(upbeat music) >> It's my pleasure, to be one of the hosts of theCUBE on cloud and the startup showcase brought to you by AWS. This is Dave Vellante and for years theCUBE has been following the trail of data. And with the relentless match of data growth this idea of a single version of the truth has become more and more elusive. Moreover, data has become the lifeblood of a digital business. And if there's one thing that we've learned throughout the pandemic, if you're not digital, you're in trouble. So we've seen firsthand, the critical importance of reliable and trusted data. And with me to talk about his company and the trends in the market is Manish Sood the CTO and co-founder of Reltio. Manish, welcome to the program. >> Thank you, Dave. It's a pleasure to be here. >> Okay, let's start with, let's go back to you and your co-founders when you started Reltio it was back in the early days of the big data movement, cloud was kind of just starting to take off, but what problems did you see then and what are enterprises struggling with today, especially with data as a source of digital innovation. >> Dave, if you look at the changes that have taken place in the landscape over the course of the last 10 years, when we started Reltio in 2011 there were a few secular trends that were coming to life. One was a cloud compute type of capabilities being provided by vendors like AWS. It was starting to pick up steam where making compute capabilities available at scale to solve large data problems was becoming real and possible. The second thing that we saw was this big trend of you know, you can not have a wall to wall, one single application that solves your entire business problem. Those visions have come and gone and we are seeing more of the best of breed application type of a landscape where even if you look within a specific function let's say sales or marketing, you have more than a dozen applications that any company is using today. And that trend was starting to emerge where we knew very well that the number of systems that we would have to work with would continue to increase. And that created a problem of where would you get the single source of truth or the single best origin of a customer, a supplier, a product that you're trying to sell, those types of critical pieces of information that are core to any business that's out there today. And, you know, that created the opportunity for us at Reltio to think about the problem at scale for every company out there, every business who needed this kind of capability and for us to provide this capability in the cloud as a software, as a service offering. So that's where, you know, the foundation of Reltio started. And the core problem that we wanted to solve was to bridge the gap that was created by all these data silos, and create a unified view of the core critical information that these companies run on. >> Yeah, the cloud is this giant, you know hyper distributed system, data by its very nature is distributed. It's interesting what you were sort of implying about you know, the days of the monolithic app are gone, but my business partner years ago John Furrier at theCUBE said, data is going to become the new development kit. And we've certainly seen that with the pandemic but tell us more about Reltio and how you help customers deal with that notion of data silos, data fragmentation, how do you solve that problem? >> So data fragmentation is what exists today. And, with the Reltio software as a service offering that we provide, we allow customers to stitch together and unify the data coming from these different fragmented siloed applications or data sources that they have within their enterprise. At the same time, there's a lot of dependence on the third party data. You know, when you think about different problems that you're trying to solve, you have for B2B type of information that in Bradstreet type of data providers, in life sciences you have IQVIA type of data providers. You know, as you look at other verticals that is a specialized third party data provider for any and every kind of information that most of the enterprise businesses want to combine with their in-house data or first party data to get the best view of who they're dealing with, who are they working with, you know who are the customers that they're serving and use that information also as a starting point for the digital transformation that they want to get to. And that's where Reltio fits in as the only platform that can help stitch together this kind of information and create a 360 degree view that spans all the data silos and provides that for real-time use, for BI and analytics to benefit from, for data science to benefit from, and then this emerging notion of data in itself is a, you know, key starting point that is used by us in order to make any decisions. Just like we go, you know, if I they wanted to look at information about you, I would go to places like LinkedIn, look up the information, and then on my next set of decisions with that information. If somebody wanted to look up information on Reltio they would go to, let's say crunchbase as an example and look up, who are the investors? How much money have we raised? All those details that are available. It's not a CRM system by itself but it is an information application that can aid and assist in the decision-making process as a starting point. And that user experience on top of the data becomes an important vehicle for us to provide