>>live from Miami, Florida It's the Q covering IBM is data in a I forum brought to you by IBM. >>Welcome back to Miami, Florida Everybody watching the Cube, the leader in live tech coverage. We go out to the events and extract the signal from the noise we're covering the IBM data and a I Forum Scott Hefner series The CMO on uh, sorry VP and CMO IBM Data. Yeah, right, I know. Here's the CMO of late again. So welcome. Welcome to the >>cake was great. Great >>event. Yeah, I've never attended one of these before. The sort of analytics University 1700 people that everybody's like. Sponges trying to learn more and more and more. >>60% higher attendance last year. Awesome. A lot of interest. >>So if we go back a couple of years ago, talks about digital transformation, people roll their eyes. They think it's a buzz word. When you talk to customers, it's really they're trying to transform their business, and data is at the center of that. So if you go back to like 2016 there's a lot of experimentation going on. Kind of throw everything against the wall, see what sticks. It seems Scott, based on the data that I see, that people are now narrowing their their bets on things like Ai ai automation machine learning containers. What are you seeing from customers? >>I think you framed it Well, I mean, if you kind of think about it, this digital transformations been going on for almost 20 years. With the advent of the Internet back around 2000 late 19 nineties, every started on the Internet doing business transactions, and slowly but surely, digital transformation was taken effect, right? And I think clients are now shifting to what we can call digital transformation two point. Oh, what's the next 20 years look like? And our view, our viewpoint from overlay from our clients is, if you think about it, it's data that fuels digital transformation. Right? Without data, there is no digital transformation is no digital. It's all data driven, evidence based decision making, using data to do things more efficiently and more effectively for your clients and your employees, and so on, so forth. But if you think about it, we've been using data as a way of looking to what has happened in the past or what is happening now in clients with digital transformation. To point out what a shift to a word of predictive data. How do you How do you predict in shape? Future outcomes, right? And if you think about it's a I that's gonna unlock predictive data. That's why we see such an intense focus on a I as a really the linchpin of digital transformation. Two point. Oh, and of course, all that data needs to be virtualized. It has to sit in a hybrid cloud environment. 94% of clients have multiple clouds. So if that unlocks the value or if a Iot of Mark's value the data and predictive ways the cloud in a multi cloud environment is that platform that has built upon, it's. That's why you see this enormous shift today. I in terms of investment priority along with hybrid multi cloud. >>So I like this this point of view, this digital transformation 2.0, because what's in their senior business in a digital business? That's how they used data. Yeah, and IBM is mission. Using your group is to help people better take advantage of data to five business outcomes. I mean, that's pretty clearly. What you guys are doing this to Dato To me. Three innovation cocktails, data plus machine intelligence or a I, and then you scale it with cloud. And so you talk about cloud to two point. Oh, really? Involves this predictive sort of a component of the equation that you're bringing into it, doesn't it? >>Yeah. When I think of this next phase, there's several things our clients trying to achieve. One is to predict and shape future outcomes, whether it be inventory, whether it be patient care, whatever it may be. Ah, customer service call. You want Toby to predict what the call's gonna be about what the client or what the customers has gone through before with the issue may be right. So this notion of predicted in shaping the outcome the second is empowering. People do higher value work. How do you make them better at what they're doing? The superpowers of being aided by a machine all right, or some kind of software, it's gonna help you be better what you do. And of course, this whole notion of automating task that people don't want to do automated experiences and intelligent ways. This all adds up to like new business models, right? And that's where a I comes in. That's what I does, and I do think it's a linchpin. What clients are looking to invest in is this notion that you need one unified platform to build upon for the future. That is, cloud service is data service is an aye aye. Service is all is one thing. One cloud native platform that runs on any cloud and completely opens up where all your data is. You run your APS wherever you want to run them secure to the core, and that's what they're looking to invest in. And >>so you guys use is the sort of tag line you can't have a I without. Ay, ay, ay, ay, ay, being information architecture. So for years on the q b been talking about bringing the cloud model to your data? Could you don't move data around? Now you're talking about bringing machine intelligence to your data wherever your data lives, to talk about why that's important and what IBM is doing both conceptually in from a product standpoint, to enable that. >>So the number one issue with the eye and actually a number one issue that sometimes results in failure with a I is didn't understand the data. Some 81% of clients do not understand the data that they're gonna need for the aye aye models. And if they do understand the doubt