 Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017, brought to you by Hortonworks. Hey, welcome back to theCUBE. I'm Lisa Martin with my co-host Peter Burris. We are live on day one of the DataWorks Summit in Silicon Valley. We've had a great day so far talking about innovation across different companies, different use cases. It's been really exciting. And now please welcome our next guest, David Lyle from Informatica. You are driving business transformation services. Yes. Welcome to theCUBE. Well, thank you, it's good to be here. It's great to have you here. So tell us a little bit about Informatica World. Peter, you were there with theCUBE. Just recently, some of the big announcements that came out of there, Informatica getting more aggressive with cloud movement, extending your master data management strategy, and you also introduced a set of AI capabilities around metadata. Exactly. Looking at those three things and your customer landscape, what's going on with Informatica customers? Where are you seeing these great new capabilities become to fruition? Absolutely. Well, one of the areas that is really wonderful that we're using in every other aspect of our life is using the computer to do the logical things it should and could be doing to help us out. So in this announcement at Informatica World, we talked about the central aspect of metadata finally being the true center of Informatica's universe. So bringing in metadata from all these- And customers' universes. Well, and customers' universes. So not seeing it as something that sits over here that's not central, but truly the thing that is where you should be focusing your attention on. And so Informatica has some card-carrying PhD, artificial intelligence, machine learning engineers, scientists that we have hired that have been working for us for several years that have built this new capability called CLARE. That's the marketing term for it, but really what it is, it's helping to apply artificial intelligence against that metadata to use the computer to do things for the developer, for the analyst, for the architect, for the business people, whatever that are dealing with these complex data transformation initiatives that they're doing, where in the past what's been happening is whatever product you're using, the product is basically keeping track of all the things that the scientist or analyst does, but isn't really looking at that metadata to help suggest the things that maybe has already been done before. Or domains of data, how come you have to tell the system that this is an address? Can't the system identify that when data looks like this it's an address already? If we think about Shazam and all these other apps that we have on our phone that can do these fantastic things with music, how come we can't do those same things with data? Well that's really what CLARE can actually do now is discover these things and help. I want to push that a little bit. So historically metadata was the thing that you created in the modeling activity and it wasn't something that you wanted to change or was expected to change frequently. In fact, in the world of transaction processing you didn't want to change. Especially you get into finance apps and things like that, you want to keep that slow. Exactly, and metadata became one of those things often had to be secured in a different way and it was one of the reasons why IT was always so slow is because all these concerns about what's the impact on metadata. We move into this big data world and we're bringing forward many of the same perspectives on how we should treat metadata and what you guys are doing is saying, that's fine, keep the metadata of that data but do a better job of revealing it and how it connects and how it could be connected. And we talked about this with Bill Schmarzo just recently that the data that's in that system can also be applied to that system. It doesn't have to be a silo and what Claire's trying to do is remove some of the artificial barriers of how we get access to data that are founded by organization or application or system and make it easier to find that data, use that data and trust the data. I got that right? You've totally got that right. So if we think about all these systems in an organization as this giant, complex hairball that in the past we may have had pockets of metadata here and there that weren't really exposed or controlled in the right way in the first place but now bringing it together. But also valuable in the context of the particular database or system that was running. It wasn't the metadata that was guarded as valuable. That just provided documentation for what was in the data. Exactly, exactly. But now with this ability to see it really for the first time and understand how it connects and impacts with other systems that are exchanging data with this or viewing data with this, we can understand then if I need occasionally to make a change to the general ledger or something that I can now understand what impact on different KPIs in the calculation stream of Tableau, business objects, Cognos, MicroStrategy, Quik, whatever that what else do I need to change? What else do I need to test? That's something computers are good at. Something that humans have had to do manually up to this point. And that's what computers are for. So question for you on the business side. So as we look at, businesses are demanding real-time access to data to make real-time decisions, manage costs, be competitive and that's driving cloud, it's driving IoT, it's driving big data and analytics. You talked about Claire and the implications of it across different people within an organization. Metadata, how does a C-suite or a senior manager care about metadata? Yeah, they don't. And that's why we don't talk about the word architecture typically with C-suite folks. We don't use the word metadata with C-suite folks. Instead, we talk about things like solving the problem of time to get the application or information that you need, reducing that time by being able to see and change and retest the things that need to be done. So we just change the discussion to either dollars or time or of course those are really equivalent. But really facilitated by this artificial intelligence. It can also then lead to the, when we get into data lakes, ensuring that those data lakes are understood better, trusted better, that people are being able to see what other people are actually using. And in other words, we kind of bring somewhat the amazon.com website model to the data lake so that people know, okay, if I'm looking for a product or data set that looks like this for my R processing data science utility or what I