>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hello, everyone. Welcome to this CUBE conversation. I'm John Furrier, host of theCUBE. We're here in our Palo Alto studios in California for a CUBE conversation with Suresh Menon, who's the senior vice president and general manager of Informatica of the master data group. Suresh, great to see you. We couldn't see you in person. Three-time CUBE alumni at Informatica World, industry executive. We're remote. Great to see you. >> Good to see you, John. Great to be back. Wish this was in person, but I think this is fantastic. >> Well, one of the things that's clear in my interviews over the past four months, we've been doing our best to hit the road and we've got a quarantine crew here. We're doing our part telling the stories that matter. Data now more than ever, COVID-19 has shown that the companies that are prepared, that have done the work, for the digital transformation, you know, putting the cliche aside, is real and the benefits are definitely there. And you're seeing things like reaction time, war rooms are being put together, because business still needs to go on. This is the reality. And so companies are seeing some exposure and some opportunities, and so a lot of things are going on. So I want to get your reaction to that, because there are changes on how customers are evolving with data. You guys have been at the forefront of that, pioneering this horizontal data fabric, data 4.0, amidst talks about. What are you seeing from customers? How are they approaching this? Because at the end of the day, they got to come out of the pandemic with a growth strategy and they got to solve the problems they've got to do today and be in position. What are you seeing for changes? >> So one of the most important things that we started seeing, there are about three big trends that we began to see starting in about late March, and share some of the data points that we saw across the world, starting with Italy, which was in the news earlier this year with the pandemic. We saw that in one week, the stats were that online or digital sales increased by 81% in a single week. And it's obvious when you lock down a large population, commerce moves to, away from the brick and mortar kind of model to being completely online and digital. The other part of it that we started seeing is we had already started seeing a lot of our customers starting to struggle with supply chain issues. As borders started closing, opening, and then closing again, how do you maintain a resilient supply chain? And a resilient supply chain also means being able to be really agile in terms of trying to identify alternate supply sources, be able to quickly onboard new suppliers, maybe in different parts of the world that are not so affected. And then finally, the last piece that we saw were every single CFO, chief financial officer, people who ran finance organizations at all of these companies, for them, it is almost as if you're driving down the highway and you suddenly run into, enter this fog bank. The first reaction is to hit the brakes, of course, because you don't know what's (microphone cuts out) so every CFO around the world started saying, I need to be able to understand what my cash flow situation is. Where is it coming in from? Where is it going out of? How do I reconcile across the geographies, lines of business? Because everybody realized that without an adequate cash reserve, who knows how long this thing is going to carry on? We need to be able to survive. And then the fourth element that has always been important for our customers is all about customer engagement, getting the best possible customer experience. That's just being turned up to 11, the volume, because as organizations are saying, there's disruption happening now. There are new ways in which consumers are going out there and buying products and services, and these things might stick. There's also an opportunity for some of these organizations to go out and enter into markets, gain market share, that they were not able to do in the past. And then how do you come out of this, whenever it is, how do we come out of it? It's always by making sure you're retaining your customers and getting more of them. So the underpinnings across all of this, whether it's supplier data, whether it's getting the most accurate product information delivered to your online channels, whether it is being able to understand your supply chain holistically with our data platform under it, and then finally customer experience depends on understanding everything end to end, including everything you need to know about your customer. So data continues to become top of mind for all of these organizations. >> You know, Suresh, we've had conversations over the past three years, and I can remember them vividly all about, and we've been really geeking out, but also getting very industry focused around, oh, the enablement of data and doing all these things, horizontal scalability, application enablement, AI CLAIRE, all these things are very relevant. But now with COVID-19, that that future's been pulled to the present. It's accelerated so fast that everything's impacted the business model. You mentioned supply chain and cash flow. The business is right there visible, and all these things are exposed and heightens the volume, as you said, and so everyone's seeing it happen. They can see the consequences, right? So this is like the most reality view of all time in any kind of is digital transformation, will it happen? So I want to get your thoughts on this, because I've been riffing on this idea of the future of work, the word work, workplaces, workforce, workloads, and workflows, right? So they all have work in them, right? We talk about workflows and workloads. That's a cloud term and a tech term. Workplace is the physical place, now home. Workforce are people, their emotional stability, their engagement. These things are all now exposed and all this new data's coming in. Now the executives have to make these decisions. This has really been a forcing function. So first, I'm sure you agree with all that, but what's your reaction to that? Because this brings up challenges that customers are facing. What's your thoughts on this massive reality? >> Yeah, I mean, this is where I think the other domain that is very important, which is most important for organizations if you have to be successful is really that employee or workforce understanding. We talk about customer 360s. We have to talk about employee 360s, right? And tie that to locations. And there are very few enlightened organizations, I would say, maybe three, four, five years ago, who had said, we really do need to understand everything about employees, where they work from, what are the different locations they go to, whether it's home and whether it's the multiple office locations that the organization might have. And it started quite realistically in the healthcare organization. There's a large healthcare provider here in California who many, many years ago decided that they want to create an employee 360, and considering it's doctors, it's nurses, it's hospital technicians and so on, who move from one hospital to another different outpatient clinics. And we are in a disaster-prone state, and what they said is I need to build this data foundation about my employees to understand where someone is at any given point in time and be able to place them so that if there is, let's say, an earthquake in one part of the state, I want to know who's affected, and more importantly, who's not affected who can go out and help. And we started seeing that mindset now go across every single organization, organizations that said, hey, I was not able to keep track, when the lockdowns were started, I was not able to keep track of which one of my employees