>> From Yorktown Heights, New York, it's theCUBE, covering IBM Cloud Innovation Day. Brought to you by IBM. >> Hi, I'm Peter Burris from Wikibon, and you're watching theCUBE being broadcast from IBM Innovation Day at the Thomas J. Watson Research Lab in beautiful Yorktown, New York. And we've had a number of great conversations thus far, we've got some more on the horizon, stay with us. Now, we've got Jim Comfort. Jim Comfort is the General Manager of Hybrid Cloud Services at IBM. Jim, welcome to theCUBE. >> Thank you, Peter, glad to be here. >> So, Jim, what does Hybrid Cloud Services as a group do? >> Actually, we run infrastructure for clients. That's our business, but we help you advise, build and manage private cloud. Advise, build and manage consumption of public cloud, Azure, Google, IBM, and we help you manage and stitch all of that together. >> So a lot of people think of cloud and they think of this monolithic thing. "If I go to the cloud, suddenly my business has changed." But there's more to it than that. There's a number of different things that a business has to be successful at to succeed at getting to the cloud. What is your perspective on that? >> Well, I completely agree. And this is kind of my first conversation with clients is, you need a business strategy, but to execute that strategy you have to realize it will touch most everything in your business. It'll touch infrastructure, it'll touch applications, it'll touch your dev ops, or your development process morph to dev ops. It'll touch your operations very profoundly, this whole SRE thought, and it will test your data governance and management as well as your security and compliance. So that's the scope that you have to comprehend. >> But most people, they start with perhaps the infrastructure first and end up with the data last. Is that the right way to think about this? >> I agree, many do, and actually I have not seen many build-it-they-will-come strategies succeed. And so what I really look for is, do you understand the business drivers? Top-line revenue growth, new markets, new insights, new data, and from that can you derive a technology strategy? What I've seen happen in many cases is, if you start from the bottom up you'll be trapped in what I call the religious wars of technology that never end. >> And most people, a lot of folks start from the bottom up, because they start from the technology side of the business. >> Correct. >> Are you seeing more business people getting engaged, and conceptualizing what the strategy needs to be? >> I am, and it starts on both sides. The business people will say, "I need to move faster than you can move, so I'm going to do something different," and the IT people will say, "I can do that for you, here's what you need." The two signatures of the most successful transformations are does the line of business and the IT have the relationship to collaborate so they actually learn together? And then if they have that, have they actually created a team that understands the new as well as they understand the existing or the old, so they can actually understand what's real, what's not, where's the hype, what really happens. And then they get into the rational, real planning decision. >> So as you think about some of the assessment challenges, because you said you go through the assessment process, what are some of the key questions that a client should start with as they think about undertaking this journey? >> Well, number one is start with the business driver. I said that already, but you have to start with understanding what you're trying to accomplish so you can make choices. And the other is, start small enough and get to the end of something so that you know what the reality is, and that's where our, this is where we bring in our methods. When you hear us talk about the garage method, you hear us talk about MVPs and all the language everyone wants to use. We like to start with something, and start that iterative cycle of learning. That's the key. >> So with an iterative cycle of learning, in many respects this whole notion of agility is predicated on this idea of being agile or iterative. But it's also empirical, knowing what the data is, knowing what the data says, and being opportunistic. How does a customer balance that as they get going, say early on in the cloud journey? >> I think, again, most of what we're talking about in digital transformations is new insights that will help your business. That could be from data that you had, it could be new data. And if they think about it, what insights am I looking for? What new experience am I trying to create, and what do I need to do that? Then you start to get people to step back and think, well, what are all the possibilities? And now, how do we tackle that? So it starts from realizing, what insight am I looking for? >> So there's a lot of invention happening in the industry. >> Oh, yeah. >> And enormous new things being created. Customers are being overwhelmed at trying to