want to do, then these are the data sets that are out there that may be useful. This is how many other people have used them or who those other people are and are those people kind of trusted, valid people that have done similar stuff to what I want to do before. Anyway, all that information that we're used to when we buy products from Amazon, we bring that now to the data lake that you're putting together so that you can actually prevent it kind of from being a swamp and actually get value at it. Once again, it's the metadata that's the key to that of getting the value out of that data. Have you seen historically that, so you're working with customers that are already using Hadoop, they've got data lakes, have you seen that historically they haven't really thought about metadata as driving this much value before? Is this sort of a, not a new problem, but are you seeing that it's not been part of their strategic approach? That's right, it's a new solution. I think you talk to anybody and they knew this problem was coming that with a data lake and the speed that we're talking about, if you don't back that up with the corresponding information that you need to really digest, you can create a new mess, a new hairball faster than you ever created the original hairball you're trying to fix in the first place. Nobody likes a hairball. Nobody likes a hairball, exactly. Well, it also seems as though, for example, at the executive level, do I have a question? Can I get this question answered? How do I get this question answered? Can I trust the answer that I get? In many respects, that's what you guys are trying to solve. Exactly, exactly. So it's not, hey, what you need to do is invest a whole bunch in the actual data of copying data and moving a bunch of data around. You're just starting with the proper observation or the proposition, yes, you can answer this question, here's how you're going to do it and you can trust it because of this trail of activities based on the metadata. Exactly, exactly. So it's about helping to kind of, hate to use the phrase again, but kind of detangle that hairball so that, or at least manage it a bit so that we can begin to move faster and solve these problems with hell of a lot more confidence than we have before. So can we switch gears? Absolutely. So let's switch gears and talk about transformations. I know that's near and dear to your heart and something you're spending a lot of time with clients in. How do you approach, when a customer comes to you, how are they approaching a transformation and what's the conversation that you're having with them? Well, it's interesting that the phrase has, I'm even thinking of changing our group's title to digital transformation services, not just because it's hot, but because, frankly, the fluid or the thing, the glue that really makes that happen is data in these different environments. But the way that we approach it is by, well, understanding what the business capabilities are that are affected by the transformation that is being discussed, looking at and prioritizing those capabilities based upon the strategic relevance of that capability along with the opportunity to improve and multiplying those together, we can then take those and rank those capabilities and look at it in conjunction with what we call a business view of the company. And from that, we can understand what the effects are on the different parts of the organization and create the corresponding plans or roadmaps that are necessary to do this digital transformation. We actually bought a little stealth acquisition of a company two years ago that's kind of the underpinnings of what my team does that is extremely helpful in being able to drive these kinds of complex transformations. In fact, big companies, several in this room, in a way, are going through the transformation of moving from a traditional software license sale transaction with the customer to a subscription monthly transaction. That changes marketing, that changes sales, that changes customer support, that changes R&D, everything changes. How do you coordinate that? What is the data that you need in order to calculate a new KPI for how I judge how well I'm doing in my company? Annual recurring revenue or something. It's a, these are all, they get into data governance. You get into all these different aspects and that's what our team's tool and approach is actually able to credibly go in and lay out this roadmap for folks that is shocking, kind of, in how it's making complex problems manageable. Not necessarily simple. Actually, it was Bill Smarzo on the last, he told me this 15 years ago. Our problem is not to make simple problems mundane, our problem, what we're trying to do is make complex problems manageable. Sounds like something Bill would say. I love it. That's an important point, though, about not saying we're going to make it simple. No. We're going to make it manageable. Exactly. Because that's much more realistic. Right. Don't you think? Exactly. Exactly. The fact that you- We can't make him simple, that's good too. That would be nice. Oh, we love that. Yeah. Oh yeah, when it happens, it's beautiful. Yeah. Well, your passion and your excitement for what you guys have just announced is palpable. So obviously just coming off that announcement, but what's next? If we look out the rest of the calendar year, what's next for Informatica and transforming digital businesses? I think it is, you could say the first 20 years almost of Informatica's existence was building that metadata center of gravity and allowing people to put stuff in, I guess you could say. So going forward, the future is getting value out. It's continually finding new ways to use it. In the same way, for instance, Apple is trying to improve Siri, right? And each release, they come out with more capabilities. Obviously Google and Amazon seems to be working a little better, but nevertheless, it's all about continuous improvement. Now, I think the things that Informatica is doing is moving that power of using that metadata also towards helping our customers more directly with the business aspect of data in a digital transformation. Excellent. Well, David, thank you so much for joining us on theCUBE. We wish you continued success. I'm sure theCUBE will be back with Informatica in the next round. Thanks for sharing your passion and your excitement for what you guys are doing. Like I said, it was very palpable and it's always exciting to have that on the show. So thank you for watching. I'm Lisa Martin for my co-host, Peter Burris. We thank you for watching theCUBE. Again, we are live on day one of the DataWorks Summit from San Jose. Stick around, we'll be right back.