 Live from Boston, Massachusetts, it's theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. Boston, everybody, where the city is bracing for a big snowstorm, still euphoric over the Patriots' big win. Aaron Colcourt is here, he's the director of engineering at FIS Global, and he's joined by Dave Fevella, who's the director of BI at FIS Global. Gentlemen, welcome to theCUBE. It's good to see you. Thanks so much for coming on. So Dave, set it up, FIS Global, the company that does a ton of work in financial services that nobody's ever heard of. Absolutely, absolutely, yeah. We serve and touch, basically, virtually every credit union or bank in the United States and have services that extend globally, and that ranges anywhere from back office services to technology services that we provide by way of mobile banking or online banking. And so, we're a Fortune 500 company with a reach, like I said, throughout the nation and globally. So you're a services company that provides sort of end-to-end capabilities for somebody who wants to start a bank or upgrade their infrastructure? Absolutely, yes, so whether you're starting a bank or whether you're an existing bank looking to offer some type of technology, whether it's back-end processing services, mobile banking, bill pay, peer-to-peer payments. So we are considered a fintech company and one of the largest fintech companies there is. And Aaron, your role as the director of engineering, maybe talk about that a little bit. My role is primarily about the mobile data analytics about creating a product that's able to not only be able to give the basic behavior of our mobile application, but be able to actually dig deeper and create interesting analytics, insights into the data to give our customers understanding about not only the mobile application, but be able to, even as we're building right now, a use case for being able to take action on that data. So, I mean, mobile obviously is sweeping the banking industry by storming. Banks have always been basically IT companies when you think about a huge component of IT, but now mobile comes in and maybe talk a little bit about sort of the big drivers in the business and how mobile is fitting in. Absolutely, so first of all, you see a shift that's happening with the end user, you, David, as a user of mobile banking, right? You probably have gone into the branch maybe once in the last 90 days, but have logged into mobile banking 10 times. So we've seen anywhere from an eight to nine time shift in usage and engagement on the digital channel. And what that means is more interactions and more touch points that the bank is getting off of the consumer behavior. And so what we're trying to do here is turn that into getting to know the customer profile better so that they could better serve in this digital channel, where there's a lot more interactions occurring. Yeah, I mean, you look at the demographic too. I mean, my kids don't even use checks, right? I mean, it's all, everything's done mobile, Venmo or whatever capabilities they have. So what's the infrastructure behind that that enables it? I mean, it can't be what it used to be. I mean, probably back end still is, but what else do you have to create to enable that? Well, it's been a tremendous amount of transformation on the back ends over the last 10 years. And particularly when we talk about how that interaction has changed from becoming a more formal experience to becoming a more intimate experience to the mobile, to the mobile client. But that more specifically to the back end, we have actually implemented Apache Spark as one of our platforms to actually help transform and move the data faster. Mobile actually creates a tremendous amount of back end activity, sometimes even more than what we're able to see in other channels. Yeah, and if you think about it, if you just kind of step back a little bit, this is about core banking, right? And as you speak to IT systems. And so if you think about all the transactions that happen on the daily, whether you're in-branch, at ATM, on a mobile device, it's processed through a core banking system. And so one of the challenges that I think this industry and fintech is up against is that you've got all these legacy old systems that have been built that can't compute all this data at a fast enough rate. And so for us, bringing in Aaron, this is about how do you actually leverage new technology and take the technical debt of the old sort of systems, data schemas and models and marry the two to provide sort of data, key data that's being generated. Without shutting down the business. Without shutting down the business. Can you elaborate on that? Because that's a non-trivial, it used to be when banks merged, could take years for the back office systems to come together. So now, let's say a bank comes to you, they have their, I don't want to say legacy systems, the systems they've built up over time, but they want the more modern capabilities. How do you marry the two? You want to take it first, Dan? Well, it is actually a very complicated process because you always have to try to understand data itself and how to put those two things together. More specifically in the mobile client, because of the way that we are able to think about how data can be transformed and transported, we came up with a very flexible mechanism to allow data to actually be interpreted on the fly and processed. So that when you talk about two different banks, by transforming it into this type of format, we're able to kind of reinterpret it and process it. Would this be, could you think of this as a very, very smart stream process where ETL would be at the most basic layer and then you're adding meaning to the data so that it shows up to the mobile client in a way that coheres to the user model that the user is experiencing on their device? I think that's a really good way of putting it. I mean, there's, we like to think of it, I call it a semantic layer of how you one treat ETL as one process and then you have a semantic layer that you basically transform the bottom bits, so to speak, into components that you can then assemble semantically so that it starts making sense to the end user. And to that point, to your integration question, it is very challenging because you're trying to marry the old with the new and we'll tease the section for tomorrow which Erin will talk about that. But for us at an enterprise grade, it has to be done very cautiously, right? And we're under heavy regulation and compliance and security. And so it's not about abandoning the old, right? It's trying to figure out how do we take that, what's been in place and been stable and then couple it with sort of new technology that we're introducing. It was interesting conversation, the old versus new and I look at your title, Dave, and it's got BI in it. I remember I interviewed Kristin Chabot