as a part of the Reltio platform capabilities. >> Awesome, thank you. And I want to get into the tech, but before we do maybe we just cut to the chase and maybe you can talk about some of the examples of Reltio and action, some of the customers that you can talk about, maybe the industries that are really adopting this. What can you tell us there Manish? >> We work across a few different verticals some of the key verticals that we work in are life sciences and travel and hospitality and financial services, insurance retail, as an example. Those are some of the key verticals for us. But to give you some examples of the type of problems that customers are solving with Reltio as the data unification platform, let's take CarMax as an example,. CarMax is a customer who's in the business of buying used cars, selling used cars servicing those used cars. And then, you know, you as a customer don't just transact with them once, you know, you've had a car for three years you go back and look at what can you trade in that car for? But in order for CarMax to provide a service to you that goes across all the different touch points whether you are visiting them at their store location trying to test drive a car or viewing information about the various vehicles on their website, or just you know, punching in the registration number of your car just to see what is the appraisal from them in terms of how much will they pay for your car. This requires a lot of data behind the scenes for them to provide a seamless journey across all touch points. And the type of information that they use relative for aggregating, unifying, and then making available across all these touch points, is all of the information about the customers, all of the information about the household, you know, the understanding that they are trying to achieve because life events can be buying signals for consumers like you and I, as well as who was the associate who helped you either in the selling of a car, buying of a car, because their business is all about building relationships for the longer term, lifetime value that they want to capture. And in that process, making sure that they're providing continuity of relationship, they need to keep track of that data. And then the vehicle itself, the vehicle that you buy yourself, there is a lot of information in order to price it right, that needs to be gathered from multiple sources. So the continuum of data all the way from consumer to the vehicle is aggregated from multiple sources, unified inside Reltio and then made available through APIs or through other methods and means to the various applications, can be either built on top of that information, or can consume that information in order to better aid and assist the processes, business processes that those applications have to run and to end. >> Well, sounds like we come along, (indistinct). >> I was just going to say that's one example and, you know across other verticals, that are other similar examples of how companies are leveraging, Reltio >> Yeah, so as you say, we've come a long way from simple linear clickstream analysis of a website. I mean, you're talking about really rich information and you know happy to dig into some other examples, but I wonder how does it work? I mean, what's the magic behind it? What's the tech look like? I mean, obviously leveraging AWS, maybe you could talk about how, so, and maybe some of the services there and some of your unique IP. >> Yeah, you know, so the unique opportunity for us when we started in 2011 was really to leverage the power of the cloud. We started building out this capability on top of AWS back in 2011. And, you know, if you think about the problem itself, the problem has been around as long as you have had more than one system to run your business, but the magnitude of the problem has expanded several fold. You know, for example, I have been in this area was responsible for creating some of the previous generation capabilities and most of the friction in those previous generation MDM or master data management type of solutions as the you know, the technical term that is used to refer to this area, was that those systems could not keep pace with the increasing number of sources or the depth and breadth of the information that customers want to capture, whether it is, you know, about a patient or a product or let's say a supplier that you're working with, there is always additional information that you can capture and you know use to better inform the decisions for the next engagement. And that kind of model where the number of sources we're always going to increase the depth and breadth of information was always going to increase. The previous generation systems were not geared to handle that. So we decided that not only would we use add scale compute capabilities in the cloud, with the products like AWS as the backbone, but also solve some of the core problems around how more sources of information can be unified at scale. And then the last mile, which is the ability to consume such rich information just locking it in a data warehouse has been sort of the problem in the past, and you talked about the clickstream analysis. Analytics has a place, but most of the analytics is a real view mirror picture of the, you know, work that you have to do versus everybody that we talk to as a potential customer wanted to solve the problem of what can we do at the point of engagement? How can we influence decisions? So, you