(techy music) >> Announcer: Live from Las Vegas, it's theCUBE, covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back, everyone, to theCUBE's live coverage of ServiceNow Knowledge 18 here in Las Vegas, Nevada. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by CJ Desai. He is the Chief Product Officer for ServiceNow. Thanks so much for coming on theCUBE again, CJ. >> Thank you, it's great to be here. First time I came was last Knowledge, which was my first Knowledge, so I'm a lot more educated and equipped this time as compared to firing round of questions from Dave last time. >> We will pick your brain, exactly. So you were up on the stage this morning, a great keynote, and you said, "Welcome to the era of great experiences." Unpack that a little bit. What do you mean by that? >> First of all, thank you for remembering that. That was supposed to be the idea. But on a serious note, we feel, if you think about even our company name is ServiceNow, so you provide service, and when you provide service, that's not a technology you provide, you provide an experience, whether it's IT service, customer service, employee, whatever the case might be. And, if you are not delivering experiences, then you are not that relevant. So we are trying to truly, and we are in the beginning of this journey, truly internalize that, that if people are using us, they call themselves service desk, insider organization, IT service desk, customer service desk, whatever the terms you want to use, there is about experiences. Rather than focusing on bits and bytes, we want to focus on experiences, deliver those experiences via our platform. It's not software as a service, it's software as an experience. It's software as an experience, that's the idea, correct. Thank you for-- >> You also talked about the eras. You know, we went back to the industrial era and then went through the ages of computing. Yeah, I was not sure if that was going to work or not, but the point I was trying to make, Dave, was just around the quality of work and how work has evolved. That's it, that was the idea. >> But I think my takeaway was even more than that, because we are entering, in my view, anyway, a new era, and I'd love to get your comments. We're moving from what is real tailwind for you, which is the Cloud era, and obviously, Cloud is an important part of the new era where you have a remote set of services to one where you have this ubiquitous set of digital services that do things like sense, hear, read, act, respond. That's a different world, and it's all about the experience, and I don't know how to define that yet. Digital, I guess, is how we define it. But what are your thoughts? >> The one thing, even simple things, and these are not simple things to understand. When I look at things like even genomic sequencing, that's so different. They are using technology to figure out how to sequence the human genome so that it can help you with your health, live longer, even things like knowing that somebody rings a doorbell at my home and I can see on my phone. Everything is connected, humans are connected, when mobile came and computer came and internet came. But things being connected is pretty exciting for me. That just transforms our lives and how we work, and I really like that it is all about us, and other than us being focusing on the technology itself. So that's the point. It's that we're humans, and let's focus on humans and experience, rather than worry about, oh, this runs two times faster than the other thing, or this thing is smaller than other thing. That's interesting, but not that interesting. >> At this conference, this is really the message that you're getting across. It's the new tag line, we are making the world of work work better for people. How does the Now platform really deliver on that promise? How does it make the employees life easier? I would say we have a bunch of use cases, but as you know, we started out early on with IT service management, and the whole idea was can we provide, as long as computers are there, as long as software is there, password reset is going to be there for a very, very long time. So, my point is that that's when it started. Okay, I need to do password reset, I want to upgrade my laptop. Every year there is a new laptop, every year there is a new phone, and that cycle will continue, and as long as we are using technology for our knowledge workers, IT help desk will be there, right? And where we are evolving is enterprise service management, because you don't, as an employee, you may deal with IT, you may deal with HR, you may have a contractual issue with legal, you may need something related to your payroll from finance. People think payroll is HR, but payroll is finance. And as you try to go across in a day in a life of an employee, you need to make it as easy as possible. So that's what we are focused on, deliver better experiences. You know, artificial intelligence that listen today, I believe, is more about optimization, rather