>> Narrator: From Chicago, it's theCube covering Veritas Vision Solution Day 2018. Brought to you by Veritas. >> Welcome back to Chicago everybody. This is theCube, the leader in live tech coverage. We're here on the ground covering the Veritas Vision Solution Days in Chicago. Just a couple weeks ago we were in New York City at the iconic Tavern on the Green. We're here at the Palmer House Hotel. Jyothi Swaroop is here, he's the Vice President of Global Marketing for Veritas. Great to see you again. >> Thanks Dave, glad to be here. >> A few weeks ago we saw you in New York. Since then you've been around the globe talking to customers. You just gave a great presentation to about 60, 70 customers here in Chicago. Obviously a lot of your customers here, New York, one of the big NFL cities, so what have you learned in the last couple of weeks? >> Well, a lot. It's been exciting, right. Since New York I've been in Dubai, Milan, Rome, all over the place. Sounds exciting but a lot of jet lag and travel but a lot of exciting customers with interesting challenges that we can solve for. But I guess I would summarize it into three parts. Obviously there are data protection challenges that we solve at Veritas and have done so over 20 years. There are a lot of storage challenges that we talked about and how they're moving to the cloud and how we can assist with that. And lastly, interesting thing is the whole compliance in AI and ML related challenges as to how do they look ahead while staying compliant with what they have already. >> There are some major trends forcing people to rethink their data protection strategies. Obviously, cloud is one, the whole security and data protection world's coming together, the edge, just the whole distributed data trend. Machine intelligence is another one. There are things that you can do with all that data, machine plus data plus cloud really changes the game. You guys have some hard news in that area. Bring us up to date, what are you announcing? >> Right, so we're announcing Veritas Predictive Insights. Really excited about this announcement because when I joined Veritas about 16 months ago, I felt like Veritas sits on top of all these exabytes of data. We protect the largest number of exabytes of data, right. So we have access to the metadata of that data. So my question to the engineering team is what are we doing with that metadata? Are we going to use it, leverage it, so our customers can benefit from it? From all of this user data that we get from other customers. And the answer was, "Yes, we're working on something. Hold on, you're new." And now we have it. So at Veritas, yes it takes 12 to 16 months to build something at scale, right. We have hundreds of engineers that have worked on this. So what we have done now is, especially with our appliances portfolio, we're able to give our customers intuitive, predictive, and proactive maintenance and support of their systems. Now what does that mean? It means firmware upgrades, patches, things like that. They don't have to be a personalized, you know, fly in an engineer in to do kind of things. They can be automated. Oracle recently at Oracle OpenWorld announced this whole autonomous database. Why can't data protection be autonomous, right? So that's how we think, right. Make everything autonomous, make everything predictive and proactive and that's what Predictive Insights is about. >> So let's unpack that a little bit. So what are the enablers that allow you to actually take this next step. Obviously you've got the data, you've got a classification engine that allows you to put data in buckets, if you will. Explain what that is and why it's important. >> I'm glad you brought up the classification engine because that was at the heart of everything Veritas did for the last 20 years, right. We call it Big Veritas Information Classifier where we classified all of the data that came in on Ingest, unlike other people, other customers and other vendors. We classified all of the data that came in from that back up and we told our customers, "Here's PII numbers, your sensitive information is structured data, is unstructured data." We did this really well for a long time. Now we wanted to take that to the next level, right. We wanted to tell our customers what's actually going on with your infrastructure. You've classified the data, you've put it in here, what can you do with it next? Where can you put it? Can you optimize it after the cloud? How much will you pay for it? Can you remove something off of it? How much do you pay for that? Can you put some data retention on prem? How much would that cost you? So we would not only want to give them information about the classification of that data, but how to monetize that data, how much money would it cost to store that data in different areas. >> So this is a case where, if you go back to something you might remember, 2006, the Federal Rules of Civil Procedure mandated that you were able to recover and deliver to a court of law electronic records. Well data classification was critical component there. This is one of those cases like, if you've got an older athlete, like Tom Brady, maybe he's not as fast as he used to be, but he's got it all up here, he knows the plays before he sees it. You guys have the experience around things like data classification which are table stakes to allow you to do this but it's still a challenge for many folks in the industry. It's a metadata problem, isn't it? >> Yup, it absolutely is. It is a metadata problem and it's a metadata advantage for us at Veritas because we sit on top of the highest amount of metadata. >> So how do I take advantage of the Veritas Predictive Insights? Where does it live? >> So where we've announced it, it'll be out there the beginning of the year, 2019. We're rolling out with our appliances portfolio first because we have more control over it because the appliances and the hardware have been integrated with our software. So we give our customers predictive insights on all of their