, you know, it did a really nice job of finding some of the the same claims that she did. But she found a few more. So, you know, I think it's, on one hand we have machines that we can augment with data and they can help us make better decisions or sift through large volumes of data but then when we're teaching the machines to classify the data or to help us with metadata classification, for example, or, you know, to help us clean it. I think that it's going to be a while before we get to the point where that's the inverse. >> Right, so in that example you gave, the human actually did a better job from the machine. Now, this amazing to me how.. What, what machines couldn't do that humans could, you know last year and all of a sudden, you know, they can. It wasn't long ago that robots couldn't climb stairs. >> And now they can. >> And now they can. >> It's really creepy. >> I think the difference now is, earlier you know, you knew that there is an issue in the data. But you don't know that how much data is corrupt or wrong, right? Now, there are tools available and they're very sophisticated tools. They can pinpoint and provide you the percentage of accuracy, right? On different categories of data that that you come across, right? Even forget about the structure data. Even when you talk about unstructured data, the data which comes from social media or the comments and the remarks that you log or are logged by the customer service representative, there are very sophisticated text analytics tools available, which can talk very accurately about the data as well as the personality of the person who is who's giving that information. >> Tough problems but it seems like we're making progress. All you got to do is look at fraud detection as an example. Folks, thanks very much.. >> Thank you. >> Thank you very much. >> ...for sharing your insight. You're very welcome. Alright, keep it right there everybody. We're live from the IBM CTO conference in San Francisco. Be right back, you're watching the Cube. (electronic music)

>> Announcer: Live, from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. >> We're back at the IBM CDO Strategy Summit in San Francisco, we're at the Parc 55, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with Krishna Venkatraman, who is with IBM, he's the Vice President of Data Science and Data Governance. Krishna, thanks for coming on. >> Thank you, thank you for this opportunity. >> Oh, you're very welcome. So, let's start with your role. Your passion is really creating value from data, that's something you told me off-camera. That's a good passion to have these days. So what's your role at IBM? >> So I work for Inderpal, who's GCDO. He's the CDO for the company, and I joined IBM about a year ago, and what I was intrigued by when I talked to him early on was, you know, IBM has so many assets, it's got a huge history and legacy of technology, enormous, copious amounts of data, but most importantly, it also has a lot of experience helping customers solve problems at enterprise scale. And in my career, I started at HP Labs many, many years ago, I've been in a few startups, most recently before I joined IBM, I was at On Deck. What I've always found is that it's very hard to extract information and insights from data unless you have the end-to-end pieces in place, and when I was at On Deck, we built all of it from scratch, and I thought this would be a great opportunity to come to IBM, leverage all that great history and legacy and skill to build something that would allow data to almost be taken for granted. So, in a sense, a company doesn't have to think about the pain of getting value extracted from data, they could just say, you know, I trust data just as I trust the other things in life, like when I go buy a book, I know all the backend stuff is done for me, I can trust the product I get. And I was interested in that, and that's the role that Inderpal offered to me. >> So the opposite of On Deck, really. On Deck was kind of a blank sheet of paper, right? And so now you have a complex organization, as Inderpal was describing this morning, so big challenge. Ginni Rometty at IBM Think talked about incumbent disruptors, so that's essentially what IBM is, right? >> Exactly, exactly. The fact is IBM has a history and a culture of making their customers successful, so they understand business problems really well. They have a huge legacy in innovation around technology, and I think now is the right time to put all of those pieces together, right? To string together a lifecycle for how data can work for you, so when you embark on a data project, it doesn't have to take six months, it could be done in two or three days, because you've cobbled together how to manage data at the backend, you've got the data science and the data science lifecycle worked out, and you know how to deploy it into a business process, because you understand the business process really well. And I think, you know, those are the mismatches that I've seen happen over and over again, data isn't ready for the application of machine learning, the machine learning model really isn't well-suited to the eventual environment in which it's deployed, but I think IBM has all of that expertise, and I feel like it's an opportunity for us to tie that together. >> And everybody's trying to get, I often say, get digital right, you know, your customers, your clients, everyone talks about digital transformation, but it's really all about the data, isn't it? Getting the data right. >> Getting the data right, that's where it starts. Tomorrow, I'm doing a panel on trust, you know, we can talk about the CDO and all the great things that are happening and extracting value, but unless you have trust at the beginning and you're doing good data governance, and you're able to understand your data, all of the rest will never happen. >> But you have to have both, alright? Because if you have trust without the data value, then okay. And you do see a lot of organizations just focusing, maybe over-rotating on that privacy and trust and security, for good reason, how do you balance that information as an asset versus liability equation? Because you're trying to get value out of it, and at the same time, you're trying to protect your organization. >> Yeah. I think it's a virtuous cycle, I think they build on each other. If customers trust you with their data, they're going to give you more of it, because they know you're going to use it responsibly, and I think that's a very positive thing, so I actually look at privacy and trust as enablers to create value, rather than somehow they're in competition. >> Not a zero-sum game. >> Not at all. >> Let's talk some more about that, I mean, when you think about it, because I've heard this before, GDPR comes up. Hey, we can turn GDPR into an opportunity, it's not just this onerous, even though it is, regulatory imposition, so maybe some examples or maybe talk through how organizations can take the privacy and trust part of the equation and turn it into value. >> So very simply, what does GDPR promise, right? It's restoring the fundamental rights of data subjects, in terms of their ownership of their data and the processing of their data and the ability to know how that data is used at any point in time. Now imagine if you're a data scientist and you could, for a problem that you're trying to solve, have the same kind of guarantees. You know all about the data, you know where it resides, you know exactly what it contains. They're very similar, you know? They both are asking for the same type of information. So, in a