>> 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)

>> Narrator: From the Silicon Angle Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman and we're here at the east coast studio for Silicon Angle Media. Happy to welcome back to the program, a many time guest, Chris Selland, who is now the Vice President of strategic growth with Unifi Software. Great to see you Chris. >> Thanks so much Stu, great to see you too. >> Alright, so Chris, we'd had you in your previous role many times. >> Chris: Yes >> I think not only is the first time we've had you on since you made the switch, but also first time we've had somebody from Unifi Software on. So, why don't you give us a little bit of background of Unifi and what brought you to this opportunity. >> Sure, absolutely happy to sort of open up the relationship with Unifi Software. I'm sure it's going to be a long and good one. But I joined the company about six months ago at this point. So I joined earlier this year. I actually had worked with Unifi for a bit as partners. Where when I was previously at the Vertica business inside of HP/HP, as you know for a number of years prior to that, where we did all the work together. I also knew the founders of Unifi, who were actually at Greenplum, which was a direct Vertica competitor. Greenplum is acquired by EMC. Vertica was acquired by HP. We were sort of friendly respected competitors. And so I have known the founders for a long time. But it was partly the people, but it was really the sort of the idea, the product. I was actually reading the report that Peter Burris or the piece that Peter Burris just did on I guess wikibon.com about distributed data. And it played so into our value proposition. We just see it's where things are going. I think it's where things are going right now. And I think the market's bearing that out. >> The piece you reference, it was actually, it's a Wikibon research meeting, we run those weekly. Internally, we're actually going to be doing them soon we will be broadcasting video. Cause, of course, we do a lot of video. But we pull the whole team together, and it was one, George Gilbert actually led this for us, talking about what architectures do I need to build, when I start doing distributed data. With my background really more in kind of the cloud and infrastructure world. We see it's a hybrid, and many times a multi-cloud world. And, therefore, one of the things we look at that's critical is wait, if I've got things in multiple places. I've got my SAS over here, I've got multiple public clouds I'm using, and I've got my data center. How do I get my arms around all the pieces? And of course data is critical to that. >> Right, exactly, and the fact that more and more people need data to do their jobs these days. Working with data is no longer just the area where data scientists, I mean organizations are certainly investing in data scientists, but there's a shortage, but at the same time, marketing people, finance people, operations people, supply chain folks. They need data to do their jobs. And as you said where it is, it's distributed, it's in legacy systems, it's in the data center, it's in warehouses, it's in SAS applications, it's in the cloud, it's on premise, It's all over the place, so, yep. >> Chris, I've talked to so many companies that are, everybody seems to be nibbling at a piece of this. We go to the Amazon show and there's this just ginormous ecosystem that everybody's picking at. Can you drill in a little bit for what problems do you solve there. I have talked to people. Everything from just trying to get the licensing in place, trying to empower the business unit to do things, trying to do government compliance of course. So where's Unifi's point in this. >> Well, having come out of essentially the data warehousing market. And now of course this has been going on, of course with all the investments in HDFS, Hadoop infrastructure, and open source infrastructure. There's been this fundamental thinking that, well the answer's if I get all of the data in one place then I can analyze it. Well that just doesn't work. >> Right. >> Because it's just not feasible. So I think really and its really when you step back it's one of these like ah-ha that makes total sense, right. What we do is we basically catalog the data in place. So you can use your legacy data that's on the main frame. Let's say I'm a marketing person. I'm trying to do an analysis of selling trends, marketing trends, marketing effectiveness. And I want to use some order data that's on the main frame, I want some click stream data that's sitting in HDFS, I want some customer data in the CRM system, or maybe it's in Sales Force, or Mercado. I need some data out of Workday. I want to use some external data. I want to use, say, weather data to look at seasonal analysis. I want to do neighborhooding. So, how do I do that? You know I may be sitting there with Qlik or Tableau or Looker or one of these modern B.I. products or visualization products, but at the same time where's the data. So our value proposition it starts with we catalog the data and we show where the data is. Okay, you've got these data sources, this is what they are, we describe them. And then there's a whole collaboration element to the platform that lets people as they're using the data say, well yes that's order data, but that's old data. So it's good if you use it up to 2007, but the more current data's over here. Do things like that. And then we also then help the person use it. And again I almost said IT, but it's not real data scientists, it's not just them. It's really about democratizing the use. Because business people don't know how to do inner and outer joins and things like that or what a schema is. They just know, I'm trying do a better job of analyzing