adopt them. The innovation side, the social side of effecting a change in the business. You mentioned some of the markers for success and putting together the strategy. Go forward a little bit. What are some of the companies that have successfully gotten to that end stage maturity doing differently? >> We have a number of very good ones. I mean, a very clear one in my mind is American Airlines, where they were really trying to change the experience. They had three distinct things that had grown up over time, the mobile experience, the kiosk experience and the Web experience. Three completely different things. They brought it together, converged it, modernized it, and now completely changed the experience and the speed with which they can now act on what they see for their clients or for their customers, all of us. But also as they get new ideas, the speed and the velocity that they can bring those in is phenomenal. >> And that improves their ecosystem, their ability to work with a lot of others as well. >> Their ecosystem, how to work with others, how to bring in new ideas. And this is all, for them it's all about client satisfaction and service to their end client, to the end user. That's what it was. It had a lot of technology dimensions, but they were very clear the experience they were trying to attack. >> So next February, IBM Think, 30-plus thousand people descending upon San Francisco. You guys are taking it over. What kind of conversations are going to be on your agenda as you work with customers and partners to get this message out? >> Well, it's really two things. I often joke the blessing and curse of IBM is the breadth of our portfolio. It's a very large place, but we actually have a very simple, clear way to talk to, advise, move, build and manage. Those are the steps you need in your journey. Now, which journey for you, which type of thing. But that, we have clarity on that, and I think you'll see that displayed at Think and get to understand it. The other thing is that we have a lot of experiential and real practical, we've made this happen for many large clients at scale, and I think that what we want people to understand is we can help you that same way. It's really pretty simple. >> Jim Comfort, General Manager Hybrid Cloud Services at IBM. Thanks for being on theCUBE. >> Thank you, Peter. >> And we'll be back momentarily with more from theCUBE at IBM Innovation Day here at the Thomas J. Watson Research Center in New York.

>> From York Town Heights, New York, it's theCUBE covering IBM Cloud Analyst Summit, brought to you by IBM. (techy music) >> Hi, welcome back, I'm Peter Burris of theCUBE, and we're having conversations here at the IBM Innovation Day at the Thomas J. Watson Research Lab in York Town Heights, New York. We've got a great conversation. Don Bolia is the general manager of cloud developer services at IBM, welcome to theCUBE, Don. >> Thank you very much. >> Or should I say welcome back to theCUBE? >> (chuckling) Yes, thank you. >> So, Don, we were talking with one of your colleagues, Hillery Hunter, who's the CTO-- >> Mm-hm. >> Of here at the cloud infrastructure team, and about the fact that everybody's talking about the rate of growth of data, and nobody's really discussing the rate of growth of software, which is perhaps even more important, ultimately, to business. What is that rate of growth look like, and how is it related to the role of cloud? >> Yeah, so it's a great question. I mean, with my role as kind of owner of our platform services from the cloud perspective, one of the things we've noticed over the last probably five or 10 years is just a massive rate and pace change with respect to iteration on the software development cycle. So, they started with mobile, I would say, and then has moved to cloud since then, where you know, the expectation is everything is updating all the time, you know, everyday, all times of the day. Within our own Kubernetes and container service, as an example, we push over 500 updates a week to that software stack on behalf of our customers, and so I think there's a rate and pace of how things are changing from that perspective, but then there's also the fact that everybody's leveraging those services to then build the next generation of software. So, in our case we have a set of base services that I provide for things like containers that then the Watson team, for example, uses to build their microservices, which are then, you know, realized as machine learning and other types of services that they provide. So, you see the stacking of software, if you will, from you know, the high iteration rate at the bottom all the way to the next level and the next level, and the ability to unlock value now is something that happens in, you know, hours in some cases, or a couple of days, whereas before just provisioning the software would've taken months, and so we're really seeing just a whole change in the way people can develop things and how quickly they can get to the end