>> 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)

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

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back everyone to the IBM CDO Summit and theCUBE's live coverage, I'm your host Rebecca Knight along with my co-host Paul Gillin. We have Joe Selle joining us. He is the Cognitive Solution Lead at IBM. And Thomas Ward, Supply Chain Cloud Strategist at IBM. Thank you so much for coming on the show! >> Thank you! >> Our pleasure. >> Pleasure to be here. >> So, Tom, I want to start with you. You are the author of Risk Insights. Tell our viewers a little bit about Risk Insights. >> So Risk Insights is a AI application. We've been working on it for a couple years. What's really neat about it, it's the coolest project I've ever worked on. And it really gets a massive amount of data from the weather company, so we're one of the biggest consumers of data from the weather company. We take that and we'd visualize who's at risk from things like hurricanes, earthquakes, things like IBM sites and locations or suppliers. And we basically notify them in advance when those events are going to impact them and it ties to both our data center operations activity as well as our supply chain operations. >> So you reduce your risk, your supply chain risk, by being able to proactively detect potential outages. >> Yeah, exactly. So we know in some cases two or three days in advance who's in harm's way and we're already looking up and trying to mitigate those risks if we need to, it's going to be a real serious event. So Hurricane Michael, Hurricane Florence, we were right on top of it and said we got to worry about these suppliers, these data center locations, and we're already working on that in advance. >> That's very cool. So, I mean, how are clients and customers, there's got to be, as you said, it's the coolest project you've ever worked on? >> Yeah. So right now, we use it within IBM right? And we use it to monitor some of IBM's client locations, and in the future we're actually, there was something called the Call for Code that happened recently within IBM, this project was a semifinalist for that. So we're now working with some non-profit groups to see how they could also avail of it, looking at things like hospitals and airports and those types of things as well. >> What other AI projects are you running? >> Go ahead. >> I can answer that one. I just wanted to say one thing about Risk Insights, which didn't come out from Tom's description, which is that one of the other really neat things about it is that it provides alerts, smart alerts out to supply chain planners. And the alert will go to a supply chain planner if there's an intersection of a supplier of IBM and a path of a hurricane. If the hurricane is vectored to go over that supplier, the supply chain planner that is responsible for those parts will get some forewarning to either start to look for another supplier, or make some contingency plans. And the other nice thing about it is that it launches what we call a Resolution Room. And the Resolution Room is a virtual meeting place where people all over the globe who are somehow impacted by this event can collaborate, share documents, and have a persistent place to resolve this issue. And then, after that's all done, we capture all the data from that issue and the resolution and we put that into a body of knowledge, and we mine that knowledge for a playbook the next time a similar event comes along. So it's a full-- >> It becomes machine learning. >> It's a machine learning-- >> Sort of data source. >> It's a full soup to nuts solution that gets smarter over time. >> So you should be able to measure benefits, you should have measurable benefits by now, right? What are you seeing, fewer disruptions? >> Yes, so in Risk Insights, we know that out of a thousand of events that occurred, there were 25 in the last year that were really the ones we needed to identify and mitigate against. And out of those we know there have been circumstances where, in the past IBM's had millions of dollars of losses. By being more proactive, we're really minimizing that amount. >> That's incredible. So you were going to talk about other kinds of AI that you run. >> Right, so Tom gave an overview of Risk Insights, and we tied it to supply chain and to monitoring the uptime of our customer data centers and things like that. But our portfolio of AI is quite broad. It really covers most of the middle and back and front office functions of IBM. So we have things in the sales domain, the finance domain, the HR domain, you name it. One of the ones that's particularly interesting to me of late is in the finance domain, monitoring accounts receivable and DSO, day sales outstanding. So a company like IBM, with multiple billions of dollars of revenue, to make a change of even one day of day sales outstanding, provides gigantic benefit to the bottom line. So we have been integrating disparate databases across the business units and geographies of IBM, pulling that customer and accounts receivable data into one place, where our CFO can look at an integrated approach towards our accounts receivable and we know where the problems are, and we're going to use AI and other advanced analytic techniques to determine what's the best treatment for that AI, for those customers who are at risk because of our predictive models, of not making their payments on time or some sort of financial risk. So we can integrate a lot of external unstructured data with our own structured data around customers, around accounts, and pull together a story around AR that we've never been able to pull before. That's very impactful. >> So speaking of unstructured data, I understand that data lakes are part of your AI platform. How so? >> For example, for Risk Insights, we're monitoring hundreds of trusted news sources at any given time. So we know, not just where the event is, what locations are at risk, but also what's being reported about it. We monitor Twitter reports about it, we monitor trusted news sources like CNN or MSNBC, or on a global basis, so it gives our risk analyst not just a view of where the event is, where it's located, but also what's being said, how severe it is, how big are those tidal waves, how big was the storm surge, how many people were affected. By applying some of the machine learning insights to these, now we can say, well if there are couple hundred thousand people without power then it's very likely there is going to be multimillions of dollars of impact as a result. So we're now able to correlate those news reports with the magnitude of impact and potential financial impact to the businesses that we're supporting. >> So the idea being that IBM is saying, look what we've done for our own business (laughs), imagine what we could do for you. As Inderpal has said, it's really using IBM as its own test case and trying to figure this all out and learning as it goes and he said, we're going to make some mistakes, we've already made some mistakes but we're figuring it out so you don't have to make those mistakes. >> Yeah that's right. I mean, if you think about the long history of this, we've been investing in AI, really, since, depending on how you look at it, since the days of the 90's, when we were doing Deep Blue and we were trying to beat Garry Kasparov at chess. Then we did another big huge push on the Jeopardy program, where we we innovated around natural language understanding and speed and scale of processing and probability correctness of answers. And then we kind of carry that right through to the current day where we're now proliferating AI across all of the functions of IBM. And there, then, connecting to your comment, Inderpal's comment this morning was around let's just use all of that for the benefit of other companies. It's not always an exact fit, it's never an exact fit, but there are a lot of pieces that can be replicated and borrowed, either people, process or technology, from our experience, that would help to accelerate other companies down the same path. >> One of the questions around AI though is, can you trust it? The insights that it derives, are they trustworthy? >> I'll give a quick answer to that, and then Tom, it's probably something you want to chime in on. There's a lot of danger in AI, and it needs to be monitored closely. There's bias that can creep into the datasets because the datasets are being enhanced with cognitive techniques. There's bias that can creep into the algorithms and any kind of learning model can start to spin on its own axis and go in its own direction and if you're not watching and monitoring and auditing, then it could be starting to deliver you crazy answers. Then the other part is, you need to build the trust of the users, because who wants to take an answer that's coming out of a black box? We've launched several AI projects where the answer just comes out naked, if you will, just sitting right there and there's no context around it and the users never like that. So we've understood now that you have to put the context, the underlying calculations, and the assessment of our own probability of being correct in there. So those are some of the things you can do to get over that. But Tom, do you have anything to add to that? >> I'll just give an example. When we were early in analyzing Twitter tweets about a major storm, what we've read about was, oh, some celebrity's dog was in danger, like uh. (Rebecca laughs) This isn't very helpful insight. >> I'm going to guess, I probably know the celebrity's dog that was in danger. (laughs) >> (laughs) actually stop saying that. So we learned how to filter those things out and say what are the meaningful keywords that we need to extract from and really then can draw conclusions from. >> So is Kardashian a meaningful word, (all laughing) I guess that's the question. >> Trending! (all laughing) >> Trending now! >> I want to follow up on that because as an AI developer, what responsibility do developers have to show their work, to document how their models have worked? >> Yes, so all of our information that we provided the users all draws back to, here's the original source, here's where the information was taken from so we can draw back on that. And that's an important part of having a cognitive data, cognitive enterprise data platform where all this information is stored 'cause then we can refer to that and go deeper as well and we can analyze it further after the fact, right? You can't always respond in the moment, but once you have those records, that's how you can learn from it for the next time around. >> I understand that building test models in some cases, particularly in deep learning is very difficult to build reliable test models. Is that true, and what progress is being made there? >> In our case, we're into the machine learning dimension yet, we're not all the way into deep learning in the project that I'm involved with right now. But one reason we're not there is 'cause you need to have huge, huge, vast amounts of robust data and that trusted dataset from which to work. So we aspire towards and we're heading towards deep learning. We're not quite there yet, but we've started with machine learning insights and we'll progress from there. >> And one of the interesting things about this AI movement overall is that it's filled with very energetic people that's kind of a hacker mindset to the whole thing. So people are grabbing and running with code, they're using a lot of open source, there's a lot of integration of the black box from here, from there in the other place, which all adds to the risk of the output. So that comes back to the original point which is that you have to monitor, you have to make sure that you're comfortable with it. You can't just let it run on its own course without really testing it to see whether you agree with the output. >> So what other best practices, there's the monitoring, but at the same time you do that hacker culture, that's not all bad. You want people who are energized by it and you are trying new things and experimenting. So how do you make sure you let them have, sort of enough rein but not free rein? >> I would say, what comes to mind is, start with the business problem that's a real problem. Don't make this an experimental data thing. Start with the business problem. Develop a POC, a proof of concept. Small, and here's where the hackers come in. They're going to help you get it up and running in six weeks as opposed to six months. And then once you're at the end of that six-week period, maybe you design one more six-week iteration and then you know enough to start scaling it and you scale it big so you've harnessed the hackers, the energy, the speed, but you're also testing, making sure that it's accurate and then you're scaling it. >> Excellent. Well thank you Tom and Joe, I really appreciate it. It's great to have you on the show. >> Thank you! >> Thank you, Rebecca, for the spot. >> I'm Rebecca Knight for Paul Gillin, we will have more from the IBM CDO summit just after this. (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 Knight*, and I'm joined by cohost, Paul Gillan*. We have a guest today, John Thomas. He is the Distinguished Engineer and Director* at IBM. Thank you so much for coming, returning to theCUBE. You're a CUBE veteran, CUBE alum. >> Oh thank you Rebecca, thank you for having me on this. >> So tell our viewers a little bit about, you're a distinguished engineer. There are only 672 in all of IBM. What do you do? What is your role? >> Well that's a good question. Distinguished Engineer is kind of a technical executive role, which is a combination of applying the technology skills, as well as helping shape IBM strategy in a technical way, working with clients, et cetera. So it is a bit of a jack of all trades, but also deep skills in some specific areas, and I love what I do (laughs lightly). So, I get to work with some very talented people, brilliant people, in terms of shaping IBM technology and strategy. Product strategy, that is part of it. We also work very closely with clients, in terms of how to apply that technology in the context of the client's use status. >> We've heard a lot today about soft skills, the importance of organizational people skills to being a successful Chief Data Officer, but there's still a technical component. How important is the technical side? What is, what are the technical skills that the CDOs need? >> Well, this is a very good question Paul. So, absolutely, so, navigating the organizational structure is important. It's a soft skill. You are absolutely right. And being able to understand the business strategy for the company, and then aligning your data strategy to the business strategy is important, right? But the underlying technical pieces need to be solid. So for example, how do you deal with large volumes of different types of data spread across a company? How do you manage that data? How do you understand the data? How do you govern that data? How do you then master leveraging the value of that data in the context of your business, right? So an understanding, a deep understanding of the technology of collecting, organizing, and analyzing that data is needed for you to be a successful CDO. >> So in terms of, in terms of those skillsets that you're looking for, and one of the things that Inderpal said earlier in his keynote, is that, there are just, it's a rare individual who truly understands the idea of how to collect, store, analyze, curatize, monetize the data, and then also have the soft skills of being able to navigate the organization, being able to be a change agent who is inspiring, inspiring the rank and file. How do you recruit and retain talent? I mean, this seems to be a major challenge. >> Expertise is, and getting the right expertise in place, and Inderpal talked about it in his keynote, which was the very first thing he did was bring in talent. Sometimes it is from outside of your company. Maybe you have a kind of talent that has grown up in your company. Maybe you have to go outside, but you've got to bring in the right skills together. Form the team that understands the technology, and the business side of things, and build this team, and that is essential for you to be a successful CDO. And to some extent, that's what Inderpal has done. That's what the analytic CDO's office has done. Seth Dobrin, my boss, is the analytics CDO , and he and the analytics CDO team actually hired people with different skills. Data engineering skills, data science skills, visualization skills, and then put this team together which understands the, how to collect, govern, curate, and analyze the data, and then apply them in specific situations. >> There's been a lot of talk about AI, at this conference, which seems to be finally happening. What do you see in the field, or perhaps projects that you've worked on, of examples of AI that are really having a meaningful business impact? >> Yeah Paul, that is a very good question because, you know, the term AI is overused a lot as you can imagine, a lot of hype around it. But I think we are past that hype cycle, and people are looking at, how do I implement successful use cases? And I stress the word use case, right? In my experience these, how I'm going to transform my business in one big boil the ocean exercise, does not work. But if you have a very specific bounded use case that you can identify, the business tells you this is relevant. The business tells you what the metrics for success are. And then you focus your attention, your efforts on that specific use case with the skills needed for that use case, then it's successful. So, you know, examples of use cases from across the industries, right? I mean everything that you can think of. Customer-facing examples, like, how do I read the customer's mind? So when, if I'm a business and I interact with my customers, can I anticipate what the customer is looking for, maybe for a cross-sell opportunity, or maybe to reduce the call handing time when a customer calls into my call center. Or trying to segment my customers so I can do a proper promotion, or a campaign for that customer. All of these are specific customer phasing examples. There also are examples of applying this internally to improve precesses, capacity planning for your infrastructure, can I predict when a system is likely to have an outage, or can I predict the traffic coming into my systems, into my infrastructure and provision capacity for that on demand, So all of these are interesting applications of AI in the enterprise. >> So when your trying, what are the things we keep hearing, is that we need to data to tell a story To, the data needs to be compelling enough so that the people, the data scientist get it but then also the other kinds of business decision makers get it to. >> Yep >> So, what are sort of, the best practices that have emerged from your experience? In terms of, being able to, for your data to tell a story that you want it to tell. >> Yeah, well I mean if the pattern doesn't exist in the data then no amount of fancy algorithms can help, you know? and sometimes its like searching for a needle in a haystack but assuming, I guess the first step is, like I said, What is the use case? Once you have a clear understanding of your use case and such metrics for your use case, do you have the data to support that use case? So for example if it's fraud detection, do you actually have the historical data to support the fraud use case? Sometimes you may have transactional data from your, transocular from your core enterprise systems but that may not be enough. You may need to alt mend it with external data, third party data, maybe unstructured data, that goes along with your transaction data. So the question is, can you identify the data that is needed to support the use case and if so can I, is that data clean, is that data, do you understand the lineage of the data, who has touched and modified the data, who owns the data. So then I can start building predictive models and machine learning, deep learning models with that data. So use case, do you have the data to support the use case? Do you understand how that sata reached you? Then comes the process of applying machine learning algorithms and deep learning algorithms against that data. >> What are the risks of machine learning and particularly deep learning, I think because it becomes kind of a black box and people can fall into the trap of just believing what comes back, regardless of whether the algorithms are really sound or the data is. What is the responsibility of data scientist to sort of show their work? >> Yeah, Paul this is fascinating and not completely solid area, right? So, bias detection, can I explain how my model behaved, can I ensure that the models are fair in their predictions. So there is a lot of research, a lot of innovation happening in the space. IBM is investing a lot into space. We call trust and transparency, being able to explain a model, it's got multiple levels to it. You need some level of AI governments itself, just like we talked about data governments that is the notion of AI governments. Which is what motion of the model was used to make a prediction? What were the imports that went into that model? What were the decisions that were, that were the features that were used to make a sudden prediction? What was the prediction? And how did that match up with ground truth. You need to be able to capture all that information but beyond that, we have got actual mechanisms in place that IBM Research is developing to look at bias detection. So pre processing during execution post processing, can I look for bias in how my models behave and do I have mechanisms to mitigate that? So one example is the open source Python library, called AIF360 that comes from IBM Research and has contributed to the open source community. You can look at, there are mechanisms to look at bias and provide some level of bias mitigation as part of your model building exercises. >> And the bias mitigation, does it have to do with, and I'm going to use an IMB term of art here, the human in the loop, is it