 Live from Las Vegas, it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Anil Chakravarti. He is the Chief Executive Officer at Informatica. Thank you so much for returning to theCUBE. Oh, my pleasure. Thanks for having me back on your show here. So on the main stage this morning, you made this, you said that AI and ML need data, but data needs ML and AI. Can you elaborate on that, riff on that a little bit? Yeah, yeah. If you look at AI and ML, hot topic obviously. Every company is trying to take advantage of new machine learning AI technologies. One of the key components of making that happen is the availability of the right data. Because you have to train these machine learning algorithms. The data scientists have to be able to find the right data and then they have to prepare the right data, make sure that they have access to the data, clean it up, and then put it into their AI models, into the AI algorithms and so on. Because the training of the algorithms is very sensitive to the quality of the data. It's really garbage and garbage out. If you don't feed it the right data, the results will be skewed. And so that's the key part of what we mean by, when we say AI machine learning needs data. The flip side is what we do and help customers, which is manage their vast complexity and scale of data. If you look at customers, petabytes of data, thousands of databases, hundreds of thousands of tables. So how do they manage all of that data? Because the management of data is not just about availability of data or the performance of those systems and so on. All that is super important, but it's also the security of the data, the governance of the data, the availability of the data to the right users at the right time. Trying to do all that manually, you just can't keep up. And that's where you need machine learning and AI to be able to do that for you in an automated manner. Anil, we've tried the past multiple years ago, every year it's the same story. You guys had the right wave data. Everyone's now talking about what you were talking about four years ago. You're continuing to talk about and adding to it. You also talk about being the Switzerland, the neutral third party, because data needs to connect around from multiple sources. You had a lot of industry players up on stage today. How is that going? How are you continuing to be that role in the industry as more and more people come in? What's this say about the momentum and for Informatica strategy? Yeah, I think it's really because of what customers really want. Take any customer, any enterprise customer, government customer of any scale, they're usually using a lot of different both on-premise and cloud technology offerings. So it could be multiple software as a service offerings, multiple, maybe public clouds where they're running it as platform as a service, a lot of different on-premise offerings, et cetera. Which means that all of those offerings that they're using have a data footprint. And from a customer's perspective, if they're using different tools to manage the data for each one of those, well they have all the problems they've always had. Data inconsistency, inability to manage it, and just who's going to learn if you're a data administrator or you're going to learn four or five different tools to manage it? So that is not really going to work. So that's where customers are demanding, hey, I need a data management platform that can help me manage the data consistently and that's where we come in. That's what helps us be the Switzerland of data. So data feeds machine learning, machine learning powers AI. This is the formula you guys talk about all the time, no data, no AI, but if data is constrained from either infrastructure legacy or regulation that's going to slow the feeder concept down or maybe incomplete data. And this is really about operationalizing AI. So you've got to solve that data problem first if you want to scale up operations around AI. What's the state of the art from Informatica? What are you guys doing in this area and where's the customer's progress in this new operationalizing of AI with data at the heart of it? Yeah, from an operationalization perspective what you need is first of all, help your data scientist and others using AI to find the right data. And finding the right data you do through the catalog for example, it'll tell you what data you can access and then what's the metadata around the data? What data, what you can use the data for. So maybe there's some data that you say, look we have the data set but we don't have the customers opt in to use the data. Why? You can't use the data. So that's the first step, finding the right data. Then getting access to the data. That's what you get through data integration, the cloud tools, big data tools, et cetera. Then you prepare the data. We have a number of tools to prepare the data to make sure that the AI and machine learning models can use them well. Then you feed the data, you run it, you get your results, but then the explainability is a big deal. Whether it's regulators or even your own internal executives, they say, oh that's the result of running the AI model but how did it come to that decision? For instance, in financial services, if you're using AI to do let's say a decision on who gets to get a loan or not, well you have to make sure that there is no bias in that. And so in order to explain the results, you need to know where the source data came from and that's what we do as well through our governance and lineage. Well we love talking about SaaS success. So you look at the cloud native, born in the cloud, great examples of how data has really been driving the new generation of innovation. The more enterprises we talk to around digital transformation, the more we hear we want to be consumer-like with a SaaS, whether it's an app for banking or an IoT app or anything. So SaaS is kind of, and you need data for that. How should an enterprise architect that solution because it's harder when you don't have clean one cloud native. So you have to bring in some cloud, you got to bring in the on-premise. Where does the data sit in all this? How do you architect the data, on-premise in the cloud or in general so that the customers have a really road map to a SaaS solution? It's a great question. What you see right now is the focus on building a true customer data platform. We obviously just acquired a company outside that helps build, get insights out of a customer data platform. The way we think of it at Informatica is you have a customer data platform where then the last mile of how you reach the customer keeps changing and evolving. That last mile could be through a call center. It could be through a web application. It could be through a mobile app. It could be through a sales person who's reaching the customer with a live interaction. It could be a lot of different ones and it could be all of them. That's where the Omni Channel comes in. The way to do what you're asking for John is to truly focus on building a customer data platform that can support multiple kinds of last mile when it comes to actually interacting with the customer. That's how you ensure a very good, consistent customer experience. And then you take advantage of whatever are the latest technologies. Tomorrow, like we were just talking about here, if there is AI-enabled bots or something else that's a better way of interacting with the customer, you're still working off of the same consistent customer data platform. That's how we see it. I want to ask you about the skills gap. We know that there's a great demand for people who are data scientists, experts in cloud and analytics, and yet there are so few qualified candidates. That's right. Your thoughts about it and then also what Informatica is doing to make sure you are recruiting and retaining the right employees. Yeah, I think I completely agree with you on the skills gap. And obviously that's also a great opportunity as well because in reality, a lot of the younger folks are looking at what careers they want to pursue with the right mindset and the right training. These would be great careers for them. And this also, the other great thing about this is this is across the country and across the world. So you don't have to be in a specific location to have a successful career as a data scientist or as a data steward, et cetera, et cetera. So I think from a training perspective, we are actually working with a number of different universities. We actually started working with Indiana University to build a curriculum that can then be available online, available to a lot of different folks. We obviously work with a lot of different system integrators and consulting partners who hire hundreds of thousands of people and they are starting to build some very, very large practices around data science. That's another avenue for career growth there. And last, we're also starting at a much younger age. You know, I think last year we talked about the next 25 program and tomorrow when Sally is back on stage, you will see an update on the next 25 program. We're trying to get kids at the middle school level interested in this as a career. And you know, real quick on the follow up on that is what curriculum specifically do you see in high demand? Is it machine learning? Is it analytics? Is it cognitive? What specific skills that you see in demand and for folks to start thinking about? I think what my advice to folks, in fact my daughter is a freshman in college too and I've been giving her the same advice because I think this is a great way to go, is when you think of skills development, first think of a broad platform that will give you the right skills regardless of the changes in technology because technology will keep changing. So what is that broad platform? The broad platform is, I think you need a background in statistics, you need a background in computer modeling and programming and you need a broad platform in overall math and again, I don't mean to scare anybody off, it's not calculus level math, but it's math that helps you understand concepts, et cetera. That's the broad foundation you need. And then you have a number of different new technologies whether it's Python, whether it's Matlab, there are a lot of different ways of approaching and doing data science, but then once you have the foundation, it's easy to pick this up and the rest of it, just like in any other job, once you start doing it, you're going to pick up the rest of it and you will become an expert there. Great, Amil Chakravarti, thank you so much for coming back to theCUBE. Perfect, thank you so much for having me. Thanks for having us, thank you. You are watching theCUBE Informatica 2019, I'm Rebecca Knight for John Furrier, stay tuned.