sense, if you solve the GDPR problem well, you have to really understand your data assets very well, and you have to have it governed really well, which is exactly the same need for data scientists. So, in a way, I seem them as, you know, they're twins, separated at some point, but... >> What's interesting, too, is you think about, we were sort of talking about this off-camera, but now, you're one step away from going to a user or customer and saying here, here's your data, do what you like with it. Now okay, in the one case, GDPR, you control it, sort of. But the other is if you want to monetize your own data, why pay the search company for clicking on an ad? Why not monetize your own data based on your reputation or do you see a day where consumers will actually be able to own, truly own their own data? >> I think, as a consumer, as well as a data professional, I think that the technologies are falling into place for that model to possibly become real. So if you have something that's very valuable that other people want, there should be a way for you to get some remuneration for that, right? And maybe it's something like a blockchain. You contribute your data and then when that data is used, you get some little piece of it as your reward for that. I don't know, I think it's possible, I haven't really... >> Nirvana. I wonder if we can talk about disruption, nobody talks about that, we haven't had a ton of conversations here about disruption, it seems to be more applying disciplines to create data value, but coming from the financial services industry, there's an industry that really hasn't been highly disrupted, you know, On Deck, in a way, was trying to disrupt. Healthcare is another one that hasn't been disrupted. Aerospace really hasn't been disrupted. Other industries like publishing, music, taxis, hotels have been disrupted. The premise is, it's the data that enables that disruption. Thoughts on disruption from the standpoint of your clients and how you're helping them become incumbent disruptors? >> I think sometimes disruption happens and then you look back and you say, that was disrupted after all, and you don't notice it when it happens, so even if I look at financial services and I look at small business lending, the expectations of businesses have changed on how they would access capital in that case. Even though the early providers of that service may not be the ones who win in the end, that's a different matter, so I think the idea that, you know, and I feel like this confluence of technologies, where's there's blockchain or quantum computing or even regulation that's coming in, that's sort of forcing certain types of activities around cleaning up data, they're all happening simultaneously. I think we will see certain industries and certain processes transform dramatically. >> Orange Bank was an example that came up this morning, an all-digital bank, you can't call them, right? You can't walk into their branch. You think banks will lose control of the payment systems? They've always done a pretty good job of hanging onto them, but... >> I don't know. I think, ultimately, customers are going to go to institutions they trust, so it's all going to end up with, do you trust the entity you've given your precious commodities to, right? Your data, your information, I think companies that really take that seriously and not take it as a burden are the ones who are going to find that customers are going to reach out to them. So it's more about not necessarily whether banks are going to lose control or whether... Which banks are going to win, is the way I would look at it. >> Maybe the existing banks might get trouble, but there's so many different interesting disruption scenarios, I mean, you think about Watson in healthcare, maybe we're at the point already where machines can make better diagnoses than doctors. You think about retail, and certain retail won't go away, obviously grocery and maybe high-end luxury malls won't go away, but you wonder about the future of retail as a result of this data disruption. Your thoughts? >> On retail? I do feel like, because the data is getting more, people are going to have more access to their own information, it will lead to a change in business models in certain cases. And the friction or the forces that used to keep customers with certain businesses may dissolve, so if you don't have friction, then it's going to end up with value and loyalty and service, and those are the ones I think that will thrive. >> Client comes to you, says, Krishna, I'm really struggling with my overall data strategy, my data platform, governance, skills, all the things that Inderpal talked about this morning, where do I start? >> I would start with making sure that the client has really thought about the questions they need answered. What is it that you really want to answer with data, or it doesn't even have to be with data, for the business, with its strategy, with its tactics, there have to be a set of questions framed up that are truly important to that business. And then starting from there, you can say, you know, let's slow it down and see what technologies, what types of data will help support answering those questions. So there has to be an overarching value proposition that you're trying to solve for. And I see, you know, that's why when, the way we work in our organization is, we look at use cases as a way to drive the technology adoption. What are the big business processes you are trying to transform, what's the value you expect to create, so we have a very robust discovery process where we ask people to answer those types of questions, we help them with it. We ask them to think through what they would do if they had the perfect answer, how they will implement it, how they will measure it. And then we start working on the technology. I often think technology is an easier question to answer once you know what you want to ask. >> Totally. Is that how you spend your time, mostly working with the lines of business, trying to help them sort of answer those questions? >> That is one part of my charter. So my charter involves basically four areas, the first is data governance, just making sure that we are creating all the tools and processes so that we can guarantee that when data is used, it is trusted, it is certified, and that it's always going to be reliable. The second piece is building up a real data competency and data science competency in the organization, so we know how to use data for different types of business value, and then the third is actually taking these client engagements internally and making sure that they are successful. So our model is what we call co-creation. We ask business teams to contribute their own resources. Data engineers, data scientists, business experts. We contribute specialized skills as well. And so we're jointly in the game together, right? So that's the third piece. And the last piece is, we're building out this platform that Inderpal showed this morning, that platform needs product management, so we are also working on, what are the fundamental pieces of functionality we want in the platform, and how do we make sure they're on the roadmap and they're prioritized in the right way. >> Excellent. Well, Krishna, thanks very much for coming to theCUBE, it was a pleasure meeting you. >> Thanks. >> Alright, keep it right there everybody, we'll be back with our next guest. You're watching theCUBE live from IBM CDO Summit in San Francisco. We'll be right back. (funky electronic music) (phone dialing)

>> 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.

