>> 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. I am your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Amit Walia, President - Product and Marketing here at Informatica. Thank you for coming back on theCUBE. So we're here at Informatica World, there's a lot of buzz, a lot of energy, obviously CLAIRE is a big story, your company got great press yesterday from The Wall Street Journal teaming up with Google to tame the data. One of the themes we keep hearing is that data needs AI, but AI needs data. Elaborate on that a little bit. >> That's a great point, in fact I would extend that and say I believe, and I will talk about that today in the closing keynote, is the language that AI needs or speaks is data. Because to be honest, without data, there's no great AI. And I think something that we've known all this while, but now that AI is really becoming pervasive and has skill, you really, really need to give it relevant, good, contextual data for a Siri or a Cortana or Alexa to make some contextual decisions, right? And we see that happening a lot in the world of enterprise now. Finally enterprises are arriving at the point where they want to use AI for P-to-P use cases, not just consumer use cases that you and me are used to. And then, to your other question, AI is a part of everything that we do in data. Because, to be honest, it really helps improve productivity, automate mundane tasks. And I think we were talking before this, there is a massive skills gap. And I think you look around, the economy's kind of fully saturated with jobs, and there's still so much more work to be done with more data, different data, so AI's helping making some of those mundane activities become a lot more easier or autonomous, if I may. >> What's the use cases for CLAIRE in AI around as it grows? Because, you know, the data world, you guys have been doing it for 25 years at Informatica, private for 4 so, innovating on the products side, but it used to be, here's the data department, they handle it. The data warehousing in the fenced out area in the company, now it's strategically part of everything, right? So you guys have the MDM, you've got the Catalog, you've got all kinds of solutions. How is that role changing within your customer base? And what are some of those use cases? Because now they have to think end-to-end, you've got Cloud and On-premise, these are challenges and opportunities. But the role of data and the data teams is expanding rapidly. >> In a significant way. A significant way. I think I kind of was joking with our practitioners yesterday that they were all becoming, they were going from heroes to superheroes, if you are enjoying the Avengers movies, and that analogy. But genuinely, because if you think about it, right, I think what we are seeing in this world, we call it the data three data where the data is becoming a platform of a sort. It is getting decoupled from the data bases, from the applications, from the infrastructure, because to truly be able to leverage AI, and build applications on top, you cannot let it be siloed and be hostage to its individual infrastructure components. So we're seeing that fundamental change happening where data as a platform is coming along, and in that context the catalog becomes a very, very pivotal start, because you want to get a full view of everything. And look, you're not going to be able to move all your data in one place, it's impossible. But understanding that through metadata is where enterprises are going, and then from there, John, as Rebecca's talked about, you can have a customer experience journey with MDM. You can have a analytics journey in the Cloud with an AWS or (inaudible) or a JCP. Or you can have a complete governance and security and privacy journey understanding anomalous activity. >> So before I go any further I just want to ask you about this one point because you guys made a big bet with the Catalog >> Okay, and it's looking good. A lot of good bets. You know, AI, Catalog, Cloud, early on the Cloud, but one of the things I hear a lot is that data's at the blood stream, you want the blood flowing around the system, the body. People looking at data like an operating system kind of architecture where you got to have the data free flowing. So the Catalog seems to be a big bet there. How is that helping the AI peeps because if you can have the data flowing -- >> Yep. No I think -- >> You're going to have feeding the machine learning >> Absolutely. >> The machine learning feeds the application of AI, you got to have the data, the data's not flowing, you can't just inject it at certain times. >> The way we think about it is, you're exactly right. I would just, in fact it's so ah, interesting, the analogy I use is that data is everywhere. It's like the blood flowing through your body, right? You're not going to get all the data in one place to do any kind of analytics, right? You're going to let it be there. So we say metadata is the new OS. Bring the metadata, which is data about the data in one place. And from there let AI run on it. And what we think about AI is that, think about this. LinkedIn is a beautiful place where they leveraged the machine learning algorithm to create and social graph about you and me. So if I'm connected with John, I know now that I can be connected with you. The same thing can happen to the data layer. So when I'm doing analytics, and I'm basically searching for some report, I don't know, through that same machine learning algorithm at the catalog level, now we can tell you, you know what? This is another table. This is another report. This is another user. And so on. And we can give you back ratings within that environment for you to do what I call analytics on your fingertips at enterprise scale. So that's an extremely powerful use case of taking analytics which is the most commonly done activity in an enterprise and make it accurate at an enterprise scale. >> Well the LinkedIn example, you know, of course I have a different opinion on that. They're a siloed platform. They don't have any API's, it's only within LinkedIn. But it begs the question, since you're both that kind of consumer, look at a company like Slack, going public, very successful, their numbers are off the charts in terms of adoption, usage, a simple utility in IRC message chat room that has a great UI on it. But their success came when they integrated. >> Sure. >> Integration was a big part of their success. They wanted to