(bright upbeat music) >> Hey, guys. Welcome back to Detroit, Michigan. Lisa Martin and John Furrier here live with "theCUBE" at KubeCon CloudNativeCon, North America. John, it's been a great day. This is day one of our coverage of three days of coverage. Kubernetes is growing up. It's maturing. >> Yeah, we got three days of wall-to-wall coverage, all about Kubernetes. We heard about Security, Large scale, Cloud native at scale. That's the big focus. This next segment's going to be really awesome. You have a fast growing private company and a practitioner, big name, blue chip practitioner, building out next-gen cloud. First transforming, then building out the next level. This is classic, what we call Super Cloud-Like interview. It's going to be great. I'm looking forward to this. >> Anytime we can talk about Super Cloud, right? Please welcome back, one of our alumni, Haseeb Budhani is here, the CEO of Rafay. Great to see you. Santhosh Pasula, also joins us, the global head of Cloud SRE at Mass Mutual. Guys, great to have you on the program. >> Thanks for having us. >> Thank you for having me. >> So, Haseeb, you've been on "theCUBE" many times. You were on just recently, with the momentum that's around us today with the maturation of Kubernetes, the collaboration of the community, the recognition of the community. What are some of the things that you're excited about with on day one of the show? >> Wow, so many new companies. I mean, there are companies that I don't know who are here. And I live in this industry, and I'm seeing companies that I don't know, which is a good thing. It means that the community's growing. But at the same time, I'm also seeing another thing, which is, I have met more enterprise representatives at this show than other KubeCons. Like when we hung out at in Valencia, for example, or even other places, it hasn't been this many people. Which means, and this is a good thing that enterprises are now taking Kubernetes seriously. It's not a toy. It's not just for developers. It's enterprises who are now investing in Kubernetes as a foundational component for their applications going forward. And that to me is very, very good. >> Definitely, becoming foundational. >> Haseeb: Yeah. >> Well, you guys got a great traction. We had many interviews at "theCUBE," and you got a practitioner here with you guys, are both pioneering, kind of what I call the next-gen cloud. First you got to get through Gen-One, which you guys done at Mass Mutual extremely well. Take us through the story of your transformation? 'Cause you're on at the front end now of that next inflection point. But take us through how you got here? You had a lot of transformation success at Mass Mutual? >> So, I was actually talking about this topic few minutes back. And the whole cloud journey in big companies, large financial institutions, healthcare industry or insurance sector, it takes generations of leadership to get to that perfection level. And ideally, the cloud for strategy starts in, and then how do you standardize and optimize cloud, right? That's the second-gen altogether, and then operationalization of the cloud. And especially if you're talking about Kubernetes, in the traditional world, almost every company is running middleware and their applications in middleware. And their containerization is a topic that came in. And Docker is basically the runtime containerization. So, that came in first, and from Docker, eventually when companies started adopting Docker, Docker Swarm is one of the technologies that they adopted. And eventually, when we were taking it to a more complicated application implementations or modernization efforts, that's when Kubernetes played a key role. And as Haseeb was pointing out, you never saw so many companies working on Kubernetes. So, that should tell you one story, right? How fast Kubernetes is growing, and how important it is for your cloud strategy. >> And your success now, and what are you thinking about now? What's on your agenda now? As you look forward, what's on your plate? What are you guys doing right now? >> So we are past the stage of proof of concepts, proof of technologies, pilot implementations. We are actually playing it, the real game now. In the past, I used the quote, like "Hello world to real world." So, we are actually playing in the real world, not in the hello world anymore. Now, this is where the real time challenges will pop up. So, if you're talking about standardizing it, and then optimizing the cloud, and how do you put your governance structure in place? How do you make sure your regulations are met? The demands that come out of regulations are met? And how are you going to scale it? And while scaling, how are you going to keep up with all the governance and regulations that come with it? So we are in that stage today. >> Haseeb talked about, you talked about the great evolution of what's going on at Mass Mutual. Haseeb talk a little bit about who? You mentioned one of the things that's surprising you about this KubeCon in Detroit, is that you're seeing a lot more enterprise folks here? Who's deciding in the organization and your customer conversations? Who are the decision makers in terms of adoption of Kubernetes these days? Is that elevating? >> Hmm. Well, this guy. (Lisa laughing) One of the things I'm seeing here, and John and I have talked about this in the past, this idea of a platform organization and enterprises. So, consistently what I'm seeing, is somebody, a CTO, CIO level, an individual is making a decision. I have multiple internal Bus who are now modernizing applications. They're individually investing in DevOps, and this is not a good investment for my business. I'm going to centralize some of this capability so that we can all benefit together. And that team is essentially a platform organization. And they're making Kubernetes a shared services platform so that everybody else can come and sort of consume it. So, what that means to us, is our customer is a platform organization, and their customer is a developer. So we have to make two constituencies successful. Our customer who's providing a multi-tenant platform, and then their customer, who's your developer, both have to be happy. If you don't solve for both, you know, constituencies, you're not going to be successful. >> So, you're targeting the builder of the infrastructure and the consumer of that infrastructure? >> Yes, sir. It has to be both. >> On the other side? >> Exactly, right. So that look, honestly, it takes iteration to figure these things out. But this is a consistent theme that I am seeing. In fact, what I would argue now, is that every enterprise should be really stepping back and thinking about what is my platform strategy? Because if you don't have a platform strategy, you're going to have a bunch of different teams who are doing different things, and some will be successful, and look, some will not be. And that is not good for business. >> Yeah, and Santhosh, I want to get to you. You mentioned your transformations, what you look forward, and your title, Global Head of Cloud, SRE. Okay, so SRE, we all know came from Google, right? Everyone wants to be like Google, but no one wants to be like Google, right? And no one is Google. Google's a unique thing. >> Haseeb: Only one Google. >> But they had the dynamic and the power dynamic of one person to large scale set of servers or infrastructure. But concept can be portable, but the situation isn't. So, Borg became Kubernetes, that's inside baseball. So, you're doing essentially what Google did at their scale, you're doing for Mass Mutual. That's kind of what's happening, is that kind of how I see it? And you guys are playing in there partnering? >> So, I totally agree. Google introduce SRE, Site Reliability Engineering. And if you take the traditional transformation of the roles, in the past, it was called operations, and then DevOps ops came in, and then SRE is the new buzzword. And the future could be something like Product Engineering. And in this journey, here is what I tell folks on my side, like what worked for Google might not work for a financial company. It might not work for an insurance company. It's okay to use the word, SRE, but end of the day, that SRE has to be tailored down to your requirements. And the customers that you serve, and the technology that you serve. >> This is why I'm coming back, this platform engineering. At the end of the day, I think SRE just translates to, you're going to have a platform engineering team? 'Cause you got to enable developers to be producing more code faster, better, cheaper, guardrails, policies. It's kind of becoming the, these serve the business, which is now the developers. IT used to serve the business back in the old days, "Hey, the IT serves the business." >> Yup. >> Which is a term now. >> Which is actually true now. >> The new IT serves the developers, which is the business. >> Which is the business. >> Because if digital transformation goes to completion, the company is the app. >> The hard line between development and operations, so that's thinning down. Over the time, that line might disappear. And that's where SRE is fitting in. >> Yeah, and then building platform to scale the enablement up. So, what is the key challenges? You guys are both building out together this new transformational direction. What's new and what's the same? The same is probably the business results, but what's the new dynamic involved in rolling it out and making people successful? You got the two constituents, the builders of the infrastructures and the consumers of the services on the other side. What's the new thing? >> So, the new thing, if I may go first. The faster market to value that we are bringing to the table, that's very important. Business has an idea. How do you get that idea implemented in terms of technology and take it into real time? So, that journey we have cut down. Technology is like Kubernetes. It makes an IT person's life so easy that they can speed up the process. In a traditional way, what used to take like an year, or six months, can be done in a month today, or less than that. So, there's definitely speed velocity, agility in general, and then flexibility. And then the automation that we put in, especially if you have to maintain like thousands of clusters. These are today, it is possible to make that happen with a click off a button. In the past, it used to take, probably, 100-person team, and operational team to do it, and a lot of time. But that automation is happening. And we can get into the technology as much as possible, but blueprinting and all that stuff made it possible. >> We'll save that for another interview. We'll do it deep time. (panel laughing) >> But the end user on the other end, the consumer doesn't have the patience that they once had, right? It's, "I want this in my lab now." How does the culture of Mass Mutual? How is it evolve to be able to deliver the velocity that your customers are demanding? >> Once in a while, it's important to step yourself into the customer's shoes and think it from their perspective. Business does not care how you're running your IT shop. What they care about is your stability of the product and the efficiencies of the product, and how easy it is to reach out to the customers. And how well we are serving the customers, right? So, whether I'm implementing Docker in the background, Docker Swam or Kubernetes, business doesn't even care about it. What they really care about, it is, if your environment goes down, it's a problem. And if your environment or if your solution is not as efficient as the business needs, that's the problem, right? So, at that point, the business will step in. So, our job is to make sure, from a technology perspective, how fast you can make implement it? And how efficiently you can implement it? And at the same time, how do you play within the guardrails of security and compliance? >> So, I was going to ask you, if you have VMware in your environment? 