>> Voiceover: Live from San Jose, it's The Cube. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to The Cube, our continuing coverage of our event, Big Data SV. We're on day two of this event. I'm Lisa Martin, with George Gilbert. We've had a great day yesterday learning a lot and really peeling back the layers of big data, looking at it from different perspectives, from challenges to opportunities. Joining us next is one of our Cube alumni, Chris Selland, the VP of Strategic Alliances from Unifi Software. Chris, great to meet you, welcome back! >> Thank you Lisa, it's great to be here. I have to say, as a alumni and a many time speaker, this venue is spectacular. Congratulations on the growth of The Cube, and this is an awesome venue. I've been on The Cube a bunch of times and this is as nice as I've ever seen it, >> Yeah, this is pretty cool, >> Onward and upward. This place is great. Isn't it cool? >> It really is. This is our 10th Big Data event, we've been having five now in San Jose, do our fifth one in New York City in the fall, and it's always interesting because we get the chance, George and I, and the other hosts, to really look at what is going on from different perspectives in the industry of big data. So before we kind of dig into that, tell us a little bit about Unifi Software, what do you guys do, what is unique and differentiating about Unifi. >> Sure, yeah, so I joined Unifi a little over a year ago. You know, I was attracted to the company because it really, I think, is aligned with where the market is going, and Peter talked this morning, Peter Burris was talking this morning about networks of data. Unifi is fundamentally a data catalog and data preparation platform, kind of combined or unified together. So, you know, so people say, "What do you do?" We're a data catalog with integrated data preparation. And the idea behind that, to go to Peter's, you know, mention of networks of data, is that data is becoming more and more distributed in terms of where it is, where it lives, where it sits. This idea of we're going to put everything in the data warehouse, and then we're going to put everything in the data lake, well, in reality, some of the data's in the warehouse, some of the data's in the lake, some of the data's in SAS applications, some of the data's in blob storage. And where is all of that data, what is it, and what can I do with it, that's really the fundamental problem that we solve. And, by the way, solve it for business people, because it's not just data scientists anymore, it's really going out into the entire business community now, you know, marketing people, operations people, finance people, they need data to do their jobs. Their jobs are becoming more data driven, but they're not necessarily data people. They don't know what schemas are, or joins are, but they know, "I need better data "to be able to do my job more effectively." So that's really what we're helping with. So, Chris, this is, it's kind of interesting, if you distill, you know, the capability down to the catalog and the prep-- >> Chris: Yep. So that it's ready for a catalog, but that sort of thing is, it's like investment in infrastructure, in terms of like building the highway system, but there're going to be, you know, for those early highways, there's got to be roots that you, a reason to build them out. What are some of those early use cases that justifies the investment in data infrastructure? >> There absolutely are, I mean, and by the way, those roots don't go away, those roots, you know, just like cities, right? New roots get built on top of them. So we're very much, you know, about, there's still data sitting in mainframes and legacy systems and you know, that data is absolutely critical for many large organizations. We do a lot of working in banking and financial services, and healthcare. They're still-- >> George: Are there common use cases that they start with? >> A lot of times-- >> Like, either by industry or just cross-sectional? >> Well, it's interesting, because, you know, analysts like yourselves have tended to put data catalog, which is a relatively new term, although some other big analyst firm that's having another conference this week, they were telling us recently that, starts with a "G," right? They were telling us that data catalog is now the number one search term they're getting. But it's been, by many annals, also kind of lumped in, lumped in's the wrong word, but incorporated with data governance. So traditionally, governance, another word that starts with "G," it's been the term. So, we often, we're not a traditional data governance platform, per se, but cataloging data has to have a foundation of security in governance. You know, think about what's going on in the world right now, both in the court of law and the court of public opinion, things like GDPR, right? So GDPR sort of says any customer data you have needs to be managed a certain way, with a certain level of sensitivity, and then there's other capabilities you need to open up to customers, like the right to be forgotten, so that means I need to have really good control, first of all, knowledge of, control over, and governance over my customer data. I talked about all those business people before. Certainly marketers are a great example. Marketers want all the customer data they can get, right? But there's social security numbers, PII, who should be able to see and use what? Because, if this data is used inappropriately, then