 From San Jose in the heart of Silicon Valley, it's theCUBE covering Big Data SV 2016. Now your host, John Furrier and Jeff Frick. Okay, welcome back, everyone. We are here live in Silicon Valley. This is Silicon Angles theCUBE, our flagship program, where we go out to the events and extract the signal from the noise. I'm John Furrier with my co-host, Peter Burris. Our next guest is Gaurav Dhillon, who's the Chairman and CEO of SnapLogic. Welcome to theCUBE. It's a pleasure to be here. Thank you for having me. Great to see you. All the action around Big Data has been going on for many years. It's our seventh year covering Hadoop World now called Strata Hadoop. Now we have our own little micro event called Big Data SV. But again, the game is still the same. People are trying to get to the apps to provide a new way to get apps out there using data at the center of the value proposition. But now what we've been saying for a couple of years, but now it's reality is the cloud is here and you're seeing it front and center. The cloud is the engine room for the analytics, for the apps, that's a really, really big deal. You guys have been pioneering essentially a new approach with your company, SnapLogic. So quickly talk about why you're different and what do you guys do and then talk about the problem that you solve. Sure, sure. Look, the enterprise has always been a retrofit job. It's not like you buy some shiny object and it lives in isolation. In the enterprise, everything's a retrofit. It has to work with what came before. So at a very simple level, what SnapLogic does, it solves the problem of halving enterprises digitally transform themselves. So what do they need to do? They need to bring in cloud apps and they need to be able to deal with the new kinds of information flows, more richer information, sometimes called and structured. And we help them manage data and flight, bringing in the cloud or moving to Hadoop or Spark or as we track things like Meizos and other platforms that are going to provide breathtaking scale. That's what we do for our customers. So I want to talk about this retrofit job. I love that phrase, I just wrote that down and I'm gonna borrow that, I'll give you full credit for it. But that is essentially describes what the enterprise is and that's why the enterprise has always been different from consumer, greenfield or whatever. Some people would call it legacy baggage, depending on how you talk to it. And the real thing is that in most cases, the legacy baggage or retrofit capabilities really wasn't adding value to the next plan. So one of the things that we hear all the time and I want to get your thoughts on how you play into this value proposition is I need to make the old stuff work better and or retrofit it into a contributing role in the enterprises or just throw it away. So a lot of times the stuff hangs around, you support it, IT ops budget supports it, keep the lights on as they say but it's not really driving anything new, new revenue. So the focus of taking something that's legacy and retrofitting it into a value, what are some of your patterns that you're seeing and what do you guys do specifically to solve that problem? So yes, first I would agree that there's a lot of like the movie Zombie Land, there's a lot of stuff in the enterprise that really ought to be there. But in the sense of thinking of that retrofit analogy and taking it further, if you have a fantastic airframe, you have a business that's a great business and it's doing well, providing cash flow and profits, it's very analogous to having a big aircraft and Avionics has changed, your engines will work, your rudder and all that still works, but you need new Avionics. So a retrofit in a sense is somebody coming in and putting flat panel displays and Avionics to help your pilots get from point A to point B in all kinds of weather and get fuel efficiency and those sorts of things. So in a sense, what we're seeing in the enterprise, whether it's young companies like Adobe across the street here, which is one of our big deployments, very large deployments globally, or it's young companies like Uber, it's at Francisco and so on, or AstraZeneca and such, is that they are essentially putting in new technology to get the most of what they have and over time they will then feather out or feather in new things as replacements. Are you making it easier for them to also make choices as to when they want to retire or exit an asset? So in other words, by making it possible to extend the life, especially software, the things that don't go away, we used to call it radical maintenance. Most people did maintenance and then there was those periodic radical maintenance as long as you bought any stuff in. It's called a oops. But are you also then making it easier for people to create that separation and independence so that you can make the appropriate business choice to perhaps exit a technology that is no longer providing the value that you want? So precisely that, and in a sense, companies should not be hostage to the technology choices that were made before this person got into the job. So what you have to do is think about a Switzerland, a neutral party in the middle that helps you have options on your application portfolio, on your analytics portfolio. So you ought to be able to upgrade without saying, well, we have to bring in a backhoe and destroy and start again. So this opportunity to put in the right sort of data and flight management, being able to track those data, being able to have metadata, data about those data, lets you in a sense be once removed from the choices that have been made or that you are making today. And future proof is up. Or that you're going to make in the future because you want to be in a position where you do sustain those options. So we've heard a lot from the various sessions here at Stroud & Hadoop, as well as here in theCUBE over the past 24 hours. This notion of increasing independence by within the deployment of applications to make it possible for developers to start doing things a different way. So the difference between the application layer and the data layer. One of the things that I know that you guys do is you call it the control plane and the data plane. The dynamics in that data plane. Talk a little bit about how your clients