>> Narrator: Live from downtown San Francisco, it's The Cube, covering IBM chief data officer strategy summit 2018, brought to you by IBM. (playful music) >> Welcome back to the IBM chief data officer strategy summit in San Francisco. We're here at the Parc 55. My name is Dave Vellante, and you're watching The Cube, the leader in live tech coverage, #IBMCDO. Seth Dobrin is here. He's the chief data officer for IBM analytics. Seth, good to see you again. >> Good to see you again, Dave. >> Many time Cube alum; thanks for coming back on. Asim Tewary, Tewary? Tewary; sorry. >> Tewary, yes. >> Asim Tewary; I can't read my own writing. Head of data science and advanced analytics at Verizon, and from Jersey. Two east coast boys, three east coast boys. >> Three east coast boys. >> Yeah. >> Welcome, gentlemen. >> Thank you. >> Asim, you guys had a panel earlier today. Let's start with you. What's your role? I mean, we talked you're the defacto chief data officer at Verizon. >> Yes, I'm responsible for all the data ingestion platform, big data, and the data science for Verizon, for wireless, wire line, and enterprise businesses. >> It's a relatively new role at Verizon? You were saying previously you were CDO at a financial services organization. Common that a financial service organization would have a chief data officer. How did the role come about at Verizon? Are you Verizon's first CDO or-- >> I was actually brought in to really pull together the analytics and data across the enterprise, because there was a realization that data only creates value when you're able to get it from all the difference sources. We had separate teams in the past. My role was to bring it all together, to have a common platform, common data science team to drive revenue across the businesses. >> Seth, this is a big challenge, obviously. We heard Caitlyn this morning, talking about the organizational challenges. You got data in silos. Inderpal and your team are basically, I call it dog-fooding. You're drinking your own champagne. >> Champagne-ing, yeah. >> Yeah, okay, but you have a similar challenge. You have big company, complex, a lot of data silos coming. Yeah, I mean, IBM is really, think of it as five companies, right? Any one of them would be a fortune 500 company in and of themselves. Even within each of those, there were silos, and then Inderpal trying to bring them across, you know, the data from across all of them is really challenging. Honestly, the technology part, the bringing it together is the easy part. It's the cultural change that goes along with it that's really, really hard, to get people to think about it as IBM's or Verizon's data, and not their data. That's really how you start getting value from it. >> That's a cultural challenge you face is, "Okay, I've got my data; I don't want to share." How do you address that? >> Absolutely. Governance and ownership of data, having clear roles and responsibilities, ensuring there's this culture where people realize that data is an asset of the firm. It is not your data or my data; it is firm's data, and the value you create for the business is from that data. It is a transformation. It's changing the people culture aspect, so there's a lot of education. You know, you have to be an evangelist. You wear multiple hats to show people the value, why they should do. Obviously, I had an advantage because coming in, Verizon management was completely sold to the idea that the data has to be managed as an enterprise asset. Business was ready and willing to own data as an enterprise asset, and so it was relatively easier. However, it was a journey to try to get everyone on the same page in terms of ensuring that it wasn't the siloed mentality. This was a enterprise asset that we need to manage together. >> A lot of organizations tell me that, first of all, you got to have top-down buy-in. Clearly, you had that, but a lot of the times I hear that the C-suite says, "Okay, we're going to do this," but the middle management is sort of, they got to PNL, they've got to make their plan, and it takes them longer to catch up. Did you face that challenge, and how do you ... How were you addressing it? >> Absolutely. What we had to do was really make sure that we were not trying to boil the ocean, that we were trying to show the values. We found champions. For example, finance, you know, was a good champion for us, where we used the data and analytics to really actually launch some very critical initiatives for the firm, asset-backed securities. For the first time, Verizon launched ABS, and we actually enabled that. That created the momentum, if you will, as to, "Okay, there's value in this." That then created the opportunity for all the other business to jump on and start leveraging data. Then we all are willing to help and be part of the journey. >> Seth, before you joined IBM, obviously the company was embarking on this cognitive journey. You know, Watson, the evolution of Watson, the kind of betting a lot on cognitive, but internally you must have said, "Well, if we're going to market this externally, "we'd better become a cognitive enterprise." One of the questions that came up on the panel was, "What is a cognitive enterprise?" You guys, have you defined it? Love to ask Asim the same question. >> Yeah, so I mean, a cognitive enterprise is really about an enterprise that uses data and analytics, and cognition to run their business, right? You can't just jump to being a cognitive enterprise, right? It's a journey or a ladder, right? Where you got to get that foundation data in order. Then you've got to start even being able to do basic analytics. Then you can start doing things like machine learning, and deep learning, and then you can get into cognition. It's not a, just jump to the top of the ladder, because there's just a lot of work that's required to do it. You can do that within a business unit. The whole company doesn't need to get there, and in fact, you'll see within a company, different part of the company will be at different stages. Kind of to Asim's point about partnering with finance, and that's my experience both at IBM and before I joined. You find a partner that's going to be a champion for you. You make them immensely successful, and everyone else will