how much are you actually looking at the humans that are part of this process >> Yeah, humans are at least at this point in time, humans are very much in the loop. This notion of Peoria high where humans are completely outside the loop is, we're not there yet so very much something that the system can for awhile set off recommendations, can provide a set of explanations and can someone who understands the business look at it and make a corrective, take corrective actions. >> There has been, however to Rebecca's point, some prominent people including Bill Gates, who have speculated that the AI could ultimately be a negative for humans. What is the responsibility of company's like IBM to ensure that humans are kept in the loop? >> I think at least at this point IBM's view is humans are an essential part of AI. In fact, we don't even use artificial intelligence that much we call it augmented intelligence. Where the system is pro sending a set of recommendations, expert advise to the human who can then make a decision. For example, you know my team worked with a prominent health care provider on you know, models for predicting patient death in the case of sepsis, sepsis-onset. This is, we are talking literally life and death decisions being made and this is not something you can just automate and throw into a magic black box, and have a decision be made. So this is absolutely a place where people with deep, domain knowledge are supported, are opt mended with, with AI to make better decisions, that's where I think we are today. As to what will happen five years from now, I can't predict that yet. >> Well I actually want to- >> But the question >> bring this up to both of you, the role, so you are helping doctor's make these decisions, not just this is what the computer program says about this patient's symptoms here but this is really, so you are helping the doctor make better decisions. What about the doctors gut, in the, his or her intuition to. I mean, what is the role of that, in the future? >> I think it goes away, I mean I think, the intuition really will be trumped by data in the long term because you can't argue with the facts. Some people do these days. (soft laughter) But I don't remember (everyone laughing) >> We have take break there for some laughter >> Intrested in your perspective onthat is there, will there, should there always be a human on the front line, who is being supported by the back end or would you see a scenario were an AI is making decisions, customer facing decisions that are, really are life and death decisions? >> So I think in the consumer invest way, I can definitely see AI making decisions on it's own. So you know if lets say a recommender system would say as you know I think, you know John Thomas, bought these last five things online. He's likely to buy this other thing, let's make an offer to him. You know, I don't need another human in the loop for that >> No harm right? >> Right. >> It's pretty straight forward, it's already happening, in a big way but when it comes to some of these >> Prepoping a mortgage, how about that one? >> Yeah >> Where bias creeps in a lot. >> But that's one big decision. >> Even that I think can be automated, can be automated if the threshold is set to be what the business is comfortable with, were it says okay, above this probity level, I don't really need a human to look at this. But, and if it is below this level, I do want someone to look at this. That's you know, that is relatively straight forward, right? But if it is a decision about you know life or death situation or something that effects the very fabric of the business that you are in, then you probably want a domain explore to look at it. In most enterprises, enterprises cases will fall, lean toward that category. >> These are big questions. These are hard questions. >> These are hard questions, yes. >> Well John, thank you so much for doing >> Oh absolutely, thank you >> On theCUBE, we really had a great time with you. >> No thank you for having me. >> I'm Rebecca Knight for Paul Gillan, we will have more from theCUBE's live coverage of IBM CDO, here in Boston, just after this. (Upbeat Music)

>> Live from Boston, it's the CUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome to the CUBE's live coverage of IBM Chief Data Officer Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight along with my co host, Paul Gillin. We're starting our coverage today. This is the very first day of the summit. We have two guests, Caitlin Halferty, she is the AI accelerator lead at IBM, and Sonia Mezzetta, the data governance technical product leader. Thank you both so much for coming on the CUBE >> Thanks for having us. >> So this is the ninth summit. Which really seems hard to belief. But we're talking about the growth of the event and just the kinds of people who come here. Just set the scene for our viewers a little bit, Caitlin. >> Sure, so when we started this event back in 2014, we really were focused on building the role of the chief data officer, and at that time, we know that there were just a handful across industries. Few in finance banking, few in health care, few in retail, that was about it. And now, you know, Gartner and Forrester, some industry analysts say there are thousands across industries. So it's not so much about demonstrating the value or the importance, now, it's about how are our Chief Data Officers going to have the most impact. The most business impact. And we're finding that they're really the decision-makers responsible for investment decisions, bringing cognition, AI to their organizations. And the role has grown and evolved. When we started the first event, we had about 20, 30 attendees. And now, we get 140, that join us in the Spring in San Francisco and 140 here today in Boston. So we've really been excited to see the growth of the community over the last four years now. >> How does that affect the relationship, IBM's relationship with the customer? Traditionally, your constituent has been the CIO perhaps the COO, but you've got this new C level executive. Now, what role do they play in the buying decision? >> There was really a lot of, I think back to, I co-authored a paper with some colleagues in 2014 on the rise of Chief Data Officer. And at that time, we interviewed 22 individuals and it was qualitative because there just weren't many to interview, I couldn't do a quantitative study. You know, I didn't have sample size. And so, it's been really exciting to see that grow and then it's not just the numbers grow, it's the impact they're having. So to you questions of what role are they playing, we are seeing that more and more their scope is increasing, their armed and equipped with teams that lead data science, machine learning, deep learning capabilities so they're differentiated from a technology perspective. And then they're really armed with the investment and budget decisions. How should we invest in technology. Use data as a strategic corporate asset to drive our progress forward in transformation. And so we've really seen a significant scope increase in terms of roles and responsibilities. And I will say though, there's still that blocking and tackling around data strategy, what makes a compelling data strategy. Is is the latest, greatest? Is it going to have an impact? So we're still working through those key items as well. >> So speaking of what makes this compelling strategy, I want to bring you into the conversation Sonia, because I now you're on the automated metadata generation initiative, which is a big push for IBM. Can you talk a little bit about what you're doing at IBM? >> Sure. So I am in charge of the data governance products internally within the company and specifically, we are talking today about the automated metadata generation tool. What we've tried to do with that particular product is to try to basically leverage automation and artificial intelligence to address metadata issues or challenges that we're facing as part of any traditional process that takes place today and trying to do curation for metadata. So specifically, what I would like to also point out is the fact that the metadata curation process in the traditional sense is something that's extremely time-consuming, very manual and actually tedious. So, one of the things that we wanted to do is to address those challenges with this solution. And to really focus in and hone in on leveraging the power of AI. And so one of the things that we did there was to basically take our traditional process, understand what were the major challenges and then focusing on how AI can address those challenges. And today at 4 p.m. I'll be giving a demo on that, so hopefully, everybody can understand the power of leveraging that. >> This may sound like a simple question, but I imagine for a lot of people outside of the CIO of the IT organization, their eyes glaze over when they hear terms like data governance. But it's really important. >> It is. >> So can you describe why it's important? >> Absolutely. >> And why metadata is important too. >> Absolutely. Well, I mean, metadata in itself is extremely critical for any data monetization position strategy, right. The other importance is in order to derive critical business insights that can lead to monetary value within a company. And the other aspect to that is data quality which Interpol talked about, right? So, in order for you to have the right data governance, you need to have right metadata in order for you to have high level of data quality can, if you don't and you're spending a lot of time cleaning dirty data and dealing with inefficiencies or perhaps making wrong business decisions based on bad data quality, it's all connected back to having the right level of data governance. >> So, I mean, I'm going to also go back to something you were talking about earlier and that's just the sheer number of CDOs that we have. We have statistic here, 90% of large global companies will have the CDO by 2019. That's really astonishing. Can you talk a little bit about what you see as sort of the top threats and opportunities that CDOs as grappling with right now. >> And let me make this tangible. I'll just describe my last two weeks, for example. I was with the CDO in person in Denver of a beer company, organization, and they were looking at some MNA opportunities and figuring out what their strategy was. I was at a bank in Chicago with the head of enterprise data government there, looking at it from a regular (mumbles) perspective. And then I was with a large multinational retail organization with their CDO and team figuring out how did they work at a sort of global scale and what did they centralize at enterprise data level. And what did they let markets and teams customize out in the field, out in the GOs. And so, that's just an example of, regardless of industry, regardless of these challenges, I'm seeing these individuals are increasingly responsible for those strategic decisions. And oftentimes, we start with the data strategy and have a good discussion about what is that organization's monetization strategy. What's the corporate business case? How are they going to make money in the future and how can we architect the data strategy that will accelerate their progress there? And again, regardless of product we're selling or retail, excuse me, our industry, those are the same types of challenges and opportunities we're grappling with. >> In the early days there was a lot of questions about the definition of the role and those CDOs set in different departments and reported to different people, are you seeing some commonality emerge now about how this role, where it sits in the organization, and what its responsibilities are? >> It's a great question, I get that all the time. And especially for organizations that recognize the need for enterprise data management. They want to invest in a senior level decision-maker. And then it's a question of where should they sit organizationally? For us internally, within IBM, we report to our Chief Financial Officer. And so, we find that to be quite a compelling fit in terms of budget. And visibility into some of those spend decisions. And we're on par in peers with our CIO, so I see that quite a bit where a Chief Data Officer is now on par and appear to the CIO. We tend to find that when it's potentially buried in the CIO's organization, you lose a little of that autonomy in terms of decision-making, so if you're able to position as partners and drive that transformation for your organization forward together, that can often work quite well. >> So that partnership, is it, I mean ideally, it is collaborative and collegial, but is it ever, are there ever tensions there and how do you recommend the companies get over, overcome those obstacles? >> Absolutely, in the fight for resources that we all have, especially talent and retaining some of our top talent, should that individual or those teams sit within a CIO's organization or a CDO's organization? How do we figure that out? I think there's always going to be the challenge of who owns what. We joke, sometimes, it feels like you own everything when you're in the data space, because you own all of the data that flows through, all your business processes, both CDO-owned and corporate HR's supply chain finance. Sometimes it feels you don't own anything. And so we joke that it's, you have to really carve that out. I think the important part is to really articulate what the data strategy is, what the CDO or enterprise data management office owns from a data perspective and then building up that platform and do it in partnership with your CIO team. And then you really start to be able to build and deploy those AI applications off that platform. That's what we've been able to see, so. >> I want to go back to something Sonia said this morning during the keynote, you talked about IBM's master metadata list catalog unifying your organization around a certain set of terms. There's 6,000 terms in that catalog. Now, how did you arrive at 6,000? And what are some rules for an organization trying to do something like that? How defined, how small should that sub-terms be? >> Sure. Well, we started off with a traditional approach which is probably something that most companies are familiar with these days. The traditional process was really just based on basically reaching out to a large number of subject matter experts across the enterprise that represent in many different data domains such as customer, offering, financial, etc. And essentially having them label this data, specifically with the business metadata that's used internally across a company. Now, another example to that is that there are different organizations across the company. We are a worldwide company. And so, what one business might call a particular piece of data, which is customer, another might call it client. Which really ended up being this very large list of 6,000 business terms which is what we're using internally. But one thing that we're trying to do to be able to kind to basically connect the different business terms is leverage knowledge management and specifically ontological relationships to be able to link the data together and make it more reasonable and provide better quality with that. >> What are the things that you were talking about, Interpol was talking about on the main stage too during the keynote, was making sure that the data is telling a story because getting by in is one of the biggest challenges. How do you recommend companies think about this and approach this very big daunting task? >> I'll start and then I'm sure you have a perspective as well. One of the things that we've seen internally and I work with my client on, is every project we initiate, we really want strong sponsorship from the business in terms of funding, making sure that the right decision-makers are involved. We've identified some projects for example, that we've been able to deploy around supply chains. So identifying the risk on our supply chain processes. Some of the risks in sites, we're going to demo a little bit later today. The AMG work that Sonia's leading. And all of those efforts are underway in partnership with the business. One of my favorite ones is around enabling our sellers to better understand information about, and data, about the customers. So like most organizations, customer data is housed in silo systems that don't necessarily talk well with each other, and so it's an effort to really pull that data together in partnership with our digital sellers and enable them to then pull up user interface, user-friendly, an app where they can identify and drill down to the types of information they need about their customers. And so our thought and recommendation based on our experience and then what I'm seeing is really having that strong partnership with the business. And the contribution funding, stakeholder involvement, engagement, and then you start to prioritize where you'll have the most impact. >> You did a program called the AI accelerator. What is that? >> We did, so when we stood up our first chief data office, it was three years ago now, we wanted to be quite transparent about the journey of driving cognition through our enterprise. And we were really targeting those CDO and processes around client master product data and then all of our enterprise processes. So that first six months was about writing the data strategy and implementing that, next we spent a year on all of our processes, really mapping out, we call it journey mapping, I think a lot of folks do that, by process. So HR, supply chain, identifying ways. How it's done today, how it will be done in a cognitive AI like future state. And then also, as we're driving out those efficiencies in automation, those reinvestment opportunities to free up that money for future initiatives. And so that was the first year, year and a half. And now, we're at the point where we've evolved far enough along that we think we're learned some lessons on the way and there's been some hurdles and stumbling blocks and obstacles. And so a year ago, we really start a cognitive enterprise blueprint and that was really intended to reflect all of our experiences, driving that transformation. A lot of customer engagements, lot of industry analysts feedback as well. And now we formalized that initiative. So now I have a really fantastic team of folks working with me. Subject matter domain expertise, really deep in different processes, solutions, folks, architects. And what we can do is pull together the right breadth and depth of IBM resources. Deploy it, customize it to customer need and really, hopefully, accelerate and apply a lot of what we've learned, lot of what the clients have learned, to accelerate their own AI transformation journey. >> But AI, IBM is the guinea pig and it showcase. And so you're learning as you go and helping customers do that too. >> Exactly and we've now built our platform, deployed that, as we mentioned, we've got about 30,000 active users, active users, using our platform. Plan to grow to 100,000. We're seeing about 600 million in business benefit internally from the work we've done. And so we want to really share that and do some good, best practice sharing and accelerate some of that process. >> IBM used the term cognitive rather than AI. What is the difference or is there one? >> I think we're starting actually to shift from cognitive to AI because of that exact perspective. AI, I think is better understood in the industry, in the market and that's what's resonating more so with clients and I think it's more reflective of what we're doing. And our particular approach is human in the loop. So we've always said rather than the black box sort of AI algorithms running behind the scenes, we want to make sure that we do that with trust and transparency, so there's a real transparency aspect to what we're doing. And the other thing I would notice, we talk about sort of your data is your data. Insights derive from that data is your insights. So we've worked quite closely with our legal teams to really articulate how your data is used. If you engage and partner with us to drive AI in your enterprise, making sure we have that trust and transparency (mumbles) clearly articulated is another important aspect for us. >> Getting right back to data governance. >> Right, right, exactly. Which is our we've come full circle. >> Well Caitlin and Sonia, thank you so much for coming on the CUBE, it was great. Great to kick off this summit together. >> Great to see you again, as always. >> I'm Rebecca Knight for Paul Gillin, stay tuned for more of the CUBE's live coverage of IBM CDO Summit here in Boston. (techno 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 Knight, along with my co-host Paul Gillin. We have two guests for this session, we have Rebecca Shockley, she is executive consultant and IBM Global Business Services, and Alfred Essa, vice president analytics and R&D at McGraw-Hill Education. Rebecca and Alfred, thanks so much for coming on theCUBE. >> Thanks for having us. >> So I'm going to start with you, Rebecca. You're giving a speech tomorrow about the AI ladder, I know you haven't finished writing it-- >> Shh, don't tell. >> You're giving a speech about the AI ladder, what is the AI ladder? >> So, when we think about artificial intelligence, or augmented intelligence, it's very pervasive, we're starting to see it a lot more in organizations. But the AI ladder basically says that you need to build on a foundation of data, so that data and information architecture's your first rung, and with that data, then you can do analytics, next rung, move into machine learning once you're getting more comfortable, and that opens up the whole world of AI. And part of what we're seeing is organizations trying to jump to the top of the ladder or scramble up the ladder really quickly and then realize they need to come back down and do some foundational work with their data. I've been doing data and analytics with IBM for 21 years, and data governance is never fun. It's hard. And people would just as soon go do something else than do data governance, data security, data