 Live from New York, extracting the signal from the noise. It's theCUBE, covering RapidMiner Wisdom 2016, brought to you by RapidMiner. Now, your hosts, Dave Vellante and Jeff Brick. Welcome back to the Big Apple, everybody. This is theCUBE. We're here at RapidMiner Wisdom 2016. Bob Lytle is here. He's the CEO of Related 2, new startup that really just announcing. Great to see you. Thanks for coming on theCUBE. Thanks for having me here, Dave. You've got an amazing background. We were talking off camera. You've been a CIO, a CTO, a sports statistician. You've got a military background. Now you're an entrepreneur and startup. Wow. How did you come to this place and what's Related 2 all about? Well, you know, how I came to this place in a long and winding story, you probably don't have time for it. Simply say that. I would call myself a citizen data scientist, very similar to what RapidMiner is going at and that's really why we use their products. So, in terms of what we do at Related 2, we're looking for enhanced data about corporates, about companies that financial services and investment companies and insurance companies can use to paint a better picture of what's going on in the market. So, from the credit bureaus, you think what a credit bureau does, they're gonna gather data from a series of financial institutions, they're gonna repackage it, they're gonna put it back out. It's very good. If you have the information, what we've found in the open data space and the public data space is that there's a significant amount of different information that's not traditionally available to those FIs. So, what we do with a series of technologies, we bring it all together, create a cluster of corporate information and then we help banks use that to leverage and make better predictions on the success of new businesses, startups like mine, all the way through the mid-tier business. So, talk about the evolution of open data sources, the publicly available data that you're now accessing versus it used to be the credit unions had their own proprietary stuff coming directly from the banks and different institutions and how that's really changing the game. Sure. Well, open data has become huge probably over the last five years, right? And you've got a series of initiatives all the way from the World Bank, US government's big into it. Canada, we've got a great new PM. What he said is I'm going to make every single part of our government open to everybody, right? And folks want to know this information. And so, you've got everything from emails and that's interesting but public registry data is getting stronger and stronger throughout the provinces in Canada. It's fairly strong in the states. Not as strong as we would like it to be. You've got some states that are great and some states that are not. The difficulty with open data has not been its availability. It's been that it's too dirty. It's too difficult to understand what this data is. And I'll tell you, I'm from Chicago originally, right? Illinois data cannot be linked with Iowa data. And because you can't link it, you can't paint a picture of a company that maybe has feed on both sides of the border, right? So, open data for company information is ripe for FIs and insurance companies to really use and leverage in a way they just haven't used. And so related to helps solve that problem? Absolutely. Through data science and other data analytics. So, I don't feel you're familiar with graph databases. Sure. Graph database, right, is all about the relationships between data. So, every piece of information, let's call it a fact, right? Think of Facebook. I'm a friend of a friend. LinkedIn, I've got connections six levels deep or whatever the case is. They all use the graph. So, graph is a new way to very quickly relate and link together with high probability companies that are suppliers, competitors. Data, you know, this person owns this company and also goes down here and also goes down here. So, when we put that data into the graph, we paint a very interesting cluster picture of a corporation that's just not available. So, great. But what you need is actionable agile analytics. So, RapidMiner, and the reason we chose this as our platform, there's lots of great tools out there, I can do in a RapidMiner in a day or two what it would take me weeks and sometimes months to do in other technologies. It's just that quick. It's a new paradigm for analytics. So, I want to come back to that, but before we do, so you're essentially expanding the market for the financial institution by identifying lower risk opportunities that they would normally run away from because they don't have enough data on them. Is that correct? Either run away from it or they take an unreasonable gamble. So, think if you are a banker, I want to line a credit, I'm a new business, I'm sitting in front of you, what are you going to use to assess what I'm about? Maybe you'll look at my credit score, right? Maybe you'll look at this. Maybe you'll take a gamble, right? What we're finding is the profitability edge in FIs is all about making that gamble a little bit smarter, right? And so, it goes down to initial loan origination. It goes down to marketing and upsell. Which winners are coming and how do I raise those? And frankly, it goes down to tuning things, right? Do I need to shut this line down because I can see that something is trending differently? This is the whole power of predictive analytics is it's given me my reasonably accurate best guess of what's happening. You put it in hands of the risk managers and that's where they're able to make their level. And