>> Narrator: Live from San Jose, it's theCUBE. Presenting big data silicon valley, brought to you be Siliconangle Media and its ecosystem partner. >> Welcome back to theCUBE we are live in San Jose, at Forger Eatery, super cool place. Our first day of our two days of coverage at our event called Big Data SV. Down the street is the Strata Data Conference, and we've got some great guests today that are going to share a lot insight and different perspectives on Big Data. This is our 10th big data event on theCUBE, our fifth in San Jose. We invite you to come on down to Forger Eatery and we also invite you to come down this evening. We've got a party going on and we've got a really cool breakfast presentation on the analysis site in the morning. Our first guest is, needs no introduction to theCUBE, he's a Cube Alumni, Murthy Mathiprakasam, did I get that right? >> Murthy: Absolutely. >> Murthy, awesome, as we're going to call him. The director of product marketing for Informatica, welcome back to theCUBE, it's great to have you back. >> Thanks for having me back, and congratulations on the 10 year anniversary. >> Yeah! So, interesting, exciting news from Informatica in the last two days, tell us about a couple of those big announcements that you guys just released. >> Absolutely, yes. So this has been very exciting year for us lots of, you know product, innovations and announcements, so just this week alone, actually there's one announcement that's probably going out right now as we speak, around API management, so one of the things, probably taking about before we started interviews you know around the trend toward cloud, lots of people doing a lot more data integration and application integration in the cloud space. But they face all the challenges that we've always seen in the data management space. Around developer productivity, and hand coding, just a lot of complexity that organizations have around maintenance. So one of the things at Informatica always brought to every domain that we cover is this ability to kind of abstract the underlying complexity, use a graphical user interface, make things at the logical level instead of the physical level. So we're bringing that entire kind of paradigm to the API management space. That's going to be very exciting, very game changing on the kind of app-to-app integration side of things. Back on the data world of course, which is what we're, you know, mainly talking about here today. We're doing a lot there as well. So we announced kind of a next generation of our data management platforms for the big data world, part of that is also a lot of cloud capabilities. 'Cause again, one of the bigger trends. >> Peter: Have you made a big bet there? >> Absolutely, and I mean this is the investment, return on investments over like 10 years, right? We were started in a kind of cloud game about 10 years ago with our platform as a service offering. So that has been continuously innovated on and we've been engineering, re-imagining that, to now include more of the big data stuff in it too, because more and more people are building data lakes in the cloud. So it's actually quite surprising, you know the rate at which the data lake kind of projects are now either migrating or just starting in the cloud environments. So given that being the trend, we were kind of innovating against that as well. So now our platform is service offerings supports the ability to connect to data sources in the cloud natively. You can do processing and gestion in the cloud. So there's a lot of really cool capabilities, again it's kind of bringing the Informatica ease of use, and kind of acceleration that comes to platform approach to the cloud environment. And there's a whole bunch of other announcements too, I mean I could spend 20 minutes, just on different innovations, but you know bringing artificial intelligence into the platform so we can talk more about that. >> Well I want to connect what you just announced with the whole notion of the data lake, 'cause it's really Informatica strength has always been in between. And it turns out that where a lot the enterprise problems have been. So the data lake has been there, but it's been big, it's been large, it was big data and the whole notion is make this as big as you can and we'll figure out what to do with it later. >> Murthy: Right. >> And now you're doing the API which is just a indication that we're seeing further segmentation and a specificity, a targeting of how we're going to use data, the value that we create out of data and apply it to business problems. But really Informatica strength is been in between. >> Murthy: Absolutely. >> It's been in, knowing where you data is, it's been in helping to build those pipelines and managing those pipelines. How have the investments that you've made over the last few years, made it possible for you to actually deliver an API orientation, that will actually work for Enterprises? >> Yeah, absolutely, and I would actually phrase it as sort of platform orientation, but you're exactly right. So what's happening is, I view this as sort of maturation of a lot of these new technologies. You know Hadoop was a very very, as you were saying kind of experimental technology four or five years ago. And we had customers too who were kind of in that experimental phase. But what's happening now is, big data isn't just a conversation with data engineers and developers, we're talking to CDO's, and Chief Data Officers, and VP's of data infrastructures about using Hadoop for Enterprise scale projects, now the minute you start having a conversation with a Chief Data Officer, you're not just talking about simple tools for ingestion and stuff like that. You're talking about security, you're talking about compliance, you're talking about GDPR if you're in Europe. So there's a whole host of sort of data management challenges, that are now relevant for the big data world, just because the big data world has become main stream. And so this is exactly to your point, where the investments that I think Informatica has been making and bringing our kind of comprehensive platform oriented approach to this space are paying off. Because for Chief Data Officer, they can't really do big data without those features. They can't not deal with security and compliance, they can't not deal with not knowing what the data is. 'Cause they're accountable for knowing what the data is, right? And so, there's a number of things that by virtue of the maturation of the industry, I think that trends are pointing toward, the enterprises kind of going more toward that platform approach. >> On that platform approach Informatica's really one of the only vendors that's talking about that, and delivering it. So that clearly is an area of differentiation. Why do you think that's nascent. This platform approach verses a kind of fit-for-purpose approach. >> Yeah, absolutely. And we should be careful with even the phrase fit-for-purpose too, 'cause I think that word gets thrown around a lot as it's one of those buzz words in the industry. Because it's sort of the positive way of saying incomplete, you know? And so, I think there are vendors who have tried to kind of address, know you one aspect of sort of one feature of the entire problem, that a Chief Data Officer would care about. They might call it fit-for-purpose, but you have to actually solve a problem at the end of the day. The Chief Data Officer's are trying to build enterprise data pipelines. You know you've got raw information from all sorts of data sources, on premise, in the cloud. You need to push that through a process, like at manufacturing process of being able to ingest it, repair it, cleanse it, govern it, secure it, master it, all the stuff has to happen in order to serve all the various communities that a Chief Data Officer has to