>> Narrator: From Sand Hill Road, in the heart of Silicon Valley, it's theCUBE, presenting the People First Network, insights from entrepreneurs and tech leaders. >> Hello and welcome to a special Cube conversation, I'm John Furrier with theCUBE. We're here at Mayfield Fund on Sand Hill Road, Venture Cap for investing here for the People First co-created production by theCube and Mayfield. Next to us, Phil Finucane who's the former CTO of Express Scripts as well as a variety of other roles. Went to Stanford, Stanford alum. >> Mm hmm. >> Good to see you, thanks for joining me for this interview. >> Thank you, thank you for having me. >> So, before we get into some of the specifics, talk about your career, you're a former CTO of Express Scripts >> Yep. >> What are some of the other journeys that you've had? Talk about your roles. >> Yeah, I've had sort of a varied career. I started off as just a computer coder for a contract coder in the mid-90s. I sort of stumbled into it, not because I had a computer science background, but because when you start coding, sort of for fun in Silicon Valley in the mid-90s, there are just lots of jobs and I was lucky to have great mentors along the way. In 2003, I joined Yahoo and came in as the lead engineer, sort of the ops guy and the build and release guy for the log in and registration team at Yahoo, so I learned how to, went from being just a coder to being somebody who know how to run and build big systems and manage them all around the world. That was in the day when everything was bare metal and I could go to a data center and actually look at my machine and say, "Wow, that one's mine," right? And you know, sort of progressed from there to being the architect by the time that I left for some of the big social initiatives at Yahoo. On my way out, the YOS, the initiative to try to build Facebook in I think 2007, 2008 to try to take them on. That didn't work out too well, but it was definitely a formative experience in my career. From there I went to Zynga, where I was the CTO for Farmville. Was really, really good at getting middle-aged women in the Midwest to come play our game, and you know, was there for >> And it was highly, >> About three years >> high growth, Farmville >> Huge growth >> Took off like a rocket ship. >> Yeah, you know, over the 10 quarters I worked on the game we had over a billion dollars in revenue and that was, you know, the Zynga IPO'd on the back of that, right? And we weren't the only game, but we were certainly >> That was one of the big games >> The big whale, us and poker were the two that really drove the value in Zynga at that point. After that, I went to American Express, where I worked in a division that sort of sat off on the side of American Express focusing on stored value products. I was the chief architect for that division. Stored value products and international currency exchange. So, you know, at one point, I was in charge of both a pre-paid platform and American Express's traveler's checks platform, believe it or not, a thing that still exists. Although it's not heavily used any more. And you know, finally, I went to Express Scripts, where I spent the last three years as the CTO for that org. >> It's interesting, you've got a very unique background, because you know, you've seen the web scale, talk about bare metal Yahoo days, I mean, I remember those days vividly, you know, dealing with database schemas, I mean certainly the scale of Yahoo front page, never mind the different services that they had, which by the way, silo-like, they had databases >> Very, oh totally >> So building a registration and identity system must've been like, really stitching together a core part of Yahoo, I mean, what a Herculean task that must've been. >> Yeah, it was a lot of fun. I learned a lot, you know, we, it was my first experience in figuring out how to deal with security around the web. You know, we had, at the beginning, some vulnerabilities here and there, as time went on, our standards around interacting around the web got better and better. Obviously, Yahoo has run into trouble around that in subsequent years, but it was definitely a big learning experience, being involved in you know, the development of the OAuth 2.0 spec and all of that, I was sort of sitting there advising the folks who were, you know, in the middle of that, doing all the work. >> And that became such a standard as we know, tokens, dealing with tokens and SAS. Really drove a lot of the SAS mobile generation that did cloud, which becomes kind of that next generation so you had, you know Web 1.0, Web 2.0, then you had the cloud era, cloud 2.0, now they're goin' DevOps and apps. I want to get your thought, and you throw crypto in there just for fun, of dealing with blockchain and then token economics and new kinds of paradigms are coming online >> It's amazing how far we've come in those years, right? I mean I look at the database that was built inside of Yahoo and this predated me, you know, this was back to circa 1996, I think, but you know, big massively scalable databases that were needed just because the traditional relational database just wouldn't work at that scale, and Yahoo was one of the first to sort of discover that. And now you look at the database technologies that are out there today that take some of those core concepts and just extend them so much further and they're so much easier to access, to use, to run, operate, all of those things than back in the days of Yahoozle, UDB, and it's amazing just to see how far we've come. >> Phil, I want to get your thoughts, because you know, talking about Yahoo and just your experiences and even today, at that time it was like changing the airplane's engine at 35,000 feet, it's really difficult. A lot of corporate enterprises right nhow are having that same kind of feeling with digital, and digital transformation, I'd say it's a cliche, but it is true this impact, the role of data that's playing and the just for value creation but also cybersecurity could put a company out of business, so there's all kinds of looming things that are opportunities and challenges, that are sizable, huge tasks that was once regulated to the full stack developers and the full web scalers, now the lonely CIO with the anemic enterprise staff has to turn around on a dime. Staff up, build a stack, build commodity, scale out, this is pretty massive, and not a lot of people are talking about this. What's your view on this? Because this is super important. >> Yeah it is, and you know, so I had kind of a shock, moving from working my whole career here on Silicon Valley and then going to American Express, which you know, is very similar in a lot of ways to Express Scripts, and the sort of corporate mindset around, "What is technology?" There is this notion that everything is IT and here in the valley, IT is you know, internal networks and laptops and those sorts of things, the stuff that's required to make your enterprise run internally. Their IT is all of your infrastructure, right? And IT is a service organization, it's not the competitive advantage in your industry, right? And so both of the places that I've gone have had really forward-thinking leaders that have wanted to change the way that their enterprise operates around technology, and move away from IT but, to technology, to thinking about engineering as a core competency. And that's a huge change, not only for the CIO >> You're saying they did have that vision >> They had the vision, but they didn't know how to get there, so my charter coming in and you know, others who were on the teams around me, our charter was to come in and help build a real engineering organization as opposed to an IT org that's very vendor-oriented, you know, that's dependent on third parties to tell you the right thing or the wrong thing, you know that hires consultants to come in and help set up architecture standards, because we couldn't do that on our own, we're not the experts on this side. You know, that's sort of the mindset in many old school companies, right? That needs, that I think needs to change. This notion that software is eating the world is still not something that people have gotten their heads around in many companies, right? >> And data's washing out old business models, so if software's eating the world, data's the tsunami that's coming in and going to take out the beach and the people there. >> Right. And so it's like, all of these things, it's one thing for, you know, a forward-thinking CEO like Tim Wentworth at Express Scripts, who was responsible for bringing me and the group in, you know, those kinds of folks, it's one thing to know that you have to make that transition it's another thing to have a sense of what that means for an engineering team, and all the more for the rest of the organization to be able to get behind it. I mean, people you know, I don't know any number of business partners who've been used to, just sort of taking a spec, throwing it over the wall, and saying, "Come back to me in two years when you're done." That's not how effective organizations work around technology. >> Let's drill into that, because one of the things that's cultural, I mean I do some of the interviews of theCUBE, I talk to leaders all the time like yourself, the theme keeps coming back, it's culture, it's process, technology, all those things you talk about, but culture is the number one issue people point to, saying, "That's the reason why "something did or didn't happen." >> Correct. >> So, you talk about throwing it over the fence, that's waterfall, so you think about the old waterfall methodology, agile, well documented, but the mindset of product thinking is a really novel concept to corporate America Not to Silicon Valley, and entrepreneurs, they got to launch a product, not roll out SAP over two years, right, or something they used to be doing. So that's a cultural mindset shift. >> It's difficult for folks, even if they want to get on board to come along some of the time. One of the real big successes we had early on at Express Scripts was, you know, transitioning our teams to Agile wasn't difficult, what was difficult was getting business partners to sort of come along and be actively engaged in that product development mindset and lifecycle and all those sorts of things. And you know, we had one partner in particular, we were migrating from a really old, really clunky customer care application that you know had taken years and years to build, took on average, a new agent took six weeks to get trained on it because it was so complex and it's Oracle Forms and you know, every field in the database was a field on this thing, and there were green screens to do the stuff that you couldn't do in Oracle Forms, so and we wanted to rebuild the application. We tried to get them to come along and say, "Okay, we're going to do it in really small chunks," but business partners were like, "No, we can't afford "to have our agents swiveling between two applications." And so finally after we got our first sort of full-feature complete, we begged to go into a call center, you know with our business partners, and sit down with a few agents and just have them use it and see if it looked like it worked, if it did the right thing, and it was amazing seeing the business partner go, over the course of an hour, from "I can't be engaged in this, "I don't want an agent swiveling, "I don't want to be, you know, delivering partial applications "I want the whole thing." to, "Oh my god, it works way better, "the design is much nicer, the agents seem to like it," you know, "Here are the next things we should work on, "These are the things we got wrong." They immediately pivoted, and it wasn't, it was because they're the experts, they know how to run their business, they know what's important in their call centers, they know what their agents need, and they had just never seen the movie before, they just had no concept you could work that way. >> So this is actually interesting, 'cause what you're saying is, a new thing, foreign to the business partners, the tech team's on board, being Agile, building product, they have to, they can't just hear the feature benefits, they got to feel it. >> Yeah, they have to see it >> This seems to be the experience of success before they can move. Is that a success you think culturally, something that people have to be mindful of? >> It's absolutely something you have to be mindful of. And that was just the first step down the path. I mean, that team made a number of mistakes that folks here I think in the valley wouldn't normally make, you know. Over-committing and getting themselves into deep water by trying to get too much done and actually getting less accomplished in the process because of it and you know, the engagement around using data to actually figure out what's the next feature that we build. When you've got this enormous application to migrate, you should probably have some insight as to you know, feature by feature, what are you going to work on next? And that was a real challenge, 'cause there's a culture of expertise-driven, you know being subject-matter driven, expertise driven as opposed to being data driven about how do you >> Let's talk about data-driven. We had an interview earlier this morning with another luminary here at the Mayfield 50th conference celebration that they're having, and he said, "Data is the new feedback mechanism." and his point was, is that if you treat the Agile as an R&D exercise from a data standpoint. Not from a product but get it out there, get the data circulating in, it's critical in formulation of the next >> It is, yeah, it's absolutely critical. That was the eye opener for me going to Zynga. Zynga had an incredible, probably still does have, an incredible product culture that every single thing gets rolled out behind an experiment. And so you know, that's great from an operational perspective, because it allows