appliances that they buy from Veritas and their systems. Going forward, we'll extend that to our software only sales motions, as well. As extending it to other software platforms and other hardware platforms from other vendors, as well. So we're working on some integrations that I can't talk about today but we want to essentially take predictive insights and move it beyond Veritas in the future. >> Okay, so, talk a little bit more about how it works. Using machine learning technology, you're building models and training the data for different customers, how does it all actually come to fruition? >> Sure, so the first thing is, you know, we're generating what we call SRS or a system reliability score, right. So our engine processes all of this information that comes from a customer's data, the usage data, and maps it to the hundreds of other customers, thousands of other customers usage data that we have to find patterns, right. So for example, if a disc hasn't had a firmware upgraded and hasn't done so for months, we can predicatively let the customer know this disc is going to fail if you didn't upgrade this. But that's not enough. We actually allow them to click a button and upgrade the firmware right there to that disc so it's done, right. So it's not only letting customers know that here's something that's going to go wrong, but here's how to fix it, as well. That's just one example of what we can do. >> Well that's key, it's like the old days. You know, you have a pager and you get an alert and then you got to go do something. You're saying you're actually building automation into the process. >> Right, it's like chat bots. You respond to the chat bot right there and it does the action for you. You don't actually have to go somewhere and figure it out. >> So you've got this SRS score. >> Jyothi: Right. >> So what happens when you cross that threshold, it tells the system, "Okay, take some remedial action," or does it allow the customer to sort of make that choice? What's next? >> Sure, so the SRS score is like a credit score, right. There's a lot of complexity underneath that score. So at the highest level we tell the score, the customer if your score is above a certain point, your systems are healthy, they're running well. If they go below a certain point, right, let's say a 700 score for a credit score, you got to go watch or widen your goal below and we'll give them the 10 or 20 reasons why the score went down. Whether it's a firmware thing or a support issue or a hard drive issue. We tell them exactly what's about to go wrong so they can go fix it before it actually goes wrong. >> What do you, actually, before we go there, just some examples, some use cases that you expect in the field, you've talked to customers about. Give us some more. >> Sure, so data, like we talk to a lot of companies with massive data centers. So one of the things that it says with our appliances, simple things like temperature changes. I was in Dubai, look, the temperature there can be crazy. It goes over 100 degrees Fahrenheit. So it says simple things like temperature changes can have massive effect on your hard drives and how that works. If my AI and ML algorithms or my software can proactively tell me the temperatures going up, this is what's going to happen, you increase the cooling, do something different, move the data somewhere, back it up. That's great for the customer. Can I take action just based on a simple thing like temperature. There's another interesting customer, here in New York actually, that came to me and said, we had this problem like every so many weeks, their discs would fail. And they thought it was their temperature because it was in the summer. It wasn't and after a lot of research, it turns out it was the fire alarms that were going off. So the fire alarm and the fire alarm testing that was going on was actually causing discs to fail. >> Because of the vibration or? >> For the vibration and the decibel level. It was interesting, right. And now our AI, ML knows that so it's recorded, we know it and we'll be better off going forward, right. We'll tell other customers now that have data centers with massive, loud, high decibel fire alarms that this could be a potential issue. I'm not saying that is the issue, but this could be a potential issue that they would have never thought of otherwise. >> So what do you expect the business impact to be? When you talk to customers about this capability, you know, under non-disclosure, etcetera, how are they seeing this impacting their business? >> So it's three things, right. Proactive support and maintenance, that's really important. The customers are tired of talking to large vendors where the support connections are horrible, right. They have to go in and raise a ticket and do certain things and then they will ship a guy over to their site who'll come and fix it. That's just too long. >> Dave: Slow and reactive. >> Slow and reactive. We want to make this proactive and autonomous, that's number one. Number two is total cost of ownership, right. So when customers are able to predict these failures, they don't have to have a certain set of money set aside for solving problems when the occur. They're like, "I know this problem has come up. I need to budget for it." So their TCO models get better and more predictable, right. And last but definitely not the least, you know, when we extend this to beyond Veritas, they will be able to do more with their data. Again, what is that more? We don't know yet today. But when we are able to extend this to beyond Veritas, customers will be able to do a lot more with their data centers. >> So a couple of things this plays into. Obviously digital transformation is all about being on all the time, you don't want to have, you don't want planned downtime or unplanned downtime. This allows you to at least plan more effectively and potentially eliminate any downtime so your data is always accessible. And it's also cloud-like in that you're automating a lot of the either recovery