were in the air at that time, crossing borders, stuck in different parts of the world. So as much as we talk about product, customer financial data, supplier data, employee data, and an employee 360, and now with a lot of state and local governments creating citizens 360s has also now become top of mind because being able to pull all of this data together, and it's not just your traditional structured data. We're also talking about all the data that you're getting, the interaction data from folks carrying their phones, mobile devices, the swipes that people are doing in and out of locations, being able to capture all of that, tie it all together. Again, we talk about an explosion in volume, which I think is to your point, bringing in more automation with CLAIRE, with artificial intelligence, machine learning techniques, is really the only way to get ahead of this, because it's not humanly possible to say, as your data scales, we need to get the same linearly, the same number of people. That's not going to happen. So technology, AI, has to solve it. >> Well, I want to get to AI in a second. It's on my list to ask you about CLAIRE, get the update there. But you mentioned 360 view of business and the employee angle's definitely relevant. Talk more about this 360 business approach, how are customers approaching it across the enterprise. Certainly now more than ever, it's critical. >> Right, so the 360s have always been around, John, and I think we've had these conversations about 360s now, for the last few years now, and a lot of organizations have gone out and said, create a 360 around a particular, whichever one specific business-critical domain that they want to create a 360 out of. So typically for most organizations, you're buying parts, raw materials from a supplier. So create a supplier 360. You really need to understand is there risk there in the supply chain? Am I allowed to do business with a lot of these suppliers? It's data that helps them create that supplier 360. The product is always important, whether you're manufacturing your own, or if you're a retailer, you're buying these from your suppliers and then selling them via your different channels. And then finally, the third one was always customers, without which none of those organizations would be in business. So customer 360 was always top of mind. But, and there are ancillary domains, whether it's that's the employee 360 we just talked about, finance 360, which are of interest maybe to specific lines of business. These are all being done in silos. If you think about creating a full 360 profile of your suppliers, of your products, of your customers, the industry has been doing it now for a few years, but where this pandemic has really taught a lot of organizations is now it's important to use that platform to start connect (microphone cuts out) a line all the way from your customers via their experience all the way back to your suppliers and all the different functions and domains and 360s that it needs to touch. And the most, I guess real-world example a lot of us had to deal with was the shortages in the grocery stores, right? And that ties all the way back to the supply chain. And you're not providing your best possible customer experience if the goods and products and services that customers want to buy from you are not available. That's when organizations started realizing, we need to start connecting the customer profiles, their preferences, to the products, our inventory, all the way back down to suppliers, and are, for example, can we turn up the production in a particular factory, but maybe that location is under one of the most stringent lockdown conditions and we're not able to bring in or increase capacity there. So how do you get a full 360 across your entire business starting with customer all the way back to supplier. That is what we are saying, the end-to-end 360 view of a business, or as we, there's too many words, we just call it business 360. >> Yeah, it's interesting, and I'm interviewing a lot of your customers lately and talking some of the situations around COVID. There's the pre-COVID, before COVID, during COVID, now looking after COVID. Some have been very happy and well-prepared because they have been using, say, Informatica, and had done the work and are taking advantage of those benefits. I've talked to other practitioners who are struggling with trying to figure out how to architect, because what your customers who've been successful have been telling me is that, look at, we're in good shape right now because we did the work prior to COVID, and now they are being forced to have a 360 view not because it's a holistic corporate mission. It's they have to, right? People are at home, so it's not like, hey, let's get a 360 view of the business and do an assessment and do better and enable things. No, no, no. There's business pressure. So they're enabled. Now new types of data's coming in. So again, back to the catalog and back to some of the things that you guys have been working on. How do you talk to your customers now that they're in COVID for the ones that have been set up before COVID and the ones now that are coming to the table saying, okay, I need to now get quickly deployed with Informatica while I'm in, during the state of COVID so I can have a growth strategy coming out of it, so I don't make these mistakes again. What's your thoughts? >> Absolutely, and I think that the, whether an organization has already, a customer has already laid the groundwork, has the foundation before COVID, and the ones who are now moving full steam ahead because they're missing capabilities in those functions. The conversation is in reality more or less the same, because even for those who have the foundation, what they're starting to see is new forms of data coming in, new forms of, new requirements being placed on the, by the business on that infrastructure, the data infrastructure, and being able to, most importantly, react very, very quickly. And even for those who are starting off right now from scratch, it's the same thing. It's need to get up and running, need to get the answers to these questions, need to get the, we need to get the problems to these solutions as soon as possible. And that the theme, or I guess the talking points for both of those customers is really two things. One is you need agility. You need to be able to bring these solutions up to life and delivering as soon as possible, which means that the capabilities, the solutions you need, whether it's bringing the catalog, understanding where your data is very, very quickly, your business critical information. How do you bring that in, all of that data, and integrate that data into a 360 solution, be able to make sure it's of the highest quality, enrich it, master it, create those 360 profiles by joining it to all of this interaction, transaction data. All that has to be done with the power of technologies like CLAIRE, with artificial intelligence, so that you are up and running in a matter of days or weeks, as opposed to months and years, because you don't have that time. And then the other one which is quite important is cloud, because all of this capability needs infrastructure, hardware to run on. And we've started seeing a lot of, let's say cloud-hesitant verticals, entire verticals now in the last two to three months suddenly going from yeah, cloud is maybe somewhere down the road, as far as our future's concerned. But to now saying, we understand that we have to go to a cloud when our technicians are not able to get access to our data centers to add new machinery in there to take care of the new demands, that migration to cloud. So it's that agility and cloud which really is the common theme when we talk to customers, both- >> Yeah, and now more than ever, they need it, 'cause it's