(hip-hop music) (electronic music) >> Announcer: Live from Fisherman's Wharf in San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. (crowd) >> Hey welcome back everybody, Jeff Fricke here with Peter Burris. We're wrapping up a very full day here at the IBM Chief Data Officer Strategy Summit Spring 2017, Fisherman's Wharf, San Francisco. An all-day affair, really an intimate affair, 170 people, but Chief Data Officers with their peers, sharing information, getting good information from IBM. And it's an interesting event. They're doing a lot of them around the country, and eventually around the world. And we're excited to have kind of the power behind the whole thing. (laughing) Caitlin Lepech, she's the one who's driving the train. Don't believe the guys in the front. She's the one behind the curtain that's pulling all the levers. So we wanted to wrap the day. It's been a really good day, some fantastic conversations, great practitioners. >> Right. >> Want to get your impression of the day? Right, it's been great. The thing I love about this event the most is this is all client-led discussion, client-led conversation. And we're quite fortunate in that we get a lot leading CDOs to come join us. I've seen quite a number this time. We tried something new. We expanded to this 170 attendees, by far the largest group that we've ever had, so we ran these four breakout session tracks. And I am hearing some good feedback about some of the discussions. So I think it's been a good and full day (laughing). >> Yes, it has been. Any surprises? Anything that kind of jumped out to you that you didn't expect? >> Yeah, a couple of things. So we structure these breakout sessions... Pointed feedback from last session was, Hey, we want the opportunity to network with peers, share use cases, learn from each other, so I've got my notes here, and that we did a function builder. So these are all our CDOs that are starting to build the CDO office. They're new in the journey, right. We've got our data integrators, so they're really our data management, data wranglers, the business optimizers, thinking about how do I make sure I've got the impact throughout the business, and then market innovators. And one of the surprises is how many people are doing really innovative things, and they don't realize it. They tell me-- >> Jeff: Oh, really. >> Ahhh, I'm just in the early stages of setting up the office. I don't have the good use cases to share. And they absolutely do! They absolutely do! So that's always the surprise, is how many are actually quite more innovative than I think they give themselves credit. >> Well, that was a pretty consistent theme that came out today, is that you can't do all the foundational work, and then wait to get that finished before you start actually innovating delivering value. >> If you want to be successful. >> (laughing) Right, and keep your job (laughing) If you're one of the 41%. So you have to be parallel tracking, that first process'll never finish, but you've got to find some short-term wins that you can execute on right away. >> And that was one of our major objectives and sort of convening this event, and continuing to invest in the CDO community, is how do I improve the failure rate? We all agree, growth in the role, okay. But over half are going to fail. >> Right. >> And we start to see some of these folks now that they're four, six years in having some challenges. And so, what we're trying to do is reduce that failure rate. >> Jeff: Yeah, hopefully they-- >> But still four to six years in is still not a bad start. >> Caitlin: Yeah, yeah. >> There's most functions that fail quick... That fail tend to fail pretty quickly. >> Yeah. >> So one of the things that I was struck by, and I want to get your feedback on this, is that 170 people, sounds like a lot. >> Caitlin: Yeah, yeah. >> But it's not so much if there is a unity of purpose. >> Caitlin: Correct, correct! >> If there's pretty clear understanding of what it is they do and how they do it, and I think the CDO's role is still evolving very rapidly. So everybody's coming at this from a different perspective. And you mentioned the four tracks. But they seem to be honing in on the same end-state. >> Absolutely. >> So talk about what you think that end-state is. Where is the CDO in five years? >> Absolutely, so I did some live polling, as we kicked off the morning, and asked a couple of questions along those lines. Where do folks report? I think we mentioned this-- >> Right. >> When we kicked off. >> Right. >> A third to the CEO, a third to CIO, and a third to a CXO-type role, functional role. And reflected in the room was about that split. I saw about a third, third, third. And, yet, regardless of where in the organization, it's how do we get data governance, right? How do we get data management, right? And then there's this, I think, reflection around, okay, machine learning, deep learning, some of these new opportunities, new technologies. What sort of skills do we need to deliver? I had an interesting conversation with a CDO that said, We make a call across the board. We're not investing to build these technical skills in-house because we know in two years the guys I had doing Python and all that stuff, it's on to the next thing. And now I've got to get machine learning, deep learning, two years I need to move to the next. So it's more identifying technologies in partnership bringing those and bringing us through, and driving the business results. >> And we heard also very frequently the role the politics played. >> Caitlin: Oh, absolutely. >> And, in fact, Fow-wad Boot from-- >> Kaiser. >> Kaiser Permanente, yeah. >> Specifically talked about this... He's looking in the stewards that he's hiring in his function. He's looking for people that have learned the fine art of influencing others. >> And I think it's a stretch for a lot of these folks. Another poll we did is, who comes from an engineering, technical background. A lot of hands in the room. And we're seeing more and more come from line of business, and more and more emphasize the relationship component of it, relationship skills, which is I think is very interesting. We also see a high number of women in CDO roles, as compared to other C-suite roles. And I like to think, perhaps, it has to do-- >> Jeff: Right, right. >> With the relationship component of it as well because it is... >> Jeff: Yeah, well-- >> Peter: That's interesting. I'm not going to touch it, but it's interesting (laughing). >> Well, no, we were-- >> (laughing) I threw it out there. >> We were at the Stanford-- >> No, no, we-- >> Women in Data Science event, which is a phenomenal event. We've covered it for a couple years, and Jayna George from Western Digital, phenomenal, super smart lady, so it is an opportunity, and I don't think it's got so much of the legacy stuff that maybe some of the other things had that people can jump in. Diane Green kicked it off-- >> Yeah. >> So I think there is a lot of examples women doing their own thing in data science. >> Yeah, I agree, and I'll give you another context. In another CUBE, another event, I actually raised that issue, relationships, because men walk into a room, they get very competitive very quickly, who's the smartest guy in the room. And on what days is blah, blah, blah. And we're talking about the need to forge relationships that facilitate influence. >> Absolutely. >> And sharing of insight and sharing of knowledge. And it was a woman guest, and she... And I said, Do you see that women are better at this than others? And she looked at me, she said, Well, that's sexist. (laughing). And it was! I guess it kind of was. >> Right, right. >> But do you... You're saying that it's a place where, perhaps, women can actually take a step into senior roles in a technology-oriented space. >> Yeah. >> And have enormous success because of some of the things that they bring to the table. >> Yeah, one quote stuck with me is, when someone comes in with great experience, really smart, Are they here to hurt me or help me? And the trust component of it and building the trust, And I think there is one event we do here, the second day of all of our CDO summits, so women in breakfast, the data divas' breakfast. And we explore some opportunities for women leaders, and it was well-attended by men and women. And I think there really is when you're establishing a data strategy for your entire organization, and you need lines of business to contribute money and funding and resources, and sign off, there is I feel sometimes like we're on the Hill. I'm back in D.C., working on Capitol Hill (laughing), and we're shopping around to deliver, so absolutely. Another tying back to what you mentioned about something that was surprising today, we started building out this trust as a service idea. And a couple people on panels mentioned thinking about the value of trust and how you instill trust. I'm hearing more and more about that, so that was interesting. >> We actually brought that up. >> Caitlin: Oh, did you! >> Yeah, we actually brought it up here in theCUBE. And it was specifically and I made an observation that when you start thinking about Watson and you start thinking about potentially-competitive offerings at some point in time they're going to offer alternative opinions-- >> Absolutely. >> And find ways to learn to offer their opinions better than their's just for competitive purposes. >> Absolutely. >> And so, this notion of trust becomes essential to the brand. >> Absolutely. >> My system is working in your best interest. >> Absolutely. >> Not my best interest. And that's not something that people have spent a lot of time thinking about. >> Exactly, and what it means when we say, when we work with clients and say, It's your data, your insight. So we certainly tap that information-- >> Sure. >> And that data to train Watson, but it's not... We don't to keep that, right. It's back to you, but how do you design that engagement model to fulfill the privacy concerns, the ethical use of data, establish that trust. >> Right. >> I think it's something we're just starting to really dig into. >> But also if you think about something like... I don't know if you ever heard of this, but this notion of principal agent theory. >> Umm-hmm. >> Where the principal being the owner, in typical-- >> Right. >> Economic terms. The agent being the manager that's working on behalf of the owner. >> Right. >> And how do their agendas align or misalign. >> Right. >> The same thing is just here. We're not talking about systems that have... Are able to undertake very, very complex problems. >> Right. >> Sometimes will do so, and people will sit back and say, I'm not sure how it actually worked. >> Yeah. >> So they have to be a good agent for the business. >> Absolutely, absolutely, definitely. >> And this notion of trust is essential to that. >> Absolutely, and it's both... It originated internally, right, trying to trust the answers you're getting-- >> Sure! >> On a client. Who's our largest... Where's our largest client opportunity, you get multiple answers, so it's kind of trusting the voracity of the data, but now it's also a competitive differentiator. As a brand you can offer that to your client. >> Right, the other big thing that came up is you guys doing it internally, and trying to drive your own internal transformation at IBM, which is interesting in of itself, but more interesting is the fact that (laughing) you actually want to publish what you're doing and how you did it-- >> Yeah. >> As a road map. I think you guys are calling it the Blueprint-- >> Yes. >> For your customers. And talk about publishing that actually in October, so I wonder if you can share a little bit more color around what exactly is this Blueprint-- >> Sure. >> How's it's going to be exposed? >> What should people look forward to? >> Sure, I'm very fortunate in that Inderpal Bhandari when he came on board as IBM's First Chief Data Officer, said, I want to be completely transparent with clients on what we're doing. And it started with the data strategy, here's how we arrived at the data strategy, here's how we're setting up our organization internally, here's how we're prioritizing selecting use cases, so client prefixes is important to us, here's why. Down at every level we've been very transparent about what we're doing internally. Here's the skill sets I'm bringing on board and why. One thing we've talked a lot about is the Business Unit Data Officer, so having someone that sits in the business unit responsible for requirements from the unit, but also ensuring that there's some level of consistency at the enterprise level. >> Right. >> So, we've had some Business Unit Data Officers that we've plucked (laughing) from other organizations that have come and joined IBM last year, which is great. And so, what we wanted to do is follow that up with an actual Blueprint, so I own the Blueprint for Inderpal, and what we want to do is deliver it along three components, so one, the technology component, what technology can you leverage. Two, the business processes both the CDO processes and the enterprise, like HR, finance, supply chain, procurement, et cetera. And then finally the organizational considerations, so what sort of strategy, culture, what talent do you need to recruit, how do you retain your existing workforce to meet some of these new technology needs. And then all the sort of relationship piece we were talking about earlier, the culture changes required. >> Right. >> How do you go out and solicit that buy-in. And so, our intent is to come back around in October and deliver that Blueprint in a way that can be implemented within organization. And, oh, one thing we were saying is the homework assignment from this event (laughing), we're going to send out the template. >> Right. And our version of it, and be very transparent, here's how we're doing it internally. And inviting clients to come back to say-- >> Right. >> You need to dig in deeper here, this part's relevant to me, along the information governance, the master data management, et cetera. And then hopefully come back in October and deliver something that's really of value and usable for our clients across the industry. >> So for folks who didn't make it today, too bad for them. >> Exactly, we missed them, (laughing) but... >> So what's the next summit? Where's it's going to be, how do people get involved? Give us a kind of a plug for the other people that wished they were here, but weren't able to make it today. >> Sure, so we will come back around in the fall, September, October timeframe, in Boston, and do our east coast version of this summit. So I hope to see you guys there. >> Jeff: Sure, we'll be there. >> It should be a lot of fun. And at that point we'll deliver the Blueprint, and I think that will be a fantastic event. We committed to 170 data executives here, which fortunately we were able to get to that point, and are targeting a little over 200 for the fall, so looking to, again, expand, continue to expand and invite folks to join us. >> Be careful, you're going to be interconnected before you know. >> (laughing) No, no, no, I want it small! >> (laughing) Okay. >> And then also as I mentioned earlier, we're starting to see more industry-specific financial services, government. We have a government CDO summit coming up, June six, seven, in Washington D.C. So I think that'll be another great event. And then we're starting to see outside of the U.S., outside of North America, more of the GO summits as well, so... >> Very exciting times. Well, thanks for inviting us along. >> Sure, it's been a great day! It's been a lot of fun. Thank you so much! >> (laughing) Alright, thank you, Caitlin. I'm Jeff Fricke with Peter Burris. You're watching theCUBE. We've been here all day at the IBM Chief Data Officer Strategy Summit, that's right the Spring version, 2017, in Fisherman's Wharf, San Francisco. Thanks for watching. We'll see you next time. (electronic music) (upbeat music)

(pulsating music) >> Live from Fisherman's Wharf in San Francisco, it's theCUBE! Covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit, Spring 2017. It's a mouthful, it's a great event, and it's one of many CDO summits that IBM's putting in around the country, and soon around the world. So check it out. We're happy to be here and really talk to some of the thought leaders about getting into the nitty gritty detail of strategy and execution. So we're excited to be joined by our next guest, Rebecca Shockley. She's an Analytics Global Research Leader for the IBM Institute for Business Value. Welcome, Rebecca. I didn't know about the IBM Institute for Business Value. >> Thank you. >> Absolutely. And Priya V. She said Priya V's good, so you can see the whole name on the bottom, but Priya V. is the CTO of Cognitive/IOT/Watson Health at IBM. Welcome, Priya. >> Thank you. >> So first off, just impressions of the conference? It's been going on all day today. You've got 170 or some-odd CDO's here sharing best practices, listening to the sessions. Any surprising takeaways coming out of any of the sessions you've been at so far? >> On a daily basis I live and breathe data. That's what I help our customers to get better at it, and today is the day where we get to talk about how can we adopt something which is emerging in that space? We talk about data governance, what we need to look at in that space, and cognitive as being the fabric that we are integrating into this data governance actually. It's a great day, and I'm happy to talk to over, like you said, 170 CDO's representing different verticals. >> Excellent. And Rebecca, you do a lot of core research that feeds a lot of the statistics that we've seen on the keynote slides, this and that. And one of the interesting things we talked about off air, was really you guys are coming up with a playbook which is really to help CDO's basically execute and be successful CDO's. Can you tell us about the playbook? >> Well, the playbook was born out of a Gartner statistic that came out I guess two or three years ago that said by 2016 you'll have 90% of organizations will have a CDO and 50% of them will fail. And we didn't think that was very optimistic. >> Jeff: 90% will have them and 50% will fail? >> Yes, and so I can tell you that based on our survey of 6,000 global executives last fall, the number is at 41% in 2016. And I'm hoping that the playbook kept them from being a failure. So what we did with the playbook is basically laid out the six key questions that an organization needs to think about as they're either putting in a CDO office or revamping their CDO offices. Because Gartner wasn't completely unfounded in thinking a lot of CDO offices weren't doing well when they made that prediction. Because it is very difficult to put in place, mostly because of culture change, right? It's a very different kind of way to think. So, but we're certainly not seeing the turnover we were in the early years of CDO's or hopefully the failure rate that Gartner predicted. >> So what are the top two or three of those six that they need to be thinking about? >> So they need to think about their objectives. And one of the things that we found was that when we look at CDO's, there's three different categories that you can really put them in. A data integrator, so is the CDO primarily focused on getting the data together, getting the quality of the data, really bringing the organization up to speed. The next thing that most organizations look at is being a business optimizer. So can they use that data to optimize their internal processes or their external relationships? And then the third category is market innovator. Can they use that data to really innovate, bring in new business models, new data monetization strategies, things like that. The biggest problem we found is that CDO's that we surveyed, and we surveyed 800 CDO's, we're seeing that they're being assessed on all three of those things, and it's hard to do all three at once, largely because if you're still having to focus on getting your data in a place where you can start doing real science against it you're probably not going to be full-time market innovator either. You can't be full-time in two different places. That's not to say as a data integrator you can't bring in data scientists, do some skunk works on some of the early work, find... and we've seen organizations really, like Bank Itau down in Brazil, really in that early stages still come up with some very innovative things to do, but that's more of a one-off, right. If you're being judged on all three of those, that I think is where the failure rate comes in. >> But it sounds like those are kind of sequential, but you can't operate them sequentially cause in theory you never finish the first phase, right? >> You never finish, you're always keeping up with the data. But for some organizations, they really need to, they're still operating with very dirty, very siloed data that you really can't bring together for analytics. Now once you're able to look at that data, you can be doing the other two, optimizing and innovating, at the same time. But your primary focus has to be on getting the data straight. Once you've got a functioning data ecosystem, then the level of attention that you have to put there is going to go down, and you can start working on, focusing on innovation and optimization more as your full-time role. But no, data integrator never goes away completely. >> And cleanser. Then, that's a great strategy. Then, as you said, then the rubber's got to hit the road. And Priya, that's where you play in, the execution point. Like you say, you like to get your hands dirty with the CDO's. So what are you seeing from your point of view? In terms of actually executing, finding early wins, easy paths to success, you know, how to get those early wins basically, right? To validate what you're doing. That's right. Like you said, it's become a universal fact that data governance and things, everything around consolidating data and the value of insights we get off it, that's been established fact. Now CDO's and the rest of the organization, the CIO's and the CTO's, have this mandate to start executing on them. And how do we go about it? That's part of my job at IBM as well. As a CTO, I work with our customers to identify where are the dominant business value? Where are those things which is completely data-driven? Maybe it is cognitive forecasting, or your business requirement could be how can I maximize 40% of my service channel? Which in the end of the day could be a cognitive-enabled data-driven virtual assistant, which is automating and bringing a TCO of huge incredible value. Those are some of the key execution elements we are trying to bring. But like we said, yes, we have to bring in the data, we have to hire the right talent, and we have to have a strategy. All those great things happen. But I always start with a problem, a problem which actually anchors everything together. A problem is a business problem which demonstrates key business values, so we actually know what we are trying to solve, and work backwards in terms of what is the data element to it, what are the technologies and toolkits that we can put on top of it, and who are the right people that we can involve in parallel with the strategy that we have already established. So that's the way we've been going about. We have seen phenomenal successes, huge results, which has been transformative in nature and not just these 170 CDO's. I mean, we want to make sure every one of our customers is able to take advantage of that. >> But it's not just the CDO, it's the entire business. So the IBM Institute on Business Value looks at an enormous amount of research, or does an enormous amount of research and looks at a lot of different issues. So for example, your CDO report is phenomenal, I think you do one for the CMO, a number of different chief officers. How are other functions or other roles within business starting to acculturate to this notion of data as a driver of new behaviors? And then we can talk about, what are some of those new behaviors? The degree to which the leadership is ready to drive that? >> I think the executive suite is really starting to embrace data much more than it has in the past. Primarily because of the digitization of everything, right. Before, the amount of data that you had was somewhat limited. Often it was internal data, and the quality was suspect. As we started digitizing all the business processes and being able to bring in an enormous amount of external data, I think organizationally executives are getting much more comfortable with the ability to use that data to further their goals within the organization. >> So in general, the chief groups are starting to look at data as a way of doing things differently. >> Absolutely. >> And how is that translating into then doing things differently? >> Yeah, so I was just at the session where we talked about how organizations and business units are even coming together because of data governance and the data