know, I'll give you an example. I think everybody's familiar with Quicken loans as the mortgage lender, and in the mortgage lending business, Quicken loans is the customer who's using Reltio as the customer data unification platform behind the scenes. But every interaction that takes place, their goal is that they have a very narrow time vendor, you know anywhere from 10 minutes to about an hour where if somebody expresses an interest in refinancing or getting a mortgage they have to close that business within that hot vendor. The conversion ratios are exponentially better in that hot vendor versus waiting for 48 hours to come back with the answer of what will you be able to refinance your mortgage at? And they've been able to use this notion of real time data where as soon as you come in through the website or if you come in through the rocket mortgage app or you're talking to a broker by calling the 1800 number they are able to triangulate that it's the same person coming from any of these different channels and respond to that person with an offer ASAP so that there is no opportunity for the competition to get in and present you with a better offer. So those are the types of things where the time to conversion or the time to action is being looked at, and everybody's trying to shrink that time down. That ability to respond in real time with the capabilities were sort of the last mile missing out of this equation, which didn't exist with previous generation capabilities, and now customers are able to benefit from that. >> That is an awesome example. I know at firsthand, I'm a customer of Quicken and rocket when you experience that environment, it's totally different, than anything you've ever seen before. So it's helpful to hear you explain like what's behind that because, it's truly disruptive and I'll tell you the other thing that sort of triggered a thought was that we use the word realtime a lot and we try to develop years ago. We said, what does real-time really mean? And the answer we landed on was, before you lose the customer, and that's kind of what you just described. And that is what gives as an example a quick and a real advantage again, having experienced it firsthand. It's pretty, pretty tremendous. So that's a nice reference. So, and the other thing that struck me is, I wanted to ask you how it's different from sort of legacy Master Data Management solutions and you sort of described that they've since to me they've got to take their traditional on-prime stack, rip it out, stick it in the iCloud, it's okay we got our stack in the cloud now. Your technical approach is dramatically different. You had the advantage of having a clean sheet of paper, right? I mean, from a CTO's perspective, what's your take? >> Yeah, the clean sheet of paper is the luxury that we have. You know, having seen this movie before having, you know looked at solving this problem with previous generation technologies, it was really the opportunity to start with a clean sheet of paper and define a cloud native architecture for solving the problem at scale. So just to give you an example, you know, across all of our customers, we are today managing about 6.5 billion consolidated profiles of people, organizations, product, locations, you know, assets, those kinds of details. And these are the types of crown jewels of the business that every business runs on. You know, for example, if you wanted to let's say you're a large company, like, you know, Ford and you wanted to figure out how much business are you doing, whether, you know another large company, because the other large company could be a global organization, could be spread across multiple geographies, could have multiple subsidiaries associated with it. It's been a very difficult to answer to understand what is the total book of business that they have with that other big customer. And, you know, being able to have the right, unified, relevant, ready clean information as the starting point that gives you visibility to that data, and then allows you to run precise analytics on top of that data, or, you know drive any kind of conclusions out of the data science type of algorithms or MLAI algorithms that you're trying to run. You have to have that foundation of clean data to work with in order to get to those answers. >> Nice, and then I had questions on just analysis, it's a SAS model I presume, how is it priced? Do you have a freemium? How do I get started? Maybe you could give us some color on that. >> Yeah, we are a SAS provider. We do everything in the cloud, offer it as a SAS offering for customers to leverage and benefit from. Our pricing is based on the volume of consolidated profiles, and I use the word profiles because this is not the traditional data model, where you have rows, columns, foreign keys. This is a profile of a customer, regardless of attribution or any other details that you want to capture. And you know, that just as an example is what we consider as a profile. So number of consolidated profiles under management is the key vector of pricing. Customers can start small and they can grow from there. We have customers who manage anywhere from a few hundred thousand profiles, you know, off these different types of data domains, customer, patient, provider, product, asset, those types of details, but then they grow and some of the customers HPInc, as a customer, is managing close to 1.5 billion profiles of B2B businesses at a global scale of B2C consumers