or your customers are starting to extend that concept throughout the legacy, throughout the retrofit, as they even start to look at the data as an asset to be leveraged for the future. Yeah, so you know if you think about it, almost any company we serve, which is an enterprise, has some kind of financial system. You may call it ERP, but there's something that they use to close their books. Chances are that is not going to change in the near term. Even Google runs Oracle financials and it's going to leave their gold at hand someday, 20 years from now. Because that's a really important thing. You have to comply with FASB and other kinds of regulatory requirements, like importing earnings and so on. So what we do is we come in and help them, people like Adobe and others, move to a whole new level by providing this technology that separates the control plane from the data plane. It sounds simple, but it's actually very, very hard because what the control plane gives you is the benefits of cloud. It lets you provision. To me, the most profound implication of the cloud is being able to treat things that would require large bodies of men and women, lifting computers, putting in disks and memory. It becomes a checkbox. You provision email. You don't buy memory and disk and chips and plug them in with soldering orange, right, as we used to do in the 90s. So by having a control plane up in the cloud, we change the management of data, the self-service of data into a provisioning exercise. By keeping the data plane and the data models on premise, we can provide efficiency and performance. And the reason for that is you have to respect data gravity. If you are looking at sensor data being emitted on your factory floor, there's no way you can efficiently move that into the cloud. Maybe in South Korea, right, where the land of the magic internet, but in the real world, we can't do it. So you have to process that data and the model has to live very close to your factory machinery and you have to install Hadoop or Spark or what have you close to that. So by separating it, we get to have our KKT2 from a performance perspective and a provisioning perspective. Now, the other piece is that we hadn't thought about at the time that we built this is certain new regulations on data are having a very deep impact into if your data's in Switzerland, it must be in Switzerland, the European Union and so on and so on. So also, you can partition your data to respect sovereign government's requirements as well. So we're really making an impact in the industry with that. Now, as we think about that over the past 50 years or so, we've had a very process-centric approach to how we created applications. So you mentioned ERP. We had accounting that had practices that we could codify and program. Very successful, did a lot. The data came along with it. So it was in many respects the application to find the data. Now we're in a world where we're starting to find new ways of leveraging the data and we want to unlock more value out of the data. Kind of suggests that there is going to be a different application model. Do you see us moving from a process-oriented world to a data model-oriented world? We're really focused on how data flows and how to capture those flows so that we can turn them into value. You know, that is why we started this company. Because we saw a change, if you think about the mid-90s, say we rewind on Time Machine here and we're 20 years ago having this conversation, the big deal is business process re-engineering. The word process is king. Everybody's buying all sorts of software to improve their processes. Where we are today is people are looking at a data-centric view. They want to run their companies in some way, like Silicon Valley companies. They want to be data-driven. They want to think, in the case of healthcare, modernizing their apparatus away from paper trails to software-cloud-driven types of things. So what we see is this transition from being process-centric to being data or data model-centric. And if we can get the data to flow, then we can unlock profit pools for these companies. In the sense that it just boggles the mind. I mean, it's day one of big data. It's day zero of the internet of things. So what is yet to come is just fantastic. That's a great point. Process, I mean data-driven improvements, a whole nother management consulting category. That'll emerge, I'm sure, from this. Someone's going to make money on that. This is the future, this is now, right? So what is the playbook for that? Because now if you say, OK, I want to be data-driven, that's going to disrupt the value chains that have existed. So in this digital transformation world we're living in, a lot of things are radically shifting. What do you see as the sequence of events if there is a sequence, or maybe it's non-linear, or maybe it's a step process of those processes of those value chain activities? Yeah. So you know, some things will stay the same. The financial data warehouse, wherever it might be, it's going to stay the same. CFN needs to do reporting to Wall Street. That's going to stay the same. But in sales and marketing, you're seeing dramatic changes. If you are a consumer packaged goods manufacturer, say you manage a brand of lipstick, social listening, and seeing what people have to say when something goes out of stock is really important. Say you're a product manager for a jet engine. You sell these engines to Boeing and Airbus. Having sensor data, having that inform your design, your fuel efficiency considerations, having that reduce the cost of maintenance while keeping safety high is going to have a dramatic implication. And on and on it goes. So I think what you're seeing is that being data-driven, in a sense, is a desire for a Fortune 500 company to adopt some of the principles that we see in Silicon Valley, where we inherently say that data doesn't misrepresent anything humans do. And that's not to say we reinvent Peter Drucker, right? Every now and then I see this great study that Google did. I'm like, so Google discovered Peter Drucker in 2016. OK. So some of that we can actually read. And retrofit it into their messaging. So some of these things we're less obvious to read about, wisdom we can read about. But having data-informed decisions and having predictive