stewardship. Especially as we're seeing more business-side use of data. When I started my career, data was very much an IT thing, right. And part of my early career was basically just getting IT and business to communicate in a way that they were saying the same things. Well now you have a lot more self-service analytics, and business leaders, business executives, making software decisions and various decisions that impact the data, without necessarily understanding the ripples that their decisions can have throughout the data infrastructure, because that's not their forte. >> So what's the outcome, what's the result of this? >> Well, you start to see organizations, it's similar to what we saw when organizations first started making data lakes, right? The whole concept of a data lake, very exciting, interesting, getting all the data in together, whether it's virtual or physical. What ended up happening is without proper governance, without proper measures in place, you ended up with a data swamp instead of a data lake. Things got very messy very quickly, and instead of creating opportunities you were essentially creating problems. And so what we're advising clients, is you really have to make sure that you're focused on taking care of that first rung, right? Your data architecture, your information architecture, and treating the data with the respect as a strategic asset that it is, and making sure that you're dealing with that data in a proper manner, right? So, basically telling them, yes we understand that's fun up there, but come back down and deal with your foundation. And for a lot of organizations, they've never really stepped into data governance, because again, data isn't what they think makes the company run, right? So banks are bankers, not data people, but at the same time, how do you run a bank without data? >> Well exactly. And I want to bring you into this conversation, Alfred, as McGraw-Hill, a company that is climbing the ladder, in a more steady fashion. What's your approach? How do you think about bringing your teams of data scientists together to work to improve the company's bottom line, to enhance the customer experience? >> First I'd sort of like to start with laying some of the context of what we do. McGraw-Hill Education has been traditionally a textbook publisher, we've been around for over a hundred years, I started with the company over a hundred years ago. (all laughing) >> You've aged well. >> But we no longer think of ourselves as a textbook publisher. We're in the midst of a massive digital transformation. We started that journey over five years ago. So we think of ourselves as a software company. We're trying to create intelligent software based on smart data. But it's not just about software and AI and data, when it comes to education it's a tale of two cities. This is not just the U.S., but internationally. Used to be, we were born, went to school, got a job, raised a family, retired, and then we die. Well now, education is not episodic. People need to be educated, it's life-long learning. It's survival, but also flourishing. So that's created a massive problem and a challenge. It's a tale of two cities, by that I mean there's an incredible opportunity to apply technology, AI, we see a lot of potential in the new technologies. In that sense, it's the best of times. The worst of times is, we're faced with massive problems. There's a lot of inequity, we need to educate a people who have largely been neglected. That's the context. So I think in now answering your question about data science teams, first and foremost, we like to get people on the teams excited about the mission. It's like, what are we trying to achieve? What's the problem that we're trying to achieve? And I think the best employees, including data scientists, they like solving hard problems. And so, first thing that we try to do is, it's not what skills you have, but do you like solving really, really hard problems. And then taking it next step, I think the exciting thing about data science is it's an interdisciplinary field. It's not one skill, but you need to bring together a combination of skills. And then you also have to excel and have the ability to work in teams. >> You said that the AI has potential to improve the education process. Now, people have only so much capacity to learn, how can AI accelerate that process? >> Yeah, so if we stand back a little bit and look at the traditional model of education, there's nothing wrong with it but it was successful for a certain period of years, and it works for some people. But now the need for education is universal, and life long. So what our basic model, current model of education is lecture mode and testing. Now from a learning perspective, learning science perspective, all the research indicates that that doesn't work. It might work for a small group of people, but it's not universally applicable. What we're trying to do, and this is the promise of AI, it's not AI alone, but I think this is a big part of AI. What we can do is begin to customize and tailor the education to each individual's specific needs. And just to give you one quick example of that, different students come in with different levels of prior knowledge. Not everyone comes into a class, or a learning experience, knowing the same things. So what we can do with AI is determine, very, very precisely, just think of it as a brain scan, of what is it each student need to know at every given point in time, and then based on that we can determine also, this is where the models and algorithms are, what are you ready to learn next. And what you might be ready to learn next and what I might be ready to learn next is going to be very different. So our algorithms also help route delivery of information and knowledge at the right time to the right person, and so on. >> I mean, you're talking about these massive social challenges. Education as solving global inequity, and not every company has maybe such a high-minded purpose. But does it take that kind of mission, that kind of purpose, to unite employees? Both of you, I'm interested in your perspectives here. >> I don't think it takes, you know, a mission of solving global education. I do firmly agree with what Al said about people need a mission, they need to understand the outcome, and helping organizations see that outcome as being possible, gives them that rally point. So I don't disagree, I think everybody needs a mission to work towards but it doesn't have to be solving-- >> You want to extract that mission to a higher level, then. >> Exactly. >> Making the world a better place. >> Exactly, or at least your little corner of the world. Again what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that. You may have a goal of, we want better relationship with our customer, right? And at least folks of my age think that's a personal one-on-one kind of thing. Understanding who you are, I can find that much more quickly by looking at all your past transactions, and all of your past behaviors, and whether you clicked this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, you know, helping the folks who are doing the work, understand that the outcome will be that we can actually treat our customers the way that you want to be treated as a person, gives them that sense of purpose, and helps them connect the dots better. >> One of the big challenges that we hear CDOs face is getting buy-in, and what you're proposing about this new model really appending the old sage on the stage model, I mean, is there a lot of pushback? Is it difficult to get the buy-in and all stakeholders to be on the same page? >> Yeah, it is, I think it's doubly difficult. The way I think about it is, it's like a shift change in hockey, where you have one shift that's on the ice and another one that's about to come on the ice, that's a period of maximum vulnerability. That's where a lot of goals are scored, people get upset, start fighting. (all laughing) That's hockey. >> That's what you do. >> Organizations and companies are faced with the same challenge. It's not that they're resisting change. Many companies have been successful with one business model, while they're trying to bring in a new business model. Now you can't jettison the old business model because often that's paying the bills. That's the source of the revenue. So the real challenge is how are you going to balance out these two things at the same time? So that's doubly difficult, right. >> I want to ask you quickly, 'cause we have to end here, but there's a terrible shortage of cybersecurity professionals, data science professionals, the universities are simply not able to keep up with demand. Do you see the potential for AI to step in and fill that role? >> I don't think technology by itself will fill that role. I think there is a deficit of talented people. I think what's going to help fill that is getting people excited about really large problems that can be solved with this technology. I think, actually I think the talent is there, what I see is, I think we need to do a better job of bringing more women, other diverse groups, into the mix. There are a lot of barriers in diversity in bringing talented people. I think they're out there, I think we could do a much better job with that. >> Recruiting them, right. Alfred, Rebecca, thanks so much for coming on theCUBE, it was a pleasure. >> Thank you so much for having us. >> I'm Rebecca Knight, for Paul Gillin, we will have more from theCUBE's live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.

>> Live from Boston, it's theCUBE covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back, everyone. We are wrapping up a day of coverage, live coverage from theCUBE at the IBM CDO Summit on a very blustery cold day here in Boston, Massachusets. I'm your host, Rebecca Knight. I've been all day with Paul Gillan, my co-host. It's been a lot of fun co-hosting with you. >> It's been a pleasure. It's been a great day. >> Yeah. >> Great guests all day. >> Absolutely, high quality. This is not your father's IBM, not your mother's IBM, this is a very different company from when you started covering IBM way back when and to-- >> 1982. >> 82, so talk about a little bit about the changes. You grew up in an IBM town. >> I grew up in an IBM town. I grew up in an IBM world where everyone dressed the same, everyone had a set of talking points, it was a very closed, dark organization, dense organization, very little information got out. Of course the company at that time was operating under a consent to prove the justice department. Been attempting to be broken up. So they were understandably nervous. We began to see that change after IBM's crisis in the late 80s and began to open up the, began to celebrate individuals where previously individualism had been discouraged at IBM. And what you see at this conference here, I don't know, I'm always amazed when I go to IBM conferences at the differences I see between the people I meet at the conferences and sort of the corporate image that is represented which is of a company that is struggling to make some transitions. I see just lots of vibrant, intelligent, enthusiastic, forward-looking people. Very, very smart people at these conferences. I don't think that gets out enough to the IBM watchers out there. >> I would agree and what we're hearing too, about from the employees themselves talking about the soft skills that are needed to succeed here at IBM but also in all sorts of industries. I mean, our first guest, Inderpal was talking about, you asked him, "What do you need to succeed as a CDO?" Well, what are sort of the traits and ability-- >> He didn't talk about technology. >> And so it wasn't really on his list. >> He didn't talk about numbers or technology at all. He talked about managing relationships, about motivating organizations-- >> Inspiring people exactly. Exactly, so having those kinds of soft skills so necessary for success in the data world but also here at IBM. And then we've also had a lot of other people on talking about IBM as this very inclusive place where you bring your authentic self to work. I write for Harvard business reviews so these are really buzzy words right now. But really, so I don't know if a lot of employees would say that about their employer. >> And when you talk to IBMers, you hear very enthusiastic people, people who love the company, who love working here. With all the diversity, the way the company's been out front in promoting minorities, in promoting women, in all kinds of ways that it really was ahead of the game in the way he treated his workforce. You know, looking at the content of the conference, a couple things really stood out for me. I've been following this area for about five years now working at the MIT CDO event, on theCUBE for a number of years and really five years ago the CDO concept, we were asking questions like, does this job have a future, what does this job look like, what are the skills that are needed, where does it fit in the organization, is this a replacement for the CIO and conflict with the CIO, what's the responsibility, what is the job, we were asking. Really three or four years ago not hearing any of that anymore. There is a lot of unanimity of opinion. This position is important, it's critical. 90 percent of large organizations will have a CDO within the next couple of years, and the role appears to be well defined and is becoming more strategic and the issues of conflict with the CIO are largely being resolved. This is a main stream corporate C level position now, and it's amazing how quickly that's happened. Really over the last four years. >> Well and Andrew Paul said when he first started out, he was a CDO in 2006. He said, when I started out data was considered exhaust, so pollution and now we really know that it is a valuable asset. >> Now it's oil. >> Exactly, now it's gold and oil, and all the other. Yeah, no what about sort of this evolution from big data, big data was the buzz word a few years ago, now it's really all about AI. >> It is, and I've been an AI skeptic for a long time just because I've heard the term AI used for many years and when we didn't have it, when it didn't exist, I am now a believer. I believe that these systems that are being built are really exhibit signs of intelligence and we are going to much quicker in the future as Cloud comes into play, as software becomes more of an assembly process. We just had the discussion of the IBM risk analysis, supply chain risk analysis application. That was essentially assembled. It wasn't really written, it was assembled from components and it's a fantastic idea. We are going to see more of these powerful applications coming about and being built by people who are not extremely technical. So I think, I was amazed to see how the evolution of this program has gone from big data to AI. Today was all about AI and they're not talkin' trash anymore this stuff is really going to work. >> Are we cautious enough, would you think, as I mean, when you're thinking about all the industries here who are now playing in AI, sometimes scampering up the AI ladder a little too quickly because they want the shiny toys, when they really need to actually dig in deep with their data. But do you, as an analyst, where do you put-- >> Well, are we ever cautious enough with new technology? I mean look what Facebook is going through right now. We always go overboard and then we have to pull back and gaze at our navel and figure out, you know, how do we do this right. I'm sure there are a lot of mistakes being made with AI right now. Fortunately, I don't think the mistakes are being made in areas where it's going to meaningfully impact people's quality of life. It's not going to, we're not going to have medical, we had some people from the healthcare field on today. It was very clear that they take AI seriously, the role of AI seriously. I think we'll see a lot of stupid applications of AI, but that's always the way new technology is, right? So you have to experiment, you have to make some mistakes before you figure out what really works and I think we're just going through a natural cycle here. What's exciting is that these applications are the most transformational I've ever seen. >> Wow, and this is from someone who's been covering this industry for many decades. >> It's hard to maintain that wild-eyed enthusiasm after all these years, but it really is, boy, I wish I was 20 years younger, because this is going to be fun to stick around and watch how this develops. How about you? >> We got to raise our kids to grow up and be data scientists. >> I have every intention of doing that. (laughing) How about you? You were more focused on the workforce and the people side of the equation. We heard a lot about that today. >> Exactly, I mean, because frankly, what is all of this stuff doing, but making our work lives more easier, more satisfying, more interesting, less tedious, less boring, less onerous. So I think, frankly, when you put it all in terms of that is our goal is to help people do their jobs better and sometimes people's jobs are saving lives, sometimes people's jobs are, you know, helping people win at Publisher's Clearinghouse Sweepstakes. But that's what it really comes down to, so if it really is helping people do these things, I mean, it is as you said, very exciting. It's an exciting time to be looking at all of this stuff. >> And a time when I think people like you and me will increasingly be able to build these kinds of applications, because the tools are getting that easy to use. >> I hope so. I'm not that good. >> Well, maybe not you. (laughing) >> You can. My kids, definitely. Well, Paul it's been a real pleasure hosting, co-hosting this show with you. >> You too, it's been great. >> I'm Rebecca Knight for Paul Gillan. This has been theCUBE's live coverage of IBM CDO Summit, we will see you next time. (upbeat 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 beautiful Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this segment, we have Susan Wagner, who is the VP Data Artificial Intelligence and Governance at Deutsche Telekom and Madhu Kochar, whose the Vice President Analytics Product Development at IBM. Thank you so much for coming on the show. >> Thank you. >> Happy to be here. Susan you're coming to us from Berlin, tell us a little bit about what you it's a relatively new job title and Paul was marveling before the cameras are rolling. Do you have artificial intelligence in your job title? Tell us a little bit about what you do at Deutsche Telekom. >> So we have a long history, working with data and this is a central role in the headquarter guiding the different data and artificial intelligence activities within Deutsche Telekom. So we have different countries, different business units, we have activities there. We have already use case catalog of 300,000 cases there and from a central point we are looking at it and saying, how are we able really to get the business benefit out of it. So we are looking at the different product, the different cases and looking for some help for the business units, how to scale things. For example, we have a case we implemented in one of our countries, it was about a call center to predict if someone calls the call center, if this is a problem, we would never have(laughing) at Deutsche Telekom but it could happen and then we open a ticket and we are working on it and then we're closing that ticket and but the problem is not solved, so the ticket comes again and the customer will call again and this is very bad for us bad for the customer and we did on AI project, there predicting what kind of tickets will come back in future and this we implemented in a way that we are able to use it not only in one country, but really give it to the next country. So our other business units other countries can take the code and use it in another country. That's one example. >> Wow. >> How would you define artificial intelligence? There's someone who has in your job-- (laughing) >> That's sometimes very difficult question I must admit. I'm normally if I would say from a scientific point, it's really to have a machine that works and feels and did everything like a human. If you look now at the hype, it's more about how we learn, how we do things and not about I would say it's about robotic and stuff like that but it's more how we are learning and the major benefit we are getting now out of artificial intelligence is really that we are able now to really work on data. We have great algorithm and a lot of progress there and we have the chips that develops so far that we are able to do that. It's far away from things like a little kid can do because little kid can just, you show them an apple and then it knows an apple is green. It's were-- >> A little kid can't open a support ticket. (laughing) >> Yeah, but that's very special, so in where we special areas, we are already very, very good in things, but this is an area, for example, if you have an (mumbles) who is able like we did to predict this kind of tickets this agreement is not able at the moment to say this as an apple and this is an orange, so you need another one. So we are far away from really having something like a general intelligence there. >> Madhu do I want to bring you into this conversation. (laughing) And a little bit just in terms of what Susan was saying the sort of the shiny newness of it all. Where do you think we are in terms of thinking about the data getting in the weeds of the data and then also sort of the innovations that we saw, dream about really impacting the bottom line and making the customer experience better and also the employee experience better? >> Yeah, so from IBM perspective, especially coming from data and analytics, very simple message, right? We have what we say your letter to AI. Everybody like Susan and every other company who is part of doing any digital transformation or modernization is talking about Ai. So our message is very simple, in order to get to the letter of AI, the most critical part is that you have access to data, right? You can trust your data, so this way you can start using it in terms of