serve. And so you're either doing all that or you're not. You know, that's the problem, that way we see the problem. And so the platform approach is a way of addressing the comprehensive set of problems that a Chief Data Officer, or these kind of of Data Executives care about, but also do it in a way, that fosters productivity and re-usability. Because the more you sort of build things in a kind of infrastructure level way, as soon as the infrastructure changes you're hosed, right? So you're seeing a lot of this in the industry now too, where somebody built something in Mapreduce three years ago, as soon as Spark came out, they're throwing all that stuff away. And it's not just, you know, major changes like that, even versions of Spark, or versions of Hadoop, can sometimes trigger a need to recode and throw away stuff. And organization can't afford this. When you're talking about 40 to 50% growth in the data overall. The last thing you want to do is make an investment that you're going to end up throwing away. And so, the platform approach to go back to your question, is the sort of most efficient pathway from an investment stand point, that an enterprise can take, to build something now that they can actually reuse and maintain and kind of scale in a very very pragmatic way. >> Well, let me push you on that a little bit. >> Murthy: Yeah. >> 'Cause what we would say is that, the fit-to-purpose is okay so long as you're true about the purpose, and you understand what it means to fit. What a lot of the open source, a lot of companies have done, is they've got a fit-to-purpose but then they do make promises that they say, oh this is fit-to-purpose, but it's really a platform. And as a consequence you get a whole bunch of, you know, duck-like solutions, (laughing) That are, you know, are they swimming, or are they flying, kind of problems. So, I think that what we see clients asking for, and this is one of my questions, what we see clients asking for is, I want to invest in technologies that allow me to sustain my investments, including perhaps some of my mistakes, if they are generating business value. >> Murthy: Right. >> So it's not a rip and replace, that's not what you're suggesting, what you're suggesting I think is, you know, use what you got, if it's creating value continue to use it, and then over time, invest the platform approach that's able to generate additional returns on top of it. Have I got that right? >> Absolutely. So it goes back to flexibility, that's the key word, I think that's kind of on the minds of a lot of Chief Data Officers. I don't want to build something today, that I know I'm going to throw away a year from now. >> Peter: I want to create options for the future. >> Create options. >> When I build them today. >> Exactly. So even the cloud, you're bringing up earlier on, right? Not everybody knows exactly what their cloud strategy is. And it's changing extremely rapidly, right? We had almost, we were seeing very few big data customers in the cloud maybe even a year or two ago? Now we're close to almost 50% of our big data business is people deploying off premise, I mean that's amazing, you know in a period of just a year or two. So Chief Data Officers are having to operate in these extreme kind of high velocity environments. The last thing you want to do is make a bet today, with the knowledge that you're going to end up having to throw away that bet in six months or a year. So the platform approach is sort of like your insurance policy because it enables you to design for today's requirements, but then very very quickly migrate or modify for new requirements that maybe be six months, a year or two down the line. >> On that front, I'd love for you to give us an example of a customer that has maybe in the last year, since you've seen so much velocity, come to you. But also had other technologies and their environment that from a cost perspective, I mean but at Peter's point there's still generating value, business value. How do you help customers that have multiple different products maybe exploring different multi-calibers, how to they come and start working with Informatica and not have to rip out other stuff, but be able to move forward and achieve ROI? >> So, it's really interesting kind of how people think about the whole rip and replace concept. So we actually had a customer dinner last night and I'm sitting next to a guy, and I was kind of asking very similar question. Tell me about your technology landscape, you know where are things going, where have things gone in the past, and he basically said there's a whole portfolio of technologies that they plan to obsolete. 'Cause they just know that, like they're probably, they don't even bother thinking about sustainability, to your point. They just want to use something just to kind of try it out. It's basically like a series of like three month trails of different technologies. And that's probably why we such proliferation of different technologies, 'cause people are just kind of trying stuff out, but it's like, I know I'm going to throw this stuff out. >> Yeah but that's, I mean, let me make sure I got that. 'Cause I want to reconcile a point. That's if they're in pilot and the pilot doesn't work. But the minute it goes into production and values being created they want to be able to sustain that stream of value. >> This is production environment. I'm glad you asked that question. So this is a customer that, and I'll tell you where I'm going to the point. So they've been using Informatica for over four years, for big data which is essentially almost the entire time big data's been around. So the reason this customers making the point is, Informatica's the only technology that is actually sustained precisely for the point that you're bringing up, because their requirements have changed wildly during this time. Even the internal politics of who needs access to data, all of that has changed radically over these four years. But the platform has enabled them to actually make those changes, and it's you know, been able to give them that flexibly. Everything else as far as, you know, developer tools, you know, visualization tools, like every year there's some kind of new thing that sort of comes out. And I don't want to be terribly harsh, there's probably one or two kind of vendors that have also persisted in those other areas. But, the point that they were trying to make to your original point is, is the point about sustainability. Like, at some point to avoid complete and utter chaos, you got to have like some foundation in the data environment. Something actually has to be something you can invest in today, knowing that as these changes internally externally are happening, you can kind of count on it and you can go to cloud you can be on Premise, you can have structured data, unstructured data, you know, for any type of data, any type of user, any type of deployment environment. I need something that I can count on, that's actually existing for four or more years. And that's where Informatica fits in. And meanwhile there's going to be a lot of other tools that, like this guy was saying, they're going to try out for three month or six months and that's great, but they're almost using it with the idea that they're going to throw it away. >> Couple questions here; What are some of the business values that you were, stating like this gentlemen, that you ere talking to last night. What's the industry that's he in and also, are there any like stats or ranges you can give us. Like, reduction in TCO, or new business models opening up. What's the business impact that Informatica is helping these customers achieve. >> Yeah, absolutely, I'll use this example, he's, I can't mention the name of the company but it's an insurance company. >> Lisa: Lot's of data. >> Lots of