>>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.

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

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back. We're here to wrap up the M I T. Chief data officer officer, information quality. It's hashtag m i t CDO conference. You're watching the Cube. I'm David Dante, and Paul Gill is my co host. This is two days of coverage. We're wrapping up eyes. Our analysis of what's going on here, Paul, Let me let me kick it off. When we first started here, we talked about that are open. It was way saw the chief data officer role emerged from the back office, the information quality role. When in 2013 the CEO's that we talked to when we asked them what was their scope. We heard things like, Oh, it's very wide. Involves analytics, data science. Some CEOs even said Oh, yes, security is actually part of our purview because all the cyber data so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed. The studios were staking their claim. The reality was the CDO also emerged out of highly regulated industries financialservices healthcare government. And it really was this kind of wonky back office role. And so that's what my compliance, that's what it's become again. We're seeing that CEOs largely you're not involved in a lot of the emerging. Aye, aye initiatives. That's what we heard, sort of anecdotally talking to various folks At the same time. I feel as though the CDO role has been more fossilized than it was before. We used to ask, Is this role going to be around anymore? We had C I. Ose tell us that the CEO Rose was going to disappear, so you had both ends of the spectrum. But I feel as though that whatever it's called CDO Data's our chief analytics off officer, head of data, you know, analytics and governance. That role is here to stay, at least for for a fair amount of time and increasingly, issues of privacy and governance. And at least the periphery of security are gonna be supported by that CD a role. So that's kind of takeaway Number one. Let me get your thoughts. >> I think there's a maturity process going on here. What we saw really in 2016 through 2018 was, ah, sort of a celebration of the arrival of the CDO. And we're here, you know, we've got we've got power now we've got an agenda. And that was I mean, that was a natural outcome of all this growth and 90% of organizations putting sea Dios in place. I think what you're seeing now is a realization that Oh, my God, this is a mess. You know what I heard? This year was a lot less of this sort of crowing about the ascendance of sea Dios and Maura about We've got a big integration problem of big data cleansing problem, and we've got to get our hands down to the nitty gritty. And when you talk about, as you said, we had in here so much this year about strategic initiatives, about about artificial intelligence, about getting involved in digital business or customer experience transformation. What we heard this year was about cleaning up data, finding the data that you've got organizing it, applying meditator, too. It is getting in shape to do something with it. There's nothing wrong with that. I just think it's part of the natural maturation process. Organizations now have to go through Tiu to the dirty process of cleaning up this data before they can get to the next stage, which was a couple of three years out for most of >> the second. Big theme, of course. We heard this from the former head of analytics. That G s K on the opening keynote is the traditional methods have failed the the Enterprise Data Warehouse, and we've actually studied this a lot. You know, my analogy is often you snake swallowing a basketball, having to build cubes. E D W practitioners would always used to call it chasing the chips until we come up with a new chip. Oh, we need that because we gotta run faster because it's taking us hours and hours, weeks days to run these analytics. So that really was not an agile. It was a rear view mirror looking thing. And Sarbanes Oxley saved the E. D. W. Business because reporting became part of compliance thing perspective. The master data management piece we've heard. Do you consistently? We heard Mike Stone Breaker, who's obviously a technology visionary, was right on. It doesn't scale through this notion of duping. Everything just doesn't work and manually creating rules. It's just it's just not the right approach. This we also heard the top down data data enterprise data model doesn't works too complicated, can operationalize it. So what they do, they kick the can to governance. The Duke was kind of a sidecar, their big data that failed to live up to its promises. And so it's It's a big question as to whether or not a I will bring that level of automation we heard from KPMG. Certainly, Mike Stone breaker again said way heard this, uh, a cz well, from Andy Palmer. They're using technology toe automate and scale that big number one data science problem, which is? They spend all their time wrangling data. We'll see if that if that actually lives up >> to his probable is something we did here today from several of our guests. Was about the promise of machine learning to automate this day to clean up process and as ah Mark Ramsay kick off the conference saying that all of these efforts to standardize data have failed in the past. This does look, He then showed how how G s K had used some of the tools that were represented here using machine learning to actually clean up the data at G S. K. So there is. And I heard today a lot of optimism from the people we talked to about the capability of Chris, for example, talking about the capability of machine learning to bring some order to solve this scale scale problem Because really organizing data creating enterprise data models is a scale problem, and the only way you can solve that it's with with automation, Mike Stone breaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of, ah, a dismay at seeing all the data problems they have to clean up, but also promised that tools are on the way that could do that. >> Yeah, The reason I'm an optimist about this role is because data such a hard problem. And while there is a feeling of wow, this is really a challenge. There's a lot of smart people here who are up for the challenge and have the d n a for it. So the role, that whole 360 thing. We talked about the traditional methods, you know, kind of failing, and in the third piece that touched on, which is really bringing machine intelligence to the table. We haven't heard that as much at this event. It's now front and center. It's just another example of a I injecting itself into virtually every aspect every corner of the industry. And again, I often jokes. Same wine, new bottle. Our industry has a habit of doing that, but it's cyclical, but it is. But we seem to be making consistent progress. >> And the machine learning, I thought was interesting. Several very guest spoke to machine learning being applied to the plumbing projects right now to cleaning up data. Those are really self contained projects. You can manage those you can. You can determine out test outcomes. You can vet the quality of the of the algorithms. It's not like you're putting machine learning out there in front of the customer where it could potentially do some real damage. There. They're vetting their burning in machine, learning in a environment that they control. >> Right, So So, Amy, Two solid days here. I think that this this conference has really grown when we first started here is about 130 people, I think. And now it was 500 registrants. This'd year. I think 600 is the sort of the goal for next year. Moving venues. The Cube has been covering this all but one year since 2013. Hope to continue to do that. Paul was great working with you. Um, always great work. I hope we can, uh we could do more together. We heard the verdict is bringing back its conference. You put that together. So we had column. Mahoney, um, had the vertical rock stars on which was fun. Com Mahoney, Mike Stone breaker uh, Andy Palmer and Chris Lynch all kind of weighed in, which was great to get their perspectives kind of the days of MPP and how that's evolved improving on traditional relational database. And and now you're Stone breaker. Applying all these m i. Same thing with that scale with Chris Lynch. So it's fun to tow. Watch those guys all Boston based East Coast folks some news. We just saw the news hit President Trump holding up jet icon contractors is we've talked about. We've been following that story very closely and I've got some concerns over that. It's I think it's largely because he doesn't like Bezos in The Washington Post Post. Exactly. You know, here's this you know, America first. The Pentagon says they need this to be competitive with China >> and a I. >> There's maybe some you know, where there's smoke. There's fire there, so >> it's more important to stick in >> the eye. That's what it seems like. So we're watching that story very closely. I think it's I think it's a bad move for the executive branch to be involved in those type of decisions. But you know what I know? Well, anyway, Paul awesome working with you guys. Thanks. And to appreciate you flying out, Sal. Good job, Alex Mike. Great. Already wrapping up. So thank you for watching. Go to silicon angle dot com for all the news. Youtube dot com slash silicon angles where we house our playlist. But the cube dot net is the main site where we have all the events. It will show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then, of course, VM World is the big kickoff for the fall season. Goto wicked bond dot com for all the research. We're out. Thanks for watching Dave. A lot day for Paul Gillon will see you next time.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody watching the Cuban leader live tech coverage. We here covering the M I t CDO conference M I t CEO Day to wrapping up. Bob Parr is here. He's a partner in principle at KPMG, and he's joined by Streetcar Krishna, who is the managing director of data science. Aye, aye. And innovation at KPMG. Gents, welcome to the Cube. Thank >> thank you. Let's start with your >> roles. So, Bob, where do you focus >> my focus? Ah, within KPMG, we've got three main business lines audit tax, an advisory. And so I'm the advisory chief date officer. So I'm more focused on how we use data competitively in the market. More the offense side of our focus. So, you know, how do we make sure that our teams have the data they need to deliver value? Uh, much as possible working concert with the enterprise? CDO uh, who's more focused on our infrastructure, Our standards, security, privacy and those >> you've focused on making KPMG better A >> supposed exactly clients. OK, >> I also have a second hat, and I also serve financial service is si Dios as well. So Okay, so >> get her out of a dual role. I got sales guys in >> streetcar. What was your role? >> Yeah, You know, I focus a lot on data science, artificial intelligence and overall innovation s o my reaction. I actually represent a centre of >> excellence within KPMG that focuses on the I machine learning natural language processing. And I work with Bob's Division to actually advance the data site off the store because all the eye needs data. And without data, there's no algorithms, So we're focusing a lot on How do we use a I to make data Better think about their equality. Think about data lineage. Think about all of the problems that data has. How can we make it better using algorithms? And I focused a lot on that working with Bob, But no, it's it's customers and internal. I mean, you know, I were a horizontal within the form, So we help customers. We help internal, we focus a lot on the market. >> So, Bob, you mentioned used data offensively. So 10 12 years ago, it was data was a liability. You had to get rid of it. Keep it no longer than you had to, because you're gonna get soon. So email archives came in and obviously thinks flipped after the big data. But so what do you What are you seeing in terms of that shift from From the defense data to the offensive? >> Yeah, and it's it's really you know, when you think about it and let me define sort of offense versus defense. Who on the defense side, historically, that's where most of CEOs have played. That's risk regulatory reporting, privacy, um, even litigation support those types of activities today. Uh, and really, until about a year and 1/2 ago, we really saw most CEOs still really anchored in that I run a forum with a number of studios and financial service is, and every year we get them together and asked him the same set of questions. This was the first year where they said that you know what my primary focus now is. Growth. It's bringing efficiency is trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation. But you know, it's It's a sign that the volume of use cases as the investments in their digital transformations are starting to kick out, as well as the volumes of data that are available. The raw material that's available to them in terms of third party data in terms of the the just the general volumes that that exist that are streaming into the organization and the overall literacy in the business units are creating this, this massive demand. And so they're having to >> respond because of getting a handle on the data they're actually finding. Word is, they're categorizing it there, there, >> yeah, organizing that. That is still still a challenge. Um, I think it's better with when you have a very narrow scope of critical data elements going back to the structure data that we're talking it with the regulatory reporting when you start to get into the three offense, the generating value, getting the customer experience, you know, really exploring. You know that side of it. There's there's a ton of new muscle that has to be built new muscle in terms of data quality, new muscle in terms of um, really more scalable operating model. I think that's a big issue right now with Si Dios is, you know, we've got ah, we're used to that limited swath of CDs and they've got Stewardship Network. That's very labor intensive. A lot of manual processes still, um, and and they have some good basic technology, but it's a lot of its rules based. And when you do you think about those how that constraints going to scale when you have all of this demand. You know, when you look at the customer experience analytics that they want to do when you look at, you know, just a I applied to things like operations. The demand on the focus there is is is gonna start to create a fundamental shift >> this week are one of things that I >> have scene, and maybe it's just my small observation space. But I wonder, if you could comment Is that seems like many CBO's air not directly involved in the aye aye initiatives. Clearly, the chief digital officer is involved, but the CDO zehr kind of, you know, in the background still, you see that? >> That's a fantastic question, and I think this is where we're seeing some off the cutting it change that is happening in the industry. And when Barbara presenter idea that we can often civilly look at data, this is what it is that studios for a long time have become more reactive in their roles. And that is that is starting to come forefront now. So a lot of institutions were working with are asking What's the next generation Roll off a CDO and why are they in the background and why are they not in the foreground? And this is when you become more often they were proactive with data and the digital officers are obviously focused on, you know, the transformation that has to happen. But the studios are their backbone in order to make the transformation. Really. And if the CDO started, think about their data as an asset did as a product did us a service. The judicial officers are right there because those are the real, you know, like the data data they're living so CDO can really become from my back office to really become a business line. We've >> seen taking the reins in machine learning in machine learning projects and cos you work with. Who >> was driving