>> Announcer: Live from Las Vegas, it's theCUBE, covering NetApp Insight 2018. Brought to you by NetApp. >> Welcome back to theCUBE, we are live. We've been here all day at NetApp Insight in Las Vegas at the Mandalay Bay. I'm Lisa Martin with Stu Miniman and we're joined by a couple of guests. One of our alumni, Bharat Badrinath, the V.P. of Product Solutions and Marketing at NetApp. Hey, Bharat, welcome back. >> Thank you, thanks for having me. >> And we've also got Renee Yao, who is a Senior Product Marketing Manager for Deep Learning and AI Systems at Nvidia. Renee, welcome to theCUBE. >> Thanks for having me. >> So guys, this is a pretty big event. NetApp's biggest customer-partner event, the keynote, standing room only this morning five thousand plus people, lot of buzz, lot of momentum. Speaking of momentum, NetApp and Nvidia just launched an interesting partnership a couple months ago. Bharat, talk to us about how NetApp is working with Nvidia to really take advantage of AI and allow your customers to do that as well. >> Sure. So, as we started talking to customers and started looking at what they were investing in, AI bubbled up, right up to the top. And given our rich history in NFS, high performance NFS, it became an obvious choice for NetApp to invest in this space. So we've been working with Nvidia for a really long time, probably close to a year, to start integrating our products with their DGX-1 supercomputer and providing it as a single package to our customers, which makes it a lot easier for them to deploy their AI instead of waiting months for testing infrastructure, which the data scientists don't want to do. We get them a pre-tested, pre-validated system and our All-Flash Fast, which has been winning multiple awards and the recent A800 announcement were perfect choice for us to integrate into this architecture for the system. >> Alright, Renee, in the keynote this morning, the Futurist, he said-- We talked about data as the new oil, he said AI is the new electricity. Maybe you can speak a little bit as to why this is so important. Having gone to a lot of shows this year, it felt like every single show I go to, I see Nvidia, arm in arm with partners, because there's a huge wave coming. >> Yes, absolutely, and I think there was this hype about data, there was this hype about AI, and I think the years of Big Data World, that's creating data, absolutely the foundation for AI, and AI as the new electricity is a very, very good analogy. And let's do some math, shall we? So Swiss Federal Railway, it's a very good customer of ours. For those of you who don't know, they're kind of like the heart or center of all the railway tracks going through, serving about 1.2 million passengers on a day-to-day basis. Securing their security is very, very important. Now, they also have a lot of switches that turn on, then the train can go by and with the tunnels and bridges and switches, so they need to make sure that these trains actually don't collide. So when one train goes by with 11 switches, that gives you 30 ways of possible routing. Two trains, 900 ways. 80 trains, 10 to the eightieth power of ways. That's more than the observed atoms in the universe. And they actually have more than 10 thousand trains. So think about, can human being possibly calculate that much data and possibilities in their brain? As smart as we all want to think we all are, they turn to DGX, and the full day of simulation on DGX-1 was only 17 seconds for them to get back results. And I think that analogy of AI as the new electricity, just talking about the speed of light, is very spot on. >> So this isn't hype anymore, this is actually reality. And you gave a really great example of how a large transportation system is using it to get almost real time information. Bharat, talk to us about NetApp storage, history, 26 years, you guys have really made a lot of pivots in terms of your digital transformation, your cultural transformation. How are you helping with, now, kind of the added power of Nvidia, helping customers to, the hype's gone, actually deploy it, live it, and benefit a business from it? >> Yeah, absolutely, I think, as you rightly pointed out, NetApp has made a lot of pivots. Right, and I think the latest journey in terms of being empowering our customers with data has been a very powerful mission for the company. We entered the Flash market a little bit later than our competitors, but we have made dramatic progress in that space. In fact, recently, based on the latest IDC report, we were number one in All-Flash market worldwide, so that is quite an accomplishment for a company which was late to the market. And having said that, that's because of the innovation engine that is still alive and well within NetApp. We're announcing, as you've seen in the conference, we're announcing a lot of new products and technology which are way ahead of what our competitors are offering, but I think it is all hinged on what our customers need. The customer benefits because, yeah, it has profound benefit of changing how customers operate, their entire operations, it can transform dramatically overnight. And as Renee pointed out, Big Data gave the foundation which collected all the data, but wasn't able to process it. But AI with the power of Nvidia and DGX is able to utilize that to create those outcomes for customers. And from our perspective, we bring two key value adds to the space. One, we're able to serve up the data at incredibly high speeds with our award-winning All-Flash systems. But more importantly, data today lives everywhere. If you think about it, edge is becoming even more important. You can't expect an autonomous car to make an instantaneous decision without the backing of data, which means it can't, everything can't reside in the cloud, it may be at the edge. Some of it may be at your data center. How do you tie all three together, edge, core, and cloud? And that's where the data fabric, the vision of data fabric that you saw today comes in the picture. So one is performance, the ability to stream up the kind of data at the speed of the new processors are demanding, at the speed the customers are demanding to make business decisions and also the edge