you to, you know, move quickly and roll things out in small increments and when it doesn't work, you can just shut it off but it's not some huge catastrophe. But it's also critical because it allows you to see what's working and what's not and the flip side of that is, some humility of the people developing the products that their ideas are not going to work sometimes just because you know this domain well doesn't mean that you're necessarily going to be the expert on exactly how everything is going to play out. And so you have to have this ability to go out, try stuff, let it fail, use that, hopefully you fail quickly, you learn what's not working and use that to inform what's the next step down the path that you take, right? And Agile plays into it, but that's for me, that's the big transition that corporations really have to struggle with, and it's hard. >> You know you're, been there done that, seen multiple waves of innovation, want to bring up something to kind of get you going here. You see this classically in the old school 90s, 80s day. Product management, product people and sales people. They're always buttin' heads, you know? Product marketing, marketing people want this sales and marketing want this, product people buttin' heads, but now with Agile, the engineering focus has been the front lines. People are building engineering teams in house. They're building custom stacks for whatever reasons, the apps are getting smarter. The engineers are getting closer to the edge, the customer if you will. How do you help companies, or how do you advise companies to think about the relationship between a product-centric culture and a sales-centric culture? Because sometimes you have companies that are all about the customer-centric, customer-centric customer-centric, product-centric and sometimes if you try to put 'em together there's always going to be an alpha-beta kind of thing there and that's the balance in this. What's your take on this? Seems to be a cutting edge topic >> Yeah, well, so you know, one of the last big initiatives that I worked on at Express Scripts. Express Scripts has the, to my knowledge, the largest automated home delivery pharmacy in the world. It's amazing if you walk into one of our pharmacies where automation is packaging and filling prescriptions and packaging and shipping and doing all of that stuff. And we've built so much efficiency into the process that we've started getting slack in the system. Every year, you're trying to figure out how to make something work better and you know, have better automation around it. And so, you know, what do you do with all of that slack? The sales team can't sign up enough new customers for Express Scripts to actually fill that capacity. And so they create a division of commoditizing this, basically white labeling your pharmacy. We called it Pharmacy as a Platform, exposing APIs to third parties who might want to come along and hey, Phil's pharmacy can now fill branded prescriptions to get sent to you in your home, right? And so that's a fantastic vision, but there's a real struggle between engineering who had all these legacy stacks that we needed to figure out how to move to be able to really live up to this, you know the core of Express Scripts was our members and not somebody else's members. And so there's a lot of rewiring at the core that needs to be done. An operations team, a product team that's, you know, running these home delivery pharmacies, and a sales team that wants to go off and sell all over the place, right? And so, you know, early on, we started off and the sales team tried to sell, like six different deals that all required different parts of the vision, but you know, they weren't really, there was no real roadmap to figure out how do you get from where we're at to the end, and we could've done any of those things, but trying to do them all at once was going to be a trainwreck. And so, you know, we stubbed our toes a couple of times along the way, but I think it just came down to having a conversation and trying to be as transparent as possible on all sides, in all sides. To you know, try to get to a place where we could be effective in delivering on the vision. The vision was right. Everybody was doing all of the right things. But if you haven't actually, with so much of this stuff, if you haven't seen the movie, if you haven't worked this way before, there's nothing I can tell you that's going to make it work magically for you tomorrow. You have to just get this together and work in small increments to figure out how to get there. >> You got to go through spring training, you got to do the reps. >> Yep, absolutely. >> All right, so on your career, as you look at what you've done in your career, and what people outside are looking at right now, you got startups trying to compete and get a market position. You have other existing suppliers who could be the old guard, retooling and replatforming, refactoring, whatever the buzz word you want to use. And then the ultimate customer who wants to consume and have the ability of having custom personalization, data analytics, unlimited elastic capability with resources for their solution. How, what advice would you give to the startup, to the supplier, and to the customer to survive this next transition of cloud 2.0, you know and data tsunami, and all the opportunities that are coming? Because if they don't, they'll be challenged a startup goes out of business, a supplier gets displaced. >> Right, I mean, well, so the startup, I don't know if I have good advice for the startup. Startups in general have to find a market that actually works for them. And so, you know, I don't know that I've got some secret key that allows startups to be effective other than don't run out of money, try to figure out how to build effectively to get you to the point where you're, you know, where you're going to win. One of my earliest, one of the earliest jobs I had in my career, I came into a startup, and I tried, one of the founders had written the initial version of the code base. I, as a headstrong engineer, was convinced that he had done horrible work, and so I sort of holed up for like, six to eight weeks doing a hundred hours a week trying to rewrite the entire code base while getting nothing done for the startup. You know, in the end, that was the one job I've ever been fired from, and I should've been fired, because, you know, honestly as a startup, you shouldn't worry about perfection from an engineering perspective. You should figure out how to try to find your marketplace. Everybody has tech debt, you can fix that as time goes on, the startup needs to figure out how to be viable more than anything else. As far as suppliers go, you know, I don't know it's interesting the, you know, I sort of look at corporate America and there are many many companies that really rely heavily on their vendors to tell them how to do things. They don't trust in their own internal engineering ability. And then there are the ones, like the teams I have built at AmEx and Express Scripts that really do want to learn it all and be independent. I would say, identify when you walk into somebody's shop which they are and sell to them appropriately. You know, I've been a Splunk customer for a long time, I love Splunk. But the Splunk sales team early on at Express Scripts tried to come in and sell me on a whole bunch of stuff that Splunk was just not good at, right? >> And you knew that. >> And I knew that, because I've been a hands-on customer every since Zynga, right? I know what it's good at, and I love it as a tool, but you know, it's not the Swiss Army knife. It can't do everything. >> Well now you got Signal FX, so now you can get the observability you need. >> Exactly, right? So yeah, I, you know, I would say, you know, for those kinds of companies, it's important to go in and understand what your customer is, you know, what your customer is asking for and respond to them appropriately. And in some cases, they're going to need your expertise, either because they're building towards it or they haven't gotten there yet, and some cases, one of the things that I have done with teams of mine in the past, was it with AppDynamics at Express Scripts, excuse me at AmEx, five or six years ago, they were sold on, you know, bringing in AppDynamics as a monitoring tool, I actually made them not bring it in, because they didn't know what they didn't know. I made them go build some basic monitoring, you know, using some open source tools, just to get some background, and then, you know, once they did, we ended up bringing AppDynamics in, but doing it in a way that they were accretive to what we were trying to accomplish and not just this thing that was going to solve all of our problems. >> And so that brings up the whole off-the-shelf general purpose software model that you were referring to. The old model was lean on your vendors. They're supplying you, and because you don't have the staff to do it yourself. That's changing, do you think that's changing? >> It is, it's changing, but again, I think there's a lot of places where people nominally want to go there, but don't know how to get there, and so, you know, people are stubbing their toes left and right. If you're doing it with this mindset of, we're constantly getting better and we're learning and it's okay to make mistakes as long as we move forward, >> It's okay to stub your toe as long as you don't cut an artery open. >> Yeah, that's true, yeah exactly >> You don't want to bleed out, that's a cybersecurity hack >> That's true, that's true. But for me a lot of the time that just comes down to how long are you waiting before you stub your toe? If you're, you know, if you wait two years before you actually try to launch something, the odds of you cutting your leg off are much higher than >> Well I want to get into the failure thing, so I think stubbing your toe brings up this notion of risk management, learning what to try, what not to do, take experiments to try to your, which is a great example. Before you get there, you mentioned suppliers. One of the things we hear and I want to get your thoughts on, is that, a lot of CIOs and C-sos, and CBOs, or whatever title is the acronym, they're trying to reduce the number of suppliers. They don't want more tools, right? They don't necessarily want another tool for the tool's sake or they might want to replatform, what does that even mean? So, we're hearing in our interviews and our discussions with partitioners, "Hey, I want to get my suppliers down, "and by the way, I want to be API driven, "so I want to start getting to a mode "where I'm dictating the relationship to suppliers." How do you respond to that? Do you see that as aspirational, real dynamic, or fiction? >> It's a good goal to give motivation, I believe it. For me, I approach the problem a little differently. I'm a big believer, well, so, because I've seen this pattern of this next tool is going to be the one that consolidates three things and it's going to be the right answer and instead of eliminating three and getting down to one, you have four, because you're, you need to unwire this new thing, there's a lot of time and effort required to get rid of, you know, your old technology stack, and move to the new one, right? I've seen that especially coming from the C-Sec for Express Scripts is an amazing guy, and you know, was definitely trying to head down that path but we stubbed our toes, we ran into problems in trying to figure out, you know, how do you move from one set of networking gear to the next set? How do you deal with, you know, all of the virus protection and all the other, there's a huge variety of tools. >> So it's not just technical debt, it's disruption >> It's disruption to the existing stack, and you've got to move from old to new, so my philosophy has always been, with technical debt, when you're in debt, and I think technical debt really does operate in a lot of ways like real debt, right? Probably good to have some of it. If you're completely debt-free, that's I've never been in that place before. >> You're comfortable. You might not be moving, >> Exactly, right? But with that technical debt, you know, there's two ways to pay down your debt. You can scrimp and save and put more money into debt principal payments as opposed to spending on other new things, or, well and/or, build productive capacity. So a huge focus for me for the engineering teams that we've built, and this is not anything new to the folks in this area, but, you know, always think about an arms race, where you're getting 1% better every day. The aggregation of marginal gains and investing in internal improvements so that your team is doubling productivity every year, which is something that's really possible for, you know, some of these engineering organizations, is the way that you deal with that, right? If you get to the point where your team is really, really productive, they can go through and eliminate all the old legacy technology. >> That's actually great advice, and it's interesting, because a lot of people just get hung up on one thing. Operating something, and then growing something, and you can have different management styles and different techniques for both, the growth team, the operating team. You're kind of bringing in and saying, we can do both. Operate with growth in mind, to 1% better approach. >> Right, you know, and for me, it's been an interesting journey, you know. I started off as the engineer and then the architect, who was always focused on just the technology, the design of the system in production. Sort of learned from there that you had to be good at the you know, all the systems that get code from a developer's desktop into production, that's a whole interrelated system that's not isolated from your production system. And then from there, it has to be the engineering team that you build has to be effective as well. And