itself. Because they are having federated units where a certain part of business is enabled and having new insights because we are actually doing these things. And new businesses like monetizing data is something which is happening now. Data as a service. Actually having data as a platform where people can build new applications. I mean the whole new segment of people as data engineers, full stack developers, and data scientists actually. I mean, they are incubated and they end up building lots of new applications which has never been part of a typical business unit. So these are the cultural and the business changes we are starting to see in many organizations actually. Some of them are leading the way because they just did it without knowing actually that's the way they should be doing it. But that's how it influences many organizations. >> I think you were looking for kind of an example as well, so in the keynote this morning one of the gentlemen was talking about working with their CFO, their risk and compliance office, and were able to take the ability to identify a threat within their ecosystem from two days down to three milliseconds. So that's what can happen once you really start being able to utilize the data that's available to an organization much more effectively, is that kind of quantum leap change in being able to understand what's happening in the marketplace, bing able to understand what's happening with consumers or customers or clients, whichever flavor you have, and we see that throughout the organization. So it's not just the CFO, but the CMO, and being able to do much more targeted, much more focused on the consumer side or the client customer side, that's better for me, right. And the marketing teams are seeing 30, 40% increase in their ability to execute campaigns because they're more data-driven now. >> So has the bit flipped where the business units are now coming to the CDO's office and pounding on the door, saying "I need my team"? As opposed to trying to coerce that you no longer use intuition? >> So it depends upon where you are, where the company is. Because what we call that is the snowball effect. It's one of the reasons you have to have the governance in place and get things going kind of in parallel. Because what we see is that most organizations go in skeptically. They're used to running on their gut instinct. That's how they got their jobs mostly, right? They had good instincts, they made good decisions, they got promoted. And so making that transition to being a data-driven organization can be very difficult. What we find though, is that once one section, one segment, one flavor, one good campaign happens, as soon as those results start to mount up in the organization, you start to see a snowball effect. And what I was hearing particularly last year when I was talking to CDO's was that it had taken them so long to get started, but now they had so much demand coming from the business that they want to look at this, and they want to look at that, and they want to look at the other thing, because once you have results, everybody else in the organization wants those same kind of results. >> Just to add to that, data is not anymore viewed as a commodity. If you have seen valuable organizations who know what their asset is, it's not just a commodity. So the parity of... >> Peter: Or even a liability is what it used to be, right? >> Exactly. >> Peter: It's expensive to hold it and store it, and keep track of it. >> Exactly. So the parity of this is very different right now. So people are talking about, how can I take advantage of the intelligence? So business units, they don't come and pound the door rather they are trying to see what data that I can have, or what intelligence that I can have to make my business different shade, or I can value add something more. That's a type of... So I feel based on the experiences that we work with our customers, it's bringing organizations together. And for certain times, yes sometimes the smartness and the best practices come in place that how we can avoid some of the common mistakes that we do, in terms of replicating 800 times or not knowing who else is using. So some of the tools and techniques help us to master those things. It is bringing organizations and leveraging the intelligence that what you find might be useful to her, and what she finds might be useful. Or what we all don't know, that we go figure it out where we can get it. >> So what's the next step in the journey to increase the democratization of the utilization of that data? Because obviously Chief Data Officers, there aren't that many of them, their teams are relatively small. >> Well, 41% of businesses, so there's a large number of them out there. >> Yeah, but these are huge companies with a whole bunch of business units that have tremendous opportunity to optimize around things that they haven't done yet. So how do we continue to kind of move this democratization of both the access and the tools and the utilization of the insights that they're all sitting on? >> I have some bolder expectations on this, because data and the way in which data becomes an asset, not anymore a liability, actually folds up many of the layers of applications that we have. I used to come from an enterprise background in the past. We had layers of application programming which just used data as one single layer. In terms of opportunities for this, there is a lot more deserving silos and deserving layers of IT in a typical organization. When we build data-driven applications, this is all going to change. It's fascinating. This role is in the front and center of everything actually, around data-driven. And you also heard enough about cognitive computing these days, because it is the key ingredient for cognitive computing. We talked about full ease of cognitive computing. It has to start first learning, and data is the first step in terms of learning. And then it goes into process re-engineering, and then you reinvent things and you disrupt things and you bring new experiences or humanize your solution. So it's on a great trajectory. It's going tochange the way we do things. It's going to give new and unexpected things both from a consumer point and from an enterprise point as well. It'll bring effects like consumerization of enterprises and what-not. So I have bolder and broader expectations out of this fascinating data world. >> I think one of the things that made people hesitant before was an unfamiliarity with thinking about using data, say a CSR on the front line using data instead of the scripts he or she had been given, or their own experience. And I think what we're seeing now is A, everybody's personal life is much more digital than it was before, therefore everybody's somewhat more comfortable with interacting. And B, once you start to see those results and they realize that they can move from having to crunch numbers and do all the background work once we can automate that through robotic process automation or cognitive process automation, and let them focus on the more interesting, higher value parts of their job, we've seen that greatly impact the culture change. The culture change question comes whether people are thinking they're going to lose their job because of the data, or whether it's going to let them do more interesting things with their jobs. And I think hopefully we're getting past that "it's me or it" stage, into the, how can I use data to augment the work that I'm doing, and get more personal satisfaction, if not business satisfaction, out of the work that I'm doing. Hopefully getting rid of some of the mundane. >> I think there's also going to be a lot of software that's created that's going to be created in different ways and have different impacts. The reality is, we're creating data incredibly fast. We know that is has enormous value. People are not going to change that rapidly. New types of algorithms are coming on, but many of the algorithms are algorithms we've had for years, so in many respects it's how we render all of that in some of the new software that's not driven by process but driven by data. >> And the beauty of it is this software will be invisible. It will be self-healing, regeneratable software. >> Invisible to some, but very very highly visible to others. I think that's one of the big challenges that IT organizations face, and businesses face. Is how do they think through that new software? So you talked about today, or historically, you talked about your application stack, where you have stacks which would have some little view of the data, and in many respects we need to free that data up, remove it out of the application so we can do new things with it. So how is that process going to either be facilitated, or impeded by the fact that in so many organizations, data is regarded as a commodity, something that's disposable. Do we need to become more explicit in articulating or talking about what it means to think of data as an asset, as something that's valuable? What do you think? >> Yeah, so in the typical application world, when we start, if you really look at it, data comes at the very end of it. Because people start designing what is going to be their mockups, where are they going to integrate with what sources, am I talking to the bank as an API, et cetera. So the data representation comes at the very end. In the current generation of applications, the cognitive applications that we are building, first we start with the data. We understand what are we working on, and we start applying, taking advantage of machines and all these algorithms which existed like you said, many many decades ago. And we take advantage of machines to automate them to get the intelligence, and then we write applications. So you see the order has changed actually. It's a complete reversal. Yes we had typical three-tier, four-tier architecture. But the order of how we perceive and understand the problem is different. But we are very confident. We are trying to maximize 40% of your sales. We are trying to create digital connected dashboards for your CFO where the entire board can make decisions on the fly. So we know the business outcome, but we are starting with the data. So the fundamental change in how software is built, and all these modules of software which you are talking about, why I mentioned invisible, is some are generatable. The AI and cognitive is advanced in such a way that some are generatable. If it understands the data underlying, it can generate what it should do with the data. That's what we are teaching. That's what ontology and all this is about. So that's why I said it's limitless, it's pretty bold, and it's going to change the way we have done things in the past. And like she said, it's only going to complement humans, because we are always better decision-makers, but we need so much of cognitive capability to aid and supplement our decision-making. So that's going to be the way that we run our businesses. >> All right. Priya's painting a pretty picture. I like it. You know, some people see only the dark side. That's clearly the bright side. That's a terrific story, so thank you. So Priya and Rebecca, thanks for taking a few minutes. Hope you enjoy the rest of the show, surrounded by all this big brain power. And I appreciate you stopping by. >> Thanks so much. >> Thank you. >> All right. Jeff Frick and Peter Burris. You're watching theCUBE from the IBM Chief Data Officers Summit, Spring 2017. We'll be right back after this short break. Thanks for watching. (drums pound) (hands clap rhythmically) >> [Computerized Voice] You really crushed it. (quiet synthesizer music) >> My name is Dave Vellante, and I'm a long-time industry analyst. I was at IDC for a number of years and ran the company's largest and most profitable business. I focused on a lot of areas, infrastructure, software, organizations, the CIO community. Cut my teeth there.

(orchestra music) >> Man: Live from Fisherman's Wharf in San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit, Spring 2017, brought to you by IBM. >> Welcome back everybody, Jeff Rick here at the Cube. We're in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, but it's an ongoing series you know, it's not just one show. They're doing them on the east coast, west coast, and starting to take it all over the world. Really, a community of chief data officers coming together with the likes of their own, talking about common issues, best practices. And of course, IBM's got something to offer as well. So, we're excited to have our next guest, Ken Jacquier here. He's the Information Governance Practice from IBM. Welcome. >> Thank you. >> So, what have you been hearing in the hallways outside of the sessions? What's kind of the hot buzz topic? >> Well, actually everybody's pretty much talking about what came up in the sessions, it's all about the talent. How do these Chief Data Officers get the talent that they need to meet the mandate they've been given? >> It's not just automatically just like connect the data, via some APIs and the magic happens (laughs). >> Sometimes the people part is the hardest part. The technology's important, the machine learning is great, the algorithms are amazing, but it does come down to people. And there's some new skill sets that these chief data >> officers need in their people, so that's what they're talking about. >> So when you think about the talent, what kinds of jobs are talking about? We know the CDO job. >> Ken: Yeah. >> What kind of jobs are now underneath the CDO that are going to help the CDO get their job done? >> Yeah, absolutely. You've got the classic data scientist role that we are all talking about, we're all excited about because that can monetize the data. That's what gets the board's attention. So there's a lot of focus there. But a term that came up in the last session that I was in that I really liked was the data translator. And the point there was data scientists can be schooled in certain things, understand their algorithms, understand machine learning, but this really important skill set they're looking for is the data translator. >> So the business is looking to drive outcomes. The chief marking officer may have an objective. >> The vice president of sales has an objective. Supply chain needs to optimize. Who is the data translator that can get from this deep, difficult, often dirty data and translate it into what the business is trying to accomplish? It's a really cool role. >> Yeah, we've actually heard about this role pretty frequently, this concept very frequently when you come right down to it. And a lot of it pertains to who is in a position to understand data quality, how data transformation works, so that the outcome in fact is what's expected as opposed to just a consequence data wrong. >> Exactly. Two examples of that that I've heard today in the initial keynote session, it came up, that in this renaissance of data, we're going to look for people to bring the left side of their brain together and the right side of their brain together. In the last session, of the ladies at a large international bank, the chief data officer there, she said, "for me, honestly, even though this is difficult, "it's not about IQ, it's about EQ." I've got to have the people that can collaborate. I've got to have the people that can communicate both with the business and with the IT side. I mean, we all know that story, right. Such a challenge to pull IT and business together, >> but data is really forcing individually talented people to actually do that wherever they reside in the org chart. >> If you're the embed, you're the embed person from the CEO office working with that business unit, you've got to listen, you've got to convince them that you can help them, so it is really a softer skill. You know, the Da Vinci word has come up a couple of times. And what made Da Vinci so amazing is he had the science, but he also had the art, and the two are very, very connected. >> Exactly what we were talking about, exactly. And the listening skill is incredibly important as well. I mean a lot of times, there's so much emphasis in communication on getting your perspective out there. A lot of times in these situations, you're trying to express your view. Way underestimated skill, listening, how important that is for this stuff to work. >> So, your formal title is Information Governance Practice? >> Ken: Yes. >> Now, governance means a lot of things to a lot of people, and I don't want to put words in your mouth, but from my >> perspective, it means how are you going to ensure, put in place rules and mechanisms and methods to ensure that works get done around a particular set of issues. So, when we talk about talent, we talk about creativity, we also can talk about governance so that we in fact get the right set of practices put in place, so not that it >> runs by itself, but it runs at a high quality. >> So one of the things that you're doing with clients, to try to take talent and rules and turn it into an actual function that does (mumbles) business values. >> Yeah, it's a great question. So again, and if anybody's listening to this and they're talking about careers, or they're thinking about work coming up, or you're coming out of college, and you're like what would I want to do, think about this conversation we're having and the opportunity here. So, you just described I've got to drive business agility, and I've got to mitigate risk. Those sound like conflicting objectives. They can't be anymore. The talent has to come in. And what we're trying to help companies with is how do you build both a culture, but then also how do you bring in talent that can be excited, and creative, and innovative to drive that business agility, but respects the fact that if we don't take care of this data, important people can get in trouble. If we don't take care of this data, our clients can be in trouble, and our credibility can be damaged. But that has to be handled in tandem. It can't be two separate functions. In the past, a lot of times, we did have maybe an EIM organization that does the institutional, keep the data quality clean, and then there were innovation teams over here playing around building the new business model acquiring companies. In this new world, all this data's coming together, and you've got to be able to develop. So the word we like to use nowadays with our clients is the appropriate governments. With your financial data, you're still going to have that locked down. You're still going to have all those policies, all those business rules. That's got to be in place. But then, there's certain data that we can maybe not manage quite as tightly. We can create a landing zone where we brought in external data or third party data, and we can let marketing have a little more freedom with that. And we can be a little more creative and innovative and I don't think they have to be opposite perspectives. If they have the right architecture and the right processes, and the right governance, you can do both. >> Is it easy for someone who's had the lockdown governance for so long to start to open up their mind and think about ways that they can open it? Or does it have to come from an external point of view that looks at it from a different lens and isn't kind of locked down by the old paradigm? >> Yeah, that's a great question. And there were three R's that came up in the meeting today in terms of talent. It was recruit. So to your point, to some degree, we're going to have to recruit new folks with new paradigms. A lot of conversation in there about what an incredible opportunity for the millennials and the newer folks in the workforce if they don't have those paradigms. On the other hand, we have to still >> retain deep institutional knowledge of our data. So that might mean retraining existing skill sets, people that really know our databases, that really know where the most important data lives, but retrain them a little bit for this new environment. And then the third R was retain. So as we build these hybrid skill sets, people that are good on the business side, good on the IT side, we make that investment. How does an organization, how does a company retrain them? And for the HR professionals out there, for the senior VPs of HR, that's where you come in. You need to help these companies write job descriptions, build career paths, show people that they can work in these environments and still grow, both financially, professional, and career wise. Does that make sense? >> That makes a ton of sense, interesting challenge. I just interviewed a millennial speaker at the Professional Businesswoman's Conference, and he just flat out said, the new paradigm from his point of view as a 26 year old, is most people aren't staying on the job for more than six years. It's almost kind of built in life sabbatical every couple three or four years. So, the retention challenge is very difficult and for that generation, so much it's kind of the purposefulness. And if you can get the purposefulness in, big motivator behavior. >> Purposefulness, being a part of something bigger. >> So that's where this balance can come in. If I'm working to appropriately govern my financial data, but I'm also given an opportunity to work with the acquisitions team that's bringing an international flavor into my company, that can give that younger person a little bit of both, and help with that retention. >> One of the challenges though when we think about governance is to ensure as you said, that the rules were appropriate. >> Ken: Yes. >> One of the other things we've heard here and we certainly know about is data as an asset is different than other assets, in that it's not following the economic scarcity because it's so easy to copy, share, combine, recombine, everything else. >> Ken: Very good point. >> As you think about combining those two things, that appropriateness of data governance for financial data is different from the appropriateness of data governance for marketing data, when you combine them, which appropriateness wins? >> (laughing) >> That's a good question. So, ultimately-- >> Do we have an answer? Is that something we're discovering, is that one of the things that we need to better understand over time? What do you think? >> Yeah I do. And you used the keyword, understand. >> So, a very old terminology in our space is data profiling, of truly understanding your data and understanding where everything lives. That's never been more important than it is today. The right amount of tagging in your data links. So to do what you just described. The answer lies within truly understanding and inventorying what you have, and then you have at least an opportunity to strike that balance. But a lot of folks are skipping that step. So just moving data, they're replicating data, >> they're populating their data links in the Hadoop systems. You've got to have governance even that environment. >> Oh absolutely. And we're seeing that being one of the greatest challenges as people try to put together these analytic pipelines. Is to ensure that there's appropriate governance at each stage in the pipeline to ensure that the outcomes are both what they expected. They can be surprised, but at least it's relevant. And that they themselves are not breaking any laws or rules, or ethical or otherwise, associated with how the data gets used. >> I'd like your economic analogy, because I think that's what customers need to do, and that's what I try to help them with. >> Depending on what their business model is, they're going to understand some concept of a supply chain. But likely they don't understand what you just said, the concept of an information supply chain. So rather than try to explain it in geek speak, with IBM tooling, or all the things we typically do, I encourage customers to think about their perception of a supply chain. How does something move from a raw material to a sold product in their industry, whether it's finance, or whether they're building airplanes or whatever >> they're doing? And then, the customer can start to relate. Okay, my data's doing the same thing isn't it? And oh, I need to start thinking, I get that, my engineering brain and my process, and I have roles in the company. I have (mumbles) that their job is to work on my supply chain out in the factory, you're saying apply those types of approaches to a supply chain for data, what you just described. And once that light bulb starts to go off, there's an opportunity to do what you just said. >> Absolutely, in fact, we specifically talk to our clients about the notion first of, the role of data, first of all, data as an asset. In other words, something that has a consequential impact on a set of activities so you can put it into with other things in supply chain. But we also talk about the value chain. The role the data plays in the value chain. Whatever metaphor, both of those concepts are not broadly understood. Because data is so sharable, is so easily copied, too frequently, people say uh, it's really not an asset. Until they start making the wrong decision widely and repeatedly. So they have to think about it as an asset, they have to think about it as a value chain, and that's where the governance becomes so crucial. It's because if you're not putting in place good governance for your value chains, then you're not creating any value pretty quickly. >> And it's interesting if we think about it. So, data's an asset. Marketing people, software companies have been using that term for a long time. But now that we're at this stage and we have chief data officers, at the C-level folks reporting into the board that have this responsibility. So now the concept's a little better understood. So now the next step is what does that mean? What do I do with my typical assets? What do I do with my human resources assets? If I manage a fleet, what do I do with that fleet? So if something's truly an asset, what do I do? What do I do with it on the general ledger? What do I do from a staffing perspective? Where does it fit into to my overall operating model? And that's kind of what we're seeing unfold here. At an event like this, that's the level of conversation that's starting to happen. Not that it's a marketing buzzword anymore, but if it's true, organizationally, what have I done with other assets? Does that apply to my data as well if I'm using that statement? >> Alright, Ken, we're going to have to leave it there. I know you've got to run off to a session, but thanks for taking a few minutes out of your day. >> Thanks gentlemen. >> Alright, he's Ken. Peter, Jeff, you're watching the Cube at the IBM Chief Data Officer Strategy Summit 2017. Thanks for watching. (easy listening music) (percussive music)