it can cause a lot of problems. So, IT kind of sits in a-- they want to enable the business, but at the same time, there's a lot of risk there. So, anyway, going back to your question, you know, the catalog market is kind of evolved out of the governance market with more of a focus on kind of, you know, enabling the business, but making sure that it's done in a secure and well-governed way. >> George: Guard rails. >> Yes, guard rails, exactly, good way to say it. So, yep, that's good, I said about 500 words, and you distilled it to about two, right? Perfect, yep. >> So, in terms of your role in strategic alliances, tell us a little about some of the partnerships that Unifi is forging, to help customers understand where all this data is, to your point earlier, the different lines of business that need it to drive, identify where's their value, and drive the business forward, can actually get it. >> Absolutely, well, certainly to your point, our customers are our partners, and we can talk about some of them. But also, strategic alliances, we work very closely with a number of, you know, larger technology companies, Microsoft is a good example. We were actually part of the Microsoft Accelerator Program, which I think they've now rebranded Microsoft for Startups, but we've really been given tremendous support by that team, and we're doing a lot of work to, kind of, we're to some degree cloud agnostic, we support AWS, we support Azure, we support Google Cloud, but we're doing a lot of our development also on the Azure cloud platform. But you know, customers use all of the above, so we need to support all of the above. So Microsoft's a very close partner of ours. Another, I'll be in two weeks, and we've got some interesting news pending, which unfortunately I can't get into today, but maybe in a couple weeks, with Adobe. We're working very closely with them on their marketing cloud, their experience cloud, which is what they call their enterprise marketing cloud, which obviously, big, big focus on customer data, and then we've been working with a number of organizations and the sort of professional services system integration. We've had a lot of success with a firm called Access Group. We announced the partnership with them about two weeks ago. They've been a great partner for us, as well. So, you know, it's all about an ecosystem. Making customers successful is about getting an ecosystem together, so it's a really exciting place to be. >> So, Chris, it's actually interesting, it sounds like there's sort of a two classic routes to market. One is essentially people building your solution into theirs, whether it's an application or, you know, >> Chris: An enabling layer. >> Yes. >> Chris: Yes. >> Even higher layer. But with corporate developers, you know, it's almost like we spent years experimenting with these data lakes. But they were a little too opaque. >> Chris: Yes. >> And you know, it's not just that you provide the guard rails, but you also provide, sort of some transparency-- >> Chris: Yes. >> Into that. Have you seen a greater success rate within organizations who curate their data lakes, as opposed to those who, you know, who don't? >> Yes, absolutely. I think Peter said it very well in his presentation this morning, as well. That, you know, generally when you see data lake, we associate it with Hadoop. There are use cases that Hadoop is very good for, but there are others where it might not be the best fit. Which, to the early point about networks of data and distributed data, so companies that have, or organizations that have approached Hadoop with a "let's use it what it's good for," as opposed to "let's just dump "everything in there and figure it out later," and there have been a lot of the latter, but the former have done, generally speaking, a lot better, and that's what you're seeing. And we actually use Hadoop as a part of our platform, at least for the data preparation and transformation side of what we do. We use it in its enabling technology, as well. >> You know, it's funny, actually, when you talk about, as Peter talked about, networks of data versus centralized repositories. Scott Gnau, CTO of Hortonworks, was on yesterday, and he was talking about how he had originally come from Teradata, and that they had tried to do work, that he had tried to push them in the direction of recognizing that not all the analytic data was going to be in Teradata, you know, but they had to look more broadly with Hadapt, and I forgot what the rest of, you know-- >> Chris: Right, Aster, and-- >> Aster, yeah. >> Chris: Yes, exactly, yep. >> But what was interesting is that Hortonworks was moving towards the "we believe "everything is going to be in the data lake," but now, with their data plane service, they're talking about, you know, "We have to give you visibility and access." You mediate access to data everywhere. >> Chris: Right. >> So maybe help, so for folks who aren't, like, all bought into Hortonworks, for example, how much, you know, explain how you work relative to data plane service. >> Well, you know, maybe I could step back and give you a more general answer, because I agree with that philosophically, right? That, as I think we've been talking about here, with the networks of data, that goes back to my prior statement that there's, you know, there's different types of data platforms that have different use cases, and different types of solutions should be built on top of them, so things are