 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 Suresh Menon. He is the senior vice president and general manager, master data management here at Informatica. Thank you so much for coming on the show. Thank you, it's great to be back. Great to welcome a CUBE alum. So a major theme of this conference is customer 360. It's about customers' need for trusted, accurate data as they embark on their own digital transformation initiatives. Can you just talk a little bit about what you're hearing, what you're hearing from customers, what their priorities are? Yeah, absolutely. You know, with MDM, the promise of MDM has always been creating a trusted, authoritative version of any business critical entity. And who are the most important business critical entities for any organization, customers, right? So almost 80 to 90% of our customers are talking about reinventing a new customer experience because some of the things that they've been telling us is that we've all learned in the past that a bad customer experience means that we've all had those experiences. We go to a hotel, we use a particular airline, we have bad experience and we say promise ourselves we'll never go back there again. So organizations have always, for years now, understood that there is a cost to not delivering a good enough customer experience. The big change that I'm hearing, at least over the last year or so now, and especially at this event, is that organizations have now been able to quantify what great customer experience can mean in terms of a premium that they can charge for their products or services. Now that is a big shift. When you start thinking about saying, if I deliver a better customer experience, I'm actually be able to charge 10 cents more for a cup of coffee. I can charge 20% more for an airline ticket. That now has a direct impact on the top line. And data drives this. Obviously, data is the key part of it. What's changed this last year? I mean, a lot's happened, we see on the regulatory side, one year anniversary of GDPR, a lot of pressure around regulation. We see, everyone sees Facebook and goes, oh my God, maybe I don't want to follow that trap. If I'm an enterprise, pressure to develop SaaS-like applications with data because we know what cloud native and born in the cloud looks like. We've seen companies come out of the woodwork from fresh start and use data as part of the input with AI application for great software. So now the enterprise want to do that. Exactly. It's hard. It's hard. And I think in a lot of organizations, mines, collective mines, there's this push and pull. Because in order to deliver that best possible customer experience, they realize they need to gather more data about us. Every touch point, every interaction, if you can gain that complete 360 view, it just means that you'd be able to deliver a better possible experience. But now you're gathering more data about customers and to your example about Facebook, now means that we are now custodians of what was an explosion of data than what we used to have before. And if you're moving those to the cloud, how do I make sure that I don't end up in the front page of the Wall Street Journal like some of the other organizations have? So there is great volumes of data being collected, but how do I manage it, secure it, govern it effectively so that we don't have those risks? I want to ask you a question because I've been talking a lot about fake news in Facebook lately because we're at digital cubes, the digital distribution, 10 years been doing it, putting out good payload with content, great guests like yourself. But there's really kind of two things. This is where I want to get your reaction to. There's the content payload and then there's the infrastructure dynamics of network effect. So Facebook is an example where there was no regulation. Obviously they were incented to actually get more data from the users. But so you got content or data, and then you got infrastructure kind of like dynamics. You guys are looking at an end-to-end, you got on-premises to cloud. That's infrastructure and that's going to be powering the AI and the SaaS. Data becomes the payload, right? So what's your, as a product management executive and someone thinking about the customer and talking to the customers, how do you view that? What's the customer's formula for success to take advantage of the best use of the content or data in digital while maximizing the opportunities around these new kinds of infrastructure, scale and technology? Yeah, I think they've come to the realization that data is not entirely sitting on-premise anymore. In the old world, to get customer data, you go to three applications, a CRM and ERP and some kind of, couple of homegrown applications and on-premise. Now, for the same functionality, whether it's voice of customer, customer experience applications, what do you call it? There's an app for it and it happens to reside in the cloud. So now you have about 1100 on average, cloud applications that store components. So where do you start bringing all of that content together? A lot of organizations have realized that, you know, do it in the cloud for two reasons because that's where bulk of this data is being generated. That's where the bulk of this data is being consumed. But the other aspect of it is, we're not no longer talking about hundreds of millions of records. As you start bringing in transaction data, interaction data, we're talking about billions of records. And where else can you scale with that much ease other than the cloud? So, but at the same time, there is a hybrid that is extremely important because those applications that are sitting on-premise are not going away. You know, they still serve up a lot of valuable customer data and continue to be frontline operational systems for a lot of the users. So a truly hybrid approach is being developed. I think that thought process is coming around where some domains live in the cloud, some domains live on-premise, but a seamless experience across both. That's great insight. I want to then follow up and ask you, okay, how does Informatica fit into that? Because you guys want to provide that kind of horizontally scalable data layer depending on where the customer's needs are at any given time. You got APIs out there, things like that. Where do you guys fit in? How do you make that a reality that the statement you just made? Yeah, and the reality is already being lived today with a few of our customers. And it is that data layer that says, we can bring data, run workloads that are behind the firewall. We can do those same workloads in the cloud if that's where you want to scale the new workloads. But at the same time, have a data layer that looks like one seamless bridge between the cloud and on-premise. And there are a number of different experiences that can help that. We've invested in cloud designing and monitoring capabilities that allow you for a completely cloud-like experience. But all of your data still resides on-premise. It's still being managed and behind your firewall, which is where a lot of the organizations are going as well, especially the more conservative, more regulated organizations. One of the things I want to get your reaction to as well, great commentary, by the way, great insight, is some success examples that might not be directly to Informatica, but kind of point to some of the patterns. Let's take Slack, for instance. They create software, it's