'Cause a lot of clients compare what vCenter does for Kubernetes is really needed. And I think that's what you guys got going on. I can say that, you're the vCenter of Kubernetes. I mean, as as metaphor, a place to manage it all, is all one paint of glass, so to speak. Is that how you see success in your environment? >> So, virtualization has gone a long way. Where we started, what we call bare metal servers, and then we virtualized operating systems. Now, we are virtualizing applications, and we are virtualizing platforms as well, right? So that's where Kubernetes plays a role. >> So, you see the need for a vCenter like thing for Kubernetes? >> There's definitely a need in the market. The way you need to think is like, let's say there is an insurance company who actually implement it today, and they gain the market advantage. Now, the the competition wants to do it as well, right? So, there's definitely a virtualization of application layer that's very critical, and it's a critical component of cloud strategy as a whole. >> See, you're too humble to say it. I'll say, you're like the vCenter of Kubernetes. Explain what that means in your term? If I said that to you, what would you react? How would you react to that? Would you say, BS, or would you say on point? >> Maybe we should think about what does vCenter do today? So, in my opinion, by the way, vCenter in my opinion, is one of the best platforms ever built. Like it's the best platform in my opinion ever built. VMware did an amazing job, because they took an IT engineer, and they made him now be able to do storage management, networking management, VM's multitenancy, access management, audit. Everything that you need to run a data center, you can do from essentially single platform. >> John: From a utility standpoint, home-run? >> It's amazing. >> Yeah. >> Because you are now able to empower people to do way more. Well, why are we not doing that for Kubernetes? So, the premise man Rafay was, well, I should have IT engineers, same engineers. Now, they should be able to run fleets of clusters. That's what people that Mass Mutual are able to do now. So, to that end, now you need cluster management, you need access management, you need blueprinting, you need policy management. All of these things that have happened before, chargebacks, they used to have it in vCenter, now they need to happen in other platforms but for Kubernetes. So, should we do many of the things that vCenter does? Yes. >> John: Kind of, yeah. >> Are we a vCenter for Kubernetes? >> No. >> That is a John Furrier question. >> All right, well, the speculation really goes back down to the earlier speed question. If you can take away the complexity and not make it more steps, or change a tool chain, or do something, then the Devs move faster. And the service layer that serves the business, the new organization, has to enable speed. This is becoming a real discussion point in the industry, is that, "Yeah, we got new tool. Look at the shiny new toy." But if it move the needle, does it help productivity for developers? And does it actually scale up the enablement? That's the question. So, I'm sure you guys are thinking about this a lot. What's your reaction? >> Yeah, absolutely. And one thing that just hit my mind, is think about the hoteling industry before Airbnb and after Airbnb. Or the taxi industry before Uber and after Uber. So, if I'm providing a platform, a Kubernetes platform for my application folks, or for my application partners, they have everything ready. All they need to do is build their application and deploy it, and run it. They don't have to worry about provisioning of the servers, and then building the Middleware on top of it, and then, do a bunch of testing to make sure they iron out all the compatible issues and whatnot. Now, today, all I say is like, "Hey, we have a platform built for you. You just build your application, and then deploy it in a development environment, that's where you put all the pieces of puzzle together. Make sure you see your application working, and then the next thing that you do is like, do the correction. >> John: Shipping. >> Shipping. You build the production. >> John: Press. Go. Release it. (laughs) That when you move on, but they were there. I mean, we're there now. We're there. So, we need to see the future, because that's the case, then the developers are the business. They have to be coding more features, they have to react to customers. They might see new business opportunities from a revenue standpoint that could be creatively built, got low code, no code, headless systems. These things are happening where there's, I call the Architectural List Environment where it's like, you don't need architecture, it's already happening. >> Yeah, and on top of it, if someone has an idea, they want to implement an idea real quick. So, how do you do it? And you don't have to struggle building an environment to implement your idea and test it in real time. So, from an innovation perspective, agility plays a key role. And that's where the Kubernetes platforms, or platforms like Kubernetes plays. >> You know, Lisa, when we talked to Andy Jassy, when he was the CEO of AWS, either one-on-one or on "theCUBE," he always said, and this is kind of happening, "Companies are going to be builders, where it's not just utility, you need that table stakes to enable that new business idea." And so, in this last keynote, he did this big thing like, "Think like your developers are the next entrepreneurial revenue generators." I think I'm starting to see that. What do you think about that? You see that coming sooner than later? Or is that an insight, or is that still ways away? >> I think it's already happening at a level, at a certain level. Now ,the question comes back to, you know, taking it to the reality. I mean, you can do your proof of concept, proof of technologies, and then prove it out like, "Hey, I got a new idea. This idea is great." And it's to the business advantage. But we really want to see it in production live where your customers are actually using it. >> In the board meetings, "Hey, we got a new idea that came in, generating more revenue, where'd that come from?" Agile Developer. Again, this is real. >> Yeah. >> Yeah. Absolutely agree. Yeah, I think both of you gentlemen said a word as you were talking, you used the word, Guardrails. We're talking about agility, but the really important thing is, look, these are enterprises, right? They have certain expectations. Guardrails is key, right? So, it's automation with the guardrails. Guardrails are like children, you know, shouldn't be heard. They're seen but not heard. Developers don't care about guardrails, they just want to go fast. >> They also bounce around a little bit, (laughs) off the guardrails. >> Haseeb: Yeah. >> One thing we know that's not going to slow down, is the expectations, right? Of all the consumers of this, the Devs, the business, the business top line, and, of course, the customers. So, the ability to really, as your website says, let's say, "Make Life Easy for Platform Teams" is not trivial. And clearly what you guys are talking about here, is you're really an enabler of those platform teams, it sounds like to me. >> Yup. >> So, great work, guys. Thank you so much for both coming on the program, talking about what you're doing together, how you're seeing the evolution of Kubernetes, why? And really, what the focus should be on those platform teams. We appreciate all your time and your insights. >> Thank you so much for having us. >> Thanks for having us. >> Our pleasure. For our guests and for John Furrier, I'm Lisa Martin. You're watching "theCUBE" Live, KubeCon CloudNativeCon from Detroit. We'll be back with our next guest in just a minute, so stick around. (bright upbeat music)

(upbeat music) >> Welcome back to theCUBE's continuing coverage of Snowflake Summit 22 live from Caesars Forum in Las Vegas. I'm Lisa Martin with Dave Vellante. We've got a couple of guests joining us now. We're going to be talking about financial services. Rinesh Patel joins us, the Global Head of Financial Services for Snowflake, and Jack Berkowitz, Chief Data Officer at ADP. Guys, welcome to the program. >> Thanks, thanks for having us. >> Thanks for having us. >> Talk to us about what's going on in the financial services industry as a whole. Obviously, we've seen so much change in the last couple of years. What does the data experience look like for internal folks and of course, for those end user consumers and clients? >> So, one of the big things happening inside of the financial services industry is overcoming the COVID wait, right? A lot of banks, a lot of institutions like ours had a lot of stuff on-prem. And then the move to the Cloud allows us to have that flexibility to deal with it. And out of that is also all these new capabilities. So the machine learning revolution has really hit the services industry, right? And so it's affecting how our IT teams or our data teams are building applications. Also really affecting what the end consumers get out of them. And so there's all sorts of consumerization of the experience over the past couple of years much faster than we ever expected it to happen. >> Right, we have these expectations as consumers that bleed into our business lives that I can do transactions. It's going to be on the swipe in terms of checking authenticity, fraud detection, et cetera. And of course we don't want things to go back in terms of how brands are serving us. Talk about some of the things that you guys have put in place with Snowflake in the last couple of years, particularly at ADP. >> Yeah, so one of the big things that we've done, is, one of the things that we provide is compensation data. So we issue a thing called the National Employment Report that informs the world as to what's happening in the U.S. economy in terms of workers. And then we have compensation data on top of that. So the thing that we've been able to do with Snowflake is to lower the time that it takes us to process that and get that information out into the fingertips of people. And so people can use it to see what's changed in terms of with the worker changes, how much people are making. And they can get it very, very quickly. And we're able to do that with Snowflake now. Used to take us weeks, now it's in a matter of moments we can get that updated information out to people. >> Interesting. It helps with the talent war and- >> Helps in the talent war, helps people adjust, even where they're going to put supply chain in reaction to where people are migrating. We can have all of that inside of the Snowflake system and available almost instantaneously. >> You guys announced the Financial Data Cloud last year. What was that like? 'Cause I know we had Frank on early, he clearly was driving the verticalization of Snowflake if you will, which is kind of rare for a relatively new software company but what's that been like? Give us the update on where you're at and biggest vertical, right? >> Absolutely, it's been an exciting 12 months. We're a platform, but the journey and the vision is more. We're trying to bring together a fragmented ecosystem across financial services. The aim is really to bring together key customers, key data providers, key solution providers all across the different Clouds that exist to allow them to collaborate with data in a seamless way. To solve industry problems. To solve industry problems like ESG, to solve industry problems like quantitative research. And we're seeing a massive groundswell of customers coming to Snowflake, looking at the Financial Services Data Cloud now to actually solve business problems, business critical problems. That's really driving a lot of change in terms of how they operate, in terms of how they win customers, mitigate risk and so forth. >> Jack, I think, I feel like the only industry that's sometimes more complicated than security, is data. Maybe not, security's still maybe more fragmented- >> Well really the intersection of the two is a nightmare. >> And so as you look out on this ecosystem, how do you as the chief data officer, how do you and your organization, what process do you use to decide, okay, which of the, like a chef, which of these ingredients am I going to put together for my business. >> It's a great question, right? There's been explosion of companies. We kind of look at it in two ways. One is we want to make sure that the software and the data can interoperate because we don't want to be in the business of writing bridge code. So first thing is, is having the ecosystem so that the things are tested and can work together. The other area is, and it's important to us is understanding the risk profile of that company. We process about 20% of the U.S. payroll, another 25% of the taxes. And so there's a risk to us that we have an imperative to protect. So we're looking at those companies are they financed, what's their management team. What's the sales experience like, that's important to us. And so technology and the experience of the company coming together are super important to us. >> What's your purview as a chief data officer, I mean, a lot of CDOs that I know came out of the back office and it was a compliance or data quality. You come out of industry from a technology company. So you're sort of the modern... You're like the modern CDO. >> Thanks. Thanks. >> Dave: What's your role? >> I appreciate that. >> You know what I'm saying though? >> And for a while it was like, oh yeah, compliance. >> So I actually- >> And then all of a sudden, boom, big deal. >> Yeah, I really have two jobs. So I have that job with data governance but a lot of data security. But I also have a product development unit, a massive business in monetization of data or people analytics or these compensation benchmarks or helping people get mortgages. So providing that information, so that people can get their mortgage, or their bank loans, or all this other type of transactional data. *So it's both sides of that equation is my reading inside. >> You're responsible for building data products? >> That's right. >> Directly. >> That's right. I've got a massive team that builds data products. >> Okay. That's somewhat unique in your... >> I think it's where CDOs need to be. So we build data products. We build, and we assist as a hub to allow other business units to build analytics that help them either optimize their cost or increase their sales. And then we help with all that