hey welcome back here ready Jeff Rick here with the key we're at the chief data scientist USA conference in downtown San Francisco and we're really excited to have a representative from IBM Sam Lightstone distinguished engineer from IBM join us Sam great to se you thank you very much pleasure to be here absolutely so we cover a ton of IBM events we're at world of Watson world lots of developer conference big the big event in New York earlier this year around strata so you know we're big fans of all the things that IBM is doing and in Rob Thomas and the SPARC group so I could go on and on but we won't go there we'll talk about what you were talking about earlier today and kind of let the cat out of the bag which is always exciting breaking news or breaking Bay there I don't know exactly how we would describe it but you talked about something new IBM data confluence yeah you could share this what's that all about yeah so it's a it's a whole new idea a whole new paradigm that were that we were incubating right now inside of IBM and it's not yet available but we're hoping to start trials in January ish timeframe but it comes from a realization that so much data is about to come upon us from distributed data sources you know everybody's got not only your cell phone but increasingly data is coming from Internet of Things you're gonna have data coming from your car data come from your glasses some smart meters on your house and it's deluge of data and the way that people like to do data science on this data today is they pull this data from these devices and put it into a central repository which is which is a perfectly legitimate strategy but it means that you're creating copies of the data and there's a certain complexity of dragging that data through the internet into some central repository so the idea that we had with data confluence is to leave the data where it is and create and allow the data all these different data sources if you can imagine cars you can imagine cell phones or smart meters on buildings allow them to find one another and collaborate on data science problems like a computational mesh so that we can bring hundreds thousands millions of microprocessors to bear on the data where it lives without moving it around and our theory is not only is that simpler for everyone because the data doesn't have to move around but we can actually bring more computation to bear because every one of those data sources has compute and has persistence and you can multiply the the opportunities right and you took a chance you ran a live demo which is you know always risky business at any anything but but there was a really interesting because concepts that you highlighted kind of organically forming adapting constellation right of these of these sources and the example you use they were solar panels but for them to do this kind of automatically if you will as opposed to someone going in and scripting and building the structure because tomorrow as you demonstrated in your demo you might want to add more or add more so exactly that dynamic functions are pretty pretty interesting yeah and it's a very powerful concept and a very necessary concept and the reason it's so necessary is these devices could be anywhere right and you could have most your devices in New York but a few of them in the Yukon or Alaska or something and you don't want them to all be equally connected right so it's important to be sensitive to create this network that is sort of geospatially aware and connectivity aware not not just sort of hard-coded you know so that so one aspect of that is to be sensitive to network latency and topology that's one reason why it has to be automatic the other reason has to be automatic is if you really want this to scale to thousands of devices you can't have some programmer trying to figure out who connects to what right it's just too hard right so making it really adaptive and automatic is super important another thing that's really important for the Internet of Things is depending on the on the circumstance but if you can imagine cell phones for example you can have a network of thousands millions of phones but at any point in time somebody some of those funds are gonna be turned off so the network has to be adaptive to the possibility that devices go offline right are there intentionally like a phone perhaps unintentionally because they break you know if you have a device on a smart meter it may simply break and then that particular device is offline for a period of time right so the network has to be resilient to that and that's part of what we've been building in particular using technology that we incubated in our UK labs in Hursley so it's it's been a great collaboration across IBM this is not just you know one you know one set of people in one lab but actually a corporate collaboration and really our goal is to make this as you say automatic but I would I would say beyond automatic to make it resilient right there's got to be resilient and fault tolerant because the complexities that we could be dealing with are just too large for human being to deal with right and clearly and distributed right that's the big thing guys we're leveraging IBM bluemix cloud you know all this stuff doesn't happen with with cloud capabilities and the demo you did here you were here the data center was concerned San Jose and the actual data elements were in in Toronto so just you know Amazon and Microsoft and Google are always you know get talked about a lot it within the cloud space but really iBM is making major players and it if not in that top three certainly right there in the fourth position as a leader in cloud and then what this cloud enables and then really kind of with the whole cognitive push you know that's a priority for Ginni and the team to really bring more intelligence he's exactly right and what data confluence you know what we're hoping not only to tap in to data science on distributed systems for IOT and also for enterprise use cases as well but really to take it to the next level of hybrid cloud because these data sources could be in the cloud and they could be on-premises they could be anywhere in the world and you can mix and match and that's really a very powerful capability for our customers many companies now struggling as their data is now part cloud and part on-premises right and in the compute as well right you could deal shift exactly compute from the edge to the cloud you know a dynamic fashion based on what the kind of optimal solution is or as you said sometimes over the edges off lined and you can't do it there it's exactly right so kind of a cool story you said this came out of a out of something called blue unicorn what is blue you know fantastic so blue unicorn was an initiative that a few of us got together on inside of IBM you probably know some of these folks Rob Thomas so I think you've interviewed gears from Karachay Leah and myself and the three of us got together and we said you know we want to find a more effective way to tap in to the creative juices of our staff we got some of the greatest minds in the world working at IBM we hire brilliant people PhDs masters of the top schools all over the world and all too often we hire these people and we tell them what they should be working on that wouldn't it be better if we could find a repeatable process for them to come to us and say here's the next big innovation that IBM ssin should have and blue unicorn came out of that desire to tap into and and nurture this creative passion of of our staff and was really designed almost like an internal VC initiative so people would would come to us with proposals and we've got those proposals we start out with hundreds and feted it down to dozens that down to just a small few that we would fund from the ones that we funded you know that would go through periodic reviews until eventually we ended up with a very small set that are still being incubated and and did a confluence happen to have been one of those projects awesome so it's different than kind of the 10% thing this is actually almost like an internal you you put your proposal together you pitch it whereas if it was an internal VC you get funded and then you go do that with your team right one thing I would say is one of the you know as we're setting up we're trying to find ways to make it work make it efficient one of the best filtering factors that we came up with is that people had to show us running code before it was funded right right and that was amazing because that meant people had to work nights and weekends they had to have that level of passion and commitment for their idea to get to that level of vetting and that was incredible that that definitely filtered the people who were super passionate about what they were doing and the people just said yeah I'd like to tinker and that was tremendous okay and then you're here at the show melting a small show tight group kind of multi industry any good takeaway surprises from the last couple days here at the chief data science USA show you know it's been an amazing conference actually and some great speakers some great insights I think one of the most useful insights for me was was I was curious to hear from this audience what is the duration of data that is important to them do they need to see data from the last hour the last month the last year the last 10 years and of course it does vary from problem to problem but many people said you know for the work that I do I need about three months to build a model and then once I have a model I'm really looking at the last two to four weeks of data to gain data science insight and that was a very important point for me especially as we continue on our work on analytics data science and IBM it's very important for us to understand the range of data that that people are using shorter than you seem sure yeah it's shorter because I know certainly in the data warehousing space that I've been working a lot of my career in people do data analytics on you know six months a year or three years right so this is this is it definitely is somewhat of a shift and it tells us something about our society that things are moving faster and the idea that's older than six months is is usually not as interesting anymore yeah really shows kind of the dynamic real-time nature it's not this is analyzing just the old stuff is interesting but not nearly as interesting as being on top of where's the spark stream somebody's other thing is funny Beth Comstock kicked off the GU minds and machines event a couple days ago she said we even walk faster in cities they've done so everything is continuing to speed up right all right so you're from now you're back here what are we gonna be talking about Wow okay well you know we just launched a few months or a few weeks ago actually the the Watson Data Platform a huge event for us and it really is for us the foundation the data foundation of all the cognitive computing that we're that IBM is coming out with it's gonna bring together data science and data storage and collaboration across you know amongst analysts and data scientists together all all one platform for all your data needs I'm hoping that a year from now I'm going to speak to you about how data confluence is a core part of that of that platform and we're gonna be raeng analytics on millions of devices all over the world all right Sam well thanks for taking a few minutes I know you gotta go catch an airplane for stopping by and sharing your insight thank you all right Sam lights on I'm Jeff Creek you're watching the cube thanks for watching

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

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

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

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

>> 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.