to core to cloud, our data fabric, which is unique and unparalleled in the industry. >> Now, I'm wondering if you could both bring us inside the customers a little bit. If I think of the traditional storage customer, I need performance, I have more and more data that I need to deal with. But Renee pointed out real outcomes, which is beyond what a traditional storage person would be doing. Who are you working with at the customers-- How do they put together-- It almost sounds like you're building a car. I've got the engine, I've got all the pieces. Who helps put this whole solution together? How does the partnership on the customer's side go together? >> That's a great question. I'll give my take and you can jump on it because she's just returned from being on road shows with joint customers and prospects. So I believe it has to be a joint decision. It's not like IT does it first and the data scientists come in later. Although it may be the case in certain instances where the data scientists start the discussion and then the IT gets brought in. In an ideal case, just like building a car, you want all the teams to be sitting together, make sure they're making the right calls because every compromise you make at one end will impact the other. So you want to make sure you make the optimal decision end to end. And that's where some of our channel partners come in who kind of bridge the data scientist team and the IT team. In some cases, customers show up with data scientists and IT teams together and some, it's one after the other. >> Absolutely. We see the same thing when we're on the road show. Literally two weeks ago, in Canada, by the way, there was a snowstorm, and it was an unforeseen snowstorm, you don't get snowstorm in October-- >> Yes, even for Canada, it was unforeseen. >> Yeah, and we had a packed room of people coming to learn about AI and in the audience, we absolutely see people from the infrastructure side, from the data center side, from the data scientist side, and they realized that they really have to start talking because none of them can afford to be reactive. For example, the data scientists, we want to do the innovation. I can't just go to the infrastructure guys and say that, "Hey, this is my workload, do something about it." And the infrastructure guys don't want to hold on to that problem and then don't know what to do with it. They really need to be ahead of everything and I think the interesting thing is, among those four cities that we're at, we see customers from the government, oil and gas, transportation, health care, and just any industry you can think of, they're all here. One specific example, do you know Mike's company that actually came to us, they have about 15 petabytes of data and that's storing 20 years of historical data and they only have two staff and they were not hiring more staff. They were like, "We just want something that's "going to be able to work and we know everything, "so just give us a solution that's going to be able to "easily scale up and out and enable us to continue to "store more data, manage more data, "and get insights out of data fast." So they came to both of us, it's just a very good, natural decision. That's why we have a partnership together as well. >> So you guys talked about kind of connecting the data scientists with the infrastructure folks. Where's the business involved in this conversation? In terms of, we want to identify new products and services to deliver faster than our competition, new markets. Talk to us about, are the data scientists and the infrastructure guys and girls following business initiatives that have been set or are the business leaders involved in these joint conversations? >> Go ahead, you take it. >> Sure. So, I think we see both. We definitely see that there's top-level executives saying that this is our initiative and we have to do it. And they will make the decision that we have to refresh our infrastructure from the ground up to make sure we're supportive of our data scientists' innovation. We've also seen brilliant minds, researchers, data scientists doing amazing things and then roll it up to the VP level and then roll it up to CEO level to say that this has to be done because this-- For example, that simulation of 17 second results, it's things that people used to cannot do in their lifetime, now they can do it in seconds, that kind of innovation just cannot be ignored. >> Yeah, we see the same thing. In fact, any team that has possession of that data or is accountable for that data is the one usually driving the decisions. Because as you mine the data, as you start deploying new techniques, you realize new opportunities, which means the business gets more interested in it and vice versa. If the business is interested, they're going to look for those answers within the data that they have. >> So last thing, Renee, you were on the Women in Tech panel that ended yesterday, Bharat and I were both in the audience, and one of the things that I thought was really inspiring about your story is that you had given us, the audience, an interesting example of a TV opportunity that you were inspired to do by the CEO of Nvidia. Give our audience who didn't have a chance to see that panel a little bit, and in the last minute, of that story and how you were able to step forward and go, "I'm going to try this." >> Yeah, of course. I think that brings us back to the concept that we have at Nvidia, the speed of light concept, and you really have to learn, act, to move at the speed of light, just like our GPUs, with extreme performance. And obviously, at that speed, none of us know everything. So what Jensen, CEO, shared with us was, in an all-hands meeting internally, he told us that none of us are here qualified to do any of our jobs, maybe besides his legal counsel and CFO. And all of us are here to learn, and we need to learn as fast and as much as we can. And we can't really just let the competition determine where our limit is, but instead is by the limit of what is possible. So that is very much a fundamental mindset change in this AI revolution. >> Well thanks so much, Renee and Bharat, for stopping by and sharing with us the exciting things that you guys are doing with NetApp. We look forward to talking with you again soon. >> Thank you. >> Me too, thanks. >> For Stu Miniman, I'm Lisa Martin. You're watching theCUBE, live from NetApp Insight 2018 in Las Vegas. Stu and I will be right back with our next guests after a short break. (techno music)

