 Live from San Francisco, it's theCUBE, covering Informatica World 2016. Brought to you by Informatica. Now, here are your hosts, John Furrier and Peter Burris. Okay, welcome back everyone. We are here live in San Francisco for exclusive coverage of Informatica World 2016. This is SiliconANGLE Media's theCUBE, our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, my co-host, Peter Burris, our next guest is Ash Parak, who's the Vice President of Marketing and Data Integration, Big Data, Data Security Informatica World. But what else do you have? Data, 3.0, no, it's data everything. Welcome back. It's data everything. Thank you, thanks for having me. So we love to have some of your customers come on and they're not really here to pimp up the testimonials but really more about the use cases and it's really clear and I love the comment from Yelana from George Washington University. We like Informatica because they are focused like a laser and it's my word, on data. Obviously data is the key. What are some of the trends happening around data? Can you just share your view of the macro landscape because everyone talks about data, you guys are laser focused on data and what is the big trend and trends you're riding? It's a great question. You, all of us were at Strata Hadoop World all through last year, the Hadoop summits, some of the other data management conferences, right? So we've been seeing a couple of these trends of all. You know, when people ask me this question about trends, what I really love about Informatica World is this, that isn't this really becoming the de facto data management place? I mean, you have the hype, you have the discussions around what's new in IoT and hey, I can actually track how my ski lifts are actually panning out. Is it really very busy right now or it's not with big data? But truly this is the place, this is the mecca where actually people who are involved in data management or really see data as an asset, they are truly converging and every single year this happens, so they listen to a lot of stuff which is a little bit out there, but they come back away to Informatica World and ask us, you know, how does a rubber meet the road? Get me more real about my data management projects. You know, Ashley, this is a good point. I want to bring this up because I, you know, Big Data SV is part of the Big Data Week with Strata Hadoop and our event at Big Data SV. We talked about this with a lot of your folks and also last year at Informatica World, my eyes were kind of opened up to Informatica. Oh yeah, private companies, kind of like that. I wasn't really sure kind of where you guys were, but I like to peg the line down the middle of the street and break things down into two sides of the street. Old way, new way, right? And you can almost look at the data warehouse industry and say there's dogma around old way. This is the way we do it. We've done it for years this way and then there's the new school which is the other side of the street, the new side, modern side, which is I say, okay, we have this. What do we do now? What do we do next? I think you guys are clearly on that new side. So congratulations, we can see that clearly. The things you talk about, the abstraction layer, moving data around, removing the friction. Absolutely. What is that key initiative? Because friction is a problem with data. You know, silos certainly are legacy infrastructure, subsystems, data, mobile devices, consumerization. These are the forces that are driving you guys to be modern. What makes it so impactful when you guys are so new and modern? So look again about being pragmatic and practical, right? I'll answer the question in this way. The way we look at our technologies and solutions, anytime we look at our new development, we always wear the hat of how is my existing customer going to reap benefits from this? So for example, big data, right? Is it a new paradigm or am I going to, you know, introduce something completely different to people who've been doing something in a traditional manner, right? There is never a right or wrong answer. There's a need for a data warehouse. I keep hearing these things around data warehouses dead, business intelligences that haven't we seen that for the last 15, 20 years, they never go away. Let's pray for a headline, but the reality is it evolves. Absolutely, it evolves, you said it, but it also evolves with a pragmatic way of approaching your business. There's a need for a data warehouse, there's a need for big data. You bring them both together, you answer bigger questions. And that's where Informatica is actually, you know, yesterday in Anil's keynote, in Jim's keynote and this morning in Amit's, I think what they spoke about was there's a natural progression. There's almost a maturity model, right? Once I start going beyond an individual's benefit, I start looking at a departmental need and then an enterprise's need and then I look at unification and how do I now standardize architectures across various ways of doing data management, right? That's where Informatica truly has been shining for the last, you know, many, many years. You talk about the dynamic, because you know, we've been following Hadoop, obviously since the beginning of creation, since theCUBE was kind of founded in the Cloudera office. It's evolved, the hype was there and also we've seen it kind of like, it's expensive, total cost of ownership's a little bit outside the realm of what people are looking at and what they want. Skill sets are hard to find, deployments are hard, so when you have something that's hard and costs more, it's usually not a formula for success, but yet Hadoop is not just Hadoop, it's big data in general. You guys seem to hit the intersection of those two. Talk about the challenges of traditional integration of data, leveraging my existing systems of data, systems of records, some engagement, but yet leveraging this Hadoop and then talk about what that means. I mean, share with the audience, if people think Hadoop, oh, big data's just Hadoop, store it, put it in the lake, put it into a pile, put it into a swamp or put there, too. How do I make it valuable? Somebody at Informatica taught me this. Data Warehouse teaches me or tells me, gives me answers about what widgets I sold in the past. Hadoop and big data, and you're right, it's not just about Hadoop, it's much more. It gives me answers to what can I sell my customer in the future. And this is really interesting, because if you're a CEO of a company, wouldn't you need answers to both those questions? So you really are talking about coexistence here. So you asked me a question about, hey, how