building models, not just predictive models but prescriptive and diagnostics. Everything needs to kind of come together, right? So that is what we are doing in data analytics. Our message is very, very simple. The innovations are coming in from the perspectives of machine learning, deep learning and making and to me that all equates to automation, right? A lot of this stuff data curation, I think you can Susan, tell how long and how manual the data curation aspects can be. Now with machine learning, getting to your latter of AI, You can do this in a matter of hours, right? And you can get to your business users, you can if your CHARM model, If your clients are not happy, your fraud, you have to detect in your bank or retail industry, it just applies to all the industry. So there is tons of innovation happening. We just actually announced a product earlier called IBM Cloud Private for Data. This is our the analytics platform which is ready with data built in governance to handle all your data curation and be building models which you can test it out, have all the DevOps and push it into production. Really, really trying to get clients like Deutsche Telekom to get their journey there faster. Very simple-- >> We've heard from many of our guests today about the importance of governance, of having good quality data before you can start building anything with it. What was that process like? How is the... what is the quality of data like at Deutsche Telekom and what work did it take to get it in that condition. >> So data quality is a major issue everywhere, because as Madhu that this is one of the essential things to really get into learning, if you want to learn, you need the data and we have in the different countries, different kind of majorities and what we are doing at the moment is that we are really doing it case by case because you cannot do everything from the beginning, so you start with one of the cases looking what to do there? How to define the quality? And then if the business asked for the next case, then you can integrate that, so you have the business impact, you have demand from the business and then you can integrate the data quality there and we are doing it really step by step because to bring it to the business from the beginning, it's very, very difficult. >> You mentioned, one of the new products that you announced just today, what are some of the-- (laughing) >> We announced it in may. >> Oh, okay, I'm sorry. >> It's okay still new. >> In terms of the other innovations in the pipeline, what I mean this is such a marvelous and exciting time for technology. What are some of the most exciting developments that you see? >> I think the most exciting, especially if I talk about what I do day out everything revolves around metadata, right? Used to be not a very sticky term, but it is becoming quite sexy all over again, right? And all the work in automatic metadata generation, understanding the lineage where the data is coming from. How easy, we can make it to the business users, then all the machine learning algorithms which we are doing in terms of our prescriptive models and predictive, right? Predictive maintenance is such a huge thing. So there's a lot of work going on there and then also one of the aspects is how do you build once and run anywhere, right? If you really look at the business data, it's behind the firewalls, Is in multicloud. How do you bring solutions which are going to be bringing all the data? Doesn't matter where it resides, right? And so there's a lot of innovation like that which we are working and bringing in onto our platform to make it really simple story make data easy access which you can trust. >> One of the remarkable things about machine learning is that the leading libraries have all been open source, Google, Facebook, eBay, others have open source their libraries. What impact do you think that has had on the speed with which machine learning is developed? >> Just amazing, right. I think that gives us that agility to quickly able to use it, enhance it, give it back to the community. That has been the one of the tenants for, I think that how everybody's out there, moving really really fast. Open source is going to play a very critical role for IBM, and we're seeing that with many of our clients as well. >> What tools are you using? >> We're using different kind of tools that depending on the departments, so the data scientists like to use our patents. (laughing) They are always use it, but we are using a lot like the Jupiter notebook, for example, to have different kind of code in there. We have in one of our countries, the classical things like thus there and the data scientists working with that one or we have the Cloud-R workbench to really bringing things into the business. We have in some business-- >> Data science experience. >> IBM, things integrated, so it it really depends a little bit on the different and that's a little bit the challenge because you really have to see how people working together and how do we really get the data, the models the sharing right. >> And then also the other challenges that all the CDOs face that we've been talking about today, the getting by in the-- >> Yes. >> The facing unrealistic expectations of what data can actually do. I mean, how would you describe how you are able to work with the business side? As a chief working in the chief data office. >> Yeah, so what I really like and what I'm always doing with the business that we are going to the business and doing really a joint approach having a workshop together like the design thinking workshop with the business and the demand has to come from the business. And then you have really the data scientists in there the data engineers best to have the operational people in there and even the controlling not all the time, but that it's really clear that all people are involved from the beginning and then you're really able to bring it into production. >> That's the term of DataOps, right? That's starting to become a big thing. DevOps was all about to agility. Now DataOps bring all these various groups together and yeah I mean that's how you we really move forward. >> So for organizations so that's both of you for organizations that are just beginning to go down the machine learning path that are excited by everything you've been hearing here. What advice would you have for them? They're just getting started. >> I think if you're just getting started to me, the long pole item is all about understanding where your data is, right? The data curation. I have seen over and over again, everybody's enthusiastic. They love the technology, but the... It just doesn't progress fast enough because of that. So invest in tooling where they have automation with machine learning where they can quickly understand it, right? Data virtualization, nobody's going to move data, right? They're sitting in bedrock systems access to that which I call dark data, is important because that is sometimes your golden nugget because that's going to help you make the decisions. So to me that's where I would focus first, everything else around it just becomes a lot easier. >> Great. >> So-- >> Do you have a best practice too? Yeah. >> Yeah. Focus on really bringing quick impact on some of the cases because they're like the management needs success, so you need some kind of quick access and then really working on the basics like Madhu said, you need to have access of the data because if you don't start work on that it will take you every time like half a year. We have some cases where we took finance department half a year to really get all that kind of data and you have to sharpen that for the future, but you need the fast equipments. You need to do both. >> Excellent advice. >> Right, well Susan and Madhu thank you so much for coming on theCUBE, it's been great having you. >> Thank you. >> Thank you. >> I'm Rebecca Knight for Paul Gillin we will have more from theCUBE's live coverage of the IBM CDO just after this. (upbeat 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 Knight, along with my co-host Paul Gillin. We're joined by Dr. Prakriteswar Santikary known as Dr Santi. He is the Vice President and Global Chief Data Officer at eResearch Technology. Thank you so much for coming back on theCUBE. >> Yeah, thank you for inviting me. >> So Dr Santi tell our viewers a little bit about eResearch Technology. You're based in Marlborough... >> Yeah, so we're in Boston, but ERT has been around since 1977 and we are a data and technology company that minimizes risks and uncertainties within clinical trial space and our customers are pharmaceutical companies, biotechnology companies, medical device companies, and where they really trust us in terms of running their clinical trials on our platform. So we have been around over 40 years, so we have seen a thing or two in the space. It's a very complex domain a very highly regulated as you know, because it's dealing with patients lives. So we take huge pride in what we do. >> We know how involved clinical trials can be long, very expensive, how are the new tools, big data impacting the cost? >> Well, that has been an age old problem within the clinical trials, usually a drug takes about eight to 12 years and costs about $2 billion from start to commercialization. So it's a very lengthy, manual and arduous process. So there are lots going on in this clinical trial domain that's tries to shorten the timeline and employing of big data technologies, modern data platform to expedite data processing, data collection from mobile devices and health technologies and all these. Artificial intelligence is playing a big role in terms of disrupting some of these domains, particularly if you see the protocol development down to patient selection, down to study design, then study monitoring. So you need to do all those things and each takes long long long time, so AI with the big data technologies is they're really making a difference. >> In what ways? >> For example, patient selection is one of the huge pin points in any clinical trial, because without patients there are no clinical trials. Particularly when you try to launch a drug, you will have to identify the patients, select the patients and not only select the patients, you have to make sure those patients stay with the clinical trials throughout the duration of the trial. So patient engagement is also a big deal. So with these big data technologies, like now you can see all this mobile health devices that patients are wearing using which you can monitor them. You can remind, send them a reminder, take your drug or you can send a text saying that there will be a clinical visit at that site come at seven o'clock, don't come at nine o'clock. So these kind of encouragement and constant feedback loop is really helping patients stay engaged. That is critical. Then matching patients with the given clinical trials is a very manual and arduous process, so that's where the algorithms is helping. So they are just cranking up real world evidence data for example claims data, prescription data and other type of genomic data and they're matching patients and the clinical trial needs. Instead of just fishing around in a big pond and find out, okay I need three patients. So go and fish around the world to get the three patients. That's why current process is very manual and these AI techniques and behind technologies and big data technologies are really disrupting this industry. >> So are the pharmaceutical companies finding that clinical trials are better today because patients are more engaged and they are getting as you said this constant reminder, take your drug, stay with us. Do you think that they are, in fact, giving them better insights into the efficacy of the drug? >> Yes because you will see their compliance rate is increasing, so because remember when they have to fill out all these diaries, like morning diaries evening diaries, when they are taking which medicine, when they are not taking. It used to be all manual paper driven, so they would forget and particularly think about a terminally ill patient, each day is so critical for them. So they don't have patience, nor do they have time to really maintain a manual diary. >> Nor do their caregivers have the time. Right. >> So this kind of automation is really helping and that is also encouraging them as well, that yeah somebody is really caring about me. We are not just a number, patient is not a number that somebody is really relating to them. So patient engagement, we have a product that specifically focuses around patient engagement. So we do all these phase one through phase four trials, one, two, three, four and then forced marketing, obviously, but through the entire process, we also do patient engagement, so that we help our customers like pharmaceutical companies and biotechnology companies so that they can run their trials with confidence. >> How about analyzing the data that you collect from the trials, are you using new techniques to gain insights more quickly? >> Yes, we are. We just recently launched a modern data platform, a data lake while we are consolidating all the data and anonymizing it and then really applying AI techniques on top of it and also it is giving us real time information for study monitoring. Like which side is not complying, with patients or not complying, so if the data quality is a big deal in clinical trials, because if the quality is good, then FDA approval, there is a chance that FDA may approve, but if the data quality is bad, forget about it, so that's why I think the quality of the data and monitoring of that trial real time to minimize any risks before they become risks. So you have to be preempted, so that's why this predictive algorithms are really helping, so that you can monitor the site, you can monitor individual patient through mHealth devices and all these and really pinpoint that, hey, your clinical trials are not going to end on time nor on budget. Because here you see the actual situation here, so, do something instead of waiting 10 years to find that out. So huge cost saving and efficiency gain. >> I want to ask about data in healthcare in general because one of the big tensions that we've talked about today is sort of what the data is saying versus what people's gut is saying and then in industry, it's the business person's gut but in healthcare it is the doctor, the caregivers' gut. So how are you, how have you seen data or how is data perceived and is that changing in terms of what the data shows that the physician about the patient's condition and what the patient needs right then and there, versus what the doctors gut is telling him that the patient needs? >> Yeah and that's where that augmentation and complementary nature, right? So AI and doctors, they're like complementing each other, So predictive algorithm is not replacing doctors the expertise, so you still need that. What AI and predictive algorithm is playing a big role is in expediting that process, so instead of sifting through manual document so sifting through this much amount of document, they would only need to do this much of document. So then that way it's minimizing that time horizon. It's all about efficiency again, so AI is not going to be replacing doctors anytime soon. We still need doctors, because remember a site is run by a primary investigator and primary investigator owns that site. That's the doctor, that's not a machine. That's not an AI algorithm, so his or her approval is the final approval. But it's all about efficiency cost cutting and bringing the drugs to the market faster. If you can cut down these 12 years by half, think about that not only are you saving lots of money, you are also helping patients because those drugs are going to get to the market six year earlier. So you're saving lots of patients in that regard as well. >> One thing that technologies like Watson can do is sort through, read millions of documents lab reports and medical journals and derive insights from them, is that helping in the process of perhaps avoiding some clinical trials or anticipating outputs earlier? >> Yes, because if you see Watson run a clinical study with Cleveland Clinic recently or Mayo Clinic I think or maybe both. While they reduce the patient recruitment time by 80%, 80%. >> How so? >> Because they sweep through all those documents, EMR results, claims data, all this data they combined-- >> Filter down-- >> Filter down and then say, for this clinical trial, here are the 10 patients you need. It's not going to recommend to who those 10 patients are but it will just tell you that, the goal is the average locations, this that, so that you just focus on getting those 10 patients quickly instead of wasting nine months to research on those 10 patients and that's a huge, huge deal. >> And how can you trust that, that is right? I mean I think that's another question that we have here, it's a big challenge. >> It is a challenge because AI is all about math and algorithm, right? So when you, so it's like, input black box, output. So that output may be more accurate than what you perceive it to be. >> But that black box is what is tripping me up here. >> So what is happening is sometimes, oftentimes, if it is a deep learning technique, so that kind of lower level AI techniques. It's very hard to interpret that results, so people will keep coming back to you and say, how did you arrive at that results? And that's where most of the, there are techniques like Machine Learning techniques that are easily interpretable. So you can convince FDA folks or other folks that here is how we've got to it, but there are a deep learning techniques that Watson uses for example, people will come and, how did you, how did you arrive at that? And it's very hard because those neural networks are multi-layers and all about math, but as I said, output may be way more accurate, but it's very hard to decipher. >> Right, exactly. >> That's the challenge. So that's a trust issue in that regard. >> Right, well, Dr. Santi, thank you so much for coming on theCUBE. It was great talking to you. >> Okay, thank you very much. Thanks for inviting. >> I'm Rebecca Knight for Paul Gillin we will have more from the IBM CDO Summit in just a little bit. (upbeat music)

>> Announcer: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officers Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, everybody. This is theCUBE, the leader in live tech coverage, and we're here covering exclusive coverage of IBM's Chief Data Officer Strategy Summit. This is the summit, as I said, they book in at each coast, San Francisco and Boston. Intimate, a lot of senior practitioners, chief data officers, data folks, people who love data. Caitlyn Halferty is back. She's the Client Engagement Executive and the Chief Data Officer office at IBM. Great. And, Allen Crane, Vice President at USAA. >> Thank you. >> Good to see you. Thanks for coming on. All right. >> Thanks for having us. >> You're welcome. Well, good day today, as I said, a very intimate crowd. You're here as a sort of defacto CDO, learning, sharing, connecting with peers. Set up your role, Allen. Tell us about that. >> At USA, we've got a distributed data and analytics organization where we have centralized functions in our hub, and then each of the lines of business have their own data offices. I happen to have responsibility for all the different ways that our members interact with us, so about 100 million phone calls a year, about a couple billion internet and digital sessions a year, most of that is on mobile, and always lookin' at the ways that we can give back time to our membership, as well as our customer service reps, who we call our member service reps, so that they can serve our members better. The faster and more predictive we can be with being able to understand our members better and prompt our MSRs with the right information to serve them, then the more they can get on to the actual value of that conversation. >> A lot of data. So, one of the things that Inderpal talked about the very first time I met him, in Boston, he talked about the Five Pillars, and the first one was you have to understand as a CDO, how your organization gets value out of data. You said that could be direct monetization or, I guess, increased revenue, cut costs. That's value. >> Right. >> That's right. >> That's the starting point. >> Right. >> So, how did you start? >> Well, actually, it was the internal monetization. So, first off, I want to say USA never sells any of our member data, so we don't think of monetization in that framework, but we do think of it terms of how do we give something that's even more precious than money back to our company and to our members and the MSRs? And, that is really that gift of time. By removing friction from the system, we've been able to reduce calls per member, through digitization activities, and reduced transfers and reduced misdirects by over 10% every year. We're doing work with AI and machine learning to be able to better anticipate what the member is calling about, so that we can get them to the right place at the right time to the right set member service representatives. And, so all these things have resulted in, not just time savings but, obviously, that translates directly to bottom line savings, but at the end of the day, it's about increasing that member service level, increasing your responsiveness, increasing the speed that you're answering the phone, and ultimately increasing that member satisfaction. >> Yeah, customer satisfaction, lowers churn rates, that's a form of monetization, >> Absolutely. >> so it's hard dollars to the CFO, right? >> Absolutely, yeah. >> All right, let's talk about the role of the CDO. This is something that we touched on earlier. >> Yes. >> We're bringing it home here. >> Yes. >> Last segment. Where are we at with the role of the CDO? It was sort of isolated for years in regulated industries, >> Correct. >> permeated to mainstream organizations. >> Correct. >> Many of those mainstream organizations can move faster, 'cause their not regulated, so have we sort of reached parody between the regulated and the