>> Announcer: Live from midtown Manhattan, it's theCUBE covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and it's ecosystem sponsors. (upbeat techno music) >> Hey, welcome back, everyone. Here live in New York City, this is theCUBE from SiliconANGLE Media Special. Exclusive coverage of the Big Data World at NYC. We call it Big Data NYC in conjunction also with Strata Hadoop, Strata Data, Hadoop World all going on kind of around the corner from our event here on 37th Street in Manhattan. I'm John Furrier, the co-host of theCUBE with Peter Burris, Head of Research at SiliconANGLE Media, and General Manager of WikiBon Research. And our next guest is one of our famous CUBE alumni, Prakash Nanduri co-founder and CEO of Paxata who launched his company here on theCUBE at our first inaugural Big Data NYC event in 2013. Great to see you. >> Great to see you, John. >> John: Great to have you back. You've been on every year since, and it's been the lucky charm. You guys have been doing great. It's not broke, don't fix it, right? And so theCUBE is working with you guys. We love having you on. It's been a pleasure, you as an entrepreneur, launching your company. Really, the entrepreneurial mojo. It's really what it's all about. Getting access to the market, you guys got in there, and you got a position. Give us the update on Paxata. What's happening? >> Awesome, John and Peter. Great to be here again. Every time I come here to New York for Strata I always look forward to our conversations. And every year we have something exciting and new to share with you. So, if you recall in 2013, it was a tiny little show, and it was a tiny little company, and we came in with big plans. And in 2013, I said, "You know, John, we're going to completely disrupt the way business consumers and business analysts turn raw data into information and they do self-service data preparation." That's what we brought to the market in 2013. Ever since, we have gone on to do something really exciting and new for our customers every year. In '14, we came in with the first Apache Spark-based platform that allowed business analysts to do data preparation at scale interactively. Every year since, last year we did enterprise grade and we talked about how Paxata is going to be delivering our self-service data preparation solution in a highly-scalable enterprise grade deployment world. This year, what's super exciting is in addition to the recent announcements we made on Paxata running natively on the Microsoft Azure HDI Spark system. We are truly now the only information platform that allows business consumers to turn data into information in a multi-cloud hybrid world for our enterprise customers. In the last few years, I came and I talked to you and I told you about work we're doing and what great things are happening. But this year, in addition to the super-exciting announcements with Microsoft and other exciting announcements that you'll be hearing. You are going to hear directly from one of our key anchor customers, Standard Chartered Bank. 150-year-old institution operating in over 46 countries. One of the most storied banks in the world with 87,500 employees. >> John: That's not a start up. >> That's not a start up. (John laughs) >> They probably have a high bar, high bar. They got a lot of data. >> They have lots of data. And they have chosen Paxata as their information fabric. We announced our strategic partnership with them recently and you know that they are going to be speaking on theCUBE this week. And what started as a little experiment, just like our experiment in 2013, has actually mushroomed now into Michael Gorriz, and Shameek Kundu, and the entire leadership of Standard Chartered choosing Paxata as the platform that will democratize information in the bank across their 87,500 employees. We are going in a very exciting way, a very fast way, and now delivering real value to the bank. And you can hear all about it on our website-- >> Well, he's coming on theCUBE so we'll drill down on that, but banks are changing. You talk about a transformation. What is a teller? An Internet of Things device. The watch potentially could be a terminal. So, the Internet of Things of people changes the game. Are the ATMs going to go away and become like broadcast points? >> Prakash: And you're absolutely right. And really what it is about is, it doesn't matter if you're a Standard Chartered Bank or if you're a pharma company or if you're the leading healthcare company, what it is is that everyone of our customers is really becoming an information-inspired business. And what we are driving our customers to is moving from a world where they're data-driven. I think being data-driven is fine. But what you need to be is information-inspired. And what does that mean? It means that you need to be able to consume data, regardless of format, regardless of source, regardless of where it's coming from, and turn it into information that actually allows you to get inside in decisions. And that's what Paxata does for you. So, this whole notion of being information-inspired, I don't care if you're a bank, if you're a car company, or if you're a healthcare company today, you need to have-- >> Prakash, for the folks watching that might not know our history as you launched on theCUBE in 2013 and have been successful every year since. You guys have really deploying the classic entrepreneurial success formula, be fast, walk the talk, listen to customers, add value. Take a minute quickly just to talk about what you guys do. Just for the folks that don't know you. >> Absolutely, let's just actually give it in the real example of you know, a customer like Standard Chartered. Standard Chartered operates in multiple countries. They have significant number of lines of businesses. And whether it's in risk and compliance, whether it is in their marketing department, whether it's in their corporate banking business, what