>> 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)