 Live from San Francisco, it's theCUBE covering Informatica World 2016. Brought to you by Informatica. Now, here are your hosts, John Furrier and Peter Burris. Okay, welcome back everyone. We are here live in San Francisco, California for Informatica World 2016. This is SiliconANGLE Media's exclusive coverage. This is theCUBE, our flagship program. We go out to the events and extract the signal from the noise. I'm John Furris, my coach. Peter Burris, our next two guests are Peter Sant, Pedro Santos, Vice President MDM Lead at CIT Group, and Adam Sigg, Director of Customer Data Services at InfoVarity. Welcome to theCUBE. Thank you. So MDM Lead, that's a good dig into that. But I want to hear about the journey with Informatica, especially around MDM, because it's changed a lot. Yes, what's changed, what's going on? Give us the quick highlights of kind of where it's come from, what's going on now, and where you think it's going to be in the future. Well, we started the journey kind of moving towards an old legacy system that we had, and converted that into the current MDM solution. In 2014, from there, we started moving, just after we did the lift and ship, we started moving until having hierarchies brought into MDM from an old system also. And now we're going to continue and to bring in kind of having that information push it down to downstream systems using unique identifiers for everybody to use throughout the firm. That's the first three phases, and the other phases I'll add on top of that. What's your thoughts? So I actually was around in 2008 at the very bleeding edge of CIT's MDM journey, and it started out with a business unit solution that didn't scale, it was a single domain, it was a flat data model. So you had to have one name and one address, and that's who you were, gosh darn it, or we weren't going to be able to master you. So since 2008, that's a legacy platform that Pedro had mentioned, we're now on multi-domain where our parties can have multiple names, they can have multiple addresses, they can have multiple tax IDs. So we actually can really have a flexible definition, say okay, is it a sales definition, is it a legal definition? We used Dunham Bradstreet as a reference data provider, hence the old flat level record, but we can now actually expand that beyond just businesses are name and address, consumers are very different, and we've grown through an acquisition recently that brought us into the consumer side as well. So email addresses and mobile phone numbers weren't real important to us back in 2008. Today after growing through acquisitions and now with Informatica's multi-domain tool, we can actually master those one to many relationships where before we had to duplicate those records to be able to master them. So you now eliminate the duplication by incorporating it in or not, I guess that? So you're duplicating the records or not? We had to in the old flat data model, so coming off that, now it's just a mapping, we can now manage those one to many relationships, so you can have multiple addresses, right? Your vacation home, your office, and your home address, but that's still you, and we can match across to all those and not duplicate you, we can rationalize you down. Pedro, talk about CIT, what do you guys do and why is this important to you guys? Obviously you want to move into an environment where you're not looking back and digging through old legacy bottlenecks, roadblocks. What's your business doing? Why is this important? Well, CIT has a whole gamut. We have an airplane, airplanes that we lease, actually, to other companies, and we have, you can go all the way from equipment, printers to loans, now that we have one West Bank, that we have quite one West Bank, and all that. So what we have to do is actually manage, MDM help us manage the risk when we have all those customers coming in. We want to know how much we have, basically, across all that customer base. So risk is the big thing. Exactly, risk is the big thing to us. Specifically, I presume that you've got a set of risk algorithms and calculations tied into some portfolio processes, but is the MDM telling you or helping you map to the data sources that are pertinent, in fact crucial to how you run those risk assessment activities, is that right? That's correct, yes. You're not doing risk inside the MDM, using MDM to sustain visibility into those data sources. That's correct. We use MDM as a tool to facilitate the work that our risk analysts do. Excellent, and are your risk analysts using MDM to identify additional data sources that they might use? That's correct, yes, they do. How's that interaction taking place within between you and them? Well, so we kind of maintain the MDM for them and kind of facilitate making the vision to them so that it's as easy as possible to them to go and make those connections. So we actually feed their systems, all their systems that they use, as well to assess the risk. But there are, when you do the mappings, there are certain tactics or certain techniques that you have to apply to make sure that the mapping is accurate, and that covers things like intellectual property ownership and payments that you might have to make, et cetera. Is that part of your evolving job? Yes, it is, and those are the rules that we actually build into MDM where we have all that accounted for. You know, I'm curious, if I can just kind of riff her for a second, and I'm curious, we talk a lot about, we talk a lot in the industry about the idea of data brokers or commercializing or monetizing data differently without necessarily getting into the details of, you know, operationally how it's going