they have to do is, a simple example could be I want to create a customer list to be able to go and run a marketing campaign. And the customer list in a particular region is not something easy for a bank like Standard Charter to come up with. They need to be able to pull from multiple sources. They need to be able to clean the data. They need to be able to shape the data to get that list. And if you look at what is really important, the people who understand the data are actually not the folks in IT but the folks in business. So, they need to have a tool and a platform that allows them to pull data from multiple sources to be able to massage it, to be able to clean it-- >> John: So, you sell to the business person? >> We sell to the business consumer. The business analyst is our consumer. And the person who supports them is the chief data officer and the person who runs the Paxata platform on their data lake infrastructure. >> So, IT sets the data lake and you guys just let the business guys go to town on the data. >> Prakash: Bingo. >> Okay, what's the problem that you solve? If you can summarize the problem that you solve for the customers, what is it? >> We take data and turn it into information that is clean, that's complete, that's consumable and that's contextual. The hardest problem in every analytical exercise is actually taking data and cleaning it up and getting it ready for analytics. That's what we do. >> It's the prep work. >> It's the prep work. >> As companies gain experience with Big Data, John, what they need to start doing increasingly is move more of the prep work or have more of the prep work flow closer to the analyst. And the reason's actually pretty simple. It's because of that context. Because the analyst knows more about what their looking for and is a better evaluator of whether or not they get what they need. Otherwise, you end up in this strange cycle time problem between people in back end that are trying to generate the data that they think they want. And so, by making the whole concept of data preparation simpler, more straight forward, you're able to have the people who actually consume the data and need it do a better job of articulating what they need, how they need it and making it presentable to the work that they're performing. >> Exactly, Peter. What does that say about how roles are starting to merge together? Cause you've got to be at the vanguard of seeing how some of these mature organizations are working. What do you think? Are we seeing roles start to become more aligned? >> Yes, I do think. So, first and foremost, I think what's happening is there is no such thing as having just one group that's doing data science and another group consuming. I think what you're going to be going into is the world of data and information isn't all-consuming and that everybody's role. Everybody has a role in that. And everybody's going to consume. So, if you look at a business analyst that was spending 80% of their time living in Excel or working with self-service BI tools like our partner's Tableau and Power BI from Microsoft, others. What you find is these people today are living in a world where either they have to live in coding scripting world hell or they have to rely on IT to get them the real data. So, the role of a business analyst or a subject matter expert, first and foremost, the fact that they work with data and they need information that's a given. There is no business role today where you can't deal with data. >> But it also makes them real valuable, because there aren't a lot of people who are good at dealing with data. And they're very, very reliant on these people to turn that data into something that is regarded as consumable elsewhere. So, you're trying to make them much more productive. >> Exactly. So, four years years ago, when we launched on theCUBE, the whole premise was that in order to be able to really drive towards a world where you can make information and data-driven decisions, you need to ensure that the business analyst community, or what I like to call the business consumer needs to have the power of being able to, A, get access to data, B, make sense of the data, and then turn that data into something that's valuable for her or for him. >> Peter: And others. >> And others, and others. Absolutely. And that's what Paxata is doing. In a collaborative, in a 21st Century world where I don't work in a silo, I work collaboratively. And then the tool, and the platform that helps me do that is actually a 21st Century platform. >> So, John, at the beginning of the session you and Jim were talking about what is going to be one of the themes here at the show. And we observed that it used to be that people were talking about setting up the hardware, setting up the clutters, getting Hadoop to work, and Jim talked about going up the stack. Well, this is one of the indicators that, in fact, people were starting to go up the stack because they're starting to worry more about the data, what it can do, the value of how it's going to be used, and how we distribute more of that work so that we get more people using data that's actually good and useful to the business. >> John: And drives value. >> And drives value. >> Absolutely. And if I may, just put a chronological aspect to this. When we launched the company we said the business analyst needs to be in charge of the data and turning the data into something useful. Then right at that time, the world of create data lakes came in thanks to our partners like Cloudera and Hortonworks, and others, and MapR and others. In the recent past, the world of moving from on premise data lakes to hybrid, multicloud data lakes is becoming reality. Our partners at Microsoft, at AWS, and others are having customers come in and build cloud-based data lakes. So, today what you're seeing is on one hand this complete democratization within the business, like at Standard Chartered, where all these business analysts are getting access to data. And on the other hand, from the data infrastructure moving into a hybrid multicloud world. And what you need is a 21st Century information management platform that serves the need of the business and to make that data relevant