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> Hi everybody, welcome back to theCUBE's coverage of the IBM Chief Data Officer Event. We're here at Fisherman's Wharf in San Francisco at the Centric Hyatt Hotel. This is the 10th anniversary of IBM's Chief Data Officer Summits. In the recent years, anyway, they do one in San Francisco and one in Boston each year, and theCUBE has covered a number of them. I think this is our eighth CDO conference. I'm Dave Vellante, and theCUBE, we like to go out, especially to events like this that are intimate, there's about 140 chief data officers here. We've had the chief data officer from AstraZeneca on, even though he doesn't take that title. We've got a panel coming up later on in the day. And I want to talk about the evolution of that role. The chief data officer emerged out of kind of a wonky, back-office role. It was all about 10, 12 years ago, data quality, master data management, governance, compliance. And as the whole big data meme came into focus and people were realizing that data is the new source of competitive advantage, that data was going to be a source of innovation, what happened was that role emerged, that CDO, chief data officer role, emerged out of the back office and came right to the front and center. And the chief data officer really started to better understand and help companies understand how to monetize the data. Now monetization of data could mean more revenue. It could mean cutting costs. It could mean lowering risk. It could mean, in a hospital situation, saving lives, sort of broad definition of monetization. But it was really understanding how data contributed to value, and then finding ways to operationalize that to speed up time to value, to lower cost, to lower risk. And that required a lot of things. It required new skill sets, new training. It required a partnership with the lines of business. It required new technologies like artificial intelligence, which have just only recently come into a point where it's gone mainstream. Of course, when I started in the business several years ago, AI was the hot topic, but you didn't have the compute power. You didn't have the data, you didn't have the cloud. So we see the new innovation engine, not as Moore's Law, the doubling of transistors every 18 months, doubling of performance. Really no, we see the new innovation cocktail as data as the substrate, applying machine intelligence to that data, and then scaling it with the cloud. And through that cloud model, being able to attract startups and innovation. I come back to the chief data officer here, and IBM Chief Data Officer Summit, that's really where the chief data officer comes in. Now, the role in the organization is fuzzy. If you ask people what's a chief data officer, you'll get 20 different answers. Many answers are focused on compliance, particularly in what emerged, again, in those regulated industries: financial service, healthcare, and government. Those are the first to have chief data officers. But now CDOs have gone mainstream. So what we're seeing here from IBM is the broadening of that role and that definition and those responsibilities. Confusing things is the chief digital officer or the chief analytics officer. Those are roles that have also emerged, so there's a lot of overlap and a lot of fuzziness. To whom should the chief data officer report? Many say it should not be the CIO. Many say they should be peers. Many say the CIO's responsibility is similar to the chief data officer, getting value out of data, although I would argue that's never really been the case. The role of the CIO has largely been to make sure that the technology infrastructure works and that applications are delivered with high availability, with great performance, and are able to be developed in an agile manner. That's sort of a more recent sort of phenomenon that's come forth. And the chief digital officer is really around the company's face. What does that company's brand look like? What does that company's go-to-market look like? What does the customer see? Whereas the chief data officer's really been around the data strategy, what the sort of framework should be around compliance and governance, and, again, monetization. Not that they're responsible for the monetization, but they responsible for setting that framework and then communicating it across the company, accelerating the skill sets and the training of existing staff and complementing with new staff and really driving that framework throughout the organization in partnership with the chief digital officer, the chief analytics officer, and the chief information officer. That's how I see it anyway. Martin Schroeder, the senior vice president of IBM, came on today with Inderpal Bhandari, who is the chief data officer of IBM, the global chief data officer. Martin Schroeder used to be the CFO at IBM. He talked a lot, kind of borrowing from Ginni Rometty's themes in previous conferences, chapter one of digital which he called random acts of digital, and chapter two is how to take this mainstream. IBM makes a big deal out of the fact that it doesn't appropriate your data, particularly your personal data, to sell ads. IBM's obviously in the B2B business, so that's IBM's little back-ended shot at Google and Facebook and Amazon who obviously appropriate our data to sell ads or sell goods. IBM doesn't do that. I'm interested in IBM's opinion on big tech. There's a lot of conversations now. Elizabeth Warren wants to break up big tech. IBM was under the watchful eye of the DOJ 25 years ago, 30 years ago. IBM essentially had a monopoly in the business, and the DOJ wanted to make sure that IBM wasn't using that monopoly to hurt consumers and competitors. Now what IBM did, the DOJ ruled that IBM had to separate its applications business, actually couldn't be in the applications business. Another ruling was that they had to publish the interfaces to IBM mainframes so that competitors could actually build plug-compatible products. That was the world back then. It was all about peripherals plugging into mainframes and sort of applications being developed. So the DOJ took away IBM's power. Fast forward 30 years, now we're hearing Google, Amazon, and Facebook coming under fire from politicians. Should they break up those companies? Now those companies are probably the three leaders in AI. IBM might debate that. I think generally, at theCUBE and SiliconANGLE, we believe that those three companies are leading the charge in AI, along with China Inc: Alibaba, Tencent, Baidu, et cetera, and the Chinese government. So here's the question. What would happen if you broke up big tech? I would surmise that if you break up big tech, those little techs that you break up, Amazon Web Services, WhatsApp, Instagram, those little techs would get bigger. Now, however, the government is implying that it wants to break those up because those entities have access to our data. Google's got access to all the search data. If you start splitting them up, that'll make it harder for them to leverage that data. I would argue those small techs would get bigger, number one. Number two, I would argue if you're worried about China, which clearly you're seeing President Trump is worried about China, placing tariffs on China, playing hardball with China, which is not necessarily a bad thing. In fact, I think it's a good thing because China has been accused, and we all know, of taking IP, stealing IP essentially, and really not putting in those IP protections. So, okay, playing hardball to try to get a quid pro quo on IP protections is a good thing. Not good for trade long term. I'd like to see those trade barriers go away, but if it's a negotiation tactic, okay. I can live with it. However, going after the three AI leaders, Amazon, Facebook, and Google, and trying to take them down or break them up, actually, if you're a nationalist, could be a bad thing. Why would you want to handcuff the AI leaders? Third point is unless they're breaking the law. So I think that should be the decision point. Are those three companies, and others, using monopoly power to thwart competition? I would argue that Microsoft actually did use its monopoly power back in the '80s and '90s, in particular in the '90s, when it put Netscape out of business, it put Lotus out of business, it put WordPerfect out of business, it put Novell out of the business. Now, maybe those are strong words, but in fact, Microsoft's bundling, its pricing practices, caught those companies off guard. Remember, Jim Barksdale, the CEO of Netscape, said we don't need the browser. He was wrong. Microsoft killed Netscape by bundling Internet Explorer into its operating system. So the DOJ stepped in, some would argue too late, and put handcuffs on Microsoft so they couldn't use that monopoly power. And I would argue that you saw from that two things. One, granted, Microsoft was overly focused on Windows. That was kind of their raison d'etre, and they missed a lot of other opportunities. But the DOJ definitely slowed them down, and I think appropriately. And if out of that myopic focus on Windows, and to a certain extent, the Department of Justice and the government, the FTC as well, you saw the emergence of internet companies. Now, Microsoft did a major pivot to the internet. They didn't do a major pivot to the cloud until Satya Nadella came in, and now Microsoft is one of those other big tech companies that is under the watchful eye. But I think Microsoft went through that and perhaps learned its lesson. We'll see what happens with Facebook, Google, and Amazon. Facebook, in particular, seems to be conflicted right now. Should we take down a video that has somewhat fake news implications or is a deep hack? Or should we just dial down? We saw this recently with Facebook. They dialed down the promotion. So you almost see Facebook trying to have its cake and eat it too, which personally, I don't think that's the right approach. I think Facebook either has to say damn the torpedoes. It's open content, we're going to promote it. Or do the right thing and take those videos down, those fake news videos. It can't have it both ways. So Facebook seems to be somewhat conflicted. They are probably under the most scrutiny now, as well as Google, who's being accused, anyway, certainly we've seen this in the EU, of promoting its own ads over its competitors' ads. So people are going to be watching that. And, of course, Amazon just having too much power. Having too much power is not necessarily an indication of abusing monopoly power, but you know the government is watching. So that bears watching. theCUBE is going to be covering that. We'll be here all day, covering the IBM CDO event. I'm Dave Vallente, you're watching theCUBE. #IBMCDO, DM us or Tweet us @theCUBE. I'm @Dvallente, keep it right there. We'll be right back right after this short break. (upbeat music)

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

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