(lively music) >> To the world. Over 31 million people have viewed theCUBE and that is the result of great content, great conversations and I'm so proud to be part of theCUBE, of a great team. Hi, I'm John Furrier. Thanks for watching theCUBE. For more information, click here. >> Narrator: Live from Fisherman's Wharf in San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. Jeff Frick here at theCUBE. It is lunchtime at the IBM CDO Summit. Packed house, you can see them back there getting their nutrition. But we're going to give you some mental nutrition. We're excited to be joined by a repeat performance of Cortnie Abercrombie. Coming on back with Vijay Vijayasankar. He's the GM Cognitive, IOT, and Analytics for IBM, welcome. >> Thanks for having me. >> So first off, did you eat before you came on? >> I did thank you. >> I want to make sure you don't pass out or anything. (group laughing) Cortnie and I both managed to grab a quick bite. >> Excellent. So let's jump into it. Cognitive, lot of buzz, IoT, lot of buzz. How do they fit? Where do they mesh? Why is it, why are they so important to one another? >> Excellent question. >> IoT has been around for a long time even though we never called it IoT. My favorite example is smart meters that utility companies use. So these things have been here for more than a decade. And if you think about IoT, there are two aspects to it. There's the instrumentation by putting the sensors in and getting the data. And the insides aspect where there's making sense of what the sensor is trying to tell us. Combining these two, is where the value is for the client. Just by putting outwardly sensors, it doesn't make much sense. So, look at the world around us now, right? The traditional utility, I will stick with the utilities to complete the story. Utilities all get dissected from both sides. On one hand you have your electric vehicles plugging into the grid to draw power. On the other hand, you have supply coming from solar roofs and so on. So optimizing this is where the cognitive and analytics kicks in. So that's the beauty of this world. All these things come together, that convergence is where the big value is. >> Right because the third element that you didn't have in your original one was what's going on, what should we do, and then actually doing something. >> Vijay: Exactly. >> You got to have the action to pull it all together. >> Yes, and learning as we go. The one thing that is available today with cognitive systems that we did not have in the past was this ability to learn as you go. So you don't need human intervention to keep changing the optimization algorithms. These things can learn by itself and improve over time which is huge. >> But do you still need a person to help kind of figure out what you're optimizing for? That's where, can you have a pure, machine-driven algorithm without knowing exactly what are you optimizing for? >> We are no where close to that today. Generally, where the system is super smart by itself is a far away concept. But there are lots of aspects of specific AI optimizing a given process that can still go into this unsupervised learning aspects. But it needs boundaries. The system can get smart within boundaries, the system cannot just replace human thought. Just augmenting our intelligence. >> Jeff: Cortnie, you're shaking you head over there. >> I'm completely in agreement. We are no where near, and my husband's actually looking forward to the robotic apocalypse by the way, so. (group laughing) >> He must be an Arnold Schwarzenegger fan. >> He's the opposite of me. I love people, he's like looking forward to that. He's like, the less people, the better. >> Jeff: He must have his Zoomba, or whatever those little vacuum cleaner things are called. >> Yeah, no. (group laughing) >> Peter: Tell him it's the fewer the people, the better. >> The fewer the people the better for him. He's a finance guy, he'd rather just sit with the money all day. What does that say about me? Anyway, (laughing) no, less with the gross. Yeah no, I think we're never going to really get to that point. Because we always as people always have to be training these systems to think like us. So we're never going to have systems that are just autonomically out there without having an intervention here and there to learn the next steps. That's just how it works. >> I always thought the autonomous vehicle, just example, cause it's just so clean. You know, if somebody jumps in front of the car, does the car hit the person, or run into the ditch? >> Where today a person can't make that judgment very fast. They're just going to react. But in computer time, that's like forever. So you can actually make rules. And then people go bananas, well what if it's a grandma on one side and kids on the other? Which do you go? Or what if it's a criminal that just robbed a bank? Do you take him out on purpose? >> Trade off. >> So, you get into a lot of, interesting parameters that have nothing to do necessarily with the mechanics of making that decision. >> And this changes the fundamentals of computing big time too, right? Because a car cannot wait to ping the Cloud to find out, you know, should I break, or should I just run over this person in front of me. So it needs to make that determination right away. And hopefully the right decision which is to break. But on the other hand, all the cars that have this algorithm, together have collective learning, which needs some kind of Cloud computing. So this whole idea of Edge computing will come and replace a lot of what exists today. So see this disruption even behind the scenes on how we architect these systems, it's a fascinating time. >> And then how much of the compute, the store is at the Edge? How much of the computed to store in the Cloud and then depending on the decision, how do you say it, can you do it locally or do you have to send it upstream or break it in pieces. >> I mean if you look at a car of the future, forget car of the future, car of the present like Tesla, that has more compute power than a small data center, at multiple CPU's, lots of RAM, a lot of hard disk. It's a little Cloud that runs on wheels. >> Well it's a little data center that runs on wheels. But, let me ask you a question. And here's the question, we talk about systems that learn, cognitive systems that are constantly learning, and we're training them. How do we ensure that Watson, for example is constantly operating in the interest of the customer, and not the interest of IBM? Now there's a reason I'm asking this question, because at some point in time, I can perceive some other company offering up a similar set of services. I can see those services competing for attention. As we move forward with increasingly complex decisions, with increasingly complex sources of information, what does that say about how these systems are going to interact with each other? >> He always with the loaded questions today. (group laughing) >> It's an excellent question, it's something that I worry about all the time as well. >> Something we worry about with our clients too. >> So, couple of approaches by which this will exist. And to begin with, while we have the big lead in cognitive computing now, there is no hesitation on my part to admit that the ecosystem around us is also fast developing and there will be hefty competition going forward, which is a good thing. 'Cause if you look at how this world is developing, it is developing as API. APIs will fight on their own merits. So it's a very pluggable architecture. If my API is not very good, then it will get replaced by somebody else's API. So that's one aspect. The second aspect is, there is a difference between the provider and the client in terms of who owns the data. We strongly believe from IBM that client owns the data. So we will not go in and do anything crazy with it. We won't even touch it. So we will provide a framework and a cartridge that is very industry specific. Like for example, if Watson has to act as a call center agent for a Telco, we will provide a set of instructions that are applicable to Telco. But, all the learning that Watson does is on top of that clients data. We are not going to take it from one Telco and put it in another Telco. That will stay very local to that Telco. And hopefully that is the way the rest of the industry develops too. That they don't take information from one and provide to another. Even on an anonymous basis, it's a really bad idea to take a clients data and then feed it elsewhere. It has all kinds of ethical and moral consequences, even if it's legal. >> Absolutely. >> And we would encourage clients to take a look at some of the others out there and make sure that that's the arrangement that they have. >> Absolutely, what a great job for an analyst firm, right? But I want to build upon this point, because I heard something very interesting in the keynote, the CDO of IBM, in the keynote this morning. >> He used a term that I've thought about, but never heard before, trust as a service. Are you guys familiar with his use of that term? >> Vijay: Yep. >> Okay, what does trust as a service mean, and how does it play out so that as a consumer of IMB cognitive services, I have a measurable difference in how I trust IBM's cognitive services versus somebody else? >> Some would call that Blockchain. In fact Blockchain has often been called trust as a service. >> Okay, and Blockchain is probably the most physical form of it that we can find at the moment, right? At the (mumbles) where it's open to everybody but then no one brand section can be tabbed by somebody else. But if we extend that concept philosophically, it also includes a lot of the concept about identity. Identity. I as a user today don't have an easy way to identify myself across systems. Like, if I'm behind the firewall I have one identity, if I am outside the firewall I have another identity. But, if you look at the world tomorrow where I have to deal with a zillion APIs, this concept of a consistent identity needs to pass through all of them. It's a very complicated a difficult concept to implement. So that trust as a service, essentially, the light blocking that needs to be an identity service that follows me around that is not restrictive to an IBM system, or a Nautical system or something. >> But at the end of the day, Blockchain's a mechanism. >> Yes. >> Trust in the service sounds like a-- >> It's a transparency is what it is, the more transparency, the more trust. >> It's a way of doing business. >> Yes. >> Sure. >> So is IBM going to be a leader in defining what that means? >> Well look, in all cases, IBM has, we have always strove, what's the right word? Striven, strove, whatever it. >> Strove. >> Strove (laughing)? >> I'll take that anyway. >> Strove, thank you. To be a leader in how we approach everything ethically. I mean, this is truly in our blood, I mean, we are here for our clients. And we aren't trying to just get them to give us all of their data and then go off and use it anywhere. You have to pay attention sometimes, that what you're paying for is exactly what you're getting, because people will try to do those things, and you just need to have a partner that you trust in this. And, I know it's self-serving to say, but we think about data ethics, we think about these things when we talk to our clients, and that's one of the things that we try to bring to the table is that moral, ethical, should you. Just because you can, and we have, just so you know walked away from deals that were very lucrative before, because we didn't feel it was the right thing to do. And we will always, I mean, I know it sounds self-serving, I don't know how to, you won't know until you deal with us, but pay attention, buyer beware. >> You're just Cortnie from IBM, we know what side you're on. (group laughing) It's not a mystery. >> Believe me, if I'm associated with it, it's yeah. >> But you know, it's a great point, because the other kind of ethical thing that comes up a lot with data, is do you have the ethical conversation before you collect that data, and how you're going to be using it. >> Exactly. >> But that's just today. You don't necessarily know what's going to, what and how that might be used tomorrow. >> Well, in other countries. >> That's what gets really tricky. >> Future-proofing is a very interesting concept. For example, vast majority of our analytics conversation today is around structure and security, those kinds of terms. But, where is the vast majority of data sitting today? It is in video and sound files, which okay. >> Cortnie: That's even more scary. >> It is significantly scary because the technology to get insights out of this is still developing. So all these things like cluster and identity and security and so on, and quantum computing for that matter. All these things need to think about the future. But some arbitrary form of data can come hit you and all these principles of ethics and legality and all should apply. It's a very non-trivial challenge. >> But I do see that some countries are starting to develop their own protections like the General Data Protection Regulation is going to be a huge driver of forced ethics. >> And some countries are not. >> And some countries are not. I mean, it's just like, cognitive is just like anything else. When the car was developed, I'm sure people said, hey everybody's going to go out killing people with their cars now, you know? But it's the same thing, you can use it as a mode of transportation, or you can do something evil with it. It really is going to be governed by the societal norms that you live in, as to how much you're going to get away with. And transparency is our friend, so the more transparent we can be, things like Blockchain, other enablers like that that allow you to see what's going on, and have multiple copies, the better. >> All right, well Cortnie, Vijay, great topics. And that's why gatherings like this are so important to be with your peer group, you know, to talk about these much deeper issues that are really kind of tangental to technology but really to the bigger picture. So, keep getting out on the fringe to help us figure this stuff out. >> I appreciate it, thanks for having us. >> Thanks. >> Pleasure. All right, I'm Jeff Frick with Peter Burris. We're at the Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit 2017. Thanks for watching. (upbeat music) (dramatic music)

>> Announcer: Live from Fisherman's Wharf in San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, it's 170 people here, all high-level CXOs learning about data, and it's part of an ongoing series that IBM is doing around chief data officers and data, part of a big initiative with Cognitive and Watson, I'm sure you've heard all about it, Watson TV if nothing else, if not going to the shows, and we're really excited to have the drivers behind this activity with us today, also Peter Burris from Wikibon, chief strategy officer, but we've got Caitlin Lepech who's really driving this whole show. She is the Communications and Client Engagement Executive, IBM Global Chief Data Office. That's a mouthful, she's got a really big card. And Cortnie Abercrombie, who I'm thrilled to see you, seen her many, many times, I'm sure, at the MIT CDOIQ, so she's been playing in this space for a long time. She is a Cognitive and Analytics Offerings leader, IBM Global Business. So first off, welcome. >> Thank you, great to be here. >> Thanks, always a pleasure on theCUBE. It's so comfortable, I forget you guys aren't just buddies hanging out. >> Before we jump into it, let's talk about kind of what is this series? Because it's not World of Watson, it's not InterConnect, it's a much smaller, more intimate event, but you're having a series of them, and in the keynote is a lot of talk about what's coming next and what's coming in October, so I don't know. >> Let me let you start, because this was originally Cortnie's program. >> This was a long time ago. >> 2014. >> Yeah, 2014, the role was just starting, and I was tasked with can we identify and start to build relationships with this new line of business role that's cropping up everywhere. And at that time there were only 50 chief data officers worldwide. And so I-- >> Jeff: 50? In 2014. >> 50, and I can tell you that earnestly because I knew every single of them. >> More than that here today. >> I made it a point of my career over the last three years to get to know every single chief data officer as they took their jobs. I would literally, well, hopefully I'm not a chief data officer stalker, but I basically was calling them once I'd see them on LinkedIn, or if I saw a press announcement, I would call them up and say, "You've got a tough job. "Let me help connect you with each other "and share best practices." And before we knew, it became a whole summit. It became, there were so many always asking to be connected to each other, and how do we share best practices, and what do you guys know as IBM because you're always working with different clients on this stuff? >> And Cortnie and I first started working in 2014, we wrote IBM's first paper on chief data officers, and at the time, there was a lot of skepticism within our organization, why spend the time with data officers? There's other C-suite roles you may want to focus on instead. But we were saying just the rise of data, external data, unstructured data, lot of opportunity to rise in the role, and so, I think we're seeing it reflected in the numbers. Again, first summit three years ago, 30 participants. We have 170 data executives, clients joining us today and tomorrow. >> And six papers later, and we're goin' strong still. >> And six papers later. >> Exactly, exactly. >> Before we jump into the details, some of the really top-level stuff that, again, you talked about with John and David, MIT CDOIQ, in terms of reporting structure. Where do CDOs report? What exactly are they responsible for? You covered some of that earlier in the keynote, I wonder if you can review some of those findings. >> Yeah, that was amazing >> Sure, I can share that, and then, have Cortnie add. So, we find about a third report directly to the CEO, a third report through the CIO's office, sort of the traditional relationship with CIOs, and then, a third, and what we see growing quite a bit, are CXOs, so functional or business line function. Originally, traditionally it was really a spin-off of CIO, a lot of technical folks coming up, and we're seeing more and more the shift to business expertise, and the focus on making sure we're demonstrating the business impact these data programs are driving for our organization. >> Yeah, it kind of started more as a data governance type of role, and so, it was born out of IT to some degree because, but IT was having problems with getting the line of business leaders to come to the table, and we knew that there had to be a shift over to the business leaders to get them to come and share their domain expertise because as every chief data officer will tell you, you can't have lineage or know anything about all of this great data unless you have the experts who have been sitting there creating all of that data through their processes. And so, that's kind of how we came to have this line of business type of function. >> And Inderpal really talked about, in terms of the strategy, if you don't start from the business strategy-- >> Inderpal? >> Yeah, on the keynote. >> Peter: Yeah, yeah, yeah, yeah. >> You are really in big risk of the boiling the ocean problem. I mean, you can't just come at it from the data first. You really have to come at it from the business problem first. >> It was interesting, so Inderpal was one of our clients as a CEO three times prior to rejoining IBM a year ago, and so, Cortnie and I have known him-- >> Express Scripts, Cambia. >> Exactly, we've interviewed him, featured him in our research prior, too, so when he joined IBM in December a year ago, his first task was data strategy. And where we see a lot of our clients struggle is they make data strategy an 18-month, 24-month process, getting the strategy mapped out and implemented. And we say, "You don't have the time for it." You don't have 18 months to come to data, to come to a data strategy and get by and get it implemented. >> Nail something right away. >> Exactly. >> Get it in the door, start showing some results right away. You cannot wait, or your line of business people will just, you know. >> What is a data strategy? >> Sure, so I can say what we've done internally, and then, I know you've worked with a lot of clients on what they're building. For us internally, it started with the value proposition of the data office, and so, we got very clear on what that was, and it was the ability to take internal, external data, structured, unstructured, and pull that together. If I can summarize it, it's drive to cognitive business, and it's infusing cognition across all of our business processes internally. And then, we identified all of these use cases that'll help accelerate, and the catalyst that will get us there faster. And so, Client 360, product catalog, et cetera. We took data strategy, got buy-in at the highest levels at our organization, senior vice president level, and then, once we had that support and mandate from the top, went to the implementation piece. It was moving very quickly to specify, for us, it's about transforming to cognitive business. That then guides what's critical data and critical use cases for us. >> Before you answer, before you get into it, so is a data strategy a means to cognitive, or is it an end in itself? >> I would say it, to be most effective, it's a succinct, one-page description of how you're going to get to that end. And so, we always say-- >> Peter: Of cognitive? >> Exactly, for us, it's cognitive. So, we always ask very simple question, how is your company going to make money? Not today, what's its monetization strategy for the future? For us, it's coming to cognitive business. I have a lot of clients that say, "We're product-centric. "We want to become customer, client-centric. "That's our key piece there." So, it's that key at the highest level for us becoming a cognitive business. >> Well, and data strategies are as big or as small as you want them to be, quite frankly. They're better when they have a larger vision, but let's just face it, some companies have a crisis going on, and they need to know, what's my data strategy to get myself through this crisis and into the next step so that I don't become the person whose cheese moved overnight. Am I giving myself away? Do you all know the cheese, you know, Who Moved My Cheese? >> Every time the new iOS comes up, my wife's like-- >> I don't know if the younger people don't know that term, I don't think. >> Ah, but who cares about them? >> Who cares about the millenials? I do, I love the millenials. But yes, cheese, you don't want your cheese to move overnight. >> But the reason I ask the question, and the reason why I think it's important is because strategy is many things to many people, but anybody who has a view on strategy ultimately concludes that the strategic process is what's important. It's the process of creating consensus amongst planners, executives, financial people about what we're going to do. And so, the concept of a data strategy has to be, I presume, as crucial to getting the