and information and ready for their consumption. While at the same time we should not forget that enterprises need governance. They need lineage. They need scale. They need to be able to move things around depending on what their business needs are. And that's what Paxata is driving. That's why we're so excited about our partnership with Microsoft, with AWS, with our customer partnerships such as Standard Chartered Bank, rolling this out in an enterprise-- >> This is a democratization that you were referring to with your customers. We see this-- >> Everywhere. >> When you free the data up, good things happen but you don't want to have IT be the constraint, you want to let them enable-- >> Peter: And IT doesn't want to be the constraint. >> They don't. >> This is one of the biggest problems that they have on a daily basis. >> They're happy to let it go free as long as it's in they're mind DevOps-like related, this is cool for them. >> Well, they're happy to let it go with policy and security in place. >> Our customers, our most strategic customers, the folks who are running the data lakes, the folks who are managing the data lakes, they are the first ones that say that we want business to be able to access this data, and to be able to go and make use out of this data in the right way for the bank. And not have us be the impediment, not have us be the roadblock. While at the same time we still need governance. We still need security. We still need all those things that are important for a bank or a large enterprise. That's what Paxata is delivering to the customers. >> John: So, what's next? >> Peter: Oh, I'm sorry. >> So, really quickly. An interesting observation. People talk about data being the new fuel of business. That really doesn't work because, as Bill Schmarzo says, it's not the new fuel of business, it's new sunlight of business. And the reason why is because fuel can only be used once. >> Prakash: That's right. >> The whole point of data is that it can be used a lot, in a lot of different ways, and a lot of different contexts. And so, in many respects what we're really trying to facilitate or if someone who runs a data lake when someone in the business asks them, "Well, how do you create value for the business?" The more people, the more users, the more context that they're serving out of that common data, the more valuable the resource that they're administering. So, they want to see more utilization, more contexts, more data being moved out. But again, governance, security have to be in place. >> You bet, you bet. And using that analogy of data, and I've heard this term about data being the new oil, etc. Well, if data is the oil, information is really the refined fuel or sunlight as we like to call it. >> Peter: Yeah. >> John: Well, you're riffing on semantics, but the point is it's not a one trick pony. Data is part of the development, I wrote a blog post in 1997, I mean 2007 that said data's the new development kit. And it was kind of riffing on this notion of the old days >> Prakash: You bet. >> Here's your development kit, SDK, or whatever was how people did things back then Enter the cloud, >> Prakash: That's right. >> And boom, there it is. The data now is in the process of the refinery the developers wanted. The developers want the data libraries. Whatever that means. That's where I see it. And that is the democratization where data is available to be integrated in to apps, into feeds, into ... >> Exactly, and so it brings me to our point about what was the exciting, new product innovation announcement we made today about Intelligent Ingest. You want to be able to access data in the enterprise regardless of where it is, regardless of the cloud where it's sitting, regardless of whether it's on-premise, in the cloud. You don't need to as a business worry about whether that is a JSON file or whether that's an XML file or that's a relational file. That's irrelevant. What you want is, do I have the access to the right data? Can I take that data, can I turn it into something valuable and then can I make a decision out of it? I need to do that fast. At the same time, I need to have the governance and security, all of that. That's at the end of the day the objective that our customers are driving towards. >> Prakash, thanks so much for coming on and being a great member of our community. >> Fantastic. >> You're part of our smart network of great people out there and entrepreneurial journey continues. >> Yes. >> Final question. Just observation. As you pinch yourself and you go down the journey, you guys are walking the talk, adding new products. We're global landscape. You're seeing a lot of new stuff happening. Customers are trying to stay focused. A lot of distractions whether security or data or app development. What's your state of the industry? How do you view the current market, from your perspective and also how the customer might see it from their impact? >> Well, the first thing is that I think in the last four years we have seen significant maturity both on the providers off software technology and solutions, and also amongst the customers. I do think that going forward what is really going to make a difference is one really driving towards business outcomes by leveraging data. We've talked about a lot of this over the last few years. What real business outcomes are you delivering? What we are super excited is when we see our customers each one of them actually subscribes to Paxata, we're a SAS company, they subscribe to Paxata not because they're doing the science experiment but because they're trying to deliver real business value. What is that? Whether that is a risk in compliance solution which is going to drive towards real cost savings. Or whether that's a top line benefit because they know what they're customer 360 is and how they can go and serve their customers better or how they can improve supply chains or how they can optimize their entire efficiency in the company. I think if you take it from that lens, what is going to be important right now is there's lots of new technologies coming in, and what's important is how is it going to drive towards those top three business drivers that I have today for the