so, I've moved from being very technology-centric to somebody who says, "Okay, I have to start "with getting the team right "and getting the culture right if we're ever going to "be able to get the technology to a good place." Mind you, I still love the technology. I'm still an architect at my core, but I've come to this realization that good technology and bad teams will get crushed by bad technologies and good teams. Because now I've seen that a couple of places, where you have old but evolving technology stacks that have gone from low availability and poor performance and low ability to get new features into production to a place where you're fixing all of that at a high rate. It starts with the team. >> You're bringing us some core Silicon Valley ethos to the IT conversation, because what you're talking about is "I'll fund an A team with a B plan any day "over a B team with an A plan." >> Right. >> And where this makes sense, I think is true, is that to your point about debt, A teams know how to manage it. >> Yeah. >> So this is kind of what you're getting at here. >> Right. >> You can take that same ethos, so it's the Agile enterprise. >> Yeah, it is >> That's what we're talking about. Okay, so hypothetical final point I want to chat with you about. Let's just say you and I were startin' a company. We're chief architects, you're the chief architect, I'm a coder, what are we doing? Do I code from horizontally scalable cloud, certainly cloud native, how would you think about building, we have an app in mind, all of our requirements defined, it's going to be data-centric, it's going to be game change and have community, it might have some crypto in there, who knows, but it's going to be fun. How do we scale this out to be really fast? How would you architect this? >> Yeah, well, you know, I do start in the cloud. I go to AWS or Azure or any of the offerings that are out there, and you know, leverage everything that they have that's already wired up already for you. I mean the thing that we've seen in the evolution of software and production systems over the last, well, forever, is you get more and more leverage every day, every year, right? And so, if you and I are startin' a new company, let's go use the tools that are there to do the things that we shouldn't be wasting our time on. Let's focus on the value for our company as much as we can. Don't over-architect. I think premature optimization is a thing that you know, I learned early on is a real problem. You should, you know >> Give an example, what that would look like. >> I've seen >> Database scale decisions done with no scale >> Correct, yeah, you know? You go off >> Let's pick this! It's the most scalable database, well we have no users yet. >> Right, you know you build the super complicated caching architecture or you know, you go design the most critical part of the system out of the gate, you know, using Assembly. You use C++ or, you use a low level language when a high level language with your three users would be just fine, right? You can get the work done in a fraction of the time. >> And get the business logic down, the IP, >> Solve the problem when it becomes a problem. Like, it's, you know, I've, any number of times, I've run into systems, I've built systems where you have some issue that you run into, and you have to go back and redesign some chunk of the system. In my experience, I'm really bad at predicting, and I think engineers are really bad at predicting what are going to be the problem areas until you run into them, so just go as simple as you can out of the gate, you know. Use as many tools as you can to solve problems that, you know, maybe as an engineer, I want to go rebuild every thing from scratch every time. I get the inclination. But it's >> It's a knee-jerk reaction to do that but you stay your course. Don't over-provision, overthink it, thus start taking steps toward the destination, the vision you want to go to, and get better, operate >> Solve the problem you have when it shows up. >> So growth mindset, execute, solve the problems when they're there. >> Right, and initially the problem that you have is finding a market, you know, not building the greatest platform in the world, right? >> Find a market, exactly. >> Right? >> Phil, thanks for taking the time >> Thank you very much, appreciate it. >> Appreciate the insights. Hey, we're here for the People First, Mayfield's 50th celebration, 50 years in business. It's a CUBE co-production, I'm John Furrier, thanks for watching >> Thanks John. (outro music)

>> Announcer: Live from Santa Clara in the heart of Silicon Valley, it's The Cube, covering Cloud Foundry Summit 2017. Brought to you by the Cloud Foundry Foundation and Pivotal. >> Welcome back, I'm Stu Miniman joined by my cohost John Troyer. Happy to welcome to the program a first-time guest but a company we've had on the program before. Brian Gregory is the director of Cloud Strategy and Engineering at Express Trips, I'm sorry, Express Scripts, and Express Scripts booth is actually right behind us on the stage. Thank you so much for joining us. >> Thank you for having me. >> All right, you were giving us a little bit of background about, I believe it's been about three years you've been with the company. Why don't you share with our audience Express Scripts company that many of us have probably used, less likely that everybody knows who you are and your role there. >> Yeah, so my role today, again, is a Cloud Strategy Engineering director, but it's really focused on building out the next-gen platform, making infrastructure irrelevant, if you will, and making our developers go faster and making everything as streamless as possible. >> And Brian, just for our audience that doesn't know Express Scripts, give us, what's the brain of the company and what are you known for. >> We serve 85 million patients today, 3,000 contractor. We have, if you go look at what we do as a business, the pharmacy benefit management side, our goal is to basically make prescriptions safer, more affordable, for all of our patients. So if you think about what we're doing, that's really what we're doing, and we like to say pharmacy smarter. And for us, that's our major goal in everything we do. So my team, anybody's team, doesn't matter what you're doing in technology, that's your end goal, to deliver value to those patients and your clients and so that's what we focus on. >> Well, Brian, it's a good thing. IT's changing all the time, at least health care, nothing's been changing radically, changing all the time. So, bring us inside what you did when you came inside the company. Your role, as you said, infrastructure. Wanted to worry about that less, that's something we hear a lot. >> Right, and one thing I should clarify is that Express Scripts has always been a technology company. If you look at their grass roots and what they were built upon, in 30 years they grew to 100 billion dollar business, really by utilizing technology at the end of the day. When I came in, I was managing some infrastructure teams and database organizations and we decided that this was, the whole digital transformation, or cloud strategy, was a real thing, so my leadership asked me to take this on as an initiative, that was my background, that's where I came from, my grass roots, if you will, from Savs and Centurylink and that became a full-time role. It came to one point where you can't do both, this is taking off and it's being a real thing. Our main goal was to say, how are we going to step outside the box? We still have to run a 100 billion dollar organization. But we got to figure out what the new is, right, you're going to invent that next light bulb, and we've got to maintain the current one. And so, for us, we wanted a full-fledged platform. It wasn't about just spitting out BM's and delivering those, I think we'd had that covered. It was really about figuring out how we're going to figure out cloud, cloud in general. And then what about multi-cloud and how do we get a platform that could seamlessly integrate with all of those. >> And Brian, what was the underlying driver there in the business? Is it you needed to develop more software, you needed to move faster, what's the why, was it cost savings and things getting out of hand? >> Yeah, all of the above. I think specifically it's about delivering value, delivering value to patients as fast and seamless as possible. And so we wanted to figure that out. The old ways, if we all go back in our years of, there were days that I would get hardware and physically, I'm going to figure out how to put the drive so that they're getting more IO or whatever. Those projects have been solved, right, and if you look at companies 10 years ago, they were like, virtualization is scary, I can't do that, I have production workload. So the trend in the market is to keep moving up the stack, and so ultimately, that's where you end up focusing on. Where do we deliver value as an IT organization? That's not for us to go build a homebrewed system that does any of those things. You can go buy those things, integrate them and figure out how they can drive value in your business. So that's what we wanted to do, get a platform. The first goal we had was actually a really interesting story. We wanted to build a new mobile application that would allow consumers to go on and make a user experience much better than it had previously been. If you wanted to order prescriptions, you could go onto this app and say, not just, can I order that, but I can also see what the prices are at different places local to you, the distance to those, and then what would it take if you did a 90-day fill for our home-delivery program and you could sign up via that method. And that was really what we were going after, that end user experience to say, how do we change the experience for our consumers. Not necessarily the back-end stuff, the day-to-day batch processing that they don't really care about how that's done as long as it's done within the time threshold that that's supposed to be done. >> Brian, can you talk a little bit about the process of getting to where we are in terms of, you talked about trying to figure out cloud, and part of that is figuring out which platform to go with and then part of it is finding the people with the skills to, once you've decided on a direction, to help you figure it out. Can you talk just a little bit about maybe that learning journey for you, for Express Scripts? >> Sure, sure. Yeah, it's really interesting, 'cause when I talk, I feel like this was five years that I've been doing this specifically at Express Scripts, when really, it's 18 months that we've really stood up our internal hybrid cloud and got our platform installed. So, yeah, it's learning by doing but the nice thing about everything we're doing, and you hear this all the time, is that we can iterate, you can make changes. It's not like you have to wait three months and then you can't shift or can't course-correct. Learning, the team's been amazing, so I grabbed some people within, they got re-trained on this is the new stack. I also brought in some people that had previously worked with me that had some of the skill sets. Really, it's people that are curious in nature and want to learn. But then you go, that's a neat story, we can stand up a cloud or use an external one, you can stand up your pads. But at the end of the day, what do you do next, how do you start to engage developers? 'Cause when we opened the doors for business, it wasn't like we had everybody standing there waiting to get in. You had to convince them of, these are the features, not convince them, show them the features and the functionality and why it mattered to them. Why does it matter now, and then you go, okay, that's great, and you start to, I would say my team focuses probably 80% of their time on teaching people how to fish, hoping that the developers get better and the consuming of the platform, they help each other and we see a lot of that happening in our slack channels and hipchat and different things, communication tools where they're helping each other, so we're not even having to answer all the questions. But then you get the whole problem of, all right, well, now we've got release management we're going to start working with and that opens a whole new can and they're transforming as well and they're definitely changing their processes. But it gets hairy when you start looking at, well, we've got to keep the tourniquet over here 'cause we can't afford any disruption or outages for our patients. But these new guys, if they check all these boxes, they should be able to deploy whenever they feel like in the middle of the day and we should feel comfortable doing that because it's now micro-service, it's not some monolith thing that they don't understand. >> Brian, can you give us a little insight on the state of your application, so I think most people understand something like cloud foundry, oh, I wanted to build that new app, that's a great use case. How many applications have you moved over, do you have a percentage you measure? I heard the Liberty Mutual keynote was like, what percentage of workloads they have there, what percentage of people code, how fast they release code on this thing, what metrics do you use? >> Yeah, so I had this conversation last night. We don't have, I can tell you that we have over 1100 applications that have moved in 18 months, so I would tell you that, when it started out, we had specific goals of migrating existing lift and shift and refactoring and things like that. What we found was that there's all this net new coming in, not just what we're doing is blowing up within the company, but they're also doing that on the developer's side and they're doing a lot of new things so those new projects clearly migrate over or come in the door starting out with cloud-first strategy. Then you start to lift and shift, and you really have