result. >> Now, we're here at the Thomas J. Watson Research Lab, and downstairs is this wall of all IBM fellows, and one of them E.F. Codd, the famous originator of database and the role that SQL played, et cetera-- >> Mm-hm. >> In relational database technology. He wrote a seminal paper back in the early 1970s about how the notion of developer was going to evolve over time, and he might've been a little aggressive in thinking that we were going to end up with these citizens developers than we actually happened, but we are seeing the role of developer changing, and we are seeing new classes of professionals become more developer-like. >> Mm-hm. >> How is that relationship changing the way that we think of developer services that you serve? >> Yeah, it's a great question. I think, first of all, software is sort of invading almost every single industry, and so, you know, people have got to have some amount of those skills to be able to function in kind of the optimal way for whatever industry they're in. So, what we're seeing is that as we've built more and more foundational services, the act of actually creating something new is more about stitching together, composing, orchestrating a set of things, as opposed to really building from scratch everything from the ground up, and you know, things like our Watson services are a great example, right? The ability to tap into something like that with a couple lines of code in an hour, as opposed to what would've taken, you know, months, years, whatever, and even really, frankly, been out of the reach of most developers to begin with is now something you can have somebody come in and do, you know, with a fairly low level of skill and get a good result on the outside. >> So, we've got more demand for code as we move to digital business, more people participating in that process, cloud also enables paths, a lot of new classes of tools that are going to increase the productivity-- >> Yep. >> Including automated code generation. How is the process, how is that tool set evolving, especially as it pertains to the cloud? >> Yeah, so I think one of the mantras of cloud is automation, and in order to standardize and automate, that's really how you get to the kind of scale that we would see in, say, a public cloud like the IBM cloud. So, it really is kind of a fundamental premise of anything you do has to be something that you automate, and so we've seen a whole class of tools, to your points, really start to emerge, which allow people to get that kind of, you know, automated capability. So, nobody thinks of, for example, creating a, you know, a build pipeline these days without using a set of tools. You know, often they're opensource tools, and there's a lot of choice within that whole spectrum of tools, and we support a bunch of different varieties, but you would never think today of having a build process that isn't totally automated, right, that can't be instantly recreated. Even the whole process of how you deploy code in a cloud these days is sort of an assumption that you can destroy that and restart at any point, and in order to do that, you really need the automation behind that, so I think it's a base premise now. I don't think you can really be at the velocity that people are expecting out of software without having a totally automated process to go through that. >> So, any digital business strategy presumes that data's an asset, and things that are related to data are assets, including software in many... Well, software is data when you come right down to it. >> Mm-hm. >> And we want to exploit that data and generate new sources of value out of that data, and that's one of the predicates of digital business, but at the same time we also want to protect those attributes of data-- >> Mm-hm. >> That are our IP, our enterprise's distinction. As we move forward with software, how do we reconcile that tension between more openness and generating a community that's capable of improving things, while at the same time ensuring that we've got good control over our IP where it actually does create a business differentiation? >> Now, that's right, and you're right, data's king. So, you know, the software can do, you know, a set of things, but most of the time it's operating on a set of that data, and that data's where the true value that you can unlock comes from. Our policy, from an IBM perspective, has always been that, you know, your data is yours, and to your point, this IP that you may want to protect, and we try to give you the tools to do that, and so a lot of our philosophy, within the cloud in particular, is around things like Bring Your Own Key, where you have control of the keys that encrypt that data that's in the cloud. In fact, we would like to be totally out of that loop, quite frankly, and have it be something that is controlled by our clients, and that they can, you know, get the value they're looking for, and so we'll never have a situation where one of our services is, you know, using or acting