data, right. Not only do they have a lot of data, but there's a lot of sensitivity around the data. Because basically the only way they grow is by identifying patterns in consumers and they want to look at it if somebody's using car insurance in, maybe it for so long they're ready to get married, they need home insurance, they have these like really really sophisticated models around human behavior. So they know when to go and position new forms of insurance. There's also obviously security government types of issues that are at play as well. So the sensitivity around data is very very important. So for them, the business value is increased revenue, and you know ability to meet kind of regulatory pressure. I think that's generally, I mean every industry has some variant of that. >> Right. >> Cost production, increase revenue, you know meeting regulatory pressures. And so Informatica facilitates that, because instead of having to hire armies of people, and having to change them out maybe every three months or six months 'cause the underlying infrastructures changing, there's this one team, the Informatica team that's actually existed for this entire journey. They just keep changing, used cases, and projects, and new data sets, new deployment models, but the platform is sort of fixed and it's something that they can count on it's robust, it enables that kind of. >> Peter: It's an asset. >> It's an asset that delivers that sustainable value that you were taking about. >> Last question, we've got about a minute left, in terms of delivering value, Informatica not the only game in town, your competitors are kind of going with this MNA partnership approach. What makes Informatica stand out, why should companies consider Informatica? >> So they say like, what there's a quote about it. Imitation is the most sincere from of flattery. Yeah! (laughing) I guess we should feel as a little bit flattered, you know, by what we're seeing in the industry, but why from a customers stand point should they, you know continue to rely on Informatica. I mean we keep pushing the envelope on innovations, right? So, one the other areas that we innovated on is machine learning within the platform, because ultimately if one of the goals of the platform is to eliminate manual labor, a great way to do that is to just not have people doing it in the first place. Have machines doing it. So we can automatically understand the structure of data without any human intervention, right? We can understand if there's a file and it's got costumer names and you know, cost and skews, it must be an order. You don't actually have to say that it's an order. We can infer all this, because of the machine learning them we have. We can give recommendations to people as they're using our platform, if you're using a data set and you work with another person, we can go to you and say hey, maybe this is a data set that you would be interesting in. So those types of recommendations, predictions, discovery, totally changes the economic game for an organization. 'Cause the last thing you want is to have 40 to 50% growth in data translate into 40 to 50% of labor. Like you just can't afford it. It's not sustainable, again, to go back to your original point. The only sustainable approach to managing data for the future, is to have a machine learning based approach and so that's why, to your question, I think just gluing a bunch of stuff together still doesn't actually get to nut of sustainability. You actually have to have, the glue has to have something in it, you know? And in our case it's the machine learning approach that ties everything together that brings a data organization together, so they can actually deliver the maximum business value. >> Literally creates a network of data that delivers business value. >> You got it. >> Well Murthy, Murthy Awesome, thank you so much for coming back to theCUBE. >> Thank you! >> And sharing what's going on the Informatica and what's differentiating you guys. We wish you a great rest of the Strata Conference. >> Awesome, you as well. Thank you. >> Absolutely, we want to thank you for watching theCUBE. I'm Lisa Martin with Peter Burris, we are live in San Jose at the Forger Eatery, come down here and join us, we've got a really cool space, we've got a part-tay tonight, so come join us. And we've got a really interesting breakfast presentation tomorrow morning, stick around and we'll be right back, with our next guest for this short break. (fun upbeat music)

>> Narrator: Live from midtown Manhattan, it's theCUBE. Covering BigData, New York City, 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Welcome back everyone, we're here live in New York City for theCUBE's coverage of BigData NYC, our event we've been running for five years, been covering BigData space for eight years, since 2010 when it was Hadoop World, Strata Conference, Strata Hadoop, Strata Data, soon to be called Strata AI, just a few. We've been theCUBE for all eight years. Here, live in New York, I'm John Furrier. Our next guest is Murthy Mathiprakasam, who is the Director of Product Marketing at Informatica. Cube alumni has been on many times, we cover Informatica World, every year. Great to see you, thanks for coming by and coming in. >> Great to see you. >> You guys do data, so there's not a lot of recycling going on in the data because we've been talking about it all week, total transformation, but the undercurrent has been a lot of AI, AI this, and you guys have the CLAIRE product, doing a lot of things there. But outside of the AI, the undertone is cloud, cloud, cloud. Governance, governance, governance. There's two kind of the drivers I'm seeing as the force of this week is, a lot of people trying to get their act together on those two fronts and you can kind of see the scabs on the industry, people, some people haven't been paying attention. And they're weak in the area. Cloud is absolutely going to be driving the BigData world, 'cause data is horizontal. Cloud's the power source that you guys have been on that. What's your thoughts, what other drivers encourage you? (mumbles) what I'm saying and what else did I miss? Security is obviously in there, but-- >> Absolutely, no, so I think you're exactly right on. So obviously governments security is a big deal. Largely being driven by the GDPR regulation, it's happening in Europe. But, I mean every company today is global, so. Everybody's essentially affected by it. So, I think data until now has always been a kind of opportunistic thing, that there's a couple guys and their organizations were looking at it as oh, let's do some experimentation. Let's do something interesting here. Now, it's becoming government managed so I think there's a lot of organizations who are, like, to your point, getting their act together, and that's driving a lot of demand for data management projects. So now, people say, well, if I got to get my act together, I don't have to hire armies of people to do it, let me look for automated machine learning based ways of doing it. So that they can actually deliver on their audit reports that they need to deliver on, and ensure the compliance that they need to ensure, but do it in a very scalable way. >> I've been kind of joking all week, and I kind of had this meme in my head, so I've been pounding on it all week, calling it the tool shed problem. The tool shed problem is, everyone's got these tools. They throw them into the tool shed. They bought a hammer and the company that sells them the hammer is trying to turn it to a lawnmower, right? You can't mow your lawn with a hammer, it's not going to work, and so this, these tools are great but it defines work. What you do, but, the platforming issue is a huge one. And you start to see people who took that view. You guys were one of them because in a platform centric view