from failures, or you know, you're pushing a button and saying okay, remediate this, patch that so you don't have the failure. So that's a sort of cloud-like approach. So you said it's available the first part of '19. And it's available, is it in appliances or? How do I get this. >> So we'll be rolling it out in appliances first, all the Veritas appliances. And then we'll extend it to software only, as well as beyond Veritas going forward. >> Awesome, Jyothi, thanks very much for taking us through the new capability. AI brought to data protection, anticipating problems before they occur, remediating them in an autonomous way. I appreciate your time. >> Thanks Dave. >> Thanks for coming back on. Alright, keep it right there everybody. We'll be back with our next guess right after this short break. You're watching theCube from Chicago, Veritas Vision Solution Day. We'll be right back. (electronic music)

>> Narrator: Live from Las Vegas It's theCUBE covering IBM Think 2018. Brought to you by IBM. >> We're back at Mandalay Bay in Las Vegas. This is IBM Think 2018. This is day three of theCUBE's wall-to-wall coverage. My name is Dave Vellante, I'm here with Peter Burris. You're watching theCUBE, the leader in live tech coverage. Daniel Hernandez is here. He's the Vice President of IBM Analytics, a CUBE alum. It's great to see you again, Daniel >> Thanks >> Dave: Thanks for coming back on >> Happy to be here. >> Big tech show, consolidating a bunch of shows, you guys, you kind of used to have your own sort of analytics show but now you've got all the clients here. How do you like it? Compare and contrast. >> IBM Analytics loves to share so having all our clients in one place, I actually like it. We're going to work out some of the kinks a little bit but I think one show where you can have a conversation around Artificial Intelligence, data, analytics, power systems, is beneficial to all of us, actually. >> Well in many respects, the whole industry is munging together. Folks focus more on workloads as opposed to technology or even roles. So having an event like this where folks can talk about what they're trying to do, the workloads they're trying to create, the role that analytics, AI, et cetera is going to play in informing those workloads. Not a bad place to get that crosspollination. What do you think? >> Daniel: Totally. You talk to a client, there are so many problems. Problems are a combination of stuff that we have to offer and analytics stuff that our friends in Hybrid Integration have to offer. So for me, logistically, I could say oh, Mike Gilfix, business process automation. Go talk to him. And he's here. That's happened probably at least a dozen times so far in not even two days. >> Alright so I got to ask, your tagline. Making data ready for AI. What does that mean? >> We get excited about amazing tech. Artificial intelligence is amazing technology. I remember when Watson beat Jeopardy. Just being inspired by all the things that I thought it could do to solve problems that matter to me. And if you look over the last many years, virtual assistants, image recognition systems that solve pretty big problems like catching bad guys are inspirational pieces of work that were inspired a lot by what we did then. And in business, it's triggered a wave of artificial intelligence can help me solve business critical issues. And I will tell you that many clients simply aren't ready to get started. And because they're not ready, they're going to fail. And so our attitude about things are, through IBM Analytics, we're going to deliver the critical capabilities you need to be ready for AI. And if you don't have that, 100% of your projects will fail. >> But how do you get the business ready to think about data differently? You can do a lot to say, the technology you need to do this looks differently but you also need to get the organization to acculturate, appreciate that their business is going to run differently as a consequence of data and what you do with it. How do you get the business to start making adjustments? >> I think you just said the magic word, the business. Which is to say, at least all the conversations I have with my customers, they can't even tell that I'm from the analytics because I'm asking them about the problems. What do you try to do? How would you measure success? What are the critical issues that you're trying to solve? Are you trying to make money, save money, those kinds of things. And by focusing on it, we can advise them then based on that how we can help. So the data culture that you're describing I think it's a fact, like you become data aware and understand the power of it by doing. You do by starting with the problems, developing successes and then iterating. >> An approach to solving problems. >> Yeah >> So that's kind of a step zero to getting data ready for AI >> Right. But in no conversation that leads to success does it ever start with we're going to do AI or machine learning, what problem are we going to solve? It's always the other way around. And when we do that, our technology then is easily explainable. It's like okay, you want to build a system for better customer interactions in your call center. Well, what does that mean? You need data about how they have interacted with you, products they have interacted with, you might want predictions that anticipate what their needs are before they tell you. And so we can systematically address them through the capabilities we've got. >> Dave, if I could amplify one thing. It makes the technology easier when you put it in these constants I think that's a really crucial important point. >> It's super simple. All of us have had to have it, if we're in technology. Going the other way around, my stuff is cool. Here's why it's cool. What problems can you solve? Not helpful for most of our clients. >> I wonder if you could comment on this Daniel. I feel like we're, the last ten years about cloud mobile, social, big data. We seem to be entering an era now of sense, speak, act, optimize, see, learn. This