on data that is really, you know, not ours to use, and so that's been a fundamental premise of the cloud as we go forward, and again, we continue to provide a set of tools that really let you manage that, and to your point, you know, not everything gets managed at the same level. Some things are highly protected, and therefore have, you know, layers and layers of security policy around them, and there's other examples where, you know, you're relatively able to make that open through a set of APIs, for example, and let everybody have access it. From our perspective, though, that's really a client choice, and so for us it's about giving the right tools so that they can do the job they need to do. >> February 2019, San Francisco, IBM's taking over San Francisco with the IBM THINK show. What types of conversations are you looking forward to having with customers? What excites you about the 2019 version? >> Yeah, so I mean it's a great venue. It is absolutely, you know, something that I look forward to every year. I know my team looks forward to it, as well. I mean, the amount of interaction we get with clients... I mean, it's really all about the client stories, so you know, what are they able to do, in my case, with our cloud services. What can I learn about what they've done, and how, you know, can we then leverage that to make our services better, and so, you know, to me it's all about, you know, what you can learn from others, and it's a great form to be able to do that and there's a lot of great things that, you know, you can dive deep on. You get access to a lot of the IBM technical experts, so I have all of my, you know, fellows and distinguished engineers there, you know, on hand, and just great conversations. There's always great insights that you get from it, highly recommend it. >> Don Bolia, IBM general manager of cloud developer services, thanks very much for being on theCUBE. >> Thank you. >> Once again, we'll be back from IBM Innovation Day here at Thomas J. Watson Research Center in York Town Heights, talk to you soon. (techy music)

(technological music) >> From Yorktown Heights, New York, it's theCUBE covering IBM Cloud Innovation Day, brought to you by IBM. >> Hi, I'm Peter Burris and we are broadcasting theCUBE from IBM Innovation Day at the Thomas J. Watson Research Lab in Yorktown, New York. We've got a great number of guests to talk about. We're going to start with Hillery Hunter, who's the CTO and vice-president of cloud infrastructure at IBM. Hillery, welcome to theCUBE. >> Thank you very much. Pleasure to be here. >> So, you're relatively new in your role. Tell us about some of the things that you're focusing on as the CTO of cloud infrastructure here at IBM. >> As CTO for cloud infrastructure, I'm focused on making our cloud the best possible place that it can be for people to bring their data, bring their applications, and overall, come into that modernization journey with us, the process of transforming to become a digital enterprise. >> So, one of the things that people talk about all the time is how fast data's being generated. Nobody seems to be talking about how fast software is being generated, and yet, that seems to be one of the advantages and potentially the liabilities of doing cloud wrong. Talk to us a little bit about how IBM sees the world of software changing as we move forward with the cloud. >> [Hillery] There are parts that are consistent with what we've seen for about the past 20 years in open source, and there are parts that certainly, we feel like are accelerating and changing. With regard to the pace of software and its change today, open source is clearly this innovation space. It's this playground where lots of people can go and can contribute. We can take... We're here at the IBM research facility. We can take the latest in innovations and math that helps us accomplish great AI and AI insights. We can take that into open source. We can take microservice integration capabilities and take it into open source and work there collaboratively with people across the industry. What we see, therefore, is a tremendous rate and pace in change of software and the capability of software and its ability to analyze data and bring insights to data and realize the promises of big data, of getting insight out of that data, is just really on a tremendous growth rate. When you move to cloud, you're not just doing what they used to say of converting capital expense on premises into opex and renting a server in the cloud. You're bringing your overall workload and modernizing it and bringing it into this era where you're able to apply through microservices and cloud-based programming methodology, you're able to bring the latest of software capability to your data and get more insights out of it. >> You're really able to alter the operating model of how not only your technology group works, but also how your business works. >> Absolutely. >> How does Red Hat play a role in this? >> We have shared principles with Red