>>from around the globe If the cube with digital coverage of M I t. Chief data officer and Information quality symposium brought to you by Silicon Angle Media >>Hello, everyone. This is Day Volonte and welcome back to our continuing coverage of the M I t. Chief Data Officer CDO I Q event Interpol Bhandari is here. He's a leading voice in the CDO community and a longtime Cubillan Interpol. Great to see you. Thanks for coming on for this. Especially >>program. My pleasure. >>So when you you and I first met, you laid out what I thought was, you know, one of the most cogent frameworks to understand what a CDO is job was where the priority should be. And one of those was really understanding how, how, how data contributes to the monetization of station aligning with lines of business, a number of other things. And that was several years ago. A lot of change since then. You know, we've been doing this conference since probably twenty thirteen and back then, you know, Hadoop was coming on strong. A lot of CEOs didn't want to go near the technology that's beginning to change. CDOs and cto Zehr becoming much more aligned at the hip. The reporting organizations have changed. But I love your perspective on what you've observed as changing in the CDO roll over the last half decade or so. >>Well, did you know that I became chief data officer in two thousand six? December two thousand and six And I have done this job four times four major overnight have created of the organization from scratch each time. Now, in December of two thousand six, when I became chief data officer, there were only four. Chief Data Officer, uh, boom and I was the first in health care, and there were three, three others, you know, one of the Internet one and credit guns one and banking. And I think I'm the only one actually left standing still doing this job. That's a good thing or a bad thing. But like, you know, it certainly has allowed me to love the craft and then also scripted down to the level that, you know, I actually do think of it purely as a craft. That is. I know, going into a mutual what I'm gonna do. They were on the central second. No, the interesting things that have unfolded. Obviously, the professions taken off There are literally thousands off chief data officers now, and there are plenty off changes. I think the main change, but the job is it's, I think, a little less daunting in terms off convincing the senior leadership that it's need it because I think the awareness at the CEO level is much, much, much better than what it waas in two thousand six. Across the world. Now, having said that, I think it is still only awareness and don't think that there's really a deep understanding of those levels. And so there's a lot off infusion, which is why you will. You kind of think this is my period. But you saw all these professions take off with C titles, right? Chief Data officer, chief analytics officer, chief digital officer and chief technology officer. See, I off course is being there for a long time. And but I think these newer see positions. They're all very, very related, and they all kind of went to the same need which had to do with enterprise transformation, digital transformation, that enterprises chief digital officer, that's another and and people were all trying to essentially feel the elephants and they could only see part of it at the senior levels, and they came up with which have a role you know, seemed most meaningful to them. But really, all of us are trying to do the same job, which is to accelerate digital transformation in the enterprise. Your comment about you kind of see that the seat eels and sea deals now, uh, partnering up much more than in the past, and I think that's in available the major driving force full. That is, in my view, anyway. It's is artificial intelligence as people try to infuse artificial intelligence. Well, then it's very technical field. Still, it's not something that you know you can just hand over to somebody who has the business jobs, but not the deep technical chops to pull that off. And so, in the case off chief data officers that do have the technical jobs, you'll see them also pretty much heading up the I effort in total and you know, as I do for the IBM case, will be building the Data and AI Enablement internal platform for for IBM. But I think in other cases you you've got Chief date officers who are coming in from a different angle. You know, they built Marghera but the CTO now, because they have to. Otherwise you cannot get a I infused into the organization. >>So there were a lot of other priorities, obviously certainly digital transformation. We've been talking about it for years, but still in many organisations, there was a sense of, well, not on my watch, maybe a sense of complacency or maybe just other priorities. Cove. It obviously has changed that now one hundred percent of the companies that we talked to are really putting this digital transformation on the front burner. So how has that changed the role of CDO? Has it just been interpolate an acceleration of that reality, or has it also somewhat altered the swim lanes? >>I think I think it's It's It's Bolt actually, so I have a way of looking at this in my mind, the CDO role. But if you look at it from a business perspective, they're looking for three things. The CEO is looking for three things from the CDO. One is you know this person is going to help with the revenue off the company by enabling the production of new products, new products of resulting in new revenue and so forth. That's kind of one aspect of the monetization. Another aspect is the CEO is going to help with the efficiency within the organization by making data a lot more accessible, as well as enabling insights that reduce into and cycle time for major processes. And so that's another way that they have monitor. And the last one is a risk reduction that they're going to reduce the risk, you know, as regulations. And as you have cybersecurity exposure on incidents that you know just keep keep accelerating as well. You're gonna have to also step in and help with that. So every CDO, the way their senior leadership looks at them is some mix off three. And in some cases, one has given more importance than the other, and so far, but that's how they are essentially looking at it now. I think what digital transformation has done is it's managed to accelerate, accelerate all three off these outcomes because you need to attend to all three as you move forward. But I think that the individual balance that's struck for individuals reveals really depends on their ah, their company, their situation, who their peers are, who is actually leading the transformation and so >>forth, you know, in the value pie. A lot of the early activity around CDO sort of emanated from the quality portions of the organization. It was sort of a compliance waited roll, not necessarily when you started your own journey here. Obviously been focused on monetization how data contributes to that. But But you saw that generally, organizations, even if they didn't have a CDO, they had this sort of back office alliance thing that has totally changed the the in the value equation. It's really much more about insights, as you mentioned. So one of the big changes we've seen in the organization is that data pipeline you mentioned and and cycle time. And I'd like to dig into that a little bit because you and I have talked about this. This is one of the ways that a chief data officer and the related organizations can add the most value reduction in that cycle time. That's really where the business value comes from. So I wonder if we could talk about that a little bit and how that the constituents in the stakeholders in that in that life cycle across that data pipeline have changed. >>That's a very good question. Very insightful questions. So if you look at ah, company like idea, you know, my role in totally within IBM is to enable Ibn itself to become an AI enterprise. So infuse a on into all our major business processes. You know, things like our supply chain lead to cash well, process, you know, our finance processes like accounts receivable and procurement that soulful every major process that you can think off is using Watson mouth. So that's the That's the That's the vision that's essentially what we've implemented. And that's how we are using that now as a showcase for clients and customers. One of the things that be realized is the data and Ai enablement spots off business. You know, the work that I do also has processes. Now that's the pipeline you refer to. You know, we're setting up the data pipeline. We're setting up the machine learning pipeline, deep learning blank like we're always setting up these pipelines, And so now you have the opportunity to actually turn the so called EI ladder on its head because the Islander has to do with a first You collected data, then you curated. You make sure that it's high quality, etcetera, etcetera, fit for EI. And then eventually you get to applying, you know, ai and then infusing it into business processes. And so far, But once you recognize that the very first the earliest creases of work with the data those themselves are essentially processes. You can infuse AI into those processes, and that's what's made the cycle time reduction. And although things that I'm talking about possible because it just makes it much, much easier for somebody to then implement ai within a lot enterprise, I mean, AI requires specialized knowledge. There are pieces of a I like deep learning, but there are, you know, typically a company's gonna have, like a handful of people who even understand what that is, how to apply it. You know how models drift when they need to be refreshed, etcetera, etcetera, and so that's difficult. You can't possibly expect every business process, every business area to have that expertise, and so you've then got to rely on some core group which is going to enable them to do so. But that group can't do it manually because I get otherwise. That doesn't scale again. So then you come down to these pipelines and you've got to actually infuse AI into these data and ai enablement processes so that it becomes much, much easier to scale across another. >>Some of the CEOs, maybe they don't have the reporting structure that you do, or or maybe it's more of a far flung organization. Not that IBM is not far flung, but they may not have the ability to sort of inject AI. Maybe they can advocate for it. Do you see that as a challenge for some CEOs? And how do they so to get through that, what's what's the way in which they should be working with their constituents across the organization to successfully infuse ai? >>Yeah, that's it's. In fact, you get a very good point. I mean, when I joined IBM, one of the first observations I made and I in fact made it to a senior leadership, is that I didn't think that from a business standpoint, people really understood what a I met. So when we talked about a cognitive enterprise on the I enterprise a zaydi em. You know, our clients don't really understand what that meant, which is why it became really important to enable IBM itself to be any I enterprise. You know that. That's my data strategy. Your you kind of alluded to the fact that I have this approach. There are these five steps, while the very first step is to come up with the data strategy that enables a business strategy that the company's on. And in my case, it was, Hey, I'm going to enable the company because it wants to become a cloud and cognitive company. I'm going to enable that. And so we essentially are data strategy became one off making IBM. It's something I enterprise, but the reason for doing that the reason why that was so important was because then we could use it as a showcase for clients and customers. And so But I'm talking with our clients and customers. That's my role. I'm really the only role I'm playing is what I call an experiential selling there. I'm saying, Forget about you know, the fact that we're selling this particular product or that particular product that you got GPU servers. We've got you know what's an open scale or whatever? It doesn't really matter. Why don't you come and see what we've done internally at scale? And then we'll also lay out for you all the different pain points that we have to work through using our products so that you can kind of make the same case when you when you when you apply it internally and same common with regard to the benefit, you know the cycle, time reduction, some of the cycle time reductions that we've seen in my process is itself, you know, like this. Think about metadata business metadata generating that is so difficult. And it's again, something that's critical if you want to scale your data because you know you can't really have a good catalogue of data if you don't have good business, meditate. Eso. Anybody looking at what's in your catalog won't understand what it is. They won't be able to use it etcetera. And so we've essentially automated business metadata generation using AI and the cycle time reduction that was like ninety five percent, you know, haven't actually argue. It's more than that, because in the past, most people would not. For many many data sets, the pragmatic approach would be. Don't even bother with the business matter data. Then it becomes just put somewhere in the are, you know, data architecture somewhere in your data leg or whatever, you have data warehouse, and then it becomes the data swamp because nobody understands it now with regard to our experience applying AI, infusing it across all our major business processes are average cycle time reduction is seventy percent, so just a tremendous amount of gains are there. But to your point, unless you're able to point to some application at scale within the enterprise, you know that's meaningful for the enterprise, Which is kind of what the what the role I play in terms of bringing it forward to our clients and customers. It's harder to argue. I'll make a case or investment into A I would then be enterprise without actually being able to point to those types of use cases that have been scaled where you can demonstrate the value. So that's extremely important part of the equation. To make sure that that happens on a regular basis with our clients and customers, I will say that you know your point is vomited a lot off. Our clients and customers come back and say, Tell me when they're having a conversation. I was having a conversation just last week with major major financial service of all nations, and I got the same point saying, If you're coming out of regulation, how do I convince my leadership about the value of a I and you know, I basically responded. He asked me about the scale use cases You can show that. But perhaps the biggest point that you can make as a CDO after the senior readership is can we afford to be left up? That is the I think the biggest, you know, point that the leadership has to appreciate. Can you afford to be left up? >>I want to come back to this notion of seventy percent on average, the cycle time reduction. That's astounding. And I want to make sure people understand the potential impacts. And, I would say suspected many CEOs, if not most understand sort of system thinking. It's obviously something that you're big on but often times within organisations. You might see them trying to optimize one little portion of the data lifecycle and you know having. Okay, hey, celebrate that success. But unless you can take that systems view and reduce that overall cycle time, that's really where the business value is. And I guess my we're real question around. This is Every organization has some kind of Northstar, many about profit, and you can increase revenue are cut costs, and you can do that with data. It might be saving lives, but ultimately to drive this data culture, you've got to get people thinking about getting insights that help you with that North Star, that mission of the company, but then taking a systems view and that's seventy percent cycle time reduction is just the enormous business value that that drives, I think, sometimes gets lost on people. And these air telephone numbers in the business case aren't >>yes, No, absolutely. It's, you know, there's just a tremendous amount of potential on, and it's it's not an easy, easy thing to do by any means. So we've been always very transparent about the Dave. As you know, we put forward this this blueprint right, the cognitive enterprise blueprint, how you get to it, and I kind of have these four major pillars for the blueprint. There's obviously does this data and you're getting the data ready for the consummation that you want to do but also things like training data sets. How do you kind of run hundreds of thousands of experiments on a regular basis, which kind of review to the other pillar, which is techology? But then the last two pillars are business process, change and the culture organizational culture, you know, managing organizational considerations, that culture. If you don't keep all four in lockstep, the transformation is usually not successful at an end to end level, then it becomes much more what you pointed out, which is you have kind of point solutions and the role, you know, the CEO role doesn't make the kind of strategic impact that otherwise it could do so and this also comes back to some of the only appointee of you to do. If you think about how do you keep those four pillars and lock sync? It means you've gotta have the data leader. You also gotta have the technology, and in some cases they might be the same people. Hey, just for the moment, sake of argument, let's say they're all different people and many, many times. They are so the data leader of the technology of you and the operations leaders because the other ones own the business processes as well as the organizational years. You know, they've got it all worked together to make it an effective conservation. And so the organization structure that you talked about that in some cases my peers may not have that. You know, that's that. That is true. If the if the senior leadership is not thinking overall digital transformation, it's going to be difficult for them to them go out that >>you've also seen that culturally, historically, when it comes to data and analytics, a lot of times that the lines of business you know their their first response is to attack the quality of the data because the data may not support their agenda. So there's this idea of a data culture on, and I want to ask you how self serve fits into that. I mean, to the degree that the business feels as though they actually have some kind of ownership in the data, and it's largely, you know, their responsibility as opposed to a lot of the finger pointing that has historically gone on. Whether it's been decision support or enterprise data, warehousing or even, you know, Data Lakes. They've sort of failed toe live up to that. That promise, particularly from a cultural standpoint, it and so I wonder, How have you guys done in that regard? How did you get there? Many Any other observations you could make in that regard? >>Yeah. So, you know, I think culture is probably the hardest nut to crack all of those four pillars that I back up and you've got You've got to address that, Uh, not, you know, not just stop down, but also bottom up as well. As you know, period. Appear I'll give you some some examples based on our experience, that idea. So the way my organization is set up is there is a obviously a technology on the other. People who are doing all the data engineering were kind of laying out the foundational technical elements or the transformation. You know, the the AI enabled one be planning networks, and so so that are those people. And then there is another senior leader who reports directly to me, and his organization is all around adoptions. He's responsible for essentially taking what's available in the technology and then working with the business areas to move forward and make this make and infuse. A. I do the processes that the business and he is looking. It's done in a bottom upwards, deliberately set up, designed it to be bottom up. So what I mean by that is the team on my side is fully empowered to move forward. Why did they find a like minded team on the other side and go ahead and do it? They don't have to come back for funding they don't have, You know, they just go ahead and do it. They're basically empowered to do that. And that particular set up enabled enabled us in a couple of years to have one hundred thousand internal users on our Central data and AI enabled platform. And when I mean hundred thousand users, I mean users who were using it on a monthly basis. We company, you know, So if you haven't used it in a month, we won't come. So there it's over one hundred thousand, even very rapidly to that. That's kind of the enterprise wide storm. That's kind of the bottom up direction. The top down direction Waas the strategic element that I talked with you about what I said, Hey, be our data strategy is going to be to create, make IBM itself into any I enterprise and then use that as a showcase for plants and customers That kind of and be reiterated back. And I worked the senior leadership on that view all the time talking to customers, the central and our senior leaders. And so that's kind of the air cover to do this, you know, that mix gives you, gives you that possibility. I think from a peer to peer standpoint, but you get to these lot scale and to end processes, and that there, a couple of ways I worked that one way is we've kind of looked at our enterprise data and said, Okay, therefore, major pillars off data that we want to go after data, tomato plants, data about our offerings, data about financial data, that s and then our work full student and then within that there are obviously some pillars, like some sales data that comes in and, you know, been workforce. You could have contractors. Was his employees a center But I think for the moment, about these four major pillars off data. And so let me map that to end to end large business processes within the company. You know, the really large ones, like Enterprise Performance Management, into a or lead to cash generation into and risk insides across our full supply chain and to and things like that. And we've kind of tied these four major data pillars to those major into and processes Well, well, yes, that there's a mechanism they're obviously in terms off facilitating, and to some extent one might argue, even forcing some interaction between teams that are the way they talk. But it also brings me and my peers much closer together when you set it up that way. And that means, you know, people from the HR side people from the operation side, the data side technology side, all coming together to really move things forward. So all three tracks being hit very, very hard to move the culture fall. >>Am I also correct that you have, uh, chief data officers that reporting to you whether it's a matrix or direct within the division's? Is that right? >>Yeah, so? So I mean, you know, for in terms off our structure, as you know, way our global company, we're also far flung company. We have many different products in business units and so forth. And so, uh, one of the things that I realized early on waas we are going to need data officers, each of those business units and the business units. There's obviously the enterprise objective. And, you know, you could think of the enterprise objectives in terms of some examples based on what I said in the past, which is so enterprise objective would be We've gotta have a data foundation by essentially making data along these four pillars. I talked about clients offerings, etcetera, you know, very accessible self service. You have mentioned south, so thank you. This is where the South seven speaks. Comes it right. So you can you can get at that data quickly and appropriately, right? You want to make sure that the access control, all that stuff is designed out and you're able to change your policies and you'd swap manual. But, you know, those things got implemented very rapidly and quickly. And so you've got you've got that piece off off the off the puzzle due to go after. And then I think the other aspect off off. This is, though, when you recognize that every business unit also has its own objectives and they are looking at some of those things somewhat differently. So I'll give you an example. We've got data any our product units. Now, those CEOs right there, concern is going to be a lot more around the products themselves And how were monetizing those box and so they're not per se concerned with, You know, how you reduce the enter and cycle time off IBM in total supply chain so that this is my point. So they but they're gonna have substantial considerations and objectives that they want to accomplish. And so I recognize that early on, and we came up with this notion off a data officer council and I helped staff the council s. So this is why that's the Matrix to reporting that we talked about. But I selected some of the key Blair's that we have in those units, and I also made sure they were funded by the unit. So they report into the units because their paycheck is actually determined. Pilot unit and which makes them than aligned with the objectives off the unit, but also obviously part of my central approach so that I can disseminate it out to the organization. It comes in very, very handy when you are trying to do things across the company as well. So when we you know GDP our way, we have to get the