sort of pervasive AI, if you will. How- is that a reasonable notion, that we're entering that era, and what do you see clients doing to take advantage of that? What's their mindset like when you talk to them? >> I think the evidence is there. You just got to look around the show and see what's possible, technically. The Watson team has been doing quite a bit of stuff around speech, around image. It's fascinating tech, stuff that feels magical to me. And I know how this stuff works and it still feels kind of fascinating. Now the question is how do you apply that to solve problems. I think it's only a matter of time where most companies are implementing artificial intelligence systems in business critical and core parts of their processes and they're going to get there by starting, by doing what they're already doing now with us, and that is what problem am I solving? What data do I need to get that done? How do I control and organize that information so I can exploit it? How can I exploit machine learning and deep learning and all these other technologies to then solve that problem. How do I measure success? How do I track that? And just systematically running these experiments. I think that crescendos to a critical mass. >> Let me ask you a question. Because you're a technologist and you said it's amazing, it's like magic even to you. Imagine non technologists, what `it's like to me. There's a black box component of AI, and maybe that's okay. I'm just wondering if that's, is that a headwind, are clients comfortable with that? If you have to describe how you really know it's a cat. I mean, I know a cat when I see it. And the machine can tell me it's a cat, or not a hot dog Silicon Valley reference. (Peter laughs) But to tell me actually how it works, to figure that out there's a black box component. Does that scare people? Or are they okay with that? >> You've probably given me too much credit. So I really can't explain how all that just works but what I can tell you is how certainly, I mean, lets take regulated industries like banks and insurance companies that are building machine learning models throughout their enterprise. They've got to explain to a regulator that they are offering considerations around anti discriminatory, basically they're not buying systems that cause them to do things that are against the law, effectively. So what are they doing? Well, they're using tools like ones from IBM to build these models to track the process of creating these models which includes what data they used, how that training was done, prove that the inputs and outputs are not anti-discriminatory and actually go through their own internal general counsel and regulators to get it done. So whether you can explain the model in this particular case doesn't matter. What they're trying to prove is that the effect is not violating the law, which the tool sets and the process around those tool sets allow you to get that done today. >> Well, let me build on that because one of the ways that it does work is that, as Ginni said yesterday, Ginni Rometty said yesterday that it's always going to be a machine human component to it. And so the way it typically works is a machine says I think this is a cat and a human validates it or not. The machine still doesn't really know if it's a cat but coming back to this point, one of the key things that we see anyway, and one of the advantages that IBM likely has, is today the folks running Operational Systems, the core of the business, trust their data sources. >> Do they? >> They trust their DB2 database, they trust their Oracle database, they trust the data that's in the applications. >> Dave: So it's the data that's in their Data Lake? >> I'm not saying they do but that's the key question. At what point in time, and I think the real important part of your question is, at what point in time do the hardcore people allow AI to provide a critical input that's going to significantly or potentially dramatically change the behavior of the core operational systems. That seems a really crucial point. What kind of feedback do you get from customers as you talk about turning AI from something that has an insight every now and then to becoming effectively, an element or essential to the operation of the business? >> One of the critical issues in getting especially machine learning models, integrated in business critical processes and workflows is getting those models running where that work is done. So if you look, I mean, when I was here last time I was talking about the, we were focused on portfolio simplification and bringing machine learning where the data was. We brought machine learning to private cloud, we brought it onto Gadook, we brought it on mainframe. I think it is a critical necessary ingredient that you need to deliver that outcome. Like, bring that technology where the data is. Otherwise it just won't work. Why? As soon as you move, you've got latency. As soon as you move, you've got data quality issues you're going to have contending. That's going to exacerbate whatever mistrust you might have. >> Or the stuff's not cheap to move. It's not cheap to ingest. >> Yeah. By the way, the Machine Learning on Z offering that we launched last year in March, April was one of our highest, most successful offerings last year. >> Let's talk about some of the offerings. I mean, at the end of the day you're in the business of selling stuff. You've talked about Machine Learning on Z X, whatever platform. Cloud Private, I know you've got perspectives on that. Db2 Event Store is something that you're obviously familiar with. SPSS is part of the portfolio. >> 50 year, the anniversary. >> Give us the update on some of these products. >> Making data ready for AI requires a design principled on simplicity. We launched in January three core offerings that help clients benefit from the capability that we deliver to capture data, to organize and control that data and analyze that data. So we delivered a Hybrid Data Management offering