unregulated, and what do you discern there in terms of patterns and states of innovation? >> Sure. I think when we kicked off these summits in 2014, many of our CDOs came from CIO type organizations, defensive posture, you know, king of the data warehouse that we joke about, and now annuls reports of that time were saying maybe 20% of large organizations were investing in the CDO or similar individual responsible for enterprise data, and now we see analysts reports coming out to say upwards of 85, even 90%, of organizations are investing in someone responsible for that role of the CDO type. In my opening remarks this morning, I polled the room to say who's here for the first time. It was interesting, 69, 70% of attendees were joining us for the first time, and I went back, okay, who's been here last year, year before, and I said who was here from the beginning, 2014 with us, and Allen is one of the individuals who's been with us. And, as much as the topics have changed and the role has grown and the purview and scope of responsibilities, some topics have remained, our attendees tell us, they're still important, top-of-mind, and data monetization is one of those. So, we always have a panel on data monetization, and we've had some good discussions recently, that the idea of it's just the external resell, or something to do with selling data externally is one view, but really driving that internal value, and the ways you drive out those efficiencies is another perspective on it. So, fortunate to have Allen here. >> Well, we've been able to, for that very reason, we've been able to grow our team from about six or seven people five years ago to well over a hundred people, that's focused on how we inefficiency out of the system. That mere 10%, when your call-per-member reduction, when you're taking 30 million calls in the bank, you know, that's real dollars, three million calls out of the system that you can monetize like that. So, it's real value that the company sees in us, and I think that, in a sense, is really how you want to be growing in a data organization, because people see value in you, are willing to give you more, and then you start getting into those interesting conversations, if I gave you more people, could you get me more results? >> Let's talk about digital transformation and how it relates to all this. Presumably, you've got a top down initiative, the CEO says, he or she says, okay, this is important. We got to do it. Boom, there's the North Star. Let's go. What's the right regime that you're seeing? Obviously, you've got to have the executive buy-in, you've got the Chief Data Officer, you have the Chief Digital Officer, the Chief Operating Officer, the CFO's always going to be there, making sure things are on track. How are you seeing that whole thing shake out, at least in your organization? >> Well, one thing that we've been seeing is digital digitization or the digital transformation is not about just going only digital. It's how does all this work together. It can't just be an additive function, where you're still taking just as many calls and so forth, but it's got to be something that that experience online has got to do something that's transformative in your organization. So, we really look at the member all the way through that whole ecosystem, and not just through the digital lens. And, that's really where teams like ours have really been able to stitch together the member experience across all their channels that they're interacting with us, whether that's the marketing channels or the digital channels or the call channel, so that we can better understand that experience. But, it's certainly a complementary one. It can't just be an additive one. >> I wonder if we could talk about complacency, in terms of digital transformation. I talk to a lot of companies and there's discussion about digital, but you talk to a lot of people who say, well, we're doing fine. Maybe not in our industry. Insurance is one that hasn't been highly disruptive, financial services, things like aerospace. I'll be retired by the time this all, I mean, that's true, right? And, probably accurate. So, are you seeing a sense of complacency or are you seeing a sense of urgency, or a mix or both? What are you seeing, Caitlyn? >> Well, it's interesting, and people may not be aware, but I'm constantly polling our attendees to ask what are top-of-mind topics, what are you struggling with, where are you seeing successes, and digital was one that came up for this particular session, which is why tomorrow's keynote, we have our Chief Digital Officer giving the morning keynote, to show how our data office and digital office are partnering to drive transformation internally. So, at least for our perspective, in the internal side of it, we have a priority initiative, a cognitive sales advisor, and it's essentially intended to bring in disparate part of customer data, obtained through many different channels, all the ways that they engage with us, online and other, and then, deliver it through sales advisor app that empowers our digital sellers to better meet their revenue targets and impact, and develop more of a quality client relationship and improve that customer experience. So, internally, at least, it's been interesting to see one of our strongest partnerships, in terms of business unit, has been our data and digital office. They say, look, the quality of the data is at the core, you then enable our digital sellers, and our clients benefit, for a better client experience. >> Well, about a year ago, we absolutely changed the organization to align the data office with the digital office, so that reports to our executive counsel level, so their peers, that reporting to the same organization, to ensure that those strategies are connected. >> Yeah, so as Caitlyn was saying, this Chief Data Officer kind of emerged from a defensive posture of compliance, governance, data quality. The Chief Digital Officer, kind of new, oftentimes associated with marketing, more of an external, perhaps, facing role, not always. And then, the CIO, we'll say, well, wait a minute, data is the CIO's job, but, of course, the CIO, she's too busy trying to keep the lights on and make everything work. So, where does the technology organization fit? >> Well, all that's together, so when we brought all those things together at the organizational level, digital, data, and technology were all together, and even design. So, you guys are all peers, reporting into the executive committee, essentially, is that right? Yes, our data, technology, and design, and digital office are all peers reporting to the same executive level. And then, one of the other pillars that Inderpal talks about is the relationship with the line of business. So, how is that connective tissue created? Well, being on the side that is responsible for how all of our members interact, my organization touches every product, every line of business, every channel that our members are interacting with, so our data is actually shared across the organization, so right now, really my focus is to make sure that that data is as accessible as it can be across our enterprise partners, it's as democratized as it can be, it's as high as quality. And then, things that we're doing around machine learning and AI, can be enabled and plugged into from all those different lines of business. >> What does success look like in your organization? How do you know you're doing well? I mean, obviously, dropping money to the bottom line, but how are you guys measuring yourselves and setting objectives? What's your North Star? >> I think success, for me, is when you're doing a good job, to the point that people say that question, could you do more if I gave you more? That, to me, is the ultimate validation. It's how we grew as an organization. You know, we don't have to play that justification game When people are already coming to the table saying, You're doing great work. How can you do more great work? >> So, what's next for these summits? Are you doing Boston again in the fall? Is that right? Are you planning >> We are, we are, >> on doing that? >> and you know, fall of last year, we released the blueprint, and the intent was to say, hey, here's the reflection of our 18 months, internal journey, as well as all our client interactions and their feedback, and we said, we're coming back in the spring and we're showing you the detail of how we really built out these internal platforms. So, we released our hybrid on-prem Cloud showcase today, which was great, and to the level of specificity that shows that the product solutions, what we're using, the Flash Storage, some of the AI components of machine learning models. >> The cognitive systems component? >> Exactly. And then, our vision, to your question to the fall, is coming back with the public Cloud showcases. So, we're already internally doing work on our public Cloud, in particular respect to our backup, some of our very sensitive client data, as well as some initial deep learning models, so those are the three pieces we're doing in public Cloud internally, and just as we made the commitment to come back and unveil and show those detail, we want to come back in the fall and show a variety of public Cloud showcases where we're doing this work. And then, hopefully, we'll continue to partner and say, hey, here's how we're doing it. We'd love to see how you're doing it. Let's share some best practices, accelerate, build these capabilities. And, I'll say to your business benefit question, what we've found is once we've built that platform, we call it, internally, a one IBM architecture, out our platform, we can then drive critical initiatives for the enterprise. So, for us, GVPR, you know, we own delivery of GVPR readiness across the IBM corporation, working with senior executives in all of our lines of business, to make sure we get there. But, now we've got the responsibility to drive out initiatives like that cross business unit, to your question on the partnerships. >> The evolution of this event seems to be, well, it's got a lot of evangelism early on, and now it's really practical, sort of sharing, like you say, the blueprint, how to apply it, a lot of people asking questions, you know, there's different levels of maturity. Now, you guys back tomorrow? You got to panel, you guys are doing a panel on data monetization? >> We're doing a panel on data monetization tomorrow. >> Okay, and then, you've got Bob Lord and Inderpal talking about that, so perfect juxtaposition and teamwork of those two major roles. >> And, this is the first time we've really showcased the data/digital partnership and connection, so I'm excited, want to appeal to the developer viewpoint of this. So, I think it'll be a great conversation about data at the core, driving digital transformation. And then, as you said, our data monetization panel, both external efforts, as well as a lot of the internal value that we're all driving, so I think that'll be a great session tomorrow. >> Well, and it's important, 'cause there's a lot of confusing, and still is a lot of confusion about those roles, and you made the point early today, is look, there's a big organizational issue you have to deal with, particularly around data silos, MyData. I presume you guys are attacking that challenge? >> Absolutely. >> Still, it's still a-- >> It's an ongoing-- >> Oh, absolutely. >> I think we're getting a lot better at it, but you've got to lean in, because if it's not internal, it's some of the external challenges around. Now we're picking Cloud vendors and so forth. Ten years ago, we had our own silos and our own warehouses, if we had a warehouse, and then, we were kind of moving into our own silos in our own databases, and then as we democratized that, we solved the one problem, but now our data's so big and compute needs are so large that we have no choice but to get more external into Cloud. So, you have to lean in, because everything is changing at such a rapid rate. >> And, it requires leadership. >> Yep. >> Absolutely. >> The whole digital data really requires excellent leadership, vision. IBM's catalyzing a lot of that conversation, so congratulations on getting this going. Last thoughts. >> Oh, I would just say, we were joking that 2014, the first couple of summits, small group, maybe 20-30 participants figuring out how to best organize from a structural perspective, you set up the office, what sort of outcomes, metrics, are we going to measure against, and those things, I think, will continue to be topics of discussion, but now we see we've got about 500 data leaders that are tracking our journey and that are involved and engaged with us. We've done a lot in North America, we're starting to do more outside the geographies, as well, which is great to see. So, I just have to say I think it's interesting to see the topics that continue to be of interest, the governance, the data monetization, and then, the new areas around AI, machine learning, data science, >> data science >> the empowering developers, the DevOps delivery, how we're going to deliver that type of training. So, it's been really exciting to see the community grow and all the best practices leveraged, and look forward to continuing to do more of that this year as well. >> Well, you obviously get a lot of value out of these events. You were here at the first one, you're here today. So, 2018. Your thoughts? >> I think the first one, we were all trying to figure out who we are, what's our role, and it varied from I'm a individual contributor, data evangelist in the organization to I'm king of the warehouse thing. >> Right. >> And, largely, from that defensive standpoint. I think, today, you see a lot more people that are leaning in, leading data science teams, leading the future of where the organizations are going to be going. This is really where the center of a lot of organizations are starting to pivot and look, and see, where is the future, and how does data become the leading edge of where the organization is going, so it's pretty cool to be a part of a community like this that's evolving that way, but then also being able to have that at a local level within your own organization. >> Well, another big take-away for me is the USAA example shows that this can pay for itself when you grow your own organization from a handful of people to a hundred plus individuals, driving value, so it makes it easier to justify, when you can demonstrate a business case. Well, guys, thanks very much for helping me wrap here. >> Absolutely. >> I appreciate you having us here. >> Thank you. >> It's been a great event. Always a pleasure, hopefully, we'll see you in the fall. >> Sounds good. Thank you so much. >> All right, thanks, everybody, for watching. We're out. This is theCUBE from IBM CDO Summit. Check out theCUBE.net for all of the videos, siliconangle.com for all the news summaries of this event, and wikibon.com for all the research. We'll see you next time. (techy music)

(techno music) >> Announcer: Live, from downtown San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody, this is theCUBE, the leader in live tech coverage. We're here at the Parc 55 in San Francisco covering the IBM CDO Strategy Summit. I'm here with Joel Horwitz who's the Vice President of Digital Partnerships & Offerings at IBM. Good to see you again Joel. >> Thanks, great to be here, thanks for having me. >> So I was just, you're very welcome- It was just, let's see, was it last month, at Think? >> Yeah, it's hard to keep track, right. >> And we were talking about your new role- >> It's been a busy year. >> the importance of partnerships. One of the things I want to, well let's talk about your role, but I really want to get into, it's innovation. And we talked about this at Think, because it's so critical, in my opinion anyway, that you can attract partnerships, innovation partnerships, startups, established companies, et cetera. >> Joel: Yeah. >> To really help drive that innovation, it takes a team of people, IBM can't do it on its own. >> Yeah, I mean look, IBM is the leader in innovation, as we all know. We're the market leader for patents, that we put out each year, and how you get that technology in the hands of the real innovators, the developers, the longtail ISVs, our partners out there, that's the challenging part at times, and so what we've been up to is really looking at how we make it easier for partners to partner with IBM. How we make it easier for developers to work with IBM. So we have a number of areas that we've been adding, so for example, we've added a whole IBM Code portal, so if you go to developer.ibm.com/code you can actually see hundreds of code patterns that we've created to help really any client, any partner, get started using IBM's technology, and to innovate. >> Yeah, and that's critical, I mean you're right, because to me innovation is a combination of invention, which is what you guys do really, and then it's adoption, which is what your customers are all about. You come from the data science world. We're here at the Chief Data Officer Summit, what's the intersection between data science and CDOs? What are you seeing there? >> Yeah, so when I was here last, it was about two years ago in 2015, actually, maybe three years ago, man, time flies when you're having fun. >> Dave: Yeah, the Spark Summit- >> Yeah Spark Technology Center and the Spark Summit, and we were here, I was here at the Chief Data Officer Summit. And it was great, and at that time, I think a lot of the conversation was really not that different than what I'm seeing today. Which is, how do you manage all of your data assets? I think a big part of doing good data science, which is my kind of background, is really having a good understanding of what your data governance is, what your data catalog is, so, you know we introduced the Watson Studio at Think, and actually, what's nice about that, is it brings a lot of this together. So if you look in the market, in the data market, today, you know we used to segment it by a few things, like data gravity, data movement, data science, and data governance. And those are kind of the four themes that I continue to see. And so outside of IBM, I would contend that those are relatively separate kind of tools that are disconnected, in fact Dinesh Nirmal, who's our engineer on the analytic side, Head of Development there, he wrote a great blog just recently, about how you can have some great machine learning, you have some great data, but if you can't operationalize that, then really you can't put it to use. And so it's funny to me because we've been focused on this challenge, and IBM is making the right steps, in my, I'm obviously biased, but we're making some great strides toward unifying the, this tool chain. Which is data management, to data science, to operationalizing, you know, machine learning. So that's what we're starting to see with Watson Studio. >> Well, I always push Dinesh on this and like okay, you've got a collection of tools, but are you bringing those together? And he flat-out says no, we developed this, a lot of this from scratch. Yes, we bring in the best of the knowledge that we have there, but we're not trying to just cobble together a bunch of disparate tools with a UI layer. >> Right, right. >> It's really a fundamental foundation that you're trying to build. >> Well, what's really interesting about that, that piece, is that yeah, I think a lot of folks have cobbled together a UI layer, so we formed a partnership, coming back to the partnership view, with a company called Lightbend, who's based here in San Francisco, as well as in Europe, and the reason why we did that, wasn't just because of the fact that Reactive development, if you're not familiar with Reactive, it's essentially Scala, Akka, Play, this whole framework, that basically allows developers to write once, and it kind of scales up with demand. In fact, Verizon actually used our platform with Lightbend to launch the iPhone 10. And they show dramatic improvements. Now what's exciting about Lightbend, is the fact that application developers are developing with Reactive, but if you turn around, you'll also now be able to operationalize models with Reactive as well. Because it's basically a single platform to move between these two worlds. So what we've continued to see is data science kind of separate from the application world. Really kind of, AI and cloud as different universes. The reality is that for any enterprise, or any company, to really innovate, you have to find a way to bring those two worlds together, to get the most use out of it. >> Fourier always says "Data is the new development kit". He said this I think five or six years ago, and it's barely becoming true. You guys have tried to make an attempt, and have done a pretty good job, of trying to bring those worlds together in a single platform, what do you call it? The Watson Data Platform? >> Yeah, Watson Data Platform, now Watson Studio, and I think the other, so one side of it is, us trying to, not really trying, but us actually bringing together these disparate systems. I mean we are kind of a systems company, we're IT. But not only that, but bringing our trained algorithms, and our trained models to the developers. So for example, we also did a partnership with Unity, at the end of last year, that's now just reaching some pretty good growth, in terms of bringing the Watson SDK to game developers on the Unity platform. So again, it's this idea of bringing the game developer, the application developer, in closer contact with these trained models, and these trained algorithms. And that's where you're seeing incredible things happen. So for example, Star Trek Bridge Crew, which I don't know how many Trekkies we have here at the CDO Summit. >> A few over here probably. >> Yeah, a couple? They're using our SDK in Unity, to basically allow a gamer to use voice commands through the headset, through a VR headset, to talk to other players in the virtual game. So we're going to see more, I can't really disclose too much what we're doing there, but there's some cool stuff coming out of that partnership. >> Real immersive experience