next 18 months? >> John: So, that's foundational. >> That's foundational. Those are the building blocks-- >> That's what is happening. Don't jump... If you're a customer, it's great to look at new technologies, etc. There's always innovation projects-- >> RND, GPOCs, whatever. Kick the tires. >> But now, if you are really going to talk the talk about saying I'm going to be, call your word, data-driven, information-driven, whatever it is. If you're going to talk the talk, then you better walk the walk by delivering the real kind of tools and capabilities that you're business consumers can adopt. And they better adopt that fast. If they're not up and running in 24 hours, something is wrong. >> Peter: Let me ask one question before you close, John. So, you're argument, which I agree with, suggests that one of the big changes in the next 18 months, three years as this whole thing matures and gets more consistent in it's application of the value that it generates, we're going to see an explosion in the number users of these types of tools. >> Prakash: Yes, yes. >> Correct? >> Prakash: Absolutely. >> 2X, 3X, 5X? What do you think? >> I think we're just at the cusp. I think is going to grow up at least 10X and beyond. >> Peter: In the next two years? >> In the next, I would give that next three to five years. >> Peter: Three to five years? >> Yes. And we're on the journey. We're just at the tip of the high curve taking off. That's what I feel. >> Yeah, and there's going to be a lot more consolidation. You're going to start to see people who are winning. It's becoming clear as the fog lifts. It's a cloud game, a scale game. It's democratization, community-driven. It's open source software. Just solve problems, outcomes. I think outcome is going to be much faster. I think outcomes as a service will be a model that we'll probably be talking about in the future. You know, real time outcomes. Not eight month projects or year projects. >> Certainly, we started writing research about outcome-based management. >> Right. >> Wikibon Research... Prakash, one more thing? >> I also just want to say that in addition to this business outcome thing, I think in the last five years I've seen a lot of shift in our customer's world where the initial excitement about analytics, predictive, AI, machine-learning to get to outcomes. They've all come into a reality that none of that is possible if you're not able to handle, first get a grip on your data, and then be able to turn that data into something meaningful that can be analyzed. So, that is also a major shift. That's why you're seeing the growth we're seeing-- >> John: Cause it's really hard. >> Prakash: It's really hard. >> I mean, it's a cultural mindset. You have the personnel. It's an operational model. I mean this is not like, throw some pixie dust on it and it magically happens. >> That's why I say, before you go into any kind of BI, analytics, AI initiative, stop, think about your information management strategy. Think about how you're going to democratize information. Think about how you're going to get governance. Think about how you're going to enable your business to turn data into information. >> Remember, you can't do AI with IA? You can't do AI without information architecture. >> There you go. That's a great point. >> And I think this all points to why Wikibon's research have all the analysts got it right with true private cloud because people got to take care of their business here to have a foundation for the future. And you can't just jump to the future. There's too much just to come and use a scale, too many cracks in the foundation. You got to do your, take your medicine now. And do the homework and lay down a solid foundation. >> You bet. >> All right, Prakash. Great to have you on theCUBE. Again, congratulations. And again, it's great for us. I totally have a great vibe when I see you. Thinking about how you launched on theCUBE in 2013, and how far you continue to climb. Congratulations. >> Thank you so much, John. Thanks, Peter. That was fantastic. >> All right, live coverage continuing day one of three days. It's going to be a great week here in New York City. Weather's perfect and all the players are in town for Big Data NYC. I'm John Furrier with Peter Burris. Be back with more after this short break. (upbeat techno music).

>> Announcer: Live from San Jose, California, it's theCUBE. Covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. Here live in Silicon Valley this is theCUBE. I'm John Furrier, covering our Big Data SV event, #BigDataSV. Our companion event to Big Data NYC, all in conjunction Strata Hadoop, the Big Data World comes together, and great to have guests come by. Donna Prlich, who's the senior VP of products and solutions at Pentaho, a Hitachi company who we've been following before Hitachi had acquired you guys. But you guys are unique in the sense that you're a company within Hitachi left alone after the acquisition. You're now running all the products. Congratulations, welcome back, great to see you. >> Yeah, thank you, good to be back. It's been a little while, but I think you've had some of our other friends on here, as well. >> Yep, and we'll be at Pentaho World, you have Orlando, I think is October. >> Yeah, October, so I'm excited about that, too, so. >> I'm sure the agenda is not yet baked for that because it's early in the year. But what's going on with Hitachi? Give us the update, because you're now, your purview into the product roadmap. The Big Data World, you guys have been very, very successful taking this approach to big data. It's been different and unique to others. >> [Donna} Yep. What's the update? >> Yeah, so, very exciting, actually. So, we've seen, especially at the show that the Big Data World, we all know that it's here. It's monetizable, it's where we, actually, where we shifted five years ago, and it's been a lot of what Pentaho's success has been based on. We're excited because the Hitachi acquisition, as you mentioned, sets us up for the next bit thing, which is IOT. And I've been hearing non-stop about machine learning, but that's the other component of it that's exciting for us. So, yeah, Hitachi, we're-- >> You guys doing a lot of