basically an IRC message chat room with, on the web, with a great user experience. But the adoption really kicked in when they built integration points into other systems. So this seems to be a fundamental piece of Informatica's opportunity is to kind of do this layer, but also integrating it, because although you might have monitoring, I might want to use a better monitoring system. So you're now thinking about integration. How do you respond to that? What are you guys doing with respect to integration? What's the product touchpoints? Can you share any commentary on that? Yeah, so the openness of our entire data architecture and all of the solutions is something that we, I think we use the word Switzerland quite often. But what it also means is that, you are able to plug in a best of breed execution engine for a particular workload on a particular platform if you so desire. If you want to plug in a AI ML model that happens to be developed on a specific, let's say an Azure or an AWS, you'll be able to bring that in because the architecture is open, completely API driven as you mentioned. So we're able to, customers have the flexibility to plug in and we try to make that a little easier for them also. As you might have seen in some of the demos yesterday, we are providing recommendations and saying for this particular segment of your workload, here are the choices that we recommend to you and that's where Clare comes in because it's very hard for users to keep up with all of the different possibilities or options that they might be having in that particular data landscape and we can provide those recommendations to them. I want to ask about something you were saying earlier and this is that companies are using data to realize that they can charge a premium for a better customer experience and that really requires a change in mindset from a gut-driven decision-making to a data-driven decision-making method and approach. How are you seeing this mindset shift? Is it, are companies still having a hard time sort of giving up, but my gut's telling me to do this? And in particular with relationship to the acquisition you made in February of Allside. Yes. Yeah, I think the good news is across the board, line of business leaders, CEOs, even boards are now recognizing customer experience, customer engagement happens to be top of mind, but there's also equally a recognition that data is what is going to help make this a reality. But so that was one of the reasons why we went out and did this acquisition of Allside because if you think about it, customer data is no longer just a handful of slowly changing attributes like a name and address and a telephone number or social media handles that you could be used to contact us, but it's really about now the thousands of interactions we might have on the websites, clickstream data, web chats, even calls into call centers. All of this and even what we are tweeting about a product or a service online is all the interactions and touch points that need to be pulled in and the dots have to be connected in order to build that customer profile. So we have to do this at scale and that's something that Allside has been doing very well, but it's now become more about just connecting the dots. So we can say, here is this customer and this is all the different touch points as customers had, all the different products they purchased from us over the last few months, few years, but now can we derive some insight, some intelligence? So if I'm connecting four pieces of information, can I infer a life event? Can I detect that an insurance customer is ready to retire? Can I detect that this family is actually shopping for a vacation to Hawaii? That's the first level of derived intelligence insight that we can now offer with Allside. The next level is also about saying, can I be understanding some of these intent? Can we also understand how happy is this customer? Have they been mentioning competitor products which can allow us to infer that this person's probably going to go off and buy a competitor's product if this problem they're having with this device or product is not resolved? So churn scoring, sentiment scoring, and now the third level on top of that, which I think is really the game changer, is now can we infer what the next best action or interaction should be based upon all these things? Can we even do things such as, as I, let's say, not a too happy customer with a particular maybe laptop that I, you know, purchase, I call the call center, as the call is coming through, can we infer what I'm calling about based upon all of the interactions I've had over the recent past and direct that call to a level two or level three technician who specialized in the laptop model that I have in order to make me continue to be a customer for life? One of the biggest challenges happening in the technology industry is the skills gap. I want to hear your thoughts on it and also how concerned are you about finding qualified candidates for your roles? So, you know, I think being a globally, you know, global organization with R&D centers distributed around the world, I think one of the luxuries we have is we're able to look across, not just, you know, we're from Silicon Valley, you know, and you know, there is a, definitely a huge competition for skills over there. I think one of the things that we've been able to do is locations like Toronto, we were just talking about, that's where all site is based, extremely cool technology that's come out there that's, you know, really transforming organizations and their approach to customers stood guard, you know, Dublin, you know, Bangalore, Chennai, Hyderabad. So, you know, we are tapping into centers that have lots of skilled, you know, folks and call it hedging our, you know, our approach and looking at this globally. Yes, there's definitely going to be even more of a demand as a lot of technology changes go for these skills. But I think, you know, by spreading, you know, that skills and having complete developed R&D centers in each of those locations helps us mitigate that problem. What about kids in school, elementary school, high school, college or even people retraining? Is there a certain discipline, stats, philosophy, ethics? Will you see data opportunities for folks that may or may not have been obvious or even in place? I mean, Berkeley just had their first graduating class of data science this year. I mean, that's so early. People want to hone in. What do you see as successful people attaining certain skills? What do you recommend? So, I think there is definitely a combination of technical skills, whether it is the newer AI and ML applications. But I think there is also, you know, in the past we would have said, let's go on higher than someone who's done computer science, you know, and is very deep into that topic. But look at the problems we're trying to solve with data and the application of AI and ML. They're all in service of a business outcome. Some kind of a business outcome. And the more we find people who are able to bridge that gap between the strong application of the newer technologies around AI and ML, and also an understanding of the broader world and the business, I think that combination of skills is really what's going to be required to succeed. Excellent. Great note to end on. Thank you so much, Suresh, for coming on the show. Thank you. Great insight. Thanks. I'm Rebecca Knight for John Furrier. You are watching theCUBE.