driving a lot of data. Now you're part of the Digital Business Group. I like the term digital business, because we talk about it all the time. Digital business, what's the difference between a digital business and a business? What's the, how they use data. >> Joel: Yeah. >> You're a data person, what does that mean? That you're part of the Digital Business Group? Is that an internal facing thing? An external facing thing? Both? >> It's really both. So our Chief Digital Officer, Bob Lord, he has a presentation that he'll give, where he starts out, and he goes, when I tell people I'm the Chief Digital Officer they usually think I just manage the website. You know, if I tell people I'm a Chief Data Officer, it means I manage our data, in governance over here. The reality is that I think these Chief Digital Officer, Chief Data Officer, they're really responsible for business transformation. And so, if you actually look at what we're doing, I think on both sides is we're using data, we're using marketing technology, martech, like Optimizely, like Segment, like some of these great partners of ours, to really look at how we can quickly A/B test, get user feedback, to look at how we actually test different offerings and market. And so really what we're doing is we're setting up a testing platform, to bring not only our traditional offers to market, like DB2, Mainframe, et cetera, but also bring new offers to market, like blockchain, and quantum, and others, and actually figure out how we get better product-market fit. What actually, one thing, one story that comes to mind, is if you've seen the movie Hidden Figures- >> Oh yeah. >> There's this scene where Kevin Costner, I know this is going to look not great for IBM, but I'm going to say it anyways, which is Kevin Costner has like a sledgehammer, and he's like trying to break down the wall to get the mainframe in the room. That's what it feels like sometimes, 'cause we create the best technology, but we forget sometimes about the last mile. You know like, we got to break down the wall. >> Where am I going to put it? >> You know, to get it in the room! So, honestly I think that's a lot of what we're doing. We're bridging that last mile, between these different audiences. So between developers, between ISVs, between commercial buyers. Like how do we actually make this technology, not just accessible to large enterprise, which are our main clients, but also to the other ecosystems, and other audiences out there. >> Well so that's interesting Joel, because as a potential partner of IBM, they want, obviously your go-to-market, your massive company, and great distribution channel. But at the same time, you want more than that. You know you want to have a closer, IBM always focuses on partnerships that have intrinsic value. So you talked about offerings, you talked about quantum, blockchain, off-camera talking about cloud containers. >> Joel: Yeah. >> I'd say cloud and containers may be a little closer than those others, but those others are going to take a lot of market development. So what are the offerings that you guys are bringing? How do they get into the hands of your partners? >> I mean, the commonality with all of these, all the emerging offerings, if you ask me, is the distributed nature of the offering. So if you look at blockchain, it's a distributed ledger. It's a distributed transaction chain that's secure. If you look at data, really and we can hark back to say, Hadoop, right before object storage, it's distributed storage, so it's not just storing on your hard drive locally, it's storing on a distributed network of servers that are all over the world and data centers. If you look at cloud, and containers, what you're really doing is not running your application on an individual server that can go down. You're using containers because you want to distribute that application over a large network of servers, so that if one server goes down, you're not going to be hosed. And so I think the fundamental shift that you're seeing is this distributed nature, which in essence is cloud. So I think cloud is just kind of a synonym, in my opinion, for distributed nature of our business. >> That's interesting and that brings up, you're right, cloud and Big Data/Hadoop, we don't talk about Hadoop much anymore, but it kind of got it all started, with that notion of leave the data where it is. And it's the same thing with cloud. You can't just stuff your business into the public cloud. You got to bring the cloud to your data. >> Joel: That's right. >> But that brings up a whole new set of challenges, which obviously, you're in a position just to help solve. Performance, latency, physics come into play. >> Physics is a rough one. It's kind of hard to avoid that one. >> I hear your best people are working on it though. Some other partnerships that you want to sort of, elucidate. >> Yeah, no, I mean we have some really great, so I think the key kind of partnership, I would say area, that I would allude to is, one of the things, and you kind of referenced this, is a lot of our partners, big or small, want to work with our top clients. So they want to work with our top banking clients. They want, 'cause these are, if you look at for example, MaRisk and what we're doing with them around blockchain, and frankly, talk about innovation, they're innovating containers for real, not virtual containers- >> And that's a joint venture right? >> Yeah, it is, and so it's exciting because, what we're bringing to market is, I also lead our startup programs, called the Global Entrepreneurship Program, and so what I'm focused on doing, and you'll probably see more to come this quarter, is how do we actually bridge that end-to-end? How do you, if you're startup or a small business, ultimately reach that kind of global business partner level? And so kind of bridging that, that end-to-end. So we're starting to bring out a number of different incentives for partners, like co-marketing, so I'll help startups when they're early, figure out product-market fit. We'll give you free credits to use our innovative technology, and we'll also bring you into a number of clients, to basically help you not burn all of your cash on creating your own marketing channel. God knows I did that when I was at a start-up. So I think we're doing a lot to kind of bridge that end-to-end, and help any partner kind of come in, and then grow with IBM. I think that's where we're headed. >> I think that's a critical part of your job. Because I mean, obviously IBM is known for its Global 2000, big enterprise presence, but startups, again, fuel that innovation fire. So being able to attract them, which you're proving you can, providing whatever it is, access, early access to cloud services, or like you say, these other offerings that you're producing, in addition to that go-to-market, 'cause it's funny, we always talk about how efficient, capital efficient, software is, but then you have these companies raising hundreds of millions of dollars, why? Because they got to do promotion, marketing, sales, you know, go-to-market. >> Yeah, it's really expensive. I mean, you look at most startups, like their biggest ticket item is usually marketing and sales. And building channels, and so yeah, if you're, you know we're talking to a number of partners who want to work with us because of the fact that, it's not just like, the direct kind of channel, it's also, as you kind of mentioned, there's other challenges that you have to overcome when you're working with a larger company. for example, security is a big one, GDPR compliance now, is a big one, and just making sure that things don't fall over, is a big one. And so a lot of partners work with us because ultimately, a number of the decision makers in these larger enterprises are going, well, I trust IBM, and if IBM says you're good, then I believe you. And so that's where we're kind of starting to pull partners in, and pull an ecosystem towards us. Because of the fact that we can take them through that level of certification. So we have a number of free online courses. So if you go to partners, excuse me, ibm.com/partners/learn there's a number of blockchain courses that you can learn today, and will actually give you a digital certificate, that's actually certified on our own blockchain, which we're actually a first of a kind to do that, which I think is pretty slick, and it's accredited at some of the universities. So I think that's where people are looking to IBM, and other leaders in this industry, is to help them become experts in their, in this technology, and especially in this emerging technology. >> I love that blockchain actually, because it's such a growing, and interesting, and innovative field. But it needs players like IBM, that can bring credibility, enterprise-grade, whether it's security, or just, as I say, credibility. 'Cause you know, this is, so much of negative connotations associated with blockchain and crypto, but companies like IBM coming to the table, enterprise companies, and building that ecosystem out is in my view, crucial. >> Yeah, no, it takes a village. I mean, there's a lot of folks, I mean that's a big reason why I came to IBM, three, four years ago, was because when I was in start-up land, I used to work for H20, I worked for Alpine Data Labs, Datameer, back in the Hadoop days, and what I realized was that, it's an opportunity cost. So you can't really drive true global innovation, transformation, in some of these bigger companies because there's only so much that you can really kind of bite off. And so you know at IBM it's been a really rewarding experience because we have done things like for example, we partnered with Girls Who Code, Treehouse, Udacity. So there's a number of early educators that we've partnered with, to bring code to, to bring technology to, that frankly, would never have access to some of this stuff. Some of this technology, if we didn't form these alliances, and if we didn't join these partnerships. So I'm very excited about the future of IBM, and I'm very excited about the future of what our partners are doing with IBM, because, geez, you know the cloud, and everything that we're doing to make this accessible, is bar none, I mean, it's great. >> I can tell you're excited. You know, spring in your step. Always a lot of energy Joel, really appreciate you coming onto theCUBE. >> Joel: My pleasure. >> Great to see you again. >> Yeah, thanks Dave. >> You're welcome. Alright keep it right there, everybody. We'll be back. We're at the IBM CDO Strategy Summit in San Francisco. You're watching theCUBE. (techno music) (touch-tone phone beeps)

>> Announcer: Live from downtown San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody. We're at the Parc 55 in Union Square and this is the Cube, the leader in live tech coverage and we're covering exclusive coverage of the IBM CDO strategy summit. IBM has these things, they book in on both coasts, one in San Francisco one in Boston, spring and fall. Great event, intimate event. 130, 150 chief data officers, learning, transferring knowledge, sharing ideas. Cayn Woodruff is here as the principle data scientist at IBM and she's joined by Ritesh Ororo, who is the director of digital analytics at HCL Technologies. Folks welcome to the Cube, thanks for coming on. >> Thank you >> Thanks for having us. >> You're welcome. So we're going to talk about data management, data engineering, we're going to talk about digital, as I said Ritesh because digital is in your title. It's a hot topic today. But Caryn let's start off with you. Principle Data Scientist, so you're the one that is in short supply. So a lot of demand, you're getting pulled in a lot of different directions. But talk about your role and how you manage all those demands on your time. >> Well, you know a lot of, a lot of our work is driven by business needs, so it's really understanding what is critical to the business, what's going to support our businesses strategy and you know, picking the projects that we work on based on those items. So it's you really do have to cultivate the things that you spend your time on and make sure you're spending your time on the things that matter and as Ritesh and I were talking about earlier, you know, a lot of that means building good relationships with the people who manage the systems and the people who manage the data so that you can get access to what you need to get the critical insights that the business needs, >> So Ritesh, data management I mean this means a lot of things to a lot of people. It's evolved over the years. Help us frame what data management is in this day and age. >> Sure, so there are two aspects of data in my opinion. One is the data management, another the data engineering, right? And over the period as the data has grown significantly. Whether it's unstructured data, whether it's structured data, or the transactional data. We need to have some kind of governance in the policies to secure data to make data as an asset for a company so the business can rely on your data. What you are delivering to them. Now, the another part comes is the data engineering. Data engineering is more about an IT function, which is data acquisition, data preparation and delivering the data to the end-user, right? It can be business, it can be third-party but it all comes under the governance, under the policies, which are designed to secure the data, how the data should be accessed to different parts of the company or the external parties. >> And how those two worlds come together? The business piece and the IT piece, is that where you come in? >> That is where data science definitely comes into the picture. So if you go online, you can find Venn diagrams that describe data science as a combination of computer science math and statistics and business acumen. And so where it comes in the middle is data science. So it's really being able to put those things together. But, you know, what's what's so critical is you know, Interpol, actually, shared at the beginning here and I think a few years ago here, talked about the five pillars to building a data strategy. And, you know, one of those things is use cases, like getting out, picking a need, solving it and then going from there and along the way you realize what systems are critical, what data you need, who the business users are. You know, what would it take to scale that? So these, like, Proof-point projects that, you know, eventually turn into these bigger things, and for them to turn into bigger things you've got to have that partnership. You've got to know where your trusted data is, you've got to know that, how it got there, who can touch it, how frequently it is updated. Just being able to really understand that and work with partners that manage the infrastructure so that you can leverage it and make it available to other people and transparent. >> I remember when I first interviewed Hilary Mason way back when and I was asking her about that Venn diagram and she threw in another one, which was data hacking. >> Caryn: Uh-huh, yeah. >> Well, talk about that. You've got to be curious about data. You need to, you know, take a bath in data. >> (laughs) Yes, yes. I mean yeah, you really.. Sometimes you have to be a detective and you have to really want to know more. And, I mean, understanding the data is like the majority of the battle. >> So Ritesh, we were talking off-camera about it's not how titles change, things evolve, data, digital. They're kind of interchangeable these days. I mean we always say the difference between a business and a digital business is how they have used data. And so digital being part of your role, everybody's trying to get digital transformation, right? As an SI, you guys are at the heart of it. Certainly, IBM as well. What kinds of questions are our clients asking you about digital? >> So I ultimately see data, whatever we drive from data, it is used by the business side. So we are trying to always solve a business problem, which is to optimize the issues the company is facing, or try to generate more revenues, right? Now, the digital as well as the data has been married together, right? Earlier there are, you can say we are trying to analyze the data to get more insights, what is happening in that company. And then we came up with a predictive modeling that based on the data that will statically collect, how can we predict different scenarios, right? Now digital, we, over the period of the last 10 20 years, as the data has grown, there are different sources of data has come in picture, we are talking about social media and so on, right? And nobody is looking for just reports out of the Excel, right? It is more about how you are presenting the data to the senior management, to the entire world and how easily they can understand it. That's where the digital from the data digitization, as well as the application digitization comes in picture. So the tools are developed over the period to have a better visualization, better understanding. How can we integrate annotation within the data? So these are all different aspects of digitization on the data and we try to integrate the digital concepts within our data and analytics, right? So I used to be more, I mean, I grew up as a data engineer, analytics engineer but now I'm looking more beyond just the data or the data preparation. It's more about presenting the data to the end-user and the business. How it is easy for them to understand it. >> Okay I got to ask you, so you guys are data wonks. I am too, kind of, but I'm not as skilled as you are, but, and I say that with all due respect. I mean you love data. >> Caryn: Yes. >> As data science becomes a more critical skill within organizations, we always talk about the amount of data, data growth, the stats are mind-boggling. But as a data scientist, do you feel like you have access to the right data and how much of a challenge is that with clients? >> So we do have access to the data but the challenge is, the company has so many systems, right? It's not just one or two applications. There are companies we have 50 or 60 or even hundreds of application built over last 20 years. And there are some applications, which are basically duplicate, which replicates the data. Now, the challenge is to integrate the data from different systems because they maintain different metadata. They have the quality of data is a concern. And sometimes with the international companies, the rules, for example, might be in US or India or China, the data acquisitions are different, right? And you are, as you become more global, you try to integrate the data beyond boundaries, which becomes a more compliance issue sometimes, also, beyond the technical issues of data integration. >> Any thoughts on that? >> Yeah, I think, you know one of the other issues too, you have, as you've heard of shadow IT, where people have, like, servers squirreled away under their desks. There's your shadow data, where people have spreadsheets and databases that, you know, they're storing on, like a small server or that they share within their department. And so you know, you were discussing, we were talking earlier about the different systems. And you might have a name in one system that's one way and a name in another system that's slightly different, and then a third system, where it's it's different and there's extra granularity to it or some extra twist. And so you really have to work with all of the people that own these processes and figure out what's the trusted source? What can we all agree on? So there's a lot of... It's funny, a lot of the data problems are people problems. So it's getting people to talk and getting people to agree on, well this is why I need it this way, and this is why I need it this way, and figuring out how you come to a common solution so you can even create those single trusted sources that then everybody can go to and everybody knows that they're working with the the right thing and the same thing that they all agree on. >> The politics of it and, I mean, politics is kind of a pejorative word but let's say dissonance, where you have maybe of a back-end syst6em, financial system and the CFO, he or she is looking at the data saying oh, this is what the data says and then... I remember I was talking to a, recently, a chef in a restaurant said that the CFO saw this but I know that's not the case, I don't have the data to prove it. So I'm going to go get the data. And so, and then as they collect that data they bring together. So I guess in some ways you guys are mediators. >> [Caryn And Ritesh] Yes, yes. Absolutely. >> 'Cause the data doesn't lie you just got to understand it. >> You have to ask the right question. Yes. And yeah. >> And sometimes when you see the data, you start, that you don't even know what questions you want to ask until you see the data. Is that is that a challenge for your clients? >> Caryn: Yes, all the time. Yeah >> So okay, what else do we want to we want to talk about? The state of collaboration, let's say, between the data scientists, the data engineer, the quality engineer, maybe even the application developers. Somebody, John Fourier often says, my co-host and business partner, data is the new development kit. Give me the data and I'll, you know, write some code and create an application. So how about collaboration amongst those roles, is that something... I know IBM's gone on about some products there but your point Caryn, it's a lot of times it's the people. >> It is. >> And the culture. What are you seeing in terms of evolution and maturity of that challenge? >> You know I have a very good friend who likes to say that data science is a team sport and so, you know, these should not be, like, solo projects where just one person is wading up to their elbows in data. This should be something where you've got engineers and scientists and business, people coming together to really work through it as a team because everybody brings really different strengths to the table and it takes a lot of smart brains to figure out some of these really complicated things. >> I completely agree. Because we see the challenges, we always are trying to solve a business