than intelligence. Yeah, we want to use your data to be able to predict, like if you see in Gmail, I don't know if you use Gmail, but if you have Gmail, you get an email, it'll suggest auto-responses. Those auto-responses are almost positive. Have you noticed that? They are never negative. >> Yeah. >> Oh, of course. >> They're like, no, I don't want to come to your meeting. (laughing) It's kind of like trying to predict most likely what you would want to say, and I think if we can use intelligence to make people more productive, that's what we want. >> I mean, I use that function. I actually like it. >> CJ: Yeah, exactly. >> You know, it gives you three choices, and one of 'em is pretty close to what I would normally, and if I'm busy, I'm done. >> Yeah, right, exactly. >> I like that. This is the other thing we've talked about. We've talked about this with Farrel this morning. Try to anticipate my needs, right? So that means you've got to infuse AI into the application and identify specific use cases. You guys have done some M&A there, you talked to the financial analysts meeting, obviously, not disclosing anything, but watch for us to do some more M&A. You got to believe that that machine intelligence space is really ripe for innovation. >> And what we believe is if I look at the big Cloud providers, like Google, are investing a lot in deep learning and many, many other technologies, so whenever they expose it, and some of them do a really good job, we will just leverage their libraries. But there are things specific to enterprise, because there are things specific to enterprise, like if you use the word network at a hardware company, that's always in context of compute network and storage. If you use the word network at a healthcare company, that's a network of physicians, networks of hospitals, networks of whatever. And if you use the word network at a Telco company, that is a whole different network. My point is we want to understand those pieces, and if we can make it easier based on your data, so if all your cases, which are, Oh, part of your network is down. Ah, that's what you mean from the context end point, so we want to use wherever folks like Google are investing, we will leverage that, but if we need to leverage, we'll do that too. >> It's interesting, we were talking to a customer today, it might have been Worldpay, and they took the CMDV language and transformed it into the language of the business. What a rare and powerful concept for somebody from IT to do that, because if the lingua franca is business, then the adoption's going to go through the roof. >> So does that make sense? >> Yeah, it makes a lot of sense. Well, I appreciate you talking about the value and the customer experience versus the technology. Certainly, it speeds and feeds you right. Boring. But the platform is important. Many products, one platform, that's unique for an enterprise software company, and you guys aspire to be the next great enterprise software company. Talk about how the platform enables you to get there. >> So I will tell you simple. You know our founder, Fred Luddy, started with the platform in 2004, so that was 14 years ago now, and his idea was you should be able to route work through the enterprise using our platform, and then we started with the IT service management and use case. The biggest advantage we have is that we are a very customer-driven organization. Many companies say that, but you see it here. Dave, you have been coming to Knowledge for a long time, I don't know about you. >> This is my first rodeo, but it's cool. >> It's the first thing you see. >> These are 80-plus person sessions, are customer sessions. They're not our sessions, where they are sharing best practices with them. So we get all these requests, CJ, we have built emergency response system using ServiceNow, CJ, we have built financial close using ServiceNow. Can you productize it? And we say, okay, thank you for the idea, which is great, thank you for the idea. How do I prioritize all of that? And, Dave, where platform comes in, because all the services I talked about today, service intelligence, service experience, user experience, they're all built in the platform, and I'm trying to be cautious, but if I want to create a brand new product on our platform, a brand new product on our platform, 40-use case, a 1.0 product where I feel comfortable the customers can use it, I would say 12 to 18 engineers. That's it. >> Rebecca: Wow. >> If I want to create one product, it's 12 to 18 engineers. So the R&D leverage, and that's the point I was trying to get across, that whether it's my own team creating product or whether our customer building apps on our product, because on platform, because we provide all the common services integration, the incremental cost to create something, now sales marketing, with my close friend, Dave Schneider, is much harder, because he has to scale it, build specialty in it and all that, but to create the product is not an issue for us on