governance and communication, we don't want to divide it up. There's a continuum to it. >> And you're a peer of the CIO and the CISO? >> Yeah, exactly. They're my peers. I actually talk to them almost every day. So I've got the CIO as a peer. >> It's a team. >> I've got the security as a peer and we get things done together. >> Talk about the alignment with business. We've been talking a lot about alignment with the data folks, the business folks, the technical folks to identify the right solutions, to be able to govern data, to monetize it, to create data products. What does that... You mentioned a couple of your cohorts, but on the business side, who are some of those key folks? >> So we're like any other big, big organization. We have lots of different business units. So we work directly with either the operational team or the heads of those business units to divine analytic missions that they'll actually execute. And at the same time, we actually have a business unit that's all around data monetization. And so I work with them every single day. And so these business units will come together. I think the big thing for us is to define value and measure that value as we go. As long as we're measuring that value as we go, then we can continue to see improvements. And so, like I said, sometimes it's bottom line, sometimes it's top line, but we're involved. Data is actually a substrate of the company. It's not a side thing to the company. >> Yeah, you are. >> ADP. >> Yeah but if they say data first but you really are data first. >> Yeah. I mean, our CEO says- >> Data's your product. >> Data's our middle name. And it literally is. >> Well, so what do you do in the Snowflake financial services data Cloud? Are you monetizing? >> Yeah. >> What's the plan? >> Yeah, so we have clients. So part of our data monetization is actually providing aggregate and anonymized information that helps other clients make business decisions. So they'll take it into their analytics. So, supply chain optimization, where should we actually put the warehouses based on the population shifts? And so we're actually using the file distribution capabilities or the information distribution, no longer files, where we use Snowflake to actually be that data cloud for those clients. So the data just pops up for our other clients. >> I think the industry's existed a lot with the physical movement of data. When you physically move data, you also physically move the data management challenges. Where do you store it? How do you map it? How do you concord it? And ultimately data sharing is taking away that friction that exists. So it's easier to be able to make informed decisions with the data at hand across two counterparties. >> Yeah, and there's a benefit to us 'cause it lowers our friction. We can have a conversation and somebody can be... Obviously the contracts have to be signed, but once they get done, somebody's up and running on it within minutes. And where it used to be, as you were saying, the movement of data and loss of control, we never actually lose control of it. We know where it is. >> Or yeah, contracts signed, now you got to go through this long process of making sure everything's cool, or a lot of times it could slow down the sale. >> That's right. >> Let's see how that's going to... Let's do a little advanced work. Now you're working without a contract. Here, you can say, "Hey, we're in the Snowflake data cloud. It's governed, you're a part of the ecosystem." >> Yeah, and the ecosystem we announced, oh gee, I think it's probably almost a year and a half ago, a relationship with ICE, Intercontinental Exchange, where they're actually taking our information and their information and creating a new data product that they in turn sell. So you get this sort of combination. >> Absolutely. The ability to form partnerships and monetize data with your partners vastly increases as a consequence. >> Talk to us about the adoption of the financial services data cloud in the last what, maybe nine months or so, since it was announced? And also in terms of the its value proposition, how does the ADP use case articulate that? >> So, very much so. So in terms of momentum, we're a global organization, as you mentioned, we are verticalized. So we have increasingly more expertise and expertise experience now within financial services that allows us to really engage and accelerate our momentum with the top banks, with the biggest asset managers by AUM, insurance companies, sovereign wealth funds on Snowflake. And obviously those data providers and solution providers that we engage with. So the momentum's really there. We're really moving very, very fast in a great market because we've got great opportunity with the capabilities that we have. I mean, ADP is just one of many use cases that we're working with and collaborations that we're taking to market. So yeah, the opportunity to monetize data and help our partners monetize the data has vastly increased within this space. >> When you think about... Oh go ahead, please. >> Yeah I was just going to say, and from our perspective, as we were getting into this, Snowflake was with us on the journey. And that's been a big deal. >> So when you think about data privacy, governance, et cetera, and public policy, it seems like you have, obviously you got things going on in Europe, and you got California, you have other states, there's increasing in complexity. You guys probably love that. (Dave laughs) More data warehouses, but where are we at with that whole? >> It's a great question. Privacy is... We hold some of the most critical information about people because that's our job to help people get paid. And we respect that as sort of our prime agenda. Part of it deals with the technology. How do you monitor, how do you see, make sure that you comply with all these regulations, but a lot of it has to do with the basic ethics of why you're doing and what you're doing. So we have a data and AI ethics board that meets and reviews our use cases. Make sure not only are we doing things properly to the regulation, but are these the types of products, are these the types of opportunities that we as a company want to stand behind on behalf of the consumers? Our company's been around 75 years. We talk about ourselves as a national asset. We have a trust relationship. We want to ensure that that trust relationship is never violated. >> Are you in a position where you can influence public policy and create more standards or framework. >> We actually are, right. We issue something every month called the National Employment Report. It actually tells you what's happening in the U.S. economy. We also issue it in some overseas countries like France. Because of that, we work a lot with various groups. And we can help shape, either data policy, we're involved in understanding although we don't necessarily want to be out in the front, but we want to learn about what's happening with federal trade commission, EOC, because at the end of the day we serve people, I always joke ADP, it's my grandfather's ADP. Well, it was actually my grandfather's ADP. (Dave laughs) He was a small businessman, and he used a ADP all those years ago. So we want to be part of that conversation because we want to continue to earn that trust every day. >> Well, plus your observation space is pretty wide. >> And you've got context and perspective on that that you can bring. >> We move somewhere between two, two and a half trillion dollars a year through our systems. And so we understand what's happening in the economy. >> What are some of the, oh sorry. >> Can your National Employment Report combined with a little Snowflake magic tell us what the hell's going to happen with this economy? >> It's really interesting you say that. Yeah, we actually can. >> Okay. (panelists laugh) >> I think when you think about the amount of data that we are working with, the types of partners that we're working with, the opportunities are infinite. They really, really are. >> So it's either a magic eight ball or it's a crystal ball, but you have it. >> We think- >> We've just uncovered that here on theCUBE. >> We think we have great partners. We have great data. We have a set of industry problems out there that we're working, collaboration with the community to be able to solve. >> What are some of the upcoming use cases Rinesh, that excite you, that are coming up in financial services- >> Great question. >> That snowflake is just going to knock out of the park. >> So look, I think there's a set of here and now problems that the industry faces, ESG's a good one. If you think about ESG, it means many different things from business ethics, to diversity, to your carbon footprint and every asset manager has to make sure they have now some form of green strategy that reflects the values of their investors. And every bank is looking to put in place sustainable lending to help their corporate customers transition. That's a big data problem. And so we're very much at the center of helping those organizations support those informed investors and help those corporates transition to a more sustainable landscape. >> Let me give you an example on Snowflake, we launched capabilities about diversity benchmarks. The first time in the industry companies can understand for their industry, their size, their location what their diversity profile looks like and their org chart profile looks like to differentiate or at least to understand are they doing the right things inside the business. The ability for banks to understand that and everything else, it's a big deal. And that was built on Snowflake. >> I think it's massive, especially in the context of the question around regulation 'cause we're seeing more and more disclosure agreements come out where regulators are making sure that there's no greenwashing taking place. So when you have really strong sources of data that are standardized, that allow that investment process to ingest that data, it does allow for a better outcome for investors. >> Real data, I mean, that diversity example they don't have to rely on a survey. >> It's not a survey. >> Anecdotes. >> It's coming right out of the transactional systems and it's updated, whenever those paychecks are run, whether it's weekly, whether it's biweekly or monthly, all that information gets updated and it's available. >> So it sounds like ADP is a facilitator of a lot of companies ESG initiatives, at least in part? >> Well, we partner with companies all the time. We have over 900,000 clients and all of them are... We've never spoken to a client who's not concerned about their people. And that's just good business. And so, yeah we're involved in that and we'll see where it goes over time now. >> I think there's tremendous opportunity if you think about the data that the ADP have in terms of diversity, in terms of gender pay gap. Huge, huge opportunity to incorporate that, as I said into the ESG principles and criteria. >> Good, 'cause that definitely is what needs to be addressed. (Lisa laughs) Guys thank you so much for joining Dave and me on the program, talking about Snowflake ADP, what you're doing together, and the massive potential that you're helping unlock with the value of data. We appreciate your insights and your time. >> Thank you for having us. >> Dave: Thanks guys. >> Thank you so much. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, live in Las Vegas at Snowflake Summit 22. Dave and I will be right back with our next guest. (upbeat music)