do you balance these two worlds? Customers are truly in the traditional world. Let's take, for example, they're still looking to make their traditional data management, data integration, life cycles much more agile. How can I cut minutes or days out of my existing data flows? That's an example there. How can I go from manual testing to more automated testing? How can I be more proactive about monitoring my operational systems? That's a traditional world, it's still existing. I still want to do analytics, but there's also this new world where give me a bridge so that I can actually go from using my skills and my existing skills and my infrastructure to actually transcend these new barriers, right? Now is that an innovation strategy or is that bridge a migration or both? How would you look at that bridge? Most people look at the old side and be kind of stagnant. So like now you have this bridge to big data. Is that an enabler? This is absolutely an enabler. It's not about throwing something that's old out. It's actually leveraging what you already have. And then, as I think you mentioned it, right? It's augmenting what you already have with some of these new world concepts like big data and IoT, et cetera. At the end of the day, there's a data conference and it's always about data, right? So whether data big or small, you still need to manage it. That's foundational to what we do. What are the top conversations you're having with customers? Because obviously, you guys have great messages. Love the story. I think it's very relevant. Love the subtraction layer. You guys are talking about this intelligent data platform. I think it's very relevant because it spans a lot. What are some of the conversations with customers? Where are they in the progress of their digital journey? Are they advanced? So what are the trending top three conversations that you have every day with customers? Wonderful question. I'll give you an example. Large enterprise, and we typically work with, full scale of large enterprises or small businesses, doesn't matter. Big departments to enterprise level ICCs within the same company. I'll give you an example of a company where within one department, they were just trying to get started with their first few data marts. Forget data warehouses. They were right there. They were trying to get started with data integration. In another department with the same company, they were actually looking at putting data into a data lake. Now if you really bring everything together and you want to bring economies of scale, you're looking at a single data management platform that can transcend either of these paradigms. That's where Informaga comes in. So you can leverage familiar skills within the entire company without rescaling them to instantly go from this level of maturity all the way to the top. What's their core problem? Well, there are a lot of things, right? Agility, flexibility. Can I actually, good quality data? That's often the very tenet of everything they do. I need to govern my data well. I need to master it. These things are not going to go away on even if it's the big data world. So the number one thing that we hear is moving data around and opening it up. And most people want to hoard on the data and like hold on to it data. Well, how do you guys have that conversation with customers because freeing up the data is something that's an organic enabling real time, certainly speeds that up. But yet most of the data governance people tend to be like, no, don't share it. How do you balance opening it up with compliance and policy? Great question. So many years ago we came up with this paradigm and we worked with a number of the industry analysts around this and customers is managed self-service. There's a concept called self-service. There needs to be a concept and understanding around managed self-service where the business owns the data, defines the rules because they're the closest to the data, right? But it's IT who then takes the results of what business does to truly operationalize it to optimize it. Without the two working together you are not going to get success, repeatable success. And that's what I mean by true success as far as data management is concerned. Or governance. I'm going to push you a little bit on that. If we think about the role of data in the business, today it's consumed by a relatively small percentage of business decision makers. Are we trying to optimize what we're doing for that small group? Are we trying to open it up to more parts of the organization, number one? And number two, if we think about IoT just conceptually, we can imagine that there are going to be more systems as users than there are people as users. How do you self-service when you're servicing systems? So let's talk about both those things. Are we focusing on making the existing folks more productive? Or are we trying to extend this out to new folks? And we'll talk about the other one in a second. Great, I think it's a two-part question but it kind of answers one major paradigm. So first, the first part. Citizen integrators is what we've been working around for the last three or four years. Rev, it's basically now a data preparation tool. It's available across PowerCenter, Cloud, Big Data, you name it. That frees up, that actually democratizes data across the board for people who truly know data and are not that technical. Very excellent like features. Let me munch data together. Let me bring it together. Let me do some light profiling on it. Let me do some light cleansing on it. And then I'll feed it into you, my IT counterpart to go ahead and do something with that. That was step one. So truly freeing it up for another persona, right? This is not an ETL architect. This is not an architect. This is truly a citizen integrator, an analyst. Someone even higher level than an analyst. So that was one part of the question, right? The second part of the question which you asked me is about IoT and the expanse of data that's being projected through these kind of devices. Sure, so at the end of the day, what exactly is this? You can derive data from any of these devices, but what really goes on is once you collect it, what do you do with it? How do you bring meaning and semantics around that? That's the real question to ask, and that's where we come in. That's what's that? That's a real question to ask, the semantics and the quality. It's great to kind of collect data from thousands and millions and millions of these sensors and whatever else, but what's the next step? You really are trying to get to a particular decision from all that data. So it seems to me as though