sales trends. I got all these different data sources, but then once I found them, once I've decided what I want to use, how do I use them? So we answer that question too. >> Yea, Chris reminds me a lot of some the early value propositions we heard when kind of Hadoop and the whole big data wave came. It was how do I get as a smaller company, or even if I'm a bigger company, do it faster, do it for less money than the things it use to be. Okay, its going to be millions of dollars and it's going to take me 18 months to roll out. Is it right to say this is kind of an extension of that big data wave or what's different and what's the same? >> Absolutely, we use a lot of that stuff. I mean we basically use, and we've got flexibility in what we can use, but for most of our customers we use HDFS to store the data. We use Hive as the most typical data form, you have flexibility around there. We use MapReduce, or Spark to do transformation of the data. So we use all of those open source components, and as the product is being used, as the platform is being used and as multiple users, cause it's designed to be an enterprise platform, are using it, the data does eventually migrate into the data lake, but we don't require you to sort of get it there as a prerequisite. As I said, this is one of the things that we really talk about a lot. We catalog the data where it is, in place, so you don't have to move it to use it, you don't have to move it to see it. But at the same time if you want to move it you can. The fundamental idea I got to move it all first, I got to put it all in one place first, it never works. We've come into so many projects where organizations have tried to do that and they just can't, it's too complex these days. >> Alright, Chris, what are some of the organizational dynamics you're seeing from your customers. You mention data scientist, the business users. Who is identifying, whose driving this issues, whose got the budget to try to fix some of these challenges. >> Well, it tends to be our best implementations are driven really, almost all of them these days, are driven by used cases. So they're driven by business needs. Some of the big ones. I've sort of talked about customers already, but like customer 360 views. For instance, there's a very large credit union client of ours, that they have all of their data, that is organized by accounts, but they can't really look at Stu Miniman as my customer. How do I look at Stu's value to us as a customer? I can look at his mortgage account, I can look at his savings account, I can look at his checking account, I can look at his debit card, but I can't just see Stu. I want to like organize my data, that way. That type of customer 360 or marketing analysis I talked about is a great use case. Another one that we've been seeing a lot of is compliance. Where just having a better handle on what data is where it is. This is where some of the governance aspects of what we do also comes into play. Even though we're very much about solving business problems. There's a very strong data governance. Because when you are doing things like data compliance. We're working, for instance, with MoneyGram, is a customer of ours. Who this day and age in particular, when there's money flows across the borders, there's often times regulators want to know, wait that money that went from here to there, tell me where it came from, tell me where it went, tell me the lineage. And they need to be able to respond to those inquiries very very quickly. Now the reality is that data sits in all sorts of different places, both inside and outside of the organization. Being able to organize that and give the ability to respond more quickly and effectively is a big competitive advantage. Both helps with avoiding regulatory fines, but also helps with customers responsiveness. And then you've got things GDPR, the General Data Protection Regulation, I believe it is, which is being driven by the EU. Where its sort of like the next Y2K. Anybody in data, if they are not paying attention to it, they need to be pretty quick. At least if they're a big enough company they're doing business in Europe. Because if you are doing business with European companies or European customers, this is going to be a requirement as of May next year. There's a whole 'nother set of how data's kept, how data's stored, what customers can control over data. Things like 'Right to Be Forgotten'. This need to comply with regulatory... As data's gotten more important, as you might imagine, the regulators have gotten more interested in what organizations are doing with data. Having a framework with that, organizes and helps you be more compliant with those regulations is absolutely critical. >> Yeah, my understanding of GDPR, if you don't comply, there's hefty fines. >> Chris: Major Fines. >> Major Fines. That are going to hit you. Does Unifi solve that? Is there other re-architecture, redesign that customers need to do to be able to be compliant? [speaking at The same Time] >> No, no that's the whole idea again where being able to leave the data where it is, but know what it is and know where it is and if and when I need to use it and where it came from and where it's going and where it went. All of those things, so we provide the platform that enables the customers to use it or the partners to build the solutions for their customers. >> Curious, customers, their adoption of public cloud, how does that play into what you are doing? They deploy more SAS environments. We were having a conversation off camera today talking about the consolidation that's happening in the software world. What does those dynamics mean for your customers? >> Well public cloud is obviously booming and growing and any organization has some public cloud infrastructure