company ready for Judy PR, I would say that this mechanism became a key key aspect of what enabled us to move forward and do it rapidly. Trouble them >>be because you had the structure that perhaps the lines of business weren't. Maybe is concerned about GDP are, but you had to be concerned with it overall. And this allowed you to sort of hiding their importance, >>right? Because think of in the case of Jeannie PR, they have to be a company wide policy and implementation, right? And if he did not have that structure already in place, it would have made it that much harder. Do you get that uniformity and consistency across the company, right, You know, So you will have to in the weapon that structure, but we already have it because way said Hey, this is around for data. We're gonna have these types of considerations that they are. And so we have this thing regular. You know, this man network that meat meets regularly every month, actually, and you know, when things like GDP are much more frequently than that, >>right? So that makes sense. We're out of time. But I wonder if we could just close if you could address the M I t CDO audience that probably this is the largest audience, Believe or not, now that it's that's virtual definitely expanded the audience, but it's still a very elite group. And the reason why I was so pleased that you agreed to do this is because you've got one of the more complex organizations out there and you've succeeded. And, ah, a lot of the hard, hard work. So what? What message would you leave the M I t CDO audience Interpol? >>So I would say that you know, it's it's this particular professional. Receiving a profession is, uh, if I have to pick one trait of let me pick two traits, I think what is your A change agent? So you have to be really comfortable with change things are going to change, the organization is going to look to you to make those changes. And so that's what aspect off your job, you know, may or may not be part of me immediately. But the those particular set of skills and characteristics and something that you know, one has to, uh one has to develop or time, And I think the other thing I would say is it's a continuous looming jaw. So you continue sexism and things keep changing around you and changing rapidly. And, you know, if you just even think just in terms off the subject areas, I mean this Syria today you've got to understand technology. Obviously, you've gotta understand data you've got to understand in a I and data science. You've got to understand cybersecurity. You've gotta understand the regulatory framework, and you've got to keep all that in mind, and you've got to distill it down to certain trends. That's that's happening, right? I mean, so this is an example of that is that there's a trend towards more regulation around privacy and also in terms off individual ownership of data, which is very different from what's before the that's kind of weather. Bucket's going and so you've got to be on top off all those things. And so the you know, the characteristic of being a continual learner, I think is a is a key aspect off this job. One other thing I would add. And this is All Star Coleman nineteen, you know, prik over nineteen in terms of those four pillars that we talked about, you know, which had to do with the data technology, business process and organization and culture. From a CDO perspective, the data and technology will obviously from consent, I would say most covert nineteen most the civil unrest. And so far, you know, the other two aspects are going to be critical as we move forward. And so the people aspect of the job has never bean, you know, more important down it's today, right? That's something that I find myself regularly doing the stalking at all levels of the organization, one on a one, which is something that we never really did before. But now we find time to do it so obviously is doable. I don't think it's just it's a change that's here to stay, and it ships >>well to your to your point about change if you were in your comfort zone before twenty twenty two things years certainly taking you out of it into Parliament. All right, thanks so much for coming back in. The Cuban addressing the M I t CDO audience really appreciate it. >>Thank you for having me. That my pleasant >>You're very welcome. And thank you for watching everybody. This is Dave a lot. They will be right back after this short >>break. You're watching the queue.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalizing and automating the data pipeline with data ops and with me is Interpol Bhandari who is the global chief data officer at IBM in Nepal has always great to see you thanks for coming on my pleasure you know the standard throw away question from guys like me is you know what keeps the chief data officer up at night well I know what's keeping you up at night it's kovat 19 how are you doing it's keeping keeping all of us yeah for sure so how you guys making out as a leader I'm interested in you know how you have responded with whether it's you know communications obviously you're doing much more stuff you know remotely you're not on airplanes certainly like you used to be but but what was your first move when you actually realized this was going to require a shift well I think one of the first things that I did was to test the ability of my organization who worked remotely this was well before the the recommendations came in from the government but just so that we wanted you know to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was good and so that was one of the first things we did along with that I think another major activity that we embarked on is even that we had created this central data and AI platform for IBM using our hybrid multi cloud approach how could that be adapting very very quickly you helped with the covert situation but those were the two big items that my team embarked on very quickly and again like I said this is well before there was any recommendations from the government or even internally within IBM any recommendations but B we decided that we wanted to run ahead and make sure that we were ready to ready to operate in that fashion and I believe a lot of my colleagues did the same yeah there's a there's a conversation going on right now just around productivity hits that people may be taking because they really weren't prepared it sounds like you're pretty comfortable with the productivity impact that you're achieving oh I'm totally comfortable with the productivity in fact I will tell you that while we've gone down this spot we've realized that in some cases the productivity is actually going to be better when people are working from home and they're able to focus a lot more on the work aspect you know and this could this runs the gamut from the nature of the job where you know somebody who basically needs to be in the front of a computer and is remotely taking care of operations you know if they don't have to come in their productivity is gonna go up somebody like myself who had a long drive into work you know which I would use on phone calls but now that entire time is can be used a lot more productivity but not maybe in a lot more productive manner so there is a we realize that that there's going to be some aspects of productivity that will actually be helped by the situation provided you're able to deliver the services that you deliver with the same level of quality and satisfaction that you've always done now there were certain other aspects where you know productivity is going to be affected so you know my team there's a lot of whiteboarding that gets done there are lots of informal conversations that spark creativity but those things are much harder to replicate in a remote in life so we've got a sense of you know where we have to do some work what things together versus where we were actually going to be more productive but all in all they are very comfortable that we can pull this off no that's great I want to stay on Kovac for a moment and in the context of just data and data ops and you know why now obviously with a crisis like this it increases the imperative to really have your data act together but I want to ask you both specifically as it relates to Co vid why data ops is so important and then just generally why at this this point in our time so I mean you know the journey we've been on they you know when I joined our data strategy centered around the cloud data and AI mainly because IBM's business strategy was around that and because there wasn't the notion of ái in enterprise right there was everybody understood what AI means for the consumer but for the enterprise people don't really understand what it meant so our data strategy became one of actually making IBM itself into an AI and a BA and then using that as a showcase for our clients and customers who look a lot like us to make them into a eye on the prize and in a nutshell what that translated to was that one had to in few AI into the workflow of the key business processes of enterprise so if you think about that workflow is very demanding why do you have to be able to deliver data and insights on time just when it's needed otherwise you can essentially slow down the whole workflow of a major process with but to be able to pull all that off you need to have your own data very very streamlined so that a lot of it is automated and you're able to deliver those insights as the people who are involved in the workflow needed so we've spent a lot of time while we were making IBM into an AI enterprise and infusing AI into our keepers and thus processes into essentially a data ops pipeline that was very very streamlined which then allowed us to very quickly adapt to the covert 19 situation and I'll give you one specific example that we'll go to you know how one would say one could essentially leverage that capability that I just talked about to do this so one of the key business processes that we had taken aim at was our supply chain you know we're a global company and our supply chain is critical we have lots of suppliers and they are all over the globe and we have different types of products so that you know it has a multiplicative fact is we go from each of those you have other additional suppliers and you have events you have other events you have calamities you have political events so we have to be able to very quickly understand the risk associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly so that was one of the key applications that we built on our central data and the Aqua and as part of a data ops pipeline that meant he ingested the ingestion of the several hundred sources of data had to be blazingly fast and also refreshed very very quickly also we had to then aggregate data from the outside from external sources that had to do with weather related events that had to do with political events social media feeds etcetera and overlay that on top of our map of interest with regard to our supply chain sites and also where they were supposed to deliver we'd also weaved in our capabilities here to track those shipments as they flowed and have that data flow back as well so that we would know exactly where where things were this is only possible because we had a streamlined data ops capability and we had built this central data Nai platform for IBM now you flip over to the covert 19 situation when go with 19 you know emerged and we began to realize that this was going to be a significant significant pandemic what we were able to do very quickly was to overlay the Kovach 19 incidents on top of our sites of interest as well as pick up what was being reported about those sites of interest and provide that over to our business continuity so this became an immediate exercise that we embarked but it wouldn't have been possible if you didn't have the foundation of the data ops pipeline as well as that central data Nai platform in place to help you do that very very quickly and adapt so so what I really like about this story and something that I want to drill into is it essentially a lot of organizations have a real tough time operationalizing AI and fusing it to use your word and the fact that you're doing it is really a good proof point that I want to explore a little bit so you're essentially there was a number of aspects of what you just described there was the data quality piece with your data quality in theory anyway is gonna go up with more data if you can handle it and the other was speed time to insight so you can respond more quickly if it's think about this Kovan situation if your days behind or weeks behind which is not uncommon you know sometimes even worse you just can't respond I mean these things change daily sometimes certainly within the day so is that right that's kind of the the business outcome and objective that you guys were after yes you know so trauma from an infused AI into your business processes by the overarching outcome metric that one focuses on is end to end cycle so you take that process the end-to-end process and you're trying to reduce the end-to-end cycle time by you know several factors several orders of magnitude we did for instance in my organization that have to do with the generation of metadata is data about data and that's usually a very time-consuming process and we've reduced that by over 95% by using AI you actually help in the metadata generation itself and that's applied now across the board for many different business processes that you know iBM has that's the same kind of principle that was you you'll be able to do that so that foundation essentially enables you to go after that cycle time reduction right off the bat so when you get to a situation like of open 19 situation which demands urgent action your foundation is already geared to deliver on that so I think actually we might have a graphic and then the second graphic guys if you bring up this second one I think this is Interpol what you're talking about here that sort of 95 percent reduction guys if you could bring that up would take a look at it so this is maybe not a co vid use case yeah here it is so that 95 percent reduction in in cycle time improving and data quality what we talked about there's actually some productivity metrics right this is what you're talking about here in this metadata example correct yeah yes the middle do that right it's so central to everything that one does with data I mean it's basically data about data and this is really the business metadata that we're talking about which is once you have data in your data Lee if you don't have business metadata describing what that data is then it's very hard for people who are trying to do things to determine whether they can even whether they even have access to the right data and typically this process has been done manually because somebody looks at the data they looks at the fields and they describe it and it could easily take months and what we did was we essentially use a deep learning and a natural language processing approach looked at all the data that we've had historically over an idea and we've automated the metadata generation so whether it was you know you were talking about both the data relevant for probit team or for supply chain or for a receivable process any one of our business processes this is one of those fundamental steps that one must go through to be able to get your data ready for action and if you were able to take that cycle time for that step and reduce it by 95% you can imagine the acceleration yeah and I liked it we were saying before you talk about the end to end a concept you're applying system thinking here which is very very important because you know a lot of a lot of points that I talked you'll they'll be they're so focused on one metric may be optimizing one component of that end to end but it's really the overall outcome that you're trying to achieve you you may sometimes you know be optimizing one piece but not the whole so that systems thinking is is very very important isn't it the system's thinking is extremely important overall no matter you know where you're involved in the process of designing the system but if you're the data guy it's incredibly important because not only does that give you an insight into the cycle time reduction but it also gives it clues you in into what standardization is necessary in the data so that you're able to support an eventual out you know a lot of people will go down the path of data governance and creation of data standard and you can easily boil the ocean trying to do that but if you actually start with an end-to-end view of your key processes and that by extension the outcomes associated with those processes as well as the user experience at the end of those processes and kind of then work backwards as to what are the standards that you need for the data that's going to feed into all that that's how you arrive at you know a viable practical data standards effort that you can essentially push forward with so there's there are multiple aspects when you take that end-to-end system you that helps the chief later one of the other tenets of data ops is really the ability across the organization for everybody to have visibility communications it's very key we've got another graphic that I want to show around the organizational you know in the right regime and this is a complicated situation for a lot of people but it's imperative guys if you bring up the first graphic it's imperative that organizations you know fine bring in the right stakeholders and actually identify those individuals that are going to participate so that there's full visibility everybody understands what their their roles are they're not in in silos so a guys if you could show us that first graphic that would be great but talk about the organization and the right regime they're Interpol yes yes I believe you're going to what you're gonna show up is actually my organization but I think it's yes it's very very illustrative of what one has to set up to be able to pull off the kind of impact you know so let's say we talked about that central data and AI platform that's driving the entire enterprise and you're infusing AI into key business processes like the supply chain you then create applications like the operational risk insights that we talked about and then extend it over to a faster merging and changing situation like the overt nineteen you need an organization that obviously reflects the technical aspects of the plan right so you have to have the data engineering arm and in my case there's a lot of emphasis around because that's one of those skill set areas that's really quite rare and but also very very powerful so they're the major technology arms of that there's also the governance arm that I talked about where you have to produce a set of standards and implement them and enforce them so that you're able to make this end-to-end impact but then there's also there's a there's an adoption where there's a there's a group that reports in to me very very you know empowered which essentially has to convince the rest of the organization to adopt but the key to their success has been in power in the sense that they are empowered to find like-minded individuals in our key business processes who are also empowered and if they agree they just move forward and go ahead and do it because you know we've already provided the central capabilities by central I don't mean they're all in one location we're completely global and you know it's it's it's a hybrid multi-cloud set up but it's central in the sense that it's one source to come for for trusted data as well as the expertise that you need from an AI standpoint to be able to move forward and deliver the business outcome so when these business schemes come together with the adoption that's where the magic hand so that's another another aspect of the organization that's critical and then we've also got a data officer council that I chair and that has to do with the people who are the chief data officer z' of the individual business units that we have and they're kind of my extended team into the rest of the organization and we leverage that bolt from a adoption of the platform standpoint but also in terms of defining and enforcing standard it helps us do want to come back the Ovid talked a little bit about business resiliency people I think you've probably seen the news that IBM's you know providing super computer resources to the government to fight coronavirus you've also just announced that some some RTP folks are helping first responders and nonprofits and providing capabilities for no charge which is awesome I mean it's the kind of thing look I'm sensitive the companies like IBM you know you don't want to appear to be ambulance-chasing in these times however IBM and other big tech companies you're in a position to help and that's what you're doing here so maybe you could talk a little bit about what you're doing in this regard and then we'll tie it up with just business resiliency and the importance of data right right so you know I'd explained the operational risk insights application that we had which we were using internally and be covert nineteen even be using it we were using it primarily to assess the risk to our supply chain from various events and then essentially react very very quickly to those through those events so you could manage the situation well we realize that this is something that you know several non government NGOs that big they could essentially use the ability because they have to manage many of these situations like natural disasters and so we've given that same capability to the NGOs to you and to help them to help them streamline their planning and their thinking by the same token but you talked about Oh with nineteen that same capability with the poet mine team data overlaid on top of them essentially becomes a business continuity planning and resilience because let's say I'm a supply chambers right now I can look the incidence of probe ignite and I can and I know where my suppliers are and I can see the incidence and I can say oh yes know this supplier and I can see that the incidence is going up this is likely to be affected let me move ahead and start making plans backup plans just in case it reaches a crisis level then on the other hand if you're somebody in our revenue planning you know on the finance side and you know where your keep clients and customers are located again by having that information overlaid with those sites you can make your own judgments and you can make your own assessment to do that so that's how it translates over into a business continuity and resilient resilience planning - we are internally doing that now - every department you know that's something that we are actually providing them this capability because we could build rapidly on what we had already done and to be able to do that and then as we get inside into what each of those departments do with that data because you know once they see that data once they overlay it to their sites of interest and this is you know anybody and everybody in IBM because no matter what department they're in there are going to be sites of interest that are going to be affected and they have an understanding of what those sites of interest mean in the context of the planning that they're doing and so they'll be able to make judgments but as we gain a better understanding of that we will automate those capabilities more and more for each of those specific areas and now you're talking about a comprehensive approach an AI approach to business continuity and resilience planning in the context of a large complicated organization like IBM which obviously will be of great interest to enterprise clients and customers right one of the things that we're researching now is trying to understand you know what about this crisis is gonna be permanent some things won't be but but we think many things will be there's a lot of learnings do you think that organizations will rethink business resiliency in this context that they might sub optimize profitability for example to be more prepared for crises like this with better business resiliency and what role would data play in that so no it's a very good question and timely question Dave so I mean clearly people have understood that with regard to such a pandemic the first line of beef right is it is it's not going to be so much on the medicine side because the vaccine is not even we won't be available for a period of time it has to go to development so the first line of defense is actually to take a quarantine like a pro like we've seen play out across the world and then that in effect results in an impact on the businesses right in the economic climate and the businesses there's an impact I think people have realized this now they will obviously factor this in into their into how they do business will become one of those things from if this is time talking about how this becomes permanent I think it's going to become one of those things that if you're a responsible enterprise you are going to be planning for you're going to know how to implement this on the second go-around so obviously you put those frameworks and structures in place and there will be a certain cost associated with them and one could argue that that could eat into the profitability on the other hand what I would say is because these two points really that these are fast emerging fluid situations you have to respond very very quickly to those you will end up laying out a foundation pretty much like we did which enables you to really accelerate your pipeline right so the data ops pipelines we talked about there there's a lot of automation so that you can react very quickly you know data ingestion very very rapidly that you're able to you know do that kind of thing the metadata generation just the entire pipeline that we're talking about that you're able to respond and very quickly bring in new data and then aggregated at the right levels infuse it into the workflows and then deliver it to the right people at the right time I will you know that will become a must now but once you do that you could argue that there is a cost associated with doing that but we know that the cycle time reductions on things like that they can run you know I mean I gave you the example of 95 percent you know on average we see like a 70% end to end cycle time era where we've implemented the approach that's been pretty pervasive with an idea across a business process so that in a sense in in essence then actually becomes a driver for profitability so yes it might you know this might back people into doing that but I would argue that that's probably something that's going to be very good long term for the enterprises involved and they'll be able to leverage that in their in their business and I think that just the competitive pressure of having to do that will force everybody down that path mean but I think it'll be eventually a good that end and cycle time compression is huge and I like what you're saying because it's it's not just a reduction in the expected loss during a crisis there's other residual benefits to the organization Interpol thanks so much for coming on the cube and sharing this really interesting and deep case study I know there's a lot more information out there so really appreciate your time all right take care buddy thanks for watching and this is Dave Allante for the cube and we will see you next time [Music]