that? Yeah. Great question. So we are seeing, like, you know, different. I would put them in buckets, right? There is no one mortal fits all. We're seeing different generations within the company's. Some off. The ones were just testing out the market. There's two keeping it in their technology space in their back office. Take idea and, you know, in in forward I d let me call them where they are starting to experiment with this. But you see, the mature organizations on the other end of the spectrum, they are integrating action, learning and a I right into the business line because they want to see ex souls having the technology right by their side so they can lead leverage. Aye, aye. And machine learning spot right for the business right there. And that is where we're seeing know some of the new models. Come on. >> I think the big shift from a CDO perspective is using a i to prep data for a That's that's fundamentally where you know, where the data science was distributed. Some of that data science has to come back and free the integration for equality for data prepping because you've got all this data third party and other from customer streaming into the organization. And you know, the work that you're doing around, um, anomaly detection is it transcends developing the rules, doing the profiling, doing the rules. You know, the very manual, the very labor intensive process you've got to get away from that >> is used in order for this to be scale goes and a I to figure out which out goes to apply t >> clean to prepare the data toe, see what algorithms we can use. So it's basically what we're calling a eye for data rather than just data leading into a I. So it's I mean, you know, you developed a technology for one off our clients and pretty large financial service. They were getting closer, like 1,000,000,000 data points every day. And there was no way manually, you could go through the same quality controls and all of those processes. So we automated it through algorithms, and these algorithms are learning the behavior of data as they flow into the organization, and they're able to proactively tell their problems are starting very much. And this is the new face that we see in in the industry, you cannot scale the traditional data governance using manual processes, we have to go to the next generation where a i natural language processing and think about on structure data, right? I mean, that is, like 90% off. The organization is unstructured data, and we have not talked about data quality. We have not talked about data governance. For a lot of these sources of information, now is the time. Hey, I can do it. >> And I think that raised a great question. If you look at unstructured and a lot of the data sources, as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the the profiling and the rules generation the way you would with standard data. So the teams, the skills that CEOs have in their organizations, have to change. You have to start to, and, you know, it's a great example where, you know, you guys were ingesting documents and there was handwriting all over the documents, you know, and >> yeah, you know, you're a great example, Bob. Like you no way would ask the client, like, you know, is this document gonna scanned into the system so my algorithm can run and they're like, Yeah, everything is good. I mean, the deal is there, but when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms breakdown now >> tribal knowledge striving Exactly. >> Exactly. So that's what we're seeing. You know, if I if we talk about the digital transformation in data in the city organization, it is this idea dart. Nothing is left unseen. Some algorithm or some technology, has seen everything that is coming into. The organization has has has a para 500. So you can tell you where the problems are. And this is what algorithms do. This scale beautifully. >> So the data quality approaches are evolving, sort of changing. So rather than heavy, heavy emphasis on masking or duplication and things like that, you would traditionally think of participating the difficult not that that goes away. But it's got to evolve to use machine >> intelligence. Exactly what kind of >> skill sets people need thio achieve that Is it Is it the same people or do we need to retrain them or bring in new skills. >> Yeah, great question. And I can talk from the inspector off. Where is disrupting every industry now that we know, right? But we knew when you look at what skills are >> required, all of the eye, including natural language processing, machine learning, still require human in the loop. And >> that is the training that goes in there. And who do you who are the >> people who have that knowledge? It is the business analyst. It's the data analyst who are the knowledge betters the C suite and the studios. They are able to make decisions. But the day today is still with the data analyst. >> Those s Emmys. Those sm >> means So we have to obscure them to really start >> interacting with these new technologies where they are the leaders, rather than just waiting for answers to come through. And >> when that happens now being as a data scientist, my job is easy because they're Siamese, are there? I deploy the technology. They're semi's trained algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re architect ing my rules. And like my, you know, what are the masking capabilities I need to have? It is evolving us. >> Does that change the >> number one problem that you hear from data scientists, which is the 80% of the time >> spent on wrangling cleaning data 10 15 20% run into sm. He's being concerned that they're gonna be replaced by the machine. Their training. >> I actually see them being really enabled now where they're spending 80% of the time doing boring job off, looking at data. Now they're spending 90% of their time looking at the elements future creative in which requires human intelligence to say, Hey, this is different because off X, >> y and Z so let's let's go out. It sounds like a lot of what machine learning is being used for now in your domain is clean things up its plumbing. It's basic foundation work. So go out. Three years after all that work has been done and the data is clean. Where are your clients talking about going next with machine learning? Bob, did you want? >> I mean, it's a whole. It varies by by industry, obviously, but, um but it covers the gamut from, you know, and it's generally tied to what's driving their strategies. So if you look at a financial service is organization as an example today, you're gonna have, you know, really a I driving a lot of the behind the scenes on the customer experience. It's, you know, today with your credit card company. It's behind the scenes doing fraud detection. You know, that's that's going to continue. So it's take the critical functions that were more data. It makes better models that, you know, that that's just going to explode. And I think they're really you can look across all the functions, from finance to to marketing to operations. I mean, it's it's gonna be pervasive across, you know all of that. >> So if I may, I don't top award. While Bob was saying, I think what's gonna what What our clients are asking is, how can I exhilarate the decision making? Because at the end of the day on Lee, all our leaders are focused on making decisions, and all of this data science is leading up to their decision, and today you see like you know what you brought up, like 80% of the time is wasted in cleaning the data. So only 20% time was spent in riel experimentation and analytics. So your decision making time was reduced to 20% off the effort that I put in the pipeline. What if now I can make it 80% of the time? They're I put in the pipeline, better decisions are gonna come on the train. So when I go into a meeting and I'm saying like, Hey, can you show me what happened in this particular region or in this particular part of the country? Previously, it would have been like, Oh, can you come back in two weeks? I will have the data ready, and I will tell you the answer. But in two weeks, the business has ran away and the CDO know or the C Street doesn't require the same answer. But where we're headed as as the data quality improves, you can get to really time questions and decisions. >> So decision, sport, business, intelligence. Well, we're getting better. Isn't interesting to me. Six months to build a cube, we'd still still not good enough. Moving too fast. As the saying goes, data is plentiful. Insights aren't Yes, you know, in your view, well, machine intelligence. Finally, close that gap. Get us closer to real time decision >> making. It will eventually. But there's there's so much that we need to. Our industry needs to understand first, and it really ingrained. And, you know, today there is still a fundamental trust issues with a I you know, it's we've done a lot of work >> watch Black box or a part of >> it. Part of it. I think you know, the research we've done. And some of this is nine countries, 2400 senior executives. And we asked some, ah, a lot of questions around their data and trusted analytics, and 92% of them came back with. They have some fundamental trust issues with their data and their analytics and and they feel like there's reputational risk material reputational risk. This isn't getting one little number wrong on one of the >> reports about some more of an >> issue, you know, we also do a CEO study, and we've done this many years in a row going back to 2017. We started asked them okay, making a lot of companies their data driven right. When it comes to >> what they say they're doing well, They say they're day driven. That's the >> point. At the end of the day, they making strategic decisions where you have an insight that's not intuitive. Do you trust your gut? Go with the analytics back then. You know, 67% said they go with their gut, So okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it. 2018 go down. No, went up 78%. So it's not aware this issue there is something We're fundamentally wrong and you hit it on. It's a part of its black box, and part of it's the date equality and part of its bias. And there's there's all of these things flowing around it. And so when we dug into that, we said, Well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that that trust issue and really it's the front part is, is exactly what we're talking about in terms of data quality, both structured more traditional approaches and unstructured, using the handwriting example in those types of techniques. But then you get into the models themselves, and it's, you know, the critical thing she had to worry about is, you know, lineage. So from an integrity perspective, where's the data coming from? Whether the sources for the change controls on some of that, they need to look at explain ability, gain at the black box part where you can you tell me the inferences decisions are those documented. And this is important for this me, the human in the loop to get confidence in the algorithm as well as you know, that executive group. So they understand there's a structure set of processes around >> Moneyball. Problem is actually pretty confined. It's pretty straightforward. Dono 32 teams are throwing minor leagues, but the data models pretty consistent through the problem with organizations is I didn't know data model is consistent with the organization you mentioned, Risk Bob. The >> other problem is organizational inertia. If they don't trust it, what is it? What is a P and l manage to do when he or she wants to preserve? Yeah, you know, their exit position. They attacked the data. You know, I don't believe that well, which which is >> a fundamental point, which is culture. Yes. I mean, you can you can have all the data, science and all the governance that you want. But if you don't work culture in parallel with all this, it's it's not gonna stick. And and that's, I think the lot of the leading organisations, they're starting to really dig into this. We hear a lot of it literacy. We hear a lot about, you know, top down support. What does that really mean? It means, you know, senior executives are placing bats around and linking demonstrably linking the data and the role of data days an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy absolutely fundamental is well, because it's not just the executives and the data scientists that have to get this. It's the guy in ops that you're trying to get you. They need to understand, you know, not only tools, but it's less about the tools. But it's the techniques, so it's not. The approach is being used, are more transparent and and that you know they're starting to also understand, you know, the issues of privacy and data usage rights. That's that's also something that we can't leave it the curb. With all this >> innovation, it's also believing that there's an imperative. I mean, there's a lot of for all the talk about digital transformation hear it everywhere. Everybody's trying to get digital, right? But there's still a lot of complacency in the organization in the lines of business in operation to save. We're actually doing really well. You know, we're in financial service is health care really hasn't been disrupted. This is Oh, it's coming, it's coming. But there's still a lot of I'll be retired by then or hanging. Actually, it's >> also it's also the fact that, you know, like in the previous generation, like, you know, if I had to go to a shopping, I would go into a shop and if I wanted by an insurance product, I would call my insurance agent. But today the New world, it's just a top off my screen. I have to go from Amazon, so some other some other app, and this is really this is what is happening to all of our kind. Previously that they start their customers, pocketed them in different experience. Buckets. It's not anymore that's real in front of them. So if you don't get into their digital transformation, a customer is not going to discount you by saying, Oh, you're not Amazon. So I'm not going to expect that you're still on my phone and you're only two types of here, so you have to become really digital >> little surprises that you said you see the next. The next stage is being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. >> No natural profile. There are two differences, right? One is external facing is absolutely the customer internal facing. It's absolutely the decision making, because that's how they're separating. The internal were, says the external, and you know most of the meetings that we goto Customer insight is the first place where analytics is starting where data is being cleaned up. Their questions are being asked about. Can I master my customer records? Can I do a good master off my vendor list? That is where they start. But all of that leads to good decision making to support the customers. So it's like that external towards internal view well, back >> to the offense versus defense and the shift. I mean, it absolutely is on the offense side. So it is with the customer, and that's a more directly to the business strategy. So it's get That's the area that's getting the money, the support and people feel like it's they're making an impact with it there. When it's it's down here in some admin area, it's below the water line, and, you know, even though it's important and it flows up here, it doesn't get the VIN visibility. So >> that's great conversation. You coming on? You got to leave it there. Thank you for watching right back with our next guest, Dave Lot. Paul Gillen from M I t CDO I Q Right back. You're watching the Cube

