 Live from Orlando, Florida, it's theCUBE. Covering Pentaho World 2017, brought to you by Hitachi Ventara. Welcome back to theCUBE's coverage of Pentaho World. He brought to you, of course, by Hitachi Ventara. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Charles Gatti. He is the business development manager at Melissa Data. Thanks so much for joining us. Great, thank you for having me. So 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 startup, you might say, and 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 strategic alliance and so what is your relationship now with Hitachi Ventara? That's a great question because now that we're with Hitachi Ventara, one of the things we're focusing on is a strategy around data quality blueprints. Data quality blueprints are something that Pentaho brought into 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 sound less technical and more engaging, I think. Now we're getting to 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 a consultative, 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 gonna 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 then the expected outcomes after you've solved that problem. So the first use case is around, it's around customer data quality with an online retail, right? So everything from preventing packages from being misplaced by using address verification and geocoding in order to prove the inequality 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 in 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, 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 ensure the quality of it but then also ensure that it's not used for nefarious purposes like I 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 two-part question. So looking at it from the perspective of what was described in the data lake, things are going into the data lake, right? 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, right? 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. Is that, does that involve AI or machine learning on your end to do that to the anomaly detectives in the data lake? There's elements of our technology that leverage pieces of that for sure. I mean, I wouldn't call it a full blown AI from that perspective but there is 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 could use Melissa data? Okay, so as luck would have it, the first use case, the first blueprint we're doing is the customer one I just mentioned but we're already talking with Hitachi Vitara about the idea of doing a financial services one, right? And so in that FinTech space, not only would you be able to leverage matching the duplication 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 FinTech 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. Yes, so 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 startup so as a function of that we future proofed ourselves because we've evolved and adapted and 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 technology set can be easily adapted and evolve over time, right? And I think those are the 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 tract to that story you heard earlier. You've also kept, you've said it a couple of times you're a 30 year old startup. How do you stay innovative? I mean now you are a 30 year old startup that now has employees in four locations across the US dealing in huge businesses. How do you keep that startup mentality the hungry mentality and the hacky mentality? I guess you're just saying too. Well you know one of the real advantages we've got there is that 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. 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. So 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 can see that the next thing and Hitachi Ventara has 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 it fit with your roadmap going forward? Right, so you know our perspective on that is much like when we first engaged with them. Data was going into the data layers. Let's 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 that you're getting the best data you can to feed those micro analytics and to feed that, that's a critical part that we see as potential. Looking ahead to, what are some of the problems that you want to solve? I mean just sort of 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? Well 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. And then when you're dealing with a massive amount of indexes, it becomes a lot faster and a lot easier to match, 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, our massive indexing table concept, right? 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. And keep in mind, I am the head of Global Sales and Alliances, so don't bust out over the two technical questions right now. So this is an identity resolution at a massive scale. Does it involve an internet of things, almost like a, you know, slap me on the wrist, like a graph, a social graph of you and all of the identities you may have running on various edge devices. You meaning a user. I think there is the potential for pieces of that. I'm a geek here. 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 stored 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. So it's not complete in any way so I wouldn't want a venture to answer the complete 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 can look at something similar, yeah. Right. So you're here at Pentaho World 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 to- Outside of business? Yeah, outside of getting some deals done, exactly. But what are you learning? What are you hearing? What are you sharing best practices and how do you do that here? Well, you know, we're pretty tightly connected into different elements of what is now Hitachi Vitara, right? So we work with their office in Singapore. We work with, you know, work with them and engaged all over the world on many different fronts. And so it's nice to be here one so that you can literally put some faces with some names, right? And as you look at some of their different initiatives like cybersecurity that I've seen over there somewhere and then some of the initiatives they've got going, they march a bit in lockstep with what we're doing and we're looking, the nice thing about being here is the ability to sort of reconcile that and see and talk about how we could go forward together with those other elements, so if that makes sense. Right. Yeah, absolutely. Well, Charles, thanks so much for coming on theCUBE. It's been a great talking to you. Thank you for having me. I appreciate it. Great. We will have more from the CUBE's live coverage of Pentaho World in just a little bit.