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Everybody welcome this special digital presentation where we're covering the topic of data ops and specifically how IBM is really operationalize ing and automating the data pipeline with data office. And with me is Interpol Bhandari, who is the global chief data officer at IBM and Paul. It's always great to see you. Thanks for coming on. >>My pleasure. >>So, you know the standard throwaway question from guys like me And you know what keeps the chief data officer up at night? Well, I know what's keeping you up that night. It's coverted 19. How you >>doing? It's keeping keeping all of us. >>Yeah, for sure. Um, So how are you guys making out as a leader I'm interested in, You know, how you have responded would whether it's communications. Obviously you're doing much more stuff you remotely You're not on airplanes. Certainly like you used to be. But But what was your first move when you actually realized this was going to require a shift? >>Well, I think one of the first things that I did wants to test the ability of my organization, You work remotely. This was well before the the recommendations came in from the government just so that we wanted to be sure that this is something that we could pull off if there were extreme circumstances where even everybody was. And so that was one of the first things we did along with that. I think another major activity that's being boxed off is even that we have created this Central Data and AI platform for idea using our hybrid, multi cloud approach. How could that the adaptive very, very quickly help them look over the city? But those were the two big items that my team and my embarked on and again, like I said, this is before there was any recommendations from the government or even internally, within IBM. Have any recommendations be? We decided that we wanted to run ahead and make sure that we were ready to ready to operate in that fashion. And I believe a lot of my colleagues did the same. Yeah, >>there's a there's a conversation going on right now just around productivity hits that people may be taking because they really weren't prepared it sounds like you're pretty comfortable with the productivity impact that you're achieving. >>Oh, I'm totally comfortable with the politics. I mean, in fact, I will tell you that while we've gone down this spot, we've realized that in some cases the productivity is actually going to be better when people are working from home and they're able to focus a lot more on the work, you know, And this could. This one's the gamut from the nature of the jaw, where you know somebody who basically needs to be in the front of the computer and is remotely taking care of operations. You know, if they don't have to come in, their productivity is going to go up Somebody like myself who had a long drive into work, you know, which I would use a phone calls, but that that entire time it can be used a lot more productivity, locked in a lot more productive manner. So there is. We realized that there's going to be some aspect of productivity that will actually be helped by the situation. Why did you are able to deliver the services that you deliver with the same level of quality and satisfaction that you want Now there were certain other aspect where you know the whole activity is going to be effective. So you know my team. There's a lot off white boarding that gets done there lots off informal conversations that spot creativity. But those things are much harder to replicate in a remote and large. So we've got a sense off. You know where we have to do some work? Well, things together. This is where we're actually going to be mobile. But all in all, they're very comfortable that we can pull this off. >>That's great. I want to stay on Cove it for a moment and in the context of just data and data ops, and you know why Now, obviously, with a crisis like this, it increases the imperative to really have your data act together. But I want to ask you both specifically as it relates to covert, why Data office is so important. And then just generally, why at this this point in time, >>So, I mean, you know, the journey we've been on. Thank you. You know, when I joined our data strategy centered around cloud data and ai, mainly because IBM business strategy was around that, and because there wasn't the notion off AI and Enterprise, right, there was everybody understood what AI means for the consumer. But for the enterprise, people don't really understand. Well, what a man. So our data strategy became one off, actually making IBM itself into an AI and and then using that as a showcase for our clients and customers who look a lot like us, you make them into AI. And in a nutshell, what that translated to was that one had two in few ai into the workflow off the key business processes off enterprise. So if you think about that workflow is very demanding, right, you have to be able to deliver. They did not insights on time just when it's needed. Otherwise, you can essentially slow down the whole workflow off a major process within an end. But to be able to pull all that off you need to have your own data works very, very streamlined so that a lot of it is automated and you're able to deliver those insights as the people who are involved in the work floor needed. So we've spent a lot of time while we were making IBM into any I enterprise and infusing AI into our key business processes into essentially a data ops pipeline that was very, very streamlined, which then allowed us to do very quickly adapt do the over 19 situation and I'll give you one specific example that will go to you know how one would someone would essentially leverage that capability that I just talked about to do this. So one of the key business processes that we have taken a map, it was our supply chain. You know, if you're a global company and our supply chain is critical, you have lots of suppliers, and they are all over the globe. And we have different types of products so that, you know, has a multiplication factors for each of those, you have additional suppliers and you have events. You have other events, you have calamities, you have political events. So we have to be able to very quickly understand the risks associated with any of those events with regard to our supply chain and make appropriate adjustments on the fly. So that was one off the key applications that we built on our central data. And as Paul about data ops pipeline. That meant we ingest the ingestion off those several 100 sources of data not to be blazingly fast and also refresh very, very quickly. Also, we have to then aggregate data from the outside from external sources that had to do with weather related events that had to do with political events. Social media feeds a separate I'm overly that on top off our map of interest with regard to our supply chain sites and also where they were supposed to deliver. We also leave them our capabilities here, track of those shipments as they flowed and have that data flow back as well so that we would know exactly where where things were. This is only possible because we had a streamline data ops capability and we have built this Central Data and AI platform for IBM. Now you flip over to the Coleman 19 situation when Corbyn 19 merged and we began to realize that this was going to be a significant significant pandemic. What we were able to do very quickly wants to overlay the over 19 incidents on top of our sites of interest, as well as pick up what was being reported about those sites of interests and provide that over to our business continuity. So this became an immediate exercise that we embark. But it wouldn't have been possible if you didn't have the foundation off the data office pipeline as well as that Central Data and AI platform even plays to help you do that very, very quickly and adapt. >>So what I really like about this story and something that I want to drill into is it Essentially, a lot of organizations have a really tough time operational izing ai, infusing it to use your word and the fact that you're doing it, um is really a good proof point that I want to explore a little bit. So you're essentially there was a number of aspects of what you just described. There was the data quality piece with your data quality in theory, anyway, is going to go up with more data if you can handle it and the other was speed time to insight, so you can respond more quickly if it's talk about this Covic situation. If you're days behind for weeks behind, which is not uncommon, sometimes even worse, you just can't respond. I mean, the things change daily? Um, sometimes, Certainly within the day. Um, so is that right? That's kind of the the business outcome. An objective that you guys were after. >>Yes, you know, So Rama Common infuse ai into your business processes right over our chain. Um, don't come metric. That one focuses on is end to end cycle time. So you take that process the end to end process and you're trying to reduce the end to end cycle time by several factors, several orders of magnitude. And you know, there are some examples off things that we did. For instance, in my organ organization that has to do with the generation of metadata is data about data. And that's usually a very time consuming process. And we've reduced that by over 95%. By using AI, you actually help in the metadata generation itself. And that's applied now across the board for many different business processes that, you know IBM has. That's the same kind of principle that was you. You'll be able to do that so that foundation essentially enables you to go after that cycle time reduction right off the bat. So when you get to a situation like over 19 situation which demands urgent action. Your foundation is already geared to deliver on that. >>So I think actually, we might have a graphic. And then the second graphic, guys, if you bring up a 2nd 1 I think this is Interpol. What you're talking about here, that sort of 95% reduction. Ah, guys, if you could bring that up, would take a look at it. So, um, this is maybe not a cove. It use case? Yeah. Here it is. So that 95% reduction in the cycle time improvement in data quality. What we talked about this actually some productivity metrics, right? This is what you're talking about here in this metadata example. Correct? >>Yeah. Yes, the metadata. Right. It's so central to everything that one does with. I mean, it's basically data about data, and this is really the business metadata that you're talking about, which is once you have data in your data lake. If you don't have business metadata describing what that data is, then it's very hard for people who are trying to do things to determine whether they can, even whether they even have access to the right data. And typically this process is being done manually because somebody looks at the data that looks at the fields and describe it. And it could easily take months. And what we did was we essentially use a deep learning and natural language processing of road. Look at all the data that we've had historically over an idea, and we've automated metadata generation. So whether it was, you know, you were talking about the data relevant for 19 or for supply chain or far receivable process any one of our business processes. This is one of those fundamental steps that one must go through. You'll be able to get your data ready for action. And if you were able to take that cycle time for that step and reduce it by 95% you can imagine the acceleration. >>Yeah, and I like you were saying before you talk about the end to end concept, you're applying system thinking here, which is very, very important because, you know, a lot of a lot of clients that I talk to, they're so focused on one metric maybe optimizing one component of that end to end, but it's really the overall outcome that you're trying to achieve. You may sometimes, you know, be optimizing one piece, but not the whole. So that systems thinking is very, very important, isn't it? >>The systems thinking is extremely important overall, no matter you know where you're involved in the process off designing the system. But if you're the data guy, it's incredibly important because not only does that give you an insight into the cycle time reduction, but it also give clues U N into what standardization is necessary in the data so that you're able to support an eventual out. You know, a lot of people will go down the part of data governance and the creation of data standards, and you can easily boil the ocean trying to do that. But if you actually start with an end to end, view off your key processes and that by extension the outcomes associated with those processes as well as the user experience at the end of those processes and kind of then work backwards as one of the standards that you need for the data that's going to feed into all that, that's how you arrive at, you know, a viable practical data standards effort that you can essentially push forward so that there are multiple aspect when you take that end to end system view that helps the chief legal. >>One of the other tenants of data ops is really the ability across the organization for everybody to have visibility. Communications is very key. We've got another graphic that I want to show around the organizational, you know, in the right regime, and it's a complicated situation for a lot of people. But it's imperative, guys, if you bring up the first graphic, it's a heritage that organizations, you know, find bringing the right stakeholders and actually identify those individuals that are going to participate so that this full visibility everybody understands what their roles are. They're not in silos. So, guys, if you could show us that first graphic, that would be great. But talk about the organization and the right regime there. Interpol? >>Yes, yes, I believe you're going to know what you're going to show up is actually my organization, but I think it's yes, it's very, very illustrative what one has to set up. You'll be able to pull off the kind of impact that I thought So let's say we talked about that Central Data and AI platform that's driving the entire enterprise, and you're infusing AI into key business processes like the supply chain. Then create applications like the operational risk in size that we talked about that extended over. Do a fast emerging and changing situation like the over 19. You need an organization that obviously reflects the technical aspects of the right, so you have to have the data engineering on and AI on. You know, in my case, there's a lot of emphasis around deep learning because that's one of those skill set areas that's really quite rare, and it also very, very powerful. So uh huh you know, the major technology arms off that. There's also the governance on that I talked about. You have to produce the set off standards and implement them and enforce them so that you're able to make this into an impact. But then there's also there's a there's an adoption there. There's a There's a group that reports into me very, very, you know, Empowered Group, which essentially has to convince the rest of the organization to adopt. Yeah, yeah, but the key to their success has been in power in the sense that they're on power. You find like minded individuals in our key business processes. We're also empowered. And if they agree that just move forward and go and do it because you know, we've already provided the central capabilities by Central. I don't mean they're all in one location. You're completely global and you know it's it's It's a hybrid multi cloud set up, but it's a central in the sense that it's one source to come for for trusted data as well as the the expertise that you need from an AI standpoint to be able to move forward and deliver the business out. So when these business teams come together, be an option, that's where the magic happens. So that's another another aspect of the organization that's critical. And then we've also got, ah, Data Officer Council that I chair, and that has to do with no people who are the chief data officers off the individual business units that we have. And they're kind of my extended teams into the rest of the organization, and we levers that bolt from a adoption off the platform standpoint. But also in terms of defining and enforcing standards. It helps them stupid. >>I want to come back over and talk a little bit about business resiliency people. I think it probably seen the news that IBM providing supercomputer resource is that the government to fight Corona virus. You've also just announced that that some some RTP folks, um, are helping first responders and non profits and providing capabilities for no charge, which is awesome. I mean, it's the kind of thing. Look, I'm sensitive companies like IBM. You know, you don't want to appear to be ambulance chasing in these times. However, IBM and other big tech companies you're in a position to help, and that's what you're doing here. So maybe you could talk a little bit about what you're doing in this regard. Um, and then we'll tie it up with just business resiliency and importance of data. >>Right? Right. So, you know, I explained that the operational risk insights application that we had, which we were using internally, we call that 19 even we're using. We're using it primarily to assess the risks to our supply chain from various events and then essentially react very, very quickly. Do those doodles events so you could manage the situation. Well, we realize that this is something that you know, several non government NGOs that they could essentially use. There's a stability because they have to manage many of these situations like natural disaster. And so we've given that same capability, do the NGOs to you and, uh, to help that, to help them streamline their planning. And there's thinking, by the same token, But you talked about over 19 that same capability with the moment 19 data over layed on double, essentially becomes a business continuity, planning and resilience. Because let's say I'm a supply chain offers right now. I can look at incidents off over night, and I can I know what my suppliers are and I can see the incidents and I can say, Oh, yes, no, this supplier and I can see that the incidences going up this is likely to be affected. Let me move ahead and stop making plans backup plans, just in case it reaches a crisis level. On the other hand, if you're somebody in revenue planning, you know, on the finance side and you know where you keep clients and customers are located again by having that information over laid that those sites, you can make your own judgments and you can make your own assessment to do that. So that's how it translates over into business continuity and resolute resilience planning. True, we are internally. No doing that now to every department. You know, that's something that we're actually providing them this capability because we build rapidly on what we have already done to be able to do that as we get inside into what each of those departments do with that data. Because, you know, once they see that data, once they overlay it with their sights of interest. And this is, you know, anybody and everybody in IBM, because no matter what department they're in, there are going to decide the interests that are going to be affected. And they haven't understanding what those sites of interest mean in the context off the planning that they're doing and so they'll be able to make judgments. But as we get a better understanding of that, we will automate those capabilities more and more for each of those specific areas. And now you're talking about the comprehensive approach and AI approach to business continuity and resilience planning in the context of a large IT organization like IBM, which obviously will be of great interest to our enterprise, clients and customers. >>Right? One of the things that we're researching now is trying to understand. You know, what about this? Prices is going to be permanent. Some things won't be, but we think many things will be. There's a lot of learnings. Do you think that organizations will rethink business resiliency in this context that they might sub optimize profitability, for example, to be more prepared crises like this with better business resiliency? And what role would data play in that? >>So, you know, it's a very good question and timely fashion, Dave. So I mean, clearly, people have understood that with regard to that's such a pandemic. Um, the first line of defense, right is is not going to be so much on the medicine side because the vaccine is not even available and will be available for a period of time. It has to go through. So the first line of defense is actually think part of being like approach, like we've seen play out across the world and then that in effect results in an impact on the business, right in the economic climate and on the business is there's an impact. I think people have realized this now they will honestly factor this in and do that in to how they do become. One of those things from this is that I'm talking about how this becomes a permanent. I think it's going to become one of those things that if you go responsible enterprise, you are going to be landing forward. You're going to know how to implement this, the on the second go round. So obviously you put those frameworks and structures in place and there will be a certain costs associated with them, and one could argue that that would eat into the profitability. On the other hand, what I would say is because these two points really that these are fast emerging fluid situations. You have to respond very, very quickly. You will end up laying out a foundation pretty much like we did, which enables you to really accelerate your pipeline, right? So the data ops pipelines we talked about, there's a lot of automation so that you can react very quickly, you know, data injection very, very rapidly that you're able to do that kind of thing, that meta data generation. That's the entire pipeline that you're talking about, that you're able to respond very quickly, bring in new data and then aggregated at the right levels, infuse it into the work flows on the delivery, do the right people at the right time. Well, you know that will become a must. But once you do that, you could argue that there's a cost associated with doing that. But we know that the cycle time reductions on things like that they can run, you know? I mean, I gave you the example of 95% 0 you know, on average, we see, like a 70% end to end cycle time where we've implemented the approach, and that's been pretty pervasive within IBM across the business. So that, in essence, then actually becomes a driver for profitability. So yes, it might. You know this might back people into doing that, but I would argue that that's probably something that's going to be very good long term for the enterprises and world, and they'll be able to leverage that in their in their business and I think that just the competitive director off having to do that will force everybody down that path. But I think it'll be eventually ago >>that end and cycle time. Compression is huge, and I like what you're saying because it's it's not just a reduction in the expected loss during of prices. There's other residual benefits to the organization. Interpol. Thanks so much for coming on the Cube and sharing this really interesting and deep case study. I know there's a lot more information out there, so really appreciate your done. >>My pleasure. >>Alright, take everybody. Thanks for watching. And this is Dave Volante for the Cube. And we will see you next time. Yeah, yeah, yeah.