follow because of shame, because they don't want to be out-competed by their peers. >> So, similar definition of a cognitive enterprise? >> Absolutely. In fact, what I would say is cognitive is a spectrum, right? Where most companies are at the low end of that spectrum where using data for decision-making, but those are reports, BI reports, and stuff like that. As you evolve to become smarter and more AI machine learning, that's when you get into predictive, where you're using the data to predict what might happen based on prior historical information. Then that evolution goes all the way to being prescriptive, where you're not only looking back and being able to predict, but you're actually able to recommend action that you want to take. Obviously, with the human involvement, because governance is an important aspect to all of this, right? Completely agree that the cognitive is really covering the spectrum of prescriptive, predictive, and using data for all your decision making. >> This actually gets into a good point, right? I mean, I think Asim has implemented some deep learning models at Verizon, but you really need to think about what's the right technology or the right, you know, the right use case for that. There's some use cases where descriptive analytics is the right answer, right? There's no reason to apply machine learning or deep learning. You just need to put that in front of someone. Then there are use cases where you do want deep learning, either because the problem is so complex, or because the accuracy needs to be there. I go into a lot of companies to talk to senior executives, and they're like, "We want to do deep learning." You ask them what the use case is, and you're like, "Really, that's rules," right? It gets back to Occam's razor, right? The simplest solution is always the answer, is always the best answer. Really understanding from your perspective, having done this at a couple of companies now, kind of when do you know when to use deep learning versus machine learning, versus just basic statistics? >> How about that? >> Yeah. >> How do you parse that? >> Absolutely. You know, like anything else, it's very important to understand what problem you're trying to solve. When you have a hammer, everything looks like a nail, and deep learning might be one of those hammers. What we do is make sure that any problem that requires explain-ability, interpret-ability, you cannot use deep learning, because you cannot explain when you're using deep learning. It's a multi-layered neural network algorithm. You can't really explain why the outcome was what it was. For that, you have to use more simpler algorithms, like decision tree, like regression, classification. By the way, 70 to 80% of the problem that you have in the company, can be solved by those algorithms. You don't always use deep learning, but deep learning is a great use case algorithm to use when you're solving complex problems. For example, when you're looking at doing friction analysis as to customer journey path analysis, that tends to be very noisy. You know, you have billions of data points that you have to go through for an algorithm. That is, you know, good for deep learning, so we're using that today, but you know, those are a narrow set of use cases where it is required, so it's important to understand what problem you're trying to solve and where you want to use deep learning. >> To use deep learning, you need a lot of label data, right? >> Yes. >> And that's-- >> A lot of what? Label data? >> Label data. So, and that's often a hurdle to companies using deep learning, even when they have a legitimate deep learning use cases. Just the massive amount of label data you need for that use case. >> As well as scale, right? >> Yeah. >> The whole idea is that when you have massive amounts of data with a lot of different variables, you need deep learning to be able to make that decision. That means you've got to have scale and real time capability within the platform, that has the elasticity and compute, to be able to crunch all that data. >> Yeah. >> Initially, when we started on this journey, our infrastructure was not able to handle that. You know, we had a lot of failures, and so obviously we had to enhance our infrastructure to-- >> You spoke to Samit Gupta and Ed earlier, about, you know, GPUs, and flash storage, and the need for those types of things to do these complex, you know, deep learning problems. We struggled with that even inside of IBM when we first started building this platform as, how do we get the best performance of ingesting the data, getting it labeled, and putting it into these models, these deep learning models, and some of the instance we use that. >> Yeah, my takeaway is that infrastructure for AI has to be flexible, you got to be great granularity. It's got to not only be elastic, but it's got to be, sometimes we call it plastic. It's got to sometimes retain its form. >> Yes. >> Right? Then when you bring in some new unknown workload, you've got to be able to adjust it without ripping down the entire infrastructure. You have to purpose built a whole next set of infrastructure, which is kind of how we built IT over the years. >> Exactly. >> I think, Dave, too, When you and I first spoke four or five years ago, it was all about commodity hardware, right? It was going to Hadoop ecosystem, minimizing, you know, getting onto commodity hardware, and now you're seeing a shift away from commodity hardware, in some instances, toward specialized hardware, because you need it for these use cases. So we're kind of making that. We shifted to one extreme, and now we're kind of shifting, and I think we're going to get to a good equilibrium where it's a balance of commodity and specialized hardware for big data, as much as I hate that word, and advanced analytics. >> Well, yeah, even your cloud guys, all the big cloud guys, they used to, you know, five, six years ago, say, "Oh, it's all commodity stuff," and now it's a lot of custom, because they're solving problems that you can't solve with a commodity. I want to ask you guys about this notion of digital business. To us, the difference between a business and a digital business is how you use data. As you become a digital business, which is