with tools that are enabled, to be highly productive. You don't have to worry about new things like a government's policy, the GDPR that might pop up, or the next Equifax that's around the corner. There's probably two or three of them going on right now. So, that's an impact, the data, who uses it, how it's used, and who's at fault or whatever. So, how does a company deal with that? And machine learning has proven to be a great horse that a lot of people are riding right now. You guys are doing it, how does a customer deal with that tsunami of potential threats? Architecture challenges, what is your solution, how do you talk about that? >> Well, I think machine learning, you know, up until now has been seen as the kind of, nice to have, and I think that very quickly, it's going to become a must have. Because, exactly like you're saying, it really is a tsunami. I mean, you could see people who are nervous about the fact that I mean, there's different estimates. It's like 40% growth in data assets from most organizations every year. So, you can try to get around this somehow with one of these (mumbles) tools or something. But at some point, something is going to break, either you just don't, run out of manpower, you can't train the manpower, people start leaving. whatever the operational challenges are, it just isn't going to scale. Machine learning is the only approach. It is absolutely the only approach that actually ensures that you can maintain data for these kind of defensive reasons like you're saying. The structure and compliance, but also the kind of offensive opportunistic reasons, and do it scalably, 'cause there's just no other way mathematically speaking, that when the data is growing 40% a year, just throwing a bunch of tools at it just doesn't work. >> Yeah, I would just amplify and look right in the camera, say, if you're not on machine learning, you're out of business. That's a straight up obvious trend, 'cause that's a precursor to AI, real AI. Alright, let's get down to data management, so when people throw around data management, it's like, oh yeah we've got some data management. There are challenges with that. You guys have been there from day one. But now if you take it out in the future, how do you guys provide the data management in a totally cloud world where now the customer certainly has public and private, or on premise but theirs might have multi cloud? So now, comes a land grab for the data layer, how do you guys play in that? >> Well, I think it's a great opportunity for these kind of middle work platforms that actually do span multiple clouds, that can span the internal environments. So, I'll give you an example. Yesterday we actually had a customer speaking at Astrada here, and he was talking about from him, the cloud is really just a natural extension of what they're already doing, because they already have a sophisticated data practice. This is a large financial services organization, and he's saying well now the data isn't all inside, some of it's outside, you've got partners, who've got data outside. How do we get to that data? Clearly, the cloud is the path for doing that. So, the fact that the cloud is a national extension a lot of organizations were already doing internally means they don't want to have a completely different approach to the data management. They want to have a consistent, simple, systematic repeatable approach to the data management that spans, as you said, on premise in the cloud. That's why I think the opportunity of a very mature and sophisticated platform because you're not rewriting and re-platforming for every new, is it AWS, is it Azure? Is it something on premise? You just want something that works, that shields you from the underlying infrastructure. >> So I put my skeptic hat on for a second and challenge you on this, because this I think is fundamental. Whether it's real or not, it's perceived, maybe in the back of the mind of the CXO or the CDO, whoever is enabled to make these big calls. If they have the keys to the kingdom in Informatica, I'm going to get locked in. So, this is a deep fear. People wake up with nightmares in the enterprise, they've seen locked in before. How do you explain that to a customer that you're going to be an enabling opportunity for them, not a lock in and foreclosing future benefits. Especially if I have an unknown scenario called multi-cloud. I mean, no one's really doing multi-cloud let's face it. I mean, I have multiple clouds with stuff on it, >> At least not intentionally. Sometimes you got a line of businesses and doing things, but absolutely I get it. >> No one's really moving workloads dynamically between clouds in real time. Maybe a few people doing some hacks, but for the most part of course, not a standard practice. >> Right. >> But they want it to be. >> Absolutely. >> So that's the future. From today, how do you preserve that position with the customer where you say hey we're going to add value, but we're not going to lock you in? >> So the whole premise again of, I mean, this goes back to classic three tier models of how you think about technology stacks, right? There's an infrastructure layer, there's a platform layer, there's an analytics layer and the whole premise of the middle of the layer, the platform layer, is that it enables flexibility in the other two layers. It's precisely when you don't have something that's kind of intermediating the data and the use of the data, that's when you run into challenges with flexibility and with data being locked in the data store. But you're absolutely right. We had dinner with a bunch of our customers last night. They were talking about they'd essentially evaluated every version of sort of BigData platform and data infrastructure platform right? And why? It was because they were a large organization and your different teams start stuff and they had to compute them out and stuff. And I was like that must have been pretty hard for you guys. Now what we were using Informatica, so it didn't really matter where the data was, we were still doing everything as far as the data management goes from a consistent layer and we integrate with all those different platforms. >> John: So you didn't get in the way? >> We didn't get in the way. >> You've actually facilitated. >> We are facilitating increased flexibility. Because without a layer like that, a fabric, or whatever you want to call it a data platform that's facilitating this the complexity's going to get very, very crazy very soon. If it hasn't already. The number of infrastructure platforms that are available like you said, on premise and on the cloud now, keeps growing. The number of analytical tools that are available is also growing. And all this is amazing innovation by the way. This is all great stuff, but to your point about it if your the chief officer of an organization going, I got to get this thing figured out somehow. I need some sanity, that's really the purpose of-- >> They just don't want to know the tool for tool's sake, they need to have it be purposeful. >> And that's why this machine learning aspect is very, very critical because I was thinking about an analogy just like you were and I was thinking, in a way you can think of data managing as sort of cleaning stuff up and there are people that have brooms and mops and all these different tools. Well, we are bringing a Roomba to market, right? Because you don't want to just create tools that transfer the laborer around, which is a little bit of what's going on. You want to actually get the laborer out of the equation, so that the people are focused on the context, business strategy and the data management is sort of cleaning itself. It's doing the work for you. That's really what Informatica's vision is. It's about being a kind of enterprise cloud data management vendor that