(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)

(upbeat music) >> Live from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to Moscone everybody, you're watching theCUBE, the leader in live tech coverage. This is day three of our coverage of IBM Think, at the newly renovated Moscone Center. I'm here with John Furrier, I'm Dave Vallante. Inderpal Bhandari is here, he's the global chief data officer at IBM, longtime CUBE alumn. Inderpal, great to see you again. >> It's my pleasure. >> You know, we met several years ago. You had just started as the chief data officer. You've now built out a global team, we've seen the blueprint that you've created, customers have begun to adopt that, we've talked to many of them, but give us the update. What's happening here at Think? you've given some talks and what's new? >> So, I think you covered the main points well. It's been about three years, and when I came on board, one of the promises I actually made to our clients, was that we were going to make IBM itself into an AI enterprise and then use those lessons to help our clients make their enterprises AI enterprises as well. Because a lot of our clients look very much like us, right? They're large, complicated organizations. So that's the journey we've been on and we've been progressing on that very well. You know, we created the data and AI backbone for the company. Now we've got various IBM processes that are making use of that backbone to introduce an AI capability, Watson, into their processes. And these range from transactional processes like accounts receivable all the way to analytical processes like those done by our chief analytics office. The entire platform and backbone is essentially the one that we've built. >> When we first met, you laid out a prescription of the things that a chief data officer should be focused on. The first thing you said is, "You've got to understand the relationship between data and monetization." And a lot of people confused in the early days of big data, oh, I got to monetize my data, I have to package it and sell it. And that's not what what you meant. I mean, it could be as simple as, how can you use data to save money? So, how has that gone, that message, how's it going internally and both externally? >> Yeah, I think data monetization is all about creating value for the company using data. And there's many parts to it. It depends on the business strategy that the company is following. Because you want to enable that. That's one way to make money. If they're able to better implement their business strategy because of certain data, then that's going to monetize and monetize far more rapidly than anything you could package and sell. The other possibility is you could take an operation that's critical to the company and make it a lot more efficient and accurate. That also could release billions of dollars in value. So it depends on the company itself. So for the case of IBM and other parts to monetization, is also enabling and helping our product partners, you know, the products that we're using in our data and AI backbone, the IBM products, and we are running through all that, so that they can then change their roadmaps based on the actual scale use cases that we've put together. So there are many different paths to monetization within the company. It depends on the specific case, but eventually it's about tying back to the business strategy and figuring out along the lines of whether you're creating new products, enabling additional revenue or efficiencies, or accuracy. It comes back to those kinds of outcomes. >> So essentially the data value, it's like beauty is in the eye of the beholder. It's contextual to the business. There's no one general purpose data implementation, right? I mean that's what you're getting at right here? >> Yes, I mean, it's not so much the implementation, it's the actual part that you take forward. It's got to address certain business outcomes, right? So the generalization is at that level, but one company might pick a very different outcome from another company. And so as a result, what you build, even though the lower levels of the stack might be the same, what you end up delivering and so forth will look very different. >> Inderpal, talk about your journey within IBM. I liked the narrative of let's do it for ourselves and then share that learnings with the customers. What outcomes were you trying to do internally at IBM to get right and then to bring to the customers? What were those key learnings? What did you learn? What was the outcome? >> Yeah, no, absolutely. So, there are many different outcomes because each process has its own outcome, right? Accounts receivable, they would have days sales outstanding, that would be for procurement, it would be the time to finish a deal. But eventually you can generalize it by saying it's all around cycle time, end to end cycle time for a process. You want to reduce that and reduce that dramatically using artificial intelligence. So that's been our main outcome that I've been focused on across all our different processes, including my own processes. Now, I think I've mentioned this in the past as well, that eventually it's not so much about technology, as it is about other factors that also accompany technology. You have the data itself, how do you prepare it, make sure that it's ready? But also the cultural aspects of the change, the organizational considerations, the business process changes, people's jobs are changing. How do you make sure that they're trained to do it the new way? How do you tap into the legacy stuff that you've got locked into legacy, and then unlock that and make that into AI processes? So there's a lot of work like that, that has to go across the lines of not just technology but data, organizational considerations, business process change. And that's the blueprint that Dave was talking about. >> Jenny made a big deal at our talk yesterday about trust the stewards of trust, your data, what does that mean specifically from a data standpoint? Does that mean you're not going to appropriate our data to serve ads? Does that mean you're going to secure the data with technology? What does it mean from your perspective? >> It's again, actually trusts cuts all across the stack. So with regard to data and clients, from our standpoint, what that means is the client's data is their data. It's going to remain their data, we will not make use of that data outside of what the client actually authorizes us to do. But not only that, we go even further and we say insights drawn from that data also belong to the client. And the reason we're able to do that is because our business model doesn't depend on the network effect as such, right? In terms of capturing data and then amassing a lot of it, learning from it. You know, getting data from A, but benefiting ourselves and C, right? That's not our model because we're in just in a different world. Our interests have aligned with the client. So it's all about making sure that their data stays their data, and the insights also stay their insights. We have no interest in actually capitalizing or monetizing the intellectual capital that our AI systems capture when working with the clients. That's why it's got to remain there. >> Are those discussions with clients evolving to the point where your commercial terms are evolving? I mean, are they sort of pushing you for different or extended commercial terms that actually explicitly state that? And are you involved in that? >> You know, those terms, we just made them available. So clients can pick up those terms. We didn't have to be pushed there, we already knew that this is because of the nature of AI and when we started working this within IBM, we realize that AI would become central to every process. Which means that it's capturing not only data, but also the intellectual capital of the company. And then if we put ourselves in the shoes of anybody else, any client who's looking at that, they would want, they'd be very sensitive to how you go about doing that. So we put those terms in right off the bat. So the clients have, they've got offerings where they can essentially choose, yeah, this is going to stay ours, you can't use it for anything else, just use it for precisely what we want you to do. That's just part of our standard approach now. >> I talked about this chapter two of the cloud, Jenny mentioned that kind of a nice reference. It's an attention grabber. Okay, chapter two, next level, cloud. But I want to get your perspective on next level data. What are you seeing the digital 2.0 or the digital generation the digitization economy happening to processes? You mentioned processes are key. How our processes changing with cloud, with data, with mobile, with these online digital assets and processes? What's changed to these processes that you see? Generally speaking or specifically? >> So, one aspect is, and this is why we refer to it as cognitive or augmented intelligence, processes are changing so that the decision makers have access to an intelligence system that helps them do a better job with the decision. Be more accurate, be quicker, et cetera, et cetera, right? Harness the whole data explosion to our advantage so that you can actually make a better decision. So that's one aspect of the process changes. I think the other aspect is the average enterprise makes use of nine different clouds. So when we look at that and we begin to understand the complexity that underlies that, for an enterprise, right? Being able to manage across these different clouds, and when you couple that also with on-prem systems, private clouds, because clients say well, for our data, we really don't want it on a public cloud, we want to do it privately. To manage across all those environments is very tough. It's very difficult. And so from a data standpoint, you have that same complexity extending into the data space. So now I worry about things like, well, we've got to make sure that if we ingest data once somewhere, we should be able to use it anywhere in an inappropriate way, right? In a trusted, governed, secured way. How do you do that? That's an example of the complexities that you have to solve as you go through this new environment. That's the 2.O. >> Knowing you ingested it just to begin with, is a good start, right? >> Yes, but being able to use it everywhere in a way that's secure, I mean, 'cause you're opening up a lot of flexibility, but then you also have to make sure that this is a trustworthy-- >> So the processes are increasing in terms of the capability, decision making, and efficiency, so you now have more process potential that's dynamic coming online, it's not just that blocking and tackling straight process, it's baked, we don't touch it. It's getting more dynamic. >> This is new ground, but nobody, I mean is, that's why I think Jenny drew the distinction between 1.0 and 2.0. 1.0 was essentially, think of it as single cloud. 2.0 is multi-cloud and things are different. Whether it be from a data standpoint, whether it be from the standpoint of products, you know, now you want products, you run them once you, I mean you write them once, you should be able to run them everywhere, right? Again, appropriately, that's the key part of this, right? In a secure, trusted manner. You can't take something that's running on one side very securely and then you start running it somewhere else and it's no longer secure, right? Then it doesn't work. >> So Inderpal, independent of the complexities of hybrid cloud, which you just sort of articulated, what are some of the challenges that you see with regard to people getting their data house in order? I mean, we definitely still see complacency. People say, ah, you know, we're a bank, we're making a lot of money, we don't really have to transform. Or, by the time we have to do it, I'll be retired. There seems to be still a lack of sense of urgency for some customers. Is that a challenge and what are some of the other challenges that you see, even maybe for those guys who want to lean in? >> I think at least what I've been seeing over the last three years that the awareness around AI has increased tremendously. And even within the last three years, clients now generally don't question that they need to go down that route. >> They feel the need to go down that route. They understand that there's a competitive advantage here and there's a danger of being left behind, but their biggest question now is where do I start? How do I do this in a way that makes me comfortable, right? So that I don't really end up losing the house while I try to go down that path. And I think that's the central need, that's the central challenge that they face, and that's exactly what we try to-- >> So they don't want to over rotate to something that's not going to give them a business return. So what do you tell them in that case? Focus on something that's going to drop, you know, save some money to the bottom line or let's try a little RPA project, or where do you start? >> You know, what we found is from an AI standpoint, you can do point projects, but you'll only get incremental value by doing those. What you really need to do is to make the whole enterprise an AI enterprise. So that every process, even the most, what seemed like the most mundane decisions. I might have told you this story before Dave, but there's somebody in my organization who labels whether the client we're working with is a government-owned entity or not. >> Okay, no, I didn't know that. >> Yes, and if you think about it, that's, you can think it's just a classification task based on what you know, but if you're able to harness the latest news releases, the latest PR releases that are coming up, you're going to make a much better decision. So it becomes an AI task. And think of all the tens of thousands of such decisions that are being made within an enterprise, and you make them more effective through AI. That's the AI enterprise. That's the promise. That's where you're going to get, not just incremental change, but monumental change. It'll just completely change the company. >> Right, so you're saying fundamentally, you've got to change the company. And so now there's a cultural aspect of that, which is obviously another challenge. People don't have the skill sets, they don't have the mindset, How are you seeing customers deal with that and how are you advising them deal with that? >> Yeah, so we've been eating our own cooking on this, so we've been through this, we know where the warts are, we know where the pitfalls are, and those are major pitfalls. You have to be prepared to address those, you know? So for instance, retraining the workforce is a major, major aspect that you have to address right off the bat if you go down this spot at scale. If you do a point project, yeah, there's no problem, right? You'll make sure you'll be able to do it. >> Low risk, yeah. >> Yeah, but if you're going to do this at scale, then the technology moves very fast. You've got to get the workforce at least comfortable to the extent that they need to do their jobs to be able to use these systems. And so you need to do that en masse as well, right? Otherwise, people will not be able to adopt it, and you won't get the desired return. The point I made about legacy, where literally, you could have billions of dollars that are locked in legacy and so it may not be that easy to apply the AI systems in that context. You have to think through that to get the maximum value of these things. So these are all aspects that go to culture, to change. You know, my boss, he keeps telling me that there are only two words to describe my job. That's not data officer, that's change agent. >> Yeah, right. >> Awesome, awesome. >> Good deal, so we have to wrap. John and I love storytelling. What's the story of IBM Think 2019, from your perspective? >> Oh, I think it's just been such a dynamic, vibrant conference. I see the energy, I think people are understanding the whole notion of the 2.0 and what it entails as the future is unfolding. And it's just been a terrific conference. >> Well, it's great to have you on theCUBE again and it's been marvelous to watch your progression over the last three years. Thanks so much for coming on and sharing. >> It's a pleasure, thank you both. You're welcome, all right, keep it right there, everybody. John and I will be back with our next guest. We're live from IBM Think, 2019. You're watching theCUBE, be right back. (upbeat music)

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

>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit, 2018 brought to you by IBM. >> Welcome back to San Francisco everybody. We're here covering the IBM CDO strategy summit. You're watching theCUBE, the leader and live tech coverage hashtag IBM CDO. Beth Smith is here, she's the general manager at Watson data and AI at IBM and of course Inderpal Bandari who's the global chief data officer at IBM. Folks, welcome back to theCUBE. It's great to see you both again. >> Good to be back. >> So I love these shows, they're intimate, practitioner really they're absorbing everything. You're getting some good questions, some good back and forth but Beth share with us what you're hearing from customers. What matters for enterprises right now in the context of the cognitive enterprise, the AI enterprise. >> So you know customers are looking at how did they get the benefit? They recognize that they've got a lot of valuable data, data that we haven't always called data. Sometimes it's documents and journals and other kinds of really unstructured things and they want to determine how can they get value from that and they look out and compare it to maybe consumer things and recognize they don't have the same volume of that. So it's important for customers, how do they get started and I would tell you that most of them start with a small project, they see value with that quickly they then say, okay how do we increment and grow from that. >> So Inderpal you had said I think I got this right, this is your fourth CDO gig. You're not new to this rodeo. Were you the first healthcare CDO is that right? >> I was. >> Dave: Okay you got it all started. >> There were four of us at that time. >> Okay so forth and four okay I did get that right. So you obviously bring a lot of experience here and one of the things you stressed today in your talk is you basically want to showcase IBM so you're applying sort of data enterprise data strategies to IBM and then you showcase that to your clients. Talk about that a little bit. >> Yeah I mean if you think about it, we are the quintessential complex enterprise. We're global, we're far-flung, we have literally thousands of products. We acquire companies, we move forward at a global scale and also we are always competing at a global scale. So there literally is that complexity that enterprises face which all our customers who are the large enterprises have to also deal with. So given all that we felt that the best way to talk about an AI enterprise is to use ourselves as a showcase. >> Okay Beth, I got to ask you about Watson's law. Okay so we had Moore's law we all know what that is. Metcalfe's law the network effect, Watson's law and I have a noodling on this a little bit. We're entering a new era which I think is underscored by... and names matter. We use a parlance in our industry whether it's cloud or a big data or internet or whatever it is and so we're trying to sort of figure out what this new era is like. What do you envision as Watson's law. I'd love to have a little riff on that. >> So first of all as we look at all those things and compare them back, they're all about opportunities to scale and how things changed because of a new scaling effect. So I would argue that the one we're in now, which we like to call Watson's law the future will determine what it's actually called is about scaling knowledge and applying knowledge so it's about how to use AI machine learning applied to data, all forms of data and turn that into knowledge and that's what's going to separate people and I would tell you that's also going to come back and give the incumbents an opportunity because the incumbents are strong in their industries, in their domains, they can leverage the data that they have, the knowledge and experience they have and then use that for how do they disrupt and really become the disruptors of the future. >> So okay what about the math behind this? I'm kind of writing down some notes as you were talking so my version of Watson's law and love your comment is innovation in the future and the current is going to be a function of the data, your ability to apply AI or cognitive to that data and then your ability to your point scale, the cloud economics. Does that make sense to you guys, is that where innovation is going to come? >> It's true but I have to go back at this point Dave of knowledge so I think you take data and you take AI or machine learning and those are sort of your ingredients. The scaling factor is going to be on knowledge and how does that ultimately get applied. Cloud again gives us an ingredient if you will that can be applied to it but the thing that'll make the difference on it, just like networking was in the past and opened up opportunities around the internet is that in the other will be how folks use knowledge. It's almost like you could think of it as a learning era and how that's not just going to be about individuals but about companies and business models etc. >> So the knowledge comes from applying cognitive to the data and then being able to scale it. Okay and then Inderpal, how do I address the access issue? I've got many if not most incumbents data are in silos. The marketing department has data, the sales department has data, the customer service department has data. How do you as a CDO address that challenge? >> Well what you've got to do is use the technology to actually help you address that challenge. So building data lakes is a good start for both structured and unstructured data where you bring data that's traditionally been siloed together but that's not always possible. Sometimes you have to let the data be where they are but you at least have to have a catalog that tells you where all the data is so that an intelligent system can then reason about that when working with somebody on a particular use case actually help them find that data and help them apply it and use it. >> So that's a metadata challenge correct? >> It's a metadata challenge in the AI world because the metadata challenge has always been there but now you have the potential to apply AI to not just create metadata but then also to apply it effectively to help business users and subject matter experts who are not data experts find the right data and work it. >> You guys make a big deal about automating some of this stuff up front as the point of creation or use automating. Classification is a good example. How are you solving that problem from a technology perspective? >> Well some of our core Watson capabilities are all about classification and then customers use that. It can be what I will call a simple use case of email classification and routing. We have a client in France that has 350,000 emails a week and they use Watson for that level of classification. You look at all sorts of different kinds of ticketing things you look at AI assistants and it comes down to how do you really understand what the intent is here and I believe classification is one of the fundamental capabilities in the whole thing. >> Yeah it's been a problem that we've been trying to solve in this industry for a while kind of pre AI and you really there's not a lot you can do if you don't have good classification if you can't automate it then you can't scale. >> That's right. >> So from a classification standpoint, I mean it's a fundamental always been fundamental problem. Machines have gotten much better at it with the AI systems and so forth but there's also one aspect that's quite interesting which is now you have open loop systems so you're not just classifying based on data that was historically present and so in that sense you're confined to always repeat your mistakes and so forth. You hear about hedge funds that implode because their models are no longer applicable because there's a Black Swan event. Now with the AI systems you have the opportunity to tap the realtime events as they're going and actually apply that to the classification as well. So when Beth talks about the different APIs that we have available to do classification, we also have NLP APIs that allow you to bring to bare this additional stuff that's going on and go from a closed-loop classification to an open-loop one. >> So I want to ask you about the black box problem. If you think about AI, I was saying this in my intro, I know when I see a dog but if I have to describe how I actually came to that conclusion, it's actually quite difficult to do and computers can show me here's a dog or I joked in Silicon Valley. I don't know if you guys watch that show Silicon Valley. Hot dog or not so your prescription at IBM is to make a white box, open that up, explain to people which I think is vitally important because when line of business people get in the room. like how'd you get to that answer and then it's going to be it's going to actually slow you down if you have arguments but how do you actually solve that black box problem? >> It's a much harder problem obviously but there are a whole host of reasons as to why you should look at it that way. One is we think it's just good business practice because when people are making business decisions they're not going to comply unless they really understand it. From my previous stint at IBM when I was working with the coaches of the NBA, they would not believe what patterns were being put forward to them until such time as we tied it to the video that showed what was actually going on. So it's that same aspect in terms of being able to explain it but there's also fundamentally more important reasons as well. You mentioned the example of looking at a dog and saying that's a dog but not being able to describe it. AI systems have that same issue. Not only that what we're finding is that you can take an AI system and you can just tweak a little bit of the data so that instead of recognizing it as a dog now it's completely fooled and it will recognize it as a rifle or something like that. Those are adversarial examples. So we think that taking this white box approach sets us up not just tactically and from a business standpoint but also strategically from a technical standpoint because if a system is able to explain it, describe it and really present its reasoning, it's not going to be fooled that easily either. >> Some of the themes that we hear from IBM, you own your own data, the Facebook blowback has actually been an unbelievable tailwind for that meme and most of the data in the world is not publicly searchable. So build on those themes and talk about how IBM is helping its customers take advantage of those two dynamics. >> So they kind of go hand-in-hand in the sense that because customers have most of the data behind their firewall if you will, within their business walls it means it's unlikely that it's annotated and labeled and used for a lot of these systems so we're focusing on how do we put together techniques to allow systems to learn more with less data. So that goes hand-in-hand with that. That's also back to the point of protecting your data because as we protect your data, you and your competitor we cannot mix that data together to improve the base models that are a part of it so therefore we have to do techniques that allow you to learn