 Live from San Francisco, it's theCUBE, covering Informatica World 2017, brought to you by Informatica. Okay, welcome back everyone. We are here live in San Francisco for Informatica World 2017. Exclusive coverage from theCUBE, third year covering the transformation of Informatica as a company. I'm John Furrier, SiliconANGLE. My co-host this week is Peter Burris, general manager of wikibond.com and head of research with SiliconANGLE Media. Our next guest is eight-time CUBE alumni, Amit Wally, executive vice president of products at Informatica. Amit, great to see you. Good to be here. Thanks for spending the time to come on. I saw you at a nice dinner last night. Yeah, always love to be back here. With all your top customers. Very happy customers. Welcome to theCUBE. Yes, thank you. We keep them happy. You know, 11th year in a row, we got the number one in customer loyalty. We work hard for that. It's a lot of exciting things happen. I just want to jump into some of the products though, because that's your wheelhouse. You guys have been an amazing product company. The boy's been kind of bullish on you guys, been very complimentary. But the one thing that when we've talked on Facebook and also on theCUBE is that not everyone knows about Informatica. I mean, they know about the old Informatica. We had Jerry Heldon yesterday talking about the transformation, how hybrid clouds here today. You guys have made great strides on the product front, the platform front, decentralizing data with control. Now you get the new brand. What's it, what's going on? Give us the update. I mean, you got to be pretty pumped now. You got some megaphone out there with the new CMO. Yeah, no, lots happening at our end. I mean, I'll just step back and paint a picture as to how we see where the industry is and then how we basically are transforming that. So my fundamental belief is that we're going through this massive transformation. I mean, pick any word, but underlying at the technology level the systems of records, all the databases and all the apps are massively fragmenting. Cloud, on-premise, hundreds and hundreds of choices. Systems of engagement for customers are fragmenting, right? And when I talk to customers, they're struggling to figure out what is their system of intelligence? What's the organizing principle? Take a great example. My customer data, and what I know about you, John, is available inside the system, within multiple databases, multiple apps, outside the systems, what you do on LinkedIn, Facebook, Twitter, how do I get a handle of you to be able to effectively engage with you? And that is a fundamental change that's happening in the industry. So what is my organizing principle to have the system of intelligence? We've honed in at the metadata layer for that. We believe, leave the data wherever it is because it's going to be in different places. Use your best-to-breed apps. Organize the metadata, because the scale and scope of that, while small, power of that is very high. And yesterday in my keynote, I announced the launch of Claire, our AI ML offering. And the idea is that we are going to be the Google of the enterprise to bring the entire metadata together. And when we apply machine learning to it, the same algorithms that LinkedIn applies, or Facebook applies for photo tagging, or relationships, or Amazon applies for recommendations, we're going to apply it for data. And make that then be what I called organizing principle, the system of intelligence for an enterprise. That's the nutshell of what we're trying to do. And so there's also this data 3.0 thing, I want to press you on this, because this is really cool. You guys have increased the surface area of addressability of data. And we talked about that last year, making it horizontally scalable yet with all the goodness of the controls, as we talked about in the past. But now you're bringing in access methods via machine learning and AI techniques to make it accessible. Think Alexa, right, people can at home. Hey, give me a song. How are you guys using the algorithms? Because now algorithms become a super important part of what to look at. And Facebook, you mentioned Facebook and Google, they've been criticized for their algorithms suppressing quality data, new cycle, things pop up once they kind of see some traction. But how do you guys tweak and enable algorithms to surface the best data possible? You know the best way to describe it is that our philosophy is different. Claire, our AI engine, our goal is to make sure we can surface all of the data to the customer. But in an organized fashion, we're not looking to say filter something. The best example is that predictive maintenance, right? If I am BMW and I'm running a robotic driven shop floor, how do I know when something's about to go down? I have a lot of old historical data on my shop floor, but real time streaming data is coming from the sensor of the robot. I want to marry the two together and then let the system tell me, boy, I feel like in the next 30 minutes, something is about to happen. So we are doing those kind of things, solving those problems. So we're not looking to filter or suppress anything. Our goal is to make sure that we can bring more and more and more data together. And with the help of machine learning, Claire, make it easy for customers to make decisions. Intelligent decisions, smart decisions, easier versus hundreds of people having to guess or predict, which ends up not being very smart. Okay, so on the roadmap side, I want you to take a minute