at this point, just about any organization. There's some very heavily regulated areas. Actually health care's probably a good example. Where there's very little public cloud. But even there we're working with... we're part of the Microsoft Accelerator Program. Work very closely with the Azure team, for instance. And they're working in some health care environments, where you have to be things like HIPAA compliant, so there is a lot of caution around that. But none the less, the move to public cloud is certainly happening. I think I was just reading some stats the other day. I can't remember if they're Wikibon or other stats. It's still only about 5% of IT spending. And the reality is organizations of any size have plenty of on-prem data. And of course with all the use of SAS solutions, with Salesforce, Workday, Mercado, all of these different SAS applications, it's also in somebody else's data center, much of our data as well. So it's absolutely a hybrid environment. That's why the report that you guys put out on distributed data, really it spoke so much to what out value proposition is. And that's why you know I'm really glad to be here to talk to you about it. >> Great, Chris tell us a little bit, the company itself, how many employees you have, what metrics can you share about the number of customers, revenue, things like that. >> Sure, no, we've got about, I believe about 65 people at the company right now. I joined like I said earlier this year, late February, early March. At that point we we were like 40 people, so we've been growing very quickly. I can't get in too specifically to like our revenue, but basically we're well in the triple digit growth phase. We're still a small company, but we're growing quickly. Our number of customers it's up in the triple digits as well. So expanding very rapidly. And again we're a platform company, so we serve a variety of industries. Some of the big ones are health care, financial services. But even more in the industries it tends to be driven by these used cases I talked about as well. And we're building out our partnerships also, so that's a big part of what I do also. >> Can you share anything about funding where you are? >> Oh yeah, funding, you asked about that, sorry. Yes, we raised our B round of funding, which closed in March of this year. So we [mumbles], a company called Pelion Venture Partners, who you may know, Canaan Partners, and then most recently Scale Venture Partners are investors. So the companies raised a little over $32 million dollars so far. >> Partnerships, you mentioned Microsoft already. Any other key partnerships you want to call out? >> We're doing a lot of work. We have a very broad partner network, which we're building up, but some of the ones that we are sort of leaning in the most with, Microsoft is certainly one. We're doing a lot of work guys at Cloudera as well. We also work with Hortonworks, we also work with MapR. We're really working almost across the board in the BI space. We have spent a lot of time with the folks at Looker. Who was also a partner I was working with very closely during my Vertica days. We're working with Qlik, we're working with Tableau. We're really working with actually just about everybody in sort of BI and visualization. I don't think people like the term BI anymore. The desktop visualization space. And then on public cloud, also Google, Amazon, so really all the kind of major players. I would say that they're the ones that we worked with the most closely to date. As I mentioned earlier we're part of the Microsoft Accelerator Program, so we're certainly very involved in the Microsoft ecosystem. I actually just wrote a blog post, which I don't believe has been published yet, about some of the, what we call the full stack solutions we have been rolling out with Microsoft for a few customers. Where we're sitting on Azure, we're using HDInsight, which is essentially Microsoft's Hadoop cloud Hadoop distribution, visualized empower BI. So we've really got to lot of deep integration with Microsoft, but we've got a broad network as well. And then I should also mention service providers. We're building out our service provider partnerships also. >> Yeah, Chris I'm surprised we haven't talked about kind of AI yet at all, machine learning. It feels like everybody that was doing big data, now has kind pivoted in maybe a little bit early in the buzz word phase. What's your take on that? You've been apart of this for a while. Is big data just old now and we have a new thing, or how do you put those together? >> Well I think what we do maps very well until, at least my personal view of what's going on with AI/ML, is that it's really part of the fabric of what our product does. I talked before about once you sort of found the data you want to use, how do I use it? Well there's a lot of ML built into that. Where essentially, I see these different datasets, I want to use them... We do what's called one click functions. Which basically... What happens is these one click functions get smarter as more and more people use the product and use the data. So that if I've got some table over here and then I've got some SAS data source over there and one user of the product... or we might see field names that we, we grab the metadata, even though we don't require moving the data, we grab the metadata, we look at the metadata and then we'll sort of tell the user, we suggest that you join this data source with that data source and see what it looks like. And if they say: ah that worked, then we say oh okay that's part of sort of the whole ML infrastructure. Then we are more likely to advise the next few folks with the one click function that, hey if you trying to do a analysis of sales trends, well you might want to use this source and that