(upbeat music) >> Hey everyone. Welcome back to the show floor in Las Vegas Snowflake Summit 22 with 7,000 plus folks here, Lisa Martin with Dave Vellante. Great to be back in person. We're excited to welcome a couple of guests that join us next. Persona Christian is here. The director of product for collaboration and Snowflake marketplace. Katie Laughlin joins us as well. The Global Head Offerings, Human Data Science Cloud at Customer IQVIA. Ladies, welcome to the program. >> Thank you. >> Thank you for having us. >> Dave: All right. Thanks for coming on. >> Katie, let's go ahead and start with you. Give the audience an overview of IQVIA. What you guys do, your mission, what you deliver? >> Yeah, sure. So, IQVIA is a healthcare focused data analytics and clinical research organization. We have 82,000 employees. We operate in a hundred countries and we have tens of thousands of data deliverables that we curate for our customers and deliver to them on a monthly basis. So, we're 100% healthcare focused, whether it's clinical research, helping our customers support their clinical trials, real world evidence, how are medicines operating in the market or commercial aspects. You know, how is your company performing overall in the market? >> How long have you been a customer of Snowflake's? >> A few years. Yeah. >> A few years, okay. Persona, tremendous growth going on right now. There's a rocket ship. You could even feel kind of like the whiplash from the keynote and all the announcements going on, but looking at the first quarter 23, fiscal 23 results, product revenue, 384 million, 85% growth tremendous momentum going on, big growth in customers. Talk to us about IQVIA, its partnership with Snowflake and the data driver award program. They, they just won. >> Yeah, absolutely. I'll start with a little bit about the Snowflake collaboration capabilities, which enable these thousands of customers to really collaborate on the data cloud to be able to break down silos between data and drive business decisions based on data and applications that live outside your own four walls as well. And this is where IQVIA, as a leader in healthcare data, bringing together data to enable healthcare organizations to be more data driven and to really drive insights. One, the data for good award, which we are really excited with for the partnership and really excited to have IQVIA be the winner of the award. >> And what does that mean? The data for good. We always love talking about that, Katie. >> Katie: Sure. What does that mean? How is that embodied at IQVIA? >> Can you say the last part? >> Yeah. How is that embodied at IQVIA? >> That's a great question. I think everyone that works at IQVIA believes in the mission, which is really to drive healthcare forward. We're really proud of a lot of the things that we do. So, with the advent of COVID, for example, we really had to pivot and help our customers. How do we keep executing on clinical trials? We supported a lot of the COVID trials that came forward and helped our customers understand how is this affecting patients in the real world? And how is it affecting your commercial operations? So, being in Vegas with tens of thousands of people around and almost nobody wearing masks, I think to myself, I'm part of the organization an organization that helped make that possible. >> So Frank Slootman today, Katie talked about compress. He talked about one pharmaceutical compressing from nine years to seven years, you guys have done a lot of obviously contract research over the years. So, what has that Snowflake journey been like? What's been the business impact of of working with that and the collaboration? >> Yeah. So my focus is really around our data as a service offering, which is where we're enabling our customers to ingest their data in modern ways. So if you imagine, you know, we've done everything from paper to big tapes of data for over 60 years of of our company being in business, now to VPN, SFTP, making multiple hops of data from one end to the other. I was just learning about one of our use cases where we're able to cut down processing time for our customers for two weeks. They data share some data with us. We do some additional processing on that. We serve it back to them and we're saving them two weeks of time to gain time to insights. >> Right. And Prasanna, collaboration transcends data sharing, right? It's almost like it's, that's, that's sort of the the first, the core of the concentric circle, right? >> Prasanna: Yeah. >> Talk about what else is embodied in collaboration. >> Yeah, that's a great question. So the first problem that we solved was getting access to data through our core sharing technology. And as you were talking about Katie, replacing FTPs and having to build APIs, which were cumbersome, and instead being able to access data on the data cloud without having to copy or move anything. That was the core sharing technology. But that solves the first problem, which is the access problem. The second problem is how do I discover what what's out there? How do I better understand it? How do I evaluate it? How do I try it and buy it? And those are all the problems that we're solving with the marketplace, which is now home to both data and applications that you can discover, try, and buy. >> Katie, talk to us about what IQVIA was doing before Snowflake? What was that life like before? How were you enabling customers to leverage data to make data driven decisions? >> Yeah, so we, as I said, we're a data and analytics company. So we provide some native analytics capabilities to our customers, but most customers, most of the large customers I would say, they're building their own data lakes. They have their own ecosystems. Some of them are adopting Snowflake and we really needed to partner with them on being able to get the data to them as quickly as possible. So like, I, I was just describing a minute ago we would have multiple hops where we deliver to a location, customer ingests it, customer does their QC. Then they process it and then it appears in their data warehouse. And now we're able to adopt their QC protocols within our own platform and deliver the data to them much more quickly. >> And what does that enable to your business from an outcomes perspective? If you look at overall Snowflake as an engine what is it enabling and empowering IQVIA to accomplish? >> So it helps us partner with our customers in modern ways. So I'm saying we've been in the data business for 60 years. So it's sometimes it's a legacy behemoth that you need to bring along to modern times. And I think for us, the shift has been night and day in terms of Snowflake's capabilities. >> So you will build data based apps in the Snowflake data cloud? Is that, is that where you're headed? >> Yes. So we have several applications that we built natively on Snowflake that we offer to our customers. >> And what will that bring you that you kind of couldn't do before? >> That we couldn't do before? I think the the ability to, we talk a lot about how you spend 80% of your time cooking the data, right? Getting it ready for insights and only 20% of your time being able to to bring those insights forward and Snowflake, it really helps us flip that ratio so that we don't have to worry so much about the scaling and the infrastructure and the data sourcing. We can focus more on driving those insights and innovations. >> So Prasanna, we talk a lot about, you have this application stack over here and it sends a database over here and then you have an analytics stack. It seems like you're enabling those worlds to come together. Is that, is that by design? Is that more organic? Can you talk about that? >> Yeah. I mean, that is essential to our our mission and our value prop is to bring it together. It's one product, it's seamless and lets you do more with your data. Benoit talked today in the opening keynote about running multiple workloads on your data and the way you do that is by having one product that allows you to to run your data, data queries but also build applications that can run against that data. >> Katie, can you share a little bit about the partnership? We'll say collaboration that IQVIA has with Snowflake in terms of your ability to influence the roadmap in the direction. We heard a lot of customer stories in the keynote and they talked a lot about Frank Slootman did, Benoit, Christian. We are listening to our customers. Do you feel that as a, a customer for the last few years? >> Yeah, absolutely. So we have a really broad partnership with Snowflake. We're a customer. We have OEM licensing where we're building applications on top of Snowflake. We're an SI partner where we're marrying our data healthcare expertise along with Snowflake technology expertise and helping customers build and utilize the data internally and as well as just, if nothing else, the Snowflake data share in order to deliver the data into their environment. >> Prasanna, what do you look for in a data driver winner? Like what stood out about IQVIA and others that aspire to that, what should they be focused on? >> Yeah, I mean, you know, we ultimately think that in every business you have business needs that you're trying to solve and business is inherently collaborative. You never solve problems with just what you have within your own four walls. And IQVIA is an example of someone that's really enabling outcomes for healthcare companies to be much faster through live access to data. Which is what we want to accomplish for the data cloud, help our company, help our customers solve business needs. >> Every company has to be a data company these days, right? There's no, you have no choice. We talked about, you know, software eating the world a few years. Now we're talking about data eating the world. For organizations, it's in any any vertical healthcare, life sciences, retail, finance. It's essential to not just have data, live data access to it, to be able to extract insights from it that you can act on. Talk about what you are doing at Snowflake as a differentiator? Is that goal of becoming the defacto standard data platform and what that enables partners like IQVIA to accomplish? >> Yeah. It starts with our fundamental architecture, which allows you to collaborate and access data without creating copies of it or sending around copies and built on top of that now, the ability to build applications and to monetize them really enables our customers to do more with their data and to monetize it and to be able to distribute it without having to deal with all the plumbing. >> That's nice. That saves you a lot of time. What do you think when you, Katie, if you talk to people that are your peers in either healthcare or other industries, what are like the top couple of recommendations that you would have for them? We have a data problem. It's all a data problem. How do we actually leverage value from this fast so we can be competitive? >> Yeah. So I think if I were to advise someone who is thinking about commercializing their data set, when if they haven't before, you know, you have to think about good data governance protocols, good data cataloging. Make sure you're, you know, conforming to all of the privacy rules that you need to and overseeing the management of that data, any changes in the data, you know, delivering that both to internal and external customers. But I think, just a quick plug for Snowflake, what I would say on a personal level is that their partner first mentality really is a pleasure, makes it a pleasure to work with them and makes it really easy for us to enable our services through, through Snowflake. >> Frank Slootman talked about mission alignment this morning, kind of a mission I thought of, of aligning on with the missions of their customers and partners. It sounds like that's what Katie's talking about from a cultural perspective. You've got that alignment here? >> Yes, absolutely. You know, we work with our partners to enable our customers to drive business value and solve the needs of their industry. >> What are some of the things that you are excited about? Fourth Annual Summit. We, I, I said 7,000 plus people we'll get numbers kind of later on. What are you excited about finally being back in person? >> Yes, of course. >> Being able to access this hugely growing population of customers and partners, what excites you about this Summit 22? >> What excites me most is the fact that we are now enabling our customers to do more, to build applications which has been a big theme at Summit, but also to be able to distribute and monetize this. So as Frank talked about this morning, helping customers drive value and more value from, from their data. >> Critical. Katie, last question for you. If we look at all the,it was a very technical keynote this morning. You talked about the great partnership, the synergies the alignment that IQVIA has with Snowflake. What are you excited about in terms of hearing and seeing and feeling and touching this week at Summit? >> Well, yesterday we won an award for Data Marketplace. Marketplace Partner of the year for healthcare and life sciences. That was really exciting for us. It was great recognition for us in terms of how we've been able to modernize on the cloud. But I'm really excited to see how much the Snowflake business has grown as well. Our General Manager for information management was telling me, he said, when I come to this conference a couple of years ago it was only a few thousand people and now it's really, it's really grown and really taken off. And it's really exciting to see how many of the different partnerships are interacting and and that we're able to take advantage of as well. >> Yeah, I think we heard earlier this morning that the first summit four years ago was a couple thousand people. Now here we are eight, eight to ten. We've also seen, Persona, I mentioned some of the product revenue numbers for fiscal 23 Q1. I also noticed that in the last four years, the number percentage of customers with a million plus ARR is grown over 1200%. Number of customers is growing, the high value customers are growing. It seems like you're on a rocket ship here with Snowflake. Would you agree? >> Yeah. We're excited with all the value that we're bringing to our customers and the growth we're seeing. >> Dave: Yeah. Way to amp it up. >> Yeah, absolutely. >> Excellent. Ladies, thank you so much for joining us talking about the partnership with IQVIA and Snowflake. Congratulations again. >> Katie: Thank you. >> Katie, on IQVIA winning the data driver award, Data for good >> Great to hear what you're doing together and how you're enabling organizations in the healthcare industry to maximize the value of data. We appreciate your insights. >> Thank you. >> Dave: Thank you guys. >> Thanks. >> For our guests, Dave Vellante, I'm Lisa Martin. You're watching the Cube's live coverage from Las Vegas of Snowflake Summit 22. Stick around, Dave and I will be right back with our next guest.