to explain because now it's a good laying out the value proposition there, but I want to tie the cloud together with this because Jerry held, yes, they said hybrid cloud's going to be a very long journey because legacy doesn't go away. You guys have a great business on prem that's been historical for you guys. As you guys have modernized, what is the connection on a product basis that's available today and that's being worked on on a roadmap basis that says, okay, you can do all this stuff with the data, but it's going to be cloud enabled. How do you get that cloud, hybrid cloud connection so the customer doesn't feel pain in moving to the cloud? No, it's a great, look, first of all, I can boldly say that we were probably the only software company in the industry that disrupted our own industry to go to the cloud. So by the way, data integration, which is our core market, 11 years ago, we invested in the cloud. We didn't know where it would go and we announced it at Informatica 11 years ago and today, 11 years later, we are the number one market share leader in cloud data integration, number one in Gartner Magic Quadrant and our cloud platform today is transacting a trillion transactions a month. So in some ways, we were disrupting ourselves, as you speak. Yeah, I mean, the cloud, I mean, the Gartner thing, I mean, I always say this, those are old metrics, but the new metric is customer traction. You guys were in the announcement with Google Spanner as they globally GA their Spanner distributed database, which is a horizontally scalable data. You have a relationship with Amazon, you're in Microsoft. What is the customer uptake and what are some use cases and give us some specifics? Three specific use cases. The customer started a journey in cloud, more connecting cloud applications. You know, I'm Salesforce, connect me to Workday, connect me to SAP, so on and so forth, simple application integration or API management, where data gravity is moving to cloud, where fundamental workloads are going and we see more and more traction is taking analytics to the cloud. I'm moving my workload to Redshift or I'm moving my workload to Azure Data Warehouse. That's where, by the way, between January and May, we have moved half a trillion data objects to cloud data warehouses, half a trillion. So clearly in that context, we work with AWS. Three years ago, we started with them, Azure, Google. Well, just to put an exclamation point on that, in January, it was a billion. So between January and now it's up to a trillion. That's huge. I mean, that's a hockey stick. The scale is a hockey stick over there because so much more is being created outside the enterprise and customers don't want to bring it on-premise. They want to say, look, I want to just put it in a Redshift or Azure Data Warehouse and I want to process there. And over a period of time, what they want, not your point, is that connect me to my on-premise data warehouse too. So let's say I've done some analytics here. Can I take the relevant analytics and move it to, let's say, my on-premise data warehouse? And over a period of time, as I get comfortable with this hybrid, I may take this workload and 100% flip over to the cloud too. But they want this bi-directional journey. And that's what we're enabling customers. It's always trying hard to cobble together things that customers' language, that they're used to speaking in to new concepts. And it seems to me that data integration is your business. That's the foundational. So the way we describe is data integration is the foundational layer. And everything else we do is what I call more value-added data management capable, like MDM. Data integration allows you to connect, bring data together. MDM is a value-added data management solution to say now I can get a 360-degree view of my customer, like a Nordstrom is using us for, or a 360-degree view of my products, or a 360-degree view of my suppliers to make more business decisions. So integration is table stakes from your standpoint. It's foundational. It's foundational. It's table stakes. It's foundational. Foundation, okay, better work. And in that context, we operate like the Switzerland in the world of data, whether it's Amazon, Google, Azure, Tomorrow Oracle, SAP, we connect the whole world. So you have a vision of where this is all going to go. So it's one thing to say, well, we've got our products set and we're moving it to a new technology base. Which is good. That'll improve productivity. But this whole concept of data management is bigger than just moving existing tooling, existing practices to a new set of platforms, no matter how much more productivity you might get out of those new platforms. It means something more. It means a way your business operates differently. Business thinks differently. It means different ways of institutionalizing work. Give us the vision that you're laying out to your product team about how, yes, we're re-platforming, we're introducing these new development technologies and all these other things. But here's where we're going. Here's the role we want to have in business. What is the role that Informatica wants to have in business? So our vision is to be what I call the system of intelligence for our customer. Because the organizing layer for that is data. So when we say data management, I mean data management is a very broad word, you could argue. But our goal is that we want to organize the enterprises data. The vision that Google has for the internet, organize the customer's data, whether it's