more with less data. One of the simplest thing is around the customization. I mean we're coming up on two years since we provided the capability to do custom models on top of visual recognition, Watson visual recognition. The other guys have been bragging about it in the last four to five months. We've been doing it in production with clients, will be two years in July so you'd say okay, well what's that about? We can end up training a base model that understands some of the basics around visual type things like your dog example and some other things but then give you the tools to very quickly and easily create your custom one that now allows you to better understand equipment that may be natural to you or how it's all installed or agricultural things or rust on a cell phone tower or whatever it may be. I think that's another example of how this all comes about to say that's the part that's important to you as a company, that's part that has to be protected that also has to be able to learn with you only spending a few days and a few examples to train it, not millions and billions. >> And that base layer is IBM, but the top layer is client IP and you're guaranteeing the clients that my IP won't seep into my competitors. >> So our architecture is one that separates that. We have hybrid models as a part of it and that piece that as you said is the client piece is separate from the rest of it. We also do it in such a way that you could see there could be a middle layer in there as well. Let's call it industry or licensed data so maybe it comes from a third party, it's not owned by the client but it's something that's again licensed not public as a part of it. That's a part of what our architecture is-- >> And you guys, we saw the block diagrams in there. You're putting together solutions for clients and it's a combination of your enterprise data architecture and you actually have hardware and software components that you can bring to bear here. Can you describe that a little bit? >> Go ahead, it's your implementation. >> Yeah so we've got again the perfect example of a large enterprise. There's significant on-prem implementations, there's private cloud implementations, there's public cloud implementations. You've got to bridge all that and do it in a way that makes it seamless and easy for an enterprise to adopt so we've worked through all that stuff. So we've learned things the hard way about well if you're truly going to do an AI data lake, you better have it on flash. For that reason we have it on flash on-prem but also on the cloud, our storage is on flash and so we were able to make those types of decisions so that we've learned through this, we want to share that with our clients. How do you involve deep learning in this space, well it's going to be proximate to your data lake so that the servers can get to all that data and run literally thousands and thousands of experiments in time that it's going to be useful for the decision. So all those hard learnings we are packaging that in the form of these showcases. We're bringing that forward but right now it's around hybrid cloud and the bridge so that because we want to talk about everything and then going forward it's all public cloud as we leverage that for the elasticity of the computer. >> Well IBM if you can do it there you can do it anywhere. It's a highly complex organization. So it's been what two years in for you now two? >> Little over two years. >> So you're making a lot of progress and I could see the practitioners eating this stuff up and that's got to feel good. I mean you have an impact obviously. >> It absolutely feels very good and I'm always in fact I kind of believe this coming into IBM that this is a company that has not only a number of products that are pertinent to this space but actually the framework to create an AI enterprise. These are not like disparate products. These are all going towards creating an AI enterprise and I think if you look across our portfolio of products and then you kind of map that back to our showcases, you'll see that come to life but in a very tangible way that yes if you truly want to create an AI enterprise, IBM is the place to be because they've got the answers across all the dimensions of the problem as opposed to just one specific dimension like let's say a data mining algorithm or something machine learning and that's basically it. When we cover the full gamut and you have to otherwise you can't really create an AI enterprise. >> In the portfolio obviously coming together IBM huge ambitions with with Watson and everybody's familiar with the ads and so you've done a great job of getting that you know top of mind but you're really starting to work with clients to implement this stuff. I know we got to end here but you had thrown out of stat 85% of executive CAI as a competitive advantage but only 20% can use it at scale so there's still that big gap, you're obviously trying to close that gap. >> Yeah so in a way I would correct it only 20% of them are using it at scale. I think Dave it's a part of how do they get started and I think that comes back to the fact that it shouldn't be a large transformational, scary multi-year project. It is about taking small things, starting with two or three or five use cases and growing and scaling that way and that's what our successful customers are doing. We give it to them in a way that they can use it directly or we leverage it within a number of solutions, like cyber security, like risk and compliance for financial services like health care that they can use it in those solution areas as well. >> Guys thanks so much for coming to theCUBE and sharing your story. We love coming to these events you see guys I used to say you see the practitioners, it's a board level discussion and these guys are right in it so good to see you guys, thank you. >> You too, thank you. >> You're welcome, all right keep it right to everybody, we'll be back with our next guest you're watching theCUBE live from the IBM Chief Data Officer Strategy Summit in San Francisco, we'll be right back.

>> Announcer: Live from Las Vegas, it's the CUBE. Covering IMB Think 2018. Brought to you by IBM. >> Hello everyone, welcome to the Cube here at IBM Think 2018. It's our flagship program where we extract the signal noise live entertainment and technology coverage here. Of course we're going to get all the data as well. Inderpal Bhandari, Global Chief Data Officer for IBM is here in the CUBE, CUBE alumni. The chief of the data for the entire company your job is pretty secure right now. Jean Merriman was talking about how data's the center of the value proposition, blockchain and A.I. Dave and I call it the innovation sandwich. You've got job security right now. >> (laughs) I guess you could put it that way. >> (laughs) So, obviously the data, all kidding aside, we've talked before in the CUBE, the importance of data and, you know, we're data driven, we're data geeks. This is a wonderful time to be in this world because the disruptive enabling that's going on with data is really been, I think, underplayed. It's been more of a tech conversation but the business benefits that this enables, I mean, just blockchain alone, what that could do for efficiencies in rewiring the value chains in a decentralized environment. And then what A.I. promises with the use of data to automate value creation, this is pretty spectacular. >> No, I would completely agree with you. I think it's a very exciting time to be in our industry. And, John, I think the challenge though, is what does it mean for the enterprise? If you put yourselves in the shoes of our customers, they're trying to understand, what does this really mean for the enterprise? What's an A.I. enterprise? What are the use cases for blockchain that play in the enterprise? And that's one of the major foci that I have within my organization, you know. And my role within IBM and the Global Chief Data Officer is to create an A.I. enterprise within IBM itself and then use that as a showcase for our customers so they're able to understand, clearly, what the use cases are that make a lot of sense. Because, frankly, IBM looks a lot like some of our customers. You know we are a large enterprise, we've been around for a while and the fits the profile for the large customers that we serve. >> Well, IBM is the perfect melting pot and Petri dish, if you will, to look at the future, 'cause you have legacy, you know, hundreds of years of being in business, so you've been around but you're also pushing the latest technologies. How has IBM been using the tech? Can you give an example, because this is the digital transformation challenge that most existing leaders have. You know, you don't need to be only five years old just to be, kind of, an old relic compared to what's on the table right now, the speed of innovation. So there has to be a constant energy on understanding how to create sustainable tech and business models and have that regenerate self-healing. I mean, this is a new normal that is just hitting us. How do you guys do it? Can you give some examples? >> Yes, no, absolutely. So we've taken the view that we want to transform our key processes within the company. And examples of these processes, they're not typical to us, they're typical of any large enterprise, you know, these could be procurement, supply chain, marketing, research, data. So we've got these end-to-end processes, which we are now transforming through the use of A.I. and blockchain, these kinds of technologies so that we are able to then re-use those as showcases. So in terms of examples of how we are making use of these today, they.. I'll give you some examples that are more, you know, just at a whole process level, for instance, supply chain. Trying to understand what are the risks to our supply chain based on emerging weather conditions, based on emerging political events. Trying to unravel all that and then essentially use that intelligent system to guide us to make the best decisions with regard to supply chain. That's kind of what I would call a process level example. I'll give you one example within data that seems to some extent quite trivial but actually there are literally thousands and thousands of such decision that are made everyday in a large enterprise. So one of the things that we do in my organization is try to understand if a client that we're dealing with is a government owned entity. And since we operate globally and there are rules that regulate how one deals with government owned entities, very important for us to get it right so that we do business ethically. And it's, you know, you might think, 'well that's a simple decision' it's actually quite complicated and a lot of different parties have a stake in the ground on this. You know, the legal department, the sales area. But now, the way the process is transforming is all that input is fed into an intelligence system that has an understanding of what we've done in the past. It can look at the external data, the news feeds that are available about that organization as well as what are the different points of view and then come to an understanding and then finally be able to explain back to us its rationale as to why it considers something a government owned entity or not. So every subject matter expert in the company should be able to make use of this technology. That's what an A.I. enterprise is and there are literally thousands and thousands such people within an enterprise. >> I mean, you're putting real complex data at their fingertips almost as easy as putting numbers on a spreadsheet. >> Inderpal: Yes. >> That's the kind of work that you guys are thinking. >> Yes, the way I would put it to you, it's more in the sense of engaging the subject matter expert in a dialog. So it's like you've got this intelligent system, Watson, that's working with this subject matter expert, taking them through the whole scenario. They come in with a use case in mind, I used the example of government owned entity or a risk insight for supply chain, they're coming in with a use case in mind, the system is guiding them through. Here's the internal data that's relevant. >> Yeah. >> Here's the external data that's relevant. Here's how you can link them. Here are the insights that you can draw from. So it's kind of a two-way street but it just ends being a much more accurate decision made much more quickly. >> Jean's talk on speech and the theme here at Think 2018 is, putting smart to work. I'll edit that for you in our conversation, putting smart data to work, 'cause that's what you're getting at here. How do you make data intelligent? I know, you know, I mean if you look at it, we can kind of go in the high levels in the clouds and look down and say, 'yeah, you know, that's a great mission.' You know it's hard as heck! >> It's it's very hard. >> So you've got an intelligent data, is it the right data, is it conceptually relevant, is it in the right place at the right time, does the application have the ability to ingest and use the data? >> How reliable it is? All that stuff comes into play and that's where, I think, you know, we've thought of IBM as having a very large portfolio of products that span from, you know, data management, data quality, those kinds of things, all the way to A.I. and Watson and so forth. Think of it more now as bringing together that portfolio into a cohesive data and cognitive framework or data and cognitive backbone for the enterprise. And that's really essentially what we're putting together. >> Inderpal I want to get your thoughts on something. I'm going to kind of go on a tangent since it just popped in my head. I wrote blog posted in 2007, way back in the day, 10 years ago, that said data's the new developer kit. And it's kind of a riff on that data's going to be the software. So we're seeing that now. I interviewed Rob Thomas earlier where he was talking about data containers. We're starting to get to that level with these Kubernetes and these cloud technologies, you now have new models emerging around data where people want to act on data, whether as a subject matter expert or developer. They are essentially develop users. So data's got to be programmable, it's got to be accessible. How do we get to a world where it's being developed on in a seamless way? Just like software's developed on. 'Cause most of the software, 90% of most software is open source, only 10%, put in a Linux foundation, is actually raw intellectual property. So you can almost think of data the same way. >> Inderpal: Yes, no no question. >> How does using data in a development context? What's your vision on that? >> So, you know, we have a blueprint to make an enterprise into am A.I. enterprise or a cognitive enterprise and it has four elements to it. One of the elements is actually data for precisely the reasons that you just annunciated. You know, developers, if they have to go off and search for data and try to find it then it's not a productive use of their time. So to some extent you have to bring the data eco system to them and that needs to be part of an A.I. enterprise. That that data is readily available for developers so that they're able to harness that. And so, now you get into all the hard questions, right? How to do you find it? What is the lineage of the data? So you need to have a super catalog enterprise-wide that enables all that and.. >> Hey, we're making up a new category as we speak it's called data ops. Data as code. We have DevOps as infrastructure's code. You know, I've been kind of, I was talking about this a year ago, didn't get any traction with the idea but what was circling in my head was if infrastructure as code, which was DevOps, which is now serverless when we look at the cloud computing as a set of programmable resources, you can almost make the stretch that data as code is a similar nirvana. >> Inderpal: Yes. >> Okay, it's available, I'm not searching for it but I don't need to reconstruct it, I don't need to essentially ingest it, yeah I'm ingesting it as a function, but, in a free-flowing world, what's your thoughts on that? What's your reaction to that? >> Well the way, you know, that's why setting up the central backbone for data and cognition is extremely important. And I think the right way to think about it is as a continuum. So you've got data and then you've got, essentially, API's on top of the data, that may, may be representing certain functions that you're running on the data. You think about that as a continuum because those functions end up with data as a result. Right? So you've got derived data. So, what the backbone needs to be able to do is give developers very quick access to all the raw data, the source data, as well as the derived data in terms that they can understand and it's easy for them to fathom what that is so that they're able to make judgments in conjunction with an intelligent system that guides them. >> Yeah, that's the key thing and that why Jean brought up Moore's law and Metcalfe's law in her speech because she's intimating at two things, faster smaller cheaper, performance improvements. Metcalfe's law is a network effect. Okay, so you know where I'm going with this, right?. So now we're in a network effect gamification world. We see blockchain, we see crypto currency, we see decentralized application developers coming on on board very quickly. So you have a world with token economics is becoming front and center and where I see innovation, certainly ICOs, initial point offerings are scaring me right now, but it is highlighting the innovation and arbitrage of an inefficient capital market, so, I just use that as a use case. But blockchain and crypto currency is an opportunity to create new business models from the enabling blockchain capability. How do you view that? Because we're still talking about data now. If you're freeing up more people to have more time to actually do their job, they're going to create new things maybe new business models and enter interstate token economics combined with blockchain, this is where we really see a lot of great innovation. Your thoughts in this area of token economics. >> Sure, yeah absolutely. So, I think there are two ways to think about it, one is in the transaction of business itself. What you're doing is you're bringing in a stakeholders for a particular business transaction and you're giving them a way to, a distributed way, a distributed way to arrive at the decision, right? As to whether or not to move forward. So, distributed consensus. You're making that very easy and simple of them so that they can rapidly reach a decision and make their decision, whether they're going to put in money, take out money et cetera. That's one aspect of it, and we literally have.. >> And by the way, consensus is now a new data source? >> Yes. >> And active real time.. >> Yes. >> Data set? >> Absolutely, it is creating, it is creating a data set, in and of its own right. So, but that's kind of one aspect of it, which is in the transaction of business, making it much more efficient, much faster and so forth. But I think it's also instructive to look at blockchain and apply it in terms of a second reuse to the process of managing data itself. So to the extent you're able to establish identities, to the extent you're able to establish permissions and roles. It's going to make governance of data much easier and much faster and much more efficient. These are typically very hard problems for enterprises to solve but I would say that as you go forward, maybe in this year or next year, you're going to see examples. >> And the opportunity too, is to actually break down some structural barriers. >> Yes. >> With this new technology. >> Absolutely. >> It's the bulldozer of innovation. It's not easy but there is a path. You guys have what, close to a hundred customers in blockchain? >> Yes. >> And it's a data story. Supply chain, blockchain, value chain, chain activities, interesting. >> It's going to just lead to a lot a lot more efficiency and accuracy as we move forward. >> Awesome! Inderpal Bhandari Global Chief Data Officer here on the CUBE, sharing his insights on data. We didn't even get to the good part around social data and graphs and all that great stuff that we love talking about. But more CUBE coverage is going to continue here. Day two coverage of IBM Think. I'm John Furrier, thanks for watching. (electronic music)

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

(inspiring piano and string 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 in downtown San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. That's a mouthful, but it's important because there's a series of these strategy summits that are happening not only in the United States, but they're expanding it all over the world, and it's really a chance for practitioners to come together, the chief data officers, to share best practices, really learn from the best, and as we love to do on theCUBE, we get the smartest people we can find, and we have them here. So first off, let me introduce Peter Burris, Chief Research Officer from Wikibon, but from IBM coming right off the keynote-- >> The smart people. >> The smart people, Inderpal Bhandari, he is the IBM Global Chief Data Officer, which is a short title and a big job, and Jesus Mantas, he's the General Manager, Cognitive Transformation, IBM Global Business Services. First off, gentlemen, welcome. >> Thank you. >> Thank you. >> It's really interesting how this chief data officer space has evolved. We've been watching it for years, back to some of the MIT CDOIQ, I think like three or four years ago nobody knew who they were, who were they going to report to, what are they going to do, what's the scope of the job. That's changed dramatically, and it really says something to IBM's credit that they just went out and got one to help really to refine and define for your customers where this is going. So first off, welcome, and let's get into it. How is the role starting to solidify as to what do chief data officers do? >> So, I'll take that. In terms of chief data officers, if you think in terms of the advent of the position, when it started out, I was one of the earliest in 2006, and I've done the job four times, and it has been continuously evolving ever since. When the job was first, in my very first job, I actually had to create the job because there was a company very interested in recruiting me, and they said they sensed that data was critical. It was a company in pharmaceutical insurance, so really very data based, right, everything is driven through data. And so, they had a sense that data was going to be extremely important, extremely relevant, but they didn't really have the position, or they didn't coin the phrase. And I suggested that there were three other chief data officers at that time in the U.S., and so, I became the fourth. At that time, it had to do with, essentially aligning data with strategy, with the strategy of the company, which means how is the company actually planning to monetize itself? Not its data, but itself. And then, essentially make sure that the data is now fit for purpose, to help them with that monetization. And so, that's all about aligning with the corporate strategy, and you have to have an officer who's capable of doing that and has that focus and is able to push that because then, once you start with that strategy, and then, there are plenty of different branches that shoot off, like governance, centralization of data, analytics, data science, and so on and so forth, and then, you have to manage that process. >> And data used to be kind of a liability, hard to think today looking back, 'cause you had to buy servers and storage, and it was expensive, and what do you do with it all? You can't analyze it. Boy, how the world has flipped. Now, data is probably one of your most important assets, but then, the big question, right, what do you do with it to really make it an asset? >> It is, it is, and it's actually fascinating to see here in the summit how even the role that was created in a few years, chief data officer, is coupled with this change in the nature of the value of that role has changed. To your point, I remember meeting some CIO friends 10 years ago that they were telling me how they were deleting data because it was too costly to have it. Now, those same CIOs would give whatever they could have to get that data back and have that history and be able to monetize the data. Because of the evolution of computing, and because now, not only the portion of the physical world that we've been able to represent with data for the last 50 years with information technology, but we're adding to that space all of this 80% of the data that even if digitized we were unable to use in processes, in decision making, in manufacturing. Now we have cognitive technology that can actually use that data, the role of the chief data officer is actually expanding significantly from what used to be the element of data science, of data governance, of data sovereignty, of data security, to now this idea of value creation with basically five times more categories of data, and it actually is a dialogue that we're having here at the summit that is the fascinating from the people who are doing this job every day. >> If you think about the challenges associated with the chief data officer, it's a job that's evolving, but partly one of the reasons why the chief data officer job is evolving is the very concept of the role that data plays in business is evolving, and that's forcing every job in business to evolve. So, the CMO's job's evolving, the CEO's job's evolving, and the CIO's job is evolving. How are you navigating this interesting combination of forces on the role of the CDO as you stake out, this is the value I'm going to bring to the business, even as other jobs start to themselves change and respond to this concept of the value of data? >> People ask me to describe my job, and there are just two words that I use to describe it. It's change agent, and that's exactly how a CDO needs to be, needs to look at their job, and also, actually act on that. Because to your point, it's not just the CDO job is evolving, it's all these other jobs are all evolving simultaneously, and there are times when I'm sitting at the table, it appears that, well, you don't really own anything because everybody else owns all the processes in the business. On the other hand, sometimes you're sitting there, and you're thinking, no, you actually own everything because the data that feeds those processes or comes out of those process is not coming back to you. I think the best way to think about the CDO job is that of a change agent. You are essentially entrusted with creating value from the data, as Jesus said, and then, enabling all the other jobs to change, to take advantage of this. >> 'Cause it's the enablement that that's where you bring the multiplier effect, it's the democratization of the data across the organization, across business roles, across departments is where you're going to get this huge multiplier. >> Yeah, and I think the role of one of the things that we're seeing and the partnership that Inderpal and I have in the way that we do this within IBM, but also, we do it for the rest of our clients is that change agency element of it is the constant infusion of design. Chief data officers were very well-known for the data science elements of it, but part of the constraint is actually no longer the computing capability or the algorithms themselves or the access to the data, which solved those constraints, is now actually preparing the business leaders to consume that and to actually create value, which changes the nature of their job as