Hat. We both have been active in the open source communities. IBM famously had billion dollars of investment in LINUX going back 20 years ago, and Red Hat is a prominent name in open source. We have a shared understanding of the value of open source and the value of rate and pace of innovation that's commensurate with what open source provides. We have a shared value around what enterprises need and a shared client-centric view that you need support on your software, that you need certifications, that you expect security, those kind of things. There's tremendous amount of shared value proposition in what we see as the rate and pace of innovation as well as then moving that into an enterprise context. Enterprises make these choices very carefully. As consumers of enterprise capabilities, we expect them to guard our data, we expect them to do things on our data in a secure way, and there are many foundational elements in philosophy that are similar between the two of us. >> You mentioned that cloud started out as this notion of capex to opex, move all your data to a single place, let somebody else deal with it. Increasingly, enterprise is starting to recognize that their data may sometimes have to remain in place. We start talking about innovation, open source, these new classes of services. What is it going to mean to bring the cloud experience to the data from IBM's perspective? >> We really see that the data today exists in multiple places, that largely because of that, people are partway through their journey to overall modernization. They're partway through their journey to the cloud. We really think that the world is going to be hybrid, meaning that... Or, the world is hybrid, I guess I would say, meaning that there is data and there is cloud function needed on premises and in public clouds. There's a need for private, dedicated environments in the public cloud as well. There's a significant amount of IT that is currently traditional in that people are in the process of modernizing, and that may initially be through a private cloud context on the journey to overall workload modernization. We also see that the world is multi-cloud. People are using upwards of 9 clouds or more in many cases, and that, in a lot of cases, has to do with this intersection of function and data residency and being able to bring together all of those pieces of where the data needs to be or where the data currently is, and then bring software function to the data is something that we see as critically important. >> Without being too specific in the use of the word binding, today, the idea is you bring your data to a cloud supplier and then, you can run the services of that cloud supplier supplies on that data. Do you and IBM foresee a world in which the customer's going to be able to control their own data and then acquire the services from the cloud and bring it to their data? Is that the direction you think it's going to go? >> Not only do we see that it will be possible, we think that it is possible and we're putting things in market already today that enable people to bring cloud function to their data. The IBM Cloud private offerings and IBM Cloud private for data enable people to, in their environment, where their data resides, bring sophisticated data, warehousing data analytics and AI capabilities. Fundamentally, that process of workload modernization is a set of steps and it starts with data and it starts with modernization of that environment and it matures then into being able to get deep insights through the power of AI on that data. >> Let me ask you one more question. In February, IBM's going to host 30,000+ people in San Francisco. Unbelievable opportunity for networking, learning, and IBM Think. What kind of conversations do you expect that you're going to be having in Think in 2019? >> I think you hit at the heart of the conversations that we're going to be having at Think and our positioning of the hybrid multi-cloud environment. Our other core tenets there are open and open source and keeping up with the rate and pace of open source as an innovation stream, providing choice in how folks are deploying cloud and deploying systems. We also are going to be having conversations around security. That's a core enterprise value proposition and ultimately, management. You want to not just declare that the world is hybrid and multi-cloud, but provide solutions to that and we believe we have strong answers to how to bring these pieces together and enable people to successfully move at the rate and pace of innovation that they need, yet in a secure context, and leverage the ability to deploy cloud capabilities where their data currently is, be that on private or public context. >> Hillery Hunter, CTO and vice president of cloud infrastructure at IBM, thanks for talking to theCUBE here today at the IBM Innovation Day. >> Thank you so much for having me. It was a pleasure. >> And, we will be back momentarily with more conversations at IBM Innovation Day.