essentially what you're doing with cognitive and AI, historically, you may have organized around, I don't know, your network, and certain you've got human skills that are involved, and your customers. I mean, IBM in your case, it's your products, your services, your portfolio, your clients. Increasingly, you're organizing around your data, aren't you? Which brings back to cultural change, but what about the data model? I presume you're trying to get to a data model where the customer service, and the sales, and the marketing aren't separate entities. I don't have to deal with them when I talk to Verizon. I deal with just Verizon, right? That's not easy when the data's all inside. How are you dealing with that challenge? >> Customer is at the center of the business model. Our motto and out goal is to provide the best products to the customers, but even more important, provide the best experience. It is all about the customer, agnostic of the channel, which channel the customer is interacting with. The customer, for the customer, it's one Verizon. The way we are organizing our data platform is, first of all, breaking all the silos. You know, we need to have data from all interactions with the customer, that is all digital, that's coming through, and creating one unified model, essentially, that essentially teaches all the journeys, and all the information about the customer, their events, their behavior, their propensities, and stuff like that. Then that information, using algorithms, like predictive, prescriptive, and all of that, make it available in all channels of engagement. Essentially, you have common intelligence that is made available across all channels. Whether the customer goes to point of sale in a retail store, or calls a call center, talks to a rep, or is on the digital channel, is the same intelligence driving the experience. Whether a customer is trying to buy a phone, or has an issue with a service related aspect of it, and that's the key, which is centralized intelligence from common data lake, and then deliver a seamless experience across all channels for that customer-- >> Independent of where I bought that phone, for example, right? >> Exactly. Maintaining the context is critical. If you went to the store and you know, you're looking for a phone, and you know, you didn't find what you're looking for, you want to do some research, if you go to the digital channel, you should be able to have a seamless experience where we should know that you went, that you're looking for the phone, or you called care and you asked the agent about something. Having that context be transferred across channel and be available, so the customer feels that we know who the customer is, and provide them with a good experience, is the key. >> We have limited time, but I want to talk about skills. It's hard to come by; we talked about that. It's number five on Inderpal's sort of, list of things you've got to do as a CDO. Sometimes you can do MNA, by the weather company. You've got a lot of skills, but that's not always so practical. How have you been dealing with the skills gap? >> Look, skill is hard to find, data scientists are hard to find. The way we are envisioning our talent management is two things we need to take care of. One, we need solid big data engineers, because having a solid platform that has real trans-streaming capability is very critical. Second, data scientists, it's hard to get. However, our plan is to really take the domain experts, who really understand the business, who understand the business process and the data, and give them the tools, automation tools for data science, that essentially, you know, will put it in a box for them, in terms of which algorithm to use, and enable them to create more value. While we will continue to hire specialized data scientists who are going to work on much more of the complex problems, the skill will come from empowering and enabling the domain experts with data science capabilities that automates choosing model development and algorithm development. >> Presumably grooming people in house, right? >> Grooming people in house, and I actually break it down a little more granular. I even say there's data engineers, there's machine learning engineers, there's optimization engineers, then there's data journalists. They're the ones that tell the story. I think we were talking earlier, Asim, about you know, it's not just PhDs, right? You're not just looking for PhDs to fill these rolls anymore. You're looking for people with masters degrees, and even in some cases, bachelors degrees. With IBM's new collar job initiative, we're even bringing on some, what we call P-TECH students, which are five year high school students, and we're building a data science program for them. We're building apprenticeships, which is, you know, you've had a couple years of college, building a data science program, and people look at me like I'm crazy when I say that, but the bulk of the work of a data science program, of executing data science, is not implementing machine learning models. It's engineering features, it's cleaning data. With basic Python skills, this is something that you can very easily teach these people to do, and then under the supervision of a principal data scientist or someone with a PhD or a masters degree, they can start learning how to implement models, but they can start contributing right away with just some basic Python skills. >> Then five, seven years in, they're-- >> Yeah. >> domain experts. All right, guys, got to jump, but thanks very much, Asim, for coming on and sharing your story. Seth, always a pleasure. >> Yeah, good to see you again, Dave. >> All right. >> Thank you, Dave. >> You're welcome. Keep it right there, buddy. >> Thanks. >> We'll be back with our next guest. This is The Cube, live from IBM CDO strategy summit in San Francisco. We'll be right back. (playful music) (phone dialing)