analytics is a whole new thing, folks. I mean, it's interesting. One of the things we always comment on the queue, we always like to look at and analyze. And we always say, and Dave Vellante and I always saw this all the time, with big data now, you can instrument your entire business end-to-end. So everything's measurable now for the first time in the history of business in the world ever. So OK, if you have access to the data for everything, that means everyone has to really truly look at their business. A lot of people don't even know their business. It's a black hole. And coupled with the move of advertising to digital advertising, so that old bro might have half my money in marketing is wasted, I don't know which half, is becoming less tried and tested. So I think what you're seeing, though. Although often it's, now I know that all my marketing is going to fit, because I have measures that can prove it. Well, we think we do better than that, but anyhow. But I guess every CEO thinks that. But just going back to what happens to the value chain that we had and what we're going to have, what we see now is a move that the value chains we had, the rear view reporting on charts and graphs. What did we sell for this thing in October last year? Those are table stakes. My phone tells me how many steps I took. It does a report on it. But what I'm looking for, and what digital business is looking for, is reports on our health. What does this mean for my health? How do I put this together with other insights I might have and think about predictions? How many things are we likely to sell in the future? So those things are starting to. It takes the whole cliche of outcome-based solutions and says, OK, you got to know what you want. And health is easy, and I want to die young. So, OK, take more steps. That's right. Or for a company more profits or larger size or a more dominant brand. So if you take that analogy further, now we have with technologies like SnapLogic, Microsoft's, machine learning, other technologies, you have the ability to supply predictive models on a self-service basis. I mean, this would take a theater full of PhDs, 300-person theater out of a big university to do 20 years ago. And now we can do this. So talk about that. We got a couple minutes left. I want you to just talk about that concept in context to your business. Because you're essentially agreeing, and I'm assuming you would agree, that the cloud now makes that possible. You know, if compute available, you have data models that can be constructed, you have leverage in things you build, share with the folks that are watching out to the people who are trying to decide how to build their businesses and choose a vendor on what the impact the cloud is, the reality of what they can do today and what you guys are providing. So I think perhaps the most important thing about the cloud is, again, provisioning. So what we have done is make Hadoop possible for humans. We walk around, and we have things on the wall where we drive our engineering teams, to make it Hadoop for humans. I don't need PhDs. I can't wait. I can't retract. I can't retain that kind of talent. It's expensive, too. Well, if you can find it. And where are you going to find it in a flyover state? Because here we've got Fang bidding up the prices. But it's very hard if you can find it to retain it. So the first thing is being able to take the friction out of these big data predictive analytics by providing sort of this Hadoop for humans capability whether Hadoop, Blacks, and Spark, Blacks from SnapLogic, irrespective of what you choose as a data platform, as a data at rest, could be any choice. But the second one is starting to really, in a sense, take hold for us, which is we feel that there's opportunities now to turbocharge some of the industry in the same way as happened in the 90s. So in the old days, I mean, we use the analogy of the furniture industry. There was a furniture industry and then IKEA came along. You went from buying wood to buying the semi-finished furniture to flatback. So this is sort of what happened in the former industry of data called data warehousing in the 90s, where you sort of were assembling it, bespoke as the same England. And that went to a particular set of models. Thank you, Ralph Gamble and Bill Inman and so on. And the same thing is now happening clearly in the world of data. We're going to IKEAize this industry and make it flatbacked. So you go, you get it, and a little bit of assembly is required. I mean, nobody buys a data warehouse. Nobody's going to buy a data lake, but if the industry has done its job of flatbacking the components, people can very trivially assemble great solutions for this. And it's customized to their business. It's not general purpose with some big gaps in the product. The T-shirt size is small, medium, large, or vertical. You might have a different thing for consumer packaged goods. You might have a different thing for industrial company like a GE. Gaur, a final comment, thoughts on the show. What's the vibe of the event here in San Jose? Strada Hadoop, Big Data Week. What's the vibe here? Yeah, it's very exciting. This is a time, it's almost like, look, in a lot of things that we do in business, we win and someone has to lose. The world of data, everybody's winning. We're not taking anything away from anyone. We're giving something. So, to someone who's an optimist and someone who is an inclusionary, inspirational style manager, this is fantastic. We are able to come in and provide some new things that makes what people have done in the past much richer, more nuanced, has more context to what they're going to do. The vibe is incredible. Plenty of beach head and plenty of fruit on the tree for everybody. Everybody, it's like, hey, could this be the happiest place on earth for a couple of days? Gaur, thanks so much for coming. You appreciate the insight and the data sharing when you're in success of SnapLogic. Appreciate it. We are going to be in Dublin for Hadoop Summit next month. Check out theCUBE there. And always here, day two live, more coverage coming, a lot more guests and then tomorrow, Thursday, all day coverage. This is theCUBE here live in Silicon Valley, covering big data weeks throughout Hadoop, big data SV. We'll be right back after this short break.