to start cherry-picking what makes sense. Some things you go, there's not really the value, there's no user experience behind this, it's literally just a monetary thing, maybe or maybe not. But you start taking the dollars that you're going to put towards migrating this, and then you're like, well, it's not really a win-win. So what we found was that allnet news coming this direction on the cloud foundry platform, and the workload that makes sense, and then we started cherry-picking things that we re-wrote in spring and they're slowly migrating. But today we're at 1100, we just hit 1100 apps, which is pretty good for 18 months. >> That's really impressive in 18 months. Any lessons learned there as to things you could do to move faster or mistakes that were made that you could tell your peers, hey, watch out for this? >> Yeah, I definitely have a lot of those but, at the same time, I feel like lessons learned are the best thing, the best thing we do is fail, literally, because then you learn something from it and you move forward. I think the one advice, some advice I would give to people is, don't get hung up on trying to be perfect. If there's a lift and shift opportunity and you only get 60 to 70% of the goodness of moving this thing, then just go ahead and do it, don't say, well, but it's not perfect, and we want to make this app completely different, 'cause that's where people get stuck on. I think once they realize they just try some things and then you can get over there and change it and make it perfect down the road. But some people are like, why would I move one to one, I should refactor this app and maybe have it multiple per one and those are great conversations but I think it keeps people, the old analysis paralysis conversation, and it's like, just try it, just go. I would say another good story we had is, we have an outcomes conference that we host, Express Scripts. We had, they were trying to come up with a new way to do basically a content management solution show a digital benefit guide, and they came in and they wanted to try one technology. Didn't work out, two weeks later they came back and were like, well, it doesn't deliver that, so we tried something else. And we tried four different things for this person and they got to where they wanted to be but the important part was that we could change on the fly. In the old days, it was like, you ordered hardware or you ordered BM's, you were stuck because he had dates to deliver and we basically, we can change things. If it doesn't work we'll try something else and you can move around and do things at scale where you couldn't before. >> It sounds like a set of outcomes that the business side, the executive side, leadership, can actually point to and recognize as helping transform the business. You're here at the conference, a sponsor of the conference, participating at the conference. And you're a pharmacy benefits company, so, that's an interesting position to be in. But it sounds like the business and management supports you and recognizes that this is helping move with velocity. >> Yes, so from the top down, we are a technology company at heart. This is what we do and the company has come a long way with, when I got there, the fact that we're here sponsoring it means a great deal to me because this is, after all, an open-source tech conference, which is amazing. It's nice to talk about, get some branding out there and talk about what we're doing so people know that, hey, there's a lot of really cool things happening. Maybe we recruit some people at the end of the day as a result, you never know. But yeah, it's really about just branding and then supporting the community and getting out the word of what we're doing and why it matters. >> Last question I have for you, Brian, is, a lot of discussion about multi-cloud and things like Kubernetes enable that. Can you share what public cloud or public cloud you use, how you look at that dynamic? >> Yeah, so we've got a few things in the works. So there's a few POC's happening, I would tell you that our internal cloud is where we have everything hosted today. We've built an internal hybrid cloud. >> So you have the data centers? >> Yeah, we have multiple data centers and we have our internal hybrid clouds built out. We are evaluating some external capabilities, we're also doing some partnerships, you can actually go read about. Our CIO just released a story about that. We're definitely looking for that, where's our burst-able capacity. With our data and with what we have going on, going external is a much different conversation than where my previous company, we're talking about Hippa compliance and a lot of different data issues that we've got to make sure we're protecting and our most important thing to do is protect that data from our patients. So there's no reason for us to go, we have to get out external at this point, but we do see that as an important part of our key going forward to say, this is part of our strategy, to say, we've got to get an external solution as well. >> Brian Gregory, always love the stories of how digital transformation are helping to impact everyone who uses prescriptions. I mean, no better way of helping people, so we'll be back with lots more coverage here of Cloud Foundry Summit 2017. Thanks for watching The Cube. (techno music) >> I remember when I had such a fantastic batting practice, I walked by a couple of sports writers in that era.

>> Hi I'm Peter Burris. Welcome to another Wikibon Action Item. (upbeat music) We're once again broadcasting from theCube's beautiful Palo Alto, California studio. I'm joined here in the studio by George Gilbert and David Floyer. And then remotely, we have Jim Kobielus, David Vellante, Neil Raden and Ralph Finos. Hi guys. >> Hey. >> Hi >> How you all doing? >> This is a great, great group of people to talk about the topic we're going to talk about, guys. We're going to talk about the notion of de-risking digital business. Now, the reason why this becomes interesting is, the Wikibon perspective for quite some time has been that the difference between business and digital business is the role that data assets play in a digital business. Now, if you think about what that means. Every business institutionalizes its work around what it regards as its most important assets. A bottling company, for example, organizes around the bottling plant. A financial services company organizes around the regulatory impacts or limitations on how they share information and what is regarded as fair use of data and other resources, and assets. The same thing exists in a digital business. There's a difference between, say, Sears and Walmart. Walmart mades use of data differently than Sears. And that specific assets that are employed and had a significant impact on how the retail business was structured. Along comes Amazon, which is even deeper in the use of data as a basis for how it conducts its business and Amazon is institutionalizing work in quite different ways and has been incredibly successful. We could go on and on and on with a number of different examples of this, and we'll get into that. But what it means ultimately is that the tie between data and what is regarded as valuable in the business is becoming increasingly clear, even if it's not perfect. And so traditional approaches to de-risking data, through backup and restore, now needs to be re-thought so that it's not just de-risking the data, it's de-risking the data assets. And, since those data assets are so central to the business operations of many of these digital businesses, what it means to de-risk the whole business. So, David Vellante, give us a starting point. How should folks think about this different approach to envisioning business? And digital business, and the notion of risk? >> Okay thanks Peter, I mean I agree with a lot of what you just said and I want to pick up on that. I see the future of digital business as really built around data sort of agreeing with you, building on what you just said. Really where organizations are putting data at the core and increasingly I believe that organizations that have traditionally relied on human expertise as the primary differentiator, will be disrupted by companies where data is the fundamental value driver and I think there are some examples of that and I'm sure we'll talk about it. And in this new world humans have expertise that leverage the organization's data model and create value from that data with augmented machine intelligence. I'm not crazy about the term artificial intelligence. And you hear a lot about data-driven companies and I think such companies are going to have a technology foundation that is increasingly described as autonomous, aware, anticipatory, and importantly in the context of today's discussion, self-healing. So able to withstand failures and recover very quickly. So de-risking a digital business is going to require new ways of thinking about data protection and security and privacy. Specifically as it relates to data protection, I think it's going to be a fundamental component of the so-called data-driven company's technology fabric. This can be designed into applications, into data stores, into file systems, into middleware, and into infrastructure, as code. And many technology companies are going to try to attack this problem from a lot of different angles. Trying to infuse machine intelligence into the hardware, software and automated processes. And the premise is that meaty companies will architect their technology foundations, not as a set of remote cloud services that they're calling, but rather as a ubiquitous set of functional capabilities that largely mimic a range of human activities. Including storing, backing up, and virtually instantaneous recovery from failure. >> So let me build on that. So what you're kind of saying if I can summarize, and we'll get into whether or not it's human expertise or some other approach or notion of business. But you're saying that increasingly patterns in the data are going to have absolute consequential impacts on how a business ultimately behaves. We got that right? >> Yeah absolutely. And how you construct that data model, and provide access to the data model, is going to be a fundamental determinant of success. >> Neil Raden, does that mean that people are no longer important? >> Well no, no I wouldn't say that at all. I'm talking with the head of a medical school a couple of weeks ago, and he said something that really resonated. He said that there're as many doctors who graduated at the bottom of their class as the top of their class. And I think that's true of organizations too. You know what, 20 years ago I had the privilege of interviewing Peter Drucker for an hour and he foresaw this, 20 years ago, he said that people who run companies have traditionally had IT departments that provided operational data but they needed to start to figure out how to get value from that data and not only get value from that data but get value from data outside the company, not just internal data. So he kind of saw this big data thing happening 20 years ago. Unfortunately, he had a prejudice for senior executives. You know, he never really thought about any other people in an organization except the highest people. And I think what we're talking about here is really the whole organization. I think that, I have some concerns about the ability of organizations to really implement this without a lot of fumbles. I mean it's fine to talk about the five digital giants but there's a lot of companies out there that, you know the bar isn't really that high for them to stay in business. And they just seem to get along. And I think if we're going to de-risk we really need to help companies understand the whole process of transformation, not just the technology. >> Well, take us through it. What is this process of transformation? That includes the role of technology but is bigger than the role of technology. >> Well, it's like anything else, right. There has to be communication, there has to be some element of control, there has to be a lot of flexibility and most importantly I think there has to be acceptability by the people who are going to be affected by it, that is the right thing to do. And I would say you start with assumptions, I call it assumption analysis, in other words let's all get together and figure out what our assumptions are, and see if we can't line em up. Typically IT is not good at this. So I think it's going to require the help of a lot of practitioners who can guide them. >> So Dave Vellante, reconcile one point that you made I want to come back to this notion of how we're moving from businesses built on expertise and people to businesses built on expertise resident as patterns in the data, or data models. Why is it that the most valuable companies in the world seem to be the ones that have the most real hardcore data scientists. Isn't that expertise and people? >> Yeah it is, and I think it's worth pointing out. Look, the stock market is volatile, but right now the top-five companies: Apple, Amazon, Google, Facebook and Microsoft, in terms of market cap, account for about $3.5 trillion and there's a big distance between them, and they've clearly surpassed the big banks and the oil companies. Now again, that could change, but I believe that it's because they are data-driven. So called data-driven. Does that mean they don't need humans? No, but human expertise surrounds the data as opposed to most companies, human expertise is at the center and the data lives in silos and I think it's very hard to protect data, and leverage data, that lives in silos. >> Yes, so here's where I'll take exception to that, Dave. And I want to get everybody to build on top of this just very quickly. I think that human expertise has surrounded, in other businesses, the buildings. Or, the bottling plant. Or, the wealth management. Or, the platoon. So I think