which gives you everything you need to collect data, it's anchored by Db2. We have the Unified Governance and Integration portfolio that gives you everything you need to organize and control that data as anchored by our information server product set. And we've got our Data Science and Businesses Analytics portfolio, which is anchored by our data science experience, SPSS and Cognos Analytics portfolio. So clients that want to mix and match those capabilities in support of artificial intelligence systems, or otherwise, can benefit from that easily. We just announced here a radical- an even radical step forward in simplification, which we thought that there already was. So if you want to move to the public cloud but can't, don't want to move to the public cloud for whatever reason and we think, by the way, public cloud for workload to like, you should try to run as much as you can there because the benefits of it. But if for whatever reason you can't, we need to deliver those benefits behind the firewall where those workloads are. So last year the Hybrid Integration team led by Denis Kennelly, introduced an IBM cloud private offering. It's basically application paths behind the firewall. It's like run on a Kubernetes environment. Your applications do buildouts, do migrations of existing workloads to it. What we did with IBM Cloud Private for data is have the data companion for that. IBM Cloud Private was a runaway success for us. You could imagine the data companion to that just being like, what application doesn't need data? It's peanut butter and jelly for us. >> Last question, oh you had another point? >> It's alright. I wanted to talk about Db2 and SPCC. >> Oh yes, let's go there, yeah. >> Db2 Event Store, I forget if anybody- It has 100x performance improvement on Ingest relative to the current state of the order. You say, why does that matter? If you do an analysis or analytics, machine learning, artificial intelligence, you're only as good as whatever data you have captured of your, whatever your reality is. Currently our databases don't allow you to capture everything you would want. So Db2 Event Store with that Ingest lets you capture more than you could ever imagine you would want. 250 billion events per year is basically what it's rated at. So we think that's a massive improvement in database technology and it happens to be based in open source, so the programming model is something that developers feel is familiar. SPSS is celebrating it's 50th year anniversary. It's the number one digital offering inside of IBM. It had 510,000 users trying it out last year. We just renovated the user experience and made it even more simple on stats. We're doing the same thing on Modeler and we're bringing SPSS and our data science experience together so that there's one tool chain for data science end to end in the Private Cloud. It's pretty phenomenal stuff. >> Okay great, appreciate you running down the portfolio for us. Last question. It's kind of a, get out of your telescope. When you talk to clients, when you think about technology from a technologist's perspective, how far can we take machine intelligence? Think 20 plus years, how far can we take it and how far should we take it? >> Can they ever really know what a cat is? (chuckles) >> I don't know what the answer to that question is, to be honest. >> Are people asking you that question, in the client base? >> No. >> Are they still figuring out, how do I apply it today? >> Surely they're not asking me, probably because I'm not the smartest guy in the room. They're probably asking some of the smarter guys-- >> Dave: Well, Elon Musk is talking about it. Stephen Hawking was talking about it. >> I think it's so hard to anticipate. I think where we are today is magical and I couldn't have anticipated it seven years ago, to be honest, so I can't imagine. >> It's really hard to predict, isn't it? >> Yeah. I've been wrong on three to four year horizons. I can't do 20 realistically. So I'm sorry to disappoint you. >> No, that's okay. Because it leads to my real last question which is what kinds of things can machines do that humans can't and you don't even have to answer this, but I just want to put it out there to the audience to think about how are they going to complement each other. How are they going to compete with each other? These are some of the big questions that I think society is asking. And IBM has some answers, but we're going to apply it here, here and here, you guys are clear about augmented intelligence, not replacing. But there are big questions that I think we want to get out there and have people ponder. I don't know if you have a comment. >> I do. I think there are non obvious things to human beings, relationships between data that's expressing some part of your reality that a machine through machine learning can see that we can't. Now, what does it mean? Do you take action on it? Is it simply an observation? Is it something that a human being can do? So I think that combination is something that companies can take advantage of today. Those non obvious relationships inside of your data, non obvious insights into your data is what machines can get done now. It's how machine learning is being used today. Is it going to be able to reason on what to do about it? Not yet, so you still need human beings in the middle too, especially when you deal with consequential decisions. >> Yeah but nonetheless, I think the impact on industry is going to be significant. Other questions we ask are retail stores going to be the exception versus the normal. Banks lose control of the payment systems. Will cyber be the future of warfare? Et cetera et cetera. These are really interesting questions that we try and cover on theCUBE and we appreciate you helping us explore those. Daniel, it's always great to see you. >> Thank you, Dave. Thank you, Peter. >> Alright keep it right there buddy, we'll be back with our next guest right after this short break. (electronic 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).