the platform. >> But this is where Cloud economics are so important, because at volume, your marginal costs go to practically zero. >> CJ: That's exactly right. >> But people may say, oh, 12 to 18, that sounds like a lot, but we're talking about an enterprise class software product here, and Fred Luddy, in the 2004 time frame, I mean, the state of enterprise software then, frankly, and now, was terrible. The guys at 37signals, I don't know if you know Jason, they made valid attempts, but it wasn't enterprise class software, it wasn't a platform. I've said, a number of times this week, the reference model for enterprise software is painfully mediocre, so you guys have done a great job, and now you've really got to take the next step and stay ahead on innovation. >> Correct on innovation card, that's what I said, innovation should be my top priority. You heard me at the Financial Analysts Day. Customer Service Management, brand new product, we actually launched it at Knowledge 16. Okay, that's when we launched it. It was engineers and teens who created that product, so many teens, the 1.0, now we have evolved quite a bit, 500 customers two weeks ago, 500 enterprise customers. You guys know that we don't go to the small line of the business. 500 in two years, eight quarters. >> And I found out last night, I think it was 75, or it might even be higher, reference customers. >> CJ: Yeah, already, using CSM. >> That's the difference. I do, we do, a lot of these shows. >> That's the platform impact. >> And you're talking about the customer focus. You do a lot of these shows. The customers talk about the impact on their business. They don't talk about how they installed some box, or like you say, runs faster. It's the business impact that really makes a difference, and that's why we're excited to be here. >> You saw today when I talked about Flow Designer and Integration Hub. IT wants to provide software so that business analysts can model business processes in a Cloud way with whoever you need to integrate with, so we are really keeping that as the north star for our customers, and how can we make their life easier, whatever they want to automate, some manual processes, all of manual processes. I remember speaking to Fred when I joined initially, and I said, "Fred, how did you think about TAM?" He said, "What do you mean, TAM?" You know, he's a funny guy, and he was serious. His point was there are so many manual workflows, how do you put a TAM around it? Every business is unique, their processes are complex, so don't box yourself and say, Oh, this is a $4 billion TAM and I'm going to get 20% of it. Every enterprise, as long as they exist, they will have manual workflows, you go and give it our platform so they can automate however they want. >> Well, I'm going to make you laugh about TAM. I'm a former industry analyst, so when you guys did the IPO way back when, well before your time-- >> CJ: 2012. >> when Frank was here, there was a research company saying this is small market, maybe it's a billion dollars and it's shrinking, so I, with some of my colleagues, developed a TAM analysis, and it was more than 30 billion. I published 30 billion, you can go on our old Wiki and see that, and the guy said to me, "Dave, you can't publish more than 30 billion. You'll look like a fool." The TAM is much, much bigger than 30 billion. You can't even quantify it, it's so large when you start looking at it. >> And now, because people are recognizing that we automate all the manual workflows in a enterprise on a Cloud platform, last week somebody published a report and I just saw the headlines, I didn't go through the details, 126 billion. So from in 2012 to that small number, and we don't know what the number is. >> Could it be bigger? >> I would have no idea. I would be completely disingenuous if I told you I know what my TAM is, but I don't think that way. I say what customer problems can I solve? >> Well, that's what I wanted to ask you. So you're here with so many different customers. Just on the show, we've had ones in payments, in insurance, in health care. What are you hearing from customers, and what are sort of your favorite applications of what you're doing? What makes you the proudest? >> Yeah, so I would say the proudest moments for me are when I'm like, wow, you do that with ServiceNow? I would have never thought that. So when I didn't expect, when I expect something, Oh, I had this routine email, text collaboration, and I switched it to ServiceNow, get it, like not a big aha moment. I had this one customer who said he has a big distribution network, all these partners, and those guys have ServiceNow, he has ServiceNow, and when they have problem with the product, their product, my customer's product, they all communicate via ServiceNow to each other. So they have created a whole ServiceNow network, truly a B2B kind of exchange, kind of, using ServiceNow. One of our median and entertainment customers who owns a bunch of parks, they refill the popcorn