that the organization of assets has always been the determining factor of how a business behaves and we institutionalized work, in other words where we put people, based on the business' understanding of assets. Do you disagree with that? Is that, are we wrong in that regard? I think data scientists are an example of reinstitutionalizing work around a very core asset in this case, data. >> Yeah, you're saying that the most valuable asset is shifting from some of those physical assets, the bottling plant et cetera, to data. >> Yeah we are, we are. Absolutely. Alright, David Foyer. >> Neil: I'd like to come in. >> Panelist: I agree with that too. >> Okay, go ahead Neil. >> I'd like to give an example from the news. Cigna's acquisition of Express Scripts for $67 billion. Who the hell is Cigna, right? Connecticut General is just a sleepy life insurance company and INA was a second-tier property and casualty company. They merged a long time ago, they got into health insurance and suddenly, who's Express Scripts? I mean that's a company that nobody ever even heard of. They're a pharmacy benefit manager, what is that? They're an information management company, period. That's all they do. >> David Foyer, what does this mean from a technology standpoint? >> So I wanted to to emphasize one thing that evolution has always taught us. That you have to be able to come from where you are. You have to be able to evolve from where you are and take the assets that you have. And the assets that people have are their current systems of records, other things like that. They must be able to evolve into the future to better utilize what those systems are. And the other thing I would like to say-- >> Let me give you an example just to interrupt you, because this is a very important point. One of the primary reasons why the telecommunications companies, whom so many people believed, analysts believed, had this fundamental advantage, because so much information's flowing through them is when you're writing assets off for 30 years, that kind of locks you into an operational mode, doesn't it? >> Exactly. And the other thing I want to emphasize is that the most important thing is sources of data not the data itself. So for example, real-time data is very very important. So what is your source of your real-time data? If you've given that away to Google or your IOT vendor you have made a fundamental strategic mistake. So understanding the sources of data, making sure that you have access to that data, is going to enable you to be able to build the sort of processes and data digitalization. >> So let's turn that concept into kind of a Geoffrey Moore kind of strategy bromide. At the end of the day you look at your value proposition and then what activities are central to that value proposition and what data is thrown off by those activities and what data's required by those activities. >> Right, both internal-- >> We got that right? >> Yeah. Both internal and external data. What are those sources that you require? Yes, that's exactly right. And then you need to put together a plan which takes you from where you are, as the sources of data and then focuses on how you can use that data to either improve revenue or to reduce costs, or a combination of those two things, as a series of specific exercises. And in particular, using that data to automate in real-time as much as possible. That to me is the fundamental requirement to actually be able to do this and make money from it. If you look at every example, it's all real-time. It's real-time bidding at Google, it's real-time allocation of resources by Uber. That is where people need to focus on. So it's those steps, practical steps, that organizations need to take that I think we should be giving a lot of focus on. >> You mention Uber. David Vellante, we're just not talking about the, once again, talking about the Uberization of things, are we? Or is that what we mean here? So, what we'll do is we'll turn the conversation very quickly over to you George. And there are existing today a number of different domains where we're starting to see a new emphasis on how we start pricing some of this risk. Because when we think about de-risking as it relates to data give us an example of one. >> Well we were talking earlier, in financial services risk itself is priced just the way time is priced in terms of what premium you'll pay in terms of interest rates. But there's also something that's softer that's come into much more widely-held consciousness recently which is reputational risk. Which is different from operational risk. Reputational risk is about, are you a trusted steward for data? Some of that could be personal information and a use case that's very prominent now with the European GDPR regulation is, you know, if I ask you as a consumer or an individual to erase my data, can you say with extreme confidence that you have? That's just one example. >> Well I'll give you a specific number on that. We've mentioned it here on Action Item before. I had a conversation with a Chief Privacy Officer a few months ago who told me that they had priced out what the fines to Equifax would have been had the problem occurred after GDPR fines were enacted. It was $160 billion, was the estimate. There's not a lot of companies on the planet that could deal with $160 billion liability. Like that. >> Okay, so we have a price now that might have been kind of, sort of mushy before. And the notion of trust hasn't really changed over time what's changed is the technical implementations that support it. And in the old world with systems of record we basically collected from our operational applications as much data as we could put it in the data warehouse and it's data marked satellites. And we try to govern it within that perimeter. But now we know that data basically originates and goes just about anywhere. There's no well-defined perimeter. It's much more porous, far more distributed. You might think of it as a distributed data fabric and the only way you can be a trusted steward of that is if you now, across the silos, without trying to centralize all the data that's in silos or across them, you can enforce, who's allowed to access it, what they're allowed to do, audit who's done what to what type of data, when and where? And then there's a variety of approaches. Just to pick two, one is where it's discovery-oriented to figure out what's going on with the data estate. Using machine learning this is, Alation is an example. And then there's another example, which is where you try and get everyone to plug into what's essentially a new system catalog. That's not in a in a deviant mesh but that acts like the fabric for your data fabric, deviant mesh. >> That's an example of another, one of the properties of looking at coming at this. But when we think, Dave Vellante coming back to you for a second. When we think about the conversation there's been a lot of presumption or a lot of bromide. Analysts like to talk about, don't get Uberized. We're not just talking about getting Uberized. We're talking about something a little bit different aren't we? >> Well yeah, absolutely. I think Uber's going to get Uberized, personally. But I think there's a lot of evidence, I mentioned the big five, but if you look at Spotify, Waze, AirbnB, yes Uber, yes Twitter, Netflix, Bitcoin is an example, 23andme. These are all examples of companies that, I'll go back to what I said before, are putting data at the core and building humans expertise around that core to leverage that expertise. And I think it's easy to sit back, for some companies to sit back and say, "Well I'm going to wait and see what happens." But to me anyway, there's a big gap between kind of the haves and the have-nots. And I think that, that gap is around applying machine intelligence to data and applying cloud economics. Zero marginal economics and API economy. An always-on sort of mentality, et cetera et cetera. And that's what the economy, in my view anyway, is going to look like in the future. >> So let me put out a challenge, Jim I'm going to come to you in a second, very quickly on some of the things that start looking like data assets. But today, when we talk about data protection we're talking about simply a whole bunch of applications and a whole bunch of devices. Just spinning that data off, so we have it at a third site. And then we can, and it takes to someone in real-time, and then if there's a catastrophe or we have, you know, large or small, being able to restore it often in hours or days. So we're talking about an improvement on RPO and RTO but when we talk about data assets, and I'm going to come to you in a second with that David Floyer, but when we talk about data assets, we're talking about, not only the data, the bits. We're talking about the relationships and the organization, and the metadata, as being a key element of that. So David, I'm sorry Jim Kobielus, just really quickly, thirty seconds. Models, what do they look like? What are the new nature of some of these assets look like? >> Well the new nature of these assets are the machine learning models that are driving so many business processes right now. And so really the core assets there are the data obviously from which they are developed, and also from which they are trained. But also very much the knowledge of the data scientists and engineers who build and tune this stuff. And so really, what you need to do is, you need to protect that knowledge and grow that knowledge base of data science professionals in your organization, in a way that builds on it. And hopefully you keep the smartest people in house. And they can encode more of their knowledge in automated programs to manage the entire pipeline of development. >> We're not talking about files. We're not even talking about databases, are we David Floyer? We're talking about something different. Algorithms and models are today's technology's really really set up to do a good job of protecting the full organization of those data assets. >> I would say that they're not even being thought about yet. And going back on what Jim was saying, Those data scientists are the only people who understand that in the same way as in the year 2000, the COBOL programmers were the only people who understood what was going on inside those applications. And we as an industry have to allow organizations to be able to protect the assets inside their applications and use AI if you like to actually understand what is in those applications and how are they working? And I think that's an incredibly important de-risking is ensuring that you're not dependent on a few experts who could leave at any moment, in the same way as COBOL programmers could have left. >> But it's not just the data, and it's not just the metadata, it really is the data structure. >> It is the model. Just the whole way that this has been put together and the reason why. And the ability to continue to upgrade that and change that over time. So those assets are incredibly important but at the moment there is no way that you can, there isn't technology available for you to actually protect those assets. >> So if I combine what you just said with what Neil Raden was talking about, David Vallante's put forward a good vision of what's required. Neil Raden's made the observation that this is going to be much more than technology. There's a lot of change, not change management at a low level inside the IT, but business change and the technology companies also have to step up and be able to support this. We're seeing this, we're seeing a number of different vendor types start to enter into this space. Certainly storage guys, Dylon Sears talking about doing a better job of data protection we're seeing middleware companies, TIBCO and DISCO, talk about doing this differently. We're seeing file systems, Scality, WekaIO talk about doing this differently. Backup and restore companies, Veeam, Veritas. I mean, everybody's looking at this and they're all coming at it. Just really quickly David, where's the inside track at this point? >> For me it is so much whitespace as to be unbelievable. >> So nobody has an inside track yet. >> Nobody has an inside track. Just to start with a few things. It's clear that you should keep data where it is. The cost of moving data around an organization from inside to out, is crazy. >> So companies that keep data in place, or technologies to keep data in place, are going to have an advantage. >> Much, much, much greater advantage. Sure, there must be backups somewhere. But you need to keep the working copies of data where they are because it's the real-time access, usually that's important. So if it originates in the cloud, keep it in the cloud. If it originates in a data-provider, on another cloud, that's where you should keep it. If it originates on your premise, keep it where it originated. >> Unless you need to combine it. But that's a new origination point. >> Then you're taking subsets of that data and then combining that up for itself. So that would be my first point. So organizations are going to need to put together what George was talking about, this metadata of all the data, how it interconnects, how it's being used. The flow of data through the organization, it's amazing to me that when you go to an IT shop they cannot define for you how the data flows through that data center or that organization. That's the requirement that you have to have and AI is going to be part of that solution, of looking at all of the applications and the data and telling you where it's going and how it's working together. >> So the second thing would be companies that are able to build or conceive of networks as data. Will also have an advantage. And I think I'd add a third one. Companies that demonstrate perennial observations, a real understanding of the unbelievable change that's required you can't just say, oh Facebook wants this therefore everybody's going to want