 from San Jose. It's theCUBE, presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. 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 started data conference and we've got some great guests today that are going to share a lot of 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 analyst side in the morning. Our first guest is, needs no introduction to theCUBE, he's a CUBE alumni, Murthy Mathiprakasam. Get that right? 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, yeah, so this has been a very exciting year for us, lots of 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, as we probably talked about before we started the 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, just a lot of complexity that organizations have around maintenance. So one of the things Informatica's 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 that logical level instead of the physical level. So we're bringing that entire kind of paradigm to the API management space. So 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 mainly talking about here today, we're doing a lot there as well. So we announced our kind of next generation of our data management platforms for the big data world. Part of that is also a lot of cloud capabilities, because again, that's one of the bigger trends. Hey, you made a big bet there. Absolutely, and I mean, this is the investment, a return on investments over like 10 years, right? You know, we were started in the 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 kind of re-engineering and 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 a trend, we were kind of innovating against that as well. So now our platform as service offering supports the ability to connect the 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 with the 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, you know, we can talk more about that. Well, I want to connect, I want to connect to what you just announced with the whole notion of the data lake, because it's really informatica strength has always been in between. And it turns out that's where a lot of 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. Right. And then now you're doing the APIs, which is just an 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 has been in between. Absolutely. It's been in knowing where your data is. It's been in helping to build those pipelines and manage 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 the maturation of a lot of these new technologies. You know, Hadoop was a very, very, like 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 were talking to CDOs and chief data officers and VPs of data infrastructure 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 mainstream. And so this is exactly to your point where the investments that I think Informatica has been making are kind of comprehensive platform-oriented approach to the space are paying off. Because for a 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 because they're accountable for knowing what the data is. And so there's a number of things by virtue of the maturation of the industry. I think the trends are pointing toward the enterprises kind of going more toward that platform approach. And on that platform approach, Informatica is 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, versus a kind of fit-for-purpose approach? Yeah, absolutely. And we should be careful with even the phrase fit-for-purpose too, because I think that word gets thrown around a lot. It's one of those buzzwords 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 one aspect or sort of one feature of the entire problem that a Chief Data Officer would care about. They might call it fit-for-purpose, but I mean, you have to actually solve a problem at the end of the day. The Chief Data Officers are trying to build enterprise data pipelines. You know, you got raw information from all sorts of data sources on-premise in the cloud. You need to push that through a process, like on a manufacturing process, of being able to ingest it, prepare it, cleanse it, govern it, secure it, master it. All this 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 you know, these kind of data executives care about, but also do it in a way that fosters productivity and reusability. 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 major changes like that, even versions of Spark or versions of Hadoop can sometimes trigger a need to recode and throw away stuff. And organizations 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 standpoint 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 on that a little bit more. So, because what we would say it is that, 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 to fit the purpose but then they make promises that they say, oh, well this is fit to purpose but it's really a platform. And as a consequence, you get a whole bunch of duck-like solutions that are, are they swimming or are they flying kind of problems? So, I think that what we see clients asking for, this is my question, 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. So, it's not a rip and replace, that's not what you're suggesting. What you're suggesting, I think, is use what you got, it's creating value, continue to use it and then over time, investor 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. I want to create options for the future with what I've built in today. Exactly, so even the cloud, as you were bringing up earlier on, not everybody knows exactly what their cloud strategy is and it's changing extremely rapidly. 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 like 50% of our big data business is people deploying off-premise. I mean, that's amazing 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 to 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 six months a year, two years 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 in their environment that from a cost perspective and maybe to Peter's point, they're still generating business value. How do you help customers that have multiple different products maybe exploring different multi-cloud approach? How do they come and start working with Informatic 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 was sitting next to a guy and I was kind of asking him a very similar question. He told me about your technology landscape, where are things going, where are things gone in the past and he basically said there's a whole portfolio of technologies that they plan to obsolete because they just know that 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 three month trials of different technologies and that's probably why we see such proliferation of different technologies because people are just kind of trying stuff out but he's like, I know I'm going to throw this stuff out. Yeah, but that's, let me make sure I got that because I want to reconcile the points. That's if they're in pilot and the pilot doesn't work but the minute it goes into production and value's 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 at 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 customer is making the point is, Informatica's the only technology that has 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 like 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 been able to give them that flexibility. Everything else as far as developer tools and visualization tools, like every year there's some kind of new thing that kind of 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 it's 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 that 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, for any type of data, any type of user, any type of deployment environment, I need something that I can count on as it's like 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 months or six months and that's great but they're almost using it with the idea that they're going to throw it away. A couple questions for you. What are some of the business values that you were seeing like this gentleman that you were talking to last night? What's the industry that he's in and also are there any like stats or ranges you can give us like the reduction in TCO or new business models opening up? What's the business impact that Informatica is helping these customers achieve? Yeah, absolutely. So I'll use this example. He's, I can't mention the name of the company but it's an insurance company. Lots of data. Lots of data, right? Not only do they have a lot of data but like 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 if somebody's using car insurance and maybe for so long they're ready to get married so they need home insurance and they have these really sophisticated models around human behavior so they know when to go and position new forms of insurance. There's also obviously security governance 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 ability to meet kind of regulatory pressure. I think that's generally, I mean every industry has some variant of that, right? Cost reduction, increased revenue, meeting kind of regulatory pressures. And so Informatica facilitates that because instead of having to hire armies of people and then having to change them out maybe every three months or six months because the underlying infrastructure is changing there's this one team, the Informatica team, that's actually existed for this entire journey and they just keep changing use 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 It's an asset. It's an asset that delivers that sustainable value that you were talking 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 M&A partnership approach. What makes Informatica stand out? Why should companies consider Informatica? So they say like, there's a quote about imitation is the most sincere form of flattery. From a flattery, yes. So I guess we should feel a little bit flatter by what we're seeing in the industry but so why from a customer standpoint should they continue to rely on Informatica? I mean, we keep pushing the envelope on innovations, right? So one of the other areas that we've 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. Just 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 customer names and costs and skews, it must be an order. You don't actually have to say that it's an order. We can infer all of this because of the machine learning that 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'd be interested in. So those types of recommendations, predictions, discovery totally changes the economic game for an organization because the last thing you want is to have 40 to 50% growth in data translate into 40 to 50% of labor. 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 I take to your question, I think just gluing a bunch of stuff together still doesn't actually get to the nut of sustainability. You actually have to have, the glue has to have something in it. And in our case, it's this machine learning approach that ties everything together, that brings a data organization together so they can actually deliver the maximum business value. It 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 and sharing what's going on at Informatica and what's differentiating you guys. We wish you a great rest of these throughout our conference. Awesome, you as well. Thank you. 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 party 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 after this short break.