well, and that's the resistance point where embedding these technologies in the workflows, in a way that they create value in the natural flow of what these jobs actually do is extremely important. Otherwise, I mean, we were having a fascinating discussion before this, even if the data is correct, many business leaders will say, "Well, I don't believe it." And then, if you don't adopt it, you don't get the value. >> You guys are putting together this wonderful community of CDOs, chief data officers, trying to diffuse what the job is, how you go about doing the job. If you're giving advice and counsel to a CEO or board of directors who are interested in trying to apply this role in their business, what should they be looking for? What type of person, what type of background, what type of skills? >> I'll take it, and then, you can. I think it's almost what I would call a new Da Vinci. >> Peter: A new Da Vinci? >> A new Da Vinci is the Renaissance of someone that is, he's got a technology background, because you need to actually understand the mathematical and the data and the technology co-engineering aspect. >> So, if not an IT background, at least a STEM background. >> Exactly, it's a STEM background, but combined with enough knowledge of business architecture. So I call it Da Vinci because if you see the most remarkable paintings and products of Da Vinci was the fusion of mathematics and arts in a way that hadn't been done before. I think the new role of a data science is someone that can be in the boardroom elegantly describing a very sophisticated problem in a very easy to understand manner, but still having the depth of really understanding what's behind it and drawing the line versus what's possible and what's likely to happen. >> I think that's right on. I think the biggest hurdle for a chief data officer is the culture change, and to do that, you actually have to be a Da Vinci, otherwise, you really can't pull that off. >> Peter: You have to be a Da Vinci? >> You have to be a Da Vinci to pull that off. It's not just, you have to appreciate not just the technology, but also the business architecture as well as the fact that people are used to working in certain ways which are now changing on them, and then, there is an aspect of anxiety that goes with it, so you have to be able to understand that, and actually, perhaps even harness that to your advantage as you move forward as opposed to letting that become some kind of a threat or counterproductive mechanism as you move forward. >> I've done a fair amount of research over the years on the relationship between business model, business model design profitability, and this is, there's a lot of different ways of attacking this problem, I'm not going to tell you I have the right answer yet, but one of the things that I discovered when talking to businesses about this is that often it fails when the business fails to, I'm going to use the word secure, but it may not be the right word, secure the ongoing rents or value streams from the intellectual property that they create as part of the strategy. Companies with great business model design also find ways to appropriate that value from what they're doing over an extended period of time, and in digital business, increasingly that's data. That raises this interesting question, what is the relationship between data, value streams over time, ownership, intellectual property? Do you have any insight into that? It's a big question. >> Yeah, no no, I mean, I think we touched on it also in the discussion, both Jesus and I touched on that. We've staked out a very clear ground on that, and when I say we, I mean IBM, the way we are defining that is we are pretty clear that for all the reasons you just outlined, the client's data has to be their data. >> Peter: Has to be? >> Has to be their data. It has to be their insight because otherwise, you run into this notion of, well, whose intellectual property is it, whose expertise is it? Because these systems learn as they go. And so, we're architecting towards offerings that are very clear on that, that we're going to make it possible for a client that, for instance, just wants to keep their data and derive whatever insight they can from that data and not let anybody else derive that insight, and it'll be possible for them to do that. As well as clients where they're actually comfortable setting up a community, and perhaps within an industry-specific setup, they will allow insights that are then shared across that. We think that's extremely important to be really clear about that up front and to be able to architect to support that, in a way that that is going to be welcomed by the business. >> Is that part of the CDO's remit within business to work with legal and work with others to ensure that the rules and mechanisms to sustain management of intellectual property and retain rents out of intellectual property, some call it the monetization process, are in place, are enforced, are sustained? >> That's always been part of the CDO remit, right. I mean, in the sense that even before cognition that was always part of it, that if we were bringing in data or if data was leaving the company that we wanted to make sure that it was being done in the right way. And so, that partnership not just with legal but also with IT, also with the business areas, that we had to put in place, and that's the essence of governance. In the broadest sense, you could think of governance as doing that, as protecting the data asset that the company has. >> They have the derivatives now, though. You're getting stacked derivatives. >> Inderpal: It's much more complicated. >> Of data, and then insight combined, so it's not just that core baseline data anymore. >> And I like to make it an element. You've heard us say for the last five years we believe that data has become the new natural resource for the business. And when you go back to other natural resources, and you see what happened with people that were in charge of them, you can kind of predict a little bit that evolution on the chief data officer role. If you were a landowner in Texas when there was no ability to basically either extract or decline petroleum, you were not preoccupied with how would you protect land rights under the line that you can see. So, as a landowner you have a job, but you were basically focused on what's over the surface. Once actually was known that below the surface there was massive amount of value that could be obtained, suddenly that land ownership expanded in responsibility. You then have to be preoccupied, "Okay, wait a minute, who owns those land rights "to actually get that oil, and who's going to do that?" I think you can project that to the role of the chief data officer. If you don't have a business model that monetizes data, you are not preoccupied to actually figure out how to govern it or how to monetize it or how to put royalties on it, you are just preoccupied with just making sure that the data you have, it was well-maintained and it could be usable. The role's massively expanding to this whole below the line where not only the data is being used for internal purposes, but it's becoming a potential element of a strategy that is new. >> The value proposition, simply stated. >> Jesus: Value proposition, exactly. >> But you're right, so I agree with that, but data as an asset has different characteristics than oil as an asset, or people as an asset. People can effectively be applied to one thing at a time. I mean, we can multitask, but right now, you're having a conversation with us, and so, IBM is not seeing you talk to customers here at the show, for example. Data does not follow the economics of scarcity. >> Jesus: Right. >> It follows a new economics, it's easy to copy, it's easy to share. If it's done right, it's easy to integrate. You can do an enormous number of things with data that you've never been able to do with any other asset ever, and that's one of the reasons why this digital transformation is so interesting and challenging, and fraught with risk, but also potentially rewarding. So, as you think about the CDO role and being the executive in the business that is looking at taking care of an asset, but a special type of asset, how that does change the idea of taking care of the energy or the oil to now doing it a little bit differently because it can be shared, because it can be combined. >> I mean, I think in the way as technology has moved from being a mechanism to provide efficiency to the business to actually being core to defining what the business is, I think every role related to technology is following that theme, so I would say, for example, Inderpal and I, when we're working with clients or on our models, he's not just focused on the data, he's actually forming what is possible for the business to do. What should be the components of the new business architecture? It's this homogenized role, and that's why I kept saying it's like, you could have been one of those Da Vincis. I mean, you get to do it every day, but I don't know if you want to comment on that. >> I think that's exactly right. You are right in the sense that it is a different kind of asset, it has certain characteristics which are different from what you'd find in, say, land or oil or something like a natural resource, but in terms of, and you can create a lot of value at times by holding onto it, or you could create a lot of value by sharing it, and we've seen examples of both metaphors. I think as part of being the CDO, it's being cognizant that there is going to be a lot of change in this role as data is changing, not just in its nature in the sense that now you have a lot more unstructured data, many different forms of data, but also in terms of that's application within the business, and this expansion to changing processes and transforming processes, which was never the case when I first did the job in 2006. It was not about process transformation. It was about a much more classic view of an asset where it's, we create this data warehouse, that becomes the corporate asset, and now, you generate some insights from it, disseminate the insights. Now it's all about actually transforming the business by changing the processes, reimagining what they could be, because the nature of data has changed. >> I have one quick question. >> Last one. >> Very quickly, well, maybe it's not a quick question, so if you could just give me a quick answer. A couple times you both have mentioned the relationship between the CDO and business architecture. Currently, there's a relationship between the CIO and IT architecture, even the CIO and data architecture at a technical level. At IBM, do you actually have staff that does business architecture work? Is there someone, is that a formal, defined set of resources that you have, or should CDOs have access to a group of people who do business architecture? What do you think? >> We've traditionally had business architects at IBM, I think for a long time, that predates me. But again, as Jesus said, their role is also evolving. As it becomes much more about process transformation, it's different than it was before. I mean, this is much more now about a collaborative effort where you essentially sit down in a squad in an agile setting, and you're working together to redesign and reinvent the process that's there. And then, there's business value. It's less about creating large monolithic architectures that span an entire enterprise. It's all about being agile, data-driven, and reacting to the changes that are happening. >> So, turning strategy into action. >> Yes. >> And I think, again, in IBM, one of the things that we have done, our CIO, that is the organization that actually is the custodian of this cognitive enterprise architecture of which Inderpal actually is part of. So, we are actually putting it all together. It used to be an organization. Most COOs have evolved from running operations to defining shared services to now have to figure out what is the digital services version of the enterprise they need to implement, and they can't do that without a CDO in place, they just can't. >> Alright, gentlemen. Unfortunately, we'll have to leave it there. For viewers at home, tune into season two with Inderpal and Jesus. Really a great topic. Congratulations on the event, and we look to forward to the next time. >> Thank you. >> Thank you very much. >> Absolutely. With Peter Burris, I'm Jeff Frick. You're watching theCUBE from the IBM Chief Data Officer Strategy Summit Spring 2017. Be right back with more after this short break. Thanks for watching. (electronic keyboard music)

I from Las Vegas Nevada it's the cube covering IBM world of Watson 2016 brought to you by IBM now here are your hosts John furrier and Dave vellante hey welcome back everyone we're here live in Las Vegas for IBM's world of Watson at the mandalay bay here this is the cube SiliconANGLE media's flagship program we go out to the events and extract the signal-to-noise I'm John Ford SiliconANGLE i'm here with dave vellante my co-host chief researcher red Wikibon calm and our next guest is inderpal bhandari who's the chief global chief data officer for IBM welcome to the cube welcome back thank you thank you meet you you have in common with Dave at the last event 10 years Papa John was just honest we just talked about the ten year anniversary of I OD information on demand and Dave's joke why thought was telling we'll set up the says that ten years ago different data conversation how do you get rid of it is I don't want the compliance and liability now it shifted to a much more organic innovative exciting yeah I need a value add what's the shift what's the big change in 10 years what besides the obvious of the Watson vision how did what it move so fast or too slow what's your take on this ya know so David used to be viewed as exhaust right the tribe is something to get rid of like you pointed out and now it's much more to an asset and in fact you know people are even talking about about quantifying it as an asset so that you can reflect it on the balance sheet and stuff like that so it certainly moved a long long way and I think part of it has to do with the fact that we are inundated with data and data does contain valuable information and to the extent that you're able to glean it and act on it efficiently and quickly and accurately it leads to a competitive advantage what's the landscape for architects out there because a lot of things that we hear is that ok i buy the day they I got a digital transformation ok but now I got to get put the data to work so I need to have it all categorized what's the setup is there a general architecture philosophy that you could share with companies that are trying to set themselves up for some baseline foundational sets of building blocks I mean I think they buy the Watson dream that's a little Headroom I just want to start in kindergarten or in little league or whatever metaphor we want to use any to baseline what's today what's the building blocks approach the building blocks approach I mean from a if you're talking about a pure technical architectural that kind of approach that's one thing if you're really going after a methodology that's going to allow you to create value from data I would back you up further I would say that you want to start with the business itself and gaining an understanding of how the business is going to go about monetizing itself not its data but you know what is the businesses monetization strategy how does the business plan to make money over the next few years not how it makes money today but over the next few years how it plans to make money that's the right starting point once you've understood that then it's basically reflecting on how data is best used in service of that and then that leads you down to the architecture the technologies the people you need the skills makes the process Tanner intuitive the way it used to be the ivory tower or we would convene and dictate policy and schemas on databases and say this is how you do it you're saying the opposite business you is going to go in and own the road map if you will the business it's a business roadmap and then figure it out yeah go back then go back well that's that's really the better way to address it than my way so the framework that we talked about in in Boston and now and just you're like the professor I'm the student so and I've been out speaking to other cheap date officers about it it's spot on this framework so let me briefly summarize it and we can I heard you not rebuilding it to me babe I'm saying this is Allah Falls framework I've stolen it but with no shame no kidding and so again we're doing a live TV it's you know he can source your head I will give him credit so but you have said they're there are two parallel and three sequential activities that have to take place for data opposite of chief data officer the two parallel our partnership with the line of business and get the skill sets right the three sequential are the thing you just mentioned how you going to monetize data access to data data sources and Trust trust the data okay so great framework and I'd say I've tested it some CEOs have said to me well I geeza that's actually better than the framework I had so they've sort of evolved as I said you're welcome and oh okay but now so let's drill into that a little bit maybe starting with the monetization piece in the early days Jonna when people are talking about Big Data it was the the mistake people made was I got to sell the data monetize the data itself not necessarily it's what you're saying yes yes I think that's the common pitfall with that when you start thinking about monetization and you're the chief data officer your brain naturally goes to well how do I monetize the data that's the wrong question the question really is how is the business planning to monetize itself what is the monetization strategy for the overall business and once you understand that then you kind of back into what data is needed to support it and that's really kind of the sets the staff the strategy in place and then the next two steps off well then how do you govern that data so it's fit for the purpose of that business lead that you just identified and finally what data is so critical that you want to centralize it and make sure that it's completely trusted so you back into those three those three steps so thinking about data sources you know people always say well should you start with internal should you start with external and the answer presumably is it depends it depends on the business so how do you how do you actually go through that decision tree what's that process like yeah I mean if you know you start with the monetization strategy of the company so for example I'll use IBM a banana and the case of IBM took me the first few months to understand that our monetization strategy was around cognitive business specifically making enterprises into cognitive businesses and so then the strategy that we have internally for IBM's data is to enable cognition within within IBM the enterprise and move forward with that and then that becomes a showcase for our customers because it is after all such a good example of a complex enterprise and so backing you know backing in from that strategy it becomes clear what are some of the critical data elements that you need to master that you need to trust that you need to centralize and you need to govern very very rigorously so that's basically how I approached it did I answer your question daivam do you get so so you touched on the on the second part I want to drill into the the third sequential activities which which is sources so i did so you did we just talk about this well the sources i mean if you had something add to that yes in terms of the i think you mentioned the internal versus external so one thing else i'll mention especially if you kind of take that 10-year outlook that we were talking about 10 years ago serials had very internal outlook in terms of the data was all internal business data today it's much more external as well there's a lot more exogenous data that we have to handle and validity and that's because we're making use of a lot more unstructured data so things like news feeds press releases articles that have just been written all our fair game to amplify the view that you have about some entity so for example if we're dealing with a new supplier you know previously we might gather some information by talking with them now we'd also be able to look at essentially everything that's out there about them and factor that in so it is a there's an element of the exogenous data that's brought to bear and then that obviously becomes part of the realm of the CDO as well to make sure that that data is available and you unusable by the business is John Kelly said something go ahead sorry well Jeff Jonas would say that's the observation space right that you want to have the news feeds it's extra metadata that could change the alchemy if you will of whatever the mix of the data is that kind of well yeah I would say you might even go further than just metadata i would say that in some some sense it's part of your intrinsic data set because you know it gives you additional information about the entities that you're collecting data on and that measuring the John Kelly in the keynote this morning he made two statements he said one is in three to five years every health care practitioners going to going to want to consult Watson and then he also said same thing for MA because watch is going to know every public piece of data about every single company right so it's would seem that within the three to five year time frame that the shift is going to be increasingly toward external data sources not necessarily the value in the lever points but in terms of the volume certainly of data is that fair I think it's a it's a fair statement I mean I think if you think of it in the healthcare context if you know a patient comes in and there's a doctor or a practitioner that's examining the patient right there they're generating some data based on their interaction but then if you think about the exogenous data that's relevant and pertinent to that case that could involve you know thousands of journals and articles and so you know your example of essentially saying that the external data could be far greater than the internal data out say we're already there okay and then the third sequential piece is trust are you gonna be able to trust the trust we talk a lot about we were down to Big Data NYC the same week you guys made your big announcement the data works everybody talks about data Lakes we joke gets the data swamp and can't really trust the data yeah we further away from a single version of the truth than we ever were so how are you dealing with that problem internally at IBM and what's the focus is it more on reporting is it more on supporting lines of business in product yeah the focus internal within IBM is in terms of driving cognition at the way I would describe it is at points where today we have significant human judgment being exercised to make decisions and that's you know thousands of points in our enterprise or complicated enterprise like IBM's and each of those decision points is actually an opportunity to inject cognitive technology and play and then bring to bear and augmented intelligence to those decisions that you know a factors in the exogenous data so leaving a much better informed decision but also them a much more accurate decision okay the two parallel activities let's start with the first one line of business you know relationships sounds like bromide why is it not just sort of a trite throwaway statement what where's the detail behind that so the detail behind that if you go back to the very first and the most important step and this whole thing with regard to the monetization strategy of the company understanding that if you don't have those deep relationships with the lines of business there's no way that you'll be able to understand the monetization strategy of the business so that's why that's a concurrent activity that has to start on day one otherwise you won't even get past the you know that that very first first base in terms of understanding what the monetization strategies are for the business and that can only really come by working directly with the business units meeting with their leadership understanding their business so you have to do that due diligence and that's where that partnership becomes critical then as you move on as you progress to that sequence you need them again so for instance once you understood the strategy and now you understood what data you need to follow that strategy and to govern it you need their help in governing the business because in many cases the businesses may be the ones collecting the data or at least controlling the source systems for that data so that partnership then just gets deeper and deeper and deeper as you move forward in that program I love the conscience of monetizing earlier and this some tweets going around you know what's holding it back cost of building it obviously and manageability but I want to bring that back and bring a developer perspective here because a lot of emphasis is on developing apps where the data is now part of the development process I wrote a blog post in 2008 saying that dated some new development kit radical at the time but reality it came out to be true and that they're looking at data as library of value to tap into so if stuffs annandale they could be sitting there for years but I could pull something out and be very relevant in context in real time and change the game on some insight and the insight economy is bob was saying so what is your strategy for IBM 21 on board more developer goodness and to how do you talk to customers were really trying to figure out a developer strategy so they can build apps and not to go back and rewrite it make it certainly mobile first etc but what's how does a date of first appt get built and I should developers be programming with you I'll give you a way to think about it right i mean and going back again to that ten-year paradigm shift right so ten years ago if somebody wanted to write an application and put it on the internet and it was based on data the hardest part was getting hold of the data because it was just very very difficult for them to get all of it to access the data and then those who did manage to get all of the data they were very successful in being able to utilize it so now with the the paradigm shift that's happened now is the approaches that you make the data available to developers and so they don't have to go through that work both in terms of accessing collecting finding that data then cleaning it it's also significant and so time consuming that it could put put back there their whole process of eventually getting to the app so to the extent that you have large stores of data that are ready to go and you can then make that available to a body of developers it just unleashes it's like having a library of code available is it all the hard work and I think that's a good way to look at it I mean that's think that's a very good way to look at it because you've also got technologies like the deep learning technologies where you can essentially train them with data so you don't need to write the code they get trained to later so I see a DevOps of data means like an agile meets I'm again you're right a lot of the cleaning and this is where you no more noise we all know that problem or data creates more noise better cleaning tools so however you can automate that yes seems to be the secret differentiator it's an accelerator it's