>>from the cube studios in Palo alto in boston connecting >>with thought leaders all around the world. This is a cute conversation. Hello and welcome to today's session of the 2021 AWS Global public sector Partner awards. I'm your host, Natalie ehrlich. Today we'll discuss the award for the most customer obsessed mission based win for state and local government. I'm pleased to introduce our guests for today's session Todd, Carey, Global Head West Business group Cognizant and David. Sullivan chief executive officer of Elizabeth river crossings. Thank you gentlemen for joining the program. >>Thanks >>Thanks Todd. >>I'd love to start with you. How are companies thinking about cloud today in their businesses? >>Well, there's some, some really exciting developments but at the heart of a cloud is changing the way companies interact with their customers, their suppliers and the way they think about business. And at cognizant it is really a customer first customer centric approach and then we work our way back to a solution. But most of the time, cloud decisions are not really made from a cost optimization or cost take out point of view. They're made from a customer experience or a business driver point of view. And how do we make businesses better? More, more scalable, more agile, more flexible and we've really built some some really great solutions that are industry specific and we've loved working with the R. C. In this capacity. >>How about you? I'd love to get your insight. Um As well. David, what what what do you see is like the main challenges and also how next gen technologies like you know, five G. Can help alleviate in those issues. >>Um Yes. First, it, like Todd said that, you know, the customer has an expectation and that expectation is raised every day by what they experienced in every other channel they work in and shop in and whatever they're doing so, so expectations are always increasing from the customer side, responsiveness personalization. They want to see all of that in everything they do, including paying their told bill. Um, and so I think as technology has changed, you know, tolling has kind of come from technology that is really 2030 years old or older. Uh, two more of a modern influence. And today we use R. F. I. D. Tags that are embedded in things like EZ Pass. But in the future it will be, it'll be your, your mobile device or your automobile itself that that triggers a total transaction and helps us process it and making in a way that is fast, convenient and most importantly accurate. >>Yeah. Well staying with you, David, I'd love to hear how working with AWS helped modernize your systems and as well as if you could give us some insight on your tracking systems. >>Yes. So with AWS, we have been working with Cognizant. Cognizant is our tolling subcontractor. So they are responsible for providing our tolling system. And we had what I would call a typical legacy tolling system. We had to data centers, both of them located pretty close together, a primary and a redundant data center and both of them very close to flood prone areas. And in our location in the southeast corner of Virginia were very vulnerable to tropical storms and tidal flooding. So part of our concern was, you know, we're exposed all our infrastructure, all our tolling infrastructure is exposed. So as we began to pursue a cloud strategy, uh the first idea was just to lift everything out of our environment and move it to a W. S. And Cognizant pull that off in about three months, uh which is really pretty incredible and we never missed a beat. Uh You know, we did it over a three day holiday weekend, but from a business transaction standpoint it all flowed once in the cloud. We began to rethink now that we're out of these legacy hardware environments, How do we get out of the legacy application environment and embrace what the cloud enables and working closely with Cognizant who had a great vision for how this could be achieved. We were able to, you know, systematically move through and migrate to a cloud first cloud oriented uh system. And uh you know, it's given us lower cost, increased availability and most importantly for our customer service agents that deal with customers or customers that deal with the web, it's given them a better experience uh shorter call times, better information and you know, and and frankly better customer satisfaction. >>Terrific. Well, thank you for that Todd. Let's shift to you. What do you see as the next phase of this digital transformation process? >>Well, as David hidden, I think it's an important theme of cloud first. I mean most companies in our clients start with that cloud forest, cloud native mentality. But for cognizant, our cloud approach is really customer first and being able to start with the client in mind and then work our way back into a technology staff or into a scalable solution. But specifically for the coal industry, there's a lot of things that are needed around revenue, predictability and looking at potential leakages. But as we hit on already of making sure that we're really delivering a great customer experience. And so with our solution, as we expect our tolling solution to really grow, we're keeping it cloud native, we're keeping it modular in nature and integration ready. So for example, are total customers can use their own roadside solutions or hand picked some of the small back office modules that they want to use. It's always going to be purpose bill and align to our customer and we see nothing but growth in this segment. It's very exciting. >>Yeah. Terrific. Well, David, you know, now that you've actually implemented this, what do you see as the next phase? What is your vision um for the future for your business in 2021? >>Well, I think, you know, for for us moving forward, um you know, we've been in this uh as Todd said, kind of a modular approach, which is great because you can make the changes and really manage your risk while you're making them. Um so you're you're moving small things. Whereas traditionally new systems meant massive investments, long, long time implementation times and you know, all in cut overs, all of which are packed with risk. So, you know, we want to reduce our risk and the solution that we have being cloud native allows us to really incrementally and quickly, just continually to improve the system. So you know, on our forecast, we would like to have a better insight into our customers and you know, support a direct app, Annie R. C. App that would allow our customers to interact with us and give us a better view of the customer um and a better experience for the customer overall. But you know, we, our goal is to build that total transaction accurately fairly. And then if the customer has an issue to be able to treat them in a way that uh that they feel respected and and valued as a customer because we we do look at it that way. >>Yeah, Terrific. I mean obviously, you know, engagement such an important issue in this area. Now I'd like to shift gears and here a little bit more about, you know, what are some of the other applications that cognizant could provide beyond tolling and let's shift this to Todd? >>Well, David had done a little bit, there's there's a lot of when we start to focus on the customer, there's a lot of opportunity there on the front side. So mobile apps, websites, the synchronization of data, but then also the way that we support that customer interacting with that data. Things like I've er automating, call centers, being able to support that customer through the entire chain of custody. There's some new and exciting applications now that we come out and David touched on a little bit too in terms of vehicles. So the vehicles to everything type motion. That's an exciting development in this segment as well to be able to continually integrate everything that's in the customer ecosystem. So whether that's uh, the, the need to pay a bill or be able to drive a car through a gate and be able to simply not touch anything but be able to have that all the way that payment process all the way through and have clear visibility into usage and insights. And then also be able to turn all that data over to a company like er, C to make good decisions based on what they see in terms of buying patterns, consumption, etcetera. There's a lot of expansion going on in this and the greatest part about this is it's built on the AWS platform. So when we architect something in a cloud native way, we can rapidly expanded and we can really streamline the investment required to jump start any kind of innovation and best of all our customers in keeping with the best model, really only pay for the actual traffic that they use so we can keep those long term costume. >>Yeah. Well, excellent point. Thank you both gentlemen for joining our program. Really loved having you. And uh, you know, that was Todd, Cary and David. Sullivan. Excuse me. And I'm your host, Natalie or like, Thank you for watching. >>Mm hmm. Mm.

>> Announcer: From San Francisco, it's theCUBE, covering OSIsoft PI World, 2018. Brought to you by OSIsoft. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the OSIsoft show, it's called PI World. It's been going on for over 15 years. We've never been here before, we're excited to be here. Really is coming at it from the operations point of view, and they've been worrying about operations and operations efficiency for years. There's people walking around with 15-year pins, which is pretty amazing. I got my first one-year pin, so that's good. So we're excited to be here and dive into the details, because we've talked about IoT and industrial IoT, and kind of coming at it from the IT side, but these guys have been working at it from the OT side for years and years and years, almost 40 years. So our first guest is joining us. He's Remi Duquette, the Global Head - Applied AI and Datacenter at Clarity Lifecycle, it's a mouthful, for Maya Heat Transfer Technologies. Remi, nice to meet you. >> Very nice meeting you, thank you for having me. >> So, give us a little bit more detail on what Maya Heat Transfer is all about, and then we'll dive into some of the specific stuff you're working on. >> So Maya Heat Transfer started about 28 years ago in the simulation of heat and getting rid of all that heat that's being emitted by a lot of data centers, all the servers and the density that's occurring these days. And we've involved into developing a software solution, leveraging the PI infrastructure for real-time monitoring, and extended it beyond, for forecasting and doing all sorts of advanced analytics from that data. >> Right, so heat is the historical enemy of electronics, and has been forever. >> Yes, continuing to be so, for sure. >> And continuing to be so, and the data centers, you know, it's an interesting evolution in the data center space, because on one hand, they're consolidating data centers, or shutting down data centers, you've got this public cloud phenomenon. On the other hand, it's density, density, density, density, density, which probably is good opportunity for you guys. >> A great opportunity. Unfortunately, you know, the problems kind of are accentuated by exactly those phenomenon of consolidation, and the cloud, and the virtualization projects that are going on. So all of that combined, makes for a really big cocktail of heat and that heat needs to be dissipated somehow. And of course, the energy efficiency of all the machines are getting better and better, but at some point, it needs to be optimized, and that's where the software component, to remove the human in the loop, really to optimize that heat distribution and removal. >> So one of the big themes here at this show is finding inefficiency. This kind of continual quest for better efficiency and using data, and big data specifically, and sensor data, to be able to get that, find the inefficiency and act on the inefficiency. So what are some of the things that you guys look at? You've been at it for a long time, but there's still a lot more opportunities to find inefficiencies. Where are you still finding inefficiencies? >> Well, I mean, the main aspect is we have a lot of building automation systems and cooling loop systems, that have been programed to try and get to the best situation in any circumstances. And, really, when you look at what we're doing now, is applying artificial intelligence to augment the abilities of those systems, to better control and get to even a better place from an energy efficiency perspective. So that's really the latest evolution, to use that big data, to learn from that data, and then further optimize your cooling environment and your heat distribution. >> Right, now I'm curious what kind of new learnings came out of kind of the hyperscale players. Obviously, big public cloud players, Amazon and Azure, Google Cloud, have giant data centers, not only for their own core businesses, but now they're building them out as public clouds. Much bigger scale than the traditional corporate data centers. They're just operating at a whole different level. >> A whole new, yeah (laughs). >> So what are some of the things that have come out of those experiences that are different than the world pre-public cloud? >> Well, if you look at the pre-public, private cloud and public cloud, you had maybe, on average, five to six kilowatt per rack in a data center, was the average power consumed by those racks. Now we're looking, you know, some of our clients have up to 50 kilowatt per rack and now you need water-cooled elements into that rack, or other cooling elements that are being, helping the situation, 'cause those kinds of densities are producing a huge amount of heat, and that's really a big concern and a big shift from the enterprise level data center that was a little bit less of a consumer of that power. >> Right, now do you guys do anything outside of the data center? I know that's your area of specialties, but we've been doing a lot of autonomous vehicle shows, and one of the things that comes over and over and over is kind of the harsh environment for compute in a car or a truck or a bus or whatever. It's not a beautifully controlled with a lot of great backup power and diesel and air conditioning. Very rough environment. So what are some of the applications that you guys can use to help get that compute power in these vehicles? >> Well, actually the evolution for us more on the software side, was to apply our deep learning, artificial intelligence components and agents to other industries. So we're leveraging the forecasting capabilities of these deep learning agents to apply to other areas. So discrete manufacturing was one example, fleet optimization, so to go back to those edge devices, so we do a lot of fleet optimization, fuel optimization on these components. And that's completely outside the data center, but it's based on the same type of deep learning technologies that we've developed for the data center. >> And all the forecasts are, as more and more the compute and the store moves out to the edge, and you've got all the industrial devices running around in the centers, it's not new news for the group at this organization, >> No, clearly (chuckles). >> But you know, you're kind of shifting that load of the heat management from the data center out to the edge. >> To the edge, correct. So it does relieve a little bit of the, let's call it the pressure, inside the data center, but at the end of the day, the density of those cloud providers is just being accentuated by the sheer number of devices. So we thought