it. There's going to be a lot of push marketing that goes on at the technology side. Alright so let's get to some Action Items. David Vellante, I'll start with you. Action Item. >> Well the future's going to be one where systems see, they talk, they sense, they recognize, they control, they optimize. It may be tempting to say, you know what I'm going to wait, I'm going to sit back and wait to figure out how I'm going to close that machine intelligence gap. I think that's a mistake. I think you have to start now, and you have to start with your data model. >> George Gilbert, Action Item. >> I think you have to keep in mind the guardrails related to governance, and trust, when you're building applications on the new data fabric. And you can take the approach of a platform-oriented one where you're plugging into an API, like Apache Atlas, that Hortonworks is driving, or a discovery-oriented one as David was talking about which would be something like Alation, using machine learning. But if, let's say the use case starts out as an IOT, edge analytics and cloud inferencing, that data science pipeline itself has to now be part of this fabric. Including the output of the design time. Meaning the models themselves, so they can be managed. >> Excellent. Jim Kobielus, you've been pretty quiet but I know you've got a lot to offer. Action Item, Jim. >> I'll be very brief. What you need to do is protect your data science knowledge base. That's the way to de-risk this entire process. And that involves more than just a data catalog. You need a data science expertise registry within your distributed value chain. And you need to manage that as a very human asset that needs to grow. That is your number one asset going forward. >> Ralph Finos, you've also been pretty quiet. Action Item, Ralph. >> Yeah, I think you've got to be careful about what you're trying to get done. Whether it's, it depends on your industry, whether it's finance or whether it's the entertainment business, there are different requirements about data in those different environments. And you need to be cautious about that and you need leadership on the executive business side of things. The last thing in the world you want to do is depend on data scientists to figure this stuff out. >> And I'll give you the second to last answer or Action Item. Neil Raden, Action Item. >> I think there's been a lot of progress lately in creating tools for data scientists to be more efficient and they need to be, because the big digital giants are draining them from other companies. So that's very encouraging. But in general I think becoming a data-driven, a digital transformation company for most companies, is a big job and I think they need to it in piece parts because if they try to do it all at once they're going to be in trouble. >> Alright, so that's great conversation guys. Oh, David Floyer, Action Item. David's looking at me saying, ah what about me? David Floyer, Action Item. >> (laughing) So my Action Item comes from an Irish proverb. Which if you ask for directions they will always answer you, "I wouldn't start from here." So the Action Item that I have is, if somebody is coming in saying you have to re-do all of your applications and re-write them from scratch, and start in a completely different direction, that is going to be a 20-year job and you're not going to ever get it done. So you have to start from what you have. The digital assets that you have, and you have to focus on improving those with additional applications, additional data using that as the foundation for how you build that business with a clear long-term view. And if you look at some of the examples that were given early, particularly in the insurance industries, that's what they did. >> Thank you very much guys. So, let's do an overall Action Item. We've been talking today about the challenges of de-risking digital business which ties directly to the overall understanding of the role of data assets play in businesses and the technology's ability to move from just protecting data, restoring data, to actually restoring the relationships in the data, the structures of the data and very importantly the models that are resident in the data. This is going to be a significant journey. There's clear evidence that this is driving a new valuation within the business. Folks talk about data as the new oil. We don't necessarily see things that way because data, quite frankly, is a very very different kind of asset. The cost could be shared because it doesn't suffer the same limits on scarcity. So as a consequence, what has to happen is, you have to start with where you are. What is your current value proposition? And what data do you have in support of that value proposition? And then whiteboard it, clean slate it and say, what data would we like to have in support of the activities that we perform? Figure out what those gaps are. Find ways to get access to that data through piecemeal, piece-part investments. That provide a roadmap of priorities looking forward. Out of that will come a better understanding of the fundamental data assets that are being created. New models of how you engage customers. New models of how operations works in the shop floor. New models of how financial services are being employed and utilized. And use that as a basis for then starting to put forward plans for bringing technologies in, that are capable of not just supporting the data and protecting the data but protecting the overall organization of data in the form of these models, in the form of these relationships, so that the business can, as it creates these, as it throws off these new assets, treat them as the special resource that the business requires. Once that is in place, we'll start seeing businesses more successfully reorganize, reinstitutionalize the work around data, and it won't just be the big technology companies who have, who people call digital native, that are well down this path. I want to thank George Gilbert, David Floyer here in the studio with me. David Vellante, Ralph Finos, Neil Raden and Jim Kobelius on the phone. Thanks very much guys. Great conversation. And that's been another Wikibon Action Item. (upbeat music)

>> Announcer: Live from Santa Clara in the heart of Silicon Valley, it's the Cube, covering Cloud Foundry Summit 2017. Brought to you by The Cloud Foundry Foundation, and Pivotal. >> Oh my Bosh! One of the fun t-shirts here at the Cloud Foundry Summit. I'm Stu Miniman joined by my co-host John Troyer. We've had a day of some really good interviews, really liked geeking out, digging into this hybrid, multi-cloud world, John. Something that feels to be coming into focus a little bit more. I had a bunch of questions coming in, and many of them, at least, I have some answers as to where they're going. What's your take on the Cloud Foundry Summit? >> Yeah, my first Cloud Foundry Summit I thought was super interesting. We got to talk to a couple users, which is always really interesting, and also some folks from the foundation. It was insightful, actually. I talked to a few vendors here, and they said, well how's the crowd? I said, not big, but the people who are here are big. Right? In terms of, there weren't 20,000 people here, there were 1,700, but the companies that are involved are serious about Cloud Foundry, they're all in, they're building apps and they're not building one or two apps, they're building thousands of apps on Cloud Foundry and moving their whole enterprise over. So, that was kind of super enlightening to me. >> Yeah, I mean, John, we know the story here. We've talked at a number of events about this. When you've got big financial companies, insurance companies, people in healthcare, if they don't become more agile, they will be Uberized. We have to have a different term, right? Uber's in the news for all the bad reasons now, so Netflix was the old term, but that digital disruption by start-ups. So, when you hear companies, oh, we're a 75-year-old company, we're a 100-year-old company, we're becoming a software company, and therefore, we're going to take our thousands of apps, and somewhere writing, we always have the new things we're writing, and then we'll move some along. So, that application really spectrum of the new stuff, and then pulling along the old one with a platform like Cloud Foundry, being that bridge to the future if you will. >> Right. Right. And, we aren't talking about a small team chatting on slack. We're talking about, in one organization, thousands of developers, coordinating on this platform. >> Yeah, absolutely. We to talked Express Scripts, I think they said they're hiring about a thousand engineers in a little more than a year. So, big companies, a lot of things to move when we're talking, Liberty Mutual is like, oh we want 75% of our IT staff to be writing code, and today they're less than 50%. So, if you're sitting in that other 50%, the writing is on the wall that you need to move in that direction, or maybe we're not the right organization for you. I'm curious, your take about that retraining of staff, we know we have a shortage of skill sets. How do they learn? How do they get, is it certifications? Is it training? What have you seen? >> Well they did just announce the Cloud Foundry certification program here today. So, I think that was an interesting component that's needed for support for this. But, really the Cloud Foundry supports all sorts of technologies and I think you see it in both the contributors here and in the technology. So, it's polyglot world, I see a lot of people, the crowd, used to, known assistments are indeed doing more programming, doing more automation, and so I think it's all of a course. I think, look it's clear, in five or 10 years the profile of people in IT is going to look a lot different. And, this is one of the leading edges of it. >> Yeah. Coming to the show and we talked about it on the intro that drumbeat of Kubernetes really gaining the hearts and minds of developers, I feel like it's been diffused a little bit. I don't know whether Kubo is the answer, but it is an answer. We've talked to some people in the ecosystem, that have other options that they're doing. As well as, of course, companies like Google, which Kubernetes came out of and Microsoft who's embracing Kubernetes, they like choice, they want people to use their platform. Keeping a more open approach for Cloud Foundry to work with other pieces of open source in the ecosystem. It's goodness? Time will tell whether this one solution makes sense. What's your take on that? >> Sure, I think Cloud Foundry has always been known as the opinionated platform. But, I think now the subtleties have come out that, yes there are certain opinions in the way things are glued together, but as James Waters pointed out, they've always had different kinds of abstractions of things running on or in the platform, in terms of whole apps or server list, we didn't really talk about today. But, so Kubernetes is sitting beside there for people who want more knobs, who already have an app, that expects that kind of scalability and management, makes sense for the Cloud Foundry. I think, they seem pretty open to embracing whatever works, and in some ways it's an analogy to what going on in the clouds like Azure and Google Cloud Platform, and that it's like, look bring us your work loads, we will run them. So, I think that's kind of an opening of at least a publicly stance of an opening. >> Yeah. I like this as Steve O'Grady said in the conversation we had with him, there's a lot of choices out there and therefore customers really, they want that. Of course there's the paradox situation. How do I keep up on all the latest and greatest? I mean, three years ago, the last time I came to the show, was like 08 Docker, totally going to disrupt this. Now it's Kubernetes, we only brought up functions as a service or as a server less, like once, and it did not seem to fit into where this plays today. But, there's options out there. Customers that are here, like what they're doing. It is moving them forward, it is enabling them to be that faster, faster, faster. More agile, meet the needs of the business and stay competitive. >> Yeah. Steve's term was different tools for different jobs or something like that right? >> We always said at Wikibon, a torse is for courses. >> Yeah. I mean a polyglot is one way that Coops' Clouds Foundry world used to talk about it. But, I think different tools is a great way. There is, we're in a technical time of great diversity. Which is awesome right? There's no monoculture here, which is super interesting, I think. >> Yeah absolutely, also the move from Cloud Foundry really started out as a predominantly, a non premises deployment and Public Cloud is seeping into it. We talked to a couple of customers that are starting to use Public Cloud, and most of them who weren't using it today were understanding where it fits. Sorting that piece out and look at solutions like Cloud Foundry as one of those pieces that are going to give them flexibility moving forward. >> Yeah. I mean I think that this is something that's going to have to develop over time. Right? It's one thing to say, I'm a layer on top of another cloud, but Amazon really wants you to use its databases, and Google Cloud really wants you to use it's services. And so, you can only stay completely independent for so long without taking advantage of those things, as you evolve these platforms. So, there is that tension there, that will play out, but it's played out over and over again at the many levels in tact. So, we'll see some standard stuff there. If Cloud Foundry has enough value, people will use it as their deployment platform on MultiCloud. Well let's talk about MultiCloud. What you think Stu? But sometimes MultiCloud is more of an ideal than a practicality for many organizations. >> Yeah. What about Pivitol? So if we look at Pivitol, number they're doing in Cloud Foundry, was, last