>> live from San Francisco, California It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> We're back. You're watching the Cube, the leader in life Tech coverage. My name is Dave Volant Day, and we're covering the IBM Chief Data officer event hashtag IBM CDO is the 10th year that IBM has been running This event on the New Cube has been covering this for the last I'd say four or five years. Beth rottenness here. She's the distinguished engineer and principal data scientist. Cognitive within GTS Large Service's organization within IBM. Bet thanks so much for coming on the Cube. >> Absolutely. Thank you for having me. >> So you're very welcome. So really interesting sort of title. I'm inferring a lot. Um, and you're sexually transforming lives through data and analytics. Talk about your role a little bit. >> So my role is to infuse workforce transformation with cognitive. I typically we go from I think you've heard the ladder to a I. But as we move up that ladder and we can actually >> apply artificial intelligence and NLP, which is a lot of what I'm doing, >> it is it's instrumental in being able to see human beings in a lot more dimensions. So when we classify humans by a particular job role skill set, we often don't know that they have a passion for things like coding or anything else. And so we're really doing a lot more where we're getting deeper and being able to match your supply and demand in house as well as know when we have a demand for someone. And this person almost meets that demand. Based on all the different dimensionality that weaken dio, >> we can >> put them into this specific training class and then allow them to go through that training class so that we can upgrade the entire upscale and reschedule the entire work force. >> So one of the challenges you're working on is trying to operationalize machine intelligence and obviously starts with that training and skill level so well, it's not easy company the size of of I B M E. You're starting the GTS group, which probably has an affinity, at least conceptually, for transformation. That's what you guys do for your clients. So how's that going? You know, where are you in that journey? >> I think that we're in the journey and we're doing really well. I think that a lot of our people and the people who are actually working on the ground, we're talking to our clients every single day. So people on the helped us, they're talking to clients and customers. They understand what that client is doing. They understand the means, the troops, the mores, the language of the customer, of the organization of the customer, in the client, giving those people skills to understand what they can do better. To help solve our client's problems is really what it's all about. So understanding how we can take all of the unstructured data, all of like the opportunity for understanding what skills those people have on the ground and then being able to match that to the problems that our clients and customers are having. So it's a great opportunity. I think, that I've been in GTS my entire career and being an I t. I think that you understand this is where you store or create or, you know, manage all of the data in an entire enterprise organization, being able to enable and empower the people to be ableto upscale and Reese kill themselves so that they can get access to that so that we can do better for our clients and customers. >> So when you think about operations, folks, you got decades of skills that have built up you. D. B A is, you got network engineers, you got storage administrators. You know the VM add men's, you know, Unix. Add men's, I mean and a lot of those jobs. Air transforming clearly people don't want to invest is much in heavy lifting and infrastructure deployment, right? They want to go up the stack, if you will. So my question is, as you identify opportunities for transformation, I presume it's a lot of the existing workforce that you're transforming. You're not like saying, Okay, guys, you're out. What is gonna go retrain or bring in new people? Gonna retrain existing folks? How's that going? What's their appetite for that? Are they eagerly kind of lining up for this? You could describe that dynamic. >> I think the bits on the ground, they're very hungry. Everyone is so, so, so hungry because they understand what's coming on. They listen to the messages, they're ready. We were also in flexing. I'm sure you've heard of the new collar program were influencing a lot of youth as early professional hires. I have 2 16 year olds in the 17 year old on my team as interns from a P Tech program in Boulder, and getting that mix in that diversity is really all what it's about. We need that diversity of thought. We need that understanding of how we can start to do these things and how people can start to reach for new ways to work. >> All right, so I love this top of the cube we've we've covered, you know, diversity, women in tech. But so let's talk about that a little bit. You just made a statement that you need that diversity. Why is it so important other than it's the right thing to do? What's the what's the business effect of bringing diversity to the table? >> I think that would. We're searching for information truth if you want. If you want to go there, you need a wide variance of thought, the white of your variance, the more standard you're me, and it's actually a mathematical theory. Um, so this is This is something that is part of our truth. We know that diversity of thoughts we've been working. I run and sponsor the LGBT Q Plus group. I do women's groups in the B A R G's and then we also are looking at neuro diversity and really understanding what we can bring in as far as like, a highly diverse workforce. Put them all together, give them the skills to succeed. Make sure that they understand that the client is absolutely the first person that they're looking at in the first person that they're using Those skills on enable them to automate, enable them to stop doing those repeatable tasks. And there's so much application of a I that we can now make accessible so that people understand how to do this at every single level. >> So it's a much wider scope of an observation space. You're sort of purposefully organizing. So you eliminate some of that sampling bias and then getting to the truth. As you say, >> I think that in order to come up with ethical and explainable, aye, aye, there's definitely a way to do this. We know how to do it. It's just hard, and I think that a lot of people want to reach for machine learning or neural nets that spit out the feature without really understanding the context of the data. But a piece of data is an artifact of a human behavior, so you have to trace it all the way back. What process? What person who put it there? Why did they put it there? What was that? When we when we look at really simple things and say, Why are all these tickets classified in this one way? It's because when you observe the human operator, they're choosing the very first thing human behaviors put data in places or human behaviors create machines to put data in places. All of this can be understood if we look at it in a little bit of a different way. >> I thought I had was. So IBM is Business is not about selling ads, so there's no one sent to future appropriate our data to sell advertising. However, if we think about IBM as an internal organism, there's certain incentive structures. There's there's budgets, there's resource is, and so there's always incentives to game the system. And so it sounds like you're trying to identify ways in which you can do the right thing right thing for the business right for people and try to take some of those nuances out of the equation. Is that >> so? From an automation perspectively build digital management system. So all the executives can go in a room and not argue about whose numbers are correct, and they can actually get down to the business of doing business. From the bottoms up perspective, we're enabling the workforce to understand how to do that automation and how to have not only the basic tenets of data management but incorporate that into a digital management system with tertiary and secondary and triangulation and correlations so that we have the evidence and we can show data providence for everything that we're doing. And we have this capability today we're enabling it and operational izing. It really involves a cultural transformation, which is where people like me come in. >> So in terms of culture, so incentives drive behavior, how have you thought through and what are you doing in terms of applying new types of incentives? And how's that working? >> So when we start to measure skills were not just looking at hard skills. We're looking at soft skills, people who are good collaborators, people who have grit, people who are good leaders, people who understand how to do things over and over and over again in a successful manner. So when you start measuring your successful people, you start incentivizing the behaviors that you want to see. And when you start measuring people who can collaborate globally in global economies that that is our business as IBM, that is who we want to see. And that's how we're incentivizing the behaviors that we want to. D'oh. >> So when I look at your background here, obviously you're you're a natural fit for this kind of transformation. So you were You have an anthropology background language. Your data scientist, you do modeling. >> I always say I'm a squishy human data scientist, but I got to work with a huge group of people to create the data science profession with an IBM and get that accredited through open group. And that's something we're very passionate about is to give people a career past so that they know where their next step is. And it's all about moving to growth and sustainable growth by making sure that the workforce knows how value they are by IBM and how valuable they are by our clients. What does >> success look like to you? >> I think success is closer than we think. I think that success is when we have everybody understanding everybody, understanding what it's like to pick up the phone and answer a customer service call from our client and customer and be able to empathize and sympathize and fix the problem. We have 350,000 human beings. We know somebody in some circle that can help fix a client's problem. I think success looks like being able to get that information to the right people at the right time and give people a path so that they know that they're on the boat together, all rowing together in order to make our clients successful. >> That's great. I love the story. Thanks so much for coming on the hearing. You're very welcome. Keep it right there, but we'll be back with our next guest is a day. Violante. We're live from Fisherman's. More for the IBM CDO Chief Data officer event. Right back sticker The cube dot net is where the

>> Live from San Francisco, California. It's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at Fisherman's Wharf at the IBM CDO conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Volante, Joe Selle is here. He's the Global Advanced Analytics and Cognitive Lead at IBM, Boston base. Joe, good to see you again. >> You to Dave. >> And Jerry Gupta, the Senior Vice President and Digital Catalyst at Swiss Re Institute at Swiss Re, great to see you. Thanks for coming on. >> Thank you for having me Dave. >> You're very welcome. So Jerry, you've been at this event now a couple of years, we've been here I think the last four or five years and in the early, now this goes back 10 years this event, now 10 years ago, it was kind of before the whole big data meme took off. It was a lot of focus I'm sure on data quality and data compliance and all of a sudden data became the new source of value. And then we rolled into digital transformation. But how from your perspective, how have things changed? Maybe the themes over the last couple of years, how have they changed? >> I think, from a theme perspective, I would frame the question a little bit differently, right? For me, this conference is a must have on my calendar, because it's very relevant. The topics are very current. So two years ago, when I first attended this conference, it was about cyber and when we went out in the market, they were not too many companies talking about cyber. And so you come to a place like this and you're not and you're sort of blown away by the depth of knowledge that IBM has, the statistics that you guys did a great job presenting. And that really helped us inform ourselves about the cyber risk that we're going on in cyber and so evolve a little bit the consistent theme is it's relevant, it's topical. The other thing that's