source and you might want to join them together this way. So it's a combination of sort of AI and ML built into the fabric of what we do, and then also the community aspect of more and more people using it. But that's, going back to your original question, That's what I think that... There was quote, I'll misquote it, so I'm not going to directly say it, but it was just.. I think it might have John Ferrier, who was recently was talking about ML and just sort of saying you know eventually we're not going to talk about ML anymore than we talk about phone business or something. It's just going to become sort of integrated into the fabric of how organizations do business and how organizations do things. So we very much got it built in. You could certainly call us an AI/ML company if you want, its actually definitely part of our slide deck. But at the same time its something that will just sort of become a part of doing business over time. But it really, it depends on large data sets. As we all know, this is why it's so cheap to get Amazon Echoes and such these days. Because it's really beneficial, because the more data... There's value in that data, there was just another piece, I actually shared it on Linkedin today as a matter of fact, about, talking about Amazon and Whole Foods and saying: why are they getting such a valuation premium? They're getting such a valuation premium, because they're smart about using data, but one of the reasons they're smart about using the data is cause they have the data. So the more data you collect, the more data you use, the smarter the systems get, the more useful the solutions become. >> Absolutely, last year when Amazon reinvented, John Ferrier interviewed Andy Jassy and I had posited that the customer flywheel, is going to be replaced by that data flywheel. And enhanced to make things spin even further. >> That's exactly right and once you get that flywheel going it becomes a bigger and bigger competitive advantage, by the way that's also why the regulators are getting interested these days too, right? There's sort of, that flywheel going back the other way, but from our perspective... I mean first of all it just makes economic sense, right? These things could conceivably get out of control, that's at least what the regulators think, if you're not careful at least there's some oversight and I would say that, yes probably some oversight is a good idea, so you've got kind of flywheels pushing in both directions. But one way or another organizations need to get much smarter and much more precise and prescriptive about how they use data. And that's really what we're trying to help with. >> Okay, Chris want to give you the final word, Unify Software, you're working on kind of the strategic road pieces. What should we look for from you in your segment through the rest of 2017? >> Well, I think, I've always been a big believer, I've probably cited 'Crossing the Chasm' like so many times on theCUBE, during my prior HP 10 year and such but you know, I'm a big believer and we should be talking about customers, we should be talking about used cases. It's not about alphabet soup technology or data lakes, it's about the solutions and it's about how organizations are moving themselves forward with data. Going back to that Amazon example, so I think from us, yes we just released 2.O, we've got a very active blog, come by unifisoftware.com, visit it. But it's also going to be around what our customers are doing and that's really what we're going to try to promote. I mean if you remember this was also something, that for all the years I've worked with you guys I've been very much... You always have to make sure that the customer has agreed to be cited, it's nice when you can name them and reference them and we're working on our customer references, because that's what I think is the most powerful in this day and age, because again, going back to my, what I said before about, this is going throughout organizations now. People don't necessarily care about the technology infrastructure, but they care about what's being done with it. And so, being able to tell those customer stories, I think that's what you're going to probably see and hear the most from us. But we'll talk about our product as much as you let us as well. >> Great thing, it reminds me of when Wikibon was founded it was really about IT practice, users being able to share with their peers. Now when the software economy today, when they're doing things in software often that can be leveraged by their peers and that flywheel that they're doing, just like when Salesforce first rolled out, they make one change and then everybody else has that option. We're starting to see that more and more as we deploy as SAS and as cloud, it's not the shrink wrap software anymore. >> I think to that point, you know, I was at a conference earlier this year and it was an IT conference, but I was really sort of floored, because when you ask what we're talking about, what the enlightened IT folks and there is more and more enlightened IT folks we're talking about these days, it's the same thing. Right, it's how our business is succeeding, by being better at leveraging data. And I think the opportunities for people in IT... But they really have to think outside of the box, it's not about Hadoop and Sqoop and Sequel and Java anymore it's really about business solutions, but if you can start to think that way, I think there's tremendous opportunities and we're just scratching the surface. >> Absolutely, we found that really some of the proof points of what digital transformation really is for the companies. Alright Chris Selland, always a pleasure to catch up with you. Thanks so much for joining us and thank you for watching theCUBE. >> Chris: Thanks too. (techno music)