who was then CEO of Tableau and he's like, old, slow, BI. Okay, now you guys here are talking about Spark. Spark's all about real time and speed and memory and everything else. Talk about the transformation in your role as this industry is transformed. Yeah, absolutely. So when we think about business intelligence and creating that intelligence layer, we elected the mobile channel, right? Because we're seeing the most inter-activities happen there. So for us an intelligent BI solution is not just data management and analytics platform. There has to be the fulfillment. You talk a lot about actioning on your data. So for us is if we could actually create intelligence layer to analytics level, how can we feed marketing solutions with this intelligence to have the full circle and insights back? I believe the gentleman, they were talking about the RISE lab in this morning's session, right? If you- They follow on to AMP, basically. Yeah, exactly. So there was all about that feedback loop, right? And so for us when we think about BI, the whole loop is from data management to end-to-end marketing solutions and then back so we could serve the mobile customer. Well, so the original promise of the data warehouse was this 365, what you just described, right? And being able to affect business outcomes. And that is now the promise of so-called big data. And even though people don't really like that term anymore. So my question is, is it same wine, new bottle, or is it really transformational? Are we going to live up to that challenge this time around as practitioners? I'd really love your input on that. I think I'd love to expand on that. Yeah, I mean, I don't think it's, I think it's a whole new bottle and a whole new wine. David here is from wine country and there's definitely the data warehouse introduced the important concepts of which is a tremendous foundation for us to stand on. You know, you always stand, like to stand on the shoulders of giants. They, it introduced the concepts, but in the case of marrying the new with the old, there's a tremendous extra third dimension, okay? So we have a velocity dimension when we start talking about Apache Spark. We can accelerate it, it might go quick, and we can get that data. There's the, there's another aspect there when we start talking about, like for example, hey, different banks have different types of way that like to talk to it. So now we're kind of like talking about, like there's variation in people's data. And Apache Spark actually is able to give that capability to process data, you know, that is different than each other and then be able to marry it down the pipe together. But, and then the additional, what I think is actually making it into a new wine is when we start talking about data, the traditional mechanism, data warehousing, that 360 view of the customer, they were thinking more of data as in, I like to think of it as let's count beans, right? Let's just come up with how many people were doing acts, how many were doing this. Accurate reporting. Exactly. And if you think about it, it was driving the business to the rear view mirror because all you had to do was base it off of the historical information and that's how we're going to drive the business. We're going to look in the rear view mirror, we're going to look at what's been going on and then we're going to see what's going on. And I think the transformation here is taking technologies and being able to say, how do we put not only predictive analytics inside play, but how do we actually allow the customer to take control and actually move forward and then as well expand those use cases for variation, use that same technology to look for, between the data points, are there more data points? That can be actually derived and move forward. George, I love that description. You have one of your reports, I remember. George had this picture of this boat and he said, oh, I imagine trying to drive the boat is looking at the wake. You know, I'm looking in the rear view mirror. But in addition to that, yeah, it's like you're driving the rear view mirror, but you also said something interesting about sort of, I guess the words I used to use were anticipating and influencing the customer. Exactly. Can you talk about how much of that is done offline, you know, like scoring profiles and how much of that is done in real time with the customer? Ah. Well, a lot of it is still being done offline, mostly because, you know, as trying to serve a bank, you have to also be able to serve their immediate needs. So really we're revolving to actually build that use case around the real time. We actually do have the technology already in place. We built the POCs, we built the technology inside for being able to move real time and we're ready to go there. What will be the difference? Me as a consumer, how will that change my experience? I think that would probably be best for you. Yeah, well, just kind of step back a little bit too, because, you know, what we're representing here is the digital channel mobile analytics, right? But there's other areas within FIS, global, right, that handles real time payments with real time analytics, such as the credit card division, right? So both are happening, you know, sort of in parallel right now. For us, from our perspective on the mobile and digital front, the experience and how that's going to change is that from a, if you're a bank, and as a bank or credit union, you're receiving this behavioral data from our product, you want to be able to offer up better services that meet your consumer profile, right? And so from our standpoint, we're working with other teams within FIS global via Spark and Cloud to essentially get that holistic profile, to offer up those services that are more targeted, that are, I think, more meaningful to the consumer when they're in the mobile banking application. So does FIS provide that sort of data service, that behavioral service, sort of as a turnkey service or as a service, or is that something that you sort of teach the bank or the credit union how to fish? That's a really good question. We stated our mission statement as helping these institutions, creating a culture of being data driven, right? So give them the taste of data in a way that democratizing data, if you will, as we talked about this morning. That concept's really important to us because with that comes, give FIS more data, right? Send them more data or help them teach us how to manage all this data to have a data science experience where we can go in and play with the data to create our own sub-targeting. Because our belief is that our clients know their customers the best, so we're here to serve them with the tools to do that. So I want to come back to the role of Spark. I mean with Hadoop, Hadoop was profound. Ship five megabytes of code to a petabyte of data, no doubt about it. But at