machine learning, a lot of machine learning? >> So we, announced our own kind of own orchestration capabilities that really target how do you, it's less about building models, and how do you enable the data scientists and data preparers to leverage the actual kind of intellectual properties that companies have in those models they've built to transform their business. So we have our own, and then the other exciting piece on the Hitachi side is, on the products, we're now at the point where we're running as Pentaho, but we have access to these amazing labs, which there's about 25 to 50 depending on where you are, whether you're here or in Japan. And those data scientists are working on really interesting things on the R & D side, when you apply those to the kind of use cases we're solving for, that's just like a kid in a candy store with technology, so that's a great-- >> Yeah, you had a built-in customer there. But before I get into Pentaho focusing on what's unique, really happening within you guys with the product, especially with machine learning and AI, as it starts to really get some great momentum. But I want to get your take on what you see happening in the marketplace. Because you've seen the early days and as it's now, hitting a whole another step function as we approach machine learning and AI. Autonomous vehicles, sensors, everything's coming. How are enterprises in these new businesses, whether they're people supporting smart cities or a smart home or automotive, autonomous vehicles. What's the trends you are seeing that are really hitting the pavement here. >> Yeah, I think what we're seeing is, and it's been kind of Pentaho's focus for a long time now, which is it's always about the data. You know, what's the data challenge? Some of the amounts of data which everybody talks about from IOT, and then what's interesting is, it's not about kind of the concepts around AI that have been around forever, but when you start to apply some of those AI concepts to a data pipeline, for instance. We always talk about that 6data pipeline. The reason it's important is because you're really bringing together the data and the analytics. You can't separate those two things, and that's been kind of not only a Pentaho-specific, sort of bent that I've had for years, but a personal one, as well. That, hey, when you start separating it, it makes it really hard to get to any kind of value. So I think what we're doing, and what we're going to be seeing going forward, is applying AI to some of the things that, in a way, will close the gaps between the process and the people, and the data and the analytics that have been around for years. And we see those gaps closing with some of the tools that are emerging around preparing data. But really, when you start to bring some of that machine learning into that picture, and you start applying math to preparing data, that's where it gets really interesting. And I think we'll see some of that automation start to happen. >> So I got to ask you, what is unique about Pentaho? Take a minute to share with the audience some of the unique things that you guys are doing that's different in this sea of people trying to figure out big data. You guys are doing well, an6d you wrote a blog post that I referenced earlier yesterday, around these gaps. How, what's unique about Pentaho and what are you guys doing with examples that you could share? >> Yeah, so I think the big thing about Pentaho that's unique is that it's solving that analytics workflow from the data side. Always from the data. We've always believed that those two things go together. When you build a platform that's really flexible, it's based on open source technology, and you go into a world where a customer says, "I not only want to manage and have a data lake available," for instance, "I want to be able to have that thing extend over the years to support different groups of users. I don't want to deliver it to a tool, I want to deliver it to an application, I want to embed analytics." That's where having a complete end-to-end platform that can orchestrate the data and the analytics across the board is really unique. And what's happened is, it's like, the time has come. Where all we're hearing is, hey, I used to think it was throw some data over and, "here you go, here's the tools." The tools are really easy, so that's great. Now we have all kinds of people that can do analytics, but who's minding the data? With that end-to-end platform, we've always been able to solve for that. And when you move in the open source piece, that just makes it much easier when things like Spark emerge, right. Spark's amazing, right? But we know there's other things on the horizon. Flink, Beam, how are you going to deal with that without being kind of open source, so this is-- >> You guys made a good bet there, and your blog post got my attention because of the title. It wasn't click bait either, it was actually a great article, and I just shared it on Twitter. The Holy Grail of analytics is the value between data and insight. And this is interesting, it's about the data, it's in bold, data, data, data. Data's the hardest part. I get that. But I got to ask you, with cloud computing, you can see the trends of commoditization. You're renting stuff, and you got tools like Kinesis, Redshift on Amazon, and Azure's got tools, so you don't really own that, but the data, you own, right? >> Yeah, that's your intellectual property, right? >> But that's the heart of your piece here, isn't it, the Holy Grail. >> Yes, it is. >> What is that Holy Grail? >> Yeah, that Holy Grail is when you can bring those two things together. The analytics and the data, and you've got some governance, you've got the control. But you're allowing the access that lets the business derive value. For instance, we just had a customer, I think Eric might have mentioned it, but they're a really interesting customer. They're one of the largest community colleges in the country, Ivy Tech, and they won an award, actually, for their data excellence. But what's interesting about them is, they said we're going to create a data democracy. We want data to be available because we know that we see students dropping out, we can't