that they don't know how to make it simple, inaccessible, especially when its ever changing and then they have all the issues of compliance and quality. And is it a trusted set of data that you're using? And that's what you mentioned about? There is no way I without an aye aye, which is information architecture. So it starts there than two. To your point is, Dad is everywhere. There's thousands of sources of data, if not more than that. So how do you normalize all that? Virtual eyes it right. And that's where you get into one platform, any cloud, so that you can access the data wherever it sits. Don't spend the money moving things around the complexity of all that. And then, finally, the third thing we're looking to do is use a I to build. I use a I to actually manage the life cycle of how do you incorporate this into your business and That's what this one platform is gonna d'oh! Versus enabling customers to piece together all this stuff. It's just it's too much. >>So this is what cloudpack for data? Yeah, it is and does. Yes. So you say Aye, aye. Free. Are you talking about picking the functions and automating components? Prioritizing? Yeah. How you apply those those algorithms. Is that right? >>Yeah. So I think Way talk about data with three big things to really focus on his data. And that is the whole nursing. You need that information architecture that's that's ready for an aye aye multicolored world. It's all about the dad in the end, right? Two is about talent, right? Talent being skills. Are you able to acquire the skills you need? So we're trying to help our customers apply. I actually generate and build a I optimize eh? So they don't need is, you know, as much skill to do it. In other words, democratize the ability to build a I models for your business. And then finally, the dad is everywhere. You need to have completely open environment. That's the run on any cloud notion. And that's why the Red had open shift is such a big component of this. So think of clients are looking to climb the ladder >>today. I >>modernize their data states, make the data simple, inaccessible, create a trusted data foundation building scale new models and infuse it throughout their business. Cloudpack for data is essentially the foundational platform that gives you the latter >>day I >>that is in earnestly extensible with things that may be important to you or certain areas of additional capabilities. So Compaq for Dad essentially is the platform that I'm referring to hear when you say you know any cloud, right? >>So I feel like we're on the cusp of this enormous productivity boom. If you look at the data, productivity in the first quarter went up now and if you believe the Bureau of Labor Statistics, but over the long term productivity numbers right, you probably can't believe in them. I think for Q one was like 3% which is a huge uptick. And I feel like it's much, much higher than the anemic whatever it was one and 1/2 1.7%. All this ay, ay, all this automation is gonna drive productivity. It's gonna have an impact on organizations. So what's your perspective? Point of view on on the depending productivity boom boom? Do you believe that premise, How our job's going to be affected, What a client seeing in terms of how their retraining people, What should we expect? >>Yeah, I think a I's gonna give people superpowers. It's gonna make them better. What they do, it's gonna make you as a consumer better at how you choose what to buy. It's gonna make the automobile drive more efficiently and more more information that's relevant to you in the dashboard. It's gonna allow you call for service on your cable company. For them to already know your history, maybe already died. Knows what why you're calling and make it a more efficient call. It's gonna make everyone more productive. It's gonna result in higher quality output because you're able to predict things right. You automate things and intelligent ways, so I don't see it as anything that replaces jobs. It's just gonna make people better at what they do. Allow them to focus on higher value work and be more efficient when you are making decisions right in that will that will result in higher productivity per per worker, right? >>I mean, we've certainly heard examples today of customers that are doing that basically, and it's not like they're firing people. They're basically taking away mundane tasks or things that maybe humans would take so long to do and then re pointing that talent somewhere else. >>Toe higher value. >>So you're seeing that in your client base? Yeah, it's starting to hit today. It's gonna be interesting to see whether or not that affects jobs. I mean, we like to say That's not I ultimately think it's gonna create more jobs. There may be some kind of dip where we've got to retrain people, maybe have to change the way in which we do. Reading right bet Smith and I were talking, reading, writing arithmetic in coding, You know, maybe one of the skills that we have to bring in, but ultimately I think it is a positive, and I'm sanguine and I'm an optimist. Um, but you're seeing examples today of people refocusing their talent. What are they focusing that talent on more strategic things? Like what? >>Well, again, I think it's just getting people to be better at what they do by giving them that predictive power of super powers to be a to do their job better. It's gonna make people better not replace >>them. So it's consumers. We're probably gonna buy more. You're >>gonna buy more, you're gonna buy the right things more. And the right things are gonna be there for you to buy the right sales because everything is gonna be able to