to work. And it seems like MDM is going to become an increasingly important tool, both in the consumer side, but very also more than likely on the production side. What role do you think MDM is going to play as you think about brokering data, but also provide it, because you're a financial services company, and do you see your job moving from a support job to someone who's actually worrying about data products or data services within your business? Well, we'll see ourselves as a data provider in a way. We want to be, with MDM, we want to be the central point where everybody comes and gets their data. Basically, come here to get the one source of the truth, the best version of the truth, and use that to make the decisions that you're going to be making with the business that you're going to be dealing with. Are you actually a clearinghouse of subscriptions to other sources of data? Do you do that, or does that come in through information services or something like that, and then it's passed to you? Well, some services, some sources send their data to us in investor spec. Also, we actually push data down to other downstream systems. So in interesting use cases, and the objective all along is the business users shouldn't necessarily need to know the term MDM. We just need to insert the quality into the process. So your original question was, do our risk analysts interact with MDM? Well, they don't know they do. But the tools they use to assess credit wordliness before they can even start that process, they have to enter the information. It goes in real time into MDM. It says, okay, this is Tom. We know Tom. You're okay to start working with Tom. If not, it sets a red flag and says someone new to the ecosystem, Denim Bradstreet doesn't have them. We need some upfront diligence before we can let you proceed through the origination process, and that hits our centralized credit control area, where before you can establish essentially the sold to or the least to or the obligor, we need to make sure we know you. We've got a credit score. We at least have some history before we even invest any time of our risk analysts and our sales folks on moving through the process of origination and lending money. So take me through the process, if you would Adam, of an analyst saying, okay, I'm performing my work. I don't necessarily know that I'm using MDM. I'm using an application that is supported by MDM. I identify a new source of data that has to be vetted, has to be part of, has to be cleared, which is an MDM task, or is part of the MDM structure. At what point in time do you guys get involved? From the very beginning. So as soon as that customer comes in the door, we want to recognize them. We want to identify them and we also want to segment them. So coming back to some of the key side effects of MDM is being able to have proper segmentation, which is required for risk reporting for the Federal Reserve. But to your question about bringing in new data, that's front and center. So whether it's 1Z2Z in terms of requests for loans or it's a merger or acquisition data set that suddenly has landed in our plight and all right, what's that overlap? We need to absorb that data, get it into our operational reporting. MDM's become that engine, whether it's 1Z2Z or big batch loads that we rationalize it down on board that data. And then it's available for Moody's as the tool we use for our risk analysts to work in. That real time is integrated with MDM under the covers, but from the risk analyst perspective, they're just searching within Moody's. They don't realize that it's tapping in to the services of MDM to pull that subscribe data back. So you started off in risk and origination. How are, or do you anticipate that the MDM approach, beyond the tool, but the approach that we're talking about is going to start being extended within CIT? So it's been a very interesting journey because credit risk was our first champion so to speak to get us into the door. But we've now been looking at the whole client on boarding process and anti-money laundering process as just a natural extension of the use case. They need to know exactly who they're dealing with. They need to know that history. So we started very narrowly with credit risk management. We're basically saying anyone that needs to identify or work with a customer or a prospect, you have to initiate your business process with MDM. So I think to your earlier comment, right, we've become a service to the organization. Your business user shouldn't necessarily know you're dealing with MDM. They should just start recognizing quality data and recognizing consistency across whatever platform, whether they're reporting or transacting or, right, just doing normal day in, day out operations, it's all the same quality baseline of data. But they also have acknowledged the disciplines to do that right. Well, that's a slippery slope because how often are, I mean, sales guys are great. They bring rent and revenue, but how disciplined are they in having quality data within Salesforce? So that discipline, we are trying to systematically apply. So okay, only the exceptions kick out, but we don't want a lot of exceptions because that's a lot of work. So we are within Wikibon, part of the research arm of SiliconANGLE Media. We're spending a lot of time thinking about how big data is going to be translated into reliable, ongoing, predictable value in the form of applications, which is how the industry's always done things. We start something bespoke, the tooling improves, and eventually it evolved into an application. We're still not really there yet for a whole bunch of big data, but we