>> 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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>> Narrator: Live, from downtown San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. (techno music) >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. And we are here at the IBM CDO Strategy Summit hashtag IBMCDO. Caitlin Halferty is here. She's a client engagement executive for the chief data officer at IBM. Caitlin great to see you again. >> Great to be here, thank you. >> And she's joined by Brandon Purcell, who's principal analyst at Forrester Research. Good to have you on. >> Thanks very much, thanks for having me. >> First time on theCUBE. >> Yeah. >> You're very welcome. >> I'm a newbie. >> Caitlin... that's right, you're a newbie. You'll be a Cube alum in no time, I promise you. So Caitlin let's start with you. This is, you've done a number of these CDO events. You do some in Boston, you do some in San Francisco. And it's really great to see the practitioners here. You guys are bringing guys like Inderpal to the table. You've announced your blueprint in it. The audience seems to be lapping up the knowledge transfer. So what's the purpose of these events? How has it evolved? And just set the table for us. >> Sure, so we started back in 2014 with our first Chief Data Officer Summit and we held that here in San Francisco. Small group, probably only had about 30 or 40 attendees. And we said let's make this community focused, peer to peer networking. We're all trying to, ya know, build the role of either the Chief Data Officer or whomever is responsible for enterprise wide data strategy for their company, a variety of different titles. And we've grown that event over, since 2014. We do Spring, in San Francisco, which tends to be a bit more on the technical side, given where we are here in San Francisco in Silicon Valley. And then we do our business focused sessions in Fall in Boston. And I have to say, it's been really nice to see the community grow from a small set of attendees. And now was are at about 130 that join us on each coast. So we've built a community in total of about 500 CDOs and data executives, >> Nice. that are with us on this journey, so they're great. >> And Brandon, your focus at Forrester, part of it is AI, I know you did some other things in analytics, the ethics of AI, which we're going to talk about. I have to ask you from Forrester's perspective, we're enter... it feels like we're entering this new era of there's digital, there's data, there's AI. They seem to all overlap. What's your point of view on all this? >> So, I'm extremely optimistic about the future of AI. I realize that the term artificial intelligence is incredibly hyped right now. But I think it will ultimately fulfill it's promise. If you think about the life cycle of analytics, analytics start their lives as customer data. As customers interact and transact with you, that creates a foot print that you then have to analyze to unleash some sort of insight. This customer's likely to buy, or churn, or belongs to a specific segment. Then you have to take action. The buzzwords of the past have really focused on one piece of that life cycle. Big data, the data piece. Not much value unless you analyze that. So then predictive analytics, machine learning. What AI promises to do is to synthesize all of those pieces, from data, to insights, to action. And continuously learn and optimize. >> It's interesting you talk about that in terms of customer churn. I mean, with the internet, there was like a shift in the balance of power to the consumer. There used to be that the brand had all the knowledge about the buyer. And then with the internet, we shop around, we walk into a store and, look at them. Then we go buy it on the internet right? Now that AI maybe brings back more balance, symmetry. I mean, what are your thoughts on that? Are the clients that you work with, trying to sort of regain that advantage? So they can better understand the customer. >> Yeah, well that's a great question. I mean, if there's one kind of central ethos to Forrester's research it's that we live in the age of the customer and understanding and anticipating customer needs is paramount to be able to compete, right? And so it's the businesses in the age of AI and the age of the customer that have the data on the customer and enable the ability to distill that into insights that will ultimately succeed. And so the companies that have been able to identify the right value exchange with consumers, to give us a sense of convenience, so that we're willing to give up enough personal data to satisfy that convenience are the ones that I think are doing well. And certainly Netflix and Amazon come to mind there. >> Well for sure, and of course that gets into the privacy and the ethics of AI. I mean everyone's making a big deal out of this. You own your data. >> Yeah. >> You're not trying to monetize, ya know, figure out which ad to click on. Maybe give us your perspective, Caitlin, on IBMs point of view there? >> Sure, so we lead with this thought around trusting your data. You're data's your data. Insights derive from that data, your insights. We spend a lot of time with our Watson Legal folks. And one of the things, pieces of material we've released today is the real detail at every level how you engage the traceability of where your data is. So you have a sense of confidence that you know how it's treated, how it's curated. If it's used in some third party fashion. The ability to know that, have visibility into it. The opt-out, opt-in opt-out set of choices. Making sure that we're not exploiting the network effect, where perhaps party C benefits from data exchange between A and B. That A and B do not, or do not have an opportunity to influence. And so what we wanted to do, here at the summit over the next couple of days is really share that in detail and our thoughts around it. And it comes back to trust and being able to have that viability and traceability of your data through the value chain. >> So of course Brandon, as a customer I'm paying IBM so I would expect that IBM would look out for my privacy and make that promise. I don't really pay Facebook right? But I get some value out of it. So what are the ethics of that? Is it a pay or no pay? Or is it a value or no value? Is it everybody really needs to play by the same rules? How to you parse all that? >> Ya know, I hate to use a vague term. But it's a reasonable expectation. Like I think that when a person interacts with Facebook, there is a reasonable expectation that they're not going to take that