an important time, and it's going to be an inflection point, for sure. There'll be winners and losers, and people want to be on the right side of history here. Suresh, I got to ask you about AI. Obviously CLAIRE's been a big part of it. Now more than ever, if you have bad data, AI can be bad too. So understanding the relationship between data and AI is super important. This is going to be critical to help people move faster and deal with more data as soon as they're dealing with now. What's your thoughts on the role AI will play? >> Oh, AI has a huge role to play. It's already begun to play a huge role in our solutions, whether we start from catalog to integration to 360 solutions. The first thing that AI can really do very, very well is, we've gone from folks who said, let's take supply chain. There were maybe three sources of supplier data that used to come into creating a supplier 360. Today, there are hundreds of sources. If you go all the way to the customer 360, and we are talking about 1,300, 1,400 different sources of data with 90% of them sitting up in the cloud. How is it humanly possible to bring all of that data together? First of all, understand where customer information is sitting across all of those different places, whether it's your clickstream data, call log data, whether it's the actual interaction data that customers are having with in-store, online, collecting all of that information, and from your traditional systems like CRM, ERP, and billing, and all of that, bringing all that together for understanding where it is, catalog gives you that Google for the enterprise view, right? It tells you where all this data is. But then once you've got that there, it also tells you what its relative quality is, what needs to be done to it, how usable is it. To your point of if it's bad data, at least what AI can do first of all is tell you that these are unreliable attributes, these are ones that can be enriched. And then, and this is where AI now moves to the next level, which is to start inferring what kind of rules that are in our, let's say, repository across integration, quality, and mastering, and bring, and matching, bring all that together and say, here, you as the developer who's been tasked with making this happen in a matter of days, we are going to infer for you what you need to do with this data, and then we will be able to go in and bring all these sources in, connect it, load it up into a 360 solution, and create those 360 profiles that everybody downstream, whether it's your engagement systems and other. So it's really about that discovery, that automation, as well as the ability to refine and suggest new rules in order to make your data better and better as you go along. I think that's really the power of CLAIRE and AI. >> I love the Google for the enterprise or data, because the metaphor really is about finding what you're looking for. It's the discovery piece, as you said, to make it easy, and Google did make it easy to find things, which is what their search engine did. But if you look at what Google did after that, they had to have large scales. SREs is what they call them, site reliability engineers, one engineer for thousands and thousands of servers, which they, revolutionizing IT and cloud. You guys are kind of thinking the same way, data scale, right? So it's Google in terms of discovery, right? Find what you're looking for, catalog, get it in, and get it out quest, make it available for applications. But you're kind of teasing out this other point where the AI comes in. That's scale. >> Yes. >> That's super important nuance. >> Absolutely. >> But it's key to the future. >> Absolutely, because when we are starting to talk about now not just tens of millions of records when it comes to customer data or product experience dat and so on. We are already talking about organizations like Dell, for example, with our customer 360, with billions of records going in, which would be equivalent to the scale of, if you look at Google search engine business back maybe 10, 12 years ago. So yes, we are talking about within the context of a single organization or a single company, we're already talking about volumes that were unthinkable even five years ago. So being able to manage that scale, be able to have architectures, technologies that are able to autoscale, and the advantage of course is now we've got an architectural platform that has microservices. As loads start increasing, be able to spawned new instances of those microservices seamlessly. Again, this is another part where AI comes in, AI being able to say, in the old days it was somebody had to see that the CPUs are overloaded to about 100% before someone realized that we have to go out and do something about it. In this new world with AI managing the ops layer, being able to look at is this customer bringing in another, in the cloud rack, cloud world, in a SaaS world, bringing in a billion records that they want to push through in the next 10 minutes, be able to anticipate that, spawn the new infrastructure and the microservices, and be able to take care of that load and then dial those back down when the work is done. This again, from an ops perspective as well, from, so we are able to scale instead of sort of having, let's say, 1000 SREs, I think, to your example, John, have only 10 SREs to make sure that every, look at the dashboard and make sure everything is going well. >> Well, I've been covering you guys for a long time. You guys know that. And I'm a big fan. I always had been a fan of the vision that's playing out. Large scale data, large scale discovery, fast and easy, integrating that into applications for business value. It's not just the data warehouse and just park something over here. This is a mindset. It's a foundational enablement model. You guys have done an amazing job. And now more than ever, it's I think more understood because of the pandemic. >> Absolutely, and people are making that direct connection between the business outcome and the value of having this data foundation that does all the things we described. >> Suresh, great to see you, and bummer we couldn't be in person, but hey, the pandemic hit. Informatica World when virtual. A lot of different events. I know you guys have a lot of things going on virtually, and you're engaging well. Everyone's working at home. Not a problem. Most of the techies can work at home. It's not a big deal. But you've got remote customers. You guys are engaging with them. And congratulations and great to see you. >> Same here. Thank you so much. >> All right, Suresh Menon. He is senior vice president, general manager of master data at Informatica. Data's more important than ever. We're seeing it, this is a foundational thing. If it's not enabling value, then it's not going to be a good solution. This is the new criteria. This is where action matters. People who need data and need to integrate into new workloads, new applications across workforces and new workplaces. This is the reality of the future. I'm John Furrier with theCUBE. Thanks for watching. (bright music)