amazing accelerator for development if you have good sets of data that are available for them to used so I want to round out my my little framework here your frame working with my my learnings for the fifth one being skills yes so this is complicated because it involves organization skills changes as pepper going through the lava here we try to get her on the cube Dave home to think the pamper okay babe yeah so should I take over pepper you want to go see pepper I want to see pepper on the cube hey sorry exact dress but so a lot of issues there there's reporting structures so what do you mean when you talk about sort of the skill sets and rescaling so and I'll describe to you a little bit about the organization that I have at IBM as an example some of that carries over and some of that doesn't the reason I say that is again I mean the skills piece there are some generic skill sets that you need for to be achieved data officer to be a successful chief data officer in an enterprise there is one pillar that I have in my organization is around data science data engineering DevOps deep learning and these are the folks who are adept at those technologies and approaches and methodologies and they can take those and apply them to the enterprise so in a sense these are the more technical people then another pillar that's again pretty generic and you have to have it is the information and data governance pillow so that anything that's flowing any data that's flowing through the data platform that I spoke off in the first pillar that those that that data is governed and fit for purpose so they have to worry about that as soon as any data is you even think of introducing that into the platform these folks have to be on that and they're essentially governing it making sure that people have the right access security the quality is good its improving there's a path to improving it and so forth I think those are some fairly generic you know skill sets that we have to get in the case of the first pillar what's difficult is that there aren't that many people with those skills and so it's hard to find that talent and so the sooner you get on it so that would that's the biggest barrier in the case of the second pillar what's the most difficult piece there is you need people who can walk the balance between monetization and governance too much governance and you essentially slow everything down and nothing moved a cuff and you're handcuffed and then you know if it's too much monetization you might run aground because you you ignored some major regulation so walking that loss of market value yeah that's what you have to really get ahead of your skis as they say and have a faceplant you'll try too hard to live boost mobile web startups like Twitter that's big cock rock concert with Twitter Facebook if you try to monetize too early yes you lose the flywheel effect of value absolutely so walking that balance is critical so that's that that's really finding the skill set to be able to do that that's that's what what's at play in that second or the third one is if you are applying it to an enterprise you have to integrate these you know this platform into the workflow off the enterprise itself otherwise you're not going to create any impact because that's where the impact gets created right that's basically where the data is that the tip of the spear to so to speak so you it's going to create value and in a large enterprise which has legacy systems which are silos which is acquiring companies and so on and so forth that's enough itself a significant job and that skill set is that's a handicapped because if you have that kind of siloed mentality you don't get the benefits of the data sharing right so what's that what's said how much how much effort would it take I'm just kind of painting that picture kind of like out there like well a lot of massively hard ya know that that's you know a lot of you know a lot of people think that data mining is all about my data you know this is my data I'm not going to give it to you the one of the functions of the chief data office is to change that mindset yeah and to stop making use of the data in a broader context than just a departmental siloed type of approach and now some data can legitimately be used only departmentally but the moment you need two or more department start using that data I mean it's essentially corporate data so are those roles a shared service everybody see that works it maybe varies but is it a shared service that reports into the chief data officer or is it embedded into the business those those skill sets that you talked about I think those skill sets are definitely part of the chief data officer you know organization now it's interesting you mentioned that about embedding them and the business units now in a in a large enterprise a complicated enterprise like IBM the different business units and that potentially have different business objectives and so forth you know you you do need a chief data officer role for each of these business units and that's something that I've been advocating that's my fault pillar and we are setting that up and then within the context of IBM so that they serve the business unit but they essentially reporting to me so that they can make use of the overall corporate structure you do their performance review the performance review is done by the business unit it is ok but the functional direction is given by me ok so I get back to still go either way oh yes that's a balance loon yeah absolutely under a lot of time for sure i'll get back to this data mining because you bring up a good point we can maybe continue on our next time we talk but data monies were all the cutting edge kind of best practices are were arsed work what we're relations are still there technically if you're here but that the dynamic of data mining is is that you're assuming no new data so with if you have a lot of data coming in most of the best data mining techniques are like a corpus you attack it and learned but if the pile of data is getting bigger faster that you could date a mine it what good is against or initial circular hole I'm going to again you know just take you back 10 years from now and now right and the differences between the two so it's very interesting points that you bring up I'll give you an example from 10 years ago this data mining example not ten years ago actually my first go-around at IBM so it's like 94 yeah one of the things I've done was we had a program a computer program that every team in the National Basketball Association started using and this was a classic data mining program it would look at the data and find insights and present them and one of the insights that it came up with and this was for a critical playoff game it told the coach you got to play your backup point guard and your backup forward now think about that which same coach would actually go with that so it's very hard for them to believe that they don't know if it's right or wrong in my own insurance and the way we got around that was we essentially pointed back to the snippets of video where those circumstances occurred and now the coach could see what is going on make a you know an informed decision flash forward to now the systems we have now can actually look at all that context all at once what's happening in the video what's happening in the audio also the data can piece together the context so data mining is very different today than what it was them now it's all about weaving the context and the story together and serving it up yeah what happened what's happening and what's going to happen kinda is the theaters of yes there are in sight writing what happened it's easy just yeah look at the data and spit out some insight what's happening now is a bit harder in memory I think that's the difference between cognition as it away versus data mining as you know we understood a few years ago great cartridge we can go for another hour but do we ever get enough love to follow up on some of the deep learning maybe come down to armonk next time we're in this certainly on the sports data we have a whole program on sports data so we love the sports with the ESPN of tech and bringing you all the action right here yes I did Doug before Moneyball you know my mistake was letting right yeah yeah right the next algorithm but that's okay you know we put a little foot mark on the cube notes for that thank you very much thank you appreciate okay live in Mandalay Bay we're right back with more live coverage I'm Sean for a table on thing great back today I am helping people

>> 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.

>> Announcer: From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality symposium brought to you by SiliconANGLE Media. >> Hi everybody. This is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years they've done this event at the Tang Center, moving into a new facility, but unfortunately going to have to wait at least a year, we'll see, But we've got a great guest. Nonetheless, Doug Laney is here. He's a Business Value Strategist, the bestselling author, an analyst, consultant then a long time CUBE friend. Doug, great to see you again. Thanks so much for coming on. >> Dave, great to be with you again as well. So can I ask you? You have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to accelerate people's attention on the importance of data and the value of data than all the great work that you've done. What do you think? (laughing) >> It's always great when organizations, actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third party valuations of their customer loyalty data, resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously off balance sheet is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. >> Well, and of course, all you have to do is look to the Trillionaire's Club. And now of course, Apple pushing two trillion to really see the value that the market places on data. But the other thing is of course, COVID, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? If you're not digital, you can't do business. And digital is all about data. >> I think the major challenge that most organizations are seeing from a data and analytics perspective due to COVID is that their traditional trend based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration, or even identifying what are the leading indicators of your business, then COVID and the economic shutdown have entirely broken those models. So it's raised the awareness of companies to say, "Hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those external, maybe global indicators or other kinds of markets that proceed our own forecasts or our own activity." And so the conversion from trend based forecast models to what we call driver based forecast models, isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business. And then recasting those models and executing on them. >> And that's a great point. If you think about COVID and how it's changed things, everything's changed, right? The ideal customer profile has changed, your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back, some kind of hybrid mode, you used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary in the coal mine. >> Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or service that you're offering. Look at us right now, we would generally be face to face and person to person, but we're using video technology to transfer this content. And then one of the things that I... It took me awhile to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, Caserta focuses on North America. But the reality is that every consultancy is now a global consultancy because we're all doing business remotely. There are no particular or real strong localization issues for doing consulting today. >> So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed the course of the early... The pre-title days it was coming out of a data quality world. And it's still vital. Of course, as we heard today from the Keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial, healthcare and government, but now, many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. >> Yeah. So one of the things that we've been doing here for the last couple of years is a running an ongoing study of how organizations are impacted by the role of the CDO. And really it's more of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often, mention the importance of data and analytics in their annual report organizations with a C level CDO, meaning a true executive are four times more often likely to be using data, to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a CIO, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. >> I want to ask you about that. You mentioned the CIO and we're increasingly seeing lines of reporting and peer reporting alter shift. The sands are shifting a little bit. In the early days the CDO and still predominantly I think is an independent organization. We've seen a few cases and increasingly number where they're reporting into the CIO, we've seen the same thing by the way with the chief Information Security Officer, which used to be considered the fox watching the hen house. So we're seeing those shifts. We've also seen the CDO become more aligned with a technical role and sometimes even emerging out of that technical role. >> Yeah. I think the... I don't know, what I've seen more is that the CDOs are emerging from the business, companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications of technologies, but today data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the CIO, but increasingly more CDOs you're seeing and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true executives. Along I advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations so intertwined. >> Well, it makes sense that the Chief Data Officer would have an affinity with the lines of business. And you're seeing a lot of organizations, really trying to streamline their data pipeline, their data life cycles, bringing that together, infuse intelligence into that, but also take a systems view and really have the business be intimately involved, if not even owned into the data. You see a lot of emphasis on self-serve, what are you seeing in terms of that data pipeline or the data life cycle, if you will, that used to be wonky, hard core techies, but now it really involving a lot more constituent. >> Yeah. Well, the data life cycle used to be somewhat short. The data life cycles, they're longer and they're more a data networks than a life cycle and or a supply chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams? And then test their feasibility, are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. But the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we've talked about previously, data has these unique qualities that it can be used over and over again, and it generate more data when you use it. And it can be used simultaneously for multiple purposes. So companies like, you mentioned, Apple and others have built business models, based on these unique qualities of data. But I think it's really incumbent upon any organization today to do so as well. >> But when you observed those companies that we talk about all the time, data is at the center of their organization. They maybe put people around that data. That's got to be one of the challenge for many of the incumbents is if we talked about the data silos, the different standards, different data quality, that's got to be fairly major blocker for people becoming a "Data-driven organization." >> It is because some organizations were developed as people driven product, driven brand driven, or other things to try to convert. To becoming data-driven, takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. So the culture change that goes along with it is really difficult. And so we're working with 150 year old consumer brand right now that wants to become more data-driven and they're very product driven. And we hear the CIO say, "We want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data." And once we realized that and started behaving in that fashion, then we'll be able to really win and thrive in our marketplace. >> So one of the key roles of a Chief Data Officers to understand how data affects the monetization of an organization. Obviously there are four profit companies of your healthcare organization saving lives, obviously being profitable as well, or at least staying within the budget, depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, "Okay, do I have to become a data broker? Am I selling data directly?" But I think, you pointed out many times and you just did that unlike oil, that's why we don't like that data as a new oil analogy, because it's so much more valuable and can be use, it doesn't fall because of its scarcity. But what are you finding just in terms of people's application of that notion of monetization? Cutting costs, increasing revenue, what are you seeing in the field? What's that spectrum look like? >> So one of the things I've done over the years is compile a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have a book in process that hopefully will be out this fall. I'm sharing a number of those inspirational examples. So that's the thing that organizations need to understand is that there are a variety of great examples out there, and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well, many clients come to me and they ask, "What are others in my industry doing?" And my flippant response to that is, "Why do you want to be in second place or third place? Why not take an idea from another industry, perhaps a digital product company and apply that to your own business." But like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics, you can report on it, you can use it internally to generate improved business process performance. And as long as you're measuring how data's being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supplier relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data, certainly in light of GDPR and the California consumer privacy act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call, Inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers. And take a referral fee or a commission, based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to variety of regulations, but I know who my diabetes patients are, and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a referral fee or a cut of that action. So we're working with customers and the financial services firm industry and in the healthcare industry on just those kinds of examples. So we've identified hundreds of millions of dollars of incremental value for organizations that from their data that we're just sitting on. >> Interesting. Doug because you're a business value strategist at the top, where in the S curve do you see you're able to have the biggest impact. I doubt that you enter organizations where you say, "Oh, they've got it all figured out. They can't use my advice." But as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever, there's too much technical data, et cetera, where are you finding you can have the biggest impact, Doug? >> Generally we don't come in and run those kinds of data monetization or information innovation exercises, unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level more immediate and vocational opportunities for data to deliver value through, to simply analytics. One of the simple examples I give is, I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly by night, mortgage companies, the moving companies, the box companies, the painters, the landscapers, all know you're moving because your data is in the U.S. and the MLS directory. And it was interesting. The only company that didn't reach out to me was my own bank, and so they lost the opportunity to introduce me to a Mortgage they'd retain me as a client, introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving, the MLS database or you can harvest it from Zillow or other sites is basically public domain data. And I was just thinking, how stupid simple would it have been for them to hire a high school programmer, give him a can of red bull and say, "Listen match our customer database to the MLS database to let us know who's moving on a daily or weekly basis." Some of these solutions are pretty simple. >> So is that part of what you do, come in with just hardcore tactical ideas like that? Are you also doing strategy? Tell me more about how you're spending your time. >> I trying to think more of a broader approach where we look at the data itself and again, people have said, "If you tortured enough, what would you tell us? We're just take that angle." We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers, internally and externally. We look at edge cases for their customers' businesses. We run through hypothesis generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through that feasibility assessment and determining which of these ideas you actually want to pursue. >> So you're of course the longtime industry watcher as well, as a former Gartner Analyst, you have to be. My question is, if I think back... I've been around a while. If I think back at the peak of Microsoft's prominence in the PC era, it was like windows 95 and you felt like, "Wow, Microsoft is just so strong." And then of course the Linux comes along and a lot of open source changes and low and behold, a whole new set of leaders emerges. And you see the same thing today with the Trillionaire's Club and you feel like, "Wow, even COVID has been a tailwind for them." But you think about, "Okay, where could the disruption come to these large players that own huge clouds, they have all the data." Is data potentially a disruptor for what appear to be insurmountable odds against the newbies" >> There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, there's certainly not going to be around for forever, but it's been really fascinating to see the transformation of some companies I think nobody really exemplifies it more than IBM where they emerged from originally selling meat slicers. The Dayton Meat Slicer was their original product. And then they evolved into Manual Business Machines and then Electronic Business Machines. And then they dominated that. Then they dominated the mainframe software industry. Then they dominated the PC industry. Then they dominated the services industry to some degree. And so they're starting to get into data. And I think following that trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or top. >> We have Inderpal Bhandari is one of our huge guests here. He's a Chief-- >> Sure. >> Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes, whether it's financially oriented or revenue opportunities. But one of the things he stressed to me in our interview was that they're on average, they're reducing the end to end cycle time from raw data to insights by 70%, that's on average. And that's just an enormous, for a company that size, it's just enormous cost savings or revenue generating opportunity. >> There's no doubt that the technology behind data pipelines is improving and the process from moving data from those pipelines directly into predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. >> Is the skills barrier is acute? It seems like it's lessened somewhat, the early Hadoop days you needed... Even data scientist... Is it still just a massive skill shortage, or we're starting to attack that. >> Well, I think companies are figuring out a way around the skill shortage by doing things like self service analytics and focusing on more easy to use mainstream type AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and the difficulty in finding people that are true data scientists. There's no real certification. And so really anybody can call themselves a data scientist but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But there are other types of skills that we don't really focus on, like the data engineering skills, there's still a huge need for data engineering. Data doesn't self-organize. There are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and then ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the Data Curator, somebody who acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. Most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about which is more valuable to an organization? How do you not have somebody who's dedicated to identifying the world of external data assets that are out there? There are 10 million data sets published by government, organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's a web content that can be harvested, there's data from your partners and suppliers, there's data from social media. So to not have somebody who's on top of all that it demonstrates gross negligence by the organization. >> That is such an enlightening point, Doug. My last question is, I wonder how... If you can share with us how the pandemic has effected your business personally. As a consultant, you're on the road a lot, obviously not on the road so much, you're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? >> Most of our clients, given that they're in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold but we're still seeing the same level of enthusiasm for data and doing things with data. In fact some companies have taken our (mumbles) that data to be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spend a lot less time on planes. So it gives me more time for writing and speaking and actually doing consulting. So that's been nice as well. >> Yeah, there's that bonus. Obviously theCUBE yes, we're not doing physical events anymore, but hey, we've got two studios operating. And Doug Laney, really appreciate you coming on. (Dough mumbles) Always a great guest and sharing your insights and have a great MIT CDOIQ. >> Thanks, you too, Dave, take care. (mumbles) >> Thanks Doug. All right. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer conference, MIT CDOIQ, will be right back, right after this short break. (bright music)

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

>> Hi everybody, this is Dave Velante with the CUBE. And when we talk to practitioners about data and AI they have troubles infusing AI into their data pipeline and automating that data pipeline. So we're bringing together the community, brought to you by IBM to really understand how successful organizations are operationalizing the data pipeline and with me to talk about that is Aliye Ozcan. Aliye, hello, introduce yourself. Tell us about who you are. >> Hi Dave, how are you doing? Yes, my name is Aliye Ozcan I'm the Data Operations Data ops Global Marketing Leader at IBM. >> So I'm very excited about this project. Go to crowdchat.net/dataops, add it to your calendar and check it out. So we have practitioners, Aliye from Harley Davidson, Standard Bank, Associated Bank. What are we going to learn from them? >> What we are going to learn from them is the data experiences. What are the data challenges that they are going through? What are the data bottlenecks that they had? And especially in these challenging times right now. The industry is going through this challenging time. We are all going through this. How the foundation that they invested. Is now helping them to pivot quickly to market demands, the new market demands fast. That is fascinating to see, and I'm very excited having individual conversations with those experts and bringing those stories to the audience here. >> Awesome, and we also have Inderpal Bhandari from the CDO office at IBM, so go to crowdchat.net/dataops, add it to your calendar, we'll see you in the crowd chat.