machine using ServiceNow. When the popcorn levels dip, they have those people who carry around the cart, Oh! The popcorn level dip, it marks the sensor, it routines the workflow, goes to the corporate, Ah, we need to fill up popcorn on by this particular ride. For me-- >> And even at my house, I love it. >> Yeah, so that's exciting to me. >> We talked to Siemens today. >> Yes, great customer. >> Awesome, and I want to run a line by you. We talk about AI a lot, machine intelligence. I wrote down during, you know, data is the fuel for AI. Well, you know we love data here at theCUBE, and he was describing that, he said, you know, even though CJ was not prescribing taking the data out, we could leave it in so it learns, right now, we take some of the data out. Well, you described that. Well, we put it to SAP HANA, we throw a little Watson in there, we do some Azure, machine learning, we use Tableau for visualization, he's probably got some Hadoop and Kafka in there, a very complicated, big data pipeline. And I said to him, Okay, in two years, do you want to do that inside of ServiceNow? He goes, "Absolutely. That would be my dream come true." So, I guess I'm laying down the gauntlet. Do you see that as a reality? >> So, we are talk to Siemens, great customer, they keep us honest, so I love that and I did actually meet the team who was in charge of their BI and reporting and they did share the same story a few months ago when I met them. And we are trying to figure out, Dave, if I knew the answer, I would have told you, but you know my style. I don't know the answer. We are seriously trying to figure out, Do we become an analytics hub? We are really good with ServiceNow data, we can build connectors with other data, but do I want to be in the BI and reporting market? Absolutely not. Do I want to help customers as their processes span across and provide them more visual credit tools than others, text-based searches, whatever they need, the answer is yes. Performance analytics, as you know, we have been moving along really at a good pace, and now we have what every single product, but this is something that Eric Miller, who runs that business, we talk about it all the time, because currently our analytics is building the platform, and now you know that data has a Cloud issue, so if you have data here, you have data there, you have data there, we are in our own Cloud. Can we build a connector, potentially, to OnPrem? Don't know the answer, but this is something, it's a fair gauntlet having to solve. >> Humbly, I'd like to give you my input, if I may. >> Yes. >> We see innovation, as I said before, it's data, applying machine learning to that data, and then leveraging Cloud economics. The project with big data projects, as you well know, is the complexity has killed them. Now you see the Cloud guys, whether it's Amazon or Microsoft, and that's where the data pipelines are being simplified and built. Now, I don't know if it's the right business decision for you guys, but wow, wouldn't that be powerful if you guys could do that, certainly, for your customers. >> And, truly, that is, as you heard me on Financial Analysts Day, I'm a huge fan of Geoffrey Moore's work, and he defines system of record, ERP CRM, system of action where we fall in, and then he has System of Intelligence, which is all the things around data and how do you harness the power of data. And that's something that I really, in our product teams, we talk about all the time, if I can solve Siemens problem with everything in ServiceNow, that'd be awesome, but is that something I want to prioritize right now, or is there something, we should give them the flexibility. I don't know. >> Well, you're one of the top product guys in our industry. It's why they found you. No, seriously, I put you up there with the greats. >> You're kind, thank you. >> It's true. You've got an incredible future ahead of you. But as a lead product person, you have to make those decisions, and you have to be very circumspect about where you put your resources. You can't just run to every customer requirement, right? >> And I tell, coincidentally, my wife asks me What's your job, by the way? I said, that's a good question. >> I'm married to a product officer, too, I feel the same way. What do you do all day? You do a lot of meetings. >> Yeah, exactly. So I said that I do a lot of meetings, and she said why do you do a lot of meetings? And I said I'm making a some decision or help my team make a decision because they already analyze a bunch of things. And I said, my hope is, as long as I can make more good decisions than bad decisions, specifically about product strategy, because you never know unless you make the chess pieces move and think of two or three steps ahead, and some things could be right and some things could be wrong. I have a simple framework on my whiteboard for every meeting. No jokes, right? So, my framework is very simple. Question number one, What customer problems we are trying to solve. If you cannot