very consistent is that you always learn something new. The struggle with large conferences like this is sometimes it becomes a lot of me too environment. But in conference that IBM organizes the CDO, in particular, I always learn something new because the practitioners, they do a really good job curating the practitioners. >> And Joe, this has always been an intimate event. You do 'em in San Francisco and Boston, it's, a couple hundred people, kind of belly to belly interactions. So that's kind of nice. But how do you scale this globally? >> Well, I would say that is the key question 'cause I think the AI algorithms and the machine learning has been proven to work. And we've infiltrated that into all of the business processes at IBM, and in many of our client companies. But we've been doing proof of concepts and small applications, and maybe there's a dozen or 50 people using it. But the the themes now are around scale AI at scale. How do you do that? Like we have a remit at IBM to get 100,000 IBMers that's the real number. On our Cognitive Enterprise Data Platform by the end of this calendar year, and we're making great progress there. But that's the key question, how do you do that? and it involves cultural issues of teams and business process owners being willing to share the data, which is really key. And it also involves technical issues around cloud computing models, hybrid public and private clouds, multi cloud environments where we know we're not the only game in town. So there's a Microsoft Cloud, there's an IBM Cloud, there's another cloud. And all of those clouds have to be woven together in some sort of a multi-cloud management model. So that's the techie geek part. But the cultural change part is equally as challenging and important and you need both to get to 100,000 users at IBM. >> You know guys what this conversation brings into focus for me is that for decades, we've marched to the cadence of Moore's laws, as the innovation engine for our industry, that feels like just so yesterday. Today, it's like you've got this data bedrock that we built up over the last decade. You've got machine intelligence or AI, that you now can apply to that data. And then for scale, you've got cloud. And there's all kinds of innovation coming in. Does that sort of innovation cocktail or sandwich makes sense in your business? >> So there's the innovation piece of it, which is new and exciting, the shiny, new toy. And that's definitely exciting and we definitely tried that. But from my perspective and the perspective of my company, it's not the shiny, new toy that's attractive, or that really moves the needle for us. It is the underlying risk. So if you have the shiny new toy of an autonomous vehicle, what mayhem is it going to cause?, right? What are the underlying risks that's what we are focused on. And Joe alluded to, to AI and algorithms and stuff. And it clearly is a very, it's starting to become a very big topic globally. Even people are starting to talk about the risks and dangers inherent in algorithms and AI. And for us, that's an opportunity that we need to study more, look into deeply to see if this is something that we can help address and solve. >> So you're looking for blind spots, essentially. And then and one of them is this sort of algorithmic risk. Is that the right way to look at it? I mean, how do you think about risk of algorithms? >> So yeah, so algorithmic risk would be I would call blind spot I think that's really good way of saying it. We look at not just blind spots, so risks that we don't even know about that we are facing. We also look at known risks, right? >> So we are one of the largest reinsurers in the world. And we insure just you name a risk, we reinsure it, right? so your auto risk, your catastrophe risk, you name it, we probably have some exposure to it. The blind spot as you call it are, anytime you create something new, there are pros and cons. The shiny, new toy is the pro. What risks, what damage, what liability can result there in that's the piece that we're starting to look at. >> So you got the potentially Joe these unintended consequences of algorithms. So how do you address that? Is there a way in which you've thought through, some kind of oversight of the algorithms? Maybe you could talk about IBM's point of view there. >> Well we have >> Yeah and that's a fantastic and interesting conversation that Jerry and I are having together on behalf of our organizations. IBM knowing in great detail about how these AI algorithms work and are built and are deployed, Jerry and his organization, knowing the bigger risk picture and how you understand, predict, remediate and protect against the risk so that companies can happily adopt these new technologies and put them everywhere in their business. So the name of the game is really understanding how as we all move towards a digital enterprise with big data streaming in, in every format, so we use AI to modify the data to a train the models and then we set some of the models up as self training. So they're learning on their own. They're enhancing data sets. And once we turn them on, we can go to sleep, so they do their own thing, then what? We need a way to understand how these models are producing results. Are they results that we agree with? Are these self training algorithms making these, like railroad trains going off the track? Or are they still on the track? So we want to monitor understand and remediate, but it's at scale again, my earlier comments. So you might be an organization, you might have 10,000 not models at work. You can't watch those. >> So you're looking at the intersection of risk and machine intelligence and then you're, if I understand it correctly applying AI, what I call machine intelligence to oversee the algorithms, is that correct? >> Well yes and you could think of it as an AI, watching over the other AI. That's really what we have 'cause we're using AI in as we envision what might or might not be the future. It's an AI and it's watching other AI. >> That's kind of mind blowing. Jerry, you mentioned autonomous vehicles before that's obviously a potential disruptor to your business. What can you share about how you guys are thinking about that? I mean, a lot of people are skeptical. Like there's not enough data, every time there's a another accident, they'll point to that. What's your point of view on that? From your corporation standpoint are you guys thinking is near term, mid term, very long term or it's sort of this journey, that there's quasi-autonomous that sort of gets us there. >> So on autonomous vehicles or algorithmic risk? >> On autonomous vehicles. >> So, the journey towards full automation is a series of continuous steps, right? So it's a continuum and to a certain extent, we are in a space now, where even though we may not have full autonomy while we're driving, there is significant feedback and signals that a car provides and acts or not in an automated manner that eventually move us towards full autonomy, right? So for example, the anti-lock braking system. That's a component of that, right? which is it prevents the car from skidding out of control. So if you're asking for a time horizon when it might have happened, yeah, at our previous firm, we had done some analysis and the horizons were as sort of aggressive as 15 years to as conservative as 50 years. But the component that we all agreed to where there was not such a wide range was that the cars are becoming more sophisticated because the cars are not just cars, any automobile or truck vehicles, they're becoming more automated. Where does risk lie at each piece? Or each piece of the value chain, right? And the answer is different. If you look at commercial versus personal. If you look at commercial space, autonomous fleets are already on the road. >> Right >> Right? And so the question then becomes where does liability lie? Owner, manufacturer, driver >> Shared model >> Shared, manual versus automated mode, conditions of driving, what decisions algorithm is making, which is when you know, the physics don't allow you to avoid an accident? Who do you end up hitting? (crosstalk) >> Again, not just the technology problem. Now, last thing is you guys are doing a panel, on wowing customers making customers the king, I think, is what the title of it is. What's that all about? And get into that a little bit? >> Sure. Well, we focus as IBM mostly on a B2B framework. So the example that I that I'll share to you is, somewhere between like making a customer or making a client the king, the example is that we're using some of our AI to create an alert system that we call Operations Risks Insights. And so the example that I wanted to share was that, we've been giving this away to nonprofit relief agencies who can deploy it around a geo-fenced area like say, North Carolina and South Carolina. And if you're a relief agency providing flood relief or services to people affected by floods, you can use our solution to understand the magnitude and the potential damage impact from a storm. We can layer up a map with not only normal geospatial information, but socio-economic data. So I can say find the relief agency and I've got a huge storm coming in and I can't cover the entire two-state area. I can say okay, well show me the area where there's greater population density than 1000 per square kilometer and the socio-economic level is, lower than a certain point and those are the people that don't have a lot of resources can't move, are going to shelter in place. So I want to know that because they need my help. >> That's where the risk is. Yeah, right they can't get out >> And we use AI to do to use that those are happy customers, and I've delivered wow to them. >> That's pretty wow, that's right. Jerry, anything you would add to that sort of wow customer experience? Yeah, absolutely, So we are a B2B company as well. >> Yeah. >> And so the span of interaction is dictated by that piece of our business. And so we tried to create wow, by either making our customers' life easier, providing tools and technologies that make them do their jobs better, cheaper, faster, more efficiently, or by helping create, goal create, modify products, such that, it accomplishes the former, right? So, Joe mentioned about the product that you launched. So we have what we call parametric insurance and we are one of the pioneers in the field. And so we've launched three products in that area. For earthquake, for hurricanes and for flight delay. And so, for example, our flight delay product is really unique in the market, where we are able to insure a traveler for flight delays. And then if there is a flight delay event that exceeds a pre established threshold, the customer gets paid without even having to file a claim. >> I love that product, I want to learn more about that. You can say (mumbles) but then it's like then it's not a wow experience for the customer, nobody's happy. So that's for Jerry. Guys, we're out of time. We're going to leave it there but Jerry, Joe, thanks so much for. >> We could go on Dave but thank you Let's do that down the road. Maybe have you guys in Boston in the fall? it'll be great. Thanks again for coming on. >> Thanks Dave. >> All right, keep it right there everybody. We'll back with our next guest. You're watching theCUBE live from IBM CDO in San Francisco. We'll be right back. (upbeat music)