have API's and let customers use the software, SAS software, with a lot of data. So they were really open. >> Yes. >> How were you guys from a business standpoint taking that concept of SAS openness connecting with other apps because I might have, bring my own app to the table as data, and integrate that piece into Informatica. How does that work? >> Very similarly. So the way we've done it is that our whole platform is fully API based. So we have opened up the API's, any application can hook on to that. So we believe that we are the Switzerland of data. So you may have any underlying infrastructure stack. On-prem, in the Cloud, multi-Cloud, whatever it is. Different applications, different Cloud applications, right? So our goal is that at the layer which is the metadata layer on which CLAIRE runs, we've opened up the API's, we've hooked to everything, and so we can consume the metadata, and there we truly provide a true data platform to our organization. So if you are running a Server Snap, a Salesforce.com, Adobe, Google, AWS, you can still bring all that stuff together and make contextual business decisions. >> One of the things you had talked about on the main stage is how the Millennials that you're hiring have higher expectations in their personal lives from the technology that they're using, and that's really pushing you to deliver different kinds of products and services that have the same level of innovation and high touch. Can you talk a little bit about that and how, and how this new generation of the workforce, and there's obviously Gen Y coming right behind it, is really pushing innovation in your company. >> Well you know, I have a fourteen-year-old, so I get a taste of that every day at home. (laughing) So you know, what they want to experience, so I, you know, I use this word, experiences are changing. And by the way they are pushing the boundary for us too. We grew up in the infrastructure software world which you know, twenty-five years ago was all, you can go down to the command line interface. Not any more. You really really have to make it simple. I think users today don't want to waste their time what I call doing mundane activities. They want to get to value fast. That's pushing the boundary for us. In fact that's where we're leveraging AI in our products to make sure we can remove the mundane clutter activities for them, for them to do value added activities. For example, I want to discover data to do some analysis. I don't want to go around discovering. Discover it for me. So that's where CLAIRE comes in and the catalog, right? Discover it for me. You know what? I don't want to figure out whether this data is accurate or not accurate. Tell me. So we are taking that philosophy, really really pushing the boundary for us, but in a good way. Because definitely those users want what I call very simplified and value added experiences. >> And that's really what SAS and consumer applications have shown us, and that's proven to be hard in the enterprise. So I got to ask you as you take this data concept to the infrastructure, a lot of enterprises are re-architecting, you hear words like multi-Cloud, hybrid Cloud, public Cloud, and you start to see a holistic new kind of persona, a Cloud architect. >> Yes. >> They're re-architecting their infrastructure to be SAS-like, to take advantage of data. >> Correct. >> That's kind of known out there, it's been reported on, we've been reporting on it. So the question is, that isn't alignment, that's not just the data people, it's data meets infrastructure. >> Absolutely. >> What's your advice to the companies out there that are doing this, because you guys have Cloud, Google, Amazon, Azure, Cloud, On-premise. You can work anywhere. What's you're advice? >> Yeah, no, I think it's a very good, it's a very topical question. Because I do think that the infra, the old days of separating different layers of the stack are are gone. Especially the old infrastructure all the way to platform as a server stack has to be very well though out together. To your point, customers running a hybrid multi-cloud world, right? So think about it, if you're in the world of improving customer experiences, I may have my marketing cloud running somewhere, I may have my sales cloud running somewhere, and a service cloud running somewhere. But to give a great experience I have to bring it all together. So you have to think about the infrastructure and the data together for enterprises to give a better experience to their customers. And I see innovative customers of companies truly think through that one and succeed. And the ones that are still lagging behind are still looking at that in silos. And then be able to have the data layer for hyper scale. Well these are all hyper scale platforms. You cannot run a little experiment over here. So we've invested in that whole concept of hyperscale, multi-cloud, hybrid cloud, and make sure it touches everything through API's. >> So we've been covering you guys for four years here at Informatica World. It's great to see the journey, nothing's really changed on the messaging and the strategy, you say you're going to do something and you keep doing it, and some little course corrections here, and acquisitions here and there to kind of accelerate it. But when we talk to your customers we hear a couple of different things. We hear platform, Informatica, when describing Informatica. You guys win the whole data thing, you're there, it's the business you're in. In the data business. But I'm hearing new words, platform. Scale. These are kind of new signals we're hearing from your customer base and some of the people here at the show. Talk about that impact, how you guys are investing in the platform, what it means for customers, and what does scale mean for your business and customers? >> No, we've heard that from our customers too. Customers said look, they all recognize that they have to invest in data as a platform. But you know, it's not like an original platform so they want it because we serve the broader state of management needs, so they want us to be like a platform. So we've invested that, couple of years ago we went completely ground-up, re-built everything, micro-services based. All API driven. Containerized. Modular. So the idea is that nobody is buying a monolithic platform. Nobody buying a platform, it just builds by itself. And they can compartmentize it, I want this now, I want that later, so like a Lego block it