is leveraging AI under the hood so that you can sort of set it and forget it. A lot of this ingestion and the cleansing, telling annals what data they should be looking for. All the stuff is just happening in an automated way and you're not in this total chaos. >> And that can be some tools will be sitting in the back for a long time. In my tool shed, when I had one back in a big enough property back east. No one has tool sheds by the way. No one does any gardening. The issue is in the day, I need to have a reliable partner. So I want you to take a minute and explain to the folks who aren't yet Informatica customers why they should be and the Informatica customers why they should stay with Informatica. >> Absolutely, so certainly the ones we have, a very loyal customer base. In fact the guy who was presenting with us yesterday, he said he's been with Informatica since 1999, going through various versions of our products and adopting new innovations. So we have a very loyal customer base, so I think that loyalty itself speaks for itself as well. As far as net new customers, I think that in a world of this increasing data complexity, it's exactly what you were saying, you need to find an approach that is going to scale. I keep hearing this word from the chief data officer, I kind of got something some going on today, I don't know how I scale it. How is this going to work in 2018 and 2019, in 2025? And it's just daunting for some of these guys. Especially going back to your point about compliance, right? So it's one thing if you have data sitting around, data so to speak, that you're not using it. But god forbid now, you got legal and regulatory concerns around it as well. So you have to get your arms around the data and that's precisely where Informatica can help because we've actually thought through these problems and we've talked about them. >> Most of them were a problem you solved because at the end of the day, we were talking about problems that have massive importance, big time consequences people can actually quantify. >> That's right. >> So what specific problem highest level do you solve is the most important, has the most consequences? >> Everything from ingestion of raw data sets from wherever like you said, in the cloud on premise, all the way through all the processes you need to make it fully usable. And we view that as one problem. There's other vendors who think that one aspect of that is a problem and it is worth solving. We really think, look at the end of the day, you got raw stuff and you have to turn it into useful stuff. Everything in there has to happen, so we might as well just give you everything and be very, very good at doing all those things. And so that's what we call enterprise cloud data management. It's everything from raw material to finished goods of insights. We want to be able to provide that in a consistent integrated and machine learning integrate it. >> Well you guys have a loyal customer base but to be fair and you kind of have to acknowledge that there is a point in time and not throw Informatica's away the big customers, big engagements. But there was a time in Informatica's history where you went private. There was some new management came in. There was a moment where the boat was taking on water, right? And you could almost look at it and say, hmm, you know, we're in this space. You guys retooled around that. Success to the team. Took it to another dimension. So that's the key thing. You know a lot of the companies become big and it's hard to get rid of. So the question is that's a statement. I think you guys done a great job. Yet, the boat might have taken on water, that's my opinion, but you can probably debate that. But I think as you get mature and you're in public, you just went private. But here's the thing, you guys have had a good product chop in Informatica, so I got to ask you the question. What cool things are you doing? Because remember, cool shiny new toys help put a little flash and glam on the nuts and bolts that scales. What are you guys doing? I know you just announced claire, some AI stuff. What's the hot stuff you're doing that's adding value? >> Yeah, absolutely, first of all, this kind of addresses your water comment as well. So we are probably one of the few vendors that spends almost about $200 million in R and D. And that hasn't changed through the acquisition. If anything, I think it actually increased a little bit because now our investors are even more committed to innovation. >> Well you're more nimble in private. A lot more nimble. >> Absolutely, a lot more ideas that are coming to the forefront. So there's never been any water just to be clear. But to answer your follow on question about some examples of this innovation. So I think Ahmed yesterday talked about some of our recent release as well but we really just keep pushing on this idea of, I know I keep saying this but it's this whole machine learning approach here of how can we learn more about the data? So one of the features, I'll give you an example, is if we can actually go look at a file and if we spot like a name and an address and some order information, that probably is a customer, right? And we know that right, because we've seen past data sets. So, there's examples of this pattern matching where you don't even have to have data that's filled out. And this is increasingly the way the data looks we are not dealing with relational tables anymore it's JSON files, it's web blogs, XML files, all of that data that you had to have that data scientists go through and parse and sift through, we just automatically recognize it now. If we can look for the data and understand it, we can match it. >> Put that in context in the order of benefits that, from the old way versus the current way, what's the pain levels? One versus the other, can you put context around that? In terms of, it's pretty significant. >> It's huge because again, back to this sort of volume and variety of data that people are trying to get into systems and do it very rapidly. I'll give you a really tangible customer case. So, this is a customer that presented at Informatica World a couple months ago. It's Jewelry TV, I can actually tell you the name. So there are one of these online kind of shopping sites and they've got a TV program that goes with the online site. So what they do is obviously when you promote something on TV, your orders go up online, right? They wanted to flip it around and they said, look, let's look at the web logs of the traffic that's on the website and then go promote that on the TV program. Because then you get a closed loop and start to have this explosion of sales. So they used Informatica, didn't have to do any of this hand coding. They just build this very quickly and with the graphical user interface that we provide, it leverages sparks streaming under the hood. So they are using all these technologies under the hood, they just didn't have to do any of the manual coding. Got this thing out in a couple days and it works. And they have been able to measure it and they're actually driving increased sales by taking the data and just getting it out to the people that need to see the data very, very quickly. So that's an example of a use case where this isn't just to your point about is this a small, incremental type of thing. No, there is a lot of money behind data if you can actually put it to good use. >> The consequences are grave and I think you've seen more and more, I mean the hacks just amplify it over and over again. It's not a cost center when you think about it. It has to be somehow configured differently as a profit center, even though it might not drive top line revenue directly like an app or anything else. It's not a cost center. If anything it will be treated as a profit center because you get hacked or someone's