there might be a shift towards the edge from a power, let's say a removal from the core data center, but in the end, it's actually the opposite that's happening. The power is really getting denser and denser inside the data center itself. >> So, last question before I let you go. What's your take on the vibe of the show, what's happening here at PI World? It's amazing, the international flavor as I'm walking around the halls. I'm seeing badges and hearing all kinds of languages. I mean, this is pretty hard-core, industrial internet happening right here. >> Oh yeah, I mean the operational technologies and the various applications and industries in which PI is used and leveraged worldwide is phenomenal. And it's a very vibrant show. It's actually quite good, when it comes down to it, a lot of people, the exchange between the end users together from different industries share their tips and tricks on how they've deployed, their various stories are just amazing. So a great, great, great PI World conference for sure. >> All right, good. Well thank you for taking a few minutes and sitting down and sharing the Maya story with us. >> Thank you for having me. >> Absolutely. All right, he's Remi, I'm Jeff. We are at OSIsoft PI World 2018 in downtown San Francisco, we'll be right back, thanks for watching. (electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering IBM Think 2018, brought to you by IBM. >> Welcome back to theCUBE. We are live on Day One of our three days of coverage of IBM Think, the inaugural single event from IBM. I'm Lisa Martin with Dave Vellante. We're at the Mandalay Bay in beautiful sunny Las Vegas, and we're excited to welcome to theCUBE for the first time, Steve Hill, the Global Head of Innovation at KPMG. Welcome. >> Thanks for having me here. >> So you are giving a talk Wednesday, you said, at the event. >> Yes. >> I want to get a little bit into your role at KPMG, as well as your session. So talk to us a little bit about what your role as the Global Head of Innovation. >> So Innovation is an overused word. I don't particular like the word innovation, but in the context of my role, it really is taking a look at our business and our clients, and saying what it is that our clients need for their futures. What's going to create relevance for our clients as we go forward, and how does our portfolio of services relate to that relevance? And if we have gaps where we see our services not serving them best, or not going to serve them best in the future, my job responsibility is to help for strategy purposes and for investment purposes, bring those points to bear, and to get either investment into those areas, right, or changes in the business as appropriate to make KPMG more relevant to our clients, and to their relevance to their clients, right, that's the whole idea. >> So, Lisa and I talk to a lot of people in theCUBE, and we talk lots about invention, startups inventing something or new technology that gets invented, but innovation to us, and I think KPMG is at the heart of this is taking an invention and actually applying it to effect change, getting it adopted, >> That's right. >> and changing a business, a societal change potentially, is that-- >> That's right, I mean, our short phrase for it is idea to cash for our clients, right. I mean at the end of the day, and I think this is profound in certainly corporate governance evolution, right. We've seen the advent of lots of escrow changes of how companies have been managed, enterprise has been managed, right. The Dutch started with the East Indian Trading Company, one of the first large global enterprises, and since that time we've seen the maturation, the new roles. The CIO role didn't exist much prior to 1950, right. Today we're starting to see innovation to be a very important skill and capability for all corporations, all enterprises, including government, right. And I think we're starting to see a maturation of corporate capability, I would say, in the innovation space, because the pace of change is so fast today, the political, economic, technological, social trends are so complex that you've got to get something in your muscle memory that helps you change your business as much as operate it effectively. >> I'd love to know who you're talking to within organizations. You mentioned CIO role, the CISO role, chief data officer. >> Steve: Right. >> Who are the minds that you're helping to bring together so that an enterprise that needs to digitalize to be competitive will survive, right, really survive these days? How do you help them really embrace a culture of innovation as really there's no other choice? How do you get these minds collectively agreeing, yes, this is the direction we need to go in? >> Yeah, I think, I mean first of all, this is a C-suite conversation and a board conversation in many cases, but the reality is when you start to look at the lack of innovation in an organization, right, and when the environment changes, competitors start to change, and the more complex it is, it's harder and harder for companies to pivot and to reinvent themselves. And we're seeing a lot of unbundling of businesses in today's environment, whether it's a company that moves packages, right, or a professional services firm, or a company that used to distribute videos, right. I mean things change and some of the irony is that sometimes the innovation in companies like Kodak, Steve Sasson invented digital camera, it took eight minutes to go from a snap to a picture, but they invented digital technology from cameras, and that the distribution of digital videos is that it actually would help to, further the demise of that organization. So that notion of how do you take change going on in the environment that you're working at, and more importantly your customers and clients, how does that convert into your business, that's a C-suite conversation, and I think innovation can be embodied in a person to help build process, meaning how do you take an idea, how do you look at the marketplace and get sensory input, convert that to ideas for strategy and for investment, and the investments have to be deployed to the field to the business, and that relationship, that whole lifecycle of innovation requires a lot of people from the enterprise to be involved in it. And I would argue the culture has to evolve because until recently most people, in fact, I would say, including current times, most people in organizations are rewarded for doing what they do well, not breaking what they do, not rethinking what they do. And the more you get into that operational mindset, that I want to wring all the efficiencies out of this process that I can. Right, the more you're wed to the status quo, the more somebody comes in from the side and takes you out. >> So I love this conversation 'cause Steve you're able to take the long view and then I want to sort of shorten it up, and then maybe put it into a longer term context. So over our, your guys 20-plus-year careers, mine a little longer, most of this industry has marched to the cadence of Moore's Law, that's where innovation came from. >> Yes. >> How do you take advantage of Moore's Law? How do you go to client server software, whatever it was, the innovation equation is changing now. It seems to be a function of, these guys have been hearing me say this all day but data that's not siloed, but data that you have access to, applying machine intelligence-- >> Yep. >> And then getting cloud, scale, economics and network effects, and then applying it to your business. >> Bingo. >> So talk about how you see the new wave of innovation in this world of digital or however you phrase it. >> Well, it's interesting, I mean, I don't hear a lot of people phrase it the way you do which I think is spot on which is, and my words are, ubiquitous access to technology which is cloud, data, and that's a huge question mark and a big C-suite conversation. Having a lot of data isn't the key, having the right lot of data is the key. Right so Dyson is moving into auto-making today, right. They have a lot of data and it's very different from what the incumbents have. Is it better or worse? We're going to see, right. And then of course smart computers which is the machine intelligence, right. Those three elements, I think they're fundamentally changing labor productivity. And what I would say is to your question is that innovation is really important here because if all you do is take those three elements and you just digitize a status quo process, you might get marginal benefits, you might get some labor productivity enhancement, you may get some marginal improvement, you may change an outsourcing agreement to an onshore RPA deal, but if that's all you do, you're setting yourself up for a disappointment because what's really going to happen with thinkers, i.e., those that have innovations, they're going to rethink the process. Most of our analog systems are created around people checking people, so you may have nine steps, I'm making it up, in a process, that in a digital world only requires one or two or zero when launching in some cases. And so if you can rethink that process to go from a nine-step to a zero-step process or a one-step that's a nano second long, that changes the dynamic of the process. In fact that's not even nirvana, right, the real nirvana is can you change your business model, right? And I would use IBM, since we're here, as an example of going from a big box with a lot of people running around it, called IBM of the past, Watson, to an API engine that David Kenny has helped to build that says, we're going to have a platform business model leveraging network effects, and I want to have a supply and a demand curve that are much faster growing than my sort of organic ways of growing a network could be, right, through people point clicking. That's innovation. >> IBM is an interesting company because it is a company with a lot of legacy, but I think gets, as you just described it, but you look at the top five companies by market value today, they're six, 700-billion dollar market companies, they are data companies not just with a lot of data, but they've put data at the core, so it's Amazon, it's Apple, it's Facebook, it's Google, et cetera. They've put data at the core whereas most organizations, I'm sure many that you deal with, they have human expertise built around other assets that aren't data. It might be factories, it might be the bottling plants, et cetera. So there's a gap, I don't know, machine, AI gap between sort of those that are innovating today, now granted the stock market can change and, >> Sure. >> Who knows, maybe the oil companies will be back involved, not to drop but how do you deal, how do you advice your clients on how to close that gap? That seems like a huge challenge. >> Well it is a huge challenge, and I think, going back to the three elements, it would be very easy for you to dive bomb into a transformation effort and say, I'm going to go and get some smart computers and hire a bunch of people that know machine intelligence and natural language process, and all that stuff, and put them in a room, and go create some applications, the bottom line is, that's not unimportant. You got to get your hand on the mountain and start climbing, but the data piece, I mean, if you don't understand how data is going to be relevant to your business and to your clients and their clients, right, in the future, you lose. And the reason why those five that you talked about earlier are so successful is they think a couple of steps ahead on the data strategy, right, and they're not thinking about, most organizations by the way, they'll say we want a data strategy and then they'll relegate the strategy thinking part to their businesses which are bifurcated, and they look at the world in silos. And they're doing exactly what they should do which is take care of those businesses, but when you step back into those five companies you've talked about, they step back from those silos and say, what is the enterprise implications, and how do I create new businesses with correlations of data that I didn't have before? I think that requires a whole different level of strategy. It's C-suite and board that has to guide those kinds of decisions. You don't see a lot of people really getting their hands dirty around intense forward-thinking data strategies at the enterprise level like we're talking about here. >> You believe we are entering or going to enter shortly a productivity renaissance. >> I agree, yes. >> That's sort of I'm talking about our off-camera conversation. Explain why you think that, compare it to sort of the Industrial Revolution. Take us through your scenario. >> Sure. So, I mean, when you think about labor, I mean, what are the things that I think those three elements will give us as a society, as a global community, is a pretty big S curve jump in labor productivity. In fact we have at KPMG some efforts to quantify what that might be, looking at what we call frontier firms, and applying those practices back to incumbents. 90% of most industry players is saying what are those differences that we can model. The fact of the matter is when you go back to the Mechanical Revolution, the Industrial Revolution, people did everything by hand prior, right. Equipment helped them do things whether it was, even the printing press saw changes in society and labor, but when you start to getting into heavy manufacture in the Industrial Revolution, productivity was enhanced dramatically, and instead of putting all of these people who were doing things by hand out of business and out of work, it actually created more jobs, a lot more jobs, and a lot more wealth for society. I think we're heading for a similar S-curve change with smart computers, cloud, and with data. And that the roboticism of people is going to be automated, and people are going to be allowed to practice and use what's between their ears a lot more. That's going to create value, insight, new questions to be asked. I mean, how many times have you ever heard this? Every time you answer a question on something that's very important, you want to understand there's two more questions to be asked. Medicine is that way for sure. But you're going to start to see massive advancement in areas where people have had to use a lot of cognitive skills, right. It's severely under-leveraged because they were doing so much roboticism and doing things that computers can start to do now. So I think you're going to start to see a renaissance, if you will, of people using their nogers in ways we haven't seen before, and that's going to change the dynamics of productivity and labor in a way that's going to create wealth for everyone. >> And it's going to change industry. So, okay, so I got a bunch of questions for you then. >> Steve: Yep. >> Here we go. And I asked this earlier but I didn't really get an answer. Will machines? >> Steve: From me or from somebody else? >> No, from somebody else. >> Steve: Okay. >> Will machines make better diagnoses than doctors and when? >> I mean, what's the regression line? I mean, the samples said, I think today you'll find machines giving better diagnoses than doctors in some cases. >> Dave: Okay. >> I don't know where the regression line sits today, but if you look at the productivity of doctors going a hundredfold, and the morals scattering around lung cancer, it's impressive. >> Dave: Yeah. >> And do you want a doctor involved? Yes, you do, because part of it is in an orthodoxy of trust which by the way ten years