articulate that, for any new product idea you have, I don't go past that question, What customer problem we are trying to solve? Second is Why now? Why do we need to solve this problem now? Like you said, there are many problems, which one are you prioritize? And then, third, Why us? Why should we solve that problem? So, if you can articulate the problem, which always is a challenge because you kind of know what problems you have, but unless you really, really understand the customer pain point, you cannot articulate it. Then you say, why now? Like why is the time right now for us to invest in this, say, analytics, as a service? Why right now? And, third, why you, as in why us? Why is ServiceNow should solve it? That, at least, gives me a guiding compass to say because I have many products, as you know, I am very protective of our platform, and all these use cases come in, every product line wants to go deeper, rightfully so, because they are trying to solve for customers, and the new products want to be built on this platform. Sometimes I say maybe a partner should build it, so we made a decision, facilities product, Should our ISB partner build it? And that's the right place because we feel they are more suited, they have the skill set, all of that. But that's it, what problem, why now, why you? >> Rebecca: Really, I love it. >> Well, the Why you? it's a great framework. The why you is unclear for the Siemens problem, and I can understand that. You take the DemOps announcement that Pat stole from you today-- >> I know, that's not cool, man. >> But that's a problem that you guys solved internally, clear problem. >> He did a nice job of articulating it, very nice job. >> Yeah, definitely. >> But we feel that there always is a process when you need a workflow across, because in planning there are a bunch of companies, as the patch, or in build there are a bunch of companies in develop there are a bunch of companies. That's fine. They could be the system of records for those chevrons and we are the workflow that cuts across. So we feel loved. We showed our value to our customers by doing that. >> Rebecca: That's great. >> I know we've got to go, but lastly, it's roadmap. Last year, you talked about how you guys do releases by alphabet, twice a year. You were really transparent today, laid out the room and talked a lot about Madrid, you laid out well into the future what you guys are doing so, as an analyst, I love that. I'm sure you're customers love it, so-- >> A lot of people to picture, so that's nice. And Twitter, a lot of people posted on social media as well, so clearly there was a customer pain point, as we call it, that they needed a roadmap. In speaking to customers last one year, number one thing, if you tell us what you're building, then we don't have to build it. If you tell us when you're shipping, then we can plan around it, and then we will set aside resources to do testing. Any Cloud software company, whether it's us, CRM software or HR software, people still test, because you cannot mess up your employee experience or customer experience, and they just said give us a predictable schedule, please, so that we know. We did say two times a year, but we were not prescriptive which quarter. It could be four months and eight months, it could be six and six, it could be seven and five. I'm currently going with the quarterly-level fidelity, and eventually, I want to get to a month-level fidelity, where I say March and September, once our internal processes are organized. >> So the other subtlety there, and I know we got to go, is the ecosystem, because you're giving visibility, they have to make bets. They're making a bet on service, but then where's the white space? They're betting on white space. If you're exposing that to them, they can say, Oh, not going to solve that problem. ServiceNow's going to solve it in two quarters. >> I agree. >> Huge difference for them. >> You guys are wonderful. Thank you so much for inviting me. >> Rebecca: Thank you for coming on the show. We appreciate it. >> No, that's awesome, thank you, thank you. >> Dave: Great to have you. >> Rebecca: Great to have you. I'm Rebecca Knight, for Dave Vellante. We'll have more from ServiceNow Knowledge 18 just after this. (techy music)