data is misused, you can be out of business. There is no profit. Look at the results of these hacks. >> The defensive argument is going to become very, very strong as these regulations come out. But, let's be clear, we work with a lot of the most advanced customers. There are people making money off of this. It can be a top line driver-- >> No it should be, it should be. That's exactly the mindset. So the final question for you before we break. I know we're out of time here. There are some chief data officers that are enabled, some aren't and that's just my observation. I don't want to pidgeonhole anyone, but some are enable to really drive change, some are just figureheads that are just managing the compliance risk and work for the CFO and say no to everything. I'm over-generalizing. But that's essentially how I see it. What's the problem with that? Because the cost center issue has, we've seen this moving before in the security business. Security should not be part of IT. That's it's own deal. >> Exactly. >> So we're kind of, this is kind of smoke, but we're coming out of the jungle here. Your thoughts on that. >> Yeah, you're absolutely right. We see a variety of models. We can see the evolution of those models and it's also very contextual to different industries. There are industries that are inherently more regulated, so that's why you're seeing the data people maybe more in those cost center areas that are focused on regulations and things like that. There's other industries that are a lot more consumer oriented. So for them, it makes more sense to have the data people be in a department that seems more revenue basing. So it's not entirely random. There are some reasons, that's not to say that's not the right model moving forward, but someday, you never know. There is a reason why this role became a CXO in the first place. Maybe it is somebody who reports to the CEO and they really view the data department as a strategic function. And it might take a while to get there, but I don't think it's going to take a long time. Again, we're talking about 40% growth in the data and these guys are realizing that now and I think we're going to see very quickly people moving out of the whole tool shed model, and moving to very systematic, repeatable practices. Sophisticated middleware platforms and-- >> As we say don't be a tool, be a platform. Murphy thanks so much for coming on to theCUBE, we really appreciate it. What's going on in Informatica real quick. Things good? >> Things are great. >> Good, awesome. Live from New York, this is theCUBE here at BigData NYC more live coverage continuing day three after this short break. (digital music)

(electronic music) >> Announcer: Live from San Jose, California, it's The Cube, covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. We are live in Silicon Valley for Big Data Silicon Valley. Our companion showed at Big Data NYC in conjunction with Strata Hadoop, Big Data Week. Our next guest is Murthy Mathiprakasam, with the director of product marketing Informatica. Did I get it right? >> Murthy: Absolutely (laughing)! >> Okay (laughing), welcome back. Good to see you again. >> Good to see you! >> Informatica, you guys had a AMIT on earlier yesterday, kicking off our event. It is a data lake world out there, and the show theme has been, obviously beside a ton of machine learning-- >> Murthy: Yep. >> Which has been fantastic. We love that because that's a real trend. And IOT has been a subtext to the conversation and almost a forcing function. Every year the big data world is getting more and more pokes and levers off of Hadoop to a variety of different data sources, so a lot of people are taking a step back, and a protracted view of their landscape inside their own companies and, saying, Okay, where are we? So kind of a checkpoint in the industry. You guys do a lot of work with customers, your history with Informatica, and certainly over the past few years, the change in focus, certainly on the product side, has been kind of interesting. You guys have what looks like to be a solid approach, a abstraction layer for data and metadata, to be the keys to the kingdom, but yet not locking it down, making it freely available, yet provide the governance and all that stuff. >> Murthy: Exactly. >> And my interview with AMIT laid it all out there. But the question is what are the customers doing? I'd like to dig in, if you could share just some of the best practices. What are you seeing? What are the trends? Are they taking a step back? How is IOT affecting it? What's generally happening? >> Yeah, I know, great question. So it has been really, really exciting. It's been kind of a whirlwind over the last couple years, so many new technologies, and we do get the benefit of working with a lot of very, very, innovative organizations. IOT is really interesting because up until now, IOT's always been sort of theoretical, you're like, what's the thing? >> John: Yeah. (laughing) What's this Internet of things? >> But-- >> And IT was always poo-pooing someone else's department (laughing). >> Yeah, exactly. But we have actually have customers doing this now, so we've been working with automative manufacturers on connected vehicle initiatives, pulling sensor data, been working with oil and gas companies, connected meters and connected energy, manufacturing, logistics companies, looking at putting meters on trucks, so they can actually track where all the trucks are going. Huge cost savings and service delivery kind of benefits from all this stuff, so you're absolutely right IOT, I think is finally becoming real. And we have a streaming solution that kind of works on top of all the open source streaming platforms, so we try to simplify everything, just like we have always done. We did that MapReduce, with Spark, now with all the streaming technologies. You gave a graphical approach where you can go in and say, Well, here's what the kind of processing we want. You'd lay it out visually and it executes in the Hadoop cluster. >> I know you guys have done a great job with the product, it's been very complimentary you guys, and it's almost as if there's been an transformation within Informatica. And I know you went private and everything, but a lot of good product shops there. You guys got a lot good product guys, so I got to ask you the question, I don't see IOT sometimes as an operational technology component, usually running their own stacks, not even plugged into IT, so that's the whole another story. I'll get to that in a second. But the trend here is you have the batch world, companies that have been in this ecosystem here that are on the show floor, at O'Reilly Media, or talking to us on The Cube. Some have been just pure play batch-related! Then the fashionable steaming technologies have come out, but what's happened with Spark, you're starting to see the collision between batch and realtime-- >> Umm-hmm. >> Called streaming or what not. And at the center of that's the deep learning, it's the IOT, and it's the AI, that's going to be at the intersection of these two colliding forces, so you can't have a one-trick pony here and there. You got to kind of have a blended, more of a holistic, horizontal, scalable approach. >> Murthy: Yes. >> So I want to get your reaction to that. And two, what product gaps and organizational gaps and process gaps emerge from this trend? And what do you guys do? So, three-part question. >> Murthy: Yeah (laughing). >> Go ahead. Go ahead. >> I'll try to cover all three. >> So, first, the collision and your reaction to that trend. >> Murthy: Yeah, yeah. >> And then the gaps. >> Absolutely. So basically, you know Informatica, we've supported every type of kind of variation of these type of environments, and so we're not really a believer in it's this or that. It's not on premise or cloud, it's