better understand patterns of what happens and predict right. And that's why you're seeing this enormous investment shift among among technologists companies. What was that? M. I. T. Sloane in the Boston Consulting Group just came out with a study. I think couple weeks ago, 92% of companies are looking to expand their investments in a I gardener came out with the study of C i ose and there in top investment areas, artificial intelligence was number one. Data and analytics was number two, which is the information architecture, right? One into as the first time it's been like that. So and I think it's for this reason of digital transformation, the predictive notion predictive enterprise, if you will, and just helping everyone be more efficient, more productive or what they do. That's really what it's about. It's not so much replacing people. They're thinking of robots and things like that. That's a small part of what we're talking about. >>Well, even when you talk to people about software robots, they love them because they don't have to do these Monday tests and dramatically impact the quality of what they're doing it again. It frees them up to do other things. >>Good, Good example. Legal Legal Nation is one of our clients that we've been working with, and they do case law for business clients. And sometimes it can take weeks, if not a month, to prepare case law documents. They're able to do that ours now because they have artificial intelligence. The background has done a lot of the case law, intelligence and finding the right dad in the right case law and helping to populate those documents where they don't have to do all the research themselves. So what does that do for the lawyer? Right? It makes them better what they do. They can shift a higher value work than just preparing the document. They could work on more cases that could spend more time on the subtleties of the case. Actually, that's a good example of what we mean here. He's not replacing the lawyer. >>Well, I'm seeing a lot of examples like this in legal fields. Also, auditing. I've talked enough. I've asked you think I'd be able to cut the auditing bill? And the answer is actually, No, because to the point you just made is they're shifting their activities to higher value. They might be charging Maur for activities that take less time. >>Customer service is is another great example. There's so many some examples of that. But it used to be. If you called, everyone treated equal right and you get onto a call. And then sometimes it's very rudimentary things. Sometimes there's gotta be a way to prioritize What are the most critical calls knowing that there's something already wrong and you know why they're calling? And if you can shift your human agents to focus on those and let let a I help with the more rudimentary ones you're making, the client's happier. But those people doing higher value work, we go on forever and ever on just different examples across different industries in different businesses, of how this is really helping people, and it all comes down to it. The three big words, which is prediction, automation and optimization. And that's what I was gonna do. And with digital transformation in just shift the whole the whole notion of using data for evidence based decision making what's happened in the past? What's happening now, too? I'm gonna I'm gonna understand its shape, the future. You could do so many things with that. >>It's amazing when you think about it. We've been at this computer industry 50 60 plus years, and you think everything's automated. It's not even close. All this technology has actually created so much more data so much on structured data. Actually, so many Maur inefficient processes in a lot of ways that now machine intelligence is beginning to attack in a big >>way. You won't find a survey because, ah, a survey of businesses where a eyes not a top aspiration trick, is how do you turn the aspirations of the outcomes? And that's what this latter day eyes all about. It's a very prescriptive approach that we've learned from our clients on howto take that journey to a I and a lot of things we talk about on this on this conversation or the real key linchpins, right? You gotta get the data right. You have to trust in the data that you're going to be used and you gotta get the talent and be able to simple find democratize how you build his models and deploy them. And then ultimately you got to get trust across your organization. And that means the models have to have explained ability, Understand? You have to help you understand how it is recommending these things, and then they're gonna buy into it. It's just gonna make them better. It's the whole notion of superpowers. >>Get that down and then you could scale. And that's really where the business and >>they all want to get there. Now the hard part is now we got to start doing it right. It's kind of like the Internet was 20 years ago. They know they want to do business transactions over the Internet and do commerce. But it didn't happen like overnight. It wasn't magic. It took. It was a journey. I think we're seeing that movie. We playing here? >>Yeah. And in fact, I think in some ways it could even happen faster now because you have the Internet because you have clouds. That's not predicting a very steep Pogue. I've s curve here. We'll have to leave it there. Scott, great to see you. Thanks >>for coming >>on. >>Any time. >>All right. Keep it right, everybody. We'll be back with our next guest right after this short break. You're watching the Cube from the IBM data and a I form in Miami. We'll be right back.