at global scale. And they continue to expand that footprint as they look at other opportunities to use, the single source of truth capabilities provided by Reltio. >> And, and your relationship with AWS, you're obviously building on top of AWS, you're taking advantage of the cloud native capabilities. Are you in the AWS marketplace? Maybe you could talk about AWS relationship a bit. >> Yeah, AWS has been a key partner for us since the very beginning. We are now on the marketplace. Customers can start with the free version of the product and start to play with the product, understand it better and then move into the paid tier, you know as they bring in more data into Reltio and, you know be also have the partnership with AWS where, you know customers can benefit from the relationship where they are able to use the spend against Reltio to offset the commitment credits that they have for AWS, you know, as a cloud provider. So, you know, we are working closely with AWS on key verticals, like life sciences, travel and hospitality as a starting point. >> Nice, love those credits. Company update, you know, head count, funding, revenue trajectory what kind of metrics are you comfortable sharing? >> So we are currently at about, you know, slightly not at 300 people overall at Reltio. We will grow from 300 to about 400 people this year itself we are, you know, we just put out a press release where we mentioned some of the subscription ARR we finished last year at about $74 million in ARR. And we are looking at crossing the hundred million dollar ARR threshold later this year. So we are on a great growth trajectory and the business is performing really well. And we are looking at working with more customers and helping them solve this, you know, data silo, fragmentation of data problem by having them leverage the Reltio capability at scale across their enterprise. >> That's some impressive growth, congratulations. We're, I'm sure adding hundred people you're hiring all over the place, but where we are some of your priorities? >> So, you know, the, as the business is growing we are spending equally, both on the R and D side of the house investing more there, but at the same time also on our go to market so that we can extend our reach, make sure that more people know about Reltio and can start leveraging the benefit of the technology that we have built on top of AWS. >> Yeah, I mean it sounds like you've obviously nailed product market fit and now you're, you know, scaling the grip, go to market. You moved from CEO into the CTO role. Maybe you could talk about that a little bit. Why, what was prompted that move? >> Problems of luxury, you know, as I like to call them once you know that you're in a great growth trajectory, and the business is performing well, it's all about figuring out ways of, you know making sure that you can drive harder and faster towards that growth milestones that you want to achieve. And, you know, for us, the story is no different. The team has done a wonderful job of making sure that we can build the right platform, you know work towards this opportunity that we see, which by the way they've just to share with you, MDM or Master Data Management has always been underestimated as a, you know, yes there is a problem that needs to be solved but the market sizing was in a, not as clear but some of the most recent estimates from analysts like Gartner, but the, you know, sort of the new incarnation of data unification and Master Data Management at about a $30 billion, yeah, TAM for this market. So with that comes the responsibility that we have to really make sure that we are able to bring this capability to a wide array of customers. And with that, I looked at, you know how could we scale the business faster and have the right team to work help us maximize the opportunity. And that's why, you know, we decided that it was the right point in time for me to bring in somebody who's worked at the stretch of, you know taking a company from just a hundred million dollars in ARR to, you know, half a billion dollars in ARR and doing it at a global scale. So Chris Highland, you know, has had that experience and having him take on the CEO role really puts us on a tremendous path or path to tremendous growth and achieving that with the right team. >> Yeah, and I think I appreciate your comments on the TAM. I love to look at the TAM and to do a lot of TAM analysis. And I think a lot of times when you define the the future TAM based on sort of historical categories, you sometimes under count them. I mean, to me you guys are in the digital business. I mean, the data transformation the company transformation business, I mean that could be order of magnitude even bigger. So I think the future is bright for your company Reltio, Manish and thank you so much for coming on the program. Really appreciate it. >> Well, thanks for having me, really enjoyed it. Thank you. >> Okay, thank you for watching. You're watching theCUBEs Startup Showcase. We'll be right back. (upbeat music)

>> Hi, I'm Katie Kupec, Global Portfolio Product Marketing Manager for IBM Master Data Management. Master Data Management is a key part within the DataOps toolchain to deliver a trusted, complete view of your customers, products and to offer unique and personalized digital experiences. To learn more about this, join us at our DataOps crowd chat event on May 27th. Hope to chat with you there.

>> 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)