organization to build a consensus about the role the data's going to play in business. >> Absolutely. >> And that is the hardest. That is the hardest job. Everybody thinks of a data officer as being a technical, highly technical person, when in fact, the best thing you can be as a chief data officer is political, very, very adept at politics and understanding what drives the business forward and how to bring results that the CEO will get behind and that the C-suite table will get behind. >> And by politics here you mean influencing others to get on board and participate in this process? >> Even just understanding, sometimes leaders of business don't articulate very well in terms of data and analytics, what is it that they actually need to accomplish to get to their end goal, and you find them kind of stammering when it comes to, "Well, I don't really know "how you as Inderpal Bhandari can help me, "but here's what I've got to do." And it's a crisis usually. "I've got to get this done, "and I've got to make these numbers by this date. "How can you help me do that?" And that's when the chief data officer kicks into gear and is very creative and actually brings a whole new mindset to the person to understand their business and really dive in and understand, "Okay, this is how "we're going to help you meet that sales number," or, "This is how we're going to help you "get the new revenue growth." >> In certain respects, there's a business strategy, and then, you have to resource the business strategy. And the data strategy then is how are we going to use data as a resource to achieve our business strategy? >> Cortnie: Yes. >> So, let me test something. The way that we at SiliconANGLE, Wikibon have defined digital business is that a business, a digital business uses data as an asset to differentially create and keep customers. >> Caitlin: Right. >> Does that work for you guys? >> Cortnie: Yeah, sure. >> It's focused on, and therefore, you can look at a business and say is it more or less digital based on how, whether it's more or less focused on data as an asset and as a resource that's going to differentiate how it's business behaves and what it does for customers. >> Cortnie: And it goes from the front office all the way to the back. >> Yes, because it's not just, but that's what, create and keep, I'm borrowing from Peter Drucker, right. Peter Drucker said the goal of business is to create and keep customers. >> Yeah, that's right. Absolutely, at the end of the day-- >> He included front end and back end. >> You got to make money and you got to have customers. >> Exactly. >> You got to have customers to make the money. >> So data becomes a de-differentiating asset in the digital business, and increasingly, digital is becoming the differentiating approach in all business. >> I would argue it's not the data, because everybody's drowning in data, it's how you use the data and how creative you can be to come up with the methods that you're going to employ. And I'll give you an example. Here's just an example that I've been using with retailers lately. I can look at all kinds of digital exhaust, that's what we call it these days. Let's say you have a personal digital shopping experience that you're creating for these new millenials, we'll go with that example, because shoppers, 'cause retailers really do need to get more millenials in the door. They're used to their Amazon.coms and their online shopping, so they're trying to get more of them in the door. When you start to combine all of that data that's underlying all of these cool things that you're doing, so personal shopping, thumbs up, thumb down, you like this dress, you like that cut, you like these heels? Yeah, yes, yes or no, yes or no. I'm getting all this rich data that I'm building with my app, 'cause you got to be opted in, no violating privacy here, but you're opting in all the way along, and we're building and building, and so, we even have, for us, we have this Metro Pulse retail asset that we use that actually has hyperlocal information. So, you could, knowing that millenials like, for example, food trucks, we all like food trucks, let's just face it, but millenials really love food trucks. You could even, if you are a retailer, you could even provide a fashion truck directly to their location outside their office equipped with things that you know they like because you've mined that digital exhaust that's coming off the personal digital shopping experience, and you've understood how they like to pair up what they've got, so you're doing a next best action type of thing where you're cross-selling, up-selling. And now, you bring it into the actual real world for them, and you take it straight to them. That's a new experience, that's a new millennial experience for retail. But it's how creative you are with all that data, 'cause you could have just sat there before and done nothing about that. You could have just looked at it and said, "Well, let's run some reports, "let's look at a dashboard." But unless you actually have someone creative enough, and usually it's a pairing of data scientist, chief data officers, digital officers all working together who come up with these great ideas, and it's all based, if you go back to what my example was, that example is how do I create a new experience that will get millenials through my doors, or at least get them buying from me in a different way. If you think about that was the goal, but how I combined it was data, a digital process, and then, I put it together in a brand new way to take action on it. That's how you get somewhere. >> Let me see if I can summarize very quickly. And again, just as an also test, 'cause this is the way we're looking at it as well, that there's human beings operate and businesses operate in an analog world, so the first test is to take analog data and turn it into digital data. IOT does that. >> Cortnie: Otherwise, there's not digital exhaust. >> Otherwise, there's no digital anything. >> Cortnie: That's right. >> And we call it IOT and P, Internet of Things and People, because of the people element is so crucial in this process. Then we have analytics, big data, that's taking those data streams and turning them into models that have suggestions and predictions about what might be the right way to go about doing things, and then there's these systems of action, or what we've been calling systems of enactment, but we're going to lose that battle, it's probably going to be called systems of action that then take and transduce the output of the model back into the real world, and that's going to be a combination of digital and physical. >> And robotic process automation. We won't even introduce that yet. >> Which is all great. >> But that's fun. >> That's going to be in October. >> But I really like the example that you gave of the fashion truck because people don't look at a truck and say, "Oh, that's digital business." >> Cortnie: Right, but it manifested in that. >> But it absolutely is digital business because the data allows you to bring a more personal experience >> Understand it, that's right. >> right there at that moment, and it's virtually impossible to even conceive of how you can make money doing that unless you're able to intercept that person with that ensemble in a way that makes both parties happy. >> And wouldn't that be cheaper than having big, huge retail stores? Someone's going to take me up on that. Retailers are going to take me up on this, I'm telling you. >> But I think the other part is-- >> Right next to the taco truck. >> There could be other trucks in that, a much cleaner truck, and this and that. But one thing, Cortnie, you talk about and you got to still have a hypothesis, I think of the early false promises of big data and Hadoop, just that you throw all this stuff in, and the answer just comes out. That just isn't the way. You've got to be creative, and you have to have a hypothesis to test, and I'm just curious from your experience, how ready are people to take in the external data sources and the unstructured data sources and start to incorporate that in with the proprietary data, 'cause that's a really important piece of the puzzle? It's very different now. >> I think they're ready to do it, it depends on who in the business you are working with. Digital offices, marketing offices, merchandising offices, medical offices, they're very interested in how can we do this, but they don't know what they need. They need guidance from a data officer or a data science head, or something like this, because it's all about the creativity of what can I bring together to actually reach that patient diagnostic, that whatever the case may be, the right fashion truck mix, or whatever. Taco Tuesday. >> So, does somebody from the chief data office, if you will, you know, get assigned to, you're assigned to marketing and you're assigned to finance, and you're assigned to sales. >> I have somebody assigned to us. >> To put this in-- >> Caitlin: Exactly, exactly. >> To put this in kind of a common or more modern parlance, there's a design element. You have to have use case design, and what are we going, how are we going to get better at designing use cases so we can go off and explore the role that data is going to play, how we're going to combine it with other things, and to your point, and it's a great point, how that turns into a new business activity. >> And if I can connect two points there, the single biggest question I get from clients is how do you prioritize your use cases. >> Oh, gosh, yeah. >> How can you help me select where I'm going to have the biggest impact? And it goes, I think my thing's falling again. (laughing) >> Jeff: It's nice and quiet in here. >> Okay, good. It goes back to what you were saying about data strategy. We say what's your data strategy? What's your overarching mission of the organization? For us, it's becoming cognitive business, so for us, it's selecting projects where we can infuse cognition the quickest way, so Client 360, for example. We'll often say what's your strategy, and that guides your prioritization. That's the question we get the most, what use case do I select? Where am I going to have the most impact for the business, and that's where you have to work with close partnership with the business. >> But is it the most impact, which just sounds scary, and you could get in analysis paralysis, or where can I show some impact the easiest or the fastest? >> You're going to delineate both, right? >> Exactly. >> Inderpal's got his shortlist, and he's got his long list. Here's the long term that we need to be focused on to make sure that we are becoming holistically a cognitive company so that we can be flexible and agile in this marketplace and respond to all kinds of different situations, whether they're HR and we need more skills and talent, 'cause let's face it, we're a technology company who's rapidly evolving to fit with the marketplace, or whether it's just good old-fashioned we need more consultants. Whatever the case may be. >> Always, always. >> Yes! >> I worked my business in. >> More consultants! >> Alright, we could go, we could go and go and go, but we're running out of time, we had a full slate. >> Caitlin: We just started. >> I know. >> I agree, we're just starting this convers, I started a whole other conversation to him. We haven't even hit the robotics yet. >> We need to keep going, guys. >> Get control. >> Cortnie: Less coffee for us. >> What do people think about when they think about this series? What should they look forward to, what's the next one for the people that didn't make it here today, where should they go on the calendar and book in their calendars? >> So, I'll speak to the summits first. It's great, we do Spring in San Francisco. We'll come back, reconvene in Boston in fall, so that'll be September, October frame. I'm seeing two other trends, which I'm quite excited about, we're also looking at more industry-specific CDO summits. So, for those of our friends that are in government sectors, we'll be in June 6th and 7th at a government CDO summit in D.C., so we're starting to see more of the industry-specific, as well as global, so we just ran our first in Rio, Brazil for that area. We're working on a South Africa summit. >> Cortnie: I know, right. >> We actually have a CDO here with us that traveled from South Africa from a bank to see our summit here and hoping to take some of that back. >> We have several from Peru and Mexico and Chile, so yeah. >> We'll continue to do our two flagship North America-based summits, but I'm seeing a lot of growth out in our geographies, which is fantastic. >> And it was interesting, too, in your keynote talking about people's request for more networking time. You know, it is really a sharing of best practices amongst peers, and that cannot be overstated. >> Well, it's community. A community is building. >> It really is. >> It's a family, it really is. >> We joke, this is a reunion. >> We all come in and hug, I don't know if you noticed, but we're all hugging each other. >> Everybody likes to hug their own team. It's a CUBE thing, too. >> It's like therapy. It's like data therapy, that's what it is. >> Alright, well, Caitlin, Cortnie, again, thanks for having us, congratulations on a great event, and I'm sure it's going to be a super productive day. >> Thank you so much. Pleasure. >> Thanks. >> Jeff Frick with Peter Burris, you're watchin' theCUBE from the IBM Chief Data Officer Summit Spring 2017 San Francisco, thanks for watching. (electronic keyboard music)

>> (lively music) (lively music) >> [Narrator] Live, from Fisherman's Wharf in San Francisco, it's theCUBE. Covering IBM Chief Data Officers Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. >> Jeff Flick here with theCUBE alongside Peter Burris, our chief research officer from Wikibon. We're at the IBM Chief Data Officers Strategy Summit Sprint 2017. It's a mouthful but it's an important event. There's 170 plus CDO's here sharing information, really binding their community, sharing best practices and of course, IBM is sharing their journey which is pretty interesting cause they're taking their own transformational journey, writing up a blue print and going to deliver it in October. Drinking their own champagne as they like to say. We're really excited to have CUBE alumni, many time visitor Seth Dobrin. He is the chief data officer of IBM Analytics. Seth welcome. >> Yeah, thanks for having me again. >> Absolutely, so again, these events are interesting. There's a series of them. They're in multiple cities. They're, now, going to go to multiple countries. And it's really intended, I believe, or tell me, it's a learning experience in this great, little, tight community for this, very specific, role. >> Yeah, so these events are, actually, really good. I've been participating in these since the second one. >> So, since the first one in Boston about 2 1/2 years ago. They're really great events because it's an opportunity for CDO's or de facto CDO's in organizations to have in depth conversations with their peers about struggles, challenges, successes. >> It really helps to, kind of, one piece says you can benchmark yourself, how are we doing as an organization and how am I doing as a CDO and where do I fit within the bigger community or within your industry? >> How have you seen it evolve? Not just the role, per say, but some of the specific challenges or implementation issues that these people have had in trying to deliver a value inside their company. >> Yeah, so when they started, three years ago, there, really, were not a whole lot of tools that CDO's could use to solve your data science problems, to solve your cloud problems, to solve your governance problem. We're starting to get to a place in the world where there are actual tools out there that help you do these things. So you don't struggle to figure out how do I find talent that can build the tools internally and deploy em. It's now getting the talent to, actually, start implementing things that already exist. >> Is the CDO job well enough defined at this point in time? Do you think that you can, actually, start thinking about tools as opposed to the challenges of the business? In other words, is every CDO different or are the practices, now, becoming a little bit more and the conventions becoming a little bit better understood and stable so you >> can outdo a better job of practicing the CDO role? >> Yeah, I think today, the CDO role is still very ill defined. It's, really, industry by industry and company by company even, CDO's play different roles within each of those. I've only been with IBM for the last four months. I've been spending a lot of that time talking to our clients. Financial services, manufacturing, all over the board and really, the CDO's in those people are all industry specific, they're in different places and even company by company, they're in different places. It really depends on where the company's are on their data and digital journey what role the CDO has. Is it really a defensive play to make sure we're not going to violate any regulations or is it an offensive play and how do we disrupt our industry instead of being disrupted because, really, every industry is in a place where you're either going to be the disruptor or you're going to be the distruptee. And so, that's the scope, the breadth of, I think, the role the CDO plays. >> Do you see it all eventually converging to a common point? Cause, obviously, the CFO and the CMO, those are pretty good at standardized functions over time that wasn't always that way. >> Well, I sure hope it does. I think CDO's are becoming pretty pervasive. I think you're starting to see, when this started, the first one I went to, there were, literally, 35 people >> and only 1/2 of then were called CDO's. We've progressed now where we've got 100 people over 170 some odd people that are here that are CDO's. Most of them have the CDO title even. >> The fact that that title is much more pervasive says that we're heading that way. I think industry by industry you'll start seeing similar responsibilities for CDO's but I don't think you'll start seeing it across the board like a CFO where a CFO does the same thing regardless of the industry. I don't think you'll see that in a CDO for quite some time. >> Well one of the things, certainly, we find interesting is that the role the data's playing in business involvement. And it, partly, the CDO's job is to explain to his or her peers, at that chief level, how using data is going to change the way that they do things from the way that they're function works. And that's part of the reason, I think, why you're suggesting that on a vertical basis that the CDO's job is different. Cause different industries are being impacted themselves by data differently. So as you think about the job that you're performing and the job the CDO's are performing, what part is technical? What part is organizational? What part is political? Et cetera. >> I think a lot of the role of a CDO is political. Most of the CDO's that I know have built their careers on stomping on people's toes. How do I drive change by infringing on other people's turf effectively? >> Peter: In a nice way. >> Well, it depends. In the appropriate way, right? >> Peter: In a productive way. >> In the appropriate way. It could be nice, it could not be nice >> depending on the politics and the culture of the organization. I think a lot of the role of a CDO, it's, almost, like chief disruption officer as much as it is data officer. I think it's a lot about using data >> but, I think, more importantly, it's about using analytics. >> So how do you use analytics to, actually, drive insights and next best action from the data? I think just looking at data and still using gut based on data is not good enough. For chief data officers to really have an impact and really be successful, it's how do you use analytics on that data whether it's machine learning, deep learning, operations research, to really change how the business operates? Because as chief data officers, you need to justify your existence a lot. The way you do that is you tie real value to decisions that your company is making. The data and the analytics that are needed for those decisions. That's, really, the role of a CDO in my mind is, how do I tie value of data based on decisions and how do I use analytics to make those decisions more effective? >> Were the early days more defensive and now, shifting to offensive? It sounds like it. That's a typical case where you use technology, initially, often to save money before you start to use it to create new value, new revenue streams. Is that consistent here? By answering that, you say they have to defend themselves sometimes when you would think it'd be patently obvious >> that if you're not getting on a data software defined train, you're going to be left behind. >> I think there's two types. There's CDO's that are there to protect freedom to operate and that's what I call, think of, as defensive. And then, there's offensive CDO's and that's really bringing more value out of existing processes. In my mind, every company is on this digital transformation journey and there's two steps to it. >> One is this data science transformation which is where you use data and analytics to accelerate your businesses current goals. How do I use data analytics to accelerate my businesses march towards it's current goals? Then there's the second stage which is the true digital transformation which is how do I use data and analytics to, fundamentally, change how my industry and my company operates? So, actually, changing the goals of the industry. For example, moving from selling physical products to selling outcomes. You can't do that until you've done this data transformation till you've started operating on data, till you've started operating on analytics. You can't sell outcomes until you've done that. It's this two step journey. >> You said this a couple of times and I want to test an idea on you and see what you think. Industry classifications are tied back to assets. So, you look at industries and they have common organization of assets, right? >> Seth: Yep. Data, as an asset, has very, very, different attributes because it can be shared. It's not scarce, it's something that can be shared. As we become more digital and as this notion of data science or analytics, the world of data places in asset and analytics plays as assets becomes more pervasive, does that start to change the notion of industry because, now, by using data differently, you can use other assets and deploy other assets differently? >> Yeah, I think it, fundamentally, changes how business operates and even how businesses are measured because you hit on this point pretty well which is data is reusable. And so as I build these data or digital assets, the quality of a company's margins should change. For every dollar of revenue I generate. Maybe today I generate 15% profit. As you start moving to a digital being a more digital company built on data and analytics, that percent of profit based on revenue should go up. Because these assets that you're building to reuse them is extremely cheap. I don't have to build another factory to scale up, I buy a little bit more compute time. Or I develop a new machine learning model. And so it's very scalable unlike building physical products. I think you will see a fundamental shift in how businesses are measured. What standards that investors hold businesses to. I think, another good point is, a mind set shift that needs to happen for companies is that companies need to stop thinking of data as a digital dropping of applications and start thinking of it as an asset. Cause data has value. It's no longer just something that's dropped on the table from applications that I built. It's we are building to, fundamentally, create data to drive analytics, to generate value, to build new revenue for a company that didn't exist today. >> Well the thing that changes the least, ultimately, is the customer. And so it suggests that companies that have customers can use data to get in a new product, or new service domains faster than companies who don't think about data as an asset and are locked into how can I take my core set up, my organization, >> my plant, my machinery and keep stamping out something that's common to it or similar to it. So this notion of customer becomes the driver, increasingly, of what industry you're in or what activities you perform. Does that make sense? >> I think everything needs to be driven from the prospective of the customer. As you become a data driven or a digital company, everything needs to be shifted in that organization from the perspective of the customer. Even companies that are B to B. B to B companies need to start thinking about what is the ultimate end user. How are they going to use what I'm building, for my business partner, my B to B partner, >> what is their, actual, human being that's sitting down using it, how are they going to use it? How are they going to interact with it? It really, fundamentally, changes how businesses approach B to B relationships. It, fundamentally, changes the type of information that, if I'm a B to B company, how do I get more information about the end users and how do I connect? Even if I don't come in direct contact with them, how do I understand how they're using my product better. That's a fundamental just like you need to stop thinking of data as a digital dropping. Every question needs to come from how is the end user, ultimately, going to use this? How do I better deploy that? >> So the utility that the customer gets capturing data about the use of that, the generation of that utility and drive it all the way back. Does the CDO have to take a more explicit role in getting people to see that? >> Yes, absolutely. I think that's part of the cultural shift that needs to happen. >> Peter: So how does the CDO do that? >> I think every question needs to start with what impact does this have on the end user? >> What is the customer perspective on this? Really starting to think about. >> I'm sorry for interrupting. I'd turn that around. I would say it's what impact does the customer have on us? Because you don't know unless you capture data. That notion of the customer impact measurement >> which we heard last time, the measureability and then drive that all the way back. That seems like it's going to become an increasingly, a central design point. >> Yeah, it's a loop and you got to start using these new methodologies that are out there. These design thinking methodologies. It's not just about building an Uber app. It's not just about building an app. It's about how do I, fundamentally, shift my business to this design thinking methodology to start thinking cause that's what design thinking is all about. It's all about how is this going to be used? And every aspect of your business you need to approach that way. >> Seth, I'm afraid they're going to put us in the chaffing dish here if we don't get off soon. >> Seth: I think so too, yeah. >> So we're going to leave it there. It's great to see you again and we look forward to seeing you at the next one of these things. >> Yeah, thanks so much. >> He's Seth, he's Peter, I'm Jeff. You're watching theCUBE from the IBM Chief Data Officers Strategy Summit Spring 2017, I got it all in in a mouthful. We'll be back after lunch which they're >> setting up right now. (laughs) (lively music) (drum beats)