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody, You're watching the Cube, the leader in live tech coverage, and we're covering the M I t CDO conference M I t. CDO. My name is David Monty in here with my co host, Paul Galen. Mike Stone breakers here. The legend is founder CTO of Of Tamer, as well as many other companies. Inventor Michael. Thanks for coming back in the Cube. Good to see again. Nice to be here. So this is kind of ah, repeat pattern for all of us. We kind of gather here in August that the CDO conference You're always the highlight of the show. You gave a talk this week on the top 10. Big data mistakes. You and I are one of the few. You were the few people who still use the term big data. I happen to like it. Sad that it's out of vogue already, but people associated with the doo doop it's kind of waning, but regardless, so welcome. How'd the talk go? What were you talking about. >> So I talked to a lot of people who were doing analytics. We're doing operation Offer operational day of data at scale, and they always make most of them make a collection of bad mistakes. And so the talk waas a litany of the blunders that I've seen people make, and so the audience could relate to the blunders about most. Most of the enterprise is represented. Make a bunch of the blunders. So I think no. One blunder is not planning on moving most everything to the cloud. >> So that's interesting, because a lot of people would would would love to debate that, but and I would imagine you probably could have done this 10 years ago in a lot of the blunders would be the same, but that's one that wouldn't have been there. But so I tend to agree. I was one of the two hands that went up this morning, and vocalist talk when he asked, Is the cloud cheaper for us? It is anyway. But so what? Why should everybody move everything? The cloud aren't there laws of physics, laws of economics, laws of the land that suggest maybe you >> shouldn't? Well, I guess 22 things and then a comment. First thing is James Hamilton, who's no techies. Techie works for Amazon. We know James. So he claims that he could stand up a server for 25% of your cost. I have no reason to disbelieve him. That number has been pretty constant for a few years, so his cost is 1/4 of your cost. Sooner or later, prices are gonna reflect costs as there's a race to the bottom of cloud servers. So >> So can I just stop you there for a second? Because you're some other date on that. All you have to do is look at a W S is operating margin and you'll see how profitable they are. They have software like economics. Now we're deploying servers. So sorry to interrupt, but so carry. So >> anyway, sooner or later, they're gonna have their gonna be wildly cheaper than you are. The second, then yet is from Dave DeWitt, whose database wizard. And here's the current technology that that Microsoft Azure is using. As of 18 months ago, it's shipping containers and parking lots, chilled water in power in Internet, Ian otherwise sealed roof and walls optional. So if you're doing raised flooring in Cambridge versus I'm doing shipping containers in the Columbia River Valley, who's gonna be a lot cheaper? And so you know the economies of scale? I mean, that, uh, big, big cloud guys are building data centers as fast as they can, using the cheapest technology around. You put up the data center every 10 years on dhe. You do it on raised flooring in Cambridge. So sooner or later, the cloud guys are gonna be a lot cheaper. And the only thing that isn't gonna the only thing that will change that equation is For example, my lab is up the street with Frank Gehry building, and we have we have an I t i t department who runs servers in Cambridge. Uh, and they claim they're cheaper than the cloud. And they don't pay rent for square footage and they don't pay for electricity. So yeah, if if think externalities, If there are no externalities, the cloud is assuredly going to be cheaper. And then the other thing is that most everybody tonight that I talk thio including me, has very skewed resource demands. So in the cloud finding three servers, except for the last day of the month on the last day of the month. I need 20 servers. I just do it. If I'm doing on Prem, I've got a provision for peak load. And so again, I'm just way more expensive. So I think sooner or later these combinations of effects was going to send everybody to the cloud for most everything, >> and my point about the operating margins is difference in price and cost. I think James Hamilton's right on it. If he If you look at the actual cost of deploying, it's even lower than the price with the market allows them to their growing at 40 plus percent a year and a 35 $40,000,000,000 run rate company sooner, Sooner or >> later, it's gonna be a race to the lot of you >> and the only guys are gonna win. You have guys have the best cost structure. A >> couple other highlights from your talk. >> Sure, I think 2nd 2nd thing like Thio Thio, no stress is that machine learning is going to be a game is going to be a game changer for essentially everybody. And not only is it going to be autonomous vehicles. It's gonna be automatic. Check out. It's going to be drone delivery of most everything. Uh, and so you can, either. And it's gonna affect essentially everybody gonna concert of, say, categorically. Any job that is easy to understand is going to get automated. And I think that's it's gonna be majorly impactful to most everybody. So if you're in Enterprise, you have two choices. You can be a disrupt or or you could be a disruptive. And so you can either be a taxi company or you can be you over, and it's gonna be a I machine learning that's going going to be determined which side of that equation you're on. So I was a big blunder that I see people not taking ml incredibly seriously. >> Do you see that? In fact, everyone I talked who seems to be bought in that this is we've got to get on the bandwagon. Yeah, >> I'm just pointing out the obvious. Yeah, yeah, I think, But one that's not quite so obvious you're is a lot of a lot of people I talked to say, uh, I'm on top of data science. I've hired a group of of 10 data scientists, and they're doing great. And when I talked, one vignette that's kind of fun is I talked to a data scientist from iRobot, which is the guys that have the vacuum cleaner that runs around your living room. So, uh, she said, I spend 90% of my time locating the data. I want to analyze getting my hands on it and cleaning it, leaving the 10% to do data science job for which I was hired. Of the 10% I spend 90% fixing the data cleaning errors in my data so that my models work. So she spends 99% of her time on what you call data preparation 1% of her time doing the job for which he was hired. So data science is not about data science. It's about data integration, data cleaning, data, discovery. >> But your new latest venture, >> so tamer does that sort of stuff. And so that's But that's the rial data science problem. And a lot of people don't realize that yet, And, uh, you know they will. I >> want to ask you because you've been involved in this by my count and starting up at least a dozen companies. Um, 99 Okay, It's a lot. >> It's not overstated. You estimated high fall. How do you How >> do you >> decide what challenge to move on? Because they're really not. You're not solving the same problems. You're You're moving on to new problems. How do you decide? What's the next thing that interests you? Enough to actually start a company. Okay, >> that's really easy. You know, I'm on the faculty of M i t. My job is to think of news new ship and investigate it, and I come up. No, I'm paid to come up with new ideas, some of which have commercial value, some of which don't and the ones that have commercial value, like, commercialized on. So it's whatever I'm doing at the time on. And that's why all the things I've commercialized, you're different >> s so going back to tamer data integration platform is a lot of companies out there claim to do it day to get integration right now. What did you see? What? That was the deficit in the market that you could address. >> Okay, great question. So there's the traditional data. Integration is extract transforming load systems and so called Master Data management systems brought to you by IBM in from Attica. Talent that class of folks. So a dirty little secret is that that technology does not scale Okay, in the following sense that it's all well, e t l doesn't scale for a different reason with an m d l e t l doesn't scale because e t. L is based on the premise that somebody really smart comes up with a global data model For all the data sources you want put together. You then send a human out to interview each business unit to figure out exactly what data they've got and then how to transform it into the global data model. How to load it into your data warehouse. That's very human intensive. And it doesn't scale because it's so human intensive. So I've never talked to a data warehouse operator who who says I integrate the average I talk to says they they integrate less than 10 data sources. Some people 20. If you twist my arm hard, I'll give you 50. So a Here. Here's a real world problem, which is Toyota Motor Europe. I want you right now. They have a distributor in Spain, another distributor in France. They have a country by country distributor, sometimes canton by Canton. Distribute distribution. So if you buy a Toyota and Spain and move to France, Toyota develops amnesia. The French French guys know nothing about you. So they've got 250 separate customer databases with 40,000,000 total records in 50 languages. And they're in the process of integrating that. It was single customer database so that they can Duke custom. They could do the customer service we expect when you cross cross and you boundary. I've never seen an e t l system capable of dealing with that kind of scale. E t l dozen scale to this level of problem. >> So how do you solve that problem? >> I'll tell you that they're a tamer customer. I'll tell you all about it. Let me first tell you why MGM doesn't scare. >> Okay. Great. >> So e t l says I now have all your data in one place in the same format, but now you've got following problems. You've got a d duplicated because if if I if I bought it, I bought a Toyota in Spain, I bought another Toyota in France. I'm both databases. So if you want to avoid double counting customers, you got a dupe. Uh, you know, got Duke 30,000,000 records. And so MGM says Okay, you write some rules. It's a rule based technology. So you write a rule. That's so, for example, my favorite example of a rule. I don't know if you guys like to downhill downhill skiing, All right? I love downhill skiing. So ski areas, Aaron, all kinds of public databases assemble those all together. Now you gotta figure out which ones are the same the same ski area, and they're called different names in different addresses and so forth. However, a vertical drop from bottom to the top is the same. Chances are they're the same ski area. So that's a rule that says how to how to put how to put data together in clusters. And so I now have a cluster for mount sanity, and I have a problem which is, uh, one address says something rather another address as something else. Which one is right or both? Right, so now you want. Now you have a gold. Let's call the golden Record problem to basically decide which, which, which data elements among a variety that maybe all associated with the same entity are in fact correct. So again, MDM, that's a rule's a rule based system. So it's a rule based technology and rule systems don't scale the best example I can give you for why Rules systems don't scale. His tamer has another customer. General Electric probably heard of them, and G wanted to do spend analytics, and so they had 20,000,000 spend transactions. Frank the year before last and spend transaction is I paid $12 to take a cab from here here to the airport, and I charged it to cost center X Y Z 20,000,000 of those so G has a pre built classification system for spend, so they have parts and underneath parts or computers underneath computers and memory and so forth. So pre existing preexisting class classifications for spend they want to simply classified 20,000,000 spent transactions into this pre existing hierarchy. So the traditional technology is, well, let's write some rules. So G wrote 500 rules, which is about the most any single human I can get there, their arms around so that classified 2,000,000 of the 20,000,000 transactions. You've now got 18 to go and another 500 rules is not going to give you 2,000,000 more. It's gonna give you love diminishing returns, right? So you have to write a huge number of rules and no one can possibly understand. So the technology simply doesn't scale, right? So in the case of G, uh, they had tamer health. Um, solve this. Solved this classification problem. Tamer used their 2,000,000 rule based, uh, tag records as training data. They used an ML model, then work off the training data classifies remaining 18,000,000. So the answer is machine learning. If you don't use machine learning, you're absolutely toast. So the answer to MDM the answer to MGM doesn't scale. You've got to use them. L The answer to each yell doesn't scale. You gotta You're putting together disparate records can. The answer is ml So you've got to replace humans by machine learning. And so that's that seems, at least in this conference, that seems to be resonating, which is people are understanding that at scale tradition, traditional data integration, technology's just don't work >> well and you got you got a great shot out on yesterday from the former G S K Mark Grams, a leader Mark Ramsay. Exactly. Guys. And how they solve their problem. He basically laid it out. BTW didn't work and GM didn't work, All right. I mean, kick it, kick the can top down data modelling, didn't work, kicked the candid governance That's not going to solve the problem. And But Tamer did, along with some other tooling. Obviously, of course, >> the Well, the other thing is No. One technology. There's no silver bullet here. It's going to be a bunch of technologies working together, right? Mark Ramsay is a great example. He used his stream sets and a bunch of other a bunch of other startup technology operating together and that traditional guys >> Okay, we're good >> question. I want to show we have time. >> So with traditional vendors by and large or 10 years behind the times, And if you want cutting edge stuff, you've got to go to start ups. >> I want to jump. It's a different topic, but I know that you in the past were critic of know of the no sequel movement, and no sequel isn't going away. It seems to be a uh uh, it seems to be actually gaining steam right now. What what are the flaws in no sequel? It has your opinion changed >> all? No. So so no sequel originally meant no sequel. Don't use it then. Then the marketing message changed to not only sequel, So sequel is fine, but no sequel does others. >> Now it's all sequel, right? >> And my point of view is now. No sequel means not yet sequel because high level language, high level data languages, air good. Mongo is inventing one Cassandra's inventing one. Those unless you squint, look like sequel. And so I think the answer is no sequel. Guys are drifting towards sequel. Meanwhile, Jason is That's a great idea. If you've got your regular data sequel, guys were saying, Sure, let's have Jason is the data type, and I think the only place where this a fair amount of argument is schema later versus schema first, and I pretty much think schema later is a bad idea because schema later really means you're creating a data swamp exactly on. So if you >> have to fix it and then you get a feel of >> salary, so you're storing employees and salaries. So, Paul salaries recorded as dollars per month. Uh, Dave, salary is in euros per week with a lunch allowance minds. So if you if you don't, If you don't deal with irregularities up front on data that you care about, you're gonna create a mess. >> No scheme on right. Was convenient of larger store, a lot of data cheaply. But then what? Hard to get value out of it created. >> So So I think the I'm not opposed to scheme later. As long as you realize that you were kicking the can down the road and you're just you're just going to give your successor a big mess. >> Yeah, right. Michael, we gotta jump. But thank you so much. Sure appreciate it. All right. Keep it right there, everybody. We'll be back with our next guest right into the short break. You watching the cue from M i t cdo Ike, you right back