the same time, it was a heavy lift. It still is a heavy lift. So talk about the role of Spark in terms of catalyzing that vision that we've been talking about. Oh, definitely. So Apache Spark, when we talk in terms of big data, big data got started with Hadoop and MapReduce was definitely an interesting concept. But Apache Spark really lifted and accelerates the entire vision of big data. When you look at, for example, MapReduce, you need to go get a team of trained engineers who are typically going to work in a lower level language like Java. And they no longer focus in on what the business objectives are. They're focused in on the programming objectives, the requirements. With Spark, because it takes a more high level of abstraction of how we process data, it means that you're more focused in on what's the actual business case? How are we actually abstracting the data? How are we moving data? But then it also gives you that same capability to go inside the actual APIs, get a little bit lower to modify it for what your specific needs. So I think the true transformation with Apache Spark is basically allowing us now, like for example, in the presentation this morning, there's a lot of people who are using Scala, we use Scala ourselves. There's now a lot of people who are using Python and everybody's using SQL. How does SQL, something that has survived so robustly for almost 30, 40 years, still keep on coming back like a boomerang on us? And it's because a language composed of four simple keywords is just so easy to use and so descriptive and declarative that allows us to actually just concentrate on the business. And I think that's actually the acceleration of Apache Spark actually brings to the business as being able to just focus in on what you're actually trying to do and focus in on your objectives. And it actually lowers the actual, that same team of engineers that you're using for MapReduce now become extremely more productive. I mean, when I look at the number of lines of codes that we had to do to figure out machine learning and Hadoop to the amount of lines that you have to do in Apache Spark, it's tremendously, it's like five lines in Apache Spark, 30 in MapReduce. And the system just responds and gives it to you a hundred times faster. Why Spark too? I mean Spark, when we saw it two years ago to your point of like this tidal wave of data, we saw more mobile phone adoption. We saw those people that were on mobile banking using it more, logging in more. And then we're seeing the proliferation of devices, right, in IoT. So for us, these are all these interaction and data points that is a tsunami that's coming our way. So that's when we strategically elected to go Spark so we can handle the volume in the compute storage. Yeah, and Aaron, what you just described is all the attention used to be on just making it work. And now it's putting it to work really. Exactly. You see that in your businesses. Quick question. Do you see now that you have this sort of lower and lower latency analytics and ability to access more of what previously were data silos, do you see services that are possible that banks couldn't have thought of before beyond just sort of making different products recommended at the appropriate moment? Are there new things that banks can offer? It's interesting, on one hand, you free up their time from an analysis standpoint to where they can actually start to get out of the weeds to think about new products and services. So from that component, yes. From the standpoint of seeing pattern recognition in the data and seeing what it can do aside from target marketing, our products are actually often used by our product owners internally to understand what are the consumers doing on the device so that they could actually come up with better services to ultimately serve them aside from marketing solutions. Notwithstanding your political affiliations, we won't go there, but there's certainly a mood of a trend toward deregulation and that's presumably good news for the financial services industry. Can you comment on that or what's the narrative going on in your customer base? Are they excited about sort of fewer regulations or is that just all political nonsense? Any thoughts? Yeah. You know, on one hand, the why people come to FIS is because we do it here to compliance and regulation. Complexity is a friend, then. Yeah, absolutely, right. So they can trust us in that regard. And so from our vantage point, you know, will it go away entirely? No, it's absolutely not, right? I think cloud introduces a whole new layer of complexity because how do you handle cloud computing and NPI and PII data in the cloud? And our customers look to us to make sure that first and foremost security for the end consumer is in place. So, but I think it's an interesting question and one that you are seeing end users click through without even viewing agreements or whatnot. They just want to get to product, right? Right. So, you know, will it go away or do we see it coming when we know? But... You guys don't read all that text, do you? No comment? Required to. You know, no matter where it goes with the politics, I think there's a theme over the last 10 years and the 10 years before it, things are transforming, things are evolving in ways and sometimes going extremely, extremely fast in ways that we don't truly can anticipate. I think if we were to think about just a mobile application or a mobile bank experience 10 years ago, all we wanted was just to be able to see just the bank balance. And now we're able to take that same application, not only see our bank balance, but be able to deposit our check or even replace the card in our pocket completely with the mobile app. And I think we're going to see the exact same types of transformations over the industry over the next 10 years whether or not it's more regulation or different regulation. I think it's going to still speak to the same services which FIS is there to help deliver. Yeah, and you're right, there are going to be new regulations because they'll evolve maybe out with the old and with the new. You're seeing global regulations around Runbook and you mentioned cloud, there's data locality and you know, it's never ending. That's great for your business. Fantastic. Yeah, it comes down to truss ultimately. Yeah. Are they still, like our customers still go to banks and credit unions because they trust them with their data, if you will, right? Or their online currency in some regard. So, you know, that's not going to change. Right. Well, Aaron, Dave, thanks very much for coming to theCUBE. Absolutely. Thank you very much for your time. All right, keep it right there, everybody. We'll be back with our next guest. This is theCUBE, we're live from Boston. Spark Summit East, hashtag Spark Summit. We'll be right back. I remember when I had such a fantastic batting practice.