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day Volante and Stew Minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. Stillman and I have pleased to have Jean Kolker on a Cuba lem. Uh, he's IBM vice president and chief data officer of the Global Technology Services division. And Seth Dobrin who's the Director of Digital Strategies. That Monsanto. You may have seen them in the news lately. Gentlemen. Welcome to the Cube, Jean. Welcome back. Good to see you guys again. Thanks. Thank you. So let's start with the customer. Seth, Let's, uh, tell us about what you're doing here, and then we'll get into your role. >> Yes. So, you know, the CDO summit has been going on for a couple of years now, and I've been lucky enoughto be participating for a couple of a year and 1/2 or so, Um, and you know, really, the nice thing about the summit is is the interaction with piers, um, and the interaction and networking with people who are facing similar challenges from a similar perspective. >> Yes, kind of a relatively new Roland topic, one that's evolved, Gene. We talked about this before, but now you've come from industry into, ah, non regulated environment. Now what's happened like >> so I think the deal is that way. We're developing some approaches, and we get in some successes in regulated environment. Right? And now I feel with And we were being client off IBM for years, right? Using their technology's approaches. Right? So and now I feel it's time for me personally to move on something different and tried to serve our power. I mean, IBM clients respected off in this striking from healthcare, but their approaches, you know, and what IBM can do for clients go across the different industries, right? And doing it. That skill that's very beneficial, I think, for >> clients. So Monsanto obviously guys do a lot of stuff in the physical world. Yeah, you're the head of digital strategy. So what does that entail? What is Monte Santo doing for digital? >> Yes, so, you know, for as head of digital strategies for Monsanto, really? My role is to number one. Help Monsanto internally reposition itself so that we behave and act like a digital companies, so leveraging data and analytics and also the cultural shifts associated with being more digital, which is that whole kind like you start out this conversation with the whole customer first approach. So what is the real impact toe? What we're doing to our customers on driving that and then based on on those things, how can we create new business opportunities for us as a company? Um, and how can we even create new adjacent markets or new revenues in adjacent areas based on technologies and things we already have existing within the company? >> It was the scope of analytics, customer engagement of digital experiences, all of the above, so that the scope is >> really looking at our portfolio across the gamut on DH, seeing how we can better serve our customers and society leveraging what we're doing today. So it's really leveraging the re use factor of the whole digital concept. Right? So we have analytics for geospatial, right? Big part of agriculture is geospatial. Are there other adjacent areas that we could apply some of that technology? Some of that learning? Can we monetize those data? We monetize the the outputs of those models based on that, Or is there just a whole new way of doing business as a company? Because we're in this digital era >> this way? Talked about a lot of the companies that have CEOs today are highly regulated. What are you learning from them? What's what's different? Kind of a new organization. You know, it might be an opportunity for you that they don't have. And, you know, do you have a CDO yet or is that something you're planning on having? >> Yes, So we don't have a CDO We do have someone acts as an essential. he's a defacto CEO, he has all of the data organizations on his team. Um, it's very recent for Monsanto, Um, and and so I think, you know, in terms of from the regular, what can we learn from, you know, there there are. It's about half financial people have non financial people, are half heavily regulated industries, and I think, you know, on the surface you would. You would think that, you know, there was not a lot of overlap, but I think the level of rigor that needs to go into governance in a financial institution that same thought process. Khun really be used as a way Teo really enable Maur R and D. Mohr you know, growth centered companies to be able to use data more broadly and so thinking of governance not as as a roadblock or inhibitor, but really thinking about governance is an enabler. How does it enable us to be more agile as it enable us to beam or innovative? Right? If if people in the company there's data that people could get access to by unknown process of known condition, right, good, bad, ugly. As long as people know they can do things more quickly because the data is there, it's available. It's curated. And if they shouldn't have access it under their current situation, what do they need to do to be able to access that data? Right. So if I would need If I'm a data scientist and I want to access data about my customers, what can I can't? What can and can't I do with that data? Number one doesn't have to be DEA Nana Mayes, right? Or if I want to access in, it's current form. What steps do I need to go through? What types of approval do I need to do to do to access that data? So it's really about removing roadblocks through governance instead of putting him in place. >> Gina, I'm curious. You know, we've been digging into you know, IBM has a very multifaceted role here. You know how much of this is platforms? How much of it is? You know, education and services. How much of it is, you know, being part of the data that your your customers you're using? >> Uh so I think actually, that different approaches to this issues. My take is basically we need Teo. I think that with even cognitive here, right and data is new natural resource worldwide, right? So data service, cognitive za za service. I think this is where you know IBM is coming from. And the BM is, you know, tradition. It was not like that, but it's under a lot of transformation as we speak. A lot of new people coming in a lot off innovation happening as we speak along. This line's off new times because cognitive with something, really you right, and it's just getting started. Data's a service is really new. It's just getting started. So there's a lot to do. And I think my role specifically global technology services is you know, ah, largest by having your union that IBM, you're 30 plus 1,000,000,000 answered You okay? And we support a lot of different industries basically going across all different types of industries how to transition from offerings to new business offerings, service, integrated services. I think that's the key for us. >> Just curious, you know? Where's Monsanto with kind of the adoption of cognitive, You know what? Where are you in that journey? >> Um, so we are actually a fairly advanced in the journey In terms of using analytics. I wouldn't say that we're using cognitive per se. Um, we do use a lot of machine learning. We have some applications that on the back end run on a I So some form of artificial or formal artificial intelligence, that machine learning. Um, we haven't really gotten into what, you know, what? IBM defined his cognitive in terms of systems that you can interact with in a natural, normal course of doing voice on DH that you spend a whole lot of time constantly teaching. But we do use like I said, artificial intelligence. >> Jean I'm interested in the organizational aspects. So we have Inderpal on before. He's the global CDO, your divisional CDO you've got a matrix into your leadership within the Global Services division as well as into the chief date officer for all of IBM. Okay, Sounds sounds reasonable. He laid out for us a really excellent sort of set of a framework, if you will. This is interval. Yeah, I understand your data strategy. Identify your data store says, make those data sources trusted. And then those air sequential activities. And in parallel, uh, you have to partner with line of business. And then you got to get into the human resource planning and development piece that has to start right away. So that's the framework. Sensible framework. A lot of thought, I'm sure, went into it and a lot of depth and meaning behind it. How does that framework translate into the division? Is it's sort of a plug and play and or is there their divisional goals that are create dissonance? Can you >> basically, you know, I'm only 100 plus days in my journey with an IBM right? But I can feel that the global technology services is transforming itself into integrated services business. Okay, so it's thiss framework you just described is very applicable to this, right? So basically what we're trying to do, we're trying to become I mean, it was the case before for many industries, for many of our clients. But we I want to transform ourselves into trusted broker. So what they need to do and this framework help is helping tremendously, because again, there's things we can do in concert, you know, one after another, right to control other and things we can do in parallel. So we trying those things to be put on the agenda for our global technology services, okay. And and this is new for them in some respects. But some respects it's kind of what they were doing before, but with new emphasis on data's A service cognitive as a service, you know, major thing for one of the major things for global technology services delivery. So cognitive delivery. That's kind of new type off business offerings which we need to work on how to make it truly, you know, once a sense, you know, automated another sense, you know, cognitive and deliver to our clients some you value and on value compared to what was done up until recently. What >> do you mean by cognitive delivery? Explained that. >> Yeah, so basically in in plain English. So what's right now happening? Usually when you have a large systems  computer IT system, which are basically supporting lot of in this is a lot of organizations corporations, right? You know, it's really done like this. So it's people run technology assistant, okay? And you know what Of decisions off course being made by people, But some of the decisions can be, you know, simple decisions. Right? Decisions, which can be automated, can standardize, normalize can be done now by technology, okay and people going to be used for more complex decisions, right? It's basically you're going toe. It turned from people around technology assisted toa technology to technology around people assisted. OK, that's very different. Very proposition, right? So, again, it's not about eliminating jobs, it's very different. It's taken off, you know, routine and automata ble part off the business right to technology and given options and, you know, basically options to choose for more complex decision making to people. That's kind of I would say approach. >> It's about scale and the scale to, of course, IBM. When when Gerstner made the decision, Tio so organized as a services company, IBM came became a global leader, if not the global leader but a services business. Hard to scale. You could scare with bodies, and the bigger it gets, the more complicated it gets, the more expensive it gets. So you saying, If I understand correctly, the IBM is using cognitive and software essentially to scale its services business where possible, assisted by humans. >> So that's exactly the deal. So and this is very different. Very proposition, toe say, compared what was happening recently or earlier? Always. You know other. You know, players. We're not building your shiny and much more powerful and cognitive, you know, empowered mouse trap. No, we're trying to become trusted broker, OK, and how to do that at scale. That's an open, interesting question, but we think that this transition from you know people around technology assisted Teo technology around people assisted. That's the way to go. >> So what does that mean to you? How does that resonate? >> Yeah, you know, I think it brings up a good point actually, you know, if you think of the whole litany of the scope of of analytics, you have everything from kind of describing what happened in the past All that to cognitive. Um, and I think you need to I understand the power of each of those and what they shouldn't should be used for. A lot of people talk. You talk. People talk a lot about predictive analytics, right? And when you hear predictive analytics, that's really where you start doing things that fully automate processes that really enable you to replace decisions that people make right, I think. But those air mohr transactional type decisions, right? More binary type decisions. As you get into things where you can apply binary or I'm sorry, you can apply cognitive. You're moving away from those mohr binary decisions. There's more transactional decisions, and you're moving mohr towards a situation where, yes, the system, the silicon brain right, is giving you some advice on the types of decisions that you should make, based on the amount of information that it could absorb that you can't even fathom absorbing. But they're still needs really some human judgment involved, right? Some some understanding of the contacts outside of what? The computer, Khun Gay. And I think that's really where something like cognitive comes in. And so you talk about, you know, in this in this move to have, you know, computer run, human assisted right. There's a whole lot of descriptive and predictive and even prescriptive analytics that are going on before you get to that cognitive decision but enables the people to make more value added decisions, right? So really enabling the people to truly add value toe. What the data and the analytics have said instead of thinking about it, is replacing people because you're never going to replace you. Never gonna replace people. You know, I think I've heard people at some of these conferences talking about, Well, no cognitive and a I is going to get rid of data scientist. I don't I don't buy that. I think it's really gonna enable data scientist to do more valuable, more incredible things >> than they could do today way. Talked about this a lot to do. I mean, machines, through the course of history, have always replaced human tasks, right, and it's all about you know, what's next for the human and I mean, you know, with physical labor, you know, driving stakes or whatever it is. You know, we've seen that. But now, for the first time ever, you're seeing cognitive, cognitive assisted, you know, functions come into play and it's it's new. It's a new innovation curve. It's not Moore's law anymore. That's driving innovation. It's how we interact with systems and cognitive systems one >> tonight. And I think, you know, I think you hit on a good point there when you said in driving innovation, you know, I've run, you know, large scale, automated process is where the goal was to reduce the number of people involved. And those were like you said, physical task that people are doing we're talking about here is replacing intellectual tasks, right or not replacing but freeing up the intellectual capacity that is going into solving intellectual tasks to enable that capacity to focus on more innovative things, right? We can teach a computer, Teo, explain ah, an area to us or give us some advice on something. I don't know that in the next 10 years, we're gonna be able to teach a computer to innovate, and we can free up the smart minds today that are focusing on How do we make a decision? Two. How do we be more innovative in leveraging this decision and applying this decision? That's a huge win, and it's not about replacing that person. It's about freeing their time up to do more valuable things. >> Yes, sure. So, for example, from my previous experience writing healthcare So physicians, right now you know, basically, it's basically impossible for human individuals, right to keep up with spaced of changes and innovations happening in health care and and by medical areas. Right? So in a few years it looks like there was some numbers that estimate that in three days you're going to, you know, have much more information for several years produced during three days. What was done by several years prior to that point. So it's basically becomes inhuman to keep up with all these innovations, right? Because of that decision is going to be not, you know, optimal decisions. So what we'd like to be doing right toe empower individuals make this decision more, you know, correctly, it was alternatives, right? That's about empowering people. It's not about just taken, which is can be done through this process is all this information and get in the routine stuff out of their plate, which is completely full. >> There was a stat. I think it was last year at IBM Insight. Exact numbers, but it's something like a physician would have to read 1,500 periodic ALS a week just to keep up with the new data innovations. I mean, that's virtually impossible. That something that you're obviously pointing, pointing Watson that, I mean, But there are mundane examples, right? So you go to the airport now, you don't need a person that the agent to give you. Ah, boarding pass. It's on your phone already. You get there. Okay, so that's that's That's a mundane example we're talking about set significantly more complicated things. And so what's The gate is the gate. Creativity is it is an education, you know, because these are step functions in value creation. >> You know, I think that's ah, what? The gate is a question I haven't really thought too much about. You know, when I approach it, you know the thinking Mohr from you know, not so much. What's the gate? But where? Where can this ad the most value um So maybe maybe I have thought about it. And the gate is value, um, and and its value both in terms of, you know, like the physician example where, you know, physicians, looking at images. And I mean, I don't even know what the error rate is when someone evaluates and memory or something. And I probably don't want Oh, right. So, getting some advice there, the value may not be monetary, but to me, it's a lot more than monetary, right. If I'm a patient on DH, there's a lot of examples like that. And other places, you know, that are in various industries. That I think that's that's the gate >> is why the value you just hit on you because you are a heat seeking value missile inside of your organisation. What? So what skill sets do you have? Where did you come from? That you have this capability? Was your experience, your education, your fortitude, >> While the answer's yes, tell all of them. Um, you know, I'm a scientist by training my backgrounds in statistical genetics. Um, and I've kind of worked through the business. I came up through the RND organization with him on Santo over the last. Almost exactly 10 years now, Andi, I've had lots of opportunities to leverage. Um, you know, Data and analytics have changed how the company operates on. I'm lucky because I'm in a company right now. That is extremely science driven, right? Monsanto is a science based company. And so being in a company like that, you don't face to your question about financial industry. I don't think you face the same barriers and Monsanto about using data and analytics in the same way you may in a financial types that you've got company >> within my experience. 50% of diagnosis being proven incorrect. Okay, so 50% 05 0/2 summation. You go to your physician twice. Once you on average, you get in wrong diagnosis. We don't know which one, by the way. Definitely need some someone. Garrett A cz Individuals as humans, we do need some help. Us cognitive, and it goes across different industries. Right, technologist? So if your server is down, you know you shouldn't worry about it because there is like system, you know, Abbas system enough, right? So think about how you can do that scale, and then, you know start imagined future, which going to be very empowering. >> So I used to get a second opinion, and now the opinion comprises thousands, millions, maybe tens of millions of opinions. Is that right? >> It's a try exactly and scale ofthe data accumulation, which you're going to help us to solve. This problem is enormous. So we need to keep up with that scale, you know, and do it properly exactly for business. Very proposition. >> Let's talk about the role of the CDO and where you see that evolving how it relates to the role of the CIA. We've had this conversation frequently, but is I'm wondering if the narratives changing right? Because it was. It's been fuzzy when we first met a couple years ago that that was still a hot topic. When I first started covering this. This this topic, it was really fuzzy. Has it come in two more clarity lately in terms of the role of the CDO versus the CIA over the CTO, its chief digital officer, we starting to see these roles? Are they more than just sort of buzzwords or grey? You know, areas. >> I think there's some clarity happening already. So, for example, there is much more acceptance for cheap date. Office of Chief Analytics Officer Teo, Chief Digital officer. Right, in addition to CEO. So basically station similar to what was with Serious 20 plus years ago and CEO Row in one sentence from my viewpoint would be How you going using leverage in it. Empower your business. Very proposition with CDO is the same was data how using data leverage and data, your date and your client's data. You, Khun, bring new value to your clients and businesses. That's kind ofthe I would say differential >> last word, you know, And you think you know I'm not a CDO. But if you think about the concept of establishing a role like that, I think I think the name is great because that what it demonstrates is support from leadership, that this is important. And I think even if you don't have the name in the organization like it, like in Monsanto, you know, we still have that executive management level support to the data and analytics, our first class citizens and their important, and we're going to run our business that way. I think that's really what's important is are you able to build the culture that enable you to leverage the maximum capability Data and analytics. That's really what matters. >> All right, We'll leave it there. Seth Gene, thank you very much for coming that you really appreciate your time. Thank you. Alright. Keep it right there, Buddy Stew and I'll be back. This is the IBM Chief Data Officer Summit. We're live from Boston right back.