year was about 275 million, so that number had been shared in one of the earnings calls. Seems like a very well position for the Fortune 1000. I'm always trying to figure out. What is the tam that they can go after? Who does it work for, and who doesn't it? At OpenStack we talked about, well great, the Telco NFV market looks great, but is that 20 or 50 companies. For something like Cloud Foundry, there's lots of big revenue that they can get by knocking down many of these Fortune 1,000's. But, it does seem to be that enterprise grade, therefore there's dollars attached to that. It is something that they, Pivitol, has done a solid job of converting that need, using open source into actual software revenues. Yes, their services and labs are a critical, critical, critical piece of what they do, but it is the subscription of software that they built. Many of their clients were on, I know , a three year subscription and lots of those renewals have started coming now. Expectation is that we could see an IPO by them by 2018. It's been reported I'm sure Michael Dell would love to have another influx of cash that he can help fund all of the the things that he's doing. What's your take on Pivitol coming out of this? >> I mean, from here it looks like Pivotal is very comfortable with it's place and who it's customers are. I didn't see a lot of hedging about, we're going after a different market, or we're going for the individual developer, or we think this can be used by almost anybody. These are big companies we're talking about. In the key note this morning for the foundation, talked about enterprise grade. Talking about security, talking about scale, talking about developer experience. They're not shy about it. They're serious when they say they are an enterprise grade platform. So, which I think is great right? You should know yourself and I really feel like both the foundation and Pivitol, a big part of the foundation, does know itself and knows who's it's customers are. >> Yeah. I guess the only thing that I look at is, so many conferences that I go to, is this a platform that SAS companies are building on? As we look at what the future of companies, and especially in the technology space, are going to look like, yes we have some of these big companies that are using it, but you know there's not the, oh okay, work day and sales force, and all these companies, I haven't seen these companies that are already just software companies using it. It's the industry, older companies that are trying to get more into software and therefore this helps with their digital transformation. The companies that are born in the cloud, I haven't seen that in there, and that's fine. There's definitely a diversity of the marketplace. >> Yeah. If you look at a spectrum, we're saying that all SAS companies are software companies, well those SAS companies may be even more software company than a manufacturer or a finance company. So, I think that's okay. One thing they have to watch with the ecosystem and the customer base is the speed of evolution, the speed of the ecosystem, new entrants coming in. Can they keep the velocity of innovation up? I'm sure that's one thing they're looking at. >> Yeah. It is interesting right? Will the millennials be using Cloud Foundry caring about it? Or is this more the boomer, the older generation that have used it? >> Hey, it's not a job versus Steve McGrady, it's not a job versus Dotnet or Microsoft World anymore, but they're still a lot of job developers and new ones coming in. I think hey, there's still COBOL programmers. >> Alright. Want to give you final takeaways. For me some good quality users talking about their stories. There's reality here as you said, there wasn't any big shift is to what Cloud Foundry or the foundation or what they are doing. There's not some big pivot that they need to do. No pun on Pivotal. But, sometimes you go and you're like, are they tone deaf? Are they drinking their own Kool Aid? I think this group understands where they fit. They're focused on delivering it, definitely a changing ecosystem from previous years and how they fit into that whole cloud environment. I'll give you the final word. >> Sure. That goes with some of what you said. The people seem very productive. They seem happy. They seems super engaged. The show floor when the sessions were in session, there was nobody here on the show floor. People are here to learn. Which means that they're here to get stuff done. It's kind of a no nonsense crowd. So, I really enjoyed the day. >> Alright well, John always a pleasure to catch up with you. Appreciate you sitting in for the day and talking about all of this. You brought some great expertise to the discussion. Big thanks to the team here. We actually had four shows this week from the Cube, so as we get towards almost July 4th, which means that we get a deep breath before the fall tour comes. So, I want to thank everybody for watching. As always, check out thecube.net for all the videos from this show and all the other shows. If you see a show that we're going to be at and you want to be on, get in touch with us. If you have a show that we're not at, please feel free to reach out to us. We're really easy to get in touch with. For my co host John Troyer, I'm Stu Miniman. Once again as always, thank you for watching the Cube and we will see you at the next show.

>> Announcer: Live from Fisherman's Wharf in San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, it's 170 people here, all high-level CXOs learning about data, and it's part of an ongoing series that IBM is doing around chief data officers and data, part of a big initiative with Cognitive and Watson, I'm sure you've heard all about it, Watson TV if nothing else, if not going to the shows, and we're really excited to have the drivers behind this activity with us today, also Peter Burris from Wikibon, chief strategy officer, but we've got Caitlin Lepech who's really driving this whole show. She is the Communications and Client Engagement Executive, IBM Global Chief Data Office. That's a mouthful, she's got a really big card. And Cortnie Abercrombie, who I'm thrilled to see you, seen her many, many times, I'm sure, at the MIT CDOIQ, so she's been playing in this space for a long time. She is a Cognitive and Analytics Offerings leader, IBM Global Business. So first off, welcome. >> Thank you, great to be here. >> Thanks, always a pleasure on theCUBE. It's so comfortable, I forget you guys aren't just buddies hanging out. >> Before we jump into it, let's talk about kind of what is this series? Because it's not World of Watson, it's not InterConnect, it's a much smaller, more intimate event, but you're having a series of them, and in the keynote is a lot of talk about what's coming next and what's coming in October, so I don't know. >> Let me let you start, because this was originally Cortnie's program. >> This was a long time ago. >> 2014. >> Yeah, 2014, the role was just starting, and I was tasked with can we identify and start to build relationships with this new line of business role that's cropping up everywhere. And at that time there were only 50 chief data officers worldwide. And so I-- >> Jeff: 50? In 2014. >> 50, and I can tell you that earnestly because I knew every single of them. >> More than that here today. >> I made it a point of my career over the last three years to get to know every single chief data officer as they took their jobs. I would literally, well, hopefully I'm not a chief data officer stalker, but I basically was calling them once I'd see them on LinkedIn, or if I saw a press announcement, I would call them up and say, "You've got a tough job. "Let me help connect you with each other "and share best practices." And before we knew, it became a whole summit. It became, there were so many always asking to be connected to each other, and how do we share best practices, and what do you guys know as IBM because you're always working with different clients on this stuff? >> And Cortnie and I first started working in 2014, we wrote IBM's first paper on chief data officers, and at the time, there was a lot of skepticism within our organization, why spend the time with data officers? There's other C-suite roles you may want to focus on instead. But we were saying just the rise of data, external data, unstructured data, lot of opportunity to rise in the role, and so, I think we're seeing it reflected in the numbers. Again, first summit three years ago, 30 participants. We have 170 data executives, clients joining us today and tomorrow. >> And six papers later, and we're goin' strong still. >> And six papers later. >> Exactly, exactly. >> Before we jump into the details, some of the really top-level stuff that, again, you talked about with John and David, MIT CDOIQ, in terms of reporting structure. Where do CDOs report? What exactly are they responsible for? You covered some of that earlier in the keynote, I wonder if you can review some of those findings. >> Yeah, that was amazing >> Sure, I can share that, and then, have Cortnie add. So, we find about a third report directly to the CEO, a third report through the CIO's office, sort of the traditional relationship with CIOs, and then, a third, and what we see growing quite a bit, are CXOs, so functional or business line function. Originally, traditionally it was really a spin-off of CIO, a lot of technical folks coming up, and we're seeing more and more the shift to business expertise, and the focus on making sure we're demonstrating the business impact these data programs are driving for our organization. >> Yeah, it kind of started more as a data governance type of role, and so, it was born out of IT to some degree because, but IT was having problems with getting the line of business leaders to come to the table, and we knew that there had to be a shift over to the business leaders to get them to come and share their domain expertise because as every chief data officer will tell you, you can't have lineage or know anything about all of this great data unless you have the experts who have been sitting there creating all of that data through their processes. And so, that's kind of how we came to have this line of business type of function. >> And Inderpal really talked about, in terms of the strategy, if you don't start from the business strategy-- >> Inderpal? >> Yeah, on the keynote. >> Peter: Yeah, yeah, yeah, yeah. >> You are really in big risk of the boiling the ocean problem. I mean, you can't just come at it from the data first. You really have to come at it from the business problem first. >> It was interesting, so Inderpal was one of our clients as a CEO three times prior to rejoining IBM a year ago, and so, Cortnie and I have known him-- >> Express Scripts, Cambia. >> Exactly, we've interviewed him, featured him in our research prior, too, so when he joined IBM in December a year ago, his first task was data strategy. And where we see a lot of our clients struggle is they make data strategy an 18-month, 24-month process, getting the strategy mapped out and implemented. And we say, "You don't have the time for it." You don't have 18 months to come to data, to come to a data strategy and get by and get it implemented. >> Nail something right away. >> Exactly. >> Get it in the door, start showing some results right away. You cannot wait, or your line of business people will just, you know. >> What is a data strategy? >> Sure, so I can say what we've done internally, and then, I know you've worked with a lot of clients on what they're building. For us internally, it started with the value proposition of the data office, and so, we got very clear on what that was, and it was the ability to take internal, external data, structured, unstructured, and pull that together. If I can summarize it, it's drive to cognitive business, and it's infusing cognition across all of our business processes internally. And then, we identified all of these use cases that'll help accelerate, and the catalyst that will get us there faster. And so, Client 360, product catalog, et cetera. We took data strategy, got buy-in at the highest levels at our organization, senior vice president level, and then, once we had that support and mandate from the top, went to the implementation piece. It was moving very quickly to specify, for us, it's about transforming to cognitive business. That then guides what's critical data and critical use cases for us. >> Before you answer, before you get into it, so is a data strategy a means to cognitive, or is it an end in itself? >> I would say it, to be most effective, it's a succinct, one-page description of how you're going to get to that end. And so, we always say-- >> Peter: Of cognitive? >> Exactly, for us, it's cognitive. So, we always ask very simple question, how is your company going to make money? Not today, what's its monetization strategy for the future? For us, it's coming to cognitive business. I have a lot of clients that say, "We're product-centric. "We want to become customer, client-centric. "That's our key piece there." So, it's that key at the highest level for us becoming a cognitive business. >> Well, and data strategies are as big or as small as you want them to be, quite frankly. They're better when they have a larger vision, but let's just face it, some companies have a crisis going on, and they need to know, what's my data strategy to get myself through this crisis and into the next step so that I don't become the person whose cheese moved overnight. Am I giving myself away? Do you all know the cheese, you know, Who Moved My Cheese? >> Every time the new iOS comes up, my wife's like-- >> I don't know if the younger people don't know that term, I don't think. >> Ah, but who cares about them? >> Who cares about the millenials? I do, I love the millenials. But yes, cheese, you don't want your cheese to move overnight. >> But the reason I ask the question, and the reason