>> live from Las Vegas. It's the queue covering Inform Attica, World 2019. Brought to you by in from Attica. >> Welcome back, everyone to the cubes. Live coverage of infra Matic A world. I am your host, Rebecca Night, along with my co host, John Furrier. We are joined by sir Rushman, and he is the senior vice president and general manager. Master Data Management here it in from Attica. 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 3 60 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 MGM, the promise of MGM has always been creating a trusted, authoritative version ofthe any business critical entity on DH who are the most important business critical entities for any organization customers. So almost 80 to 90% off. You know, if our customers are talking about re inventing a new customer experience because some >> of the >> things that they've been telling us is that we've all learned, you know, in the past that bad customer experience means that, you know, we've all had those experiences. We goto 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 you know, you're also now and especially at this event, is that organizations have now been able to quantify what great customer experience can mean in terms ofthe a premium that they can charge for that products or services. Now that is a big shift. When you start thinking about saying if I'd deliver a better customer experience, I'm actually be able to charge 10 cents more for a cup of coffee. I can charge, you know, 20% more for an airline ticket that now has a direct impact on the top line >> and data drives. This obviously data's a key part of it. What's changed this last year, I mean a lot happened. We see on the regular tourist my one year anniversary of GDP are 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. Woman Enterprise pressure to develop sass like applications with data because we know what cloud native and born the Cloud looks like. We've seen companies come out of the woodwork from his fresh start and used data as part of the input with a IE application for great software. So now the enterprise I want to do that exactly. It's hard, >> it's hard. And I think you know, they're in a lot of organizations minds, you know, collective minds. This is cushion pulled because in order to deliver that best possible customer experience, they realize they need to gather more data about us, right? Every in every touch, point, every interaction. If you can gain that complete 3 60 view, it just means that you'd be able to deliver better possible experience. But now you're gathering more data about customers into your example about Facebook. Now means that we in our custodians off what was you know, 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, you know, in the front page of The Wall Street Journal? You know, like some of the other organizations have. So there is great, you know, volumes of data being collected. But how do I manage it? Secure it government effectively so that we don't have those? >> Don't ask a question. I have been talking a lot about fake news and Facebook lately because, you know, we're digital Cuba's official distribution. 10 years been doing it, putting out good payload with content. Great gets like yourself. But this really kind of too things. That's 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, I'll say they were incentive to actually get more data from the users, but she 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 it structure, and that's going to be powering the aye Aye, And the SAS data becomes the payload, right? So what? You're a zoo, a product management executive and someone thinking about the customer and talking to customers. How do you view that? What's the customers formula for success to take advantage of the best use of the content or data and digital while maximizing the opportunities around these new kinds of infrastructure scale and technology? >> Yeah, I think you know, they've come to the realization that data is not entirely sitting on premise animal, you know, in the in the in the old World, to get customer data, you go 23 applications of CR m nd R B and some kind of, you know, a couple of homegrown applications in on premise now for the same functionality. But that's wise of customer customer experience applications that whatever you call it, there's an app for it. And it happened to reside in the clouds. So now you have about 1,100 on average cloud applications that store components. So where do you 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 the 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, but I just thought bringing in transaction data interaction later don't know billions of records, And where else can you scale with that? Much is other than the club s O. But at the same time, that is, there is a hybrid that is extremely important because those applications 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 operation systems for a lot of the user. So a truly hybrid approach is being developed. I think that thought process is coming around where some domains live in the clouds. Some domains live on premise, but it's seamless experience across book. >> That's great insight I wanted Then follow up and ask you Okay, how did in from Attica fitted that because you guys want to provide that kind of horrors? Office scaleable data layer, depending on where the customer's needs are at any given time you got a pea Eye's out. There's things that Where do you guys How do you make that a reality? That statement you just made? >> Yeah. And the reality is eyes already being, you know, being lived today with a few of the few of our customers on it is that data layer that says, you know, we can, you know, bring data run work loads that are behind the firewall. We can do the same work, load 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 that a number of different experiences that can, you know, help that we've invested in cloud, you know, designing and monitoring capabilities that allow view for a completely cloud like experience. But all of the data still decides on premise. It's still being managed and behind your firewalls, which is where a lot of the organizations are going as well, especially more conservative, more regulated organisations. >> One of things. I want to get your reaction to a swell, great great commentary, By the way, Great Insight is some success examples that might not be directly the inn from Attica, but kind of point to some of the patterns. Let's take slack, for instance, Great software. It's basically an IRC measures chat room with on the Web with 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 informatics. Opportunity is, you 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 So you're now thinking about immigration. How do you respond to that? What are you guys doing? Respected. Integration? What's What's the product touchpoints can He shared a commentary >> on Yeah, So you know, the openness off our entire data architecture and all of the solutions is something that we you know, I think they use the word Switzerland quite often. But what it also means is that you know, 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 you know I am a model that happened to be developed on a specific let's say, an azure or a W You'd be ableto bring that in because the architecture's open completely FBI driven as a zoo mentioned. So we're able tto. Our customers have the flexibility to plug in, and we try to make that a little easier for them also, you know, as you might have seen some of the demos yesterday, we are providing recommendations and saying, You know, for this particular segment of your work, Lord, here are the choices that we recommend to you. And that's where Claire Gia, you know, comes in because it's very hard for users to keep up with all of the different possibilities. You know, our options that they might be having in that particular day, the landscape, and we can provide those recommendations to them. >> I want to ask about something you were saying earlier, and this is the company's heir 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 how are you seeing this? This mindset shift is it? Our company is still having a hard time sort of giving up my guts, telling me to do this in particular, with relationship to the new thie acquisition you made in February of all site. >> Yes. You know, I think the good news is, you know, across the board line of business leaders, CEOs, even boards are now recognizing custom experience. Customer engagement happened to be top of mind, but