data and sell it or monetize it to some third party, like Cambridge Analytica. And that's where they dropped the ball in that case. But, that's just in the actual data collection itself. There's also, there are also inherent ethical issues in how the data is actually transformed and analyzed. So just because you don't have like specific characteristics or attributes in data, like race and gender and age and socioeconomic status, in a multidimensional data set there are proxies for those through something called redundant encoding. So even if you don't want to use those factors to make decisions, you have to be very careful because they're probably in there anyway. And so you need to really think about what are your values as a brand? And when can you actually differentiate treatment, based on different attributes. >> Because you can make accurate inferences from that. >> Brandon: Yeah you're absolutely (mumbles). >> And is it the case of actually acting on that data? Or actually the ability to act on that data? If that makes sense to you. In other words, if an organization has that data and could, in theory, make the inference, but doesn't. Is that crossing the line? Is it the responsibility of the organization to identify those exposures and make sure that they can not be inferred? >> Yeah, I think it is. I think that that is incumbent upon our organizations today. Eventually regulators are going to get around to writing rules around this. And there's already some going into effect of course in Europe, with GDPR at the end of this month. But regulators are usually slow to catch up. So for now it's going to have to be organizations that think about this. And think about, okay, when is it okay to treat different customers differently? Because if we, if we break that promise, customers are going to ultimately leave us. >> That's a hard problem. >> Right, right. >> You guys have a lot of these discussions internally? >> We do. >> And can you share those with us? >> Yeah, absolutely, we do. And we get a lot of questions. We often engage at the data strategy perspective. And it starts with, hey we've got great activity occurring in our business units, in our functional areas, but we don't really have a handle on the enterprise wide data strategy. And at that point we start talking about trust, and privacy, and security, and what is your what does your data flows look like. So it starts at that initial data strategy discussion. And one other thing I mentioned in my opening remarks this morning is, we released this blueprint and it's intended, as you said, to put a framework in process and reflect a lot of the lessons learned that we're all going through. I know you mentioned that many companies are looking at AI adoption, perhaps more so than we realized. And so the framework was intended to help accelerate that process. And then our big announcement today has been around the showcases, in particular our platform showcase. So it's really the platform we've built, within our organization. The components, the products, the capabilities that drives for us. And then with the intent of hopefully being, illustrative and helpful to clients that are looking to build similar capabilities. >> So let's talk about adoption. >> Brandon: Yeah, sure. >> Ya know, we... you often hear this bromide that we live in a world where, that pace of change is so fast. And things are changing so quickly it's hard to deny that. But then when you look at adoption of some of the big themes in our time. Whether it's big data or AI, digital, block chains, there are some major barriers to adoption. So you see them adopted in pockets. What's your perspective, and Forrester's perspective on adoption of, let's call it machine intelligence? >> Yeah, sure, so I mean, every year Forrester does a global survey of business and technology decision leaders called Business Technographics. And we ask folks about adoptions rates of certain technologies. And so when it comes to AI, globally, 52% of companies have adopted AI in some way. And another 20% plan to in the next 12 months. What's interesting to me, actually, is when you break that down geographically, the highest adoption rate, 60 plus percent, is in APAC, followed by North America, followed by Europe. And when you think about the privacy regulations in each of those geographies, well there are far fewer in APAC than there are, and will be, in Europe. And that's, I think kind of hamstringing adoption in that geography. Now is that a problem for Europe? I don't think so actually. I think AI, the way AI is going to be adopted in Europe is going to be more refined and respectful of customers' intrinsic right to privacy. >> Dave: Ya know I want... Go ahead. >> I've got to, I have to say Dave, I have to put a plug in. I've been a huge fan of Brandon's, for a long time. I've actually, ya know, a few years now of his research. And some of the research that you're mentioning, I hope people are reading it. Because we find these reports to be really helpful to understand, as you said, the specifics of adoptions, the trends. So I've got to put a plug in there. >> Thanks Caitlin. >> Because, the quality of the work and the insights are incredible. So that is why I was quite excited when Brandon accepted our offer to join us here in this session. >> Awesome. Yeah, so, let's dig into that a little bit. >> Brandon: Sure. >> So it seems like, so 52%, I'm wondering, what the other 48 are doing? They probably are, and they just don't know it. So it's possible that the study looks at, a strategy to adopt, presumably. I mean actively adopting. But it seems, I wonder if I could run this by you, get your comment. It seems that people will, organizations will more likely be buying AI as embedded in applications or systems or just kind of invisible. Then they won't necessarily be building it. I know many are trying to probably build it today. And what's your thought on that? In terms of just AI infused everywhere? >> So the first foray for most enterprises into this world of AI is chat bots for customer service. >> Dave: Sure. >> I mean we get a ton of inquires at Forrester about that. And there are a number of solutions. Ya know, IBM certainly has one for, that fulfill that need. And that's a very