>> It's the Cube covering IBM cheap Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts Day villain day and still minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We here at the Chief Data Officers Summit that IBM is hosting in Boston. I'm joined by Courtney Abercrombie. According your your title's too long. I'm just gonna call you a cognitive rockstar on >> Alec Crane is >> here from Yusa. System by President, Vice President at that firm. Welcome to the Cube. Great to see you guys. Thank you. So this event I love it. I mean, we first met at the, uh, the mighty chief data officer conference. You were all over that networking with the CEO's helping him out and just really, I think identified early on the importance of this constituency. Why? How did you sort of realize and where have you taken it? >> It's more important than it's ever been. And we're so grateful every time that we see a new chief data officer coming in because you just can't govern and do data by committee. Um, if you really hope to be transformational in your company. All these huge, different technologies that are out there, All this amazing, rich data like weather data and the ability to leverage, you know, social media information, bringing that all together and really establishing an innovation platform for your company. You can't do that by committee. You really have to have a leader in charge of it. and that’s what chief data officers are here to do. And so every time we see one, we're so grateful >> that just so >> that we just heard from Inderpal Bhandari on his recommendation for how you get started. It was pretty precise and prescriptive. But I wonder, Alan. So tell us about the chief data officer role at USAA. Hasn't been around for a while. Of course, it's a regulated business. So probably Maur, data oriented are cognizant than most businesses. But tell us about your journey. >> We started probably about 4 or 5 years ago, and it was a combination of trying to consolidate data and analytics operations and then decentralized them, and we found that there was advantages and pros and cons of doing both. You'd get the efficiencies, but once you got the efficiencies, you'd lose the business expertise, and then we'd have to tow decentralize. So we ended up landing a couple of years ago. What we call a hub and spoke system where we have centralized governance and management of key data assets, uh, data modelling data science type work. And then we still allow the, uh, various lines of business to have their own data offices. And the one I run for USAA is our distribution channels office for all of the data and analytics. And we take about 100,000,000 phone calls a year. About 2,000,000,000 webb interactions. Mobile interactions. We take about 18,000 hours. That's really roughly two years of phone conversation data in per day. Uh, we take about 50,000,000 lines of, uh, Web analytic traffic per day as well. So trying to make sense of that to nurture remember, relationships, reinforce trust and remove obstacles >> for your supporting the agent systems. Is that right? >> I support the agent systems as well as the, um, digital >> systems. Okay. And so the objective is obviously toe to grow the business, keep it running, keep the customers happy. Very operate, agent Just efficient. Okay. Um and so when you that's really interesting. This sort of hub and spoke of decentralization gets you speed and closer to the business. Centralization get you that that efficiency. Do you feel like you found that right balance? I mean, if you think so. I >> think you know, early on, we it was mme or we had more cerebral alignment, you know, meaning that it seemed logical to us. But actually, once the last couple of years, we've had some growing pains with roles, responsibilities, overlaps, some redundancy, those types of things. But I think we've landed in a good place. And that's that's what I'm pretty proud of because we've been able to balance the agility with the governance necessary toe, have good governance and put in place, but then also be able to move at the speed the businessmen. >> So Courtney, one of things we heard one of the themes this morning within IBM it's of the role of the chief Data officer's office is to really empower the lines of business with data so that you can empower your customers is what Bob Tatiana was telling us, right? With data. So how are you doing? That is you have new services. You have processes or how is that all working >> right? We dio We have a lot of things, actually, because we've been working so much with people like Allen's group who have been leaders at, quite frankly, in establishing best practices on even how to set up these husbands votes. A lot of people are, you know, want to talk, Teo, um, the CDO and they've spun off even a lot of CEOs into other organizations, in fact, but I mean, they're really a leader in this area. So one of the things that we've noticed is you know, the thing that gives everybody the biggest grief is trying to figure out how to work with unstructured data. Um, and all this volume of data, it's just insane. And just like I was saying in the panel earlier, only about 5% of your actual internal data is enough to actually create a context around your customers. You really have to be able to go with all this exogenous data to understand what were the bigger ramifications that were going on in any customer event, whether it's a call in or whether it's, uh, you know, I'm not happy today with something that you tried to sell me or something that you didn't respond too fast enough, which I'm sure Alan could, you know, equate to. But so we have this new data as a service that we've put together based on the way the weather data has, the weather company has put their platform together. We're using a lot of the same kind of like micro services that you saw Bob put on the screen. You know, everything from, I mean, open source. As much open sources we can get, get it. And it's all cloud based. So and it's it's ways to digest and mix up both that internal data with all of that big, voluminous external data. >> So I'm interested in. So you get the organizational part down. Least you've settled on approach. What are some of the other big challenges that you face in terms of analytics and cognitive projects? Your organization? How are you dealing with those? >> Well, uh, >> to take a step back, use a We're, uh, financial services company that supports the military and their families. We now have 12 million members, and we're known for our service. And most of the time, those moments of truth, if you will, where our service really shines has been when someone talks to you, us on the phone when those member service reps are giving that incredible service that they're known for on the reason being is that the MSR is the aggregator of all that data. When you call in, it's all about you. There's two screens full of your information and the MSR is not interested in anything else but just serving you, our digital experiences more transactional in orientation. And it was It's more utilitarian, and we're trying to make it more personal, trying to make it more How do we know about you? And so one of the cues that were that were taking from the MSR community through cognitive learning is we like to say the only way to get into the call is to get into the call, and that is to truly get into the speech to text, Then do the text mining on that to see what are the other topics that are coming out that could surface that we're not actually capturing. And then how do we use those topics at a member level two then help inform the digital experience to make it more personal. How do I detect life events? Our MSR's are actually trained to listen for things like words like fiance, marriage moving, maybe even a baby crying in the background. How do we take that knowledge and turn that into something that machine learning can give us insights that can feedback into our digital transact actions. So >> this's what our group. >> It's a big task. So So how are >> you doing that? I mean, it's obviously we always talk about people processing technology. Yeah, break that down for us. I mean, how are you approaching that massive opportunity? >> Part of it is is, uh, you know, I look at it. It is like a set of those, you know, Russian nesting dolls. You know, every time you solve one problem, there's another problem inside of it. The first problem is getting access to the data. You know, where and where do you store? We're taking in two years of data per day of phone call data into a system where you put all that right and then you're where you put a week's worth a month's worth a quarter's worth of data like that. Then once you solve that problem, how do you read Act all that personal information So that that private information that you really don't need that data exhaust that would actually create a liability for you in our in our world so that you can really stay focused on what of the key themes that the member needs? And then the third thing is now had. Now that you've got access to the data, it's transcribed for you. It's been redacted from its P I I type work well, now you need the horse power and of analysts on, we're exploring partnerships with IBM, both locally and in in the States as well as internationally to look at data science as a service and try to understand How can we tap into this huge volume of data that we've got to explore those types of themes that are coming up The biggest challenges in typical transaction logging systems. You have to know what your logging You have to know what you're looking for before you know what to put the date, where to put the data. And so it's almost like you kind of have to already know that it's there to know how much you're acquiring for it and what we need to do more as we pivot more towards machine learning is that we need the data to tell us what's important to look at. And that's really the vat on the value of working with these folks. >> So obviously, date is increasingly on structure we heard this morning and whatever, 80 90% is structured. So here you're no whatever. You're putting it into whatever data fake swamp, ocean, everything center everywhere, and you're using sort of machine learning toe both find signal, but also protected yourself from risk. Right. So you've got a T said you gotta redact private information. So much of that information could be and not not no schema? Absolutely. Okay, So you're where are you in terms of solving that problem in the first inning or you deeper than that, >> we're probably would say beyond the first inning, but we so we've kind of figured out what that process is to get the data and all the piece parts working together. We've made some incredible insights already. Things that people, you know, I had no idea that was there. Um, but, uh, I'd say we still have a long way to go. Is particularly terms of scaling scaling the process, scaling the thie analytics, scaling the partnerships, figuring out how do we get the most throughput? I would say it's It's one of those things. We're measuring it on, maybe having a couple of good wins this year. A couple of really good projects that have come across. We want to kind of take that tube out 10 projects next year in this space. And that's how we're kind of measuring the velocity and the success >> data divas. I walked away and >> there was one of them Was breakfast this morning. Data divas. You hold this every year. >> D'oh! It's growing. Now we got data, >> dudes. So I was one of the few data dudes way walked in >> one of the women chief date officers. I got no problem with people calling me a P. >> I No. Yeah, I just sell. Sit down. Really? Bath s o. But also, >> what's the intent of that? What learning is that you take out of those? >> I think it's >> more. It's You know, you could honestly say this isn't just a data Debo problem. This is also, you know, anybody who feels like they're not being heard. Um, it's really easy to get drowned out in a lot of voices when it comes to data and analytics. Um, everybody has an opinion. I think. Remember, Ursula is always saying, Ah, all's fair in love, war and data. Um and it feels like, you know, sometimes you go, I'll come to the table and whoever has the loudest voice and whoever bangs their test the loudest, um, kind of wins the game. But I think in this case, you know, a lot of women are taking these roles. In fact, we saw, you know, a while back from Gardner that number about 25% of chief data officers are actually women because the role is evolving out of the business lines as opposed Thio more lines. And so I mean, it makes sense that, you know, were natural collaborators. I mean, like the biggest struggle and data governance isn't setting up frameworks. It's getting people to actually cooperate and bring data to the table and talk about their business processes that support that. And that's something that women do really well. But we've got to find our voice and our strength and our resolve. And we've got to support each other in trying to bring more diverse thinking to the table, you know? So it's it's all those kinds of issues and how do you balance family? I mean, >> we're seeing >> more and more. You know, I don't know if you know this, but there's actual statistics around millennials and that males are actually starting to take on more more role of being the the caregiver in the family. So I mean as we see that it's an interesting turnabout because now all the sudden, it's no longer, you know, women having that traditional role of, you know, I gotta always be home. Now we're actually starting to see a flip of that, which is which is, >> You know, I think it's kind of welcome. My husband's definitely >> I say he's a better parent than me. >> Friday. It's >> honest he'll watch this and he >> can thank me later that it was >> a great discussion this morning. Alan, I want to get your feedback on this event and also you participate in a couple of sessions yesterday. Maybe you could share with our audience Some of the key takeaways in the event of general and specific ones that you worked on yesterday. >> Well, I've been fortunate to come to the event for a couple of years now. And when we were just what 50 or so of us that were showing up? So, you know, I see that the evolution just in a couple of years time conversations have really changed. First meeting that we had people were saying, Where do you report in the organization? Um, how many people do you have? What do you do for your job? They were very different answers to any of that everywhere. From I'm an independent contributor that's a data evangelist to I run legions of data analysts and reporting shops, you know, and so forth and everything in between. And so what I see what it's offers in first year was really kind of a coalescing of what it really means to be a data officer in the company that actually happened pretty quickly in my mind, Um, when by seeing it through through the lens of my peers here, the other thing was when you when you think about the topics the topics are getting a lot more pointed. They're getting more pointed around the monetization of data communicating data through visualization, storytelling, key insights that you, you know, using different technologies. And we talked a lot yesterday about storytelling and storytelling is not through visual days in storytelling is not just about like who has the most, you know, colors on on a slide or or ah you know, animation of your bubble charts and things like that. But sometimes the best stories are told with the most simple charts because they resonate with your customers. And so what I think is it's almost like kind of getting a back to the basics when it comes to taking data and making it meaningful. We're only going to grow our organizations and data and data scientists and analysts. If we can communicate to the rest of the organization, our value and the key to creating that value is they can see themselves in our data. >> Yeah, the visit is we like to call it sometimes is critical to that to that storytelling. Sometimes I worry and we go onto these conferences and you go into a booth and look what we can do with machine learning, and we would just be looking at just this data. So what do I do? What >> I do with all this? Yeah. >> I don't know how it would make sense of it. So So is there a special storyteller role within your organization or you all storytellers? Do you cross train on that? Or >> it's funny you'd ask that one of the gentlemen of my team. He actually came to me about six months ago, and he says I'm really good at at the analysis part, but I really have a passion for things like Photoshopped things like, uh uh, uh the various, uh, video and video editing type software. He says I want to be your storyteller. I want to be creating a team of data and analytics storytellers for the rest of the organization. So we pitched the idea to our central hub and spoke leadership group. They loved it. They loved the idea. And he is now, um, oversubscribed. You would say in terms of demand for how do you tell the data? How do you tell the data story and how it's moving the business forward? And that takes the form kind of everything from infographics tell you also about how do you make it personal when, when? Now 7,000 m s. Ours have access to their own data. You know, really telling that at a at a very personal level, almost like a vignette of animus are who's now able to manage themselves using the data that they were not able able tto have before we're in the past, only managers had access to their performance results. This video, actually, you know, pulls on the heartstrings. But it it not only does that, but it really tells the story of how doing these types of things and creating these different data assets for the rest of your organization can actually have a very meaningful benefit to how they view work and how they view autonomy and how they view their own personal growth. >> That's critical, especially in a decentralized organization. Leased a quasi decentralized organization, getting everybody on the same page and understand You know what the vision is and what the direction is. It s so often if you don't have that storytelling capability, you have thousands of stories, and a lot of times there's dissonance. I mean, I'm not saying there's not in your in your organization, but have you seen the organization because of that storytelling capability become Mohr? Yeah, Joe. At least Mohr sort of effective and efficient, moving forward to the objectives. Well, >> you know, as a as a data person, I'm always biased thatyou know data, you know, can win an argument if presented the right way. It's the The challenge is when you're trying to overcome or go into a direction. And in this case, it was. We wanted to give more autonomy. Toothy MSR community. Well, the management of that call center were 94 year old company. And so the management of that of that call center has been doing things a certain way for many, many, many, many years. And the manager's having access to the data. The reps not That was how we did things, you know. And so when you make a change like that, there's a lot of hesitation of what is this going to do to us? How is this going to change? And what we're able to show with data and with through these visualizations is you really don't have anything to worry about? You're only gonna have upside, you know, in this conversation because at the end of the day, what's going to empower people this having access and power of >> their own destiny? Yeah, access is really the key isn't because we've all been in the meetings where somebody stands up and they've got some data point in there pounding the table, >> right? Oftentimes it's a man, all right. It >> is a powerful pl leader on jamming data down your throats, and you don't necessarily know the poor sap that he's, you know, beating up. Doesn't think Target doesn't have access to the data. This concept of citizen data scientists begins to a level that playing field doesn't want you seeing that >> it does. And I want to actually >> come back to what you're saying because there's a larger thought there, which is that we don't often address, and that's this change banishment concept. I mean, we we look at all these. I mean, everybody looks at all these technologies and all this information, and how much data can you possibly get your >> hands on? But at the end of >> the day, it's all about trying to create an outcome. A some joint outcome for the business and it could be threatening. It could be threatening to the C suite people who are actually deploying the use of these data driven tools because >> it may go >> against their gut. And, you >> know, oftentimes the poor messenger of that, >> When when you have to be the one that stands up and go against that, that senior vice presidents got it, the one who's pounding and saying No, but I know better >> That could be a >> tough position to be in without having some sort of change management philosophy going on with the introduction of data and analytics and with the introduction of tools, because there's a whole reframing that, Hey, my gut instinct that got me here all the way to the top doesn't necessarily mean that it's going to continue to scale in this new world with all of all of our competitors and all these, you know, massive changes going on in the market place right now. My guts not going to get me there anymore. So it's hard, it's hard, and I think a lot of executives don't really know to invest in that change management, if you know that goes with it that you need to change philosophies and mindsets and slowly introduced visualizations and things that get people slowly onboard, as opposed to just throwing it at him and saying here, believe it. >> Think I mean, it wasn't that >> long ago. Certainly this this millennium, where you know, publications like Harvard Business Review had, uh, cover stories on why gut feel, you know, beats, you know, analysis by paralysis. >> That seems to be changing. And >> the data purists would say the data doesn't lie. It was long as you could interpret it correctly. Let the data tell us what to do, as opposed to trying to push an agenda. But they're still politics. >> There's just things out >> there that you can't even perceive of that air coming your way. I mean, like, Blockbuster Netflix, Alibaba versus standard retailers. I mean, >> there's just things out >> there that without the use of things like machine learning and being comfortable with the use, the things like mission learning a lot of people think of that kind of stuff is >> Well, don't get your >> hoodoo voodoo into my business. You know, I don't know what that algorithm stuff does. It's >> going Yeah, I mean, e. I mean to say, What the hell is this? And now, yeah, it's coming and >> you need to get ready. >> There's an >> important role, though I think instinct, you know, you don't want to dismiss a 20 year leader in a particular operations because they've they've they've getting themselves where they're at because in large part, maybe they didn't have all the data. But they learned through a lot of those things, and I think it's when you marry those things up. And if you kenbrell in a kind of humble way to that kind of leader and win them over and show how it may be validating some of their, um uh yeah, that some of their points Or maybe how it explains it in a different way. Maybe it's not exactly what they want to see, but it's helping to inform their business, and you come into him as a partner, as opposed to gotcha, you know. Then then you know you can really change the business that way. And >> what is it? Was Linda Limbic brain is it just doesn't feel right. Is that the part of the brain that informs you that? And so It's hard to sometimes put, but you're right. Uh, there there is a component of this which is gut feel instinct and probably relates to to experience. So it's It's like, uh, when, when, uh, Deep blue beat Garry Kasparov. We talk about this all the time. It turns out that the best chess player in the world isn't a machine. It's a It's a human in the machine. >> That's right. That's exactly right. It's always the training that people training these things, that's where it gets its information. So at the end of the day, you're right. It's always still instinct to some >> level. I could We gotta go. All right. Last word on the event. You know what's next? >> Don't love my team. Data officer. Miss, you guys. It is good >> to be here. We appreciate it. All right, We'll leave it there. Thank you, guys. Thank you. All right, keep right. Everybody, this is Cuba. Live from IBM Chief Data Officer, Summit in Boston Right back. My name is Dave Volante.