ago, you wouldn't put your credit card online to buy anything, right. It's the same kind of orthodoxy. But I do think that machines can read so much more data, interpolate so many more correlations than people that when you add that to an oncologist for example and cancer, you have a super oncologist capabilities which is really what you're looking for. We're not looking to replace the oncologist per se, what we're looking to do is get the productivity of the oncologist from two to 200. >> I was talking about diagnoses. So you would say yes, okay. >> Yep. >> Will large retail stores mostly disappear in your opinion? >> No, I think they'll change. I think that the customer experience is still, we're still people, we need physical space, and we need physical things to touch, smell, and feel. I think those things will change, but we'll still need experiences. >> I'm going to keep going 'cause Steve's playing along. Will driving and owning your own car become an exception? >> Yes. >> Okay. >> I can elaborate if you want. >> Please, yeah, go ahead. >> So, I mean, the first, I mean, we actually did at KPMG a study called islands of autonomy which modeled LA and San Diego, Atlanta and Chicago, and we modeled how do people move. And we did this for a reason because autonomous vehicles are often times amalgamated as one thing. Oh well autonomous vehicle is coming so you better sell your sports cars and your SUVs, not so fast. The reality is mobility is very different based on where you are. If you're in the middle of Kansas or something, you're going to need a truck to run around in your farm, but if you're in LA or Atlanta or Chicago, you're going to move with autonomy, with autonomous vehicles, and then you're going to really enable mobility as a service very clearly, but differently. The way people move in these cities is different, and if the US auto industry understands those differences, and extrapolates those to a global marketplace, they're going to be very advantaged as mobility as a service becomes real, but the first car that goes, I hate all of the viewers that love this category, but sedan is the first cars to go. I would say sports cars, I race cars, so I love sports cars. People still ride horses today but they don't need them for transportation. And SUVs, right, specialty vehicles that you may, it may not, the economies may not be there, but as we know transportation and car ownership, it's going to change fundamentally, and that's going to have a massive effect on FS, right, insurance companies, banks that are doing loans today. It's going to have a big effect on healthcare. Mobility as a service is going to transcend to healthcare, mobile healthcare in ways that we can't see. >> You got great perspective. I got one more for you, maybe a couple more. Do you think traditional banks will lose control over payment systems? >> Well, a lot of them are already nervous about that, wouldn't you think? >> Yeah, but it hasn't happened yet though. >> I understand, the bottom line is no 'cause I think the traditional banks are getting smarter and they're leveraging their own innovation horsepower to understand things like Blockchain, and how to incorporate those things into their business models. So the answer is I think the way they do, look, banks exist because of one reason, trust. They have trusted brands, right. As long as they can stay current enough to be relevant to your banking needs, you're going to stay with that trusted brand. I think the trick for banks is how do they move fast enough, leverage the technologies that make your life easier, and not waiting three or four days for bank clearing of a check, for example. >> That's they say if you're-- >> And get to that trust in a new way. >> Unless you're a Bitcoin millionaire or a billionaire. >> You still need a bank. >> Maybe somewhere down the line. >> Yeah. >> Okay, last one, I promise. Will robots and maybe even RPA reverse offshore manufacturing advantages? >> Yes. >> Can you elaborate and give us a sense of-- >> I think, first of all, if you really look at what RPA is doing in many ways, is disintermediating the value of geographic location in many ways, right. So where I may have had, again this is important that you understand, so I can still go offshore today and get labor arbitrage and get margin, but I'm not rethinking the business. What I really want to do is own, I want to have more control and I want to have more flexibility and growth in that back office function. So it would behoove when you think about our RPA, and bring in our RPA technology so I have it one onshore, two, leverage the data more securely potentially, and then leverage that data as part of my lake to say how do I use that data to correlate to get to what I really need which is customer relevance at the front office, right. So, look, I think that this whole notion of you're in a different country, and therefore the labor pools are different, and therefore their arbitrage will get benefits from that, those days are over. I mean, it's just a question of when does it die. >> Dave: The data value offsets that arbitrage advantage. >> Well, forget that. The arbitrage is dead itself because the machines, >> Yeah, yeah, right. >> You're talking about orders that have made it to a cheaper per unit cost for an RPA, for a bot to do something than it is for a person that has to eat, sleep, take vacation, and get sick, and all that stuff. And so no matter where they are in the world. So what I would say is that notion is dead. It's just not buried. And overtime we're going to migrate again to machines doing all that robotic stuff. But, again, those people, they're going to do different things. It's not like we're going to see hordes, hundreds of thousands and millions of people not be able to work, I think they're going to be doing different things using their heads in different ways. >> Lisa: I like that answer. >> That's a plan. >> Dave: It's good. >> There's a price somewhere? >> I'm absolutely wrong, I just don't know how wrong, right. >> Well, it's fun to think about, and you provided some context. It was very useful. So, thank you. >> And I imagine folks that are attending your session at IBM Think on Wednesday are going to hear a little bit more into that. So thanks for sharing. >> We going to see some specifics, yeah. >> Thanks for sharing your insights, Steve, and for joining us on theCUBE. You guys, the innovation equation is changing, and I thank you for letting me sit between a very innovative and informative conversation. >> Thank you both. It was fun. >> Thanks Steve. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE live on Day One of IBM Think 2018. Head over to thecube.net to watch all of our videos with our guests, and siliconanglemedia.com for all the written articles about that. Also check out Wikibon, find out what our analysts are saying about all things digital transformation, Blockchain, AI, ML, et cetera. Dave and I are going to be right back after a short break with our next guest. We'll see you then. (upbeat music)

>>live from San Francisco. It's the Cube covering Informatica World 2016. Brought to you by Informatica. Now here are your hosts John Furrier and Peter Burress. Okay. Welcome back, everyone. We are here live in San Francisco for Informatica World 2016. Exclusive coverage from Silicon Angle Media is the Cube. This is our flagship programme. We go out to the events and extract the signal to noise. I'm John from my co host, Peter Burst. We have tree conflict comedy Global Head of Data Management and Steve Jones, global vice president. Big data from Capt. Jeff and I insights and data. You. Good to see you again. You sure you're welcome back. Welcome to the Cube. Thank you. And you've got my name right? It was a tongue twister, but, uh, we were talking about big data before we started rolling and kind of like where we've come to talk about over the really big data. You look back only a few years ago. Go back five years, Duke movement to where it is now. The modernisation is certainly loud and clear, but it's just not about Hadoop anymore. There's a lot of operational challenges and also the total cost of owners who want to get your thoughts. What's the trends? What do you guys see as the big trends now relative to this modernisation of taking open source the next big day to the next level? >>I think part of the pieces were actually about to publish a report we've done within the massacre on exactly that question, Uh, particular and governance and how people are making it operational. We did a report recently with our captain consulting division around Operation Analytics. Really fascinating thing that found out was the two real interesting in governance, right? The age old thing on governance has been the business doesn't engage. Well, guess what we found when you look at big data programmes is when the big data programmes start to deliver value. Guess who wants to take them over business? Guess who then actually starts leading the governance efforts, the business. So suddenly, this piece where the history of sort of data management has been, you know, going you really care about quality and the business, to be honest, going? Yeah, we don't care that much. We're still using excel, um, to the stage of which you're delivering real analytical value those pieces are going through. It's something we've been on a long journey for. I mean, we talked the other day. 2011 was the first time at camp we published a white paper on on our learnings around Big Data and governance. Um, it's amazing. Five years ago, we were talking about actually how you do governance and big data because of some of our more, uh, sort of forward looking clients. But that shift and what we're finding in that the report is the fact that people are really looking to replace this substrate. It's absolutely not about just about Hadoop, but that's the foundation, right? And unlike sort of historical pieces where there hasn't really been a data foundation, there's been lots of data silos but not a data foundation. Companies are looking to move towards actual firm data foundations across their entire business. That's a huge leap for it organisations to make and in terms of its impact on, you know, MDM and data quality and pace of delivery. Um, and those are the pieces. >>So also talk about the trends outside the US, for instance, because now you have in the UK uh, talk about that because your clients have a global footprint. The governance then crosses over the boundaries, blurring if you will virtual. But you still have physical, uh, locations. Well, I am sort of the UK and based out of London, And, uh so I see that side of the pond more often than, uh, this side. But the trends are pretty similar. And what Steve said, in fact, we were joking about it yesterday and we said, It's not for the tweet, but maybe, you know, was a little bit more big data doesn't need data quality. And my other favorite statement is MDM is dead. Long live India. Both of them are relevant. Big data doesn't need data quality in the sense that you cleanse all your data and put it into a TD WR uh, or a data lake because you can't only part of it is data owned by you. The rest comes from external sources where it needs quality is building the context on top when the end user of the analysts have a view, and there, if you build the context, then even good data could turn too bad, because in a particular context. That data is no more relevant. But bad data can turn to good because you're bringing in the context. And there was this eggs example we were talking about. You know, you you run a marketing campaign and you have all these likes and tweets and everybody loved it. Somebody then said, Okay, how about how good is this campaign? That's great. We need more. How good is it in the context of sales? Guess what? When the campaign ran, there was no difference to your sales. So then this good data that you had on the marketing campaign has turned back just to the company. That was a wasted effort that marketing. So you need contextual quality, not pure data quality. You know, if you look at e t l. You transform you do data quality before you, Lord. Now you're talking of E l t. And that's where you need quality. You need the linkages, the references, this data changes the data, and real time has been the conversation earlier so far today, the context defines the quality quality. A data swamp could be a data, you know, clean and environment. I mean, one >>of the reasons why we should presented that we present my presentation That I did on Monday was on avoiding a data swamp. So we actually think. But what we say is you've already got it. The myth is that you don't have data swamp right today, which is Oh, we've got my perfect data warehouse and it's got a perfect schemer. Really? And what does your business use Excel spreadsheets? Where do they get the data from? Well, they get from S a p. They download this and we got a macro. Somebody wrote in 1998 which means we can't upgrade that despot desktop from office 97. Right? So that desktop is office 97 because it's the only one that has a supply chain spreadsheet on. So the reality is you have the spread. Have it today. I think to the point you said about the country difference. One of the things we've seen, I think from a sort of a culture difference between Europe and here in the U. S. Is the U. S. Has been very much the technology pioneer, right is well, you know, the Hadoop stuff. The sparks of all that technology push European companies are seeing a lot of have taken quite a while to get into the, uh the Hadoop marketplace, but particularly the larger manufacturers, Um and sort of I'd say the more robust, like pharmaceuticals and these large scale organisations are now going all in. But after thinking about it. So what I mean is is that we've seen sort of lots of POC is used to be, like, four or five years ago. People doing PhDs here in North America. They're very technically centric. And then people like Okay, >>Exactly. Whereas >>over in now, in Europe, we're seeing more people going. Okay, We know where we want to get, too, because we've seen all the technology. Now it works. We're gonna start with thinking about the governance and thinking about that. What's the right way to go about this? So I think from a timing perspective, the thing that was interesting we felt beginning of last year that we begin to see some earlier states. Larger programmes in Europe, Maybe towards the end of the reality was by the middle of the year we were seeing very, very large pieces. There was almost a switch that happened, but we've our return, this notion of governance because it's really important. And you've said it here today about 20 times the rules of data Governments have been written piecemeal over the past few decades. Uh, started off by saying, uh is which application owns what data? And is the data quality enough so that the application runs or not? Uh, then compliance kind of kicked in, and we utilised compliance related rules to write the new rules of data governance. What is data governance in the context of big data? And the reason I ask questions specifically and maybe put some bounds on it is we're trying to get to a point where the business puts a value on data trade data as an asset that has a value. And the only way we're gonna be able to do that is through governance rules to support it. So what does data governance mean in a big data context, I >>think, Yeah. So the value is really the impact, and I go back to a very simple