>> 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)

>> Narrator: Live from Stanford University, it's theCUBE covering the Women in Data Science Conference 2017. >> Hi welcome back to theCUBE, I'm Lisa Martin and we are live at Stanford University at the second annual Women in Data Science one day tech conference. We are joined by one of the speakers for the event today, Claudia Perlich, the Chief Scientist at Dstillery, Claudia, welcome to theCUBE. >> Claudia: Thank you so much for having me. It's exciting. >> It is exciting! It's great to have you here. You are quite the prolific author, you've won data mining competitions and awards, you speak at conferences all around the world. Talk to us what you're currently doing as the Chief Scientist for Dstillery. Who's Dstillery? What's the Chief Scientist's role and how are you really leveraging data and science to be a change agent for your clients. I joined Dstillery when it was still called Media6Degrees as a very small startup in the New York ad tech space. It was very exciting. I came out of the IBM Watson Research Lab and really found this a new challenging application area for my skills. What does a Chief Scientist do? It's a good question, I think it actually took the CEO about two years to finally give me a job description, (laughter) and the conclusion at that point was something like, okay there is technical contribution, so I sit down and actually code things and I build prototypes and I play around with data. I also am referred to as Intellectual Leadership, so I work a lot with the teams just kind of scoping problems, brainstorming was may work or dozen, and finally, that's what I'm here for today, is what they consider an Ambassador for the company, so being the face to talk about the more scientific aspects of what's happening now in ad tech, which brings me to what we actually do, right. One of the things that happened over the recent past in advertising is it became an incredible playground for data signs because the available data is incomparable to many other fields that I have seen. And so Dstillery was a pioneer in that space starting to look at initially social data things that people shared, but over the years it has really grown into getting a sense of the digital footprint of what people do. And our primary business model was to bring this to marketers to help them on a much more individualized basis identify who their customers current as well as futures are. Really get a very different understanding than these broad middle-aged soccer mom kind of categories to honor the individual tastes and preferences and actions that really truly reflect the variety of what people do. I'm many things as you mentioned, I publish mom, what's a mom, and I have a horse, so there are many different parts to me. I don't think any single one description fully captures that and we felt that advertising is a great space to explore how you can translate that and help both sides, the people that are being interacted with, as well as the brands that want to make sure that they reach the right individuals. >> Lisa: Very interesting. Well, as buyers journey as changed to mostly online, >> Exactly. >> You're right, it's an incredibly rich opportunity for companies to harness more of that behavioral information and probably see things that they wouldn't have predicted. We were talking to Walmart Labs earlier and one of the interesting insights that they shared was that, especially in Silicon Valley where people spend too much time in the car commuting-- (laughter) You have a long commute as well by train. >> Yes. >> And you'd think that people would want, I want my groceries to show up on my doorstep, I don't want to have to go into the store, and they actually found the opposite that people in such a cosmopolitan area as Silicon Valley actually want to go into the store and pick up-- >> Claudia: Yep. >> Their groceries, so it's very interesting how the data actually can sometimes really change. It's really the scientific method on a very different scale >> Claudia: Much smaller. >> But really using the behavior insights to change the shopping experience, but also to change the experience of companies that are looking to sell their products. >> I think that the last part of the puzzle is, the question is no longer what is the right video for the Super Bowl, I mean we have the Super Bowl coming up, right? >> Lisa: Right. Right. >> They did a study like when do people pay attention to the Super Bowl. You can actually tell, cuz you know what people don't do when they pay attention to the Super Bowl? >> Lisa: Mm,hmm. >> They're not playing around with their phones. They're actually not playing-- >> Lisa: Of course. >> Candy Crush and all these things, so what we see in the ad tech environment, we actually see that the demand for the digital ads go down when people really focus on what's going on on the big screen. But that was a diversion ... >> Lisa: It's very interesting (laughter) though cuz it's something that's very tangible and very ... It's a real world applications. Question for you about data science and your background. You mentioned that you worked with IBM Watson. Forbes has just said that Data Scientist is the best job to apply for in 2017. What is your vision? Talk to us about your team, how you've grown that up, how you're using big data and science to really optimize the products that you deliver to your customers. >> Data Science is really many, many different flavors and in some sense I became a Data Scientist long before the term really existed. Back then I was just a particular weird kind of geek. (laughter) You