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. Everybody. This is the Cube. The leader in live coverage. My name is David wanted. I'm here with my co host, Paul Gill, in this day to coverage of the M I t cdo I Q conference. A lot of acronym stands for M I. T. Of course, the great institution. But Chief Data officer information quality event is his 13th annual event. Lars to Maria's here is the managing partner of Brass Rat Capital. Cool name Lars. Welcome to the Cube. Great. Very much. Glad I start with a name brass around Capitol was That's >> rat is reference to the M I t school. Okay, Beaver? Well, he is, but the students call it a brass rat, and I'm third generation M i t. So it's just seen absolutely appropriate. That is a brass rods and capital is not a reference to money, but is actually referenced to the intellectual capital. They if you have five or six brass rats in the same company, you know, we Sometimes engineers arrive and they could do some things. >> And it Boy, if you put in some data data capital in there, you really explosions. We cause a few problems. So we're gonna talk about some new regulations that are coming down. New legislation that's coming down that you exposed me to yesterday, which is gonna have downstream implications. You get ahead of this stuff and understand it. You can really first of all, prepare, make sure you're in compliance, but then potentially take advantage for your business. So explain to us this notion of open government act. >> Um, in the last five years, six years or so, there's been an effort going on to increase the transparency across all levels of government. Okay, State, local and federal government. The first of federal government laws was called the the Open Data Act of 2014 and that was an act. They was acted unanimously by Congress and signed by Obama. They was taking the departments of the various agencies of the United States government and trying to roll up all the expenses into one kind of expense. This is where we spent our money and who got the money and doing that. That's what they were trying to do. >> Big picture type of thing. >> Yeah, big picture type thing. But unfortunately, it didn't work, okay? Because they forgot to include this odd word called mentalities. So the same departments meant the same thing. Data problem. They have a really big data problem. They still have it. So they're to G et o reports out criticizing how was done, and the government's gonna try and correct it. Then in earlier this year, there was another open government date act which said in it was signed by Trump. Now, this time you had, like, maybe 25 negative votes, but essentially otherwise passed Congress completely. I was called the Open as all capital O >> P E >> n Government Data act. Okay, and that's not been implemented yet. But there's live talking around this conference today in various Chief date officers are talking about this requirement that every single non intelligence defense, you know, vital protection of the people type stuff all the like, um, interior, treasury, transportation, those type of systems. If you produce a report these days, which is machine, I mean human readable. You must now in two years or three years. I forget the exact invitation date. Have it also be machine readable. Now, some people think machine riddle mil means like pdf formats, but no, >> In fact, what the government did is it >> said it must be machine readable. So you must be able to get into the reports, and you have to be able to extract out the information and attach it to the tree of knowledge. Okay, so we're all of sudden having context like they're currently machine readable, Quote unquote, easy reports. But you can get into those SEC reports. You pull out the net net income information and says its net income, but you don't know what it attaches to on the tree of knowledge. So, um, we are helping the government in some sense able, machine readable type reporting that weaken, do machine to machine without people being involved. >> Would you say the tree of knowledge You're talking about the constant >> man tick semantic tree of knowledge so that, you know, we all come from one concept like the human is example of a living thing living beast, a living Beeston example Living thing. So it also goes back, and they're serving as you get farther and farther out the tree, there's more distance or semantic distance, but you can attach it back to concept so you can attach context to the various data. Is this essentially metadata? That's what people call it. But if I would go over see sale here at M I t, they would turn around. They call it the Tree of Knowledge or semantic data. Okay, it's referred to his semantic dated, So you are passing not only the data itself, but the context that >> goes along with the data. Okay, how does this relate to the financial transparency? >> Well, Financial Transparency Act was introduced by representative Issa, who's a Republican out of California. He's run the government Affairs Committee in the House. He retired from Congress this past November, but in 2017 he introduced what's got referred to his H R 15 30 Um, and the 15 30 is going to dramatically change the way, um, financial regulators work in the United States. Um, it is about it was about to be introduced two weeks ago when the labor of digital currency stuff came up. So it's been delayed a little bit because they're trying to add some of the digital currency legislation to that law. >> A front run that Well, >> I don't know exactly what the remember soul coming out of Maxine Waters Committee. So the staff is working on a bunch of different things at once. But, um, we own g was asked to consult with them on looking at the 15 30 act and saying, How would we improve quote unquote, given our technical, you know, not doing policy. We just don't have the technical aspects of the act. How would we want to see it improved? So one of the things we have advised is that for the first time in the United States codes history, they're gonna include interesting term called ontology. You know what intelligence? Well, everyone gets scared by the word. And when I read run into people, they say, Are you a doctor? I said, no, no, no. I'm just a date. A guy. Um, but an intolerant tea is like a taxonomy, but it had order has important, and an ontology allows you to do it is ah, kinda, you know, giving some context of linking something to something else. And so you're able Thio give Maur information with an intolerant that you're able to you with a tax on it. >> Okay, so it's a taxonomy on steroids? >> Yes, exactly what? More flexible, >> Yes, but it's critically important for artificial intelligence machine warning because if I can give them until ology of sort of how it goes up and down the semantics, I can turn around, do a I and machine learning problems on the >> order of 100 >> 1000 even 10,000 times faster. And it has context. It has contacts in just having a little bit of context speeds up these problems so dramatically so and it is that what enables the machine to machine? New notion? No, the machine to machine is coming in with son called SP R M just standard business report model. It's a OMG sophistication of way of allowing the computers or machines, as we call them these days to get into a standard business report. Okay, so let's say you're ah drug company. You have thio certify you >> drugged you manufactured in India, get United States safely. Okay, you have various >> reporting requirements on the way. You've got to give extra easy the FDA et cetera that will always be a standard format. The SEC has a different format. FERC has a different format. Okay, so what s p r m does it allows it to describe in an intolerant he what's in the report? And then it also allows one to attach an ontology to the cells in the report. So if you like at a sec 10 Q 10 k report, you can attach a US gap taxonomy or ontology to it and say, OK, net income annual. That's part of the income statement. You should never see that in a balance sheet type item. You know his example? Okay. Or you can for the first time by having that context you can say are solid problem, which suggested that you can file these machine readable reports that air wrong. So they believe or not, There were about 50 cases in the last 10 years where SEC reports have been filed where the assets don't equal total liabilities, plus cheryl equity, you know, just they didn't add >> up. So this to, >> you know, to entry accounting doesn't work. >> Okay, so so you could have the machines go and check scale. Hey, we got a problem We've >> got a problem here, and you don't have to get humans evolved. So we're gonna, um uh, Holland in Australia or two leaders ahead of the United States. In this area, they seem dramatic pickups. I mean, Holland's reporting something on the order of 90%. Pick up Australia's reporting 60% pickup. >> We say pick up. You're talking about pickup of errors. No efficiency, productivity, productivity. Okay, >> you're taking people out of the whole cycle. It's dramatic. >> Okay, now what's the OMG is rolling on the hoof. Explain the OMG >> Object Management Group. I'm not speaking on behalf of them. It's a membership run organization. You remember? I am a >> member of cold. >> I'm a khalid of it. But I don't represent omg. It's the membership has to collectively vote that this is what we think. Okay, so I can't speak on them, right? I have a pretty significant role with them. I run on behalf of OMG something called the Federated Enterprise Risk Management Group. That's the group which is focusing on risk management for large entities like the federal government's Veterans Affairs or Department offense upstairs. I think talking right now is the Chief date Officer for transportation. OK, that's a large organization, which they, they're instructed by own be at the, um, chief financial officer level. The one number one thing to do for the government is to get an effective enterprise worst management model going in the government agencies. And so they come to own G let just like NIST or just like DARPA does from the defense or intelligence side, saying we need to have standards in this area. So not only can we talk thio you effectively, but we can talk with our industry partners effectively on space. Programs are on retail, on medical programs, on finance programs, and so they're at OMG. There are two significant financial programs, or Sanders, that exist once called figgy financial instrument global identifier, which is a way of identifying a swap. Its way of identifying a security does not have to be used for a que ce it, but a worldwide. You can identify that you know, IBM stock did trade in Tokyo, so it's a different identifier has different, you know, the liberals against the one trading New York. Okay, so those air called figgy identifiers them. There are attributes associated with that security or that beast the being identified, which is generally comes out of 50 which is the financial industry business ontology. So you know, it says for a corporate bond, it has coupon maturity, semi annual payment, bullets. You know, it is an example. So that gives you all the information that you would need to go through to the calculation, assuming you could have a calculation routine to do it, then you need thio. Then turn around and set up your well. Call your environment. You know where Ford Yield Curves are with mortgage backed securities or any portable call. Will bond sort of probabilistic lee run their numbers many times and come up with effective duration? Um, And then you do your Vader's analytics. No aggregating the portfolio and looking at Shortfalls versus your funding. Or however you're doing risk management and then finally do reporting, which is where the standardized business reporting model comes in. So that kind of the five parts of doing a full enterprise risk model and Alex So what >> does >> this mean for first? Well, who does his impact on? What does it mean for organizations? >> Well, it's gonna change the world for basically everyone because it's like doing a clue ends of a software upgrade. Conversion one's version two point. Oh, and you know how software upgrades Everyone hates and it hurts because everyone's gonna have to now start using the same standard ontology. And, of course, that Sarah Ontology No one completely agrees with the regulators have agreed to it. The and the ultimate controlling authority in this thing is going to be F sock, which is the Dodd frank mandated response to not ever having another chart. So the secretary of Treasury heads it. It's Ah, I forget it's the, uh, federal systemic oversight committee or something like that. All eight regulators report into it. And, oh, if our stands is being the adviser Teff sock for all the analytics, what these laws were doing, you're getting over farm or more power to turn around and look at how we're going to find data across the three so we can come up consistent analytics and we can therefore hopefully take one day. Like Goldman, Sachs is pre payment model on mortgages. Apply it to Citibank Portfolio so we can look at consistency of analytics as well. It is only apply to regulated businesses. It's gonna apply to regulated financial businesses. Okay, so it's gonna capture all your mutual funds, is gonna capture all your investment adviser is gonna catch her. Most of your insurance companies through the medical air side, it's gonna capture all your commercial banks is gonna capture most of you community banks. Okay, Not all of them, because some of they're so small, they're not regularly on a federal basis. The one regulator which is being skipped at this point, is the National Association Insurance Commissioners. But they're apparently coming along as well. Independent federal legislation. Remember, they're regulated on the state level, not regularly on the federal level. But they've kind of realized where the ball's going and, >> well, let's make life better or simply more complex. >> It's going to make life horrible at first, but we're gonna take out incredible efficiency gains, probably after the first time you get it done. Okay, is gonna be the problem of getting it done to everyone agreeing. We use the same definitions >> of the same data. Who gets the efficiency gains? The regulators, The companies are both >> all everyone. Can you imagine that? You know Ah, Goldman Sachs earnings report comes out. You're an analyst. Looking at How do I know what Goldman? Good or bad? You have your own equity model. You just give the model to the semantic worksheet and all turn around. Say, Oh, those numbers are all good. This is what expected. Did it? Did it? Didn't you? Haven't. You could do that. There are examples of companies here in the United States where they used to have, um, competitive analysis. Okay. They would be taking somewhere on the order of 600 to 7. How 100 man hours to do the competitive analysis by having an available electronically, they cut those 600 hours down to five to do a competitive analysis. Okay, that's an example of the type of productivity you're gonna see both on the investment side when you're doing analysis, but also on the regulatory site. Can you now imagine you get a regulatory reports say, Oh, there's they're out of their way out of whack. I can tell you this fraud going on here because their numbers are too much in X y z. You know, you had to fudge numbers today, >> and so the securities analyst can spend Mme. Or his or her time looking forward, doing forecasts exactly analysis than having a look back and reconcile all this >> right? And you know, you hear it through this conference, for instance, something like 80 to 85% of the time of analysts to spend getting the data ready. >> You hear the same thing with data scientists, >> right? And so it's extent that we can helped define the data. We're going thio speed things up dramatically. But then what's really instinct to me, being an M I t engineer is that we have