analogy people, When you didn't have computers, you had your ledges. You locked it up in a safe and took the key home. So you protected who had access to your data? You then put it on PCs. But then you give them access with Loggins. Then you said, Well, I'll tell you what you can do with my data. That was the era of B I. Because you had reports all they could do was print a report. Now you've given them access to do whatever they want with data. Now, how do you know? First thing on the governance aspect is what are they doing with the data? Where did they get the data for which they used to come up with that? What is the exposure to your organisation if somebody has, you know, uh, traded around, they traded around with labour rates or, uh, you know, fix them or done something you're talking about. And then you work backwards, Arlene. Age. So now I need to know first thing what? Not just who accesses my data. And I need to know. What are they doing that I need to know where they got the data with it. >>Well, I think this is >>You don't know what they're when they're going to access it and what they're going to do with at any given time. But I >>think that's the thing is where we have the This is where the sort of contention comes in. Right. To be honest between the areas back to the value is from a data management data governance that those things are all true, right? We need to know those pieces. The other reality is that today how do you show the business, Actually that they value the pieces, which is ultimately the outcome. So the piece we're finding on the research and the research we're about to publish soon with Informatica is one of things it's really finding. Is that where when do you get the business to care about governance? And the answer is when you demonstrate an outcome which relies on having good governance. So if you do a set of analytics and you prove that this is going to improve the effectiveness, the bottom line, the top line or whatever, the firm and particularly Operational analytics customer analytics, where they're real measurable numbers, we can save you 6% on your global supply chain costs. But in order to do that, you need a single view of product and parts, which means you need to do a product. MDM Well, that's a very easy way to get the business engaging government, as opposed to we need to do product MDM What? >>We're going to 3 60 view of the customer. >>So you So we're still pricing the value of data based on the outcome? Absolutely. And then presumably at some point, there is some across all those different utilisation and that will become the true value of the data. Is that I think the piece, I'd say in terms of that, if we sum it up, it's sort of it becomes a challenge because ultimately the business pays. Right? So one of the things I like about the big data stuff and the programmes are doing these large scale companies is the ability to deliver value to an area. So what we call insight at the point of action, and that's the bit where I pay. So, yes, I could sum it up in Theoretically and the C I can say, Well, I'm delivering this much value, but it's at those points of action. And if you say to something right, I deliver you $2 million. It costs you $100,000. That's much better than we have to say in totality. This delivers you, you know, $2 billion and it costs you $20 million or $200 million. That's an abstract piece, whereas except when I'm thinking about investment BAC, because I need to be able to appropriate the right set of resources, financial and otherwise, to the data based not just on individual exploitations but across an entire range of applications. Tyre range of utilisation, right? I think I think so. But again, in terms of the ability to bill and charges that if I can, my total is the summation of the individuals. So that's why I worked with the CFO once you have the CIA was in the room, said the business case for their for one of their programmes, and CFO said, Well, if I had, it took all your business cases and adding together this company twice the size and cost nothing to run. So there's been a history of theoretical use cases. So what we're seeing, I think on the data and the outcome side is the fact that particular Operation Analytics they're absolutely quantifiable outcomes. So while then you can say? Well, yes, If you then add this up. We need to make an investment on based platform. The two things we're finding are because you can use these much more agile technologies. These projects don't take 12 months to deliver first value, so you can. And because the incremental cost of working in a lake environment is so much less, you know, I don't have a 12 month schema change problem. So that's one of the things we're seeing is the ability to say yes as a strategy. We're going to spend 20 million or whatever over the next five years on this. But every three months, I'm going to prove to you that I've delivered value back because one thing I've seen on data governance, sort of strategic programmes historically is 18 months in. What have you delivered? What have you done for me? Proves that it has value right that >>you've forgotten. And I think also what we're seeing with big data initiatives is the failed fast methodology like the drug trials and farmers. So what's your project? It's actually the sum of all the all the programmes you've run. And we were talking about apportioning uh the budget, whose budget? Because it's now being done by the individual businesses in their own areas. So there's no CF or sitting there and saying, Well, this is the budget I give I t. And this is how you apportion it. It's all at the point of the business and they find we'll do all these fail fast programmes and I've then hit one, which makes me big bucks. And I love this concept because essentially talking about the horizontal disruption, which is what cloud and data does just fantastic. And I'm sure this is driving a lot of client engagements for you guys. So I got to ask a question on that thread Jerry Held talked about earlier today. I want to ask the question. He made a comment, but alternative questions. You guys, he said. Most CFOs know where their assets are. When you ask him to go down, the legend they go, Oh, yeah, they asked. What's about data? Where the data assets. The question is, when you go talk to your clients, uh, what do they look at when they say data assets? Because you're bringing up in the notion of not inventory of data I'm sitting around whether it's dirty, clean, you can argue and things will happen. But when it gets put to use for a purpose, Peter says, data with a purpose that's this would keep on narrative. What is there a chief data officer like a CFO role that actually knows what's going on? And probably no. But how do you have the clients? They're just share some colour because this is now a new concept of who's tracking the asset value. >>And I think there's two bits and I'll start without it. And then if you talk specifically post an L, which I think is a great example of what happens with data when it becomes an asset, is the ability to understand the totality of data within any nontrivial organisation is basically zero because it's not just inside your firewalls. I'd also question the idea that CFOs know where all the assets are. I'm working with a very large manufacturer, and after they've sold it, they need to service it, and they can't tell you where every asset is because that information now lives within a client. So actually knowing where all of the assets they need to service are, they might know their physical plants and factories are. But some of these assets a pretty big things they don't know necessarily where they are on planet Earth. So the piece on data is really to the stage of because it's also external data, right? So really the piece for me about government and other ones Do I understand the relationships of these pieces in terms of the do I value data as an individual pieces because of what I can do with it? Sometimes the data itself is the value, But most of the time we're finding in terms of when people describe value, it's to the outcome that it's based upon. And that's something that's much easier to define than how much is my, uh, product master worth. Well, I can't really say that, but you know what? I can absolutely say that 6% reduction in my supply chain costs because I have a product master. But I think post and l is a great example of what happens when you go the next step on data >>because you're looking at addressed it. And actually, it's not just posting now. We were talking to another uh, male company. A postal company. Where? Data asset. Okay, my address is our data assets, but I have multiple addresses for one person, and what they wanted to offer was based on the value of the packages that you get delivered. They wanted to give you a priority or a qualification of the addresses. They said this is a more trustworthy address because anything about £50 this person gets it delivered there. This is a lot of mail. So do you consider the insurance or the value of the packages that you get delivered to be a data asset? Most people wouldn't. They would say, Yeah, the addresses a asset. That's the data asset. But there's a second part to it, which you don't even know. So the answer really is yes and no. And it all is contextual because in a particular context, you can see if I know where everybody lives. I know where everybody is and I have all the address. You almost got to look back after the outcome and kind of reverse track the data and say, OK, that stream. I >>would say that people who start with we've had 30 years of trying to say it's the data object that has the value, and it's never ever happened. As soon as we're starting talking about the outcome and then backtracking and going in order to this outcome, we needed addresses which historically issues that would have been the value. But actually it was It was that plus the analytics of prioritising them for risk that suddenly that's a lot more valuable. That outcome of you know, what this person tends to be here, this area people seem to see as lower risk. This is where I can therefore look at the work office for those people. It gives you more information about the >>notion of the data swamp turning into data quality because the context, Sri says, is really key. Because now, if you can move data to context in real time data in motion where people call these days the buzzword. But that's the value. When you when you when you stumble upon that, that's where you say, Well, I thought I had bad data. No, Actually, it's hanging around waiting to be used as potential energy. As you know, it's the same thing with questionable. They're moving from being a postal supplier to delivering packages. Now, you know they have a very short window to deliver packages. So just how do you get to a building? Do you have to go through the backyard? Do you have to call somebody to get it? Now that data becomes valuable because otherwise you know all their deliveries go off the radar screen, right? Because they just shot to schedule >>was going to say about the quality. Want a great example of qualities that we spend a lot of times say process data and manufacturing will clean it up before it goes in the reporting structure, which is great, and that gives you a really great operational reports. There's now an entire business of people doing the digital discovery of processes so they can use the bad data to discover what your processes are and where your operational processes are currently breaking down process. If I cleaned up the data, they wouldn't be able to do their jobs. And it's this fascinating stuff we're finding a lot with. The data science piece is its ability to get different value out of data, >>chemical reactions, alchemy. It's all the interactions of the data. This is interesting. And I want to ask you guys, I know we have a minute left, and I want to have you guys take a minute to explain to the audience Cap Gemini and how people how you engage with the customer, uh, and context to their progress. Where are your customers? On the progress bar of these kinds of Congress? Because we have a nice conversation. I'd love to do an hour for this. Go up. We can geek out. But reality is day to run a business, right? So and in the tier one system integrators like captain and I all have kind of different differentiation. What do you guys do differently with this area of your practise? How are you engaging with your customers? And where are they on the progress bar of Are they like while you're talking gibberish to me, are they on board? Where are they? >>I think I think we've got a bit of a man. We've been on this journey a lot longer than most. Like I say, 2011. We're talking actual data governance and big data. You don't talk about that if you haven't been doing it for a while. we were the first systems integrated and as we Cloudera pivotal with massive partner with homework. So most of what's interesting is when people talk about data lakes and some people are thinking that stuff new. We're talking about the problem of most of our clients are now looking at the problem of having We will have multiple data lakes for P. I reasons for operational efficiency reasons from budget reasons. Whatever it may be we're looking at, how do you collaborate beyond the firewall? So I'd say, Obviously, we've got a continuity of customers. But a lot of our customers are going beyond the stage at which they're worrying about big data within their four walls to the stage of how do I collaborate beyond my four walls? And this, for us, is the switch on governance and data, and what we do is is the difference between sort of capture announcement other ones is. So when's recess is the global MGM guy and Gold Data Management guy? He actually his team is in all of the countries, so he has P and l responsibility for that. When I have it for big data in the >>country, you're out implementing the value extraction >>were in multi. I mean, it's really at the stage of kicking tyres. We're at the stage >>behind the kicking tyres a long way back in 2000, 11 >>1,002,011. By now, sort >>of driving the Ferrari on the autobahn. You know, 90 miles an hour straight, narrow. It's a lot more work to do, right. There's always a lot more things keep changing and that's that's the best part >>of what we do next. And that's the point for us is the reason we're in this is that it's what's next and I think that people, the reason governments are changing fundamentally is this move towards global collaboration. So the more you look at health exchanges and all of these things, the more people collaborate outside the four walls. That for us, is the problem we want to solve next, which is why we're working on industrialising what we now consider the boring stuff which is building a data lake and doing the internals and ingestion in those pieces that were not interested in putting bodies on that. It's about how you solve the next problem. >>Stephen Pre, thank you so much for joining the Cuba because you're good to see you again. And welcome to the Cuban love nightclub. You made it, um, great to have you love to do it. Do this again and again. I love the context. I love that you guys are on this, you know, data quality at the right time. Really? Right message? Certainly we think certainly relevant. So thanks for sharing your insights on here. And And the data on the Cube live streaming from San Francisco. You're watching the Cuba right back. It's always fun to come back to the cube because