>> Live from San Francisco, California, it's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at San Francisco. We're here at Fisherman's Wharf covering the IBM Chief Data Officer event #IBMCDO. This is the tenth year of this event. They tend to bookend them both in San Francisco and in Boston, and you're watching theCUBE, the leader in live tech coverage. My name is Dave Valante. John Thomas is here, Cube alum and distinguished engineer, Director of Analytics at IBM, and somebody who provides technical direction to the data science elite team. John, good to see you again. Steve Aliouk is back. He is the Vice President of Deep Learning in the Global Chief Data Office, thanks for comin' on again. >> No problem. >> Let's get into it. So John, you and I have talked over the years at this event. What's new these days, what are you working on? >> So Dave, still working with clients on implementing data science and AI data use cases, mostly enterprise clients, and seeing a variety of different things developing in that space. Things have moved into broader discussions around AI and how to actually get value out of that. >> Okay, so I know one of the things that you've talked about is operationalizing machine intelligence and AI and cognitive and that's always a challenge, right. Sounds good, we see this potential but unless you change the operating model, you're not going to get the type of business value, so how do you operationalize AI? >> Yeah, this is a good question Dave. So, enterprises, many of them, are beginning to realize that it is not enough to focus on just the coding and development of the models, right. So they can hire super-talented Python TensorFlow programmers and get the model building done, but there's no value in it until these models actually are operationalized in the context of the business. So one aspect of this is, actually we know, we are thinking of this in a very systematic way and talking about this in a prescriptive way. So, you've got to scope your use cases out. You got to understand what is involved in implementing the use case. Then the steps are build, run, manage, and each of these have technical aspects and business aspects around, right. So most people jump right into the build aspect, which is writing the code. Yeah, that's great, but once you build the code, build the models by writing code, how do you actually deploy these models? Whether that is for online invocation or back storing or whatever, how do you manage the performance of these models over time, how do you retrain these models, and most importantly, when these models are in production, how do I actually understand the business metrics around them? 'Cause this goes back to that first step of scoping. What are the business KPI's that the line of business cares about? The data scientist talks about data science metrics, position and recall and Area Under the ROC Curve and accuracy and so on. But how do these relate to business KPI's. >> All right, so we're going to get into each of those steps in a moment, but Steve I want to ask you, so part of your charter, Inderpal, Global Chief Data Officer, you guys have to do this for IBM, right, drink your own champagne, dog footing, whatever you call it. But there's real business reasons for you to do that. So how is IBM operationalizing AI? What kind of learnings can you share? >> Well, the beauty is I got a wide portfolio of products that I can pull from, so that's nice. Like things like AI open to Watson, some of the hardware components, all that stuffs kind of being baked in. But part of the reason that John and I want to do this interview together, is because what he's producing, what his thoughts are kind of resonates very well for our own practices internally. We've got so many enterprise use cases, how are we deciding, you know, which ones to work on, which ones have the data, potentially which ones have the biggest business impact, all those KPI's etcetera, also, in addition to, for the practitioners, once we decide on a specific enterprise use case to work on, when have they reached the level where the enterprise is having a return on investment? They don't need to keep refining and refining and refining, or maybe they do, but they don't know these practitioners. So we have to clearly justify it, and scope it accordingly, or these practitioners are left in this kind of limbo, where they're producing things, but not able to iterate effectively for the business, right? So that process is a big problem I'm facing internally. We got hundreds of internal use cases, and we're trying to iterate through them. There's an immense amount of scoping, understanding, etcetera, but at the same time, we're building more and more technical debt, as the process evolves, being able to move from project to project, my team is ballooning, we can't do this, we can't keep growing, they're not going to give me another hundred head count, another hundred head count, so we're definitely need to manage it more appropriately. And that's where this mentality comes in there's-- >> All right, so I got a lot of questions. I want to start unpacking this stuff. So the scope piece, that's we're setting goals, identifying the metrics, success metrics, KPI's, and the like, okay, reasonable starting point. But then you go into this, I think you call it, the explore or understanding phase. What's that all about, is that where governance comes in? >> That's exactly where governance comes in. Right, so because it is, you know, we all know the expression, garbage in, garbage out, if you don't know what data you're working with for your machine learning and deep learning enterprise projects, you will not have the resource that you want. And you might think this is obvious, but in an enterprise setting, understanding where the data comes from, who owns the data, who work on the data, the lineage of that data, who is allowed access to the data, policies and rules around that, it's all important. Because without all of these things in place, the models will be questioned later on, and the value of the models will not realized, right? So that part of exploration or understanding, whatever you want to call it, is about understanding data that has to be used by the ML process, but then at a point in time, the models themselves need to be cataloged, need to be published, because the business as a whole needs to understand what models have been produced out of this data. So who built these models? Just as you have lineage of data, you need lineage of models. You need to understand what API's are associated with the models that are being produced. What are the business KPI's that are linked to model metrics? So all of that is part of this understand and explore path. >> Okay, and then you go to build. I think people understand that, everybody wants to start there, just start the dessert, and then you get into the sort of run and manage piece. Run, you want a time to value, and then when you get to the management phase, you really want to be efficient, cost-effective, and then iterative. Okay, so here's the hard question here is. What you just described, some of the folks, particularly the builders are going to say, "Aw, such a waterfall approach. Just start coding." Remember 15 years ago, it was like, "Okay, how do we "write better software, just start building! "Forget about the requirements, "Just start writing code." Okay, but then what happens, is you have to bolt on governance and security and everything else so, talk about how you are able to maintain agility in this model. >> Yeah, I was going to use the word agile, right? So even in each of these phases, it is an agile approach. So the mindset is about agile sprints and our two week long sprints, with very specific metrics at the end of each sprint that is validated against the line of business requirements. So although it might sound waterfall, you're actually taking an agile approach to each of these steps. And if you are going through this, you have also the option to course correct as it goes along, because think of this, the first step was scoping. The line of business gave you a bunch of business metrics or business KPI's they care about, but somewhere in the build phase, past sprint one or sprint 2, you realize, oh well, you know what, that business KPI is not directly achievable or it needs to be refined or tweaked. And there is that circle back with the line of business and a course correction as it was. So it's a very agile approach that you have to take. >> Are they, are they, That's I think right on, because again, if you go and bolt on compliance and governance and security after the fact, we know from years of experience, that it really doesn't work well. You build up technical debt faster. But are these quasi-parallel? I mean there's somethings that you can do in build as the scoping is going on. Is there collaboration so you can describe, can you describe that a little bit? >> Absolutely, so for example, if I know the domain of the problem, I can actually get started with templates that help me accelerate the build process. So I think in your group, for example, IBM internally, there are many, many templates these guys are using. Want to talk a little bit about that? >> Well, we can't just start building up every single time. You know, that's again, I'm going to use this word and really resonate it, you know it's not extensible. Each project, we have to get to the point of using templates, so we had to look at those initiatives and invest in those initiatives, 'cause initially it's harder. But at least once we have some of those cookie-cutter templates and some of them, they might have to have abstractions around certain parts of them, but that's the only way we're ever able to kind of tackle so many problems. So no, without a doubt, it's an important consideration, but at the same time, you have to appreciate there's a lot of projects that are fundamentally different. And that's when you have to have very senior people kind of looking at how to abstract those templates to make them reusable and consumable by others. >> But the team structure, it's not a single amoeba going through all these steps right? These are smaller teams that are, and then there's some threading between each step? >> This is important. >> Yeah, that's tough. We were just talking about that concept. >> Just talking about skills and >> The bind between those groups is something that we're trying to figure out how to break down. 'Cause that's something he recognizes, I recognize internally, but understanding that those peoples tasks, they're never going to be able to iterate through different enterprise problems, unless they break down those borders and really invest in the communication and building those tools. >> Exactly, you talk about full stack teams. So you, it is not enough to have coding skills obviously. >> Right. What is the skill needed to get this into a run environment, right? What is the skill needed to take metrics like not metrics, but explainability, fairness in the moderates, and map that to business metrics. That's a very different skill from Python coding skills. So full stack teams are important, and at the beginning of this process where someone, line of business throws 100 different ideas at you, and you have to go through the scoping exercise, that is a very specific skill that is needed, working together with your coders and runtime administrators. Because how do you define the business KPI's and how do you refine them later on in the life cycle? And how do you translate between line of business lingo and what the coders are going to call it? So it's a full stack team concept. It may not necessarily all be in one group, it may be, but they have to work together across these different side loads to make it successful. >> All right guys, we got to leave it there, the trains are backing up here at IBM CDO conference. Thanks so much for sharing the perspectives on this. All right, keep it right there everybody. You're watchin' "theCUBE" from San Francisco, we're here at Fisherman's Wharf. The IBM Chief Data Officer event. Right back. (bubbly electronic music)