>>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?

>>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. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>> live from San Francisco, California. It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> Welcome back to Fisherman's Fisherman's Wharf in San Francisco. Everybody, my name is David wanted. You're watching the Cube, the leader in live tech coverage, you ought to events. We extract the signal from the noise. We're here. The IBM CDO event. This is the 10th anniversary of this event. Caitlin Hallford is here. She's the director of a I Accelerator and client success at IBM. Caitlin, great to see you again. Wow. 10 years. Amazing. They and Carlo Apple Apple Glace e is here. Who is the program director for data and a I at IBM. Because you again, my friend. Thanks for coming on to Cuba. Lums. Wow, this is 10 years, and I think the Cube is covered. Probably eight of these now. Yeah, kind of. We bounce between San Francisco and Boston to great places for CEOs. Good places to have intimate events, but and you're taking it global. I understand. Congratulations. Congratulations on the promotion. Thank you. Going. Thank you so much. >> So we, as you know well are well, no. We started our chief date officer summits in San Francisco here, and it's gone 2014. So this is our 10th 1 We do two a year. We found we really have a unique cohort of clients. The join us about 100 40 in San Francisco on the spring 140 in Boston in the fall, and we're here celebrating the 10th 10 Summit. >> So, Carlo, talk about your role and then let's get into how you guys, you know, work together. How you hand the baton way we'll get to the client piece. >> So I lead the Data Center League team, which is a group within our product development, working side by side with clients really to understand their needs as well developed, use cases on our platform and tools and make sure we are able to deliver on those. And then we work closely with the CDO team, the global CEO team on best practices, what patterns they're seeing from an architecture perspective. Make sure that our platforms really incorporating that stuff. >> And if I recall the data science that lead team is its presales correct and could >> be posted that it could, it really depends on the client, so it could be prior to them buying software or after they bought the software. If they need the help, we can also come in. >> Okay, so? So it can be a for pay service. Is that correct or Yeah, we can >> before pay. Or sometimes we do it based on just our relation with >> It's kind of a mixed then. Right? Okay, so you're learning the client's learning, so they're obviously good, good customers. And so you want to treat him right >> now? How do you guys work >> together? Maybe Caitlin, you can explain. The two organizations >> were often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test will literally will prove it out of skill internally using IBM itself as an example. And then, as we build out the capability, work with Carlo and his team to really drive that in a product and drive that into market, and we share a lot of client relationships where CEOs come to us, they're want advice and counsel on best practices across the organization. And they're looking for latest applications to deploy deploy known environments and so we can capture a lot of that feedback in some of the market user testing proved that out. Using IBM is an example and then work with you to really commercialized and bring it to market in the most efficient manner. >> You were talking this morning. You had a picture up of the first CDO event. No Internet, no wife in the basement. I love it. So how is this evolved from a theme standpoint? What do you What are the patterns? Sure. So when >> we started this, it was really a response. Thio primarily financial service is sector regulatory requirements, trying to get data right to meet those regulatory compliance initiatives. Defensive posture certainly weren't driving transformation within their enterprises. And what I've seen is a couple of those core elements are still key for us or data governance and data management. And some of those security access controls are always going to be important. But we're finding his videos more and more, have expanded scope of responsibilities with the enterprise they're looked at as a leader. They're no longer sitting within a c i o function there either appear or, you know, working in partnership with, and they're driving enterprise wide, you know, initiatives for the for their enterprises and organizations, which has been great to see. >> So we all remember when you know how very and declared data science was gonna be the number one job, and it actually kind of has become. I think I saw somewhere, maybe in Glass door was anointed that the top job, which is >> kind of cool to see. So what are you seeing >> with customers, Carlo? You guys, you have these these blueprints, you're now applying them, accelerating different industries. You mentioned health care this morning. >> What are some >> of those industry accelerators And how is that actually coming to fruition? Yes. >> So some of the things we're seeing is speaking of financial clients way go into a lot of them. We do these one on one engagements, we build them from custom. We co create these engineering solutions, our platform, and we're seeing patterns, patterns around different use cases that are coming up over and over again. And the one thing about data science Aye, aye. It's difficult to develop a solution because everybody's date is different. Everybody's business is different. So what