(Upbeat music) >> Announcer: Live from Orlando Florida, It's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's coverage of PentahoWorld, brought to you, of course, by Hitachi Vantara, I'm your host Rebecca Knight along with my cohost James Kobielius. We're joined by Charles Gaddy, he is the Business Development Manager at Melissa Data. Thanks so much for joining us. >> Great, thank you for having me. >> So tell us, tell our viewers a little bit about Melissa Data and what you do there. >> Well, Melissa is a data quality and identity assurance company, so we have been around for 30 years. And we're a 30 year old start up you might say. Very innovative in what we do, and the way we address our problems. We are the strategic partner for Pentaho as it relates to data quality. So most of our data quality solutions are embedded and available within the Pentaho stack. So my particular role there is to facilitate global sales and alliances, and Pentaho is one of our global alliances. >> Okay, so that's the, it's a strategic alliance, and so what is your relationship now with Hitachi Vantara? >> That's a great question, because now that we're with Hitachi Vantara, one of the things we're focusing on is a strategy around data quality blue prints. Data quality blueprints are something that Pentaho brought in to that relationship, or that new company, right? And it's a powerful way that they sell their solutions, and craft the message around their solutions in a way that sounds less technical and more engaging, I think. And I'll give you a bit of an opinion there, and so we're very excited to be one of the first companies, from a partner perspective, to do a blueprint that's not strictly Pentaho based. >> Is it, you're talking about blueprints, is it a consultative marketing and sales tool? Or is it a solution accelerator template, or a bit of both? >> You stole my thunder, I was going to say I think it's a bit of both actually, yes. The nice thing that I've seen about the other ones they've done and the one that we're crafting is, you're taking a use case, effectively, and you're breaking down what you're bringing to that use case, with a sprinkle of technology, so that they know it is a technical solution, as well as a consultative sale. Then you're telling them about the problem you're going to solve with it, and the expected outcomes after you've solved that problem. So, the first use case is around customer data quality, within online retail, right. So, everything from preventing packages from being misplaced by using address verification, and geocoding in order to improve the quality of address data that you're shipping, all the way through to customer demographics, so you can understand and overlay demographic information about the customers you're targeting online. All of these solutions, we bring the data piece of that, and Pentaho brings the other elements to make that combined blueprint. >> So just in hearing you say those things, I'm thinking back to what we heard on the main stage today, about the potential of the dark side, in the sense of the models maybe being used for nefarious reasons, I mean, how do you guard against that? >> Well, you know, there's that AI component, which was very much of the Skynet comment I believe, and then there's data quality, which, having been around data quality for quite a while, there's a rules based element to that, that isn't necessarily AI based, so you don't necessarily have as much of that dark side to deal with, what you are rightfully pointing out, is the idea that you're using elements of data that represent someone's identity potentially, right. And how do you protect and safeguard that? And our 30 years in the business really gives us an insight on how to protect the data in ways that insure the quality of it, but then also insure that it's not used for nefarious purposes, like you said. >> Okay, so as you know, Pentaho co-founder James Dixon coined the term "the data lake". So how has Melissa partnered and integrated with Pentaho in that way? >> And how does data governance and quality ride upon and leverage the data lake to be effective? >> Okay, so it's a two part question. Looking at it from the perspective of what was described in the data lake, things are going in to the data lake. Well, you can take two approaches to it, I guess. You can try to boil that data lake, which is very challenging, you know. Or you can extract quality information out of it, and so, data quality, whether you're pushing data quality into the lake, or whether you're trying to extract actionable intelligence out of the lake, fits on both sides and gives you that step towards analytics and intelligence that you need. Right, otherwise it's a lake. The other side you mentioned is the governance side of it. So, our components that run, and our services that run as a part of what is offered with Pentaho, give elements of a feature like profiling, so you're able to profile the data as it's moving between these different places, see the anomalies, potentially address the anomalies, if that's something you need to do, or at least be aware of them so you know what's going on, right, and you're constantly monitoring. >> Does that involve AI or machine learning on your end to do that, the anomaly detection within the data lake? >> There's elements of our technology that leverage pieces of that for sure. I wouldn't call it full blown AI from that perspective, but there are some patents and some proprietary technology that we have, that gives us a unique approach on how to profile that data, and how to make that profiled information actionable within Pentaho. >> So, you talked about the retailer use case, and that's how we can make sure the packages are delivered to the right places, and the demographic. What are some other examples of ways that we can use Melissa Data? >> Okay, so as luck would have it, the first blueprint we're doing is the customer one I just mentioned, but we're already talking with Hitachi Vantara about the idea of doing a financial services one, right. And so in that fin tech space, not only would you be able to leverage matching deduplication, which they call more of an identity resolution in that element, but you'd also be able to leverage the elements of data that we bring to bear to say that you are who you say you are. So you bundle those together in a fin tech, or a financial services model, and you've got a different use case from customers and online retail, but you still have a very compelling joint offering as you're pushing data through. >> Which is particularly relevant in light of the Equifax breach, which will haunt us for the rest of our lives, we keep hearing about this. >> Yes, you have to be very careful with