narrow use case, right? And it's also a value added of use case. If you can take more of those call center agents out of the loop, or at least accelerate or make them better at their jobs, then you're going to see efficiency gains. But this isn't this company wide AI transformation. It's just one very narrow use case. And usually that's, most elements of that are pre-built. We talked this morning, or the speakers this morning talked about commoditization of certain aspects of machine learning and AI. And it's very true. I mean, machine learning algorithms, many of them have been around for a long time, and you can access them for multiple different platforms. Even natural language processing, which a few years ago was highly inaccurate, is getting really, really accurate. So when, in a world where all of these things are commoditized, it's going to end up being how you implement them that's going to drive differentiation. And so, I don't think there's any problem with buying solutions that have been pre-built. You just have to be very thoughtful about how you use them to ultimately make decisions that impact the customer experience. >> I want to, in the time we have remaining, I want to get into the tech radar, the sort of taxonomy of AI or machine intelligence. You've done some work here. How do you describe, can you paint a picture, for what that taxonomy looks like? >> So I think most people watching realize AI is not one specific thing right? It's a bunch of components, technologies that stitched together lead to something that can emulate certain things that humans do, like sense the world around us, see, read, hear, that can think or reason. That's the machine learning piece. And that can then take action. And that's the kind of automation piece. And there are different core technologies that make up each of those faculties. The kind of emerging ones are deep learning. Of course you hear about it all the time. Deep learning is inherently the use of artificial neural networks, usually to take some unstructured data, let's say pictures of cats, and identify this is actually a cat right? >> Who would have thought? That we're led to this boom right? >> Right exactly. That was something you couldn't do five or six years ago, right? You couldn't actually analyze picture data like you analyze row and column data. So that's leading to a transformation. The problem there is that not a lot of people have this massive number of pictures of cats that are consistently and accurately labeled cat, not cat, cat, not cat. And that's what you need to make that viable. So a lot of vendors, and Watson has an API for this have already trained a deep neural network to do that so the enterprises aren't starting from scratch. And I think we'll see more and more of these kind of pre-trained solutions and companies gravitating towards the pre-trained solutions. And looking for differentiation, not in the solutions themselves, but again how they actually implement it to impact the customer experience. >> Hmmm, well that's interesting, just hearing you sense, see, read, hear, reason, act. These are words that describe not the past era. This is a new era that we're entering. We're in the cloud era now. We can sort of all agree with that. But these, the cloud doesn't do these things. We are clearly entering a new wave. Maybe it's driven by Watson's Law, or whatever holds out. Caitlin I'll give you the last word. Put a bumper sticker on this event, and where we're at here in 2018? >> I'll say, it's interesting to watch the themes evolve over the last few years. Ya know, we started with sort of a defensive posture. Most of our data executives were coming perhaps from an IT type background. We see a lot more with line of business, and chief operations type role. And we've seen the, we still king of the data warehouse, that's sort of how we described at the time. And now, I see our data leaders really driving transformation. They're responsible for both the data as well as the digital transformation. On the data side, it's the AI focus. And trying to really understand the deep learning capabilities, machine learning, that they're bringing to bear. So it's been, for me, it's been really interesting to see the topics evolve, see the role in the strategic piece of it. As well as see these guys elevated, in terms of influence within their organization. And then, our big topic this year was around AI and understanding it. And so, having Brandon to share his expertise was very exciting for me because, he's our lead analyst in the AI space. And that's what our attendees are telling us. They want to better understand, and better understand how to take action to implement and see those business results. So I think we're going to continue to see more of that. And yeah, it's been great to see, great to see it evolve. >> Well congratulations on taking the lead, this is a very important space. Ya know, a lot of people didn't really believe in it early on, thought the Chief Data Officer role would just sort of disappear. But you guys, I think, made the right investment and a good call, so congratulations on that. >> I was laughed out of the room when I proposed, I said hey we're hearing of this, doing a market scan of Chief Data Officer, either by title or something similar, titled responsible for enterprise wide data. I was laughed out of the room. I said let me do a qualitative piece. Let me interview 20 and just show, and then you're right, it was the thought was, role's going to go by the wayside. And I think we've seen the opposite. >> Oh yeah, absolutely. >> Data has grown in importance. The associative capabilities have grown. And I'm seeing these individuals, their scope, their sphere of responsibility really grow quite a bit. >> Yeah Forrester's tracked this. I mean, you guys I think just a few years ago was like eh, yeah 20% of organizations have a Chief Data Officer and now it's much much higher than that. >> Yeah, yeah, it's approaching 50%. >> Yeah, so, good. Alright Brandon, Caitlin, thanks very much for coming on theCUBE. >> Thanks for having us. >> Thank you, it was great. >> Keep it right there everybody. We'll be back, at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE. (techno music) (telephone tones)