not realtime or batch. We want to make it simple and no matter how you want to process the data, or where you want to process it. So customers who use our platform for their realtime or streaming solutions, are using the same interface, as if they were doing it batched. We just run it differently under the hood. And so, that simplifies and makes a lot of these initiatives more practical because you might start with a certain latency, and you think maybe it's okay to do it at one speed. Maybe you decide to change. It could be faster or slower, and you don't have to go through code rewrites and just starting completely from scratch. That's the benefit of the abstraction layer, like you were saying. And so, I think that's one way that organizations can shield themselves from the question because why even pose that question in the first... Why is it either this or that? Why not have a system that you can actually tune and maybe today you want to start batch, and tomorrow you evolve it to be more streaming and more realtime. Help me on the-- >> John: On the gaps-- >> Yes. >> Always product gaps because, again, you mentioned that you're solving it, and that might be an integration challenge for you guys. >> Yep. >> Or an integration solution for you guys, challenge, opportunity, whatever you guys want to call it. >> Absolutely! >> Organizational gaps maybe not set up for and then processed. >> Right. I think it was interesting that we actually went out to dinner with a couple of customers last night. And they were talking a lot about the organizational stuff because the technology they're using is Informatica, so that's part's easy. So, they're like, Okay, it's always the stuff around budgeting, it's around resourcing, skills gap, and we've been talking about this stuff for a long time, right. >> John: Yeah. >> But it's fascinating, even in 2017, it's still a persistent issue, and part of what their challenge was is that even the way IT projects have been funded in the past. You have this kind of waterfall-ish type of governance mechanism where you're supposed to say, Oh, what are you going to do over the next 12 months? We're going to allocate money for that. We'll allocate people for that. Like, what big data project takes 12 months? Twelve months you're going to have a completely (laughing) different stack that you're going to be working with. And so, their challenge is evolving into a more agile kind of model where they can go justify quick-hit projects that may have very unknown kind of business value, but it's just getting by in that... Hey, sometime might be discovered here? This is kind of an exploration-use case, discovery, a lot of this IOT stuff, too. People are bringing back the sensor data, you don't know what's going to coming out of that or (laughing)-- >> John: Yeah. >> What insights you're going to get. >> So there's-- >> Frequency, velocity, could be completely dynamic. >> Umm-hmm. Absolutely! >> So I think part of the best practice is being able to set outside of this kind of notion of innovation where you have funding available for... Get a small cross-functional team together, so this is part of the other aspect of your question, which is organizationally, this isn't just IT. You got to have the data architects from IT, you got to have the data engineers from IT. You got to have data stewards from the line of business. You got business analysts from the line of business. Whenever you get these guys together-- >> Yeah. >> Small core team, and people have been talking about this, right. >> John: Yeah. >> Agile development and all that. It totally applies to the data world. >> John: And the cloud's right there, too, so they have to go there. >> Murthy: That's right! Exactly. So you-- >> So is the 12-month project model, the waterfall model, however you want... maybe 24 months more like it. But the problem on the fail side there is that when they wake up and ship the world's changed, so there's kind of a diminishing return. Is that kind of what you're getting out there on that fail side? >> Exactly. It's all about failing fast forward and succeeding very quickly as well. And so, when you look at most of the successful organizations, they have radically faster project lifecycles, and this is all the more reason to be using something like Informatica, which abstracts all the technology away, so you're not mired in code rewrites and long development cycles. You just want to ship as quickly as possible, get the organization by in that, Hey, we can make this work! Here's some new insights that we never had before. That gets you the political capital-- >> John: Yeah. >> For the next project, the next project, and you just got to keep doing that over and over again. >> Yeah, yeah. I always call that agile more of a blank check in a safe harbor because, in case you fail forward, (laughing) I'm failing forward. (laughing) You keep your job, but there's some merit to that. But here's the trick question for you: Now let's talk about hybrid. >> Umm-hmm. >> On prem and cloud. Now, that's the real challenge. What are you guys doing there because now I don't want to have a job on prem. I don't want to have a job on the cloud. That's not redundancy, that's inefficient, that's duplicates. >> Yes. >> So that's an issue. So how do you guys tee it up there for the customer? And what's the playbook for them, and people who are trying to scratching their heads saying, I want on prem. And Oracle got this right. Their earnings came out pretty good, same code on prem, off prem, same code base. So workloads can move depending upon the use cases. >> Yep. >> How do you guys compare? >> Actually that's the exact same approach that we're taking because, again, it's all about that customer shouldn't have to make the either or-- >> So for you guys, interfacing code same on prem and cloud. >> That's right. So you can run our big data solutions on Amazon, Microsoft, any kind of cloud Hadoop environment. We can connect to data sources that are in the cloud, so different SAAS apps. >> John: Umm-hmm. >> If you want to suck data out of there. We got all the out-of-the-box connectivity to all the major SAAS applications. And we can also actually leverage a lot of these new cloud processing engines, too. So we're trying to be the abstraction layer, so now it's not just about Spark and Spark streaming, there's all these new platforms that are coming out in the cloud. So we're integrating with that, so you can use our interface and then push down the processing to a cloud data processing system. So there's a lot of opportunity here to use cloud, but, again, we don't want to be... We want to make things more flexible. It's all about enabling flexibility for the organization. So if they want to go cloud, great. >> John: Yep. >> There's plenty of organizations that if they don't want to go cloud, that's fine, too. >> So if I get this right, standard interface on prem and cloud for the usability, under the hood it's integration points in clouds, so that data sources, whatever they are and through whatever could be Kinesis coming off Amazon-- >> Exactly! >> Into you guys, or Ah-jahs got some stuff-- >> Exactly! >> Over there, That all works under the hood. >> Exactly! >> Abstracts from the user. >> That's right! >> Okay, so the next question is, okay, to go that way, that means it's a multicloud world. You probably agree with that. Multicloud meaning, I'm a customer. I might have multiple workloads on multiple clouds. >> That's where it is today. I don't know if that's the endgame? And obviously all this is changing very, very quickly. >> Okay (laughing). >> So I mean, Informatica we're neutral across multiple vendors and everything. So-- >> You guys are Switzerland. >> We're the Switzerland (laughing), so we work with all the major cloud providers, and there's new one