there's also equally react. You know, a recognition that data is what is going to help, you know, make this a reality. But so that was one of the reasons why you went out and, you know, do this acquisitions also, because if you think about it, customer data is no longer just a handful of slowly changing attributes like a name and address and telephone number or social media handles that, you know, you could be used to contact us. But it's really about now. Thousands of interactions we might have on the websites Click stream data Web chat, you know, even calls into call centers. All of this and even what we're tweeting about a product or service online is all the interactions and touch points that need to be pulled in and the dogs have to be connected in order. Bill that customer profile. So we have to do the scale, and that's something that Alcide, you know, 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 the all the different Touchpoints customers had all the different products of purchase from us over the last few months. Few years. But now can we derive some inside some intelligence? So if I'm connecting four pieces of information cannot in for a life event, can I detect that an insurance customers ready to retire? Can I detect that this family is actually shopping for a vacation to Hawaii? That's the first level off Dr Intelligence Insight that we can now offer with. Also, the next level is also about saying >> cannot be >> understanding. You know, some of these, you know, intent. Can we also understand how happy is this customer, you know, have been mentioning competitive product, which can allow us to infer that person probably going to go off and buy a competitors product. If this problem they're having with this device or product is not resolved, so turn scoring, sentiment scoring. And now the third level on top of that which I think is really the game changer, is now. Can we in for what the next best action or interaction should be based upon all these things? Can we even do things such as, as I left here, not too happy customer with a particular maybe laptop that I, you know, perches I called the call center can before as a call is coming through, can we in for what I'm calling about based upon all of the interactions have had over the recent past and direct that call to 11 to 11 3 Technician who specialized in the laptop model >> that I have >> in orderto make me continue to be a customer for life. >> One of the biggest challenge is happening in the in the technology industry is the skills gap. I want to hear your thoughts on it and also how they help my 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 and 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, way 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 Alcide is based. Extremely cool technology that's come out, that that's, you know, really transforming organisations and their approach. The customers stood guard, doubling bangle or Chennai Hyderabad. So you know, we are tapping into centers that have lots of skilled, you know, folks on DH calling 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 and D centers in each of those locations helps us mitigate the farm. >> 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 that's so early. People wanna hone in. What's what do you see? Its success for people attaining certain certain skills. What do you recommend? >> So I think that is definitely a combination ofthe technical skills, whether it is the new a n M L applications. But I think that is also, you know, in the past, we would have said, Let's go on higher than someone who has done computer science You know, on is very deep in that topic. But look at the problems we're trying to solve with data on the application of the animal. They're all in service of a business outcome, some kind of a business on DH more, we find people who are able to bridge the gap between strong application off the newer technologies on a animal and also an understanding off the broader world. And the business, I think, is really the combination of skills is really what's going to be required to succeed. >> Excellent, great note to end on. Thank you so much, sir. Arrest for coming on the show. >> Thank you. Thanks. >> I'm Rebecca Knight for John Furrier. You are watching the Cube.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Welcome back everyone. This is theCUBE's exclusive coverage of Informatica World 2018. Live here in Las Vegas at the Venetian Hotel. I'm John Furrier, co-host with Peter Burris. Here for the next two days of wall to wall coverage. Our next guest is is Suresh Menon, Senior Vice President and General Manager of the Master Data Management group within Informatica. He's got the keys to the kingdom, literally. Welcome back, good to see you. >> Thank you for having me. >> The key of all this pun intended is the data. And the cataloging's looking good. There's a lot of buzz around cataloging. What you guys have as a core product. Your customers love the product. The world's changing. Where are we, what's the update? >> Catalog is extremely important. Not just to enterprise data, the entire landscape by itself. But it's equally very exciting for MDM. Cause what has the potential to to is transform the way in how quickly people can get value out of MDM. Cause a combination of metadata and artificial intelligence through machine learning is what can create self-configuring, self-operating, even self maintaining Master Data Management. And that's extremely important because in today's world, the digital world that we live in, the explosion of data. The explosion of data sources. The new kinds of data that MDM is being asked to master, correlate and link with is becoming so huge that it's not humanly going to be possible to manage/curate this data. And you need to have AINML, and the underlying metadata awareness that the catalog brings, in order to solve these new problems. >> So Suresh, after you came onto theCUBE last year. You left and I said, there's a question I should've asked him. I'm going to put you on the spot. If you could do it. If you could create a new term for this Master Data Management. And where it's going. What would you call it? >> Yeah. You know Master Data Management has been around not for very long. About eight or nine years. It doesn't begin to describe the kind of problem that we're trying to solve here today. The only one that I can think of is 360's. It's more about getting the complete holistic view of all the business critical entities that you as an organization need to know. And 360 has traditionally been used around customer. But it's not only about the customer. You need to understand what products the customer owns. Engineer a 360 around their product. You need to understand how those customers interact with employees. You need an employee 360. You need an asset 360. How can you even begin to do householding, if you don't do a location 360? >> I want to build on that. In many respects it's the ability to sustain the context of data for different personas, for different applications, for different utilizations. So in many respects, Master Data Management really is the contextual framework by which an organization consumes data. Have I got that right? >> Absolutely. It is the you know. Another way to describe that would be it is what delivers the consistent authoritative description where you have the semantics being completely differently described in all of these cloud applications. We've gone very far away from the days maybe ten years ago, where you had a handful of CRM and ERP applications that you needed to disambiguate this information. Today I think I was reading this morning that an organization on average has 1,050 different cloud applications. And 3/4 of them are not connected to anything. And the describing, creating, authoring