getting more distributed. I think that, you know, Hortonworks, like every company, has to make the investments that are, as we are, making their customers successful. So, using Hadoop, and Hortonworks is one of our supported Hadoop platforms, we do work with them on engagements, but you know, it's all about making customers successful, ultimately. It's not about a particular product, it's about, you know, which data belongs in which location, and for what use case and what purpose, and then at the same time, when we're taking all of these different data sets and data sources, and cataloging them and preparing them and creating our output, where should we put that and catalog that, so we can create kind of a continuous improvement cycle, as well, and for those types-- >> A flywheel. >> A flywheel, exactly, continuous improvement flywheel, and for those types of purposes, you know, that's actually great use case for, you know, Hortonworks, Hadoop. That's a lot of what we typically use it for. We can actually put the data any place our customers define, but that's very often what we do with it, and then, but doing it in a very structured and organized way. As opposed to, you know, a lot of the early Hadoop, and not specific to any particular distro that went bad, were, it was just like, "Let's just dump it all "into Hadoop because it's cheaper." You know, "Let's, 'cause it's cheaper than the warehouse, "so let's just put it all in there, "and we'll figure what to do with it later." That's bad, but if you're using it in a structured way, it can be extremely useful. At the same point, and at the same time, not everything's going to go there belongs there, if you're being thoughtful about it. So you're seeing a lot more thoughtfulness these days, which is good. Which is good for customers, and it's good for us in the vendor side. Us, Hortonworks, everybody, so. >> So is there, maybe you can tell us of the different approaches to, like, the advantage of integrating the data prep with the catalogized service, because as soon as you're done with data prep it's visible within the catalog. >> Chris: Absolutely, that's one, yep. >> When, let's say when people do derive additional views into the data, how are they doing that in a way that then gets also registered back in the catalog, for further discovery? >> Yeah, well, having the integrated data preparation which is a huge differentiator from us, there are a lot of data catalog products out there, but our huge differentiator, one of them, is the fact that we have integrated data preparation. We don't have to hand off to another product, so that, as you said, gives us the ability to then catalog our output and build that flywheel, that continuous improvement flywheel, and it also just basically simplifies things for customers, hence our name. So, you know, it really kind of starts there. I think I, the second part of your question I didn't really, rewind back on that for me, it was-- >> Go ahead. >> Well, I'm not sure I remember it, right now, either. >> We all need more coffee. >> Exactly, we all need more coffee. >> So I'll ask you this last question, then. >> Yes, please. >> What are, so here we are in March 2018, what are you looking forward to, in terms of momentum and evolution of Unifi this year? >> Well, a lot of it, and tying into my role, I mentioned we will be at Adobe Summit in two weeks, so if you're going to be at Adobe Summit, come see us there, some of the work that we're doing with our partner, some of the events we're doing with people like Microsoft and Access, but really it's also just customer success, I mean, we're seeing tremendous momentum on the customer side, working with our customers, working with our partners, and again, as I mentioned, we're seeing so much more thoughtfulness in the market, these days, and less talk about, you know, the speeds and feeds, and more around business solutions. That's really also where our professional services, system integration partners, many of whom I've been with this week, really help, because they're building out solutions. You know, GDPR is coming in May, right? And you're starting to really see a groundswell of, okay, you know, and that's not about, you know, speeds and feeds. That's ultimately about making sure that I'm compliant with, you know, this huge regulatory environment. And at the same time, the court of public opinion is just as important. You know, we want to make sure that we're doing the right thing with data. Spread it throughout organization, make ourselves successful and make our customers successful. So, it's a lot of fun. >> That's, fun is good. >> Exactly, fun is good. >> Well, we thank you so much, Chris, for stopping back by The Cube and sharing your insights, what you're hearing in the big data industry, and some of the momentum that you're looking forward to carrying throughout the year. >> It's always a pleasure, and you, too. So, love the venue. >> Lisa: All right. >> Thank you, Lisa, thank you, George. >> Absolutely. We want to thank you for watching The Cube. You're watching our coverage of our event, Big Data SV, hashtag BigDataSV, for George, I almost said George Martin. For George Gilbert. >> George: I wish. >> George R.R., yeah. You would not be here if you were George R.R. Martin. >> George: No, I wouldn't. >> That was a really long way to say thank you for watching. I'm Lisa Martin, for this George. Stick around, we'll be right back with our next guest. (techno music)