>> 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 Las Vegas It's theCube covering AWS re:Invent 2017 presented by AWS, Intel, and our ecosystem of partners. >> Welcome back to our live coverage. theCube here at AWS re:Invent 2017 Our fifth year covering Amazon Web Services and their massive growth. I'm John Furrier, my co-host Lisa Martin. Here our next guest is CTO of WANdisco, Jugane Sundar. Welcome back to theCube. >> Thank you John. >> You guys are everywhere. WANdisco around the table and all these deals so you guys have been doing extremely well with (indistinct talking) property. What's new? You got some news? >> Yes we do, we recently announced integration with Amazon's AWS Snowball device which gives you the ability to do migration of on-premises workload into the Cloud without down time, and then the end result is a hybrid cloud environment that you can have an active for right environment on both sides. That's a unique capability, nobody else can do that today. >> What does it mean for AWS and their customers 'cause they're very customer focused. What are you guys bringing to the table? >> We bring a whole lot of big data workloads, analytics workloads, IoT workloads into their Cloud. And the beauty of the cloud is that you may have a 20 node cluster on-premises but you can run analytics with a 1000 nodes up in the Cloud on demand and pay just for that use. We think it's a very powerful value proposition. >> Where are you seeing the most traction? We've talking about the massive growth at 18 billion dollar annual runway that fit AWS and Andy's conversation with you John the other day said we haven't gotten that big on startups alone. So even some of the things like the advertising that AWS is now starting to do suggests they're going up the stack to the Enterprise and to the Sea Suites. Where are you guys seeing the most traction with AWS? Is it in the Enterprise space, is it in the start up space, both? >> So somewhat because of our route, what we're finding is that the large majority of Big Data customers and analytics customers from the last two, three years are all considering some form of addition of a cloud to their environment. If it's not a wholesale migration, it's a hybrid environment. It's bursting out into the cloud type of use case and what you're finding is that growth of on-premise Big Data and analytics systems is slowing down because once you get to the Cloud, the plethora of tools you have, the facilities that the scale brings to you is just unmatched. That's the trend we really see in the market. >> We've seen a lot of people go and use it in the marketplace. Juniper Networks for instance, are seeing some activity at the network. Who would have thought a network player is gonna to pick it in the Cloud, but this is what industrial-strength cloud looks like. You guys have the active active. Where does that fit in for the customers who wanna leverage the apps, and don't wanna worry about the networks? >> Exactly, the traditional model of thinking was use the Cloud for back up. You have your on-premise stuff. The cheapest way to back it up is into the Cloud. But that's really just scratching the tip of the iceberg. Once you put your data up in the Cloud, you have the ability to have it strongly consistently replicated then you can do amazing things from the Cloud. You can do a whole new analytics system. Perhaps you want to experiment with Spark in the Cloud and have it on on high on-premise that works very well. Now that both sides are actively writeable, you can create partitions of your data that are dynamically generated written to both sides. These are things that people did not consider. Once they stumble upon it, it just opens their mind to a whole new way of operating. >> Business Park, I've heard some rumors and rumblings in the developer community here that they're running Spark on Lando. People always hacking with new stuff. So Lando server list I think is coming down. How does that relate to some of things that are driving WANdisco's, how do you relate to that? Does that help you? Does that hurt you guys? >> It helps us, the way we look at it. We're all about strong replication of storage. Lando is no storage, you talk to the underlying storage of some kind. It's S3, it's EBS volumes whatever. So long as the storage comes through our system. Any growth, any simple easy way for applications to be written is hugely positive for us. >> What are the start ups out there? We've seen a lot of start ups really missed the mark. They misfired on the Cloud and you seen some stars that have played it well. They've got in the tornadoes as we say. In fact, Geoffrey Moore, I think is rewriting his book Inside the Tornado, which is a management paradigm. But there really seems to be a new business model. You guys are like ever green at WANdisco because you're unique (indistinct talking) property. How are you guys working with that business model and what are some of the things you're seeing with start ups and companies who are trying to play the cloud but are misfiring? >> Right so WANdisco as you know stands for Wide Area Network Distributed Computing, and the Cloud is like a huge bonus to it. It's all about the Wide Area Network. We are now consolidating a bunch of work in the cloud, but guess what? It's gonna go back to going to go into the edge in some way 'cause the edges are getting smarter. You need replication between those. We see a lot of that coming up in the next two, three, five years. IoT workloads and use cases all involve somewhat of edge smart computing. We replicate between those really well. >> Lisa, we always talk about the trend is your friend. In your case, Cloud is your friend. >> Indeed, it is. The Cloud is all about