inside the four walls or outside in the context of the business processes. Now I'll translate that for you in two ways. We used to optimize for the IT technical user. Couple of years ago we made a big pivot to put an end to it. We are also optimizing it for the business user because data now is such a powerful asset that business users want direct access to it. So one of the things you would see from us in the last three, four years is that we have been putting out a lot of out-of-the-box data solutions. Intelligent Data Lake is a great example of that. So we are giving IT full control of it, but we have a bimodal experience where a business user can self-service analytics. I just want to walk in as a marketing analyst and understand what was my lead to revenue conversion. I don't care about all the underlying infrastructure. I don't know what to do, but I just want to do my job. But IT also wants to make sure that business users are accessing it as governance, security, compliance issues. So we are marrying the two together. That's a very high bar for ourselves. So let me see if I can follow up on that because I want to make sure that at least I understand it. When you say you want to be the Google for an enterprise's data, there's actually a couple of subtle things in there. First off, number one, is that Google is looking at mainly public data. And you want to look at public and an enterprise's private data. As you said, that requires a whole level of functionality that Google doesn't worry about. Like privacy, like ownership, like management, like control. Secondly, increasingly, the enterprise concept, especially when it comes to data, is being able to get access to any data anywhere. So it's not organize the internet, it's not organize the enterprise's data, it's organize all data for that enterprise. You got that right? Exactly, and we don't own the data. The enterprise owns the data, big difference for us. But the enterprise is also going to go out to all those sources that Google is looking at. That's exactly right. You're right, the data within the enterprise and outside the enterprise, for the enterprise. And we don't own the data. We want to bring it together for the enterprise to consume and operate and execute a lot more easily and efficiently. So we're not talking about just small corners of data. We're talking about the enterprise, all data that's possible. We are going outside the world. We're looking at unstructured data because we want to, for example, when you are, let's say on Twitter, today we're going to be tweeting. That's unstructured data, but it's about you and me. And now today, if Nordstrom wants to figure out something what John likes, what John thinks, they want that. They want that. We are bringing that together within the MDM to say, oh, you know what John bought from you. Here's what John is saying on Facebook, or here's what John is saying on Twitter. Marry the two together and you understand John a whole lot. But that's what we want to do. And make it addressable and make it available to not only databases and systems, but developers. Oh, absolutely. In context for develop, yeah. And I think this is when I asked a question about data management, kind of the vision of data management. In many respects, it's the enterprise's access to data that's relevant to it. Number one, the ability from a metadata standpoint to know where it is and have the properties of ownership and privacy and rights and privileges and identities. And then number two, the ability to move it around. Absolutely. According to, as you noted, the integration laws that the... That's exactly right. And because we've been operating for the enterprise for the last 25 years, we understand what they need, what regulations, what security concerns, what governance and compliance issues. So if I had to summarize that context as, look, we want to organize the enterprise's data, whether it's inside the four walls or outside for them, at their level of scale and security and governance. And then, and then with the help of Claire, democratize that for any user to truly use it. The democratization is a big angle. And I want to ask you that because, you know, as much as you see the future, and I think you do, I've been talking to you many times, and here in the keynote, customers aren't in the future. You've got to kind of come to Earth and get to reality. So I got to ask you the question for customers. Because they're trying to just do, I'm trying to move to the cloud. I got some VM where I got Amazon over here. I got Azure. I've really fully baked out my full, you know, how I'm going to integrate cloud into my business model. What are some of the use cases that you guys are engaging customers with? Because you have a good vision, products are solid. When you go out to the field, talk to customers, what are the use cases? What are you engaging them on? The journey to cloud is a big use case. And in the journey to cloud, as I said, there are two specific journeys customers are on. One is I'm deploying these thousands and thousands of hundreds and hundreds of enterprise SaaS apps. Help me weave them together in the context of data integration or MDM. Second is the whole data gravity going to cloud. I talked about data warehousing analytics. Second is all of that. Move my data warehousing, but give me the flexibility in the hybrid. As I said, right? I want to bring outside data within Redshift but connected tomorrow. So those are two biggest use cases we see. Third, we see that rides on both