are starting to see it happen what we call, for example, big data micro applications like fraud detection. What is the relationship between big data, your data scientists, the people really driving some of these analytics stuff, and how that gets translated into being embedded within the process through MDM-like disciplines? So one of the things that I always struggle with is MDM is not big data, but it is important to big data. So, and it's a metaphor that works well because we have the concept of data lakes now. Well, MDM is your most effective net to cast into that data lake of transactions, right? So you may have 10 duplicate toms, but if you don't know there's 10 duplicate toms, you're going to cast your net and you only get one or two back. So you're not going to get the full picture of Tom. You're not going to understand his clickstream analytics on the webpage, his ATM transactions over time. MDM gives you that net to say, I know exactly how to pull all of Tom's transactions out of that data lake, and I know how to do it across all of our systems and all of our business units. So MDM is basically that more effective net to cast into the data lake and pull Tom's fish out. So the efficiency of this is the great fishman. We love fish metaphors, you know, we've got to find the fish. We have our own tool we call Fish Finder that we've built, data listening engine. It's true, you want to get the good fish and also get the accurate version of Zay Tom. So what you're saying essentially is that allows the impact to be more of a holistic view of that person, vis-a-vis other insights could be across selling, could be, am I getting this right? And it couldn't be, it could be any transactional data. It could be, oh, I know Burlington, Northern Santa Fe has 600 engines and I need to know as GE Locomotive, all the telemetry on that diesel engine across all their products for the last six years, right? So we're trying to improve 0.1% fuel economy because that's millions of dollars. So again, MDM rationalizing who your customer is, what their serial numbers on those engines are, all right, go into my telemetry lake and pull back their specific data. That's why I say MDM is the key to the kingdom because essentially not only is it a net you're throwing in the water, the fish, it's actually a smart net that's filtering in real time. So it's an active net, if that's what you mean, and that it's a, the key word you use was rationalizing. It's that process of actively rationalizing to tie together the creativity of big data, data science and the operationalizing of that in the context, from our perspective. And the impact of data governance. So data governance is always knitting that net, right? Adding new toms in, new engines in. So every time you cast that net, it's continually the most up to date, the most effective net. So I got to get Pedro's response on it. I love that and you get way in on this. The effect to the organization. Now you're a magician, right? So now you're doing not only the data, storage stuff and setting the table and making sure things are working. MDM kind of makes things magical at this point. What's the reaction been to some of the naysayers and or some of the less sophisticated analytical folks who are like, whoa, is it really working? Is that the demo version? How do you do that? How are you getting that? Share the reaction and some of the commentary around what goes on after the fact. It's amazing. I mean, when folks look at it, they actually surprise that we actually have all that data from all different sources in just one place. And they can actually have a 360 overview of all those records in one place. And it's easy when I get calls from folks and say, well, I know this company is here. What else can you tell me about it? I just send them a report of all that that is related to that also going up all the headquarters, the global ultimate and all that. It spreads like a crack addiction basically. Exactly. And so they actually tell all their departments, whoa, you know, this... We got an arms dealer on the 10th floor. Check out the MDM, what they do for us. One of the other... That spreads because it contains. Exactly, yes. One of the other cube guests we had on today actually suggested that maybe what we need to do is we need to have a data help desk. An individual who has visibility into, or for whom the MDM is the core tool, helping people identify this, help them understand the characteristics of the data, how to use it, how not to use it. That's very important, how not to use it. Well, you're talking about self-service. The end game is self-service, right? Well, you tie it back to the naysayer comment, right? Now those people are going to find a platform and we want to hear from them because that makes the product better, it makes the solution better because they're going to find the corner cases that we need to continue to stitch up. Well, the help desk, right? That'll draw them in instead of them finding their own way around and then we hear it three, four, six months later. Well, let's have the means through a help desk, right? Bring it to us. It'll make it better faster. If that's becoming the core asset that is being provided as opposed to this or the laptop or the printer, then we probably ought to elevate that. And by the way, on the self-service issue, you're absolutely right that we want to do self-service, but we also want to be continuously adding new data. And data is not the most obviously thing to a lot of people. Well, in the DevOps world, they use the word recipes like Chef Puppet and so what we're seeing data, similar things, as you discover more