know all of a sudden it's-- >> Now it has a name. (laughter) >> Right and the reputation to be fun and so you see really many different application areas depending very different skillsets. What is originally the focus of our company has always been around, can we predict what people are going to do? That was always the primary focus and now you see that it's very nicely reflected at the event too. All of sudden communicating this becomes much bigger a part of the puzzle where people say, "Okay, I realize that you're really "good at predicting, but can you tell me why, "what is it these nuggets of inside-- >> Interpretation, right. >> "That you mentioned. Can you visualize what's going on?" And so we grew a team initially from a small group of really focused machine learning and predictive skills over to the broader can you communicate it. Can you explain to the customer archieve brands what happened here. Can you visualize data. That's kind of the broader shift and I think the most challenging part that I can tell in the broader picture of where there is a bit of a short coming in skillset, we have a lot of people who are really good today at analyzing data and coding, so that part has caught up. There are so many Data Science programs. What I still am looking for is how do you bring management and corporate culture to the place where they can truly take advantage of it. >> Lisa: Right. >> This kind of disconnect that we still have-- >> Lisa: Absolutely. >> How do we educate the management level to be comfortable evaluating what their data science group actually did. Whether they working on the right problems that really ultimately will have impact. I think that layer of education needs to receive a lot more emphasis compared to what we already see in terms of this increased skillset on just the sheer technical side of it. >> You mentioned that you teach-- >> Claudia: Mm,hmm. >> Before we went live here, that you teach at NYU, but you're also teaching Data Science to the business folks. I would love for you to expand a little bit more upon that and how are you helping to educate these people to understand the impact. Cuz that's really, really a change agent within the company. That's a cultural change, which is really challenging-- >> Claudia: Very much so. >> Lisa: What's their perception? What's their interest in understanding how this can really drive value? >> What you see, I've been teaching this course for almost six years now, and originally it was really kind of the hardcore coders who also happened to get a PhD on the side, who came to the course. Now you increasingly have a very broad collection of business minded people. I typically teach in the part-time, meaning they all have day jobs and they've realized in their day jobs, I need this. I need that. That skill. That knowledge. We're trying to get on the ground where without having to teach them python and ARM whatever the new toys are there. How can you identify opportunities? How do you know which of the many different flavors of Data Science, from prediction towards visualization to just analyzing historical data to maybe even causality. Which of these tools is appropriate for the task at hand and then being able to evaluate whether the level of support that a machine can only bring, is it even sufficient? Because often just because you can analyze data doesn't mean that the reliability of the model is truly sufficient to support then a downstream business project. Being able to really understand those trade offs without necessarily being able to sit down and code it yourself. That knowledge has become a lot more valuable and I really enjoy the brainstorming when we're just trying to scope a project when they come with problems from their day job and say, "Hey, we're trying to do that." And saying, "Are you really trying to do that?" "What are you actually able to execute? "What kind of decisions can you make?" This is almost like the brainstorming in my own company now brought out to much broader people working in hospitals, people working in banking, so I get exposed to all of these kinds of problems said and that makes it really exciting for me. >> Lisa: Interesting. When Dstillery is talking to customer or prospective customers, is this now something that you're finding is a board level conversation within businesses? >> Claudia: No, I never get bored of that, so there is a part of the business that is pretty well understood and executed. You come to us, you give us money, and we will execute a digital campaign, either on mobile phones, on video, and you tell me what it is that you want me to optimize for. Do you want people to click on your ad? Please don't say yes, that's the worst possible things you may ask me to do-- (laughter) But let's talk about what you're going to measure, whether you want people to show up in your store, whether you really care about signing up for a test drive, and then the system automatically will build all the models that then do all the real-time bidding. Advertising, I'm not sure how many people are aware, as your New York Times page loads, every single ad slot on that side is sold in a real-time auction. About 50 billion times a day, we receive a request whether we want to bid on the opportunity to show somebody an ad. >> Lisa: Wow. >> So that piece, I can't make 50 billion decisions a day. >> Lisa: Right. >> It is entirely automated. There's this fully automated machine learning that just serves that purpose. What makes it interesting for me now that ... Now this is kind of standard fare if you want to move over and is more interesting parts. Well, can you for instance predict