of them is self-service analytics. If I'm able to do both of these, then I'm much more easily able to do self-service analytics. So those three are the ones. Are the primary use cases right now? Those are the three primary use cases. Second one, on the other hand, we see governance and compliance come up very big. I mean, clearly customers are realizing that all of this rearchitecture that's happening, you still need the same governance income. If I am a large bank, if I'm a large insurance company, the laws didn't change for me. Cloud may have come, Hadoop may have come, the laws still stay the same. So governance and compliance is a huge one for us. Look at GDPR. There is a deadline in May 2018. And customers are unprepared for that. So that's the number two I see, governance, a lot I see. And in Europe it's even worse. I mean, you can get a top line, is that the top line, 4%? That's the idea. By the way, customers don't realize if you're a US company, even if you transact with one single European entity, you are now- Well the liability is there. So let's just go to the root cause of what causes that liability potential and that's security. Quickly, security obviously is on the mind of you guys. You're an interesting security product. You guys are digging in the product. What's the product vision on security? So that's another, the last one I was going to say, four years ago, I mean, we saw that coming, that security is an unsolved problem at the data layer and that's where the world is going to organize itself. So we invested and we have to invest ahead of the curve. So we launched the product security source. Today, it's basically is the industry's number one product, 11 awards at RSA. And Raymond James is a customer deployed within their four walls. 7,000 databases go through security source to give them a full view of my sensitive data who's accessing it, all of those risks that are now coming to the data layer. As data gets democratized, the security issues become bigger and broader. Final question for you. I want you to take a minute to end the segment because I want to give you the chance to say that because you know, I'm a big fan of the product work, watching you guys go private and seeing the transition with the new management team. The product guys came in. I've said this on theCUBE many times. You get the brand marketing going on now, new CMO, things going to be pumping out there. What is special about Informatica right now from a product standpoint? What makes you guys unique? You guys done some good things, products coming down the bike. What are the guiding principles for you as the leader of the product team to continue to stay on that wave and innovate and make these products valuable to customers? I think the biggest change I would say is that we are innovating at the space of a startup but we have the scale and breadth in the world of data management that is unparalleled to anyone. In this space, whether it's the traditional architecture, a big data architecture, a real time streaming architecture or a cloud architecture or it's MDM and security and governance, nobody can do it at scale as us. And by the way, we firmly believe in the best-of-breed concept. All of those capabilities are best-of-breed within their own market. So our belief is that, look, we can solve the customer's transition a lot more seamlessly and a lot more risk-free and a lot more in a future proof way. And of course, we are modeling ourselves to move at the pace of a startup. I mean, I call ourselves the hardest pre-IPO billion dollar. I was just going to ask the revenue question. Billion dollar, by the way, billion dollar in revenue company, not billion dollar in market camp company. You're doing over a billion in revenue. You're doing a billion in revenue. I'm going to add one more thing to that. I mean, I'm not even going to test it. We are especially impressed that you have made very, very bold promises the past few years and you've executed on them. You're one of the few companies in this space in the whole data management, this emerging data management in the X-generation world that has executed on the promises that it's made. Your promises make sense and yet all the things that you said are excellent. Your promises make sense, but your execution makes it safe for customers. Well, we had some critical analysis yesterday, so we're not going to just all fawn over you guys, but you know, we had some, you know, there's some things you work, but the big bets are paying out. You guys made some great bets. The cloud bet was key. Congratulations. Amit, great to see you on, come on theCUBE, thanks for spending the time. You got a keynote coming up this afternoon. Real quick, what's going to be the topic? Well, I'm going to talk a lot about how Clare will be able to solve a lot of future-looking problems. So today's keynote is all about the futures and what the vision of the future is. So I'm going to showcase a few examples of what machine learning and AI can do to increase productivity and help ease the pain of our users and customers. Get that data integrated, democratize it and create freedom for the data to fly around and get those apps addressing it. This is theCUBE bringing you all the data here inside theCUBE, but soon we'll have an AI bot doing all the interviews in the future sometime. This is I'm John Furrier with Peter Birch. We'll do in them today at Informatica. Day two, exclusive coverage from theCUBE. We'll be back with more coverage after the short break. Stay with us.