why I think it's important is because strategy is many things to many people, but anybody who has a view on strategy ultimately concludes that the strategic process is what's important. It's the process of creating consensus amongst planners, executives, financial people about what we're going to do. And so, the concept of a data strategy has to be, I presume, as crucial to getting the organization to build a consensus about the role the data's going to play in business. >> Absolutely. >> And that is the hardest. That is the hardest job. Everybody thinks of a data officer as being a technical, highly technical person, when in fact, the best thing you can be as a chief data officer is political, very, very adept at politics and understanding what drives the business forward and how to bring results that the CEO will get behind and that the C-suite table will get behind. >> And by politics here you mean influencing others to get on board and participate in this process? >> Even just understanding, sometimes leaders of business don't articulate very well in terms of data and analytics, what is it that they actually need to accomplish to get to their end goal, and you find them kind of stammering when it comes to, "Well, I don't really know "how you as Inderpal Bhandari can help me, "but here's what I've got to do." And it's a crisis usually. "I've got to get this done, "and I've got to make these numbers by this date. "How can you help me do that?" And that's when the chief data officer kicks into gear and is very creative and actually brings a whole new mindset to the person to understand their business and really dive in and understand, "Okay, this is how "we're going to help you meet that sales number," or, "This is how we're going to help you "get the new revenue growth." >> In certain respects, there's a business strategy, and then, you have to resource the business strategy. And the data strategy then is how are we going to use data as a resource to achieve our business strategy? >> Cortnie: Yes. >> So, let me test something. The way that we at SiliconANGLE, Wikibon have defined digital business is that a business, a digital business uses data as an asset to differentially create and keep customers. >> Caitlin: Right. >> Does that work for you guys? >> Cortnie: Yeah, sure. >> It's focused on, and therefore, you can look at a business and say is it more or less digital based on how, whether it's more or less focused on data as an asset and as a resource that's going to differentiate how it's business behaves and what it does for customers. >> Cortnie: And it goes from the front office all the way to the back. >> Yes, because it's not just, but that's what, create and keep, I'm borrowing from Peter Drucker, right. Peter Drucker said the goal of business is to create and keep customers. >> Yeah, that's right. Absolutely, at the end of the day-- >> He included front end and back end. >> You got to make money and you got to have customers. >> Exactly. >> You got to have customers to make the money. >> So data becomes a de-differentiating asset in the digital business, and increasingly, digital is becoming the differentiating approach in all business. >> I would argue it's not the data, because everybody's drowning in data, it's how you use the data and how creative you can be to come up with the methods that you're going to employ. And I'll give you an example. Here's just an example that I've been using with retailers lately. I can look at all kinds of digital exhaust, that's what we call it these days. Let's say you have a personal digital shopping experience that you're creating for these new millenials, we'll go with that example, because shoppers, 'cause retailers really do need to get more millenials in the door. They're used to their Amazon.coms and their online shopping, so they're trying to get more of them in the door. When you start to combine all of that data that's underlying all of these cool things that you're doing, so personal shopping, thumbs up, thumb down, you like this dress, you like that cut, you like these heels? Yeah, yes, yes or no, yes or no. I'm getting all this rich data that I'm building with my app, 'cause you got to be opted in, no violating privacy here, but you're opting in all the way along, and we're building and building, and so, we even have, for us, we have this Metro Pulse retail asset that we use that actually has hyperlocal information. So, you could, knowing that millenials like, for example, food trucks, we all like food trucks, let's just face it, but millenials really love food trucks. You could even, if you are a retailer, you could even provide a fashion truck directly to their location outside their office equipped with things that you know they like because you've mined that digital exhaust that's coming off the personal digital shopping experience, and you've understood how they like to pair up what they've got, so you're doing a next best action type of thing where you're cross-selling, up-selling. And now, you bring it into the actual real world for them, and you take it straight to them. That's a new experience, that's a new millennial experience for retail. But it's how creative you are with all that data, 'cause you could have just sat there before and done nothing about that. You could have just looked at it and said, "Well, let's run some reports, "let's look at a dashboard." But unless you actually have someone creative enough, and usually it's a pairing of data scientist, chief data officers, digital officers all working together who come up with these great ideas, and it's all based, if you go back to what my example was, that example is how do I create a new experience that will get millenials through my doors, or at least get them buying from me in a different way. If you think about that was the goal, but how I combined it was data, a digital process, and then, I put it together in a brand new way to take action on it. That's how you get somewhere. >> Let me see if I can summarize very quickly. And again, just as an also test, 'cause this is the way we're looking at it as well, that there's human beings operate and businesses operate in an analog world, so the first test is to take analog data and turn it into digital data. IOT does that. >> Cortnie: Otherwise, there's not digital exhaust. >> Otherwise, there's no digital anything. >> Cortnie: That's right. >> And we call it IOT and P, Internet of Things and People, because of the people element is so crucial in this process. Then we have analytics, big data, that's taking those data streams and turning them into models that have suggestions and predictions about what might be the right way to go about doing things, and then there's these systems of action, or what we've been calling systems of enactment, but we're going to lose that battle, it's probably going to be called systems of action that then take and transduce the output of the model back into the real world, and that's going to be a combination of digital and physical. >> And robotic process automation. We won't even introduce that yet. >> Which is all great. >> But that's fun. >> That's going to be in October. >> But I really like the example that you gave of the fashion truck because people don't look at a truck and say, "Oh, that's digital business." >> Cortnie: Right, but it manifested in that. >> But it absolutely is digital business because the data allows you to bring a more personal experience >> Understand it, that's right. >> right there at that moment, and it's virtually impossible to even conceive of how you can make money doing that unless you're able to intercept that person with that ensemble in a way that makes both parties happy. >> And wouldn't that be cheaper than having big, huge retail stores? Someone's going to take me up on that. Retailers are going to take me up on this, I'm telling you. >> But I think the other part is-- >> Right next to the taco truck. >> There could be other trucks in that, a much cleaner truck, and this and that. But one thing, Cortnie, you talk about and you got to still have a hypothesis, I think of the early false promises of big data and Hadoop, just that you throw all this stuff in, and the answer just comes out. That just isn't the way. You've got to be creative, and you have to have a hypothesis to test, and I'm just curious from your experience, how ready are people to take in the external data sources and the unstructured data sources and start to incorporate that in with the proprietary data, 'cause that's a really important piece of the puzzle? It's very different now. >> I think they're ready to do it, it depends on who in the business you are working with. Digital offices, marketing offices, merchandising offices, medical offices, they're very interested in how can we do this, but they don't know what they need. They need guidance from a data officer or a data science head, or something like this, because it's all about the creativity of what can I bring together to actually reach that patient diagnostic, that whatever the case may be, the right fashion truck mix, or whatever. Taco Tuesday. >> So, does somebody from the chief data office, if you will, you know, get assigned to, you're assigned to marketing and you're assigned to finance, and you're assigned to sales. >> I have somebody assigned to us. >> To put this in-- >> Caitlin: Exactly, exactly. >> To put this in kind of a common or more modern parlance, there's a design element. You have to have use case design, and what are we going, how are we going to get better at designing use cases so we can go off and explore the role that data is going to play, how we're going to combine it with other things, and to your point, and it's a great point, how that turns into a new business activity. >> And if I can connect two points there, the single biggest question I get from clients is how do you prioritize your use cases. >> Oh, gosh, yeah. >> How can you help me select where I'm going to have the biggest impact? And it goes, I think my thing's falling again. (laughing) >> Jeff: It's nice and quiet in here. >> Okay, good. It goes back to what you were saying about data strategy. We say what's your data strategy? What's your overarching mission of the organization? For us, it's becoming cognitive business, so for us, it's selecting projects where we can infuse cognition the quickest way, so Client 360, for example. We'll often say what's your strategy, and that guides your prioritization. That's the question we get the most, what use case do I select? Where am I going to have the most impact for the business, and that's where you have to work with close partnership with the business. >> But is it the most impact, which just sounds scary, and you could get in analysis paralysis, or where can I show some impact the easiest or the fastest? >> You're going to delineate both, right? >> Exactly. >> Inderpal's got his shortlist, and he's got his long list. Here's the long term that we need to be focused on to make sure that we are becoming holistically a cognitive company so that we can be flexible and agile in this marketplace and respond to all kinds of different situations, whether they're HR and we need more skills and talent, 'cause let's face it, we're a technology company who's rapidly evolving to fit with the marketplace, or whether it's just good old-fashioned we need more consultants. Whatever the case may be. >> Always, always. >> Yes! >> I worked my business in. >> More consultants! >> Alright, we could go, we could go and go and go, but we're running out of time, we had a full slate. >> Caitlin: We just started. >> I know. >> I agree, we're just starting this convers, I started a whole other conversation to him. We haven't even hit the robotics yet. >> We need to keep going, guys. >> Get control. >> Cortnie: Less coffee for us. >> What do people think about when they think about this series? What should they look forward to, what's the next one for the people that didn't make it here today, where should they go on the calendar and book in their calendars? >> So, I'll speak to the summits first. It's great, we do Spring in San Francisco. We'll come back, reconvene in Boston in fall, so that'll be September, October frame. I'm seeing two other trends, which I'm quite excited about, we're also looking at more industry-specific CDO summits. So, for those of our friends that are in government sectors, we'll be in June 6th and 7th at a government CDO summit in D.C., so we're starting to see more of the industry-specific, as well as global, so we just ran our first in Rio, Brazil for that area. We're working on a South Africa summit. >> Cortnie: I know, right. >> We actually have a CDO here with us that traveled from South Africa from a bank to see our summit here and hoping to take some of that back. >> We have several from Peru and Mexico and Chile, so yeah. >> We'll continue to do our two flagship North America-based summits, but I'm seeing a lot of growth out in our geographies, which is fantastic. >> And it was interesting, too, in your keynote talking about people's request for more networking time. You know, it is really a sharing of best practices amongst peers, and that cannot be overstated. >> Well, it's community. A community is building. >> It really is. >> It's a family, it really is. >> We joke, this is a reunion. >> We all come in and hug, I don't know if you noticed, but we're all hugging each other. >> Everybody likes to hug their own team. It's a CUBE thing, too. >> It's like therapy. It's like data therapy, that's what it is. >> Alright, well, Caitlin, Cortnie, again, thanks for having us, congratulations on a great event, and I'm sure it's going to be a super productive day. >> Thank you so much. Pleasure. >> Thanks. >> Jeff Frick with Peter Burris, you're watchin' theCUBE from the IBM Chief Data Officer Summit Spring 2017 San Francisco, thanks for watching. (electronic keyboard music)