problem. It's important to marry IT as well as the business side. We have the technical expert but we don't have domain experts, subject matter experts who knows the business in IT, right? So it's very very important to collaborate closely with the business, right? And data scientist a intermediate layer between the IT as well as business I will say, right? Because a data scientist as they, over the years, as they try to analyze the information, they understand business better, right? And they need to collaborate with IT to either improve the quality, right? That kind of challenges they are facing and I need you to, the data engineer has to work very hard to make sure the data delivered to the data scientist or the business is accurate as much as possible because wrong data will lead to wrong predictions, right? And ultimately we need to make sure that we integrate the data in the right way. >> What's a different cultural dynamic that was, say ten years ago, where you'd go to a statistician, she'd fire up the SPSS.. >> Caryn: We still use that. >> I'm sure you still do but run some kind of squares give me some, you know, probabilities and you know maybe run some Monte Carlo simulation. But one person kind of doing all that it's your point, Caryn. >> Well you know, it's it's interesting. There are there are some students I mentor at a local university and you know we've been talking about the projects that they get and that you know, more often than not they get a nice clean dataset to go practice learning their modeling on, you know? And they don't have to get in there and clean it all up and normalize the fields and look for some crazy skew or no values or, you know, where you've just got so much noise that needs to be reduced into something more manageable. And so it's, you know, you made the point earlier about understanding the data. It's just, it really is important to be very curious and ask those tough questions and understand what you're dealing with. Before you really start jumping in and building a bunch of models. >> Let me add another point. That the way we have changed over the last ten years, especially from the technical point of view. Ten years back nobody talks about the real-time data analysis. There was no streaming application as such. Now nobody talks about the batch analysis, right? Everybody wants data on real-time basis. But not if not real-time might be near real-time basis. That has become a challenge. And it's not just that prediction, which are happening in their ERP environment or on the cloud, they want the real-time integration with the social media for the marketing and the sales and how they can immediately do the campaign, right? So, for example, if I go to Google and I search for for any product, right, for example, a pressure cooker, right? And I go to Facebook, immediately I see the ad within two minutes. >> Yeah, they're retargeting. >> So that's a real-time analytics is happening under different application, including the third-party data, which is coming from social media. So that has become a good source of data but it has become a challenge for the data analyst and the data scientist. How quickly we can turn around is called data analysis. >> Because it used to be you would get ads for a pressure cooker for months, even after you bought the pressure cooker and now it's only a few days, right? >> Ritesh: It's a minute. You close this application, you log into Facebook... >> Oh, no doubt. >> Ritesh: An ad is there. >> Caryn: There it is. >> Ritesh: Because everything is linked either your phone number or email ID you're done. >> It's interesting. We talked about disruption a lot. I wonder if that whole model is going to get disrupted in a new way because everybody started using the same ad. >> So that's a big change of our last 10 years. >> Do you think..oh go ahead. >> oh no, I was just going to say, you know, another thing is just there's so much that is available to everybody now, you know. There's not this small little set of tools that's restricted to people that are in these very specific jobs. But with open source and with so many software-as-a-service products that are out there, anybody can go out and get an account and just start, you know, practicing or playing or joining a cackle competition or, you know, start getting their hands on.. There's data sets that are out there that you can just download to practice and learn on and use. So, you know, it's much more open, I think, than it used to be. >> Yeah, community additions of software, open data. The number of open day sources just keeps growing. Do you think that machine intelligence can, or how can machine intelligence help with this data quality challenge? >> I think that it's it's always going to require people, you know? There's always going to be a need for people to train the machines on how to interpret the data. How to classify it, how to tag it. There's actually a really good article in Popular Science this month about a woman who was training a machine on fake news and, you know, it did a really nice job of finding some of the the same claims that she did. But she found a few more. So, you know, I think it's, on one hand we have machines that we can augment with data and they can help us make better decisions or sift through large volumes of data but then when we're teaching the machines to classify the data or to help us with metadata classification, for example, or, you know, to help us clean it. I think that it's going to be a while before we get to the point where that's the inverse. >> Right, so in that example you gave, the human actually did a better job from the machine. Now, this amazing to me how.. What, what machines couldn't do that humans could, you know last year and all of a sudden, you know, they can. It wasn't long ago that robots couldn't climb stairs. >> And now they can. >> And now they can. >> It's really creepy. >> I think the difference now is, earlier you know, you knew that there is an issue in the data. But you don't know that how much data is corrupt or wrong, right? Now, there are tools available and they're very sophisticated tools. They can pinpoint and provide you the percentage of accuracy, right? On different categories of data that that you come across, right? Even forget about the structure data. Even when you talk about unstructured data, the data which comes from social media or the comments and the remarks that you log or are logged by the customer service representative, there are very sophisticated text analytics tools available, which can talk very accurately about the data as well as the personality of the person who is who's giving that information. >> Tough problems but it seems like we're making progress. All you got to do is look at fraud detection as an example. Folks, thanks very much.. >> Thank you. >> Thank you very much. >> ...for sharing your insight. You're very welcome. Alright, keep it right there everybody. We're live from the IBM CTO conference in San Francisco. Be right back, you're watching the Cube. (electronic music)

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. You're watching "The Cube" at IBM's CDO Strategy Summit from San Francisco. We'll be right back. (music playing)

>> Announcer: Live from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. (upbeat music) >> Welcome back to San Francisco, everybody. You're watching theCUBE, the leader in live tech coverage. We're covering the IBM Chief Data Officer Strategy Summit #ibmcdo. Ed Walsh is here. He's the General Manager of IBM Storage, and Steven Eliuk who's the Vice President of Deep Learning in the Global Chief Data Office at IBM, Steven. >> Yes, sir. >> Good to see you again. Welcome to The CUBE. >> Pleasure to be here. So there's a great story. We heard Inderpal Bhandari this morning talk about the enterprise data blueprint and laying out to the practitioners how to get started, how to implement, and we're going to have a little case study as to actually how you're doing this. But Ed, set it up for us. >> Okay, so we're at this Chief Data Officer Summit in the Spring, we do it twice a year and really get just Chief Data Officers together to think through their different challenges and actually share. So that's where we're at the Summit. And what we've, as IBM, as kind of try to be a foot forward, be that cognitive enterprise and showing very transparently what we're doing at our organization be more data-driven. And we've talked a bunch of different times. Everyone needs to be data-driven. Everyone wants to be data-driven, but it's really challenging for organizations. So what we're doing is with this blueprint which we're showing as a showcase, in fact you can actually physically come in and see our environment. But more importantly we're being very transparent on all the different components, high-level processes, what we did in governance, but also down to the Lilly Technology level and sharing that with our... Not because they want to do all of it, but maybe they want to do some of it or half of it, but it would be a blueprint that's worked. And then we're being transparent about what we're getting internally for our own transformation as IBM. Because really if we looked at this as a platform, it's really an enterprise cognitive data platform that all of IBM uses on all our transformation work. So our client, in fact, is Steven, and I think you can give what are we doing. By the way, it also, same type of infrastructure allows you to do what we did in the national labs, the largest supercomputers in the world, same infrastructure and the same thing we're trying to do, is make it easier for people to get insights from the data at scale in the enterprise. So that's why I want to bring Steven on. >> I joked with Inderpal. I said, "Well, if you can do it at IBM, "if you can do it there you can do it anywhere," (Ed laughing) because he's point oh. We're at a highly complex organization. So Steven, take us through how you got started and what you're doing. >> For sure, so I'm what's referred to probably as a difficult customer. So because we're so multifaceted we have so many different use cases internally in the orders of hundreds, it doesn't mean that I can just say, "Hey, this is a specific pattern that I need, Ed. "You need to make sure your hardware is sufficient in this area," because the next day I'm going to be hitting him and say, "Hey Ed, I need you to make sure "that it's also efficient in terms of bandwidth as well." And that's the beauty of working in this domain, is that I have those hundreds of use cases and it means that I'm hitting low latency requirements, bandwidth requirements, extensibility requirements because I have a huge number of headcount that I'm bringing on as well. And if I'm good now I don't have to worry about in six months to be stating, "Hey, I need to roll out new infrastructure "so I can support these new data scientists "and effectively so that they can get outcomes quicker." And I'd need to make sure that all the infrastructure behind the scenes is extensible and supports my users. And what I don't want them to have to worry about specifically is how that infrastructure works. I want them to focus on those use cases, those enterprise use cases, and I want them to touch as many of those use cases as possible. >> So Inderpal laid out sort of his five things that a CDO should do. He starts with develop a clear data strategy. So as the doer in the organization, how'd you go about doing that? Presumably you participated in that data strategy, but you're representing the lines of business presumably to make sure that it's of value to them. You can accelerate business value, but how did you start? I mean that's a big challenge, chewy. >> For sure, yeah, it's a huge challenge. And I think effectively curating, locating, governing, and quality aspects of that data is one of the first aspects. And where does that data reside, though, and how do we access it quickly? How does it support structured and unstructured data effectively? Those are all really important questions that had to come to light. And that's some of the approaches that we took. We look at the various business units and we look at are they curating the data correctly? Is it the data that we need? Maybe we have to augment that curation process before we actually are able to kind of apply new techniques, new machine-learning techniques, to that use case. There's a number of different aspects that kind of get rolled into that, and bringing effective storage and effective compute to the table really accelerates us in that journey. >> So Ed, what are the fundamental aspects of the infrastructure that supports this sort of emerging workload? >> Yeah, no, good question. And some of it is what we're going to talk about, what's a storage layer and what's a compute layer, but also what are the tools we're putting in place to use a lot of these open-source toolsets and make it easier for people to use but also use that underlying infrastructure better. So if you look at the high level, we use a storage infrastructure that is built for these AI workloads which is closer to an HPC workload. So the same infrastructure we use, we use the term ESS or elastic storage server. It's a combination. It's a turnkey solution, half rack, full rack. But it can start very small and grow to the biggest supercomputers in the world like what we're doing in the national labs, like the largest top five supercomputers in the world. But what that is is a file system called Spectrum Scale. Allows you to scale up at the performance but also low latency, gets added to the metadata but also high throughput. So we can do layers on that either on flash being all the hot tiers'll be on flash because it's not just the throughput you need which is high. So our lowest end box's close to like what, 26 gigabytes a second. Our highest one like national labs is 4.9 terabytes a second throughput. But it's also the low latency quick access. So we have a storage infrastructure but then we also have high-performance compute. So what we have is our Power Systems, our POWER9 Systems with GPUs, and the idea is how do you, we use the term feed the beast? How do you have the right throughput or IOPS to get the data close to that CPU or the GPU? The Power Systems have a unique bandwidth, so it's not like what you just find from a Comodo, the Intel servers. It's a much faster throughput, so it allows us to actually get data between the GPU CPU in storage or memory very fast. So you can get these deep learning times, and maybe you can share some of that. The learning times go up dramatically, so you get the insight. And then we're also putting layers on top which are IBM Cloud Private, is basically how do you have a hybrid cloud container-based service that allows you to move things seamlessly across and not have to wrestle with how to put all these things together either so it works seamlessly between a public cloud and private cloud? Then we have these toolsets, and I talked about this last time. It might not seem like storage or what you have in APU but we use the term PowerAI, is taking all these machine-learning tools because everyone always used open source. But we make them one more scale but also to ease your use. So how do you use a bunch of great GPUs and CPUs, great throughput, and how do you scale that? A lot of these tools were basically to be run on one CPU. So to be distributed, key research from IBM allows you to actually with PowerAI take the same TensorFlow workflows or dot dot dot and run it across a grid dramatically changing what you're doing from learning times. But anyway you can probably give more, I think, but it's a multiple layer. It's not one thing but it's not what you use for digital storage infrastructure, compute infrastructure for normal workloads. It is custom so you can't... A lot of people try to deploy maybe their NAS storage box and maybe it's flash and try to deploy it. And you can get going that way but then you hit a wall real quick. This is purposely built for AI. >> So Beth Smith was on earlier. She threw out a stat. She said that 85% of their, based on some research, I'm not sure if it was IBM or Forrest or Gartner, said 85% of customers they talked to said AI will be a competitive advantage but only 20% can use it today at scale. So obviously scale is a big challenge, and I want to ask you to comment on another potential challenge. We always talk about elastic infrastructure. You scale up, scale down, or end of month, okay. We sometimes use this concept of plastic infrastructure. Basically plastic maintains its shape because these workloads are so diverse. I don't want to have to rip down my infrastructure and bring in a new one every time my workload changes. So I wonder if you can talk about the sort of requirements from your perspective both in terms of scale and in terms of adaptability to changing workloads. >> Well, I think one of the things that Ed brought up that's really, really important is these open-source frameworks assume that it's running on a single system. They assume that storage is actually local, and that's really the only way that you get really effective throughput from it, is if it's local. So extending it via PowerAI, via these appliances and so forth means that you can use petabytes of storage at a distance and still have good throughput and not have those GP utilization coming down because these are very expensive devices. So if the storage is the blocker, is their controller and he's limiting that flow of data then ultimately you're not making the most effective use of those very expensive computational mediums. But more importantly it means that your time from ideation to product is slowed down, so you're not able to get those business outcomes. That means your competitor could get those business outcomes if they don't have it. And for me what's really important is I mentioned this briefly earlier, is that I need those specialists to touch as much of the data or as much as those enterprise use cases as possible. At the end of the year it's not about touching three use cases. It's the touching three this year, five, ten, more and more and more. And with the infrastructure being storage and computation, all of that is key attributes to kind of seeing that goal. >> Without having to rip that down and then repurpose building it every time. >> Steven: Yeah. >> And just being able to deal with the grid as a grid and you can place workloads across a grid. >> 100%. >> That's our Spectrum compute products that we've been doing for all the major banks in the world to do that and take these workloads and place them across a grid is also a key piece of this. So we always talk about the infrastructures being hey, Ed, that's not storage or infrastructure. No, you need that. And that's why it's part of my portfolio to actually build out the overall infrastructure for people to build on prim but also talk about everything we did with you on prim is hybrid. It's goes to the Cloud natively because some workloads we believe will be on the Cloud for good reasons, and you need to have that part of it. So everything we're going with you is hybrid cloud today, not in the future, today. >> No, 100%, and that's one of the requirements in our organization that we call A-1 architecture. If we write it for our own prim we have to be able to run it on the Cloud and it has to have the same look and feel and painted glass and things like that as well. So it means we only have to write it once, so we're incredibly efficient because we don't have to write it multiple times for different types of infrastructure. Likewise we have expectations from the data scientists that the performance all still have to be up to par as well. We want to really be moving the computation directly to where the data resides and we know that it's not just on prim, it's not in the Cloud, it's a hybrid scenario. >> So don't hate me for asking you this, Ed, but you've only been here for a couple years. Did you just stumble into this? You got this vast portfolio, you got this tooling, you got cloud. You got a part of your organization saying we got to do on prim. The other part's saying we got to do public. Or was this designed to the workload? Was kind of a little bit of both? >> Well, I think luck is good, but it's a embarrassment of riches inside IBM between our primary research, some of the things we were just talking about. How do you run these frameworks in a distributed fashion and not designed that way and do it performing at scale? That's our primary, that's research. That's not even in my group. What we're doing is for workload management. That's in storage, but we have these toolsets. The key thing is work with the clients to figure out what they're trying to do. Everyone's trying to be data-driven, so as we looked at what you need to do to be truly data-driven, it's not just having faster storage although that's important. It's not about the throughput or having to scale up. It's not about having just the CPUs. It's not just about having the open frameworks, but it's how to put that all together that we're invisible. In fact you said it earlier. He doesn't want his users to know at all what's underneath. He just wants to run their workload. You have people from my organization because I'm one of your customers. You're my customer but we go to you and say, "We're trying to use your platform "for a 360 view of the client," and our not data scientists, not data engineers, but ops team can use his platform. So anyway, so I actually think it's because IBM has its broad portfolio that we can bring together. And when IBM shows up which we're showing up in AI together in the Cloud, that's when you see something that we can truly do that you can't get from other organizations. And it's because of the technology differentiation we have from the different groups, but also the industry contacts that we bring. >> 100%. >> And also when you're dealing with data it is the trust. We can engage the clients at a high level and help them because we're not a single-product company. We might be more complex, but when we show up and bring the solution set we can really differentiate. And I think that's when IBM shows up. It's pretty powerful. >> And I think it's moved from "trust me" as well to "show me," and we're able to show it now because we're eating what we're producing. So we're showing. They called it a blueprint. We're using that effectively inside the organization. >> So now that you've sort of built this out internally you spend a lot of time with clients kind of showing them or...? >> Probably 15% of my time. >> So not that much. >> No, no, because I'm in charge of internal transformation operations. They're expecting outcomes from us. But at the same time there's clients that are in the exact same boat. The realization that this is really interesting. There's a lot of noise, a lot of interesting stuff in AI out there from Google, from Facebook, from Amazon, from all, Microsoft, but image recognition isn't important to me. How do I do it for my own organization? I have legacy data from 50 years. This is totally different, and there's no Git repo that I can go to and download them all and use it. It's totally custom, and how do I handle that? So it's different for these guys. >> What's on your wishlist? What's on Ed's to do list? >> Oh geez, uh... I want it so simple for my data scientists that they don't have to worry about where the data's coming from. Whether it be a traditional relational database or an object store, I want it to feed that data effectively and I don't want to have to have them looking into where the data is to make sure the computation's there. I want it just to flow effortlessly. That's really the wishlist. Likewise, I think if we had new accelerators in general outside the box, not something from the traditional GPU viewpoint, maybe data flow or something in new avant-garde-type stuff, that would be interesting because I think it might open up a new train of thought in the area just like GPUs did for us. >> Great story. >> Yeah I know, I think it's... So we're talking about AI for business, and I think what you're seeing is we're trying to showcase what IBM's doing to be really an AI business. And what we've done in this platform is really a showcase. So we're trying to be as transparent as possible not because it's the only way to do it but it's a good example of how a very complex business is using AI to get dramatically better and everyone's using the same kind of platform. >> Well, we learned, we effectively learned being open is much better than being closed. Look at the AI community. Because of its openness that's where we're at right now. And following the same lead we're doing the same thing, and that's why we're making everything available. You can see it and we're doing it, and we're happy to talk to you about it. >> Awesome, all right, so Steven, you stay here. >> Yeah. >> We're going to bring Sumit on and we're going to drill down into the cognitive platform. >> That's good. This guy, thanks for setting it up. I really, really appreciate it. >> Thank you very much. >> All right, good having you guys. All right, keep it right there, everybody. We'll be back at the IBM CDO Strategy Summit. You're watching theCUBE. 