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now here are your hosts. Day villain Day >> and stew Minimum. We're back. Welcome to Boston, Everybody. This is the IBM Chief Data Officer Summit. This is the Cube, the worldwide leader in live tech coverage. Inderpal. Bhandari is here. He's the newly appointed chief data officer at IBM. He's joined, but joined by Bob Picciano who is the senior vice president of IBM Analytics Group. Bob. Great to see again Inderpal. Welcome. Thank you. Thank you. So good event, Bob, Let's start with you. Um, you guys have been on the chief data officer kicked for several years now. You ahead of the curve. What, are you trying to achieve it? That this event? Yes. So, >> Dave, thanks again for having us here. And thanks for being here is well, tto help your audience share in what we're doing here. We've always appreciated that your commitment to help in the the masses understand all the important pulses that are going on the industry. What we're doing here is we're really moderating form between chief date officers on. We started this really on the curve. As you said 2014, where the conference was pretty small, there were some people who were actually examining the role, thinking about becoming a chief did officer. We probably had a few formal cheap date officers we're talking about, you know, maybe 100 or so people who are participating in the very 1st 1 Now you can see it's not, You know, it's it's grown much larger. We have hundreds of people, and we're doing it multiple times a year in multiple cities. But what we're really doing is bringing together a moderated form, Um, and it's a privilege to be able to do this. Uh, this is not about selling anything to anybody. This is about exchanging ideas, understanding. You know what, the challenges of the role of the opportunities which changing about the role, what's changing about the market and the landscape, what new risks might be on the horizon? What new opportunities might be on the horizon on we you know, we really liketo listen very closely to what's going on so we can, you know, maybe build better approach is to help their mother. That's through the services we provide or whether that's through the cloud capabilities were offering or whether that's new products and services that need to be developed. And so it gives us a great understanding. And we're really fortunate to have our chief data officer here, Interpol, who's doing a great job in IBM and in helping us on our mission around really becoming a cognitive enterprise and making analytics and insight on data really be central to that transformation. >> So, Dr Bhandari, new, uh, new to the chief date officer role, not nude. IBM. You worked here and came back. I was first exposed to roll maybe 45 years ago with the chief Data officer event. OK, so you come in is the chief data officer in December. Where do you start? >> So, you know, I've had the fortune of being in this role for a long time. I was one of the earliest created, the role for healthcare in two thousand six. Then I have honed that roll over three different Steve Data officer appointments at health care companies. And now I'm at IBM. So I do have, you know, I do view with the job as a craft. So it's a practitioner job and there's a craft to it. And do I answer your question? There are five things that you have to do to get moving on the job, and three of those have to be non sequentially and to must be done and powerful but everything else. So the five alarm. The first thing is you've got to develop a data strategy and data strategy is around, is focused around having an understanding ofthe how the company monetize is or plans to monetize itself. You know, what is the strategic monetization part of the company? Not so much how it monetize is data. But what is it trying to do? How is it going to make money in the future? So in the case of IBM, it's all around cognition. It's around enabling customers to become cognitive businesses. So my data strategy or our data strategy, I should say, is focused on enabling cognition becoming a cauldron of enterprise. You know, we've now realized that impacto prerequisite for cognition. So that's the data strategy piece. And that's the very first thing that needs to be done because once you understand that, then you understand what data is critical for the company, so you don't boil the ocean instead, what you do is you begin to govern exactly what's necessary and make sure it's fit for purpose. And then you can also create trusted data sources around those critical data assets that are critical for the for the monetization strategy of the company's. Those three have to go in sequence because if you don't know what you can do to adequately kind of three, and they're also significant pitfalls if you don't follow that sequence because you can end up pointing the ocean and the other two activities that must be done concurrently. One is in terms ofthe establishing deep partnerships with the other areas of the company the key business units, the key functional units because that's how you end up understanding what that data strategy ought to be. You know, if you don't have that knowledge of the company by making that effort that due diligence, that it's very difficult to get the data strategy right, so you've got to establish those partnerships and then the 5th 1 is because this is a space where you do require very significant talent. You have to start developing that talent and that all the organizational capability right from day one. >> So, Bob, you said that, uh, data is the new middle manager. You can't have an effective middle manager come unless you at least have some framework that was just described. >> Yeah, absolutely. So, you know, when Interpol talks about that fourth initiative about the engagement with the business units and making sure that we're in alignment on how the company's monetizing its value to its clients, his involvement with our team goes way beyond how he thinks about what date it is that we're collecting in the products that you're offering and what we might understand about our customers or about the marketplace. His involvement goes also into how we're curating the right user experience for who we want to win power with our products and offerings. Sometimes that's the role of the chief date officer. Sometimes that's the role of a data engineer. Sometimes it's the role of a data scientist. You mentioned data becoming the new middle management middle manager. We think the citizen analyst is ushering in that from from their seat, But we also need to be able to, from a perspective, to help them eliminate the long tail and and get transparency, the information. And sometimes it's the application developer. So we, uh, we collaborate on a very frequent basis, where, when we think about offering new capabilities to those roles, well, what's the data implication of that? What's the governance implication of that? How do we make it a seamless experience? So as people start to move down the path of igniting all of the innovation across those roles, there is a continuum to the information to using To be able to do that, how it's serving the enterprise, how it leads to that transformation to be a cognitive enterprise on DH. That's a very, very close collaboration >> we're moving from. You said you talked the process era to what I just inserted to an insight era. Yeah, um, and I have a question around that I'm not sure exactly how to formulate it, but maybe you can help. In the process, era technology was unknown. The process was very well, Don't know. Well known, but technology was mysterious. But with IBM and said help today it seems as though process is unknown. The technology's pretty known look at what uber airbnb you're doing the grabbing different technologies and putting them together. But the process is his new first of all, is that a reasonable observation? And if so, what does that mean for chief data officers? >> So the process is, you know, is new in the sense that in terms ofthe making it a cognitive process, it's going to end up being new, right? So the memorization that you >> never done it before, but it's never been done before, right >> in that sense. But it's different from process automation in the past. This is much more about knowledge, being able to scale knowledge, not just, you know, across one process, but across all the process cities that make up a company. And so in there. That goes also to the comment about data being the middle manager. I mean, if you've essentially got the ability to scale and manage knowledge, not just data but knowledge in terms of the insights that the people who are working these processes are coming up in conjunction with these data and intelligent capabilities, that that that that that of the hub right, it's the intelligence system that's had the Hubble this that's enabling all that so that That's really what leads Teo leads to the so called civilization >> way had dates to another >> important aspect of this is the process is dramatically different in the sense that it's ongoing. It's it's continuous, right, the process and your intimacy with uber and the trust that you're developing. A brand doesn't start and stop with one transaction and actually, you know branches into many different things. So your expectations, a CZ that relationships have all changed. So what they need to understand about you, what they need to protect about you, how they need to protect you in their transformation, the richness of their service needs to continue to evolve. So how they perform that task on the abundance of information they have available to perform that task. But the difficulty of being able to really consume it and make use of it is is a change. The other thing is, it's a lot more conversational, right? So the process isn't a deterministic set of steps that someone at a desk can really formulate in a business rule or a static process. It's conversationally changes. It needs to be dis ambiguity, and it needs to introduce new information during the process of disintegration. And that really, really calls upon the capabilities of a cognitive system that is rich and its ability to understand and interact with natural language to potentially introduce other sources of rich information. Because you might take a picture about what you're experiencing and all those things change that that notion from process to the conversational element. >> Dr. Bhandari, you've got an interesting role. Companies like IBM I think about the Theo with the CDO. Not only do you have your internal role, but you're also you know, a model for people going out there. You come too. Events like this. You're trying to help people in the role you've been a CDO. It's, um, health care organization to tell Yu know what's different about being kind of internal role of IBM. What kind of things? IBM Obviously, you know, strong technology culture, But tell us a little bit inside. You've learned what anything surprise you. You know, in your time that you've been doing it. >> Oh, you know, over the course ofthe time that I've been doing the roll across four different organizations, >> I guess specifically at IBM. But what's different there? >> You know, I mean IBM, for one thing, is a the The environment has tremendous scale. And if you're essentially talking about taking cognition to the enterprise, that gives us a tremendous A desperate to try out all the capabilities that were basically offering to our to our customers and to home that in the context of our own enterprise, you know, to build our own cognitive enterprise. And that's the journey that way, sharing with our with our customers and so forth. So that's that's different in in in in it. That wasn't the case in the previous previous rules that I had. And I think the other aspect that's different is the complexity of the organisation. This is a large global organization that wasn't true off the previous roles as well. They were Muchmore, not America century, you know, organizations. And so there's a There's an aspect there that also then that's complexity of the role in terms ofthe having to deal with different countries, different languages, different regulations, it just becomes much more complex. >> You first became a CDO in two thousand six, You said two thousand six, which was the same year as the Federal Rules of Civil Procedure came out and the emails became smoking guns. And then it was data viewed as a liability, and now it's completely viewed as an asset. But traditionally the CDO role was financial services and health care and government and highly regulated businesses. And it's clearly now seeping into new industries. What's driving that? Is that that value? >> Well, it is. I mean, it's, I think, that understanding that. You know, there's a tremendous natural resource in in the information in the data. But there is, you know, very much you know, union Yang around that notion of being responsible. I mean, one of the things that we're very proud of is the type of trust that we established over 105 year journey with our clients in the types of interactions we have with one another, the level of intimacy that we have in their business and very foundation away, that we serve them on. So we can never, ever do anything to compromise that you know. So the focus on really providing the ability to do the necessary governance and to do the necessary data providence and lineage in cyber security while not stifling innovation and being able to push into the next horizon. Interpol mentioned the fact that IBM, in and of itself, we think of ourselves as a laboratory, a laboratory for cognitive information innovation, a laboratory for design and innovation, which is so necessary in the digital era. And I think we've done a really good job in the spaces, but we're constantly pushing the envelope. A good example of that is blockchain, a technology that you know sometimes people think about and nefarious circumstances about, You know, what it meant to the ability to launch a Silk Road or something of that nature. We looked at the innovation understanding quite a lot about it being one of the core interview innovators around it, and saw great promise in being able to transform the way people thought about, you know, clearing multiparty transactions and applied it to our own IBM credit organization To think about a very transparent hyper ledger, we could bring those multiple parties together. People could have transparency and the transactions have a great deal of access into that space, and in a very, very rapid amount of time, we're able to take our very sizable IBM credit organization and implement that hyper ledger. Also, while thinking about the data regulation, the data government's implications. I think that's a really >> That's absolutely right. I mean, I think you know, Bob mentioned the example about the IBM credit organizer Asian, but there is. There are implications far beyond that. Their applications far beyond that in the data space. You know, it affords us now the opportunity to bring together identity management. You know, the profiles that people create from data of security aspects and essentially combined all of these aspects into what will then really become a trusted source ofthe data. You know, by trusted by me, I don't mean internally, but trusted by the consumers off the data. The subject's off the data because you'll be able to do that much in a way that's absolutely appropriate, not just fit for business purpose, but also very, very respectful of the consent on DH. Those aspects the privacy aspect ofthe data. So Blockchain really is a critical technology. >> Hype alleges a great example. We're IBM edge this week. >> You're gonna be a world of Watson. >> We will be a world Watson. We had the CEO of ever ledger on and they basically brought 1,000,000 diamonds and bringing transparency for the diamond industry. It's it's fraught with, with fraud and theft and counterfeiting and >> helping preserve integrity, the industry and eliminating the blood diamonds. And they right. >> It's fascinating to see how you know this bitcoin. You know, when so many people disparaged it is a currency, but not just the currency. You know, you guys IBM saw that early on and obviously participated in the open source. Be, You know, the old saying follow the money with us is like follow the data. So if I understand correctly, your job, a CDO is to sort of super charge of the business lines with the data strategy. And then, Bob, you're job is the line of business managers the supercharge your customers, businesses with the data strategy. Is that right? Is that the right value >> chain? I think you nailed it. Yeah, that's >> one of the things people are struggling with these days is, you know, if they can get their own data in house, then they've also gotta deal with third party. That industry did everything like that. IBM's role in that data chain is really interesting. You talked this morning about kind of the Weather Channel and kind of the data play there. Yeah, you know what? What's IBM is rolling. They're going forward. >> It's one of the most exciting things. I think about how we've evolved our strategy. And, you know, we're very fortunate to have Jimmy at the helm. Who really understands, You know, that transformational landscape on DH, how partnerships really change the ability to innovate for the companies we serve on? It was very obvious in understanding our client's problems that while they had a wealth of information that we were dealing with internally, there was great promise and being able to introduce these outside signals. If you will insights from other sources of data, Sometimes I call them vectors of information that could really transform the way they were thinking about solving their customer problem. So, you know, why wouldn't you ever want to understand that customers sentiment about your brand or about the product or service? And as a consequence to that, you know, capabilities that are there on Twitter or we chat or line are essential to that, depending on where your brand is operating in your branch, probably operating in a multinational space anyway, so you have to listen to all those signals and they're all in multiple language and sentiment is very, very bespoke. It's a different language, so you have to apply sophisticated machine learning. We've invented new algorithms to understand how to glean the signal at all that white noise. You use the weather example as well. You know, we think about the economic impact of climate atmosphere, whether on business and its profound. It's 1/2 trillion dollars, you know, in each calendar year that are, you know, lost information, lost assets, lost opportunity, misplaced inventory, you know, un delivered inventory. And we think we can do a better job of helping our clients take the weather excuses out of business in a variety of different industries. And so we've focused our initiatives on that information integration, governance, understanding new analytics toe to introduce those outside signals directly in the heart and want to place it on the desk of the chief data officer of those who are innovating around information and data. >> My my joke last Columbus. If they was Dell's buying DMC, IBM is buying the weather company. What does What does that say? My question is Interpol. When when Emma happens. And Bob, when you go out and purchase companies that are data driven, what role does the chief data officer play in both em in a pre and post. >> So, you know, I think the one that there being a cop, just gonna touch on a couple of points that Bob Major and I'll address your question directly as well. Uh, in terms of the role of the chief data officer, I think you're giving me that question before how that's he walled. The one very interesting thing that's happening now with what IBM is doing is previously the chief data officer. All at least with regard to the data, Not so much the strategy, but the data itself was internal focused. You know, you kind of worried about the data you had in house or the data you're bringing in now you've gotta worry as much about the exogenous status and because, you know, that's so That's one way that that role has changed considerably and is changing and evolving, and it's creating new opportunities for us. The other is again. In the past, the chief state officer all was around creating a warehouse for analytics and separated out from the operational processes. That's changing, too, because now we've got to transform these processes themselves. So that's, you know, that's that's another expanded role to come back to. Acquisitions emanate. I mean, I view that as essentially another process that, you know, company has. And so the chief data officer role is pretty key in terms of enabling that world in terms ofthe data, but also in terms ofthe giving, you know, guidance and advice. If, for instance, the acquisition isn't that problem itself, then you know, then we would be more closely involved. But if it's beyond that in terms of being able to get the right data, do that process as well as then once you've acquired the company in being able to integrate back the critical data assets those out of the key aspect, it's an ongoing role. >> So you've got the simplest level. You've got data sources and all the things associated with that. And then you've got your algorithms and your machine learning, and we're moving beyond sort of do tow cut costs into this new era. But so hot Oh cos adjudicate. And I guess you got to do both. You've got to get new data sources and you've got to improve this continuous process. By that you talked about how do you guide your customers as to where they put their resource? No. And that's >> really Davis. You have, you know, touching out again. That's really the benefit of this sort of a forum. In this sort of a conference, it's sharing the best practices of how the top experts in the world are really wrestling with that and identifying. I think you know Interpol's framework. What do you do sequentially to build the disciplines, to build a solid corn foundation, to make the connections that are lined with the business strategy? And then what do you do concurrently along that model to continue to operate? And how do you How do you manage and make sure your stakeholders understand what's being done? What they need to continue to do to evolve the innovation and come join us here and we'll go through that in detail. But, you know, he deposited a greatjob sharing his framers of success, and I think in the other room, other CEOs are doing that now. >> Yeah, I just wanted to quickly add to Bob's comment. The framework that I described right? It has a check and balance built into it because if you are all about governance, then the Sirio role becomes very defensive in nature. It's all about making sure you within the hour, you know, within the guard rails and so forth. But you're not really moving forward in a strategic way to help the company. And and that's why you know, setting it up by driving it from the strategy don't just makes it easier to strike that plus >> clerical and more about innovation here. We talked about the D and CDO today meaning data, but really, I think about it is being a great crucible for for disruption in information because you've disruption off. I called the Chief Disruption Office under Sheriff you >> incident in Data's digitalis data. So there's that piece of Ava's Well, we have to go. I don't want to go. So that way one last question for each of you. So Interpol, uh, thinking about and you just kind of just touched on it. He's not just playing defense, you know, thinking more offense this role. Where do you want to take it. What do your you know, sort of mid term, long term goals with this role? >> It's the specific role in IBM or just in general specifically. Well, I think in the case of I B M, we have the data strategy pretty well defined. Now it's all about being able to enable a cognitive enterprise. And so in, You know, in my mind and 2 to 3 years, we'll have completely established how that ought to be done, you know, as a prescription. And we'll also have our clients essentially sharing in that in that journey so that they can go off and create cognitive enterprises themselves. So that's pretty well set. You know, I have a pretty short window to three years to make that make that happen, And I think it's it's doable. And I think it will be, you know, just just a tremendous transformation. >> Well, we're excited to be to be watching and documenting that Bob, I have to ask you a world of washing coming up. New name for new conference. We're trying to get Pepper on, trying to get Jimmy on. Say, what should we expect? Maybe could. Although it was >> coming, and I think this year we're sort of blowing the roof off on literally were getting so big that we had to move the venue. It is very much still in its core that multiple practitioner, that multiple industry event that you experienced with insight, right? So whether or not you're thinking about this and the auspices of managing your traditional environments and what you need to do to bring them into the future and how you tie these things together, that's there for you. All those great industry tracks around the product agendas and what's coming out are are there. But the level of inspiration and involvement around this cognitive innovation space is going to be front and center. We're joined by Ginny Rometty herself, who's going to be very special. Key note. We have, I think, an unprecedented lineup of industry leaders who were going to come and talk about disruption and about disruption in the cognitive era on then. And as always, the most valuable thing is the journeys that our clients are partners sharing with us about how we're leading this inflection point transformation, the industry. So I'm very much excited to see their and I hope that your audience joins us as well. >> Great. We'll Interpol. Congratulations on the new roll. Thank you. Get a couple could plug, block post out of your comments today, so I really appreciate that, Bob. Always a pleasure. Thanks so much for having us here. Really? Appreciate. >> Thanks for having us. >> Alright. Keep right, everybody, this is the Cube will be back. This is the IBM Chief Data Officer Summit. We're live from Boston. You're back. My name is Dave Volante on DH. I'm along.