information around all these business critical entities. MDM is becoming the center of this ultra-connected universe in another way that I would look at it. >> It's also a key part of making data addressable. And we talked about this last year. But something that I have observed that's been happening since last year. The storage vendors have been radically changing their view. They're going to be have storage, but their data layer is sitting in all the clouds. That's interesting. That means that they're seeing that there's a data abstraction kind of underneath Informatica if you will. If that happens then you have to be working across all the clouds. Are customers seeing that? Are they coming to you saying that? Or are you guys getting out front? How do you view that dynamic? >> Customers are seeing that, have been seeing that for the last two to three years. As they have started taking these monolithic, very comprehensive, on premise applications to a fragmented set of applications in the cloud. Where do they keep a layer where they have all this business critical data in one place? And they're beginning to realize that as they move these things to the cloud, these applications are moving to the cloud, it's going from one to a couple of hundred. Master data is being seen as that layer that basically connects all these pieces of information together. And very importantly for a lot of these organizations, data that's proprietary to them. That they don't necessarily want locked up in an application that may or may not be there a couple of years down the road. >> The value shifting from state commodity. Even I was talking last week with the guys from NetApp about a great solid state drive they're going to have. But that values up top where the data is. And they have the data stored. So why not facilitate? And you guys can take it and integrate it into the applications, into the workloads. How is that going with respect to say catalog or the edge, for instance? How should a customer think about MDM? If they have to architect it out, what's the playbook? >> The number one thing is where the catalog comes in is first of all trying to identify in this highly fragmented universe you now have. As to where all your fragments, or master data reside. This is where the catalog comes in. It gives you in one Google-like text search, tells you where all the customer master attributes are residing across the landscape. Third party, on premise, in the cloud. The catalog will also tell you what the relative quality is of those those attributes. And then by apply AINML to it, be able to now figure out how those pieces of data can be transformed, cleansed, enriched and brought into MDM. The catalog has a role to play within MDM. What are the most appropriate matching and linking rules? What are the most appropriate survivorship trust tools that you need to apply? And how do you secure all that data that's now sitting in MDM? Because it's now in the cloud, and you know data security and protection is top of mind for most-- >> Talk about AI over at MDM. Because last year Claire was announced. We've seen certainly with GDPR that AI will play a role. Machine learning and AI. It's all coming together. The relationship between MDM and AI. Natural to me, seems like it's natural. How do you guys see the fit between AI and MDM? >> It is fundamental to MDM. And where we've begun our investment in AINML is one of the most core capabilities around MDM, which is being able to recognize potential duplicates. Or detect non-obvious relationships across this vast set of master data that's coming in. We've applied AINML, and we'll see a demo of that tomorrow, and we'll here in Vegas, is using machine learning on top of the world's best matching algorithms, in order to infer what are the most appropriate strategies in order to link and discover these entities? And build a relationship graph, without a human having to introspect the data. >> One of our predictions is that over the course of the next few years companies are actually going to start thinking about networks of data. That data is going to get the network formation treatment. That devices, and pages, and identities and services that we've gotten in the past. It does seem as though MDM could play a very, very important role in as you said identifying patterns in the data, utilization of the data. What constitutes a data node? What constitutes an edge? Number of different ways of thinking about it. Is that the direction that you see? First of all, do you agree with that notion of networks of data? And is that the direction you see MDM playing in the future? >> Absolutely. Because up until now MDM was used to solve the problem of creating a distinct node of data. Where we absolutely had to ensure that whatever it is then node was describing is actually the entire, complete, comprehensive entity. Now the next step, the new frontier for MDM is now about trying to understand the relationships across those nodes. And absolutely. MDM is both about that curation that governs, which is very important for GDPR and all of the other initiatives out there. But equally importantly now being able to understand how these entities are related across those, the graph of all of those nodes now. >> Weave in the role that security's going to play. Because MDM can... Well we'll step back. Everybody has historically figured that either data is secure or it's not. Largely because it was focused on a device. And if you have a device, and secure the device, all the data on that device got equally secured. Nowadays data is much more in flight. It's all over the place. It's a lot of different sources. The role that security plays in crafting the node, in privatizing data and turning it into an asset, is really important. But it could really use the information that's MDM to ensure that we are applying the appropriate levels of security, and types of security. Do you see an evolving role between MDM and data security? >> I would actually describe it differently. I would say that security is now the core design principal for MDM. It has to be baked into everything that we do around designing MDM for the future. Because like you said, we've again gone away from some handful of sources, bringing data into MDM in a highly protected, on premise environment with a very limited number of consumers. Now we have thousands of applications delivering that data to MDM. And you've got thousands of business users. Tens of thousands of them. Applications all leveraging that master data in the context of those interfaces. Security has never bee more important for MDM. This is again another way of security. And I want to bring catalog back again. Catalog is going to automatically tell the MDM configuration developer that these are pieces of data that should be protected. This is PII data. The the health data. This is credit data. That security is implicit in the design of those MDM initiatives. >> I think that's huge with cloud and connected edge in the network that is critical. I got to ask you. I now we're tight on time. I want to get one more question in. Define intelligent MDM. I've heard that term. What does that mean to you? You mentioned security design in the beginning. I get that, what that is. But I heard the term intelligent MDM. What is the definition of that? What does it mean? >> It really means MDM that is built for three new imperatives. One is being able to scale, what I would call digital scale. It's no longer enterprise scale. It is about being able to make sense of interactions and relationships, and being able to use the power of the catalog, and AINML, in order to connect all of these dots. Because connecting these dots is what's going to deliver immense business value to those organizations. Facilitate the rise of the business user, and their requirements. Intuitive interfaces that allow them to perform their day to day interaction with MDM. And finally time to value. Intelligent MDM should be up and running, not in months or years, but in weeks if not days. And this is where the power of catalog, power of machine learning, can make this a reality. >> That's a great clip. I'm going to clip that. That's awesome. And then putting it into action, that's the key to success. Suresh, thanks for coming on. Great to see you. >> Thank you very much. >> As always. You've got the keys to the kingdom, literally. MDM is at the center of it all, the things going on with data from cloud, edge computing, all connected. I'm John Furrier with Peter Burrs bringing all the action here at Informatica World 2018. We'll be back with more after this short break.