do they want fewer false positives or fewer false negatives? You know, depends on who you're talking to. So, the financial industry is huge, right? I think what most FIs are looking for is a reasonable risk-based solution that allows them to make their best guess using their own models, right? So, what they're looking for is maybe, you know, a little bit broadly, how can I leverage what I've got to cross-sell into this environment, right? Now, if you're a collection agency, you're looking for the whole thing. And a collection agency is going to take false positives all day long in a different mode because that's the business they're in in receivables, right? The customer's going to have a unique story, right? The data starts, you've got to have the clean data, then you've got to be able to analyze that and put it in the hands of the decision makers. And so, a way that a bank might mitigate a risk is say, okay, small business owner, you've got to sign a personal guarantee and go through a rectal exam. Does that change? Or, they still do that and they have higher confidence. You know, I think that needs to change and here's why. If you are a mainline bank, right, there are folks out there that could eat your lunch, right? And it's the alternative lenders. And they're coming out now and in 24 hours, I'm going to be able to give you this and I'm going to be able to give you a line of credit in this way and so on. So, the competitive nature of the market is going to value companies who can make that easiest for a business owner decision without some of the things that you talked about, exams or otherwise, right? So, the quicker you can originate a loan or extend a line based on actionable information, you're going to win that market. And that's what this new kind of data gives you that you just don't get from a traditional bureau model. Bureau model is great and don't get me wrong here. You need both. Right now, the banks are only dealing with one and maybe they're not even dealing with that. But your strategy is to identify the limits of that bureau model and then expand into the territory. So, you know, our partnerships, right, are all about enhancing what you have, not replacing that. So what we want to do is help folks take a good risk model and make it a better risk model. Or on a marketing side, take a good strategy and then say, hey, listen, I like this company. Tell me about the profile of that company and find me another 50, just like these guys, because that's where I want to make my next investment. So this idea of citizen data scientist, so it's a good one because normally it's been insights for a few that take forever to get into the hands of the business people. So the concept of citizen data scientist is operationalizing analytics, so the end user who's closer to the customer can use it and leverage it. Great, how does that apply specifically to your business? Right, so from our business, right, I'll just say it applies to me personally, right? I'm not a trained statistician. I hire people who do this officially, right? But I love to dabble with the data, right? So here's what happens. There's a series of folks who call them CTOs, call them MBAs, call them data-driven leaders and executives and they are not coming from a traditional statistician background and they're dying for actionable information. So when you think about where RapidMiner plays in that market, is they could very easily take a guy like me, a CTO, and say, let me give you a tool that gets you very, very close to the exact prediction that you would get if you were going back to your analytics shop, right? So it's part of a whole continuum of what do we do and what do we know with big data that's out there? It shouldn't be in the hands of the few and I think tools like RapidMiner, again this is why we picked it, makes it so powerful that even a guy like me can go out there and find those assessments and then we go into the back office and we say, you prove it, you validate it and then we bring it out. And you sell an app, you sell a service or both? Right, so our stock and trade is the data itself and clean, curated, open data, very, very important because it's very difficult to take action on something that doesn't match and doesn't link. We have analytics services, right? Some of the products that we'll be rolling out later in the year are related to things like algorithms and some of those predictive scores. You know, what I'm very excited about is the research angle of our data. So we just spent all our time talking about FIs. If you're an investigative journalist, an academic researcher, a strategic planner and a market, you may need a very deep and complete profile of either an individual company or a series of companies and so one of the things we have on our roadmap is to launch an immersive online capability for individuals to go and subscribe and research all of the same data that the banks are using in some different modes. Graph database technology is fundamentally visual, right? You're talking about things, link to things, link to things and the ability to use that as an individual going to try to investigate something, look at a supplier network, look at the competitive landscape. Frankly, it's not out there today, it's incredibly powerful. So one of the problems with predictive analytic solutions in companies and applying machine learning is people spend all their time cleaning the data to fit the algorithm and they don't have time to shape the algorithm to fit the data. If I understand it correctly, you're giving them clean data so they can spend more time on improving