the data that you utilize, absolutely. >> One of the terms we keep hearing a lot is future proofing. What does that mean to you at Melissa Data? How do you describe your approach to future proofing your business? >> So, it's interesting because, as I mentioned, we're pretty much a 30 year old start up, so as a function of that, we future proofed ourselves. Because we've evolved and adapted, you have to be nimble, you have to be agile, as well as embracing agile concepts, which, there's two different meanings there, if you will. And so, in looking at that, you want to make sure that you've got the right technology set, and that that technology set can be easily adapted and evolve over time, right. I think those are they key things we've done as a company, with the solutions we've built, and much like, I heard today on the keynote, that Hitachi had focused to do, we've done a very similar thing, because we started in direct marketing, with a database of zip codes. And now we offer matching, and we offer these cloud solutions and identity. So we've had a very similar track to that story you heard earlier. >> You've said it a couple of times, you're a 30 year old start up. How do you stay innovative? I mean, you're a 30 year old start up that now has employees in four locations across the U.S. dealing in huge businesses. How do you keep that start up mentality? The hungry mentality, and the hack-y mentality, I guess I should say too? >> One of the real advantages we've got there, is our CEO and founder has always innovated. From the first company before Melissa, all the way up through today, he's always been one to say we need to try that next thing, right. Pentaho, five or six years ago, was that next thing that he and our VP of strategy said we should try, and now I'm sitting here with you today. There's a top down, bottom up approach, if that makes sense to you, because if you have an idea, you can bring that idea forward as well. >> You consider the next thing, and Hitachi Vantara's been saying that in spades today here at this event, it's also a Wikibon research focus, the Edge, Edge computing, Edge analytics, data, machine data coming from Edge devices, how is Melissa Data, in partnership with Pentaho, moving towards this Edge to outcome frame of reference, or frame for building innovative solutions, where does that fit with your roadmap going forward? >> So our perspective on that, much like when we first engaged with them, data was going into the data lake, let's just get it all in there, get it all in there, get it all in there, get it all in there, right. Well, eventually you have to make that data actionable. You're going to have a reverse scenario with the Edge. There's a lot of data, small amounts, small chunks, that are going to be everywhere, I think it was talked about being on cell phones, and everywhere else. The idea that you can extend the reach of data quality along with the reach of analytics, to actually make sure you're getting the best data you can, to feed those microanalytics, to feed that, that's a critical part that we see as potential. >> Looking ahead, what are some of the problems that you want to solve, just sort of in the next year, the next five years, what are some of the things that you're thinking about and keeping you up at night right now. >> We're doing some very interesting things with globally unique identifiers, I'll call them that, not a GUID in that sense, but the idea that every address on the planet could be indexed, right. And then the idea beyond that was every email and every phone and every identity around that could be indexed. Then when you're dealing with a massive amount of indexes, becomes a lot faster and a lot easier to match, to dedupe, to do other data quality tasks. So, it's one of the projects that our CEO is very interested in, is this sort of indexing or massive indexing table concept. And so that's one of the things I know we're very focused on as an organization, and how that can feed all of our other technologies. >> How would that work, I mean, I know it's a research process in motion, but >> And keep in mind I am the head of global sales and alliances, so don't bust out all the too technical a question. (laughter) >> Yeah, so this is identity resolution at a massive scale, does it involve an internet of things, almost like a, slap me on the wrist, a graph, a social graph of you and all the identities you may have running on various Edge devices? You meaning a user. >> I think there is the potential for pieces. >> Remember, I'm a geek here so. >> Yeah, yeah there's a potential for pieces of that to be used in that way. Like an example we got approached about was, someone who wanted to have a cookie that represented the address that they just captured from this particular interaction on the web, right. Well, imagine if you could use this table of addresses that was indexed, right, to get that number back, and you just store that number constantly with that cookie, you'd never have to store that address data again, you could match that index against other indexes, and the uses go on and on and on. >> James: Right. >> So it's not complete in any way, so I wouldn't want to venture to answer the implete part of your question, but the idea that you can represent things with a series of numbers is how the internet got started, effectively, right, so you could look at something similar. >> Right. >> So you're here at PentahoWorld, and you said you're a biz dev manager, what is your, what do you hope to take away from it? I mean, are you talking? >> You mean outside of business? (laughter) >> Get some deals done, exactly. But what are you learning, what are you hearing, are you sharing best practices, and how do you do that here? >> Well, we're pretty tightly connected into different elements of what is now Hitachi Vantara, right, so we work with their office in Singapore, we work with them engaged all over the world, on many different fronts, and so it's nice to be here one, so you can literally put some faces with some names, right. And as you look at some of their different initiatives, like cyber security that I've seen, over there somewhere, and some of the other initiatives they've got going, they march a bit in lock step with what we're doing, and the nice thing about being here, is the ability to sort of reconcile that and see and talk about how we can go forward together with those elements, if that makes sense. >> James: Right. >> Absolutely. Well Charles, thanks so much for coming on theCUBE, it's been a great talking to you. >> James: Yeah absolutely. >> Thank you for having me, I appreciate it. >> We will have more from theCUBE's live coverage of PentahoWorld in just a little bit. (upbeat music)