we're trying to do is build these. We can't just build a widget that's going to solve the problem, because then you have to force your data into that, and we're seeing that that doesn't really work. So building a platform for these clients. But these accelerators, which are a set of core code source code notebooks, industry models in terms a CZ wells dashboards that allow them to quickly build out these use cases around a turn or segmentation on dhe. You know some other models we can grab the box provide the models, provide the know how with the source code, as well as a way for them to train them, deploy them and operationalize them in an organization. That's kind of what we're doing. >> You prime the pump >> prime minute pump, we call them there right now, we're doing client in eights for wealth management, and we're doing that, ref SS. And they come right on the box of our cloudpack for data platform. You could quickly click and install button, and in there you'll get the sample data files. You get no books. You get industry terms, your governance capability, as well as deployed dashboards and models. >> So talk more about >> cloudpack for data. What's inside of that brought back the >> data is a collection of micro Service's Andi. It includes a lot of things that we bring to market to help customers with their journey things from like data ingestion collection to all the way Thio, eh? I model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration way have a lot of capability. Part >> of it's actually tooling. It's not just sort of so how to Pdf >> dualism entire platform eso. So the platform itself has everything you need an organization to kind of go from an idea to data ingestion and governance and management all the way to model training, development, deployment into integration into your business process. >> Now Caitlin, in the early days of the CDO, saw CDO emerging in healthcare, financialservices and government. And now it's kind of gone mainstream to the point where we had Mark Clare on who's the head of data neighborhood AstraZeneca. And he said, I'm not taking the CDO title, you know, because I'm all about data enablement and CDO. You know, title has sort of evolved. What have you seen? It's got clearly gone mainstream Yep. What are you seeing? In terms of adoption of that, that role and its impact on organizations, >> So couple of transit has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the U. S. So we did our first inaugural summit in Tokyo. In Japan, there's a number of day leaders in Japan that are really eager to jump start their transformation initiatives. Also did our first Dubai summit. Middle East and Africa will be in South Africa next month at another studio summit. And what I'm seeing is outside of North America a lot of activity and interest in creating an enabling studio light capability. Data Leader, Like, um, and some of these guys, I think we're gonna leapfrog ahead. I think they're going to just absolutely jump jump ahead and in parallel, those traditional industries, you know, there's a new federal legislation coming down by year end for most federal agencies to appoint a chief data officer. So, you know, Washington, D. C. Is is hopping right now, we're getting a number of agencies requesting advice and counsel on how to set up the office how to be successful I think there's some great opportunity in those traditional industries and also seeing it, you know, outside the U. S. And cross nontraditional, >> you say >> Jump ahead. You mean jump ahead of where maybe some of the U. S. >> Absolute best? Absolutely. And I'm >> seeing a trend where you know, a lot of CEOs they're moving. They're really closer to the line of business, right? They're moving outside of technology, but they have to be technology savvy. They have a team of engineers and data scientists. So there is really an important role in every organization that I'm seeing for every client I go to. It's a little different, but you're right, it's it's definitely up and coming. Role is very important for especially for digital transformation. >> This is so good. I was gonna say one of the ways they are teens really, partner Well, together, I think is weaken source some of these in terms of enabling that you know, acceleration and leap frog. What are those pain points or use cases in traditional data management space? You know, the metadata. So I think you talk with Steven earlier about how we're doing some automated meditate a generation and really using a i t. O instead of manually having to label and tag that we're able to generate about 85% of our labels internally and drive that into existing product. Carlos using. And our clients are saying, Hey, we're spending, you know, hundreds of millions of dollars and we've got teams of massive teams of people manual work. And so we're able to recognize it, adopts something like that, press internally and then work with you guys >> actually think of every detail developer out there that has to go figure out what this date is. If you have a tool which we're trying to cooperate the platform based on the guidance from the CDO Global CEO team, we can automatically create that metadata are likely ingested and provide into platform so that data scientists can start to get value out >> of it quickly. So we heard Martin Schroeder talked about digital trade and public policy, and he said there were three things free flow of data. Unless it doesn't make sense like personal information prevent data localization mandates, yeah, and then protect algorithms and source code, which is an I P protection thing. So I'm interested in how your