the algorithm and aligning with their business goals. Absolutely, so and it's, I mean, frankly this is something that, you know, I wish that the open data community and the governments were already giving out but the fact is I can show you, you know, 20 different ways to spell Edmonton and all of those came from the Canadian government. You say, we're getting this, right? Well, that's fine, so what's the right one? What I want to do is make it simple and easy for folks to be able to use the raw data if that's what you do, right? And a lot of banks and a lot of analytic shops, they've got the things that they need but they're dying for that data, so that's one model. Interesting to me, again, on top of that is then to conduct that analysis alongside them and provide that service. So how does it work? I'm a customer with a problem, I have a corpus of data, maybe centralized, may not be centralized, how do you engage? Right, so, you know, in the engagement model we're first going to figure out exactly what they're trying to accomplish, right? Are they looking for predictions? Are they looking for cleansing? Maybe a little bit more boring but very powerful. Are they looking for a pens? I can show you the places that know the names of the company but don't know the owners of the company. You would think, how could they not know it? They don't. Ship the file, let's append it, let's analyze it and let's send it back, then you take it. That's kind of a simple business model but it's actually very needed and very powerful. The same thing applies for multiple locations, same thing applies for business licenses. It's a key thing and so I was asked last week to take a look at how good is Google at finding licensed tow truck drivers in Toronto? Google's gonna find lots of tow trucks so they asked us, hey, let's do this analysis and go look at the top five search engines and let's go find licensed tow truck drivers. I found a lot of tow truck drivers, most of them were unlicensed. They go, what, you know, how could you not have this? Why doesn't Google know that it's licensed and put that up in the thing? I'm not trying to get into their business but the concept is that extra piece of data is the edge that helps you get, in this case, the right location-based service, right? Now, that analysis took me a day and a half using the tool set, right? And that's, again, the reason why we look at RapidMiner for this is the speed of getting the data, tweaking it, turning it, cleansing it, come up with the assessment and then punch that back out. I was able to get it Monday morning and the research is out the door to see that. So on that example, on that open data set that you used to find out who is licensed and not licensed, does Google crawl that? You know, apparently not in the way that they need to, right, and frankly, that may or may not be the right business model. You think you Google's model? You know, they want good data and they want to sell the advertising. They want to get the eyeballs, they want to sell ancillary services, right? Now, me as a consumer, right? Toronto's not a warm place, right? It's winter, it's late at night, I'm pulling up my phone and I'm gonna go to the maps for this and I want to find a tow truck driver. I think I'm gonna better find somebody who's allowed to do it and who's gonna provide the right kind of service. And so those are other adjacencies and angles that we would want over there. And you use RapidMiner as part of your cleansing process. Can you describe that a little bit? What makes RapidMiner so awesome? So, you know, number one, it's visual. Back to the digital, the citizen data scientist, it's a visual tool that makes it easy for me to understand. Right, I could be a brainiac, I could be a person who's exploring and I can still see the whole thing in my cockpit. Grab my data in, I can twist it and tweak it very quickly to come up with an analyzable data set, right? So, you know, since I'm a visual guy, the ability to grab in and bring in a new operator or run a linear regression or go and do a K score, those are things that I can do in any language. And I happen to be a programmer at heart, I don't know if you could tell us, so I'd love to jump down there and do some art programming or run some Python scripts. As a business that's in the business of analyzing things, I value speed more than the excitement of writing a beautiful piece of code. So it's code optional, right? Is it what they call it? A code or no code or a lot of code. If I want to jump into the code, I can, but what I find more and more is that folks, they don't want to know what's behind the curtain, they want the actionable agile results. So, you know, that's the reason that we went with this platform and I'm delighted to do it. All right, a lot of time, but last word on the company's funding, self-funding. Right now we're a bootstrap company, you know, we value heavily partnerships so I named some of the folks that we'll probably be connecting with, you know, as a small business. What's more important to me is can we provide the right kind of service to those banks, right? And to those data providers. So it's clean data, actionable analytics, analyze, in a graph, right? Which is finding the data that you didn't know was there in being able to take action on that whole cluster. That's really the power of what we do. All right, Bob Lytle, related to, good luck with the startup and thanks for coming to theCUBE. Thanks guys, we appreciate it. All right, keep right there, everybody. We'll be back at RapidMiner Wisdom 2016. This is theCUBE, we're in New York City, right back.