MDM magic, you bottle it up, right? I mean, you can get to that point of, all right, so we start small. That's the key, that's the key that you will hear that everywhere. Start MDM small, start, add a few attributes and then keep growing the appetite and adding more attributes. And as we, yeah, that's exactly right. As we on board. It gets some winds under your belt. An additional business process. So credit risk cares about Dunnebrad Street data because they have the corporate family trees. Well, sales and marketing may have a totally different data set or we expand into the healthcare arena, right? Totally different mapping. We want to know the number of beds within a hospital. So that as we on board new business processes, we're going to start looking for more niche data providers. And MDM gives us that means to start saying, it's the best to breed. We don't need one silver bullet data source. We want the best data source. You don't run into a roadblock or a hole. You don't go into a big black pit, you know, when you see a new data set, it's not model properly. MDM allows you to bring that in aggressively. And that comes back to right casting the proper net. You don't boil the ocean with MDM. You don't have thousands and thousands of attributes, right? You need those key attributes to bring the data together. And then that lets you reach into the broader set of attributes. So yeah, it's it's a great salesman for now. Informatica, I just see it now. Oh, the MDM is a magical tool. Just trust me, it's magic. Buy it. I mean, it's almost that easy right now. Whoa. No, don't believe me. I don't work for Informatica. Yeah, but that's the goal is to get to these experiences, abstract away the complexities. That's the beautiful thing about software is that you want to get the complexity out of the hands of the end game, just delivering the data and getting people to use it. I think what is true, John, is that the disciplines that we're describing here won't be repeatable and won't be reliable and won't have any predictability unless there's software support for them. And so it's the combination of the hard work of the people that think about this and curate the data and take care of it, but also the tooling that makes it not only easier, but also sustainable. Because the goal is to add more to this against the same set of disciplines and not degrade the data in the process. Exactly, and if it's going to take six months to add a new source and new attribute, you've lost the game already. It needs to be flexible to say, yeah, we may have a one-time list we want to spin up and we want to get that in in six business days or we have an acquisition that comes in when we need to get it in in two weeks, right? Correct, yes. You burned some midnight oil, but you did it. You got it in and delivered value very quickly to the business. So guys, I want you guys to spend a minute real quickly on the last word here on this segment to share with the audience what's popping here at the show. Honestly, what bubbles out for you guys, obviously with the MDM focus, you guys have had a journey, some brute force, but also some good innovation as well and some good stuff's happening in your world. Sure you got a pay raise, other people coming to you, the dealer, the MDM dealer. But here at the show, what's new? That's some new announcements. Some are on top of existing stuff and some new products. What boils up to the top for you guys? What's the, wow, that's going to be kick ass. That's super sexy, let's share some thoughts. So two things, one from a product perspective and then from a theme. Entity 360 for MDM, IDD has been, excuse me, a very effective stewardship tool, but it wasn't glamorous. It really didn't hold the attention of the business users. So we would occasionally lose business users because it didn't have a glamorous UI. Entity 360 has that promise. I'm really excited about that in version 10.2. From a theme, most of the speakers that were a governance subject had the most questions. So we're starting to see governance becoming more and more important, not just being able to understand how do you match the data. Now, what's the definition of a customer, of a provider, of a patient? Sounds like a very simple straightforward thing, but when you start merging patients or doctors together in a database, that can get pretty scary pretty quickly and having guidance and lessons learned from your peers in the industry or in IT, talk about how do we coach the business? How do we make those decisions and those definitions? Not heard the level of conversation in past years that we're hearing this year. Andrew? Well, from a tool point of view, I'm very excited to see the new capabilities that they have on the version 10 of MDM, basically what Adam was mentioning there, how we're going to basically make IDD better. One of the key things that my customers tell me is, you know, I would like to see IDD be better. And so I'm looking forward to that. From a theme, I see everywhere data powers, this data powers that. So I'm thinking I'm taking home data powers, everything, which is the theme, I believe. Now you're seeing the results. You guys can get more power. You're the power source. You're brokering the data. You're enabling a lot of people to be successful. Well, congratulations guys. Thanks for sharing the excitement here in theCUBE. Really appreciate it. Thank you. We're getting down and dirty talking data here on theCUBE, broadcasting live from San Francisco. I'm John Furrier with Peter Burris. We're here inside theCUBE. We'll be right back with more. You're watching theCUBE. It's always fun to come back to theCUBE.