which of the 15 different creatives I have for Jobani, should I show you? >> Lisa: Mm,hmm. >> The one with the woman running, or the one with the kid opening, so there is no nuances to it and exploring these new challenges or going into totally new areas talking about, for instance churn prediction, I know an awful lot about people, I can predict very many things and a lot of them go far beyond just how you interact with ads, it's almost the most boring part. We can see people researching diabetes. We can provide snapshots to farmer telling them here's really where we see a rise of activity on a certain topic and maybe this is something of interest to understand which population is driving those changes. These kinds of conversations really making it exciting for me to bring the knowledge of what I see back to many different constituents and see what kind of problems we can possibly support with that. >> Lisa: It's interesting too. It sounds like more, not just providing ad technology to customers-- >> Claudia: Yeah. >> You're really helping them understand where they should be looking to drive value for their businesses. >> Claudia: That's really been the focus increasingly and I enjoy that a lot. >> Lisa: I can imagine that, that's quite interesting. Want to ask you a little bit before we wrap up here about your talk today. I was looking at your, the title of your abstract is, "Beware what you ask for: The secret life of predictive models". (laughter) Talk to us about some of the lessons you learn when things have gone a little bit, huh, I didn't expect that. >> I'm a huge fan of predictive modeling. I love the capabilities and what this technology can do. This being said, it's a collection of aha moments where you're looking at this and this, this doesn't really smell right. To give you an example from ad tech, and I alluded to this, when people say, "Okay we want a high click through rate." Yes, that means I have to predict who will click on an ad. And then you realize that no matter what the campaign, no matter what the product, the model always chooses to show the ad on the flashlight app. Yeah, because that's when people fumble in the dark. The model's really, really good at predicting when people are likely to click on an ad, except that's really not what you intended-- >> Right. >> When you asked me to do that. >> Right. >> So it's almost the best and powerful that they move off into a sidetracked direction you didn't even know existed. Something similar happened with one of these competitions that I won. For Siemens Medical where you had to identify an FMI images of breast, which of these regions are most likely benign or which one have cancer. In both models we did really, really well, all was good. Until we realized that the patient ID was by far the most predictive feature. Now this really shouldn't happen. Your social security number shouldn't be able to predict-- >> Lisa: Right. >> Anything really. It wasn't the social security number, but when we started looking a little bit deeper, we realized what had happened is the data set was a sample from different sources, and one was a treatment center, and one was a screening center and they had certain ranges of patient IDs, so the model had learned where the machine stood, not what the image actually contained about the probability of having cancer. Whoever assembled the data set possibly didn't think about the downstream effect this can have on modeling-- >> Right. >> Which brings us back to the data science skill as really comprehensive starting all the way from the beginning of where the data is collected, all the way down to be extremely skeptical about your own work and really make sure that it truly reflects what you want it to do. You asked earlier like what makes really good Data Scientists. The intuition to feel when something is wrong and to be able to pinpoint and trace it back with the curiosity of really needing to understand everything about the whole process. >> Lisa: And also being not only being able to communicate it, but probably being willing to fail. >> Claudia: That is the number one really requirement. If you want to have a data-driven culture, you have to embrace failure, because otherwise you will fail. >> Lisa: How do you find the reception (laughter) to that fact by your business students. Is that something that they're used to hearing or does it sound like a foreign language to them? >> I think the majority of them are in junior enough positions that they-- >> Lisa: Okay. >> Truly embrace that and if at all, they have come across the fact that they weren't allowed to fail as often as they had wanted to. I think once you go into the higher levels of conversation and we see that a lot in the ad tech industry where you have incentive problems. We see a lot of fraud being targeted. At the end of the day, the ad agency doesn't want to confess to the client that yeah they just wasted five million dollars-- >> Lisa: Right. >> Of ad spend on bots, and even the CMO might not be feeling very comfortable confessing that to the CO-- >> Right. >> Claudia: Being willing to truly face up the truth that sometimes data forces you into your face, that can be quite difficult for a company or even an industry. >> Lisa: Yes, it can. It's quite revolutionary. As is this event, so Claudia Perlich we thank you so much for joining us-- >> My pleasure. >> Lisa: On theCUBE today and we know that you're going to be mentoring a lot of people that are here. We thank you for watching theCUBE. We are live at Stanford University from the Women in Data Science Conference. I am Lisa Martin and we'll be right back (upbeat music)