>> Live from San Francisco, California, it's the Cube, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Hello, everyone. Welcome to historic Fisherman's Wharf in San Francisco. We're covering the IBM Chief Data Officer event, #IBMCDO. This is the Cube's, I think, eighth time covering this event. This is the tenth year anniversary of the IBM CDO event, and it's a little different format today. We're here at day one. It's like a half day. They start at noon, and then the keynotes. We're starting a little bit early. We're going to go all day today. My name is Dave Volante. Steve Eliuk is here. He's a Cube alum and Vice President of Deep Learning and the Global Chief Data Officer at IBM. And Tim Humphrey, the VP at the Chief Data Office at IBM. Gents, welcome to the Cube. >> Welcome, glad to be here. >> So, couple years ago, Ginni Rometty, at a big conference, talked about incumbent disruptors, and the whole notion was that you've got established businesses that need to transform into data businesses. Well, that struck me, that well, if IBM's going to sell that to its customers, it has to go through its own transformation, Steve. So let's start there. What is IBM doing to transform into a data company? >> Well, I've been at IBM for, you know, two years now, and luckily I'm benefiting from a lot of that transformation that's taken place over the past three or four years. So, internally, getting (mumbling) in order, understanding it, going through various different foundation stones, building those building blocks so that we can gather new insights and traverse through the cognitive journey. One of the nice things though, is that we have such a wide, diverse set of data within the company. So for different types of enterprise use cases that have benefits from AI, we have a lot of data assets that we can pull from. Now, keeping those data assets in good order is a challenging task in itself. And I'm able to pull from a lot of different tools that IBM's building for our customers. I get to use them internally, look at them, evaluate them, give them real practitioner's point of view to ultimately get insight for our internal business practices, but also for our customers in turn. >> Okay, so, when you think about a data business, they've got data at the core. I'm going to draw a, like, simple conceptual picture, and you've got people around it, maybe you've got processes around it. IBM, hundred-plus-year-old company, you've got different things at the core. It's products. It's people. It's business process. So maybe you could talk, Tim, about how you guys have gone about putting data at the center of the universe. Is that the right way to think about it? >> It is the right way to think about it, and I like how you were describing it. Because when you think about IBM, we've been around over a hundred years, and we do business in roughly over 170 countries. And we have businesses that span hardware, software, services, financing. And along the way, we've also acquired and divested a lot of companies and a lot of businesses. So what that leaves you with is a very fragmented data landscape, right? You know, to support regulations in this country, taxes, tax rules in another country, and having all these different types of businesses. Some you inherit. Some are born from within your company. It just leaves a lot of data silos. And as we see transformations being so important, and data is at the heart of that transformation, it was important for us to really be able to organize ourselves such that access to data is not a problem. Such that being able to combine data across disciplines from finance to HR to sales to marketing to procurement. That was the big challenge, right? And to do this in a way that really unlocks the value of the data, right? It's very easy to use somebody like one of my good, smart friends here, Steven Eliuk to develop models within a domain. But when you talk about cross-functional, complex data coming together to enable models, that's like the Holy Grail of transformation. Then we can deliver real business value. Then you're not waiting to make decisions. Then you can actually be ahead of trends. And so that's what we've been trying to do And the thought and the journey that we have been on is build a enterprise data platform. So, take the concept of a data lake. Bring in all your data sources into one place, but on top of that, make it more than just a data lake. Bring the services and capabilities that allow you to deliver insights from data together with the data so we have a data platform. And our Cognitive Enterprise data platform sort of enables that transformation, and it makes people like my good friend here much more productive and much more valuable to the business. >> This sounds like just a massive challenge. It's not just a technology challenge, obviously. You've got cultural. I mean, people, "This is my data." >> Yes. >> (laughs) And I'm referring, Tim, you're talking like you're largely through this process, right? So it first of all is... Can you talk about-- >> Basically, I will say this. This is a journey. You're never done, right? And one of the reasons why it is a journey is, if you're going to have a successful business, your business is going to keep transforming. Things are going to keep changing. And even in our landscape today, regulations are going to come. So there's always going to be some type of challenge. So I like to say, we're in a journey. We're not finished. (laughing) We're well down the path, and we've learned a lot. And one of the things we have learned, you hit on it, is culture, right? And it's a little hard to say, okay, I'm opening things up. I don't own the data. The company owns the data. There is that sort of cultural change that has to go along with this transformation. >> And there are technology challenges. I mean, when I first started in this business, AI was a hot concept, but you needed, like, massive supercomputers to actually make them work. Today, you now see their sort of rebirth. You know, (mumbling) talks about the AI winter, and now it's like the AI spring. >> Yeah. >> So how are you guys applying machine intelligence to make IBM a better business? >> Well, ultimately, the technology is really, basically transitioned us from the Dark Ages forward. Previously in the supercomputer mentality, didn't fit well for a lot of AI tasks. Now with GPUs and accelerators and FBGAs and things like that, we're definitely able, along with the data and the curated data that we need, to just fast-track. You know, the practitioners would spend an amazing amount of time gathering, crowdsourcing data, getting it in good order, and then the computational challenges were tough. Now, IBM came to the market with a very interesting computer. The POWER8 and POWER9 architecture has NVLink, which is a proprietary Nvidia, interconnect directly to the CPU. So we can feed GPUs a lot quicker for certain types of tasks. And for certain types of tasks that could mean, you know, you get to market quicker, or we get insights for enterprise problems quicker. So technology's a big deal, but it doesn't just center around GPUs. If you're slow to get access to the data, then that's a big problem. So the governance (mumbling) aspects are just as important, in addition to that, security, privacy, et cetera, also important. The quality of the data, where the data is. So it's and end-to-end system, and if there's any sort of impedance on any of it, it slows down the entire process. But then you have very expensive practitioners who are trying to do their job that are waiting on data or waiting on results. So it's really an end-to-end process. >> Okay, so let's assume for a second the technology box is checked. And again, as you say, Tim, it's a journey, and technology's going to continue to evolve. But we're at a point in technology now where this stuff actually can work. But what about data quality? What about compliance and governance? How are you dealing with the natural data quality problem? Because I'm a PNL manager. I'm saying, well, we're making data decisions, but if I don't like the decision, I'm going to attack the quality of the data. (laughing) So who adjudicates all that, and how have you resolved those challenges? >> Well, I like to think of... I'm an engineer by study, and I just like to think of simple formulas. Garbage in, garbage out. It applies to everything, and it definitely applies to data. >> (laughs) >> Your insights, the models, anything that you build is only going to be as good as the data foundation you have. So one of the key things that we've embarked on a journey on is, how do we standardize all aspects of data across the company? Now, you might say, hey, that's not a hard challenge, but it's really easy to do standards in a silo. For this organization, this is how we're going to call terms like geography, and this is how we'll represent these other terms. But when you do that across functions, it becomes conflict, right? Because people want to do it their own way. So we're on the path of standardizing data across the enterprise. That's going to allow us to have good definitions. And then, as you mentioned earlier, we are trying to use AI to be able to improve our data quality. One of the most important things about data is the metadata, the data that describes the data. >> Mm-hm. >> And we're trying to use AI to enhance our metadata. I'd love for Steven to talk a little bit about this, 'cause this is sort of his brainchild. But it's fascinating to me that we can be on a AI transformation, data can be at the heart of it, and we can use AI (laughs) to help improve the quality of our data. >> Right. >> It's fascinating. >> So the metadata problem is (mumbling) because you've talked about data length before. Then in this day and age, you're talking schema lists. Throw it into a data lake and figure out because you have to be agile for your business. So you can't do that with just human categorization, and you know, it's got to-- >> It could take hours, maybe years. >> For a company the size of IBM, the market would shift so fast, right? So how do you deal with that problem? >> That's exactly it. We're not patient enough to do the normative kind of mentality where you just throw a whole bunch of bodies at it. We're definitely moving from that non-extensible man count, full-time-employee type situation, to looking for ways that we can utilize automation. So around the metadata, quality and understanding of that data was incredibly problematic, and we were just hiring people left, right, and center. And then it's a really tough job that they have dealing with so many different business islands, et cetera. So looking for ways that we could automate that process, we finally found away to do it. So there's a lot of curated data. Now we're looking at data quality in addition to looking at regulatory and governance issues, in addition to automating the labeling of business metadata. And the business metadata is the taxonomy that everything is linked together. We understand it under the same normative umbrella. So then when one of the enterprise use cases says, "Hey, we're looking for additional data assets," oh, it's (snaps) in the cloud here, or it's in a private instance here. But we know it's there, and you can grab it, right? So we're definitely at probably the tail end of that curve now, and it started off really hard, but it's getting easier. So that's-- >> Guys, we got to leave it there. Awesome discussion. I hope we can pick it up in the future when maybe we have more metadata than data. >> (laughs) >> And metadata's going to become more and more valuable. But thank you so much for sharing a little bit about IBM's transformation. It was great having you guys on. >> Thank you. >> Alright, keep it right there, everybody. We'll be back with our next guest right after this short break. You're watching the Cube at IBM CDO in San Francisco. Right back. (electronic music) >> Alright, long clear. Alright, thank you guys. Appreciate it, I wish we had more time.