great possibilities. An A I I mean, really great possibilities. Right now, most of the A miles or pattern matching like you know, this idea using face shield technology that's just really doing patterns. You can do wonderful predictive analytics of a I and but we just need to give ah lot of the a m a. I am a I models the contact so they can run more quickly. OK, so we're going to see a world which is gonna found funny, But we're going to see a world. We talk about semantic analytics. Okay. Semantic analytics means I'm getting all the inputs for the analysis with context to each one of the variables. And when I and what comes out of it will be a variable results. But you also have semantics with it. So one in the future not too distant future. Where are we? We're in some of the national labs. Where are you doing it? You're doing pipelines of one model goes to next model goes the next mile. On it goes Next model. So you're gonna software pipelines, Believe or not, you get them running out of an Excel spreadsheet. You know, our modern Enhanced Excel spreadsheet, and that's where the future is gonna be. So you really? If you're gonna be really good in this business, you're gonna have to be able to use your brain. You have to understand what data means You're going to figure out what your modeling really means. What happens if we were, You know, normally for a lot of the stuff we do bell curves. Okay, well, that doesn't have to be the only distribution you could do fat tail. So if you did fat tail descriptions that a bell curve gets you much different results. Now, which one's better? I don't know, but, you know, and just using example >> to another cut in the data. So our view now talk about more about the tech behind this. He's mentioned a I What about math? Machine learning? Deep learning. Yeah, that's a color to that. >> Well, the tech behind it is, believe or not, some relatively old tech. There is a technology called rd F, which is kind of turned around for a long time. It's a science kind of, ah, machine learning, not machine wearing. I'm sorry. Machine code type. Fairly simplistic definitions. Lots of angle brackets and all this stuff there is a higher level. That was your distracted, I think put into standard in, like, 2000 for 2005. Called out. Well, two point. Oh, and it does a lot at a higher level. The same stuff that already f does. Okay, you could also create, um, believer, not your own special ways of a communicating and ontology just using XML. Okay, So, uh, x b r l is an enhanced version of XML, okay? And so some of these older technologies, quote unquote old 20 years old, are essentially gonna be driving a lot of this stuff. So you know you know Corbett, right? Corba? Is that what a maid omg you know, on the communication and press thing, do you realize that basically every single device in the world has a corpus standard at okay? Yeah, omg Standard isn't all your smartphones and all your computers. And and that's how they communicate. It turns out that a lot of this old stuff quote unquote, is so rigidly well defined. Well done that you can build modern stuff that takes us to the Mars based on these old standards. >> All right, we got to go. But I gotta give you the award for the most acronyms >> HR 15 30 fi G o m g s b r >> m fsoc tarp. Oh, fr already halfway. We knew that Owl XML ex brl corba, Which of course >> I do. But that's well done. Like thanks so much for coming. Everyone tried to have you. All right, keep it right there, everybody, We'll be back with our next guest from M i t cdo I Q right after this short, brief short message. Thank you

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back >> to M I. T. Everybody watching the Cube, The leader in live tech coverage. My name is Dave Volonte here with Paul Guillen. My co host, Tom Davenport, is here is the president's distinguished professor at Babson College. Huebel? Um, good to see again, Tom. Thanks for coming on. Glad to be here. So, yeah, this is, uh let's see. The 13th annual M I t. Cdo lucky. >> Yeah, sure. As this year. Our seventh. I >> think so. Really? Maybe we'll offset. So you gave a talk earlier? She would be afraid of the machines, Or should we embrace them? I think we should embrace them, because so far, they are not capable of replacing us. I mean, you know, when we hit the singularity, which I'm not sure we'll ever happen, But it's certainly not going happen anytime soon. We'll have a different answer. But now good at small, narrow task. Not so good at doing a lot of the things that we do. So I think we're fine. Although as I said in my talk, I have some survey data suggesting that large U. S. Corporations, their senior executives, a substantial number of them more than half would liketo automate as many jobs as possible. They say. So that's a little scary. But unfortunately for us human something, it's gonna be >> a while before they succeed. Way had a case last year where McDonald's employees were agitating for increasing the minimum wage and tThe e management used the threat of wrote of robotics sizing, hamburger making process, which can be done right to thio. Get them to back down. Are you think we're going to Seymour of four that were maybe a eyes used as a threat? >> Well, I haven't heard too many other examples. I think for those highly structured, relatively low level task, it's quite possible, particularly if if we do end up raising the minimum wage beyond a point where it's economical, pay humans to do the work. Um, but I would like to think that, you know, if we gave humans the opportunity, they could do Maur than they're doing now in many cases, and one of the things I was saying is that I think companies are. Generally, there's some exceptions, but most companies they're not starting to retrain their workers. Amazon recently announced they're going to spend 700,000,000 to retrain their workers to do things that a I and robots can't. But that's pretty rare. Certainly that level of commitment is very rare. So I think it's time for the companies to start stepping up and saying, How can we develop a better combination of humans and machines? >> The work by, you know, brain Nelson and McAfee, which is a little dated now. But it definitely suggests that there's some things to be concerned about. Of course, ultimately there prescription was one of an optimist and education, and yeah, on and so forth. But you know, the key point there is the machines have always replace humans, but now, in terms of cognitive functions, but you see it everywhere you drive to the airport. Now it's Elektronik billboards. It's not some person putting up the kiosks, etcetera, but you know, is you know, you've you've used >> the term, you know, paid the cow path. We don't want to protect the past from the future. All right, so, to >> your point, retraining education I mean, that's the opportunity here, isn't it? And the potential is enormous. Well, and, you know, let's face it, we haven't had much in the way of productivity improvements in the U. S. Or any other advanced economy lately. So we need some guests, you know, replacement of humans by machines. But my argument has always been You can handle innovation better. You can avoid sort of race to the bottom at automation sometimes leads to, if you think creatively about humans and machines working as colleagues. In many cases, you remember in the PC boom, I forget it with a Fed chairman was it might have been, Greenspan said, You can see progress everywhere except in the product. That was an M. I. T. Professor Robert Solow. >> OK, right, and then >> won the Nobel Prize. But then, shortly thereafter, there was a huge productivity boom. So I mean is there may be a pent up Well, God knows. I mean, um, everybody's wondering. We've been spending literally trillions on I t. And you would think that it would have led toe productivity, But you know, certain things like social media, I think reduced productivity in the workplace and you know, we're all chatting and talking and slacking and sewing all over the place. Maybe that's is not conducive to getting work done. It depends what you >> do with that social media here in our business. It's actually it's phenomenal to see political coverage these days, which is almost entirely consist of reprinting politicians. Tweets >> Exactly. I guess it's made life easier for for them all people reporters sitting in the White House waiting for a press conference. They're not >> doing well. There are many reporters left. Where do you see in your consulting work your academic work? Where do you see a I being used most effectively in organizations right now? And where do you think that's gonna be three years from now? >> Well, I mean, the general category of activity of use case is the sort of someone's calling boring I. It's data integration. One thing that's being discussed a lot of this conference, it's connecting your invoices to your contracts to see Did we actually get the stuff that we contracted for its ah, doing a little bit better job of identifying fraud and doing it faster so all of those things are quite feasible. They're just not that exciting. What we're not seeing are curing cancer, creating fully autonomous vehicles. You know, the really aggressive moonshots that we've been trying for a while just haven't succeeded at what if we kind of expand a I is gonna The rumor, trawlers. New cool stuff that's coming out. So considering all these new checks with detective Aye, aye, Blockchain new security approaches. When do you think that machines will be able to make better diagnoses than doctors? Well, I think you know, in a very narrow sense in some cases, that could do it now. But the thing is, first of all, take a radiologist, which is one of the doctors I think most at risk from this because they don't typically meet with patients and they spend a lot of time looking at images. It turns out that the lab experiments that say you know, these air better than human radiologist say I tend to be very narrow, and what one lab does is different from another lab. So it's just it's gonna take a very long time to make it into, you know, production deployment in the physician's office. We'll probably have to have some regulatory approval of it. You know, the lab research is great. It's just getting it into day to day. Reality is the problem. Okay, So staying in this context of digital a sort of umbrella topic, do you think large retail stores roll largely disappeared? >> Uh, >> some sectors more than others for things that you don't need toe, touch and feel, And soon before you're to them. Certainly even that obviously, it's happening more and more on commerce. What people are saying will disappear. Next is the human at the point of sale. And we've been talking about that for a while. In In grocery, Not so not achieve so much yet in the U. S. Amazon Go is a really interesting experiment where every time I go in there, I tried to shoplift. I took a while, and now they have 12 stores. It's not huge yet, but I think if you're in one of those jobs that a substantial chunk of it is automata ble, then you really want to start looking around thinking, What else can I do to add value to these machines? Do you think traditional banks will lose control of the payment system? Uh, No, I don't because the Finn techs that you see thus far keep getting bought by traditional bank. So my guess is that people will want that certainty. And you know, the funny thing about Blockchain way say in principle it's more secure because it's spread across a lot of different ledgers. But people keep hacking into Bitcoin, so it makes you wonder. I think Blockchain is gonna take longer than way thought as well. So, you know, in my latest book, which is called the Aye Aye Advantage, I start out talking by about Tamara's Law, This guy Roy Amara, who was a futurist, not nearly as well known as Moore's Law. But it said, You know, for every new technology, we tend to overestimate its impact in the short run and underestimated Long, long Ryan. And so I think a I will end up doing great things. We may have sort of tuned it out of the time. It actually happens way finally have autonomous vehicles. We've been talking about it for 50 years. Last one. So one of the Democratic candidates of the 75 Democratic ended last night mentioned the chief manufacturing officer Well, do you see that automation will actually swing the pendulum and bring back manufacturing to the U. S. I think it could if we were really aggressive about using digital technologies in manufacturing, doing three D manufacturing doing, um, digital twins of every device and so on. But we are not being as aggressive as we ought to be. And manufacturing companies have been kind of slow. And, um, I think somewhat delinquent and embracing these things. So they're gonna think, lose the ability to compete. We have to really go at it in a big way to >> bring it. Bring it all back. Just we've got an election coming up. There are a lot of concern following the last election about the potential of a I chatbots Twitter chat bots, deep fakes, technologies that obscure or alter reality. Are you worried about what's coming in the next year? And that that >> could never happen? Paul. We could never see anything deep fakes I'm quite worried about. We don't seem. I know there's some organizations working on how we would certify, you know, an image as being really But we're not there yet. My guess is, certainly by the time the election happens, we're going to have all sorts of political candidates saying things that they never really said through deep fakes and image manipulation. Scary? What do you think about the call to break up? Big check. What's your position on that? I think that sell a self inflicted wound. You know, we just saw, for example, that the automobile manufacturers decided to get together. Even though the federal government isn't asking for better mileage, they said, We'll do it. We'll work with you in union of states that are more advanced. If Big Tak had said, we're gonna work together to develop standards of ethical behavior and privacy and data and so on, they could've prevented some of this unless they change their attitude really quickly. I've seen some of it sales force. People are talking about the need for data standard data protection standards, I must say, change quickly. I think they're going to get legislation imposed and maybe get broken up. It's gonna take awhile. Depends on the next administration, but they're not being smart >> about it. You look it. I'm sure you see a lot of demos of advanced A I type technology over the last year, what is really impressed you. >> You know, I think the biggest advances have clearly been in image recognition looking the other day. It's a big problem with that is you need a lot of label data. It's one of the reasons why Google was able to identify cat photos on the Internet is we had a lot of labeled cat images and the Image net open source database. But the ability to start generating images to do synthetic label data, I think, could really make a big difference in how rapidly image recognition works. >> What even synthetic? I'm sorry >> where we would actually create. We wouldn't have to have somebody go around taking pictures of cats. We create a bunch of different cat photos, label them as cat photos have variations in them, you know, unless we have a lot of variation and images. That's one of the reasons why we can't use autonomous vehicles yet because images differ in the rain and the snow. And so we're gonna have to have synthetic snow synthetic rain to identify those images. So, you know, the GPU chip still realizes that's a pedestrian walking across there, even though it's kind of buzzed up right now. Just a little bit of various ation. The image can throw off the recognition altogether. Tom. Hey, thanks so much for coming in. The Cube is great to see you. We gotta go play Catch. You're welcome. Keep right. Everybody will be back from M I t CDO I Q In Cambridge, Massachusetts. Stable, aren't they? Paul Gillis, You're watching the Cube?