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>> Narrator: Live, from downtown San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. (techno music) >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. And we are here at the IBM CDO Strategy Summit hashtag IBMCDO. Caitlin Halferty is here. She's a client engagement executive for the chief data officer at IBM. Caitlin great to see you again. >> Great to be here, thank you. >> And she's joined by Brandon Purcell, who's principal analyst at Forrester Research. Good to have you on. >> Thanks very much, thanks for having me. >> First time on theCUBE. >> Yeah. >> You're very welcome. >> I'm a newbie. >> Caitlin... that's right, you're a newbie. You'll be a Cube alum in no time, I promise you. So Caitlin let's start with you. This is, you've done a number of these CDO events. You do some in Boston, you do some in San Francisco. And it's really great to see the practitioners here. You guys are bringing guys like Inderpal to the table. You've announced your blueprint in it. The audience seems to be lapping up the knowledge transfer. So what's the purpose of these events? How has it evolved? And just set the table for us. >> Sure, so we started back in 2014 with our first Chief Data Officer Summit and we held that here in San Francisco. Small group, probably only had about 30 or 40 attendees. And we said let's make this community focused, peer to peer networking. We're all trying to, ya know, build the role of either the Chief Data Officer or whomever is responsible for enterprise wide data strategy for their company, a variety of different titles. And we've grown that event over, since 2014. We do Spring, in San Francisco, which tends to be a bit more on the technical side, given where we are here in San Francisco in Silicon Valley. And then we do our business focused sessions in Fall in Boston. And I have to say, it's been really nice to see the community grow from a small set of attendees. And now was are at about 130 that join us on each coast. So we've built a community in total of about 500 CDOs and data executives, >> Nice. that are with us on this journey, so they're great. >> And Brandon, your focus at Forrester, part of it is AI, I know you did some other things in analytics, the ethics of AI, which we're going to talk about. I have to ask you from Forrester's perspective, we're enter... it feels like we're entering this new era of there's digital, there's data, there's AI. They seem to all overlap. What's your point of view on all this? >> So, I'm extremely optimistic about the future of AI. I realize that the term artificial intelligence is incredibly hyped right now. But I think it will ultimately fulfill it's promise. If you think about the life cycle of analytics, analytics start their lives as customer data. As customers interact and transact with you, that creates a foot print that you then have to analyze to unleash some sort of insight. This customer's likely to buy, or churn, or belongs to a specific segment. Then you have to take action. The buzzwords of the past have really focused on one piece of that life cycle. Big data, the data piece. Not much value unless you analyze that. So then predictive analytics, machine learning. What AI promises to do is to synthesize all of those pieces, from data, to insights, to action. And continuously learn and optimize. >> It's interesting you talk about that in terms of customer churn. I mean, with the internet, there was like a shift in the balance of power to the consumer. There used to be that the brand had all the knowledge about the buyer. And then with the internet, we shop around, we walk into a store and, look at them. Then we go buy it on the internet right? Now that AI maybe brings back more balance, symmetry. I mean, what are your thoughts on that? Are the clients that you work with, trying to sort of regain that advantage? So they can better understand the customer. >> Yeah, well that's a great question. I mean, if there's one kind of central ethos to Forrester's research it's that we live in the age of the customer and understanding and anticipating customer needs is paramount to be able to compete, right? And so it's the businesses in the age of AI and the age of the customer that have the data on the customer and enable the ability to distill that into insights that will ultimately succeed. And so the companies that have been able to identify the right value exchange with consumers, to give us a sense of convenience, so that we're willing to give up enough personal data to satisfy that convenience are the ones that I think are doing well. And certainly Netflix and Amazon come to mind there. >> Well for sure, and of course that gets into the privacy and the ethics of AI. I mean everyone's making a big deal out of this. You own your data. >> Yeah. >> You're not trying to monetize, ya know, figure out which ad to click on. Maybe give us your perspective, Caitlin, on IBMs point of view there? >> Sure, so we lead with this thought around trusting your data. You're data's your data. Insights derive from that data, your insights. We spend a lot of time with our Watson Legal folks. And one of the things, pieces of material we've released today is the real detail at every level how you engage the traceability of where your data is. So you have a sense of confidence that you know how it's treated, how it's curated. If it's used in some third party fashion. The ability to know that, have visibility into it. The opt-out, opt-in opt-out set of choices. Making sure that we're not exploiting the network effect, where perhaps party C benefits from data exchange between A and B. That A and B do not, or do not have an opportunity to influence. And so what we wanted to do, here at the summit over the next couple of days is really share that in detail and our thoughts around it. And it comes back to trust and being able to have that viability and traceability of your data through the value chain. >> So of course Brandon, as a customer I'm paying IBM so I would expect that IBM would look out for my privacy and make that promise. I don't really pay Facebook right? But I get some value out of it. So what are the ethics of that? Is it a pay or no pay? Or is it a value or no value? Is it everybody really needs to play by the same rules? How to you parse all that? >> Ya know, I hate to use a vague term. But it's a reasonable expectation. Like I think that when a person interacts with Facebook, there is a reasonable expectation that they're not going to take that data and sell it or monetize it to some third party, like Cambridge Analytica. And that's where they dropped the ball in that case. But, that's just in the actual data collection itself. There's also, there are also inherent ethical issues in how the data is actually transformed and analyzed. So just because you don't have like specific characteristics or attributes in data, like race and gender and age and socioeconomic status, in a multidimensional data set there are proxies for those through something called redundant encoding. So even if you don't want to use those factors to make decisions, you have to be very careful because they're probably in there anyway. And so you need to really think about what are your values as a brand? And when can you actually differentiate treatment, based on different attributes. >> Because you can make accurate inferences from that. >> Brandon: Yeah you're absolutely (mumbles). >> And is it the case of actually acting on that data? Or actually the ability to act on that data? If that makes sense to you. In other words, if an organization has that data and could, in theory, make the inference, but doesn't. Is that crossing the line? Is it the responsibility of the organization to identify those exposures and make sure that they can not be inferred? >> Yeah, I think it is. I think that that is incumbent upon our organizations today. Eventually regulators are going to get around to writing rules around this. And there's already some going into effect of course in Europe, with GDPR at the end of this month. But regulators are usually slow to catch up. So for now it's going to have to be organizations that think about this. And think about, okay, when is it okay to treat different customers differently? Because if we, if we break that promise, customers are going to ultimately leave us. >> That's a hard problem. >> Right, right. >> You guys have a lot of these discussions internally? >> We do. >> And can you share those with us? >> Yeah, absolutely, we do. And we get a lot of questions. We often engage at the data strategy perspective. And it starts with, hey we've got great activity occurring in our business units, in our functional areas, but we don't really have a handle on the enterprise wide data strategy. And at that point we start talking about trust, and privacy, and security, and what is your what does your data flows look like. So it starts at that initial data strategy discussion. And one other thing I mentioned in my opening remarks this morning is, we released this blueprint and it's intended, as you said, to put a framework in process and reflect a lot of the lessons learned that we're all going through. I know you mentioned that many companies are looking at AI adoption, perhaps more so than we realized. And so the framework was intended to help accelerate that process. And then our big announcement today has been around the showcases, in particular our platform showcase. So it's really the platform we've built, within our organization. The components, the products, the capabilities that drives for us. And then with the intent of hopefully being, illustrative and helpful to clients that are looking to build similar capabilities. >> So let's talk about adoption. >> Brandon: Yeah, sure. >> Ya know, we... you often hear this bromide that we live in a world where, that pace of change is so fast. And things are changing so quickly it's hard to deny that. But then when you look at adoption of some of the big themes in our time. Whether it's big data or AI, digital, block chains, there are some major barriers to adoption. So you see them adopted in pockets. What's your perspective, and Forrester's perspective on adoption of, let's call it machine intelligence? >> Yeah, sure, so I mean, every year Forrester does a global survey of business and technology decision leaders called Business Technographics. And we ask folks about adoptions rates of certain technologies. And so when it comes to AI, globally, 52% of companies have adopted AI in some way. And another 20% plan to in the next 12 months. What's interesting to me, actually, is when you break that down geographically, the highest adoption rate, 60 plus percent, is in APAC, followed by North America, followed by Europe. And when you think about the privacy regulations in each of those geographies, well there are far fewer in APAC than there are, and will be, in Europe. And that's, I think kind of hamstringing adoption in that geography. Now is that a problem for Europe? I don't think so actually. I think AI, the way AI is going to be adopted in Europe is going to be more refined and respectful of customers' intrinsic right to privacy. >> Dave: Ya know I want... Go ahead. >> I've got to, I have to say Dave, I have to put a plug in. I've been a huge fan of Brandon's, for a long time. I've actually, ya know, a few years now of his research. And some of the research that you're mentioning, I hope people are reading it. Because we find these reports to be really helpful to understand, as you said, the specifics of adoptions, the trends. So I've got to put a plug in there. >> Thanks Caitlin. >> Because, the quality of the work and the insights are incredible. So that is why I was quite excited when Brandon accepted our offer to join us here in this session. >> Awesome. Yeah, so, let's dig into that a little bit. >> Brandon: Sure. >> So it seems like, so 52%, I'm wondering, what the other 48 are doing? They probably are, and they just don't know it. So it's possible that the study looks at, a strategy to adopt, presumably. I mean actively adopting. But it seems, I wonder if I could run this by you, get your comment. It seems that people will, organizations will more likely be buying AI as embedded in applications or systems or just kind of invisible. Then they won't necessarily be building it. I know many are trying to probably build it today. And what's your thought on that? In terms of just AI infused everywhere? >> So the first foray for most enterprises into this world of AI is chat bots for customer service. >> Dave: Sure. >> I mean we get a ton of inquires at Forrester about that. And there are a number of solutions. Ya know, IBM certainly has one for, that fulfill that need. And that's a very narrow use case, right? And it's also a value added of use case. If you can take more of those call center agents out of the loop, or at least accelerate or make them better at their jobs, then you're going to see efficiency gains. But this isn't this company wide AI transformation. It's just one very narrow use case. And usually that's, most elements of that are pre-built. We talked this morning, or the speakers this morning talked about commoditization of certain aspects of machine learning and AI. And it's very true. I mean, machine learning algorithms, many of them have been around for a long time, and you can access them for multiple different platforms. Even natural language processing, which a few years ago was highly inaccurate, is getting really, really accurate. So when, in a world where all of these things are commoditized, it's going to end up being how you implement them that's going to drive differentiation. And so, I don't think there's any problem with buying solutions that have been pre-built. You just have to be very thoughtful about how you use them to ultimately make decisions that impact the customer experience. >> I want to, in the time we have remaining, I want to get into the tech radar, the sort of taxonomy of AI or machine intelligence. You've done some work here. How do you describe, can you paint a picture, for what that taxonomy looks like? >> So I think most people watching realize AI is not one specific thing right? It's a bunch of components, technologies that stitched together lead to something that can emulate certain things that humans do, like sense the world around us, see, read, hear, that can think or reason. That's the machine learning piece. And that can then take action. And that's the kind of automation piece. And there are different core technologies that make up each of those faculties. The kind of emerging ones are deep learning. Of course you hear about it all the time. Deep learning is inherently the use of artificial neural networks, usually to take some unstructured data, let's say pictures of cats, and identify this is actually a cat right? >> Who would have thought? That we're led to this boom right? >> Right exactly. That was something you couldn't do five or six years ago, right? You couldn't actually analyze picture data like you analyze row and column data. So that's leading to a transformation. The problem there is that not a lot of people have this massive number of pictures of cats that are consistently and accurately labeled cat, not cat, cat, not cat. And that's what you need to make that viable. So a lot of vendors, and Watson has an API for this have already trained a deep neural network to do that so the enterprises aren't starting from scratch. And I think we'll see more and more of these kind of pre-trained solutions and companies gravitating towards the pre-trained solutions. And looking for differentiation, not in the solutions themselves, but again how they actually implement it to impact the customer experience. >> Hmmm, well that's interesting, just hearing you sense, see, read, hear, reason, act. These are words that describe not the past era. This is a new era that we're entering. We're in the cloud era now. We can sort of all agree with that. But these, the cloud doesn't do these things. We are clearly entering a new wave. Maybe it's driven by Watson's Law, or whatever holds out. Caitlin I'll give you the last word. Put a bumper sticker on this event, and where we're at here in 2018? >> I'll say, it's interesting to watch the themes evolve over the last few years. Ya know, we started with sort of a defensive posture. Most of our data executives were coming perhaps from an IT type background. We see a lot more with line of business, and chief operations type role. And we've seen the, we still king of the data warehouse, that's sort of how we described at the time. And now, I see our data leaders really driving transformation. They're responsible for both the data as well as the digital transformation. On the data side, it's the AI focus. And trying to really understand the deep learning capabilities, machine learning, that they're bringing to bear. So it's been, for me, it's been really interesting to see the topics evolve, see the role in the strategic piece of it. As well as see these guys elevated, in terms of influence within their organization. And then, our big topic this year was around AI and understanding it. And so, having Brandon to share his expertise was very exciting for me because, he's our lead analyst in the AI space. And that's what our attendees are telling us. They want to better understand, and better understand how to take action to implement and see those business results. So I think we're going to continue to see more of that. And yeah, it's been great to see, great to see it evolve. >> Well congratulations on taking the lead, this is a very important space. Ya know, a lot of people didn't really believe in it early on, thought the Chief Data Officer role would just sort of disappear. But you guys, I think, made the right investment and a good call, so congratulations on that. >> I was laughed out of the room when I proposed, I said hey we're hearing of this, doing a market scan of Chief Data Officer, either by title or something similar, titled responsible for enterprise wide data. I was laughed out of the room. I said let me do a qualitative piece. Let me interview 20 and just show, and then you're right, it was the thought was, role's going to go by the wayside. And I think we've seen the opposite. >> Oh yeah, absolutely. >> Data has grown in importance. The associative capabilities have grown. And I'm seeing these individuals, their scope, their sphere of responsibility really grow quite a bit. >> Yeah Forrester's tracked this. I mean, you guys I think just a few years ago was like eh, yeah 20% of organizations have a Chief Data Officer and now it's much much higher than that. >> Yeah, yeah, it's approaching 50%. >> Yeah, so, good. Alright Brandon, Caitlin, thanks very much for coming on theCUBE. >> Thanks for having us. >> Thank you, it was great. >> Keep it right there everybody. We'll be back, at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE. (techno music) (telephone tones)