>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM Chief Data Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight along with my co-host Paul Gillin. We're joined by Ash Dhupar, he is the Chief Analytics Officer at Publishers Clearing House. Thank you so much for coming on theCUBE. >> Thank you Rebecca for calling me here. >> So Publishers Clearing House is a billion-dollar company. We think of it as the sweepstakes company, we think of the giant checks and be the Prize Patrol surprising contestants, but it's a whole lot more than that. Tell our viewers a little bit, just explain all the vast amount of businesses that you're in. >> Sure, so, in a nutshell, we are a media and entertainment company with a large base of customers, about 100 million customers who are motivated with the chance to win. That's the sweepstakes angle to it. And we have, you can categorize the business into two buckets. One is our media and entertainment side, which is the publishing side. And then the other is our retail side which is where we sell merchandise to our customers. Think of us as a catalog and an e-commerce company. On the media and entertainment side, we have a very good engagement with our customers, we get about two billion page views on a monthly basis on our website. We, about 15 million unique customers on a monthly basis are coming to the site and they spend a considerable amount of time with us on an average, anywhere between 12 to 15 minutes, depending on, you know the type of the customers. Some of our very heavily-engaged customers can spend as much as about two hours a day with us. (Rebecca and John laughs) >> Trying to win that, that either the big prize or there are small prizes like, if you go on our site, there's a winner everyday, like there could be 1,000 dollar winner everyday playing a certain type of a game. So that's the media and the entertainment side of our business, that's completely ad-supported. And then we are the retail side of the business is we are in direct mail, so the traditional, we would send someone a direct mail package. And an e-commerce company as well. Just as a small nugget of information, we are. We send almost about 400 million pieces of physical mail which is including our packages that are sent and so on and so forth and though also still a large direct mail company. Still profitable and still growing. >> I'm sure the US Postal Service is grateful for your support. (laughs) They need all the help they can get. You collect, essentially, the prize money, is your cost of data acquisition and you have a huge database you told us earlier before we started filming of about 100 million people, that you have data on just in the US alone. Now what are you doing at the upper limits of what you're able to do with this data. How are you using this strategically other than just you know personalized email? >> Sure, so I think using data is a core asset for us. We are utilizing in giving our customers better experiences by utilizing the data we have on them. Marrying it with other data sources as well. So that we can personalize the experience. So that we can make your experience when you come on the site better. Or if we are sending something to you in mail, we give you products that are relevant to you. So to bring it down to a little more tactical level, in case of when you are on our site, then on our e-commerce site, there's a product recommendation engine, right? Which goes in and recommends products to you on what products to buy. Those product recommendation engines drive a significant amount of sales, almost about 40% of our sales are driven by the prior recommendation engines that is all understanding of the customer, what you're buying, what you're likely to buy and the algorithms behind it are built with that. >> Can you give another example though, of how, if I were, I mean you said all these customers are united by a common desire to win and to play a game and to win. >> Right. >> But what are some other ways beyond product recommendation engines, which are now sort of old hat. >> Right. >> What other ways are you enhancing the customers experience and personalizing it? >> Sure, sure. So, I'll give you a recent example of where we are utilizing some of the data to give a more relevant experience to the customer. So when a customer comes on our website, right when you're coming to register with us. So, as you register, as you fill in the form, after you give your name, address and your email address and you hit submit, at that very second, there are some algorithms that are running behind the scenes to understand how are you likely to engage with us. How are you going to, let's say, because we have a diverse business, are you likely to buy something from us? Or are you not likely to buy something from us? And if you're not likely to buy something from us, which means I can get you to, and you know not waste your time in showing you merchandise, but I can give you an experience of free-to-play games and you can, within free-to-play games, what type of games like understanding the persona of the person. We could say, hey, you probably are a lotto player or you are a word game puzzle player and we could give you and direct you to those experiences that are more relevant to you. In case of, if you're going to buy something from us, are you likely to buy, you know highly likely to buy or less likely to buy. Depending on that, should I show you just 10 or 15 products or should I show you like more than that? Are you more likely to buy a magazine? So making it more relevant for the customer experience is where it is all about. We use a lot of this data to, to make that happen. >> So analytics is really core to your business. It's the, completely strategic. Where do you sit in the organization, organizational layout, how is that reflected in the way your job is integrated into the organization? >> Sure, so, it is, I'm part of the C-Suite. And I think our CEO, he had this vision, thing he started. He loves data first of all. (laughs) >> Lucky for you. (laughs) >> Thank you. And he truly believes that data and analytics can drive growth and bring innovation from different areas if we utilize it in the best possible way. So A, I am part of that team. And work very closely with each of the business owners. That's the key, out here is like you know, it is, analytics is not in one corner but in the center of all the, all the business areas giving them either insights or building algorithms for them so that we can make either better decisions or we can power growth, depending on which way we are looking at it. >> You're the Chief Analytics Officer and we're here at the Chief Data Summit here, of here. How different are the roles in your mind and do they work together? I mean you have a CTO that is responsible for sort of Chief Data Officer. >> Yes. >> Responsibilities. How do you two collaborate and work together? >> It is a very tight collaboration. And they're two separate jobs but it is a very tight collaboration, we work hand in hand with each other. And the best part I would say is that you know, we're all focused and we're all driving towards how can we drive growth? That's the bottom line, that is where the bucks stops for all of us in the companies. Are we building projects? Are we doing things that is going to grow the company or not? So the collaboration with the CTO is A, a critical piece. They own the infrastructure, as well as the data and when you own the data, which is, in a way, is slightly, I would say, data governance I would say is a thankless job (laughs) believe it or not. But it is a critical job. It is if your data is not right, it is not going to work for whatever you're trying to do, it's the garbage in garbage out, we all know about that. And we work very closely. If there are CAPEX proposals that needs to be put in place because we're going after a certain big project, whether it's putting things together in one place or a 360 view of the customer. All of that is worked hand in hand. We work together in working towards that. >> What is your big data infrastructure like? Is it on the Cloud? Is it your own? Are you Adobe based? What do you use? >> All of the above. >> Oh. (laughter) No, so, what we have is because we are such an old company, you know we still have our legacy Db2 infrastructure. A lot of our backend databases, lot of our backend processes are all attached to that. We have a warehouse, a sequel server warehouse. We also, for our web analytics, we use Google's BigQuery. That's where you collect a lot of data on a daily basis. And recently, I think about three years ago, we went into the Cloud environment. We have a map, our cluster, which was cloud-based and now, we have brought in on prem very recently. >> Back from the Cloud. >> Back from the Cloud, on prem. And there was very good reasoning why we did that. I think frankly, it's cheaper on a longer term to bring that on prem and you are a lot more in control with all the issues with data privacy. So it is. >> Which, I hope you don't mind my interrupting but we have to wrap here and I need to get that question in. (laughs) >> Yes. >> You have data on 100 million consumers. What are you doing with all of the attention being paid for privacy right now? What are you doing to ensure the. >> We have a very, very I would say integrated infrastructure, data governance, data. There's a whole slew of, I would say, people and process around that to make sure that our date is not exposed. Now luckily, it's it's not like PII to the level that it's a health care data. So you are not really, you have information that is crazy but you still have the PII, the name and address of these customers. And as an example, none of the PII data is actually available to even to the analytics folks. It's all stripped, the PII's stripped off. You give us an ID to the customer and frankly the analytics team don't need the PII information to build any algorithms as well. So there is a whole process around keeping the data secure. >> Great, well Ash, thank you so much for coming on theCUBE, it was a pleasure having you. >> Thank you and thank you for inviting me. >> I'm Rebecca Knight for Paul Gillin. We will have more from IBM CDO Summit just after this. (techno music)

(light music) >> Live from Boston. It's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Night. And I'm joined by my co-host, Paul Gillen. We have two guests for this segment. We have Steven Eliuk, who is the Vice President of Deep Learning Global Chief Data Officer at IBM. And Christopher Bannocks, Group Chief Data Officer at IMG. Thanks so much for coming on theCUBE. >> My pleasure. >> Before we get started, Steve, I know you have some very important CUBE fans that you need-- >> I do. >> To give a shout out to. Please. >> For sure. So I missed them on the last three runs of CUBE, so I'd like to just shout out to Santiago, my son. Five years old. And the shortest one, which is Elana. Miss you guys tons and now you're on the air. (all laughing) >> Excellent. To get that important piece of business out. >> Absolutely. >> So, let's talk about Metadata. What's the problem with Metadata? >> The one problem, or the many (chuckles)? >> (laughing) There are a multitude of problems. >> How long ya got? The problem is, it's everywhere. And there's lots of it. And bringing context to that and understanding it from enterprise-wide perspective is a huge challenge. Just connecting to it finding it, or collecting centrally and then understanding the context and what it means. So, the standardization of it or the lack of standardization of it across the board. >> Yeah, it's incredibly challenging. Just the immense scale of metadata at the same time dealing with metadata as Chris mentioned. Just coming up with your own company's glossary of terms to describe your own data. It's kind of step one in the journey of making your data discoverable and governed. Alright, so it's challenging and it's not well understood and I think we're very early on in these stages of describing our data. >> Yeah. >> But we're getting there. Slowly but surely. >> And perhaps in that context it's not only the fact that it's everywhere but actually we've not created