we've spent an enormous amount of time in the data industry focusing on the sources. Yeah. Access. Yeah, accessing the sources, moving it around, transforming it, formatting it, even just formatting it. Which is very important, which is very important. And not enough time thinking about the sinks. Where is the data going to go? Who needs to use it? In what format are they going to use it in? Both people and systems. And here's a thought. Does Informatica have to step up and become more of a leader in thinking about how people and systems ultimately will consume data as opposed to just how we're going to capture it and move it and transform it? Great, great thought here. In fact, they started getting bubbled up in the last try at Hadoop World Conference as well. People started coming to us and I'll answer your question indirectly and directly. People started coming up to us and saying, asking us real hard questions of, now that I have a Hadoop, now that I have big data, et cetera, what do I do with it? How do I start going from experimentation or a little sandbox to truly creating, you know, we may not love the name Data Lake, but truly getting those insights that we've, the promised land. What we actually told them was, listen, there's a journey. It's not about just taking a credit card and swiping it and setting up your little Hadoop server, et cetera, and then trying to create a sandbox and then using hand coding, et cetera, right? To get your data out of there. You really need to look at this as a systematic approach and you're absolutely right. We can do a lot more around driving thought leadership around this, but awareness is coming. People are already realizing that data, small or big, you still have to do the fundamental things that govern data, which is not just accessing it, which is the most important thing, getting the data in, but then governing it, qualifying it, certifying it, mastering it, blending, prepping. There's another question that comes up. Oh, you know what? I'm just going to use a self-service data preparation tool. It's a standalone one. I download it, I'll use it. I'm done. My big data is now certified. That's not true, because think of the word, self-service data prep, here's why. All the tools that are standalone today, without having the last mile in it, are only going to do one thing. First, it's self-serving. It's done for a single problem. It's not for repeatable results. There is no IT last mile there, where they actually take the results and operationalize it. There's no crowdsourcing. If you, me, and another 10, 15 of us really want to pay attention and actually provide input on a particular asset, a data entity, we just can't with those kind of tools. They're very limiting. Yeah, they're Excel-like, they're spreadsheet-like, they're very useful. I love the concept, but without it being part and parcel of an end-to-end platform that manages data, whether it's big or small, you aren't going to get the results that you need. So let's talk, let me see if I can take what you just said, because I agree with you strongly. The, I think I do. What percentage of users of the value, the data value that an informatic and related tools are going to provide are going to be consuming, looking at screens in what we typically think about self-service, versus consuming by having events or alerts or options or something else presented to them so that they're not sitting there staring at a screen, they're actually acting upon a recommendation. Because it seems as though we are focused in an enormous amount on the screen and not as much as we need on the recommendation and the action. Thank you for bringing that up. That was my third point, the distinction between standalone self-service data preparation tools and tools like ours, like our InfoMaker data preparation. It's part and parcel of the underlying platform. So I already told you about, hey, it's built not just for the self, it's built for crowdsourcing. That was one. The second piece that I talked about was operationalizing the results. And you brought up the third point, which is absolutely key. Here's where it lies in, where you want to use machine learning, artificial intelligence, et cetera, or the system to provide recommendations. Even your crowdsourced assets should be able to provide you user ratings and recommendations so that you have your next best steps. That's where we are going. In fact, we are already there with Dataprep. You are? Yeah. That's as part of, but don't forget, that's exactly the point I'm trying to make. Without the underlying metadata management that we have, live data map, et cetera, you really can't make a standalone data preparation tool, skip and dance. No, again, totally agree. And there needs to be some set of professionals that are responsible for making sure that that works, as well as some set of professionals that are looking at the screens and identifying the assets that would be most valuable by virtue of doing the self-service evaluation, and then turning that into insight into how these various assets are going to be moved around an organization and consumed. And it seems unless we're talking about all three of those layers and more beyond that, that we're presenting this as though it's going to be something simple. And as Jerry Held said, we talked about this in the first session, data is hard and it's always going to be hard. We can make it easier and we have to make it easier, but the most valuable uses of it, in many respects, will be uses when people don't even know that they're using it. They're just acting upon a recommendation. Well, that's the point about the operationalizing as key, I mean the standalone tools that operate in a vacuum, so to speak, is similar to siloed systems. They're only as good as their integration, right? So back to the integration point, if you have some insight that's not operationalized, there's no value, right? So not real time, it's a one-off. And to his point, without the metadata-driven muscle, you aren't going to be able to get this artificial intelligence, the machine learning kind of things like user ratings, recommendations, et cetera, which are truly based on human behavior or are helping you take that one last step or one last mile. Ash, thanks so much for coming on theCUBE. Really appreciate your insights and the data that you're sharing. Certainly, we'll operationalize it by broadcasting it live, which we are. Go to youtube.com slash SiliconANGLE for the videos. I'm John Furrier with Peter Burris, you're watching theCUBE live in San Francisco for Informatica World 2016. Hi, this is Chris Devaney from DataRobot.