customers air Reacting to that framework, I presume the protect the algorithms and source code I p. That's near and dear right? They want to make sure that you're not taking models and then giving it to their competitors. >> Absolutely. And we talk about that every time we go in there and we work on projects. What's the I p? You know, how do we manage this? And you know, what we bring to the table with the accelerators is to help them jump start them right, even though that it's kind of our a p we created, but we give it to them and then what they derive from that when they incorporate their data, which is their i p, and create new models, that is then their i. P. So those air complicated questions and every company is a little different on what they're worried about with that, so but many banks, we give them all the I P to make sure that they're comfortable and especially in financial service is but some other spaces. It's very competitive. And then I was worried about it because it's, ah, known space. A lot of the algorithm for youse are all open source. They're known algorithms, so there's not a lot of problem there. >> It's how you apply them. That's >> exactly right how you apply them in that boundary of what >> is P, What's not. It's kind of >> fuzzy, >> and we encourage our clients a lot of times to drive that for >> the >> organisation, for us, internally, GDP, our readiness, it was occurring to the business unit level functional area. So it was, you know, we weren't where we needed to be in terms of achieving compliance. And we have the CEO office took ownership of that across the business and got it where we needed to be. And so we often encourage our clients to take ownership of something like that and use it as an opportunity to differentiate. >> And I talked about the whole time of clients. Their data is impor onto them. Them training models with that data for some new making new decisions is their unique value. Prop In there, I'd be so so we encourage them to make sure they're aware that don't just tore their data in any can, um, service out there model because they could be giving away their intellectual property, and it's important. Didn't understand that. >> So that's a complicated one. Write the piece and the other two seem to be even tougher. And some regards, like the free flow of data. I could see a lot of governments not wanting the free flow of data, but and the client is in the middle. OK, d'oh. Government is gonna adjudicate. What's that conversation like? The example that he gave was, maybe was interpolate. If it's if it's information about baggage claims, you can you can use the Blockchain and crypt it and then only see the data at the other end. So that was actually, I thought, a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. But how is that conversation going with clients? >> Leo. Those can involve depending on the country, right and where you're at in the industry. >> But some Western countries are strict about that. >> Absolutely. And this is why we've created a platform that allows for data virtualization. We use Cooper nannies and technologies under the covers so that you can manage that in different locations. You could manage it across. Ah, hybrid of data centers or hybrid of public cloud vendors. And it allows you to still have one business application, and you can kind of do some of the separation and even separation of data. So there's there's, there's, there's an approach there, you know. But you gotta do a balance. Balance it. You gotta balance between innovation, digital transformation and how much you wanna, you know, govern so governs important. And then, you know. But for some projects, we may want to just quickly prototype. So there's a balance there, too. >> Well, that data virtualization tech is interesting because it gets the other piece, which was prevent data localization mandates. But if there is a mandate and we know that some countries aren't going to relax that mandate, you have, ah, a technical solution for that >> architecture that will support that. And that's a big investment for us right now. And where we're doing a lot of work in that space. Obviously, with red hat, you saw partnership or acquisition. So that's been >> really Yeah, I heard something about that's important. That's that's that's a big part of Chapter two. Yeah, all right. We'll give you the final world Caitlyn on the spring. I guess it's not spring it. Secondly, this summer, right? CDO event? >> No, it's been agreed. First day. So we kicked off. Today. We've got a full set of client panel's tomorrow. We've got some announcements around our meta data that I mentioned. Risk insights is a really cool offering. We'll be talking more about. We also have cognitive support. This is another one. Our clients that I really wanted to help with some of their support back in systems. So a lot of exciting announcements, new thought leadership coming out. It's been a great event and looking forward to the next next day. >> Well, I love the fact >> that you guys have have tied data science into the sea. Sweet roll. You guys have done a great job, I think, better than anybody in terms of of, of really advocating for the chief data officer. And this is a great event because it's piers talking. Appears a lot of private conversations going on. So congratulations on all the success and continued success worldwide. >> Thank you so much. Thank you, Dave. >> You welcome. Keep it right there, everybody. We'll be back with our next guest. Ready for this short break. We have a panel coming up. This is David. Dante. You're >> watching the Cube from IBM CDO right back.