>> Narrator: Live from San Francisco, it's theCUBE, covering Informatica World 2017, brought to you by Informatica. (driving techno music) >> Hey, welcome back everyone. Live here in San Francisco, Informatica World 2017, this is theCUBE's exclusive coverage from SiliconANGLE Media. I'm John Furrier, host of theCUBE, with my co-host Peter Burris, head of research at SiliconANGLE Media, also General Manager of wikibon.com, doing all the cutting edge research on data, data value, what's it mean, cloud, etc. Check it out at wikibon.com. Next guest is Suresh Menon, who's the SVP and General Manager of Master Data Management Informatica. The key to success, the central brains. MDM, great, hot area. Suresh, thanks for coming on theCUBE. Appreciate it. >> Thank you for having me. >> So, MDM has been in almost all the conversations we've had, some overtly and some kind of implied through... Take a minute to describe what you're managing and what the role is in that data fabric, in that Data 3.0 vision, why Master Data Management is so important. >> Right, if you think about Master Data Management, there are two ways to look at it. The first one would be in terms of MDM, let's follow the definition. Master Data is really about all the business critical entities that any organization is, you know, should be concerned about. So if you think about customers and products, that's the two most critical ones, and that's really where Master Data Management began. But then you should also think about employees, locations and channels, suppliers, as all being the business critical entities that every organization should care about. Master Data Management is about making sure that you have the most trusted, authoritative and consistent data about these entities, which can then fuel the rest of your enterprise. MDM has been used in the past to fulfill certain specific business objectives or outcomes, such as improving customer centricity, making sure that you're onboarding suppliers with a minimal amount of risk, and also to make sure that your products as being described and syndicated out to the web are done in the most efficient manner. >> You guys have the Industry Perspective Monday night. What was the insight from the industry? I mean, how was the industry... I know Peter's got a perspective on this. He thinks there's opportunity, big time, to reposition kind of how this is thought, but what's the industry reaction to MDM? >> The industry reaction is renewed excitement in MDM. MDM started off about 10 years ago. A lot of early adopters were there. And as is usual with a lot of early adopters, there was a quick dip into the cycle of disillusionment. What you've seen over the last couple of years and the excitement from Monday is the resurgence about MDM, and looking at MDM as being a force of disruption for the digital transformation that most organizations are going through, and actually being at the center of that disruption. >> Well it's interesting, I almost liken this to... I'm not a physicist, I wish I was, perhaps... Physics encounters a problem, and then people look at this problem and they say "Oh my goodness, that's, how are we going to solve that?" And then somebody says "Oh, I remember a math technique that I can apply to solve this problem and it works beautifully." I see MDM almost in the same situation. Oh, we've got this enormous amount of data. It's coming from a lot of different sources. How do we reconcile those all those sources? Oh, what a... oh, wait a minute. We had this MDM thing a number of years ago. How about if we took that MDM and tried to apply it to this problem, would it work? And it seems to fit pretty nicely now. Do you agree with that? >> I agree with that. There's also a re-defninition of MDM. Because sometimes when you look at what people think about, "Oh, that was MDM from seven years ago. How does that apply to the problems I'm dealing with today, with IoT data, social network data, interaction data that I need to make sense of. Wasn't MDM for the structured world and how does it apply for the new world?" And this is really the third phase of MDM, going from batch analytics, fueling old real-time applications, whether it was marketing, customer service and so on. And now, providing the context that is necessary to connect dots across this billions and billions of data that is coming in, and being able to provide that insight and the outcome that organizations are hoping to achieve by bringing all this together. >> You mentioned... I just want to jump in for a second, cause you mentioned unstructured data and also the speed of data, getting the value. So data as a service, these trends are happening, right? The role of data isn't just, okay, unstructured, now deal with it. You've got to be ready for any data injection to an application being available. >> Suresh: Yes. >> I mean, that's a big fact too, isn't it? >> Absolutely, and organizations are looking at what used to be a batch process that could run overnight, to now saying "I'm getting this data in real time and I need to be able to act on it right now." This could be organizations saying, "I'm using MDM to connect all of this interaction data that's coming in, and being able to make the right offer to that customer before my competition can." Shortening that time between getting a signal to actually going out and making the most relevant offer, has become crucial. And it also applies to other things such as, you identify risk across any part of your organization, being able to act upon that in real time as opposed to find out later and pay the expense. >> I know this is not a perfect way of thinking about it, but perhaps it will be a nice metaphor for introducing what I'm going to say. I've always thought about MDM as the system of record for data. >> Suresh: Yes. >> Right? And as we think about digital business, and we think about going after new opportunities and new types of customers, new classes of products, we now have to think about how we're going to introduce and translate the concepts of design into data. So we can literally envision what that new system of record for data is going to look like. What will be the role of MDM as we start introducing more design principles into data? Here's where we are, here's where we need to be, here's how we're going to move, and MDM being part of that change process. Is that something you foresee for MDM? >> Absolutely, and also, the definition of... MDM in the past used to be considered as, let's take a small collection of slowly changing attributes, and that's what we master for through the course of time. Instead now, MDM is becoming in this digital age, as you're bringing in tens of thousands of attributes even about a customer and a supplier, MDM being part of that process that can grow, and at the same time, those small collection of attributes important as a kernel inside of this information, it's that kernel that provides the connection, the missing link, if you will, across all of these. And