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. In Cambridge, Massachusetts. Everybody. You're watching the cube. The leader in live tech coverage. This is M I t CDO I Q the chief data officer and information quality conference. Someday Volonte with my co host, Paul Galen. Professor Dr Stewart, Mad Nick is here. Longtime Cube alum. Ah, long time professor at M i. T soon to be retired, but we're really grateful that you're taking your time toe. Come on. The Cube is great to see you again. >> It's great to see you again. It's been a long time. She worked together and I really appreciate the opportunity to share our spirits. Hear our mighty with your audience. Well, it's really been fun >> to watch this conference evolved were full and it's really amazing. We have to move to a new venue >> next year. I >> understand. And data we talk about the date explosion all the time, But one of the areas that you're focused on and you're gonna talk about today is his ethics and privacy and data causes so many concerns in those two areas. But so give us the highlight of what you're gonna discuss with the audience today. We'll get into >> one of things that makes it so challenging. It is. Data has so many implications. Tow it. And that's why the issue of ethics is so hard to get people to reach agreement on it. We're talking people regarding medicine and the idea big data and a I so know, to be able to really identify causes you need mass amounts of data. That means more data has to be made available as long as it's Elsa data, not mine. Well, not my backyard. If he really So you have this issue where on the one hand, people are concerned about sharing the data. On the other hand, there's so many valuable things would gain by sharing data and getting people to reach agreement is a challenge. Well, one of things >> I wanted to explore with you is how things have changed you back in the day very familiar with Paul you as well with Microsoft, Department of Justice, justice, FTC issues regarding Microsoft. And it wasn't so much around data was really around browsers and bundling things today. But today you see Facebook and Google Amazon coming under fire, and it's largely data related. Listen, Liz Warren, last night again break up big tech your thoughts on similarities and differences between sort of the monopolies of yesterday and the data monopolies of today Should they be broken up? What do you thought? So >> let me broaden the issue a little bit more from Maryland, and I don't know how the demographics of the audience. But I often refer to the characteristics that millennials the millennials in general. I ask my students this question here. Now, how many of you have a Facebook account in almost every class? Facebook. You realize you've given away a lot of nation about yourself. It it doesn't really occurred to them. That may be an issue. I was told by someone that in some countries, Facebook is very popular. That's how they cordoned the kidnappings of teenagers from rich families. They track them. They know they're going to go to this basketball game of the soccer match. You know exactly what I'm going after it. That's the perfect spot to kidnap them, so I don't know whether students think about the fact that when they're putting things on Facebook than making so much of their life at risk. On the other hand, it makes their life richer, more enjoyable. And so that's why these things are so challenging now, getting back to the issue of the break up of the big tech companies. One of the big challenges there is that in order to do the great things that big data has been doing and the things that a I promises do you need lots of data. Having organizations that can gather it all together in a relatively systematic and consistent manner is so valuable breaking up the tech companies. And there's some reasons why people want to do that, but also interferes with that benefit. And that's why I think it's gonna be looked at real Kim, please, to see not only what game maybe maybe breaking up also what losses of disadvantages we're creating >> for ourselves so example might be, perhaps it makes United States less competitive. Visa VI China, in the area of machine intelligence, is one example. The flip side of that is, you know Facebook has every incentive to appropriate our data to sell ads. So it's not an easy, you know, equation. >> Well, even ads are a funny situation for some people having a product called to your attention that something actually really want. But you never knew it before could be viewed as a feature, right? So, you know, in some case of the ads, could be viewed as a feature by some people. And, of course, a bit of intrusion by other people. Well, sometimes we use the search. Google, right? Looking >> for the ad on the side. No longer. It's all ads. You know >> it. I wonder if you see public public sentiment changing in this respect. There's a lot of concerns, certainly at the legislative level now about misuse of data. But Facebook user ship is not going down. Instagram membership is not going down. Uh, indication is that that ordinary citizens don't really care. >> I know that. That's been my I don't have all the data. Maybe you may have seen, but just anecdotally and talking to people in the work we're doing, I agree with you. I think most people maybe a bit dramatic, but at a conference once and someone made a comment that there has not been the digital Pearl Harbor yet. No, there's not been some event that was just so onerous. Is so all by the people. Remember the day it happened kind of thing. And so these things happen and maybe a little bit of press coverage and you're back on your Facebook. How their instagram account the next day. Nothing is really dramatic. Individuals may change now and then, but I don't see massive changes. But >> you had the Equifax hack two years ago. 145,000,000 records. Capital one. Just this week. 100,000,000 records. I mean, that seems pretty Pearl Harbor ish to me. >> Well, it's funny way we're talking about that earlier today regarding different parts of the world. I think in Europe, the general, they really seem to care about privacy. United States that kind of care about privacy in China. They know they have no privacy. But even in us where they care about privacy, exactly how much they care about it is really an issue. And in general it's not enough to move the needle. If it does, it moves it a little bit about the time when they show that smart TVs could be broken into smart. See, TV sales did not Dutch an inch. Not much help people even remember that big scandal a year ago. >> Well, now, to your point about expects, I mean, just this week, I think Equifax came out with a website. Well, you could check whether or not your credentials were. >> It's a new product. We're where we're compromised. And enough in what has been >> as head mind, I said, My wife says it's too. So you had a choice, you know, free monitoring or $125. So that way went okay. Now what? You know, life goes >> on. It doesn't seem like anything really changes. And we were talking earlier about your 1972 book about cyber security, that many of the principles and you outlined in that book are still valid today. Why are we not making more progress against cybercriminals? >> Well, two things. One thing is you gotta realize, as I said before, the Cave man had no privacy problems and no break in problems. But I'm not sure any of us want to go back to caveman era because you've got to realize that for all these bad things. There's so many good things that are happening, things you could now do, which a smartphone you couldn't even visualize doing a decade or two ago. So there's so much excitement, so much for momentum, autonomous cars and so on and so on that these minor bumps in the road are easy to ignore in the enthusiasm and excitement. >> Well and now, as we head into 2020 affection it was. It was fake news in 2016. Now we've got deep fakes. Get the ability to really use video in new ways. Do you see a way out of that problem? A lot of people looking a Blockchain You wrote an article recently, and Blockchain you think it's on hackable? Well, think again. >> What are you seeing? I think one of things we always talk about when we talk about improving privacy and security and organizations, the first thing is awareness. Most people are really small moment of time, aware that there's an issue and it quickly pass in the mind. The analogy I use regarding industrial safety. You go into almost any factory. You'll see a sign over the door every day that says 520 days, his last industrial accident and then a sub line. Please do not be the one to reset it this year. And I often say, When's the last time you went to a data center? And so assign is at 50 milliseconds his last cyber data breach. And so it needs to be something that is really front, the mind and people. And we talk about how to make awareness activities over companies and host household. And that's one of our major movements here is trying to be more aware because we're not aware that you're putting things at risk. You're not gonna do anything about it. >> Last year we contacted Silicon Angle, 22 leading security experts best in one simple question. Are we winning or losing the war against cybercriminals? Unanimously, they said, we're losing. What is your opinion of that question? >> I have a great quote I like to use. The good news is the good guys are getting better than a firewall of cryptographic codes. But the bad guys are getting batter faster, and there's a lot of reasons for that well on all of them. But we came out with a nautical talking about the docking Web, and the reason why it's fascinating is if you go to most companies if they've suffered a data breach or a cyber attack, they'll be very reluctant to say much about unless they really compelled to do so on the dock, where they love to Brent and reputation. I'm the one who broke in the Capital One. And so there's much more information sharing that much more organized, a much more disciplined. I mean, the criminal ecosystem is so much more superior than the chaotic mess we have here on the good guys side of the table. >> Do you see any hope for that? There are service's. IBM has one, and there are others in a sort of anonymous eyes. Security data enable organizations to share sensitive information without risk to their company. You see any hope on the collaboration, Front >> said before the good guys are getting better. The trouble is, at first I thought there was an issue that was enough sharing going on. It turns out we identified over 120 sharing organizations. That's the good news. And the bad news is 120. So IBM is one and another 119 more to go. So it's not a very well coordinated sharing. It's going just one example. The challenges Do I see any hope in the future? Well, in the more distant future, because the challenge we have is that there'll be a cyber attack next week of some form or shape that we've never seen before and therefore what? Probably not well prepared for it. At some point, I'll no longer be able to say that, but I think the cyber attackers and creatures and so on are so creative. They've got another decade of more to go before they run out of >> Steve. We've got from hacktivists to organized crime now nation states, and you start thinking about the future of war. I was talking to Robert Gates, aboutthe former defense secretary, and my question was, Why don't we have the best cyber? Can't we go in the oven? It goes, Yeah, but we also have the most to lose our critical infrastructure, and the value of that to our society is much greater than some of our adversaries. So we have to be very careful. It's kind of mind boggling to think autonomous vehicles is another one. I know that you have some visibility on that. And you were saying that technical challenges of actually achieving quality autonomous vehicles are so daunting that security is getting pushed to the back burner. >> And if the irony is, I had a conversation. I was a visiting professor, sir, at the University of Niece about a 12 14 years ago. And that's before time of vehicles are not what they were doing. Big automotive tele metrics. And I realized at that time that security wasn't really our top priority. I happen to visit organization, doing really Thomas vehicles now, 14 years later, and this conversation is almost identical now. The problems we're trying to solve. A hider problem that 40 years ago, much more challenging problems. And as a result, those problems dominate their mindset and security issues kind of, you know, we'll get around him if we can't get the cot a ride correctly. Why worry about security? >> Well, what about the ethics of autonomous vehicles? Way talking about your programming? You know, if you're gonna hit a baby or a woman or kill your passengers and yourself, what do you tell the machine to Dio, that is, it seems like an unsolvable problem. >> Well, I'm an engineer by training, and possibly many people in the audience are, too. I'm the kind of person likes nice, clear, clean answers. Two plus two is four, not 3.94 point one. That's the school up the street. They deal with that. The trouble with ethic issues is they don't tend to have a nice, clean answer. Almost every study we've done that has these kind of issues on it. And we have people vote almost always have spread across the board because you know any one of these is a bad decision. So which the bad decision is least bad. Like, what's an example that you used the example I use in my class, and we've been using that for well over a year now in class, I teach on ethics. Is you out of the design of an autonomous vehicle, so you must program it to do everything and particular case you have is your in the vehicle. It's driving around the mountain and Swiss Alps. You go around a corner and the vehicle, using all of senses, realize that straight ahead on the right? Ian Lane is a woman in a baby carriage pushing on to this onto the left, just entering the garage way a three gentlemen, both sides a road have concrete barriers so you can stay on your path. Hit the woman the baby carriage via to the left. Hit the three men. Take a shop, right or shot left. Hit the concrete wall and kill yourself. And trouble is, every one of those is unappealing. Imagine the headline kills woman and baby. That's not a very good thing. There actually is a theory of ethics called utility theory that says, better to say three people than to one. So definitely doing on Kim on a kill three men, that's the worst. And then the idea of hitting the concrete wall may feel magnanimous. I'm just killing myself. But as a design of the car, shouldn't your number one duty be to protect the owner of the car? And so people basically do. They close their eyes and flip a coin because they don't want anyone. Those hands, >> not an algorithmic >> response, doesn't leave. >> I want to come back for weeks before we close here to the subject of this conference. Exactly. You've been involved with this conference since the very beginning. How have you seen the conversation changed since that time? >> I think I think it's changing to Wei first. As you know, this record breaking a group of people are expecting here. Close to 500 I think have registered s o much Clea grown kind of over the years, but also the extent to which, whether it was called big data or call a I now whatever is something that was kind of not quite on the radar when we started, I think it's all 15 years ago. He first started the conference series so clearly has become something that is not just something We talk about it in the academic world but is becoming main stay business for corporations Maur and Maur. And I think it's just gonna keep increasing. I think so much of our society so much of business is so dependent on the data in any way, shape or form that we use it and have >> it well, it's come full circle. It's policy and I were talking at are open. This conference kind of emerged from the ashes of the back office information quality and you say the big date and now a I guess what? It's all coming back to information. >> Lots of data. That's no good. Or that you don't understand what they do with this. Not very healthy. >> Well, doctor Magic. Thank you so much. It's a >> relief for all these years. Really Wanna thank you. Thank you, guys, for joining us and helping to spread the word. Thank you. Pleasure. All right, keep it right, everybody. Paul and >> I will be back at M I t cdo right after this short break. You're watching the cue.