structural solutions in a consistent way across industries to be able to structure it and manage it in an appropriate way. >> So, help people do it better. What are some of the best practices for creating, managing metadata? >> Well you can look at diff, I mean, it's such a broad space you can look at different ones. Let's just take the work we do around describing our data and we do that for for the purposes of regulation. For the purposes of GDPR et cetera et cetera. It's really about discovering and providing context to the data that we have in the organization today. So, in that respect it's creating a catalog and making sure that we have the descriptions and the structures of the data that we manage and use in the organization and to give you perhaps a practical example when you have a data quality problem you need to know how to fix it. So, you store, so you create and structure metadata around well, where does it come from, first of all. So what's the journey it's taken to get to the point where you've identified that there's a problem. But also then, who do we go to to fix it? Where did it go wrong in the chain? And who's responsible for it? Those are very simple examples of the metadata around, the transformations the data might have come through to get to its heading point. The quality metrics associated with it. And then, the owner or the data steward that it has to be routed back to to get fixed. >> Now all of those are metadata elements >> All of those, yeah. >> Right? >> 'Cause we're not really talking about the data. The data might be a debit or a credit. Something very simple like that in banking terms. But actually it's got lots of other attributes associated with it which essentially describe that data. So, what is it? Who owns it? What are the data quality metrics? How do I know whether what it's quality is? >> So where do organizations make mistakes? Do they create too much metadata? Do they create poor, is it poorly labeled? Is it not federated? >> Yes. (all laughing) >> I think it's a mix of all of them. One of the things that you know Chris alluded to and you might of understood is that it's incredibly labor-intensive task. There's a lot of people involved. And when you get a lot of people involved in sadly a quite time-consuming, slightly boring job there's errors and there's problem. And that's data quality, that's GDPR, that's government owned entities, regulatory issues. Likewise, if you can't discover the data 'cause it's labeled wrong, that's potential insight that you've now lost. Because that data's not discoverable to a potential project that's looking for similar types of data. Alright, so, kind of step one is trying to scribe your metadata to the organization. Creating a taxonomy of metadata. And getting everybody on board to label that data whether it be short and long descriptions, having good tools et cetera. >> I mean look, the simple thing is... we struggle as... As a capability in any organization we struggle with these terms, right? Metadata, well ya know, if you're talking to the business they have no idea what you're talking about. You've already confused them the minute you mentioned meta. >> Hashtag. >> Yeah (laughs) >> It's a hashtag. >> That's basically what it is. >> Essentially what it is it's just data about data. It's the descriptive components that tell you what it is you're dealing with. If you just take a simple example from finance; An interest rate on it's own tells you nothing. It could be the interest rate on a savings account. It can the interest rate on a bond. But on its own you have no clue, what you're talking about. A maturity date, or a date in general. You have to provide the context. And that is it's relationships to other data and the contexts that it's in. But also the description of what it is you're looking at. And if that comes from two different systems in an organization, let's say one in Spain and one in France and you just receive a date. You don't know what you're looking at. You have not context of what you're looking at. And simply you have to have that context. So, you have to be able to label it there and then map it to a generic standard that you implement across the organization in order to create that control that you need in order to govern your data. >> Are there standards? I'm sorry Rebecca. >> Yes. >> Are there standards efforts underway industry standard why difference? >> There are open metadata standards that are underway and gaining great deal of traction. There are an internally use that you have to standardize anyway. Irrespective of what's happening across the industry. You don't have the time to wait for external standards to exist in order to make sure you standardize internally. >> Another difficult point is it can be region or country specific. >> Yeah. >> Right, so, it makes it incredibly challenging 'cause every region you might work in you might have to have a own sub-glossary of terms for that specific region. And you might have to control the export of certain data with certain terms between regions and between countries. It gets very very challenging. >> Yeah. And then somehow you have to connect to it all to be able to see what it all is because the usefulness of this is if one system calls exactly the same, maps to let's say date. And it's local definition of that is maturity date. Whereas someone else's map date to birthdate you know you've got a problem. You just know you've got a problem. And exposing the problem is part of the process. Understanding hey that mapping's wrong guys. >> So, where do you begin? If your mission is to transform your organization to be one that is data-centric and the business side is sort of eyes glazing over at the mention of metadata. What kind of communication needs to happen? What kind of teamwork, collaboration? >> So, I mean teamwork and collaboration are absolutely key. The communication takes time. Don't expect one blast of communication to solve the problem. It is going to take education and working with people to actually get 'em to realize the importance of things. And to do that you need to start something. Just the communication of the theory doesn't work. No one can ever connect to it. You have to have people who are working on the data for a reason that is business critical. And you need have them experience the problem to recognize that metadata is important. Until they experience the problem you don't get the right amount of traction. So you have to start small and grow. >> And you can use potentially the whip as well. Governance, the regulatory requirements that's a nice one to push things along. That's often helpful. >> It's helpful, but not necessarily popular. >> No, no. >> So you have to give-- >> Balance. >> We're always struggling with that balance. There's a lot of regulation that drives the need for this. But equally, that same regulation essentially drives all of the same needs that you need for analytics. For good measurement of the data. For growth of customers. For delivering better services to customers. All of these things are important. Just the web click information you have that's all essentially metadata. The way we interact with our clients online and through mobile. That's all metadata. So it's not all whip or stick. There's some real value that is in there as well. >> These would seem to be a domain that is ideal for automation. That through machine learning contextualization machines should be able to figure a lot of this stuff out. Am I wrong? >> No, absolutely right. And I think there's, we're working on proof of concepts to prove that case. And we have IBM AMG as well. The automatic metadata generation capability using machine learning and AI to be able to start to auto-generate some of this insight by using existing catalogs, et cetera et cetera. And we're starting to see real value through that. It's still very early days but I think we're really starting to see that one of the solutions can be machine learning and AI. For sure. >> I think there's various degrees of automation that will come in waves for the next, immediately right now we have certain degrees where we have a very small term set that is very high confidence predictions. But then you want to get specific to the specificity of a company which have 30,000 terms sometimes. Internally, we have 6,000 terms at IBM. And that level of specificity to have complete automation we're not there yet. But it's coming. It's a trial. >> It takes time because the machine is learning. And you have to give the machine enough inputs and gradually take time. Humans are involved as well. It's not about just throwing the machine at something and letting it churn. You have to have that human involvement. It takes time to have the machine continue to learn and grow and give it more terms. And give it more context. But over time I think we're going to see good results. >> I want to ask about that human-in-the-loop as IBM so often calls it. One of the things that Nander Paul Bendery was talking about is how the CDO needs to be a change engine in chief. So how are the rank and file interpreting this move to automation and increase in machine learning in their organizations? Is it accepted? It is (chuckles) it is a source of paranoia and worry? >> I think it's a mix. I think we're kind of blessed at least in the CDO at IBM, the global CDO. Is that everyone's kind of on board for that mission. That's what we're doing >> Right, right. >> There's team members 25, 30 years on IMBs roster and they're just as excited as I am and I've only been there for 16 months. But it kind of depends on the project too. Ones that have a high impact. Everyone's really gung ho because we've seen process times go from 90 days down to a couple of days. That's a huge reduction. And that's the governance regulatory aspects but more for us it's a little bit about we're looking for the linkage and availability of data. So that we can get more insights from that data and better outcomes for different types of enterprise use cases. >> And a more satisfying work day. >> Yeah it's fun. >> That's a key point. Much better to be involved in this than doing the job itself. The job of tagging and creating metadata associated with the vast number of data elements is very hard work. >> Yeah. >> It's very difficult. And it's much better to be working with machine learning to do it and dealing with the outliers or the exceptions than it is chugging through. Realistically it just doesn't scale. You can't do this across 30,000 elements in any meaningful way or a way that really makes sense from a financial perspective. So you really do need to be able to scale this quickly and machine learning is the way to do it. >> Have you found a way to make data governance fun? Can you gamify it? >> Are you suggesting that data governance isn't fun? (all laughing) Yes. >> But can you gamify it? Can you compete? >> We're using gamification in various in many ways. We haven't been using it in terms of data governance yet. Governance is just a horrible word, right? People have really negative connotations associated with it. But actually if you just step one degree away we're talking about quality. Quality means better decisions. And that's actually all governance is. Governance is knowing where your data is. Knowing who's responsible for fixing if it goes wrong. And being able to measure whether it's right or wrong in the first place. And it being better means we make better decisions. Our customers have better engagement with us. We please our customers more and therefore they hopefully engage with us more and buy more services. I think we should that your governance is something we invented through the need for regulation. And the need for control. And from that background. But realistically it's just, we should be proud about the data that we use in the organization. And we should want the best results from it. And it's not about governance. It's about us being proud about what we do. >> Yeah, a great note to end on. Thank you so much Christopher and Steven. >> Thank you. >> Cheers. >> I'm Rebecca Night for Paul Gillen we will have more from the IBM CDO Summit here in Boston coming up just after this. (electronic music)