wide area network computing and we are the ones who can really replicate-- >> How does a customer know what to do when it comes down to getting involved with WANdisco? It's not obvious. Spell it out, why do they need you guys? When do you get involved? What specific things should be red flags to a potential customer or a customer who says I'm gonna go in on the Cloud. Unpack that. >> Let me give you a simple example. We look at Amazon S3, it's a Cloud service storage. But do you know that it's actually on a per region basis. When you create a bucket to put objects into the bucket, it's located in one region. If you want it replicated elsewhere, they have cross-region replication which is an eventually consistent replication system that doesn't give you the consistent results that you want. If you have such a situation employing our technology immediately gives you consistent replication. Be it Cloud regions, Cloud to Cloud or on-premise to Cloud. The end result is the minute you step into replication across the land, every solution out there doesn't do it consistently and that's our core-- >> And that's your unique IP. >> Indeed, it is. >> Okay so I'm seeing Amazon racing their roll out regions. They got one coming in China, one in the Middle East. That's a big part of the strategy. Does that help you or what does that do? >> Absolutely it helps us a great deal, partly because customers now do not look at their applications as a single region applications. That doesn't fly anymore. The the notion that my banking app cannot work because a data center went down is just not acceptable in the modern world anymore. The fact that we depend so much on the services means they need to be up all the time. More regions, more data replication. That's why we step in. >> So that sounds like a lot of symbiosis here. You talk about S3 and replication challenges. So tell us how WANdisco is actually helping AWS. That's one example but help you us understand the symbiosis with your relationship with AWS. >> The best example I can give you is a large travel service company in the internet. They had to Adobe infrastructure that was growing out of control. They wanted to manage costs by moving some workloads to Amazon but didn't really know where to start, because you can't do such a thing as take a copy of the data, ship it off on a Snowball into the Cloud and tell the users of that data, stop writing to it now. It's gonna be available in the Cloud, a week, 10 days from now. Then you can start writing again. That's just not acceptable. This is live data problem. The problem here is that you need to be able to ship out your data on Snowballs, continue to write the on-premise storage. When it shows up in the Cloud, start writing that. Both are consistently replicated, you have a proper hybrid Cloud environment. So this was a great bonus to them. As for AWS, they watched this and they look at it as a easy way to move vast majority of data from on-premise big Data analytics systems. >> Have they been a fuel to your fire, in a sense that they've been on this incredible acceleration of their innovation and as Andy Joci said many times to you John. It's speed and customer focus. So how has their accelerated pace of innovation helped fuel WANdisco's so that like you were saying the unique value. How have they really ignited that? >> So they started off with just plain Snowball two years ago. Last year they announced Snowball Edge which is a pretty improved device. Now they have in the works, capability to do some compute on those boxes. That's very interesting to us. Now our services can decide on the Snowball, It arrives at a customer site. He plugs it in, turns it on instant replication capabilities Those are fueled both by Amazon's drive and extreme speed and our own capabilities. So Amazon is a wonderful partner for us partly because their charge to us innovation is quite amazing. >> Snowball, snow mobile, it's gonna be a white Christmas for you guys. Business is good. >> Business is great. >> Okay, final question. What's the conversations you're having here this year, share with us some of the quick conversations you're having in the hallways, meetings, Amazon got execs, partners. >> So most of the conversation are about moving workloads from on-premise into the Cloud. I personally am very interested in IoT use cases because I see the volume of data and the ability for us to do some interesting replications at being critical. That's where our focus is right now. >> Jugane Sundar, CTO of WANdisco. Big announcement, partnership with Amazon Web Services and Snowball replication active active. Great solution for replication. You got regions across regions. Check out WANdisco. Thanks for coming by, great to see you again. Congratulations on all your success. This is theCube, live coverage day one. It's coming down to an end. The halls open, we got two more days of packed two Cubes. Stay tuned for more, we got some great guest coming up, stay with us. (uptempo techno music)