builds. And, you know what, through an API it also hooks into any of the existing infrastructure they have, or anything new that they want to bring in. So that really pushed the boundary for us. We invested in that. By the way, that platform today, in the Cloud, which you call IICS, runs eight trillion transactions a month. Eight trillion transactions a month. And by the way, last Informatica World, it was running two-and-a-half trillion transactions. So in one year it's gone from two-and-a-half to eight. So we are seeing that really hyper scale. >> And you, and I'm going to ask you if you believe, just, and you can answer yes or no or maybe, or answer on your own, do you believe that data is critical for SAS success? >> Oh absolutely. No doubt about it. I have not met a single customer who ever said anything different. In fact, the thing that I see is like, it's becoming more and more and more a sea-level conversation. That hey, what are we going to do with our data? How do we bring that data together to make decisions? How do we leverage AI and data together? It's truly in our sea-level discussion, whereas it was never a sea-level discussion years ago, it was more about what application am I going to use? What infrastructure am I going to use? Now they're all about, how do I manage this data? >> I wanted to talk about ethics (laughs) and this is, because recently had published a paper about Tech for Good, and it's about this idea of using AI and machine learning to help society achieve better outcomes, and then also to help us measure it's impact on our welfare beyond GDP. Because think about the value that technology brings to our lives. What's your take on this? I mean how much value do you think AI brings to the enterprise in terms of this Tech for Good idea? >> No, so, by the way one of Informatica's values is "Do good". And we are firm believers in that look, there is an economic value to everything in life. But then we all have something to give back to the society. There is something to create value out there which is outside the realm of just pure economics which is the point you were asking. And we are firm believers in that. I do think that by the way, there is a very high bar for all of us in the industry to make sure that not only, it's not just about ethics of AI also at the same time, because we cannot abuse the data. We're collecting a lot of information. You and me as consumers are giving a lot of information and I talked about that yesterday as well, that we believe that the ethics of AI are going to play a fundamental and differentiating role going forward. I think the Millennials we're talking about, they are very aware of that one. They are very purposeful. So they'll look back and say, who actually has a values system to take this technology innovation and do something better with it, not just creating money out of it. And I think I totally agree, and by the way in the very early stages, industry has to still learn that, and internalize that, then do something about it. >> Well Amit, yeah I think you're right on, early days, and I can give you an anecdotal example is that this year, University of California, Berkeley, graduated its first inaugural class of data science analytics. First! First ever class for them. They're a pioneer, they're usually having protests and doing things with revolutionary things. That shows it's so early. So the question I got to ask you is, you've got your fourteen-year-old, you know I have kids, we follow each other on Facebook. I'm always asked the question and I want to get this exposed. People are really discovering new ways to learn. Not just in school, you got YouTube videos, you've got CUBE videos, you got all kinds of great things out there. But really people are trying to figure out where to double down on, what dials to turn, what classes to take, what disciplines are going to help me. It used to be oh, go into computer science, you'll get a great job. And certainly that's still true. But there's now new opportunities for people, data's now grown from you know, programming deeply to ethics. And you don't need to have a CS degree to get in and be successful to fill the job openings or contribute to society. So what are those areas that you see that people who are watching might say hey you know what? I'm good at that, I'm good at art, I'm good at society or philosophy or I'm really good at math or, what skills do you, should people think about if they want to be successful in data? >> You know, I think it's a very foundational question. I think you're right, I think programming has become a lot easier. So I think if I'd stepped back to the days we graduated, right? It's become a lot easier so I don't think that necessarily learning programming is a differentiating, I do think that back where you were going, people who'd generally think about what to do with that. I think there is analytical skills that we all need, but I think the soft skills I believe in the society, we are kind of leaving behind, right? A little bit of the psychology of how users want to use something. Design thinking. By the way I still think that design thinking is not yet completely out there. Um, the ability to marry what I call the left brain to the right brain, I mean, I think that's key. And I do think that we cannot run away completely to the right brain, as much as I am an analytical person myself. I think marrying the left and the right, I do believe, like I, as I said I have a fourteen-year-old. My advice to all those who say, he wants to do Computer Science, is to take enough psychology or design classes to kind of have that balance. So my encouragement would be have the balance. We cannot all just be hyper-analytical. We have to kind of have the balance to see ... >> I think just be smart, balance, I mean again, I have not found one, well I guess the answers are stats and math, have the check, that's easy to say, but ... >> The emotional skills. But you need more of those, I think a little bit more of those left-brain skills also to complement them. >> Well and also for the experience, study art, music, what delights people. What inspires the passion? >> I agree with that. >> Yeah. Absolutely. Amit, always a pleasure to see you. Thank you so much. >> Thank you very much. Always a pleasure to be here. >> Great conversation. Good insight. >> I'm Rebecca Knight for John Furrier, stay tuned at theCUBE's live coverage at Informatica World. (Upbeat music)