>> Live from Barcelona, Spain it's theCUBE! Covering Cisco Live! Europe brought to you by Cisco and its ecosystem partners. >> Hello everyone, welcome back to theCube's live coverage here in Barcelona, Spain for Cisco Live! Europe 2019. We're at day three of three days of coverage I'm John Furrier with Dave Vellante Our next two guests we're going to talk about privacy data Michelle Dennnedy, VP and Chief Privacy officer at Cisco and Robert Waitman who is the Director of Security and Trust. Welcome back, we had them last year and everything we talked about kinda's happening on steroids here this year >> Yep. >> Welcome back >> Thank you glad to be here >> Thanks for having us >> So security, privacy all go hand in hand. A lot going on. You're seeing more breaches you're seeing more privacy challenges Certainly GDPR's going to the next level. People are, quote, complying here's a gig of data go figure it out. So there's a lot happening, give us the update. >> Well, as we suggested last year it was privacypalooza all year long running up to the enforcement deadline of May 25, 2018. There were sort of two kinds of companies. There's one that ran up to that deadline and said woohoo we're ready to drive this baby forward! And then there's a whole nother set of people who are still sort of oh my gosh. And then there's a third category of people who still don't understand. I had someone come up to me several weeks ago and say what do I do? When is this GDPR going to be a law? I thought oh honey you need a hug >> Two years ago, you need some help. >> And some companies in the US, at least were turning off their websites. Some media companies were in the news for actually shutting down their site and not making it available because they weren't ready. So a lot of people were caught off guard, some were prepared but still, you said people would be compliant, kind of and they did that but still more work to do. >> Lots more work to do and as we said when the law was first promulgated two and a half years ago GDPR and the deadline A, It's just one region but as you'll hear as we talk about our study it's impacting the globe but it's also not the end of anything it's the beginning of the information economy at long last. So, I think we all have a lot to do even if you feel rather confident of your base-level compliance now it's time to step up your game and keep on top of it. >> Before we get into some of the details of the new finding you guys have I want you to take a minute to explain how your role is now centered in the middle of Cisco because if you look at the keynotes data's in the center of a lot of things in this intent based network on one side and you've got cloud and edge on the other. Data is the new ingredient that's feeding applications and certainly collective intelligence for security. So the role of data is critical. This is a big part of the Cisco tech plan nevermind policy and or privacy and these other things you're in the middle of it. Explain your role within Cisco and how that shapes you. >> How we sort of fit in. Well it's such a good question and actually if you watch our story through theCUBE we announced, actually on data privacy day several years ago that data is the new currency and this is exactly what we're talking about the only way that you can operationalize your data currency is to really think about it throughout the platform. You're not just pleasing a regulator you're not just pleasing your shareholders you're not just pleasing your employee base. So, as such, the way we organize our group is my role sits under the COO's office our Chief Operations Office under the office of John Stewart who is our Chief Trust officer. So security, trust, advanced research all live together in operations. We have sister organizations in places like public policy, legal, marketing, the sales groups the people who are actually operationalizing come together for a group. My role really is to provide two types of strategy. One, rolling out privacy engineering and getting across inside and outside of the company as quickly as possible. It's something new. As soon as we have set processes I put them into my sister organization and they send them out as routine and hopefully automated things. The other side is the work Robert and I do together is looking at data valuation models. Working about the economics of data where does it drive up revenue and business and speed time to closure and how do we use data to not just be compliant in the privacy risk but really control our overall risk and the quality of our information overall. It's a mouth full >> So that's interesting and Robert, that leads me to a question when we've seen these unfunded mandates before we saw it with Y2K, the Enron backlash certainly the United States the Federal Rules of Civil Procedure. And the folks in the corner office would say oh, here we go again. Is there any way to get more value beyond just reducing risk and complying and have you seen companies be able to take data and value and apply it based on the compliance and governance and privacy policies? >> Dave that's a great question. It's sort of the thought that we had and the hypothesis was that this was going to be more valuable than just for the compliance reasons and one of the big findings of the study that we just released this week was that in fact those investments you know we're saying that good privacy is very good for business. It was painful, some firms stuck their head in the sand and said I don't want to even do this but still, going through the GDPR preparation process or for any of the privacy regulations has taken people to get their data house in order and it's important to communicate. We wanted to find out what benefits were coming from those organizations that had made those investments and that's really what came out in our study this week for international data privacy day we got into that quite a bit. >> What is this study? can you give us some details on it? >> It's the Data Privacy Benchmark study we published this week for international data privacy day. It's sort of an opportunity to focus on data privacy issues both for consumers and for businesses sort of the one day a year kind of like mother's day that you should always think of your mom but mother's day's a good day so you should always think of privacy when you're making decisions about your data but it's a chance to raise awareness. So we published our study this year and it was based on over thirty-two hundred responses from companies around the world from 18 countries all sorts of sizes of companies and the big findings were in fact around that. Privacy has become a serious and a boardroom level issue that the awareness has really skyrocketed for companies who are saying before I do business with you I want to know how you're using my data. What we saw this year is that seven out of eight companies are actually seeing some sales delay from their customers asking those kinds of questions. But those that have made the investment getting ready for GDPR or being more mature on privacy are seeing shorter delays. If you haven't gotten ready you're seeing 60% longer delays. And even more interestingly for us too is when you have data breaches and a lot of companies have them as we've talked about those breaches are not nearly as impactful. The organizations that aren't ready for GDPR are seeing three times as many records impacted by the breach. They're seeing system downtime that's 50% longer and so the cost of the whole thing is much more. So, kind of the question of is this still something good to do? Not only because you have to do it when you want to avoid 4% penalties from GDPR and everything else but it's something that's so important for my business that drives value. >> So the upshot there is that you do the compliance. Okay, check the box, we don't want to get fined So you're taking your medicine basically. Turns into an upside with the data you're seeing from your board. Sales benefit and then just preparedness readiness for breaches. >> Right, I mean it's a nice-- >> Is that right? >> That's exactly right John you've got it right. Then you've got your data house in order I mean there's a logic to this. So then if you figured out where your data is how to protect it, who has access to it you're able to deal with these questions. When customers ask you questions about that you're ready to answer it. And when something bad goes wrong let's say there is a breach you've already done the right things to control your data. You've got rid of the data you don't need anymore. I mean 50% of your data isn't used for anything and of course we suggest that people get rid of that that makes it less available when and if a breach occurs. >> So I got to ask you a question on the data valuation because a lot of the data geeks and data nerds like myself saw this coming. We saw data, mostly on the tech side if you invested in data it was going to feed applications and I think I wrote a blog post in 2007 data's going to be part of the development kits or development environment you're seeing that now here. Data's now part of application development it's part of network intelligence for security. Okay, so yes, check, that's happening. At the CFO level, can you value the data so it's a balance sheet item? Can you say we're investing in this? So you start to see movement you almost project, maybe, in a few years, or now how do you guys see the valuation? Is it going to be another kind of financial metric? >> Well John, it's a great point. Seeing where we're developing around this. So I think we're still in somewhat early days of that issue. I think the organizations that are thinking about data as an asset and monetizing its value are certainly ahead of this we're trying to do that ourselves. We probed on that a little bit in the survey just to get a sense of where organizations are and only about a third of organizations are doing those data mature things. Do they have a complete data map of where their stuff is? Do they have a Chief Data Officer? Are they starting to monetize in appropriate ways, their data? So, there's a long way to go before organizations are really getting the value out of that data. >> But the signals are showing that there's value in the data. Obviously the number of sales there's some upside to compliance not just doin it to check the box there's actually business benefits. So how are you guys thinking about this cause you guys are early adopters or leaders in this how are you thinking about the data measurement of it? Can you share your insights on that? >> Yeah, so you know, data on the balance sheet Grace Hopper 1965, right? data will one day be on the corporate balance sheet because it's in most cases more valuable than the hardware that processes. This is the woman who's making software and hardware work for us, in 1965! Here we are in 2019. It's coming on the balance sheet. She was right then, I believe in it now. What we're doing is, even starting this is a study of correlation rather than causation. So now we have at least the artifacts to say to our legal teams go back and look at when you have one of our new improved streamline privacy sheets and you're telling in a more transparent fashion a deal. Mark the time that you're getting the question. Mark the time that you're finishing. Let's really be much more stilletto-like measuring time to close and efficiency. Then we're adding that capability across our businesses. >> Well one use case we heard on theCUBE this week was around privacy and security in the network versus on top of the network and one point that was referenced was when a salesperson leaves they take the contacts with them. So that's an asset and people get sued over it. So this again, this is a business policy thing. so business policy sounds like... >> Well in a lot of the solutions that exist in the marketplace or have existed I've sat on three encrypted email companies before encrypted email was something the market desired. I've sat on two advisory boards of-- a hope that you could sell your own data to the marketers. Every time someone gets an impression you get a micro cent or a bitcoin. We haven't really got that because we're looking on the periphery. What we're really trying to do is let's look at what the actual business flow and processes are in general and say things like can we put a metric on having less records higher impact, and higher quality. The old data quality in the CDO is rising up again get that higher quality now correlate it with speed to innovation speed to close, launch times the things that make your business run anyway. Now correlate it and eventually find causal connections to data. That's how we're going to get that data on the balance sheet. >> You know, that's a great point the data quality issue used to be kind of a back office records management function and now it's coming to the fore and I just make an observation if you look at what were before Facebook fake news what were the top five companies in the United States in terms of market value Amazon, Google, Facebook was up there, Microsoft, Apple. They're all data companies and so the market has valued them beyond the banks, beyond the oil companies. So you're starting to see clearer evidence quantifiable evidence that there's value there. I want to ask you about we have Guillermo Diaz coming up shortly, Michelle and I want to ask you your thoughts on the technical function. You mentioned it's a board level issue now, privacy. How should the CIO be communicating to the board about privacy? What should that conversation be like? >> Oh my gosh. So we now report quarterly to the board so we're getting a lot of practice We'll put it that way. I think we're on the same journey as the security teams used to you used to walk into the board and go here's what ransomware is and all of these former CFOs and sales guys would look at you and go ah, okay, onto the financials because there wasn't anything for them to do strategically. Today's board metrics are a little soft. It's more activity driven. Have you done your PIAs? Have you passed some sort of a third party audit? Are you getting rejected for third party value chain in your partner communities? That's the have not and da da da. To me I don't want my board telling us how to do operations that's how we do. To really give the board a more strategic view what we're really trying to do is study things like time to close and then showing trending impacts. The one conversation with John Chambers that's always stuck in my head is he doesn't want to know what today's snapshot is cause today's already over give me something over time, Michelle, that will trend. And so even though it sounds like, you know who cares if your sales force is a little annoyed that it takes longer to get this deal through legal well it turns out when you multiply that in a multi-billion dollar environment you're talking about hundreds of millions of dollars probably a week, lost to inefficiency. So, if we believe in efficiency in the tangible supply chain that's the more strategic view I want to take and then you add on things like here's a risk portfolio a potential fair risk reporting type of thing