>> From Cambridge, Massachusetts. It's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. We're here at MIT, sweltering Cambridge, Massachusetts. You're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante. I'm here with my co-host, Paul Gillin. Special coverage of the MITCDOIQ. The Chief Data Officer event, this is the 13th year of the event, we started seven years ago covering it, Mark Ramsey is here. He's the Chief Data and Analytics Officer Advisor at Ramsey International, LLC and former Chief Data Officer of GlaxoSmithKline. Big pharma, Mark, thanks for coming onto theCUBE. >> Thanks for having me. >> You're very welcome, fresh off the keynote. Fascinating keynote this evening, or this morning. Lot of interest here, tons of questions. And we have some as well, but let's start with your history in data. I sat down after 10 years, but I could have I could have stretched it to 20. I'll sit down with the young guns. But there was some folks in there with 30 plus year careers. How about you, what does your data journey look like? >> Well, my data journey, of course I was able to stand up for the whole time because I was in the front, but I actually started about 32, a little over 32 years ago and I was involved with building. What I always tell folks is that Data and Analytics has been a long journey, and the name has changed over the years, but we've been really trying to tackle the same problems of using data as a strategic asset. So when I started I was with an insurance and financial services company, building one of the first data warehouse environments in the insurance industry, and that was in the 87, 88 range, and then once I was able to deliver that, I ended up transitioning into being in consulting for IBM and basically spent 18 years with IBM in consulting and services. When I joined, the name had evolved from Data Warehousing to Business Intelligence and then over the years it was Master Data Management, Customer 360. Analytics and Optimization, Big Data. And then in 2013, I joined Samsung Mobile as their first Chief Data Officer. So, moving out of consulting, I really wanted to own the end-to-end delivery of advanced solutions in the Data Analytics space and so that made the transition to Samsung quite interesting, very much into consumer electronics, mobile phones, tablets and things of that nature, and then in 2015 I joined GSK as their first Chief Data Officer to deliver a Data Analytics solution. >> So you have long data history and Paul, Mark took us through. And you're right, Mark-o, it's a lot of the same narrative, same wine, new bottle but the technology's obviously changed. The opportunities are greater today. But you took us through Enterprise Data Warehouse which was ETL and then MAP and then Master Data Management which is kind of this mapping and abstraction layer, then an Enterprise Data Model, top-down. And then that all failed, so we turned to Governance which has been very very difficult and then you came up with another solution that we're going to dig into, but is it the same wine, new bottle from the industry? >> I think it has been over the last 20, 30 years, which is why I kind of did the experiment at the beginning of how long folks have been in the industry. I think that certainly, the technology has advanced, moving to reduction in the amount of schema that's required to move data so you can kind of move away from the map and move type of an approach of a data warehouse but it is tackling the same type of problems and like I said in the session it's a little bit like Einstein's phrase of doing the same thing over and over again and expecting a different answer is certainly the definition of insanity and what I really proposed at the session was let's come at this from a very different perspective. Let's actually use Data Analytics on the data to make it available for these purposes, and I do think I think it's a different wine now and so I think it's just now a matter of if folks can really take off and head that direction. >> What struck me about, you were ticking off some of the issues that have failed like Data Warehouses, I was surprised to hear you say Data Governance really hasn't worked because there's a lot of talk around that right now, but all of those are top-down initiatives, and what you did at GSK was really invert that model and go from the bottom up. What were some of the barriers that you had to face organizationally to get the cooperation of all these people in this different approach? >> Yeah, I think it's still key. It's not a complete bottoms up because then you do end up really just doing data for the sake of data, which is also something that's been tried and does not work. I think it has to be a balance and that's really striking that right balance of really tackling the data at full perspective but also making sure that you have very definitive use cases to deliver value for the organization and then striking the balance of how you do that and I think of the things that becomes a struggle is you're talking about very large breadth and any time you're covering multiple functions within a business it's getting the support of those different business functions and I think part of that is really around executive support and what that means, I did mention it in the session, that executive support to me is really stepping up and saying that the data across the organization is the organization's data. It isn't owned by a particular person or a particular scientist, and I think in a lot of organization, that gatekeeper mentality really does put barriers up to really tackling the full breadth of the data. >> So I had a question around digital initiatives. Everywhere you go, every C-level Executive is trying to get digital right, and a lot of this is top-down, a lot of it is big ideas and it's kind of the North Star. Do you think that that's the wrong approach? That maybe there should be a more tactical line of business alignment with that threaded leader as opposed to this big picture. We're going to change and transform our company, what are your thoughts? >> I think one of the struggles is just I'm not sure that organizations really have a good appreciation of what they mean when they talk about digital transformation. I think there's in most of the industries it is an initiative that's getting a lot of press within the organizations and folks want to go through digital transformation but in some cases that means having a more interactive experience with consumers and it's maybe through sensors or different ways to capture data but if they haven't solved the data problem it just becomes another source of data that we're going to mismanage and so I do think there's a risk that we're going to see the same outcome from digital that we have when folks have tried other approaches to integrate information, and if you don't solve the basic blocking and tackling having data that has higher velocity and more granularity, if you're not able to solve that because you haven't tackled the bigger problem, I'm not sure it's going to have the impact that folks really expect. >> You mentioned that at GSK you collected 15 petabytes of data of which only one petabyte was structured. So you had to make sense of all that unstructured data. What did you learn about that process? About how to unlock value from unstructured data as a result of that? >> Yeah, and I think this is something. I think it's extremely important in the unstructured data to apply advanced analytics against the data to go through a process of making sense of that information and a lot of folks talk about or have talked about historically around text mining of trying to extract an entity out of unstructured data and using that for the value. There's a few steps before you even get to that point, and first of all it's classifying the information to understand which documents do you care about and which documents do you not care about and I always use the story that in this vast amount of documents there's going to be, somebody has probably uploaded the cafeteria menu from 10 years ago. That has no scientific value, whereas a protocol document for a clinical trial has significant value, you don't want to look through manually a billion documents to separate those, so you have to apply the technology even in that first step of classification, and then there's a number of steps that ultimately lead you to understanding the relationship of the knowledge that's in the documents. >> Side question on that, so you had discussed okay, if it's a menu, get rid of it but there's certain restrictions where you got to keep data for decades. It struck me, what about work in process? Especially in the pharmaceutical industry. I mean, post Federal Rules of Civil Procedure was everybody looking for a smoking gun. So, how are organizations dealing with what to keep and what to get rid of? >> Yeah, and I think certainly the thinking has been to remove the excess and it's to your point, how do you draw the line as to what is excess, right, so you don't want to just keep every document because then if an organization is involved in any type of litigation and there's disclosure requirements, you don't want to have to have thousands of documents. At the same time, there are requirements and so it's like a lot of things. It's figuring out how do you abide by the requirements, but that is not an easy thing to do, and it really is another driver, certainly document retention has been a big thing over a number of years but I think people have not applied advanced analytics to the level that they can to really help support that. >> Another Einstein bro-mahd, you know. Keep everything you must but no more. So, you put forth a proposal where you basically had this sort of three approaches, well, combined three approaches. The crawlers to go, the spiders to go out and do the discovery and I presume that's where the classification is done? >> That's really the identification of all of the source information >> Okay, so find out what you got, okay. >> so that's kind of the start. Find out what you have. >> Step two is the data repository. Putting that in, I thought it was when I heard you I said okay it must be a logical data repository, but you said you basically told the CIO we're copying all the data and putting it into essentially one place. >> A physical location, yes. >> Okay, and then so I got another question about that and then use bots in the pipeline to move the data and then you sort of drew the diagram of the back end to all the databases. Unstructured, structured, and then all the fun stuff up front, visualization. >> Which people love to focus on the fun stuff, right? Especially, you can't tell how many articles are on you got to apply deep learning and machine learning and that's where the answers are, we have to have the data and that's the piece that people are missing. >> So, my question there is you had this tactical mindset, it seems like you picked a good workload, the clinical trials and you had at least conceptually a good chance of success. Is that a fair statement? >> Well, the clinical trials was one aspect. Again, we tackled the entire data landscape. So it was all of the data across all of R&D. It wasn't limited to just, that's that top down and bottom up, so the bottom up is tackle everything in the landscape. The top down is what's important to the organization for decision making. >> So, that's actually the entire R&D application portfolio. >> Both internal and external. >> So my follow up question there is so that largely was kind of an inside the four walls of GSK, workload or not necessarily. My question was what about, you hear about these emerging Edge applications, and that's got to be a nightmare for what you described. In other words, putting all the data into one physical place, so it must be like a snake swallowing a basketball. Thoughts on that? >> I think some of it really does depend on you're always going to have these, IOT is another example where it's a large amount of streaming information, and so I'm not proposing that all data in every format in every location needs to be centralized and homogenized, I think you have to add some intelligence on top of that but certainly from an edge perspective or an IOT perspective or sensors. The data that you want to then make decisions around, so you're probably going to have a filter level that will impact those things coming in, then you filter it down to where you're going to really want to make decisions on that and then that comes together with the other-- >> So it's a prioritization exercise, and that presumably can be automated. >> Right, but I think we always have these cases where we can say well what about this case, and you know I guess what I'm saying is I've not seen organizations tackle their own data landscape challenges and really do it in an aggressive way to get value out of the data that's within their four walls. It's always like I mentioned in the keynote. It's always let's do a very small proof of concept, let's take a very narrow chunk. And what ultimately ends up happening is that becomes the only solution they build and then they go to another area and they build another solution and that's why we end up with 15 or 25-- (all talk over each other) >> The conventional wisdom is you start small. >> And fail. >> And you go on from there, you fail and that's now how you get big things done. >> Well that's not how you support analytic algorithms like machine learning and deep learning. You can't feed those just fragmented data of one aspect of your business and expect it to learn intelligent things to then make recommendations, you've got to have a much broader perspective. >> I want to ask you about one statistic you shared. You found 26 thousand relational database schemas for capturing experimental data and you standardized those into one. How? >> Yeah, I mean we took advantage of the Tamr technology that Michael Stonebraker created here at MIT a number of years ago which is really, again, it's applying advanced analytics to the data and using the content of the data and the characteristics of the data to go from dispersed schemas into a unified schema. So if you look across 26 thousand schemas using machine learning, you then can understand what's the consolidated view that gives you one perspective across all of those different schemas, 'cause ultimately when you give people flexibility they love to take advantage of it but it doesn't mean that they're actually doing things in an extremely different way, 'cause ultimately they're capturing the same kind of data. They're just calling things different names and they might be using different formats but in that particular case we use Tamr very heavily, and that again is back to my example of using advanced analytics on the data to make it available to do the fun stuff. The visualization and the advanced analytics. >> So Mark, the last question is you well know that the CDO role emerged in these highly regulated industries and I guess in the case of pharma quasi-regulated industries but now it seems to be permeating all industries. We have Goka-lan from McDonald's and virtually every industry is at least thinking about this role or has some kind of de facto CDO, so if you were slotted in to a CDO role, let's make it generic. I know it depends on the industry but where do you start as a CDO for an organization large company that doesn't have a CDO. Even a mid-sized organization, where do you start? >> Yeah, I mean my approach is that a true CDO is maximizing the strategic value of data within the organization. It isn't a regulatory requirement. I know a lot of the banks started there 'cause they needed someone to be responsible for data quality and data privacy but for me the most critical thing is understanding the strategic objectives of the organization and how will data be used differently in the future to drive decisions and actions and the effectiveness of the business. In some cases, there was a lot of discussion around monetizing the value of data. People immediately took that to can we sell our data and make money as a different revenue stream, I'm not a proponent of that. It's internally monetizing your data. How do you triple the size of the business by using data as a strategic advantage and how do you change the executives so what is good enough today is not good enough tomorrow because they are really focused on using data as their decision making tool, and that to me is the difference that a CDO needs to make is really using data to drive those strategic decision points. >> And that nuance you mentioned I think is really important. Inderpal Bhandari, who is the Chief Data Officer of IBM often says how can you monetize the data and you're right, I don't think he means selling data, it's how does data contribute, if I could rephrase what you said, contribute to the value of the organization, that can be cutting costs, that can be driving new revenue streams, that could be saving lives if you're a hospital, improving productivity. >> Yeah, and I think what I've shared typically shared with executives when I've been in the CDO role is that they need to change their behavior, right? If a CDO comes in to an organization and a year later, the executives are still making decisions on the same data PowerPoints with spinning logos and they said ooh, we've got to have 'em. If they're still making decisions that way then the CDO has not been successful. The executives have to change what their level of expectation is in order to make a decision. >> Change agents, top down, bottom up, last question. >> Going back to GSK, now that they've completed this massive data consolidation project how are things different for that business? >> Yeah, I mean you look how Barron joined as the President of R&D about a year and a half ago and his primary focus is using data and analytics and machine learning to drive the decision making in the discovery of a new medicine and the environment that has been created is a key component to that strategic initiative and so they are actually completely changing the way they're selecting new targets for new medicines based on data and analytics. >> Mark, thanks so much for coming on theCUBE. >> Thanks for having me. >> Great keynote this morning, you're welcome. All right, keep it right there everybody. We'll be back with our next guest. This is theCUBE, Dave Vellante with Paul Gillin. Be right back from MIT. 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