be efficient, people can't get the data that they need, we have old school reporting. So they took Pentaho, and they really transformed the way they think about running their organization and their community colleges. Now they're adding predictive to that. So they've got this data democracy, but now they're looking at things like, "Okay we an see where certain classes are over capacity, but what if we could predict, next year, not only which classes are over capacity, what's the tendency of a particular student to drop out?" "What could we do to intervene?" That's where the kind of cool machine learning starts to apply. Well, Pentaho is what enables that data democracy across the board. I think that's where, when I look at it from a customer perspective, it's really kind of, it's only going to get more interesting. >> And with RFID and smart phones, you could have attendance tracking, too. You know, who's not showing up. >> Yeah absolutely. And you bring Hitachi into the picture, and you think about, for instance, from an IOT perspective, you might be capturing data from devices, and you've got a digital twin, right? And then you bring that data in with data that might be in a data lake, and you can set a threshold, and say, "Okay, not only do we want to be able to know where that student is," or whatever, "we want to trigger something back to that device," and say, "hey, here's a workshop for you to login to right away, so that you don't end up not passing a class." Or whatever it is, it's a simplistic model, but you can imagine where that starts to really become transformative. >> So I asked Eric a question yest6erday. It was from Dave Valante, who's in Boston, stuck in the snowstorm, but he was watching, and I'll ask you and see how it matches. He wrote it differently on Crouch, it was public, but this is in my chat, "HDS is known for main frames, historically, and storage, but Hitachi is an industrial giant. How is Pentaho leveraging the Hitachi monster?" >> Yes, that's a great way to put it. >> Or Godzilla, because it's Japan. >> We were just comparing notes. We were like, "Well, is it an $88 billion company or $90 billion. According to the yen today, it's 88. We usually say 90, but close enough, right? But yeah, it's a huge company. They're in every industry. Make all kinds of things. Pretty much, they've got the OT of the world under their belt. How we're leveraging it is number one, what that brings to the table, in terms of the transformations from a software perspective and data that we can bring to the table and the expertise. The other piece is, we've got a huge opportunity, via the Hitachi channel, which is what's seeing for us the growth that we've had over the last couple of years. It's been really significant since we were acquired. And then the next piece is how do we become part of that bigger Hitachi IOT strategy. And what's been starting to happen there is, as I mentioned before, you can kind of probably put the math together without giving anything away. But you think about capturing, being able to capture device data, being able to bring it into the digital twin, all of that. And then you think about, "Okay, and what if I added Pentaho to the mix?" That's pretty exciting. You bring those things together, and then you add a whole bunch of expertise and machine learning and you're like, okay. You could start to do, you could start to see where the IOT piece of it is where we're really going to-- >> IOT is a forcing function, would you agree? >> Yes, absolutely. >> It's really forcing IT to go, "Whoa, this is coming down fast." And AI and machine learning, and cloud, is just forcing everyone. >> Yeah, exactly. And when we came into the big data market, whatever it was, five years ago, in the early market it's always hard to kind of get in there. But one of the things that we were able to do, when it was sort of, people were still just talking about BI would say, "Have you heard about this stuff called big data, it's going to be hard." You are going to have to take advantage of this. And the same thing is happening with IOT. So the fact that we can be in these environments where customers are starting to see the value of the machine generated data, that's going to be-- >> And it's transformative for the business, like the community college example. >> Totally transformative, yeah. The other one was, I think Eric might have mentioned, the IMS, where all the sudden you're transforming the insurance industry. There's always looking at charts of, "I'm a 17-year-old kid," "Okay, you're rate should be this because you're a 17-year-old boy." And now they're starting to track the driving, and say, "Well, actually, maybe not, maybe you get a discount." >> Time for the self-driving car. >> Transforming, yeah. >> Well, Donna, I appreciate it. Give us a quick tease here, on Pentaho World coming in October. I know it's super early, but you have a roadmap on the product side, so you can see a little bit around the corner. >> Donna: Yeah. >> What is coming down the pike for Pentaho? What are the things that you guys are beavering away at inside the product group? >> Yeah, I think you're going to see some really cool innovations we're doing. I won't, on the Spark side, but with execution engines, in general, we're going to have some really interesting kind of innovative stuff coming. More on the machine learning coming out, and if you think about, if data is, you know what, is the hard part, just think about applying machine learning to the data, and I think you can think of some really cool things, we're going to come up with. >> We're going to need algorithms for the algorithms, machine learning for the machine learning, and, of course, humans to be smarter. Donna, thanks so much for sharing here inside theCUBE, appreciate it. >> Thank you. >> Pentaho, check them out. Going to be at Pentaho World in October, as well, in theCUBE, and hopefully we can get some more deep dives on, with their analyst group, for what's going on with the engines of innovation there. More CUBE coverage live from Silicon Valley for Big Data SV, in conjunction with Strata Hadoop, I'm John Furrier. Be right back with more after this short break. (techno music)