if we want to do a new business Do we light up a business in the Ukraine right now versus Barcelona? That is a strategic conversation that is board level. We've forgotten that by giving them activity. >> Interesting what you say about Chambers. John you just interviewed John Chambers and he was the first person, in the mid 90s to talk about a virtual close, if you remember that. So, obviously, what you're talking about is way beyond that. >> Yeah and you're exactly right. Let's go back to those financial roots. One of the things we talk about in privacy engineering is getting people's heads-- the concept that the data changes. So, the day before your earnings that data will send Chuck Robbins to jail if someone is leaking it and causing people to invest accordingly. The day after, it's news, we want everyone to have it. Look at how you have to process and handle and operationalize in 24 hours. Figuring out those data stories helps it turn it on its head and make it more valuable. >> You know, you mentioned John Chambers one of the things that I noticed was he really represented Silicon Valley well in Washington DC and there's been a real void there since he retired. You guys still have a presence there and are doing stuff there and you see Amazon with Theresa Carlson doing some great work there and you still got Oracle and IBM in there doing their thing. How is your presence and leadership translating into DC now? Can you give us an update of what's happening at-- >> So, I don't know if you caught a little tweet from a little guy named Chuck Robbins this week but Chuck is actually actively engaged in the debate for US federal legislation for privacy. The last thing we want is only the lobbyists as you say and I love my lobbyists wherever you are we need them to help give information but the strategic advisors to what a federal bill looks like for an economy as large and complex and dependent on international structure we have to have the network in there. And so one of the things that we are doing in privacy is really looking at what does a solid bill look like so at long last we can get a solid piece of federal legislation and Chuck is talking about it at Davos as was everyone else, which was amazing and now you're going to hear his voice very loudly ringing through the halls of DC >> So he's upping his game in leadership in DC >> Have you seen the size of Chuck Robbins? Game upped, privacy on! >> It's a great opportunity because we need leadership in technology in DC so-- >> To affect public policy, no doubt >> Absolutely. >> And globally too. It's not just DC and America but also globally. >> Yeah, we need to serve our customers. We win when they win. >> Final question, we got to get wrapped up here but I want to get you guys a chance to talk about what you guys announced here at the show what's going on get the plug in for what's going on Cisco Trust. What's happening? >> Do you want to plug first? >> Well, I think a few things we can add. So, in addition to releasing our benchmark study this week and talking about that with customers and with the public we've also announced a new version of our privacy data sheets. This was a big tool to enable salespeople and customers to see exactly how data is being used in all of our products and so the new innovation this week is we've released these very nice, color created like subway maps, you know? They make it easy for you to navigate around it just makes it easy for people to see exactly how data flows. So again, something up on our site at trust.cisco.com where people can go and get that information and sort of make it easy. We're pushing towards simplicity and transparency in everything we do from a privacy standpoint and this is really that trajectory of making it as easy as possible for anyone to see exactly how things go and I think that's the trajectory we're on that's where the legislation both where GDPR is heading and federal legislation as well to try to make this as easy as reading the nutrition label on the food item. To say what's actually here? Do I want to buy it? Do I want to eat it? And we want to make that that easy >> Trust, transparency accountability comes into play too because if you have those things you know who's accountable. >> It's terrifying. I challenge all of my competitors go to trust.cisco.com not just my customers, love you to be there too go and look at our data subway maps. You have to be radically transparent to say here's what you get customer here's what I get, Cisco, here's where my third party's. It's not as detailed as a long report but you can get the trajectory and have a real conversation. I hope everybody gets on board with this kind of simplification. >> Trust.cisco.com we're going to keep track of it. Great work you guys are doing. I think you guys are leading the industry, Congratulations. >> Thank you. >> This is not going to end, this conversation continues will continue globally. >> Excellent >> Thanks for coming on Michelle, appreciate it. Robert thanks for coming on. CUBE coverage here day three in Barcelona. We'll be back with more coverage after this break.

>> Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We are here with Joseph Selle, he is the Cognitive Transformation Lead at IBM. Thanks so much for joining us, Joe. >> Hi, Rebecca, thank you. Hi, Dave. >> Good to see you, Joe. >> You, too. >> So, your job is to help drive the internal transformation of IBM. Tell our viewers what that means and then talk about your approach. >> Right, it a very exciting, frankly, it's one of the best jobs I've ever had personally. It's wonderful. We're transforming the company from the inside out. We're engaging with all of the functional areas within IBM's operations, and we're challenging those functional teams to breakdown their business process and reinvent it using some new tooling. And in this case, it's cognitive approaches to data analysis, and to crowd sourcing information, and systems that learn. We've talked a lot about at this conference, machine learning and deep learning. We're providing all of these tools to these functional teams so they can go reinvent HR and procurement, and even our M&A process, everything is fair game. So, it's very exciting and it really allows us to reinvent IBM. >> So, reinventing all of these individual functions, I mean, where to do you start? How do you begin to build the blueprint? >> Well, in our case, where we started was we had to get the whole company thinking about a large-scale enterprise, cultural transformation. We have a company of 300-some odd thousand people, employees, speaking all languages, all over the globe. So, how do you move that mass? So, we had cognitive jam, that's basically a technology enabled brainstorm session that spreads across the entire globe. And, by engaging about 300,000 IBM'ers, we were able to call and bring together all kinds of very disruptive, interesting ideas to remake all these business processes. We culled those ideas, and through some prioritization, almost a shark tank-like process, we ended up with a few that were really worthy, we felt, of investment. We've put money in, and our cognitive reinvention was born. Just like that. >> That's a lot of brain power. (laughs) >> Well, that's why it's wonderful to be at IBM, 'cause we have hundreds of thousands of brainy people working for us. >> You have talked about, when he was a controller during the Gerstner transformation, I don't know were you there back then? >> Yes, I was. >> Okay, so you guys were young pups back then, still young pups, I guess. But, he talked about, as the controller, he was an unhappy customer because he didn't have the data. So, can you talk about, sort of, what's different today? I mean, it's a lot different, obviously, the state of the industry, the technology, the amount of the data, et cetera. But, maybe talk about data as the starting point and how that was different from, maybe, the Gerstner transformation. >> The early days. >> Which was epic, by the way. You know, took IBM to new levels and be part of what the company is today. >> And this story that I'm going to tell you, is generally applicable to most any company that's global in nature. The data are not visible and they're not easy to see and discern any value from in the early stages of your transformation. So, when Jim was controller, he had data that was one, hard to get, and two, he had no tools to organize it except for, maybe, some smart people with Excel and, whatever it was back then, LotusPro, or something, I can't remember the name of that. (laughter) >> Something that ran on OS/2. >> There was no tooling, no approach. And, the whole idea of big data was not even around at that point. Because the data was organized and disorganized in little towers and databases all around, but there wasn't a flood of data. So, what's different between those days and this time period that we're in is, you can see data now and data are everywhere. And they're coming at us in high, high volumes and at high speeds. If you think about The Weather Company, one of the acquisitions we made two years ago, that is a stream of huge, big data, coming at us very fast. You can think about The Weather Company as a giant internet of things, device, which is pulling data from the sky and from people interacting with the environment, and bringing that all together. And now, what can we do with that data? Well, we can use it to help predict when we're going to have a supply chain disruption, or, I mean in an almost obvious sense, or we can use it when we're trying to respond to some sort of operational disturbance. If we're looking at where we can reroute things, or if we're trying to anticipate some sort of blockage on our supply chain, incoming supply chain, or outgoing supply chain of products. Very important, and we just see much more now then Jim ever could when he was a controller. >> In the scope of your data initiative, is everything, I mean, he's mentioned supply chain, you got customer data? >> It is, it is. But, I'll say that, you know, if a company's going to embark down this path, you don't want to try to boil the ocean at the start. You want to try to go after some selective business challenges, that are persistent challenges that you wish you had a way to solve because a lot of value's at play. So, you go in there and you solve a few problems. You deal with a data integrity and access problem, on a, sort of a, confined basis. And you do this, maybe, several times across different parts of your company. Then, once you've done that four or five times, or some small number of times, you begin to learn how to handle the problem more generally, and you can distill approaches and tools that can then be applied broadly. And where we are in our evolution, is that Inderpal and Jim, and the internal workings of IBM, were building a cognitive enterprise data platform. So, we're taking all of these point solutions that I just referred to, bringing them together onto a platform, and applying some common tooling to all of these common types of problems around data organization, and governance, and meta-data tagging, and all this geeky stuff that you have to be able to do if you're going to make any value. You know, if you're going to make an important, valuable business decision, based on a stream of data. >> So, where has it had tangible, measurable, business impact, this sort of cognitive initiative? >> Well, a couple of the areas where we're most mature, one would be in supply chain and procurement. We've been able to take jobs that, frankly, involve a lot of churning analysis, and be able to say to a procurement specialist, okay, what used to take you six hours, or an hour, or what ever the task was, we can shrink that down using a cognitive tool, down to just a few minutes. So, procurement, we've been able to get staffing efficiencies, and we've been able, even more importantly, to make sure that we're buying things at the best possible price. Because those same analysts want to know what's happening in the market, where's the market sentiment going? Is this market tightening or loosening? Is it a buyer or a seller market? If we're trolling the web, bringing back information on the micro-movements of all the regional markets in various electronics commodities, we know an aggregate, whether we should be hard bargainers or easy bargainers, essentially. So, that's procurement. But, you could talk about human resources, where the Watson tool can recommend a game plan for how you would manage the career of a person. You don't want to lose your star people. And it's wonderful that deep, subject matter experts in HR know how to anticipate what you're thinking, and those are the people you want in charge of HR. But, there's a lot of other people who aren't, maybe, as good as that one person at HR, now the system can help you by giving you a playbook, making you a better HR manager. So, that's HR, but I got one more that's really exciting that I'm working on right now in the area of M&A. So, IBM and any large company that has multiple offerings and geographies is involved in M&A. We're using cognition and big data to speed up our M&A process. Now, we have a small team of M&A, so we're not going to make millions of dollars of staffing efficiencies, but, if we can capture a company, if we can be the first one to make an offer on a company, rather than the third one, then we're going to get the best company. And if you can bring the best company in, like The Weather Company as an example in that space, or like any other type of data-mining company or something, you want the best company. And if you can use cognition to enhance your process to move very quickly, that's going to really help you. >> So, this is a huge transformation of the business model, but then you've also talked about the cultural transformation of IBM. How would you describe this new IBM, going through this transformation? How would you describe the culture and collaboration? >> So, luckily, we're pretty far along in the transformation and we're at a stage where we actually have a data platform that's been deployed internally. And, people know about the potential of cognition to redefine and remake their business processing, create all this value. So, now we're getting people to come on to the platform as citizen analysts, if you want to call them that, they're not operations PhD's, they're not necessarily data scientists, they're regular business analysts. They're coming onto the platform and they're finding data and they're finding tools to manipulate that data. They're coming in on a self-service model and being able to gain insights to bring back into their business decisions without the CIO office being involved. >> So that's a workbench on the Cloud, essentially, is that right? >> Yes, that it a good way to put it, yep. >> Workbench, we out of trademark that. (laughs) >> Let's do that. >> Good descriptor, I think. >> Well, Joe, thanks so much for joining us, it's been a pleasure talking to you. >> My pleasure, thank you. >> Thanks, thanks a lot. >> I'm Rebecca Knight, for Dave Vellante, we will have more from IBM CDO Summit just after this.

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