>> live from Boston, Massachusetts. >> It's the Cube >> covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day villain Day and >> stew minimum. Welcome back to Boston, everybody. This is the IBM Chief Data Officer Summit. And this is the Cube, the worldwide leader in live tech coverage. Caitlin Lepic is here. She's an executive within the chief data officer office at IBM. And she's joined by Dave Shoot Mel, who's a research director at, uh D. C. And he covers cognitive systems and content analytics. Folks, welcome to the Cube. Good to see you. Thank you. Can't. Then we'll start with you. You were You kicked off the morning and I referenced the Forbes article or CDOs. Miracle workers. That's great. I hadn't read that article. You put up their scanned it very quickly, but you set up the event. It started yesterday afternoon at noon. You're going through, uh, this afternoon? What's it all about? This is evolved. Since, what, 2014 >> it has, um, we started our first CDO summit back in 2014. And at that time, we estimated there were maybe 200 or so CDOs worldwide, give or take and we had 30, 30 people at our first event. and we joked that we had one small corner of the conference room and we were really quite excited to start the event in 30 2014. And we've really grown. So this year we have about 170 folks joining us, 70 of which are CEOs, more acting, the studios in the organization. And so we've really been able to grow the community over the last two years and are really excited to see to see how we can continue to do that moving forward. >> And IBM has always had a big presence at the conference that we've covered the CDO event. So that's nice that you can leverage that community and continue to cultivate it. Didn't want to ask you, so it used that we were talking when we first met this morning. It used to be dated was such a wonky topic, you know, data was data value. People would try to put a value on data, and but it was just a really kind of boring but important topic. Now it's front and center with cognitive with analytics. What are you seeing in the marketplace. >> Yeah, I think. Well, what we're seeing in the market is this emphasis on predictive applications, predictive analytics, cognitive applications, artificial intelligence of deep learning. All of those those types of applications are derived and really run by data. So unless you have really good authoritative data to actually make these models work, you know, the systems aren't going to be effective. So we're seeing an emerging marketplace in both people looking at how they can leverage their first party data, which, you know, IBM is really talking about what you know, Bob Picciotto talked about this morning. But also, we're seeing thie emergency of a second party and third party data market to help build these models out even further so that I think that's what we're really seeing is the combination of the third party data along with the first party data really being the instrument for building these kind of predictive models, you know, they're going to take us hopefully, you know, far into the future. >> Okay, so, Caitlin square the circle for us. So the CDO roll generally is not perceived. Is it technology role? Correct. Yet as Davis to saying, we're talking about machine learning cognitive. Aye, aye. These air like heavy technical topics. So how does the miracle worker deal with all this stuff generally? And how does IBM deal with it inside the CDO office? Specifically? >> Sure. So it is. It's a very good point, you know, Traditionally, Seo's really have a business background, and we find that the most successful CDO sit in the business organization. So they report somewhere in a line of business. Um, and there are certainly some that have a technical background, but far more come from business background and sit in the business. I can't tell you how we are setting up our studio office at IBM. Um, so are new. And our first global chief date officer joined in December of last year. Interpol Bhandari, um and I started working for him shortly thereafter, and the way he's setting up his office is really three pillars. So first and foremost, we focused on the data engineering data sign. So getting that team in place next, it's information, governance and policy. How are we going to govern access, manage, work with data, both data that we own within our organization as well as the long list of of external data sources that that we bring in and then third is the business integration filler. So the idea is CDOs are going to be most successful when they deliver those data Science data engineering. Um, they manage and govern the data, but they pull it through the business, so ensuring that were really, you know, grounded in business unit and doing this. And so those there are three primary pillars at this point. So prior >> to formalizing the CDO role at I b m e mean remnants of these roles existed. There was a date, equality, you know, function. There was certainly governance in policy, and somebody was responsible to integrate between, you know, from the i t. To the applications, tow the business. Were those part of I t where they sort of, you know, by committee and and how did you bring all those pieces together? That couldn't have been trivial, >> and I would say it's filling. It's still going filling ongoing process. But absolutely, I would say they typically resided within particular business units, um, and so certainly have mature functions within the unit. But when we're looking for enterprise wide answers to questions about certain customers, certain business opportunities. That's where I think the role the studio really comes in and what we're What we're doing now is we are partnering very closely with business units. One example is IBM analytic. Seen it. So we're here with Bob Luciano and other business units to ensure that, as they provide us, you know, their data were able to create the single trusted source of data across the organization across the enterprise. And so I agree with you, I think, ah, lot of those capabilities and functions quite mature, they, you know, existed within units. And now it's about pulling that up to the enterprise level and then our next step. The next vision is starting to make that cognitive and starting to add some of those capabilities in particular data science, engineering, the deep learning on starting to move toward cognitive. >> Dave, I think Caitlin brought up something really interesting. We've been digging into the last couple of years is you know, there's that governance peace, but a lot of CEOs are put into that role with a mandate for innovation on. That's something that you know a lot of times it has been accused of not being all that innovative. Is that what you're seeing? You know what? Because some of the kind of is it project based or, you know, best initiatives that air driving forward with CEOs. I think what we're seeing is that enterprises they're beginning to recognize that it's not just enough to be a manufacturer. It's not just enough to be a retail organization. You need to be the one of the best one of the top two or the top three. And the only way to get to that top two or top three is to have that innovation that you're talking about and that innovation relies on having accurate data for decision making. It also relies on having accurate data for operations. So we're seeing a lot of organizations that are really, you know, looking at how data and predictive models and innovation all become part of the operational fabric of a company. Uh, you know, and if you think about the companies that are there, you know, just beating it together. You know Amazon, for example. I mean, Amazon is a completely data driven company. When you get your recommendations for, you know what to buy, or that's all coming from the data when they set up these logistics centers where they're, you know, shipping the latest supplies. They're doing that because they know where their customers are. You know, they have all this data, so they're they're integrating data into their day to day decision making. And I think that's what we're seeing, You know, throughout industry is this this idea of integrating decision data into the decision making process and elevating it? And I think that's why the CDO rule has become so much more important over the last 2 to 3 years. >> We heard this morning at 88% percent of data is dark data. Papa Geno talked about that. So thinking about the CEOs scope roll agenda, you've got data sources. You've gotto identify those. You gotta deal with data quality and then Dave, with some of the things you've been talking about, you've got predictive models that out of the box they may not be the best predictive models in the world. You've got iterated them. So how does an organization, because not every organizations like Amazon with virtually unlimited resource is capital? How does an organization balance What are you seeing in terms of getting new data sources? Refining those data source is putting my emphasis on the data vs refining and calibrating the predictive models. How organizations balancing that Maybe we start with how IBM is doing. It's what you're seeing in the field. >> So So I would say, from what we're doing from a setting up the chief data office role, we've taken a step back to say, What's the company's monitor monetization strategy? Not how your mind monetizing data. How are how are you? What's your strategy? Moving forward, Um, for Mance station. And so with IBM we've talked about it is moved to enabling cognition throughout the enterprise. And so we've really talked about taking all of your standard business processes, whether they be procurement HR finance and infusing those with cognitive and figuring out how to make those smarter. We talking examples with contracts, for example. Every organization has a lot of contracts, and right now it's, you know, quite a manual process to go through and try and discern the sorts of information you need to make better decisions and optimize the contract process. And so the idea is, you start with that strategy for us. IBM, it's cognitive. And that then dictates what sort of data sources you need. Because that's the problem you're trying to solve in the opportunity you're chasing down. And so then we talk about Okay, we've got some of that data currently residing today internally, typically in silos, typically in business units, you know, some different databases. And then what? What are longer term vision is, is we want to build the intelligence that pulls in that internal data and then really does pull in the external data that we've that we've all talked about. You know, the social data, the sentiment analysis, analysis, the weather. You know, all of that sort of external data to help us. Ultimately, in our value proposition, our mission is, you know, data driven enablement cognition. So helps us achieve our our strategy there. >> Thank you, Dad, to that. Yeah, >> I mean, I think I mean, you could take a number of examples. I mean, there's there's ah, uh, small insurance company in Florida, for example. Uh, and what they've done is they have organized their emergency situation, their emergency processing to be able to deal with tweets and to be able to deal with, you know, SMS messages and things like that. They're using sentiment analysis. They're using Tex analytics to identify where problems are occurring when hurricane happens. So they're what they're doing is they're they're organizing that kind of data and >> there and there were >> relatively small insurance company. And a lot of this is being done to the cloud, but they're basically getting that kind of sentiment analysis being ableto interpret that and add that to their decision making process. About where should I land a person? Where should I land? You know, an insurance adjuster and agent, you know, based on the tweets, that air coming in rather than than just the phone calls that air coming into the into the organization, you know? So that's a That's a simple example. And you were talking about Not everybody has the resources of an Amazon, but, you know, certainly small insurance companies, small manufacturers, small retail organizations, you, Khun get started by, you know, analyzing your You know what people are saying about you. You know, what are people saying about me on Twitter? What are people saying about me on Facebook? You know how can I use that to improve my customer service? Uh, you know, we're seeing ah whole range of solutions coming out, and and IBM actually has a broad range of solutions for things like that. But, you know, they're not the only points out there. There's there's a lot of folks do it that kind of thing, you know, in terms of the dark data analysis and barely providing that, you know, as part of the solution to help people make better decisions. >> So the answers to the questions both You're doing both new sources of data and trying to improve the the the analytics and the models. But it's a balancing act, and you could come back to the E. R. A. Y question. It sounds like IBM strategies to supercharge your existing businesses by infusing them with new data and new insights. Is >> that correctly? I would say that is correct. >> Okay, where is in many cases, the R A. Y of analytics projects that date have been a reduction on investment? You know, I'm going to move stuff from my traditional W two. A dupe is cheaper, and we feels like Dave, we're entering a new wave now maybe could talk about that a little bit. >> Yeah. I mean, I think I think there's a desk in the traditional way of measuring ROI. And I think what people are trying to do now is look at how you mentioned disruption, for example. You know what I think? Disruption is a huge opportunity. How can I increase my sales? How can I increase my revenue? How can I find new customers, you know, through these mechanisms? And I think that's what we're starting to see in the organization. And we're starting to see start ups that are dedicated to providing this level of disruption and helping address new markets. You know, by using these kinds of technologies, uh, in in new and interesting ways. I mean, everybody uses the airbnb example. Everybody uses uber example. You know that these are people who don't own cars. They don't know what hotel rooms. But, you know, they provide analytics to disrupt the hotel industry and disrupt the taxi industry. It's not just limited to those two industries. It's, you know, virtually everything you know. And I think that's what we're starting to see is this height of, uh, virtual disruption based on the dark data, uh, that people can actually begin to analyze >> within IBM. Uh, the chief data officer reports to whom. >> So the way we've set up in our organization is our CBO reports to our senior vice president of transformation and operations, who then reports to our CEO our recommendation as we talked with clients. I mean, we see this as a CEO level reporting relationship, and and oftentimes we advocate, you know, for that is where we're talking with customers and clients. It fits nicely in our organization within transformation operations, because this line is really responsible for transforming IBM. And so they're really charged with a number of initiatives throughout the organization to have better skills alignment with some of the new opportunities. To really improve process is to bring new folks on board s. So it made sense to fit within, uh, organization that the mandate is really transformation of the company of the >> and the CDO was a peer of the CIA. Is that right? Yes. >> Yes, that's right. That's right. Um, and then in our organization, the role of split and that we have a chief data officer as well as a chief analytics officer. Um, but, you know, we often see one person serving both of those roles as well. So that's kind of, you know, depend on the organizational structure of the company. >> So you can't run the business. So to grow the business, which I guess is the P and L manager's role and transformed the business, which is where the CDO comes. >> Right? Right, right. Exactly. >> I can't give you the last word. Sort of Put a bumper sticker on this event. Where do you want to see it go? In the future? >> Yes. Eso last word. You know, we try Tio, we tried a couple new things. Uh, this this year we had our deep dive breakout sessions yesterday. And the feedback I've been hearing from folks is the opportunity to talk about certain topics they really care about. Is their governance or is innovation being able to talk? How do you get started in the 1st 90 days? What? What do you do first? You know, we we have sort of a five steps that we talk through around, you know, getting your data strategy and your plan together and how you execute against that. Um And I have to tell you, those topics continue to be of interest to our to our participants every year. So we're going to continue to have those, um, and I just I love to see the community grow. I saw the first Chief data officer University, you know, announced earlier this year. I did notice a lot of PR and media around. Role of studio is miracle workers, As you mentioned, doing a lot of great work. So, you know, we're really supportive. Were big supporters of the role we'll continue to host in person events. Uh, do virtual events continue to support studios? To be successful on our big plug is will be world of Watson. Eyes are big IBM Analytics event in October, last week of October in Vegas. So we certainly invite folks to join us. There >> will be, >> and he'll be there. Right? >> Get still, try to get Jimmy on. So, Jenny, if you're watching, talking to come on the Q. >> So we do a second interview >> and we'll see. We get Teo, And I saw Hillary Mason is going to be the oh so fantastic to see her so well. Excellent. Congratulations. on being ahead of the curve with the chief date officer can theme. And I really appreciate you coming to Cube, Dave. Thank you. Thank you. All right, Keep right there. Everybody stew and I were back with our next guest. We're live from the Chief Data Officers Summit. IBM sze event in Boston Right back. My name is Dave Volante on DH. I'm a longtime industry analysts.

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