absolutely, it's a journey that MDM can fuel. >> We think that's crucially important. So for example, what we like to say is we can demarcate the industry. We think we're in the middle of a demarcation point, I guess I should say. Where for the first 50 years we had known process, unknown technology. Now we're looking at known technology generally speaking, but extremely unknown process. Let me explain what I mean by that. We used to have very stylized, as you said, structured data. Accounting is a stylized data form, slow moving changes etc. And that's what kind of MDM was originally built for, to capture that system of record for those things. Now we're talking about trying to create digital twins of real world things that behave inconsistently, that behave unpredictably, especially human beings. And now we're trying to capture more data about them, and bring them in to the system. Highly unstructured, highly uncertain, learning and training. So, help us connect this notion of machine learning, artificial intelligence back to MDM, and how do you see MDM evolving to be able to take this massive, new and uncertain types of data, but turn it into assets very quickly. >> Absolutely. It's a crucial part of what MDM is all about today and going forward into the future. It is the combination of both the metadata understanding about what it is that these data sets are going to be about, and then applying artificial intelligence through machine learning on top of it, so that... MDM was always about well-curated data. How can you curate data by human curation, how is that possible when you've got these real time transactions coming in at such high speed and such high volume? This is where artificial intelligence can detect those streams, be able to infer the relationships across these different streams, and then be able to allow for that kind of relationship exploration and persistence, which is key to all of this. Completely new algorithms that are being built now, it augments... >> Does it enhance master data, or extracts it away? What's the impact... like ClAIRE, for instance. What's the impact to MDM? More relevant, less relevant? >> Even more relevant, and three key areas of relevance. Number one is about automating the initial putting together about MDM, and then also automating the ongoing maintenance. Reacting to changes, both within the organization and outside the organization, and being able to learn from previous such interactions and making MDM self-configuring. The second part of it is stewardship. If you think about MDM, in the past you always had stewards, a small number of stewards in an organization who would go out and curate this data. We now have tens of thousands of businesses across the organization saying, "I want to interact with this master data, I have a role to play here." For those business users now, you have tens of thousands of them, and then thousands and thousands of attributes. Machine learning is the only way that you can stop this data explosion from causing a human explosion in terms of how do you manage this. >> John: Yeah, a meltdown. >> Yeah, a meltdown. MDM both is going to be improved through these technologies, but MDM also has to capture these crucial new sources of data and represent them to the business. >> New metadata, right? >> Yeah, all these artificial intelligence systems and machine learning stuff is going to be generating data that has to be captured somehow, and MDM's a crucial part of that. >> Exactly, right. >> So let me ask you a question. >> If we can boil this down really simply... >> John: He's excited about MDM. >> Look, I'm excited about data, this is so... If we kind of think about this, we had an accounting system, well let me step back. In the world where we were talking about hard assets, we had an accounting system that had a fixed asset module. So we put all our assets in there, we put depreciation schedules on it, we said, "Okay, who's got what? Who owns it, who owns the other things?" Is MDM really become the data asset system within the business? Is that too far a leap for you? >> I don't think so. I mean, if you think about, if master data was all about making sure that the business critical data, everything that the organization runs on, the business is running on, and now if you think of that, that's the data that's going to fuel, um, enable this digital disruption that these organizations want to do with that data, MDM's at the heart of that. And finally, the last piece I think, your point about the artificial intelligence, the third part of where MDM increases its relevance is, you have the insight now. The data is being put together, we've curated that data, we've discovered those relationships through machine learning. What next? What's next is really about not just putting that data in the hands of a user or inside of a consuming application, but instead, recommending what that application or user needs to do with that data. Predict what the next product is that a customer is going to buy, and make that next best offer recommendation to a system or a user. >> Suresh, you're the GM now, you've got the view of the landscape, you've got a business to run. Charge customers for the product, subscription, cloud, on-premise license, volving. You've got a new CMO. You've got to now snap into the storyline. What's your role in the storyline? Obviously, the story's got to be coherent around one big message and there's got to be the new logo we see behind here. What's your contribution to the story, and how are you guys keeping in cadence with the new marketing mission? >> This has been a very closely run project, this entire re-branding. It's not just a new logo and a new font for the company's name. This has been a process that began many, many months ago. It started from a look at what the direction of our products are across MDM. We worked very closely with Sally and her team to... >> John: So You've been involved. >> Absolutely, yes. >> The board certainly has. >> Both board members said they were actively involved as well. >> Yeah, this has been a... >> What do you think about it, are you excited? >> It's fantastic. >> It think it's one of those once-in-a-generation opportunities that we get where we've got such a broad breadth of capabilities across the company, and now to be able to tell that story in a way that we've never been able to before. >> It's going to help pull you into the wind that's blowing at your back. You guys have great momentum on the product site, congratulations. Now you got the... the brand is going to be building. >> Fantastic, yes. >> Okay, so what's the final question? Outlook for next year? How's the business going, you excited by things? >> Very much so. MDM has been across the board for Informatica, and I'm sure you've seen here at the conference, the interest in MDM, the success stories with MDM, large organizations like Coca-Cola and GE redoing the way they do business all powered through MDM. MDM has never been more relevant than it is now. >> And the data tsunami is here and coming and not stopping, the waves are hitting. IoT. Gene learning. >> Suresh: Right. >> Batching. >> Batching, absolutely. >> With enable frederated MDM, we'll be able to do this on a global scale, and master class... >> We'll have to have you come into our studio and do an MDM session. You guys are like, this is a great topic. Suresh, thank you so much for coming on theCUBE, really appreciate it. General Manager of the MDM Business for Informatica Master Data Management. Was once a cottage industry, now full blown, part of the data fabric at Informatica. Thanks so much for sharing on theCUBE. We're bringing you all the master CUBE interviews here in San Francisco for theCUBE's coverage of Informatica World. Back after this short break, stay with us. (techno music)