that we're constantly signing up also, but it's unclear how the market rule shipped out. >> Umm-hmm. >> There's just so much information out there. I think it's unlikely that you're going to see mass consolidation. We all know who the top players are, and I think that's where a lot of large enterprises are investing, but we'll see how things go in the future, too. >> Where should customers spend their focus because this you're seeing the clouds. I was just commenting about Google yesterday, with AMIT, AI, and others. That they're to be enterprise-ready. You guys are very savvy in the enterprising, there's a lot of table stakes, SLAs to integration points, and so, there's some clouds that aren't ready for prime time, like Google for the enterprise. Some are getting there fast like Amazon Ah-jahs super enterprise-friendly. They have their own problems and opportunities. But they are very strong on the enterprise. What do you guys advise customers? What are they looking at right now? Where should they be spending their time, writing more code, scripts, or tackling the data? How do you guys help them shift their focus? >> Yeah, yeah! >> And where-- >> And definitely not scripts (laughing). >> It's about the worst thing you can do because... And it's all for all the reasons we understand. >> Why is that? >> Well, again, we we're talking about being agile. There's nothing agile about manually sitting there, writing Java code. Think about all the developers that were writing MapReduce code three or four years ago (laughing). Those guys, well, they're probably looking for new jobs right now. And with the companies who built that code, they're rewriting all of it. So that approach of doing things at the lowest possible level doesn't make engineering sense. That's why the kind of abstraction layer approach makes so much better sense. So where should people be spending their time? It's really... The one thing technology cannot do is it can't substitute for context. So that's business context, understanding if you're in healthcare there's things about the healthcare industry that only that healthcare company could possibly know, and know about their data, and why certain data is structured the way it is. >> John: Yeah. >> Or financial services or retail. So business context is something that only that organization can possibly bring to the table, and organizational context, as you were alluding to before, roles and responsibilities, who should have access to data, who shouldn't have access to data, That's also something that can be prescribed from the outside. It's something that organizations have to figure out. Everything else under the hood, there's no reason whatsoever to be mired in these long code cycles. >> John: Yeah. >> And then you got to rewrite it-- >> John: Yeah. >> And you got to maintain it. >> So automation is one level. >> Yep. >> Machine learning is a nice bridge between the taking advantage of either vertical data, or especially, data for that context. >> Yep. >> But then the human has to actually synthesize it. >> Right! >> And apply it. That's the interface. Did I get that right, that progression? >> Yeah, yeah. Absolutely! And the reason machine learning is so cool... And I'm glad you segway into that. Is that, so it's all about having the machine learning assist the human, right. So the humans don't go away. We still have to have people who understand-- >> John: Okay. >> The business context and the organizational context. But what machine learning can do is in the world of big data... Inherently, the whole idea of big data is that there's too much data for any human to mentally comprehend. >> John: Yeah. >> Well, you don't have to mentally comprehend it. Let the machine learning go through, so we've got this unique machine learning technology that will actually scan all the data inside of Hadoop and outside of Hadoop, and it'll identify what the data is-- >> John: Yeah. >> Because it's all just pattern matching and correlations. And most organizations have common patterns to their data. So we figured up all this stuff, and we can say, Oh, you got credit card information here. Maybe you should go look at that, if that's not supposed to be there (laughing). Maybe there's a potential violation there? So we can focus the manual effort onto the places where it matters, so now you're looking at issues, problems, instead of doing the day-to-day stuff. The day-to-day stuff is fully automated and that's not what organizations-- >> So the guys that are losing their jobs, those Java developers writing scripts, to do the queries, where should they be focusing? Where should they look for jobs? Because I would agree with you that their jobs would be because the the MapReduce guys and all the script guys and the Java guys... Java has always been the bulldozer of the programming language, very functional. >> Murthy: Yep. >> But where those guys go? What's your advice for... We have a lot of friends, I'm sure you do, too. I know a lot of friends who are Java developers who are awesome programmers. >> Yeah. >> Where should they go? >> Well, so first, I'm not saying that Java's going to go away, obviously (laughing). But I think Java-- >> Well, I mean, Java guys who are doing some of the payload stuff around some of the deep--- >> Exactly! >> In the bowels of big data. >> That's right! Well, there's always things that are unique to the organization-- >> Yeah. >> Custom applications, so all that stuff is fine. What we're talking about is like MapReduce coding-- >> Yeah, what should they do? What should those guys be focusing on? >> So it's just like every other industry you see. You go up the value stack, right. >> John: Right. >> So if you can become more of the data governor, the data stewards, look at policy, look at how you should be thinking about organizational context-- >> John: And governance is also a good area. >> And governance, right. Governance jobs are just going to explode here because somebody has to define it, and technology can't do this. Somebody has to tell the technology what data is good, what data is bad, when do you want to get flagged if something is going wrong, when is it okay to send data through. Whoever decides and builds those rules, that's going to be a place where I think there's a lot of opportunities. >> Murthy, final question. We got to break, we're getting the hook sign here, but we got Informatica World coming up soon in May. What's going to be on the agenda? What should we expect to hear? What's some of the themes that you could tease a little bit, get people excited. >> Yeah, yeah. Well, one thing we want to really provide a lot of content around the journey to the cloud. And we've been talking today, too, there's so many organizations who are exploring the cloud, but it's not easy, for all the reasons we just talked about. Some organizations want to just kind of break away, take out, rip out everything in IT, move all their data and their applications to the cloud. Some of them are taking more of a progressive journey. So we got customers who've been on the leading front of that, so we'll be having a lot of sessions around how they've done this, best practices that they've learned. So hopefully, it's a great opportunity for both our current audience who's always looked to us for interesting insights, but also all these kind of emerging folks-- >> Right. >> Who are really trying to figure out this new world of data. >> Murthy, thanks so much for coming on The Cube. Appreciate it. Informatica World coming up. You guys have a great solution, and again, making it easier (laughing) for people to get the data and put those new processes in place. This is The Cube breaking it down for Big Data SV here in conjunction with Strata Hadoop. I'm John Furrier. More live coverage after this short break. (electronic music)